A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize

USDA-ARS?s Scientific Manuscript database

An interactome is the genome-wide roadmap of protein-protein interactions that occur within an organism. Interactomes for humans, the fruit fly, and now plants such as Arabidopsis thaliana and Oryza sativa have been generated using high throughput experimental methods. It is possible to use these ...

Interactome disassembly during apoptosis occurs independent of caspase cleavage.

PubMed

Scott, Nichollas E; Rogers, Lindsay D; Prudova, Anna; Brown, Nat F; Fortelny, Nikolaus; Overall, Christopher M; Foster, Leonard J

2017-01-12

Protein-protein interaction networks (interactomes) define the functionality of all biological systems. In apoptosis, proteolysis by caspases is thought to initiate disassembly of protein complexes and cell death. Here we used a quantitative proteomics approach, protein correlation profiling (PCP), to explore changes in cytoplasmic and mitochondrial interactomes in response to apoptosis initiation as a function of caspase activity. We measured the response to initiation of Fas-mediated apoptosis in 17,991 interactions among 2,779 proteins, comprising the largest dynamic interactome to date. The majority of interactions were unaffected early in apoptosis, but multiple complexes containing known caspase targets were disassembled. Nonetheless, proteome-wide analysis of proteolytic processing by terminal amine isotopic labeling of substrates (TAILS) revealed little correlation between proteolytic and interactome changes. Our findings show that, in apoptosis, significant interactome alterations occur before and independently of caspase activity. Thus, apoptosis initiation includes a tight program of interactome rearrangement, leading to disassembly of relatively few, select complexes. These early interactome alterations occur independently of cleavage of these protein by caspases. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

Receptor Tyrosine Kinase MET Interactome and Neurodevelopmental Disorder Partners at the Developing Synapse

PubMed Central

Xie, Zhihui; Li, Jing; Baker, Jonathan; Eagleson, Kathie L.; Coba, Marcelo P.; Levitt, Pat

2016-01-01

Background Atypical synapse development and plasticity are implicated in many neurodevelopmental disorders (NDDs). NDD-associated, high confidence risk genes have been identified, yet little is known about functional relationships at the level of protein-protein interactions, which are the dominant molecular bases responsible for mediating circuit development. Methods Proteomics in three independent developing neocortical synaptosomal preparations identified putative interacting proteins of the ligand-activated MET receptor tyrosine kinase, an autism risk gene that mediates synapse development. The candidates were translated into interactome networks and analyzed bioinformatically. Additionally, three independent quantitative proximity ligation assays (PLA) in cultured neurons and four independent immunoprecipitation analyses of synaptosomes validated protein interactions. Results Approximately 11% (8/72) of MET-interacting proteins, including SHANK3, SYNGAP1 and GRIN2B, are associated with NDDs. Proteins in the MET interactome were translated into a novel MET interactome network based on human protein-protein interaction databases. High confidence genes from different NDD datasets that encode synaptosomal proteins were analyzed for being enriched in MET interactome proteins. This was found for autism, but not schizophrenia, bipolar disorder, major depressive disorder or attentional deficit hyperactivity disorder. There is correlated gene expression between MET and its interactive partners in developing human temporal and visual neocortices, but not with highly expressed genes that are not in the interactome. PLA and biochemical analyses demonstrate that MET-protein partner interactions are dynamically regulated by receptor activation. Conclusions The results provide a novel molecular framework for deciphering the functional relations of key regulators of synaptogenesis that contribute to both typical cortical development and to NDDs. PMID:27086544

PodNet, a protein-protein interaction network of the podocyte.

PubMed

Warsow, Gregor; Endlich, Nicole; Schordan, Eric; Schordan, Sandra; Chilukoti, Ravi K; Homuth, Georg; Moeller, Marcus J; Fuellen, Georg; Endlich, Karlhans

2013-07-01

Interactions between proteins crucially determine cellular structure and function. Differential analysis of the interactome may help elucidate molecular mechanisms during disease development; however, this analysis necessitates mapping of expression data on protein-protein interaction networks. These networks do not exist for the podocyte; therefore, we built PodNet, a literature-based mouse podocyte network in Cytoscape format. Using database protein-protein interactions, we expanded PodNet to XPodNet with enhanced connectivity. In order to test the performance of XPodNet in differential interactome analysis, we examined podocyte developmental differentiation and the effect of cell culture. Transcriptomes of podocytes in 10 different states were mapped on XPodNet and analyzed with the Cytoscape plugin ExprEssence, based on the law of mass action. Interactions between slit diaphragm proteins are most significantly upregulated during podocyte development and most significantly downregulated in culture. On the other hand, our analysis revealed that interactions lost during podocyte differentiation are not regained in culture, suggesting a loss rather than a reversal of differentiation for podocytes in culture. Thus, we have developed PodNet as a valuable tool for differential interactome analysis in podocytes, and we have identified established and unexplored regulated interactions in developing and cultured podocytes.

SH3 interactome conserves general function over specific form

PubMed Central

Xin, Xiaofeng; Gfeller, David; Cheng, Jackie; Tonikian, Raffi; Sun, Lin; Guo, Ailan; Lopez, Lianet; Pavlenco, Alevtina; Akintobi, Adenrele; Zhang, Yingnan; Rual, Jean-François; Currell, Bridget; Seshagiri, Somasekar; Hao, Tong; Yang, Xinping; Shen, Yun A; Salehi-Ashtiani, Kourosh; Li, Jingjing; Cheng, Aaron T; Bouamalay, Dryden; Lugari, Adrien; Hill, David E; Grimes, Mark L; Drubin, David G; Grant, Barth D; Vidal, Marc; Boone, Charles; Sidhu, Sachdev S; Bader, Gary D

2013-01-01

Src homology 3 (SH3) domains bind peptides to mediate protein–protein interactions that assemble and regulate dynamic biological processes. We surveyed the repertoire of SH3 binding specificity using peptide phage display in a metazoan, the worm Caenorhabditis elegans, and discovered that it structurally mirrors that of the budding yeast Saccharomyces cerevisiae. We then mapped the worm SH3 interactome using stringent yeast two-hybrid and compared it with the equivalent map for yeast. We found that the worm SH3 interactome resembles the analogous yeast network because it is significantly enriched for proteins with roles in endocytosis. Nevertheless, orthologous SH3 domain-mediated interactions are highly rewired. Our results suggest a model of network evolution where general function of the SH3 domain network is conserved over its specific form. PMID:23549480

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures.

PubMed

Li, Yongsheng; Sahni, Nidhi; Yi, Song

2016-11-29

Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.

The Protein Interactome of Streptococcus pneumoniae and Bacterial Meta-interactomes Improve Function Predictions.

PubMed

Wuchty, S; Rajagopala, S V; Blazie, S M; Parrish, J R; Khuri, S; Finley, R L; Uetz, P

2017-01-01

The functions of roughly a third of all proteins in Streptococcus pneumoniae , a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein's function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae . We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae , the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins.

Using neighborhood cohesiveness to infer interactions between protein domains.

PubMed

Segura, Joan; Sorzano, C O S; Cuenca-Alba, Jesus; Aloy, Patrick; Carazo, J M

2015-08-01

In recent years, large-scale studies have been undertaken to describe, at least partially, protein-protein interaction maps, or interactomes, for a number of relevant organisms, including human. However, current interactomes provide a somehow limited picture of the molecular details involving protein interactions, mostly because essential experimental information, especially structural data, is lacking. Indeed, the gap between structural and interactomics information is enlarging and thus, for most interactions, key experimental information is missing. We elaborate on the observation that many interactions between proteins involve a pair of their constituent domains and, thus, the knowledge of how protein domains interact adds very significant information to any interactomic analysis. In this work, we describe a novel use of the neighborhood cohesiveness property to infer interactions between protein domains given a protein interaction network. We have shown that some clustering coefficients can be extended to measure a degree of cohesiveness between two sets of nodes within a network. Specifically, we used the meet/min coefficient to measure the proportion of interacting nodes between two sets of nodes and the fraction of common neighbors. This approach extends previous works where homolog coefficients were first defined around network nodes and later around edges. The proposed approach substantially increases both the number of predicted domain-domain interactions as well as its accuracy as compared with current methods. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human.

PubMed

Vo, Tommy V; Das, Jishnu; Meyer, Michael J; Cordero, Nicolas A; Akturk, Nurten; Wei, Xiaomu; Fair, Benjamin J; Degatano, Andrew G; Fragoza, Robert; Liu, Lisa G; Matsuyama, Akihisa; Trickey, Michelle; Horibata, Sachi; Grimson, Andrew; Yamano, Hiroyuki; Yoshida, Minoru; Roth, Frederick P; Pleiss, Jeffrey A; Xia, Yu; Yu, Haiyuan

2016-01-14

Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ∼ 50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution. Copyright © 2016 Elsevier Inc. All rights reserved.

Physical and in silico approaches identify DNA-PK in a Tax DNA-damage response interactome

PubMed Central

Ramadan, Emad; Ward, Michael; Guo, Xin; Durkin, Sarah S; Sawyer, Adam; Vilela, Marcelo; Osgood, Christopher; Pothen, Alex; Semmes, Oliver J

2008-01-01

Background We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the functional consequences of these myriad Tax activities. The physical mapping process involved the affinity isolation of Tax complexes followed by sequence identification using tandem mass spectrometry. To date we have mapped 250 cellular components within this interactome. Here we present our approach to prioritizing these interactions via an in silico culling process. Results We first constructed an in silico Tax interactome comprised of 46 literature-confirmed protein-protein interactions. This number was then reduced to four Tax-interactions suspected to play a role in DNA damage response (Rad51, TOP1, Chk2, 53BP1). The first-neighbor and second-neighbor interactions of these four proteins were assembled from available human protein interaction databases. Through an analysis of betweenness and closeness centrality measures, and numbers of interactions, we ranked proteins in the first neighborhood. When this rank list was compared to the list of physical Tax-binding proteins, DNA-PK was the highest ranked protein common to both lists. An overlapping clustering of the Tax-specific second-neighborhood protein network showed DNA-PK to be one of three bridge proteins that link multiple clusters in the DNA damage response network. Conclusion The interaction of Tax with DNA-PK represents an important biological paradigm as suggested via consensus findings in vivo and in silico. We present this methodology as an approach to discovery and as a means of validating components of a consensus Tax interactome. PMID:18922151

Mapping transcription factor interactome networks using HaloTag protein arrays.

PubMed

Yazaki, Junshi; Galli, Mary; Kim, Alice Y; Nito, Kazumasa; Aleman, Fernando; Chang, Katherine N; Carvunis, Anne-Ruxandra; Quan, Rosa; Nguyen, Hien; Song, Liang; Alvarez, José M; Huang, Shao-Shan Carol; Chen, Huaming; Ramachandran, Niroshan; Altmann, Stefan; Gutiérrez, Rodrigo A; Hill, David E; Schroeder, Julian I; Chory, Joanne; LaBaer, Joshua; Vidal, Marc; Braun, Pascal; Ecker, Joseph R

2016-07-19

Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we created high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein-protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thousands of novel interactions. Validation in a benchmarked in vitro pull-down assay revealed that a random subset of TF-NAPPA validated at the same rate of 64% as a positive reference set of literature-curated interactions. Moreover, using a bimolecular fluorescence complementation (BiFC) assay, we confirmed in planta several interactions of biological interest and determined the interaction localizations for seven pairs. The application of HaloTag-NAPPA technology to plant hormone signaling pathways allowed the identification of many novel transcription factor-protein interactions and led to the development of a proteome-wide plant hormone TF interactome network.

Disease networks. Uncovering disease-disease relationships through the incomplete interactome.

PubMed

Menche, Jörg; Sharma, Amitabh; Kitsak, Maksim; Ghiassian, Susan Dina; Vidal, Marc; Loscalzo, Joseph; Barabási, Albert-László

2015-02-20

According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data have sufficient coverage to map out modules associated with each disease. Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases. For example, diseases with overlapping network modules show significant coexpression patterns, symptom similarity, and comorbidity, whereas diseases residing in separated network neighborhoods are phenotypically distinct. These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases, even if they do not share primary disease genes. Copyright © 2015, American Association for the Advancement of Science.

Photocrosslinking approaches to interactome mapping

PubMed Central

Pham, Nam D.; Parker, Randy B.; Kohler, Jennifer J.

2012-01-01

Photocrosslinking approaches can be used to map interactome networks within the context of living cells. Photocrosslinking methods rely on use of metabolic engineering or genetic code expansion to incorporate photocrosslinking analogs of amino acids or sugars into cellular biomolecules. Immunological and mass spectrometry techniques are used to analyze crosslinked complexes, thereby defining specific interactomes. Because photocrosslinking can be conducted in native, cellular settings, it can be used to define context-dependent interactions. Photocrosslinking methods are also ideally suited for determining interactome dynamics, mapping interaction interfaces, and identifying transient interactions in which intrinsically disordered proteins and glycoproteins engage. Here we discuss the application of cell-based photocrosslinking to the study of specific problems in immune cell signaling, transcription, membrane protein dynamics, nucleocytoplasmic transport, and chaperone-assisted protein folding. PMID:23149092

Receptor Tyrosine Kinase MET Interactome and Neurodevelopmental Disorder Partners at the Developing Synapse.

PubMed

Xie, Zhihui; Li, Jing; Baker, Jonathan; Eagleson, Kathie L; Coba, Marcelo P; Levitt, Pat

2016-12-15

Atypical synapse development and plasticity are implicated in many neurodevelopmental disorders (NDDs). NDD-associated, high-confidence risk genes have been identified, yet little is known about functional relationships at the level of protein-protein interactions, which are the dominant molecular bases responsible for mediating circuit development. Proteomics in three independent developing neocortical synaptosomal preparations identified putative interacting proteins of the ligand-activated MET receptor tyrosine kinase, an autism risk gene that mediates synapse development. The candidates were translated into interactome networks and analyzed bioinformatically. Additionally, three independent quantitative proximity ligation assays in cultured neurons and four independent immunoprecipitation analyses of synaptosomes validated protein interactions. Approximately 11% (8/72) of MET-interacting proteins, including SHANK3, SYNGAP1, and GRIN2B, are associated with NDDs. Proteins in the MET interactome were translated into a novel MET interactome network based on human protein-protein interaction databases. High-confidence genes from different NDD datasets that encode synaptosomal proteins were analyzed for being enriched in MET interactome proteins. This was found for autism but not schizophrenia, bipolar disorder, major depressive disorder, or attention-deficit/hyperactivity disorder. There is correlated gene expression between MET and its interactive partners in developing human temporal and visual neocortices but not with highly expressed genes that are not in the interactome. Proximity ligation assays and biochemical analyses demonstrate that MET-protein partner interactions are dynamically regulated by receptor activation. The results provide a novel molecular framework for deciphering the functional relations of key regulators of synaptogenesis that contribute to both typical cortical development and to NDDs. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

A proteome-scale map of the human interactome network

PubMed Central

Rolland, Thomas; Taşan, Murat; Charloteaux, Benoit; Pevzner, Samuel J.; Zhong, Quan; Sahni, Nidhi; Yi, Song; Lemmens, Irma; Fontanillo, Celia; Mosca, Roberto; Kamburov, Atanas; Ghiassian, Susan D.; Yang, Xinping; Ghamsari, Lila; Balcha, Dawit; Begg, Bridget E.; Braun, Pascal; Brehme, Marc; Broly, Martin P.; Carvunis, Anne-Ruxandra; Convery-Zupan, Dan; Corominas, Roser; Coulombe-Huntington, Jasmin; Dann, Elizabeth; Dreze, Matija; Dricot, Amélie; Fan, Changyu; Franzosa, Eric; Gebreab, Fana; Gutierrez, Bryan J.; Hardy, Madeleine F.; Jin, Mike; Kang, Shuli; Kiros, Ruth; Lin, Guan Ning; Luck, Katja; MacWilliams, Andrew; Menche, Jörg; Murray, Ryan R.; Palagi, Alexandre; Poulin, Matthew M.; Rambout, Xavier; Rasla, John; Reichert, Patrick; Romero, Viviana; Ruyssinck, Elien; Sahalie, Julie M.; Scholz, Annemarie; Shah, Akash A.; Sharma, Amitabh; Shen, Yun; Spirohn, Kerstin; Tam, Stanley; Tejeda, Alexander O.; Trigg, Shelly A.; Twizere, Jean-Claude; Vega, Kerwin; Walsh, Jennifer; Cusick, Michael E.; Xia, Yu; Barabási, Albert-László; Iakoucheva, Lilia M.; Aloy, Patrick; De Las Rivas, Javier; Tavernier, Jan; Calderwood, Michael A.; Hill, David E.; Hao, Tong; Roth, Frederick P.; Vidal, Marc

2014-01-01

SUMMARY Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ~14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ~30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a “broader” human interactome network than currently appreciated. The map also uncovers significant inter-connectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high quality interactome models will help “connect the dots” of the genomic revolution. PMID:25416956

The Protein Interactome of Streptococcus pneumoniae and Bacterial Meta-interactomes Improve Function Predictions

PubMed Central

Rajagopala, S. V.; Blazie, S. M.; Parrish, J. R.; Khuri, S.; Finley, R. L.

2017-01-01

ABSTRACT The functions of roughly a third of all proteins in Streptococcus pneumoniae, a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein’s function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae. We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae, the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins. PMID:28744484

Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

PubMed Central

Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

2014-01-01

Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size.

PubMed

Sambourg, Laure; Thierry-Mieg, Nicolas

2010-12-21

As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism Saccharomyces cerevisiae. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required. We examined the yeast interactome from a new perspective, by taking into account how thoroughly proteins have been studied. We discovered that the set of literature-curated protein-protein interactions is qualitatively different when restricted to proteins that have received extensive attention from the scientific community. In particular, these interactions are less often supported by yeast two-hybrid, and more often by more complex experiments such as biochemical activity assays. Our analysis showed that high-throughput and literature-curated interactome datasets are more correlated than commonly assumed, but that this bias can be corrected for by focusing on well-studied proteins. We thus propose a simple and reliable method to estimate the size of an interactome, combining literature-curated data involving well-studied proteins with high-throughput data. It yields an estimate of at least 37, 600 direct physical protein-protein interactions in S. cerevisiae. Our method leads to higher and more accurate estimates of the interactome size, as it accounts for interactions that are genuine yet difficult to detect with commonly-used experimental assays. This shows that we are even further from completing the yeast interactome map than previously expected.

Inborn errors of metabolism and the human interactome: a systems medicine approach.

PubMed

Woidy, Mathias; Muntau, Ania C; Gersting, Søren W

2018-02-05

The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies.

Network-based study reveals potential infection pathways of hepatitis-C leading to various diseases.

PubMed

Mukhopadhyay, Anirban; Maulik, Ujjwal

2014-01-01

Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement.

Network-Based Study Reveals Potential Infection Pathways of Hepatitis-C Leading to Various Diseases

PubMed Central

Mukhopadhyay, Anirban; Maulik, Ujjwal

2014-01-01

Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement. PMID:24743187

A TRPV2 interactome-based signature for prognosis in glioblastoma patients.

PubMed

Doñate-Macián, Pau; Gómez, Antonio; Dégano, Irene R; Perálvarez-Marín, Alex

2018-04-06

Proteomics aids to the discovery and expansion of protein-protein interaction networks, which are key to understand molecular mechanisms in physiology and physiopathology, but also to infer protein function in a guilt-by-association fashion. In this study we use a systematic protein-protein interaction membrane yeast two-hybrid method to expand the interactome of TRPV2, a cation channel related to nervous system development. After validation of the interactome in silico , we define a TRPV2-interactome signature combining proteomics with the available physio-pathological data in Disgenet to find interactome-disease associations, highlighting nervous system disorders and neoplasms. The TRPV2-interactome signature against available experimental data is capable of discriminating overall risk in glioblastoma multiforme prognosis, progression, recurrence, and chemotherapy resistance. Beyond the impact on glioblastoma physiopathology, this study shows that combining systematic proteomics with in silico methods and available experimental data is key to open new perspectives to define novel biomarkers for diagnosis, prognosis and therapeutics in disease.

A TRPV2 interactome-based signature for prognosis in glioblastoma patients

PubMed Central

Dégano, Irene R.; Perálvarez-Marín, Alex

2018-01-01

Proteomics aids to the discovery and expansion of protein-protein interaction networks, which are key to understand molecular mechanisms in physiology and physiopathology, but also to infer protein function in a guilt-by-association fashion. In this study we use a systematic protein-protein interaction membrane yeast two-hybrid method to expand the interactome of TRPV2, a cation channel related to nervous system development. After validation of the interactome in silico, we define a TRPV2-interactome signature combining proteomics with the available physio-pathological data in Disgenet to find interactome-disease associations, highlighting nervous system disorders and neoplasms. The TRPV2-interactome signature against available experimental data is capable of discriminating overall risk in glioblastoma multiforme prognosis, progression, recurrence, and chemotherapy resistance. Beyond the impact on glioblastoma physiopathology, this study shows that combining systematic proteomics with in silico methods and available experimental data is key to open new perspectives to define novel biomarkers for diagnosis, prognosis and therapeutics in disease. PMID:29719613

Evidence for dynamically organized modularity in the yeast protein-protein interaction network

NASA Astrophysics Data System (ADS)

Han, Jing-Dong J.; Bertin, Nicolas; Hao, Tong; Goldberg, Debra S.; Berriz, Gabriel F.; Zhang, Lan V.; Dupuy, Denis; Walhout, Albertha J. M.; Cusick, Michael E.; Roth, Frederick P.; Vidal, Marc

2004-07-01

In apparently scale-free protein-protein interaction networks, or `interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the `hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: `party' hubs, which interact with most of their partners simultaneously, and `date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes-or modules -to each other, whereas party hubs function inside modules.

The IBD interactome: an integrated view of aetiology, pathogenesis and therapy.

PubMed

de Souza, Heitor S P; Fiocchi, Claudio; Iliopoulos, Dimitrios

2017-12-01

Crohn's disease and ulcerative colitis are prototypical complex diseases characterized by chronic and heterogeneous manifestations, induced by interacting environmental, genomic, microbial and immunological factors. These interactions result in an overwhelming complexity that cannot be tackled by studying the totality of each pathological component (an '-ome') in isolation without consideration of the interaction among all relevant -omes that yield an overall 'network effect'. The outcome of this effect is the 'IBD interactome', defined as a disease network in which dysregulation of individual -omes causes intestinal inflammation mediated by dysfunctional molecular modules. To define the IBD interactome, new concepts and tools are needed to implement a systems approach; an unbiased data-driven integration strategy that reveals key players of the system, pinpoints the central drivers of inflammation and enables development of targeted therapies. Powerful bioinformatics tools able to query and integrate multiple -omes are available, enabling the integration of genomic, epigenomic, transcriptomic, proteomic, metabolomic and microbiome information to build a comprehensive molecular map of IBD. This approach will enable identification of IBD molecular subtypes, correlations with clinical phenotypes and elucidation of the central hubs of the IBD interactome that will aid discovery of compounds that can specifically target the hubs that control the disease.

Bacterial Interactomes: Interacting Protein Partners Share Similar Function and Are Validated in Independent Assays More Frequently Than Previously Reported*

PubMed Central

Shatsky, Maxim; Allen, Simon; Gold, Barbara L.; Liu, Nancy L.; Juba, Thomas R.; Reveco, Sonia A.; Elias, Dwayne A.; Prathapam, Ramadevi; He, Jennifer; Yang, Wenhong; Szakal, Evelin D.; Liu, Haichuan; Singer, Mary E.; Geller, Jil T.; Lam, Bonita R.; Saini, Avneesh; Trotter, Valentine V.; Hall, Steven C.; Fisher, Susan J.; Brenner, Steven E.; Chhabra, Swapnil R.; Hazen, Terry C.; Wall, Judy D.; Witkowska, H. Ewa; Biggin, Mark D.; Chandonia, John-Marc; Butland, Gareth

2016-01-01

Numerous affinity purification-mass spectrometry (AP-MS) and yeast two-hybrid screens have each defined thousands of pairwise protein-protein interactions (PPIs), most of which are between functionally unrelated proteins. The accuracy of these networks, however, is under debate. Here, we present an AP-MS survey of the bacterium Desulfovibrio vulgaris together with a critical reanalysis of nine published bacterial yeast two-hybrid and AP-MS screens. We have identified 459 high confidence PPIs from D. vulgaris and 391 from Escherichia coli. Compared with the nine published interactomes, our two networks are smaller, are much less highly connected, and have significantly lower false discovery rates. In addition, our interactomes are much more enriched in protein pairs that are encoded in the same operon, have similar functions, and are reproducibly detected in other physical interaction assays than the pairs reported in prior studies. Our work establishes more stringent benchmarks for the properties of protein interactomes and suggests that bona fide PPIs much more frequently involve protein partners that are annotated with similar functions or that can be validated in independent assays than earlier studies suggested. PMID:26873250

Interactome of E. piscicida and grouper liver proteins reveals strategies of bacterial infection and host immune response.

PubMed

Li, Hui; Zhu, Qing-Feng; Peng, Xuan-Xian; Peng, Bo

2017-01-03

The occurrence of infectious diseases is related to heterogeneous protein interactions between a host and a microbe. Therefore, elucidating the host-pathogen interplay is essential. We previously revealed the protein interactome between Edwardsiella piscicida and fish gill cells, and the present study identified the protein interactome between E. piscicida and E. drummondhayi liver cells. E. drummondhayi liver cells and bacterial pull-down approaches were used to identify E. piscicida outer membrane proteins that bind to liver cells and fish liver cell proteins that interact with bacterial cells, respectively. Eight bacterial proteins and 11 fish proteins were characterized. Heterogeneous protein-protein interactions between these bacterial cells and fish liver cells were investigated through far-Western blotting and co-immunoprecipitation. A network was constructed based on 42 heterogeneous protein-protein interactions between seven bacterial proteins and 10 fish proteins. A comparison of the new interactome with the previously reported interactome showed that four bacterial proteins overlapped, whereas all of the identified fish proteins were new, suggesting a difference between bacterial tricks for evading host immunity and the host strategy for combating bacterial infection. Furthermore, these bacterial proteins were found to regulate the expression of host innate immune-related proteins. These findings indicate that the interactome contributes to bacterial infection and host immunity.

Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives.

PubMed

Romero-Durán, Francisco J; Alonso, Nerea; Yañez, Matilde; Caamaño, Olga; García-Mera, Xerardo; González-Díaz, Humberto

2016-04-01

The use of Cheminformatics tools is gaining importance in the field of translational research from Medicinal Chemistry to Neuropharmacology. In particular, we need it for the analysis of chemical information on large datasets of bioactive compounds. These compounds form large multi-target complex networks (drug-target interactome network) resulting in a very challenging data analysis problem. Artificial Neural Network (ANN) algorithms may help us predict the interactions of drugs and targets in CNS interactome. In this work, we trained different ANN models able to predict a large number of drug-target interactions. These models predict a dataset of thousands of interactions of central nervous system (CNS) drugs characterized by > 30 different experimental measures in >400 different experimental protocols for >150 molecular and cellular targets present in 11 different organisms (including human). The model was able to classify cases of non-interacting vs. interacting drug-target pairs with satisfactory performance. A second aim focus on two main directions: the synthesis and assay of new derivatives of TVP1022 (S-analogues of rasagiline) and the comparison with other rasagiline derivatives recently reported. Finally, we used the best of our models to predict drug-target interactions for the best new synthesized compound against a large number of CNS protein targets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Next-generation sequencing coupled with a cell-free display technology for high-throughput production of reliable interactome data

PubMed Central

Fujimori, Shigeo; Hirai, Naoya; Ohashi, Hiroyuki; Masuoka, Kazuyo; Nishikimi, Akihiko; Fukui, Yoshinori; Washio, Takanori; Oshikubo, Tomohiro; Yamashita, Tatsuhiro; Miyamoto-Sato, Etsuko

2012-01-01

Next-generation sequencing (NGS) has been applied to various kinds of omics studies, resulting in many biological and medical discoveries. However, high-throughput protein-protein interactome datasets derived from detection by sequencing are scarce, because protein-protein interaction analysis requires many cell manipulations to examine the interactions. The low reliability of the high-throughput data is also a problem. Here, we describe a cell-free display technology combined with NGS that can improve both the coverage and reliability of interactome datasets. The completely cell-free method gives a high-throughput and a large detection space, testing the interactions without using clones. The quantitative information provided by NGS reduces the number of false positives. The method is suitable for the in vitro detection of proteins that interact not only with the bait protein, but also with DNA, RNA and chemical compounds. Thus, it could become a universal approach for exploring the large space of protein sequences and interactome networks. PMID:23056904

The functional interactome of GSTP: A regulatory biomolecular network at the interface with the Nrf2 adaption response to oxidative stress.

PubMed

Bartolini, Desirée; Galli, Francesco

2016-04-15

Glutathione S-transferase P (GSTP), and possibly other members of the subfamily of cytosolic GSTs, are increasingly proposed to have roles far beyond the classical GSH-dependent enzymatic detoxification of electrophilic metabolites and xenobiotics. Emerging evidence suggests that these are essential components of the redox sensing and signaling platform of cells. GSTP monomers physically interact with cellular proteins, such as other cytosolic GSTs, signaling kinases and the membrane peroxidase peroxiredoxin 6. Other interactions reported in literature include that with regulatory proteins such as Fanconi anemia complementation group C protein, transglutaminase 2 and several members of the keratin family of genes. Transcription factors downstream of inflammatory and oxidative stress pathways, namely STAT3 and Nrf2, were recently identified to be further components of this interactome. Together these pieces of evidence suggest the existence of a regulatory biomolecular network in which GSTP represents a node of functional convergence and coordination of signaling and transcription proteins, namely the "GSTP interactome", associated with key cellular processes such as cell cycle regulation and the stress response. These aspects and the methodological approach to explore the cellular interactome(s) are discussed in this review paper. Copyright © 2016 Elsevier B.V. All rights reserved.

Simultaneously measuring multiple protein interactions and their correlations in a cell by Protein-interactome Footprinting

PubMed Central

Luo, Si-Wei; Liang, Zhi; Wu, Jia-Rui

2017-01-01

Quantitatively detecting correlations of multiple protein-protein interactions (PPIs) in vivo is a big challenge. Here we introduce a novel method, termed Protein-interactome Footprinting (PiF), to simultaneously measure multiple PPIs in one cell. The principle of PiF is that each target physical PPI in the interactome is simultaneously transcoded into a specific DNA sequence based on dimerization of the target proteins fused with DNA-binding domains. The interaction intensity of each target protein is quantified as the copy number of the specific DNA sequences bound by each fusion protein dimers. Using PiF, we quantitatively reveal dynamic patterns of PPIs and their correlation network in E. coli two-component systems. PMID:28338015

Network Approach to Disease Diagnosis

NASA Astrophysics Data System (ADS)

Sharma, Amitabh; Bashan, Amir; Barabasi, Alber-Laszlo

2014-03-01

Human diseases could be viewed as perturbations of the underlying biological system. A thorough understanding of the topological and dynamical properties of the biological system is crucial to explain the mechanisms of many complex diseases. Recently network-based approaches have provided a framework for integrating multi-dimensional biological data that results in a better understanding of the pathophysiological state of complex diseases. Here we provide a network-based framework to improve the diagnosis of complex diseases. This framework is based on the integration of transcriptomics and the interactome. We analyze the overlap between the differentially expressed (DE) genes and disease genes (DGs) based on their locations in the molecular interaction network (''interactome''). Disease genes and their protein products tend to be much more highly connected than random, hence defining a disease sub-graph (called disease module) in the interactome. DE genes, even though different from the known set of DGs, may be significantly associated with the disease when considering their closeness to the disease module in the interactome. This new network approach holds the promise to improve the diagnosis of patients who cannot be diagnosed using conventional tools. Support was provided by HL066289 and HL105339 grants from the U.S. National Institutes of Health.

Perturbation of the mutated EGFR interactome identifies vulnerabilities and resistance mechanisms.

PubMed

Li, Jiannong; Bennett, Keiryn; Stukalov, Alexey; Fang, Bin; Zhang, Guolin; Yoshida, Takeshi; Okamoto, Isamu; Kim, Jae-Young; Song, Lanxi; Bai, Yun; Qian, Xiaoning; Rawal, Bhupendra; Schell, Michael; Grebien, Florian; Winter, Georg; Rix, Uwe; Eschrich, Steven; Colinge, Jacques; Koomen, John; Superti-Furga, Giulio; Haura, Eric B

2013-11-05

We hypothesized that elucidating the interactome of epidermal growth factor receptor (EGFR) forms that are mutated in lung cancer, via global analysis of protein-protein interactions, phosphorylation, and systematically perturbing the ensuing network nodes, should offer a new, more systems-level perspective of the molecular etiology. Here, we describe an EGFR interactome of 263 proteins and offer a 14-protein core network critical to the viability of multiple EGFR-mutated lung cancer cells. Cells with acquired resistance to EGFR tyrosine kinase inhibitors (TKIs) had differential dependence of the core network proteins based on the underlying molecular mechanisms of resistance. Of the 14 proteins, 9 are shown to be specifically associated with survival of EGFR-mutated lung cancer cell lines. This included EGFR, GRB2, MK12, SHC1, ARAF, CD11B, ARHG5, GLU2B, and CD11A. With the use of a drug network associated with the core network proteins, we identified two compounds, midostaurin and lestaurtinib, that could overcome drug resistance through direct EGFR inhibition when combined with erlotinib. Our results, enabled by interactome mapping, suggest new targets and combination therapies that could circumvent EGFR TKI resistance.

TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks.

PubMed

Lepoivre, Cyrille; Bergon, Aurélie; Lopez, Fabrice; Perumal, Narayanan B; Nguyen, Catherine; Imbert, Jean; Puthier, Denis

2012-01-31

Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph. We extend the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions. The database includes: (i) predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices), (ii) potential regulatory interactions inferred from systematic analysis of ChIP-seq experiments, (iii) regulatory interactions curated from the literature, (iv) predicted post-transcriptional regulation by micro-RNA, (v) protein kinase-substrate interactions and (vi) physical protein-protein interactions. In order to easily retrieve and efficiently analyze these interactions, we developed In-teractomeBrowser, a graph-based knowledge browser that comes as a plug-in for Transcriptome-Browser. The first objective of InteractomeBrowser is to provide a user-friendly tool to get new insight into any gene list by providing a context-specific display of putative regulatory and physical interactions. To achieve this, InteractomeBrowser relies on a "cell compartments-based layout" that makes use of a subset of the Gene Ontology to map gene products onto relevant cell compartments. This layout is particularly powerful for visual integration of heterogeneous biological information and is a productive avenue in generating new hypotheses. The second objective of InteractomeBrowser is to fill the gap between interaction databases and dynamic modeling. It is thus compatible with the network analysis software Cytoscape and with the Gene Interaction Network simulation software (GINsim). We provide examples underlying the benefits of this visualization tool for large gene set analysis related to thymocyte differentiation. The InteractomeBrowser plugin is a powerful tool to get quick access to a knowledge database that includes both predicted and validated molecular interactions. InteractomeBrowser is available through the TranscriptomeBrowser framework and can be found at: http://tagc.univ-mrs.fr/tbrowser/. Our database is updated on a regular basis.

Rewiring of the inferred protein interactome during blood development studied with the tool PPICompare.

PubMed

Will, Thorsten; Helms, Volkhard

2017-04-04

Differential analysis of cellular conditions is a key approach towards understanding the consequences and driving causes behind biological processes such as developmental transitions or diseases. The progress of whole-genome expression profiling enabled to conveniently capture the state of a cell's transcriptome and to detect the characteristic features that distinguish cells in specific conditions. In contrast, mapping the physical protein interactome for many samples is experimentally infeasible at the moment. For the understanding of the whole system, however, it is equally important how the interactions of proteins are rewired between cellular states. To overcome this deficiency, we recently showed how condition-specific protein interaction networks that even consider alternative splicing can be inferred from transcript expression data. Here, we present the differential network analysis tool PPICompare that was specifically designed for isoform-sensitive protein interaction networks. Besides detecting significant rewiring events between the interactomes of grouped samples, PPICompare infers which alterations to the transcriptome caused each rewiring event and what is the minimal set of alterations necessary to explain all between-group changes. When applied to the development of blood cells, we verified that a reasonable amount of rewiring events were reported by the tool and found that differential gene expression was the major determinant of cellular adjustments to the interactome. Alternative splicing events were consistently necessary in each developmental step to explain all significant alterations and were especially important for rewiring in the context of transcriptional control. Applying PPICompare enabled us to investigate the dynamics of the human protein interactome during developmental transitions. A platform-independent implementation of the tool PPICompare is available at https://sourceforge.net/projects/ppicompare/ .

Next Generation Protein Interactomes for Plant Systems Biology and Biomass Feedstock Research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ecker, Joseph Robert; Trigg, Shelly; Garza, Renee

Biofuel crop cultivation is a necessary step in heading towards a sustainable future, making their genomic studies a priority. While technology platforms that currently exist for studying non-model crop species, like switch-grass or sorghum, have yielded large quantities of genomic and expression data, still a large gap exists between molecular mechanism and phenotype. The aspect of molecular activity at the level of protein-protein interactions has recently begun to bridge this gap, providing a more global perspective. Interactome analysis has defined more specific functional roles of proteins based on their interaction partners, neighborhoods, and other network features, making it possible tomore » distinguish unique modules of immune response to different plant pathogens(Jiang, Dong, and Zhang 2016). As we work towards cultivating heartier biofuel crops, interactome data will lead to uncovering crop-specific defense and development networks. However, the collection of protein interaction data has been limited to expensive, time-consuming, hard-to-scale assays that mostly require cloned ORF collections. For these reasons, we have successfully developed a highly scalable, economical, and sensitive yeast two-hybrid assay, ProCREate, that can be universally applied to generate proteome-wide primary interactome data. ProCREate enables en masse pooling and massively paralleled sequencing for the identification of interacting proteins by exploiting Cre-lox recombination. ProCREate can be used to screen ORF/cDNA libraries from feedstock plant tissues. The interactome data generated will yield deeper insight into many molecular processes and pathways that can be used to guide improvement of feedstock productivity and sustainability.« less

Large-scale De Novo Prediction of Physical Protein-Protein Association*

PubMed Central

Elefsinioti, Antigoni; Saraç, Ömer Sinan; Hegele, Anna; Plake, Conrad; Hubner, Nina C.; Poser, Ina; Sarov, Mihail; Hyman, Anthony; Mann, Matthias; Schroeder, Michael; Stelzl, Ulrich; Beyer, Andreas

2011-01-01

Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future. Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genome-wide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular. Our interactome is available via the hPRINT web server at: www.print-db.org. PMID:21836163

Ocean plankton. Determinants of community structure in the global plankton interactome.

PubMed

Lima-Mendez, Gipsi; Faust, Karoline; Henry, Nicolas; Decelle, Johan; Colin, Sébastien; Carcillo, Fabrizio; Chaffron, Samuel; Ignacio-Espinosa, J Cesar; Roux, Simon; Vincent, Flora; Bittner, Lucie; Darzi, Youssef; Wang, Jun; Audic, Stéphane; Berline, Léo; Bontempi, Gianluca; Cabello, Ana M; Coppola, Laurent; Cornejo-Castillo, Francisco M; d'Ovidio, Francesco; De Meester, Luc; Ferrera, Isabel; Garet-Delmas, Marie-José; Guidi, Lionel; Lara, Elena; Pesant, Stéphane; Royo-Llonch, Marta; Salazar, Guillem; Sánchez, Pablo; Sebastian, Marta; Souffreau, Caroline; Dimier, Céline; Picheral, Marc; Searson, Sarah; Kandels-Lewis, Stefanie; Gorsky, Gabriel; Not, Fabrice; Ogata, Hiroyuki; Speich, Sabrina; Stemmann, Lars; Weissenbach, Jean; Wincker, Patrick; Acinas, Silvia G; Sunagawa, Shinichi; Bork, Peer; Sullivan, Matthew B; Karsenti, Eric; Bowler, Chris; de Vargas, Colomban; Raes, Jeroen

2015-05-22

Species interaction networks are shaped by abiotic and biotic factors. Here, as part of the Tara Oceans project, we studied the photic zone interactome using environmental factors and organismal abundance profiles and found that environmental factors are incomplete predictors of community structure. We found associations across plankton functional types and phylogenetic groups to be nonrandomly distributed on the network and driven by both local and global patterns. We identified interactions among grazers, primary producers, viruses, and (mainly parasitic) symbionts and validated network-generated hypotheses using microscopy to confirm symbiotic relationships. We have thus provided a resource to support further research on ocean food webs and integrating biological components into ocean models. Copyright © 2015, American Association for the Advancement of Science.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations.

PubMed

Perez-Lopez, Áron R; Szalay, Kristóf Z; Türei, Dénes; Módos, Dezső; Lenti, Katalin; Korcsmáros, Tamás; Csermely, Peter

2015-05-11

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

NASA Astrophysics Data System (ADS)

Perez-Lopez, Áron R.; Szalay, Kristóf Z.; Türei, Dénes; Módos, Dezső; Lenti, Katalin; Korcsmáros, Tamás; Csermely, Peter

2015-05-01

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

PubMed Central

Perez-Lopez, Áron R.; Szalay, Kristóf Z.; Türei, Dénes; Módos, Dezső; Lenti, Katalin; Korcsmáros, Tamás; Csermely, Peter

2015-01-01

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates. PMID:25960144

How perfect can protein interactomes be?

PubMed

Levy, Emmanuel D; Landry, Christian R; Michnick, Stephen W

2009-03-03

Any engineered device should certainly not contain nonfunctional components, for this would be a waste of energy and money. In contrast, evolutionary theory tells us that biological systems need not be optimized and may very well accumulate nonfunctional elements. Mutational and demographic processes contribute to the cluttering of eukaryotic genomes and transcriptional networks with "junk" DNA and spurious DNA binding sites. Here, we question whether such a notion should be applied to protein interactomes-that is, whether these protein interactomes are expected to contain a fraction of nonselected, nonfunctional protein-protein interactions (PPIs), which we term "noisy." We propose a simple relationship between the fraction of noisy interactions expected in a given organism and three parameters: (i) the number of mutations needed to create and destroy interactions, (ii) the size of the proteome, and (iii) the fitness cost of noisy interactions. All three parameters suggest that noisy PPIs are expected to exist. Their existence could help to explain why PPIs determined from large-scale studies often lack functional relationships between interacting proteins, why PPIs are poorly conserved across organisms, and why the PPI space appears to be immensely large. Finally, we propose experimental strategies to estimate the fraction of evolutionary noise in PPI networks.

Interactome Networks and Human Disease

PubMed Central

Vidal, Marc; Cusick, Michael E.; Barabási, Albert-László

2011-01-01

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease. PMID:21414488

Bacterial interactomes: Interacting protein partners share similar function and are validated in independent assays more frequently than previously reported.

DOE PAGES

Shatsky, Maxim; Allen, Simon; Gold, Barbara; ...

2016-05-01

Numerous affinity purification – mass-spectrometry (AP-MS) and yeast two hybrid (Y2H) screens have each defined thousands of pairwise protein-protein interactions (PPIs), most between functionally unrelated proteins. The accuracy of these networks, however, is under debate. Here we present an AP-MS survey of the bacterium Desulfovibrio vulgaris together with a critical reanalysis of nine published bacterial Y2H and AP-MS screens. We have identified 459 high confidence PPIs from D. vulgaris and 391 from Escherichia coli. Compared to the nine published interactomes, our two networks are smaller; are much less highly connected; have significantly lower false discovery rates; and are much moremore » enriched in protein pairs that are encoded in the same operon, have similar functions, and are reproducibly detected in other physical interaction assays. Lastly, our work establishes more stringent benchmarks for the properties of protein interactomes and suggests that bona fide PPIs much more frequently involve protein partners that are annotated with similar functions or that can be validated in independent assays than earlier studies suggested.« less

Emerging role of the Jun N-terminal kinase interactome in human health.

PubMed

Guo, Xiao-Xi; An, Su; Yang, Yang; Liu, Ying; Hao, Qian; Tang, Tao; Xu, Tian-Rui

2018-02-08

The c-Jun N-terminal kinases (JNKs) are located downstream of Ras-mitogen activated protein kinase signaling cascades. More than 20 years of study has shown that JNKs control cell fate and many cellular functions. JNKs and their interacting proteins form a complicated network with diverse biological functions and physiological effects. Members of the JNK interactome include Jun, amyloid precursor protein, and insulin receptor substrate. Recent studies have shown that the JNK interactome is involved in tumorigenesis, neuron development, and insulin resistance. In this review, we summarize the features of the JNK interactome and classify its members into three groups: upstream regulators, downstream effectors, and scaffold partners. We also highlight the unique cellular signaling mechanisms of JNKs and provide more insights into the roles of the JNK interactome in human diseases. © 2018 International Federation for Cell Biology.

Deriving Heterospecific Self-Assembling Protein-Protein Interactions Using a Computational Interactome Screen.

PubMed

Crooks, Richard O; Baxter, Daniel; Panek, Anna S; Lubben, Anneke T; Mason, Jody M

2016-01-29

Interactions between naturally occurring proteins are highly specific, with protein-network imbalances associated with numerous diseases. For designed protein-protein interactions (PPIs), required specificity can be notoriously difficult to engineer. To accelerate this process, we have derived peptides that form heterospecific PPIs when combined. This is achieved using software that generates large virtual libraries of peptide sequences and searches within the resulting interactome for preferentially interacting peptides. To demonstrate feasibility, we have (i) generated 1536 peptide sequences based on the parallel dimeric coiled-coil motif and varied residues known to be important for stability and specificity, (ii) screened the 1,180,416 member interactome for predicted Tm values and (iii) used predicted Tm cutoff points to isolate eight peptides that form four heterospecific PPIs when combined. This required that all 32 hypothetical off-target interactions within the eight-peptide interactome be disfavoured and that the four desired interactions pair correctly. Lastly, we have verified the approach by characterising all 36 pairs within the interactome. In analysing the output, we hypothesised that several sequences are capable of adopting antiparallel orientations. We subsequently improved the software by removing sequences where doing so led to fully complementary electrostatic pairings. Our approach can be used to derive increasingly large and therefore complex sets of heterospecific PPIs with a wide range of potential downstream applications from disease modulation to the design of biomaterials and peptides in synthetic biology. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Protein Inference from the Integration of Tandem MS Data and Interactome Networks.

PubMed

Zhong, Jiancheng; Wang, Jianxing; Ding, Xiaojun; Zhang, Zhen; Li, Min; Wu, Fang-Xiang; Pan, Yi

2017-01-01

Since proteins are digested into a mixture of peptides in the preprocessing step of tandem mass spectrometry (MS), it is difficult to determine which specific protein a shared peptide belongs to. In recent studies, besides tandem MS data and peptide identification information, some other information is exploited to infer proteins. Different from the methods which first use only tandem MS data to infer proteins and then use network information to refine them, this study proposes a protein inference method named TMSIN, which uses interactome networks directly. As two interacting proteins should co-exist, it is reasonable to assume that if one of the interacting proteins is confidently inferred in a sample, its interacting partners should have a high probability in the same sample, too. Therefore, we can use the neighborhood information of a protein in an interactome network to adjust the probability that the shared peptide belongs to the protein. In TMSIN, a multi-weighted graph is constructed by incorporating the bipartite graph with interactome network information, where the bipartite graph is built with the peptide identification information. Based on multi-weighted graphs, TMSIN adopts an iterative workflow to infer proteins. At each iterative step, the probability that a shared peptide belongs to a specific protein is calculated by using the Bayes' law based on the neighbor protein support scores of each protein which are mapped by the shared peptides. We carried out experiments on yeast data and human data to evaluate the performance of TMSIN in terms of ROC, q-value, and accuracy. The experimental results show that AUC scores yielded by TMSIN are 0.742 and 0.874 in yeast dataset and human dataset, respectively, and TMSIN yields the maximum number of true positives when q-value less than or equal to 0.05. The overlap analysis shows that TMSIN is an effective complementary approach for protein inference.

Quantitative interactome reveals that porcine reproductive and respiratory syndrome virus nonstructural protein 2 forms a complex with viral nucleocapsid protein and cellular vimentin.

PubMed

Song, Tao; Fang, Liurong; Wang, Dang; Zhang, Ruoxi; Zeng, Songlin; An, Kang; Chen, Huanchun; Xiao, Shaobo

2016-06-16

Porcine reproductive and respiratory syndrome virus (PRRSV) is an Arterivirus that has heavily impacted the global swine industry. The PRRSV nonstructural protein 2 (nsp2) plays crucial roles in viral replication and host immune regulation, most likely by interacting with viral or cellular proteins that have not yet been identified. In this study, a quantitative interactome approach based on immunoprecipitation and stable isotope labeling with amino acids in cell culture (SILAC) was performed to identify nsp2-interacting proteins in PRRSV-infected cells with an nsp2-specific monoclonal antibody. Nine viral proteins and 62 cellular proteins were identified as potential nsp2-interacting partners. Our data demonstrate that the PRRSV nsp1α, nsp1β, and nucleocapsid proteins all interact directly with nsp2. Nsp2-interacting cellular proteins were classified into different functional groups and an interactome network of nsp2 was generated. Interestingly, cellular vimentin, a known receptor for PRRSV, forms a complex with nsp2 by using viral nucleocapsid protein as an intermediate. Taken together, the nsp2 interactome under the condition of virus infection clarifies a role of nsp2 in PRRSV replication and immune evasion. Viral proteins must interact with other virus-encoded proteins and/or host cellular proteins to function, and interactome analysis is an ideal approach for identifying such interacting proteins. In this study, we used the quantitative interactome methodology to identify the viral and cellular proteins that potentially interact with the nonstructural protein 2 (nsp2) of porcine reproductive and respiratory syndrome virus (PRRSV) under virus infection conditions, thus providing a rich source of potential viral and cellular interaction partners for PRRSV nsp2. Based on the interactome data, we further demonstrated that PRRSV nsp2 and nucleocapsid protein together with cellular vimentin, form a complex that may be essential for viral attachment and replication, which partly explains the role of nsp2 in PRRSV replication and immune evasion. Copyright © 2016 Elsevier B.V. All rights reserved.

A Systems Biology Methodology Combining Transcriptome and Interactome Datasets to Assess the Implications of Cytokinin Signaling for Plant Immune Networks.

PubMed

Kunz, Meik; Dandekar, Thomas; Naseem, Muhammad

2017-01-01

Cytokinins (CKs) play an important role in plant growth and development. Also, several studies highlight the modulatory implications of CKs for plant-pathogen interaction. However, the underlying mechanisms of CK mediating immune networks in plants are still not fully understood. A detailed analysis of high-throughput transcriptome (RNA-Seq and microarrays) datasets under modulated conditions of plant CKs and its mergence with cellular interactome (large-scale protein-protein interaction data) has the potential to unlock the contribution of CKs to plant defense. Here, we specifically describe a detailed systems biology methodology pertinent to the acquisition and analysis of various omics datasets that delineate the role of plant CKs in impacting immune pathways in Arabidopsis.

Do cancer proteins really interact strongly in the human protein-protein interaction network?

PubMed

Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

2011-06-01

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. © 2011 Elsevier Ltd. All rights reserved.

Do cancer proteins really interact strongly in the human protein-protein interaction network?

PubMed Central

Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

2011-01-01

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. PMID:21666777

Recent Progress in CFTR Interactome Mapping and Its Importance for Cystic Fibrosis.

PubMed

Lim, Sang Hyun; Legere, Elizabeth-Ann; Snider, Jamie; Stagljar, Igor

2017-01-01

Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a chloride channel found in secretory epithelia with a plethora of known interacting proteins. Mutations in the CFTR gene cause cystic fibrosis (CF), a disease that leads to progressive respiratory illness and other complications of phenotypic variance resulting from perturbations of this protein interaction network. Studying the collection of CFTR interacting proteins and the differences between the interactomes of mutant and wild type CFTR provides insight into the molecular machinery of the disease and highlights possible therapeutic targets. This mini review focuses on functional genomics and proteomics approaches used for systematic, high-throughput identification of CFTR-interacting proteins to provide comprehensive insight into CFTR regulation and function.

Deconstruction of the beaten Path-Sidestep interaction network provides insights into neuromuscular system development

PubMed Central

Li, Hanqing; Watson, Ash; Olechwier, Agnieszka; Anaya, Michael; Sorooshyari, Siamak K; Harnett, Dermott P; Lee, Hyung-Kook (Peter); Vielmetter, Jost; Fares, Mario A; Garcia, K Christopher; Özkan, Engin

2017-01-01

An ‘interactome’ screen of all Drosophila cell-surface and secreted proteins containing immunoglobulin superfamily (IgSF) domains discovered a network formed by paralogs of Beaten Path (Beat) and Sidestep (Side), a ligand-receptor pair that is central to motor axon guidance. Here we describe a new method for interactome screening, the Bio-Plex Interactome Assay (BPIA), which allows identification of many interactions in a single sample. Using the BPIA, we ‘deorphanized’ four more members of the Beat-Side network. We confirmed interactions using surface plasmon resonance. The expression patterns of beat and side genes suggest that Beats are neuronal receptors for Sides expressed on peripheral tissues. side-VI is expressed in muscle fibers targeted by the ISNb nerve, as well as at growth cone choice points and synaptic targets for the ISN and TN nerves. beat-V genes, encoding Side-VI receptors, are expressed in ISNb and ISN motor neurons. PMID:28829740

Preparation of Gap Junctions in Membrane Microdomains for Immunoprecipitation and Mass Spectrometry Interactome Analysis.

PubMed

Fowler, Stephanie; Akins, Mark; Bennett, Steffany A L

2016-01-01

Protein interaction networks at gap junction plaques are increasingly implicated in a variety of intracellular signaling cascades. Identifying protein interactions of integral membrane proteins is a valuable tool for determining channel function. However, several technical challenges exist. Subcellular fractionation of the bait protein matrix is usually required to identify less abundant proteins in complex homogenates. Sufficient solvation of the lipid environment without perturbation of the protein interactome must also be achieved. The present chapter describes the flotation of light and heavy liver tissue membrane microdomains to facilitate the identification and analysis of endogenous gap junction proteins and includes technical notes for translation to other integral membrane proteins, tissues, or cell culture models. These procedures are valuable tools for the enrichment of gap junction membrane compartments and for the identification of gap junction signaling interactomes.

A highly efficient approach to protein interactome mapping based on collaborative filtering framework.

PubMed

Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng

2015-01-09

The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.

A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework

PubMed Central

Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng

2015-01-01

The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly. PMID:25572661

A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework

NASA Astrophysics Data System (ADS)

Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng

2015-01-01

The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.

The Protein Interactome of Mycobacteriophage Giles Predicts Functions for Unknown Proteins.

PubMed

Mehla, Jitender; Dedrick, Rebekah M; Caufield, J Harry; Siefring, Rachel; Mair, Megan; Johnson, Allison; Hatfull, Graham F; Uetz, Peter

2015-08-01

Mycobacteriophages are viruses that infect mycobacterial hosts and are prevalent in the environment. Nearly 700 mycobacteriophage genomes have been completely sequenced, revealing considerable diversity and genetic novelty. Here, we have determined the protein complement of mycobacteriophage Giles by mass spectrometry and mapped its genome-wide protein interactome to help elucidate the roles of its 77 predicted proteins, 50% of which have no known function. About 22,000 individual yeast two-hybrid (Y2H) tests with four different Y2H vectors, followed by filtering and retest screens, resulted in 324 reproducible protein-protein interactions, including 171 (136 nonredundant) high-confidence interactions. The complete set of high-confidence interactions among Giles proteins reveals new mechanistic details and predicts functions for unknown proteins. The Giles interactome is the first for any mycobacteriophage and one of just five known phage interactomes so far. Our results will help in understanding mycobacteriophage biology and aid in development of new genetic and therapeutic tools to understand Mycobacterium tuberculosis. Mycobacterium tuberculosis causes over 9 million new cases of tuberculosis each year. Mycobacteriophages, viruses of mycobacterial hosts, hold considerable potential to understand phage diversity, evolution, and mycobacterial biology, aiding in the development of therapeutic tools to control mycobacterial infections. The mycobacteriophage Giles protein-protein interaction network allows us to predict functions for unknown proteins and shed light on major biological processes in phage biology. For example, Giles gp76, a protein of unknown function, is found to associate with phage packaging and maturation. The functions of mycobacteriophage-derived proteins may suggest novel therapeutic approaches for tuberculosis. Our ORFeome clone set of Giles proteins and the interactome data will be useful resources for phage interactomics. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Systematic analyses of the ultraviolet radiation resistance-associated gene product (UVRAG) protein interactome by tandem affinity purification.

PubMed

Son, Ji-Hye; Hwang, Eurim C; Kim, Joungmok

2016-03-01

Ultraviolet radiation resistance-associated gene product (UVRAG) was originally identified as a protein involved in cellular responses to UV irradiation. Subsequent studies have demonstrated that UVRAG plays as an important role in autophagy, a lysosome-dependent catabolic program, as a part of a pro-autophagy PIK3C3/VPS34 lipid kinase complex. Several recent studies have shown that UVRAG is also involved in autophagy-independent cellular functions, such as DNA repair/stability and vesicular trafficking/fusion. Here, we examined the UVRAG protein interactome to obtain information about its functional network. To this end, we screened UVRAG-interacting proteins using a tandem affinity purification method coupled with MALDI-TOF/MS analysis. Our results demonstrate that UVRAG interacts with various proteins involved in a wide spectrum of cellular functions, including genome stability, protein translational elongation, protein localization (trafficking), vacuole organization, transmembrane transport as well as autophagy. Notably, the interactome list of high-confidence UVRAG-interacting proteins is enriched for proteins involved in the regulation of genome stability. Our systematic UVRAG interactome analysis should provide important clues for understanding a variety of UVRAG functions.

Towards Establishment of a Rice Stress Response Interactome

PubMed Central

Seo, Young-Su; Chern, Mawsheng; Bartley, Laura E.; Han, Muho; Jung, Ki-Hong; Lee, Insuk; Walia, Harkamal; Richter, Todd; Xu, Xia; Cao, Peijian; Bai, Wei; Ramanan, Rajeshwari; Amonpant, Fawn; Arul, Loganathan; Canlas, Patrick E.; Ruan, Randy; Park, Chang-Jin; Chen, Xuewei; Hwang, Sohyun; Jeon, Jong-Seong; Ronald, Pamela C.

2011-01-01

Rice (Oryza sativa) is a staple food for more than half the world and a model for studies of monocotyledonous species, which include cereal crops and candidate bioenergy grasses. A major limitation of crop production is imposed by a suite of abiotic and biotic stresses resulting in 30%–60% yield losses globally each year. To elucidate stress response signaling networks, we constructed an interactome of 100 proteins by yeast two-hybrid (Y2H) assays around key regulators of the rice biotic and abiotic stress responses. We validated the interactome using protein–protein interaction (PPI) assays, co-expression of transcripts, and phenotypic analyses. Using this interactome-guided prediction and phenotype validation, we identified ten novel regulators of stress tolerance, including two from protein classes not previously known to function in stress responses. Several lines of evidence support cross-talk between biotic and abiotic stress responses. The combination of focused interactome and systems analyses described here represents significant progress toward elucidating the molecular basis of traits of agronomic importance. PMID:21533176

Computational Framework for Analysis of Prey–Prey Associations in Interaction Proteomics Identifies Novel Human Protein–Protein Interactions and Networks

PubMed Central

Saha, Sudipto; Dazard, Jean-Eudes; Xu, Hua; Ewing, Rob M.

2013-01-01

Large-scale protein–protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific “bait” protein and its associated “prey” proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait–prey and cocomplexed preys (prey–prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein–protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait–prey and prey–prey interactions can be used to refine network topology and extend known protein networks. PMID:22845868

Ovarian Cancer Differential Interactome and Network Entropy Analysis Reveal New Candidate Biomarkers.

PubMed

Ayyildiz, Dilara; Gov, Esra; Sinha, Raghu; Arga, Kazim Yalcin

2017-05-01

Ovarian cancer is one of the most common cancers and has a high mortality rate due to insidious symptoms and lack of robust diagnostics. A hitherto understudied concept in cancer pathogenesis may offer new avenues for innovation in ovarian cancer biomarker development. Cancer cells are characterized by an increase in network entropy, and several studies have exploited this concept to identify disease-associated gene and protein modules. We report in this study the changes in protein-protein interactions (PPIs) in ovarian cancer within a differential network (interactome) analysis framework utilizing the entropy concept and gene expression data. A compendium of six transcriptome datasets that included 140 samples from laser microdissected epithelial cells of ovarian cancer patients and 51 samples from healthy population was obtained from Gene Expression Omnibus, and the high confidence human protein interactome (31,465 interactions among 10,681 proteins) was used. The uncertainties of the up- or downregulation of PPIs in ovarian cancer were estimated through an entropy formulation utilizing combined expression levels of genes, and the interacting protein pairs with minimum uncertainty were identified. We identified 105 proteins with differential PPI patterns scattered in 11 modules, each indicating significantly affected biological pathways in ovarian cancer such as DNA repair, cell proliferation-related mechanisms, nucleoplasmic translocation of estrogen receptor, extracellular matrix degradation, and inflammation response. In conclusion, we suggest several PPIs as biomarker candidates for ovarian cancer and discuss their future biological implications as potential molecular targets for pharmaceutical development as well. In addition, network entropy analysis is a concept that deserves greater research attention for diagnostic innovation in oncology and tumor pathogenesis.

The interactome of CCT complex - A computational analysis.

PubMed

Narayanan, Aswathy; Pullepu, Dileep; Kabir, M Anaul

2016-10-01

The eukaryotic chaperonin, CCT (Chaperonin Containing TCP1 or TriC-TCP-1 Ring Complex) has been subjected to physical and genetic analyses in S. cerevisiae which can be extrapolated to human CCT (hCCT), owing to its structural and functional similarities with yeast CCT (yCCT). Studies on hCCT and its interactome acquire an additional dimension, as it has been implicated in several disease conditions like neurodegeneration and cancer. We attempt to study its stress response role in general, which will be reflected in the aspects of human diseases and yeast physiology, through computational analysis of the interactome. Towards consolidating and analysing the interactome data, we prepared and compared the unique CCT-interacting protein lists for S. cerevisiae and H. sapiens, performed GO term classification and enrichment studies which provide information on the diversity in CCT interactome, in terms of protein classes in the data set. Enrichment with disease-associated proteins and pathways highlight the medical importance of CCT. Different analyses converge, suggesting the significance of WD-repeat proteins, protein kinases and cytoskeletal proteins in the interactome. The prevalence of proteasomal subunits and ribosomal proteins suggest a possible cross-talk between protein-synthesis, folding and degradation machinery. A network of chaperones and chaperonins that function in combination can also be envisaged from the CCT interactome-Hsp70 interactome analysis. Copyright © 2016 Elsevier Ltd. All rights reserved.

RASopathies: Presentation at the Genome, Interactome, and Phenome Levels.

PubMed

Pevec, Urska; Rozman, Neva; Gorsek, Blaz; Kunej, Tanja

2016-05-01

Clinical symptoms often reflect molecular correlations between mutated proteins. Alignment between interactome and phenome levels reveals new disease genes and connections between previously unrelated diseases. Despite a great potential for novel discoveries, this approach is still rarely used in genomics. In the present study, we analyzed the data of 6 syndromes belonging to the RASopathy class of disorders (RASopathies) and presented them as a model to study associations between genome, interactome, and phenome levels. Causative genes and clinical symptoms were collected from OMIM and NCBI GeneReviews databases for 6 syndromes: Noonan, Noonan syndrome with multiple lentigines, neurofibromatosis type 1, cardiofaciocutaneous, and Legius and Costello syndrome. The STRING tool was used for the identification of protein interactions. Six RASopathy syndromes were found to be associated with 12 causative genes. We constructed an interactome of RASopathy proteins and their neighbors and developed a database of 328 clinical symptoms. The collected data was presented at genome, interactome, and phenome levels and as an integrated network of all 3 data types. The present study provides a baseline for future studies of associations between interactome and phenome in RASopathies and could serve as a novel approach to analyze phenotypically and genetically related diseases.

Epigenetic Drug Repositioning for Alzheimer's Disease Based on Epigenetic Targets in Human Interactome.

PubMed

Chatterjee, Paulami; Roy, Debjani; Rathi, Nitin

2018-01-01

Epigenetics has emerged as an important field in drug discovery. Alzheimer's disease (AD), the leading neurodegenerative disorder throughout the world, is shown to have an epigenetic basis. Currently, there are very few effective epigenetic drugs available for AD. In this work, for the first time we have proposed 14 AD repositioning epigenetic drugs and identified their targets from extensive human interactome. Interacting partners of the AD epigenetic proteins were identified from the extensive human interactome to construct Epigenetic Protein-Protein Interaction Network (EP-PPIN). Epigenetic Drug-Target Network (EP-DTN) was constructed with the drugs associated with the proteins of EP-PPIN. Regulation of non-coding RNAs associated with the target proteins of these drugs was also studied. AD related target proteins, epigenetic targets, enriched pathways, and functional categories of the proposed repositioning drugs were also studied. The proposed 14 AD epigenetic repositioning drugs have overlapping targets and miRs with known AD epigenetic targets and miRs. Furthermore, several shared functional categories and enriched pathways were obtained for these drugs with FDA approved epigenetic drugs and known AD drugs. The findings of our work might provide insight into future AD epigenetic-therapeutics.

A Synthetic Coiled-Coil Interactome Provides Heterospecific Modules for Molecular Engineering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reinke, Aaron W.; Grant, Robert A.; Keating, Amy E.

2010-06-21

The versatile coiled-coil protein motif is widely used to induce and control macromolecular interactions in biology and materials science. Yet the types of interaction patterns that can be constructed using known coiled coils are limited. Here we greatly expand the coiled-coil toolkit by measuring the complete pairwise interactions of 48 synthetic coiled coils and 7 human bZIP coiled coils using peptide microarrays. The resulting 55-member protein 'interactome' includes 27 pairs of interacting peptides that preferentially heteroassociate. The 27 pairs can be used in combinations to assemble sets of 3 to 6 proteins that compose networks of varying topologies. Of specialmore » interest are heterospecific peptide pairs that participate in mutually orthogonal interactions. Such pairs provide the opportunity to dimerize two separate molecular systems without undesired crosstalk. Solution and structural characterization of two such sets of orthogonal heterodimers provide details of their interaction geometries. The orthogonal pair, along with the many other network motifs discovered in our screen, provide new capabilities for synthetic biology and other applications.« less

HTS-Net: An integrated regulome-interactome approach for establishing network regulation models in high-throughput screenings

PubMed Central

Rioualen, Claire; Da Costa, Quentin; Chetrit, Bernard; Charafe-Jauffret, Emmanuelle; Ginestier, Christophe

2017-01-01

High-throughput RNAi screenings (HTS) allow quantifying the impact of the deletion of each gene in any particular function, from virus-host interactions to cell differentiation. However, there has been less development for functional analysis tools dedicated to RNAi analyses. HTS-Net, a network-based analysis program, was developed to identify gene regulatory modules impacted in high-throughput screenings, by integrating transcription factors-target genes interaction data (regulome) and protein-protein interaction networks (interactome) on top of screening z-scores. HTS-Net produces exhaustive HTML reports for results navigation and exploration. HTS-Net is a new pipeline for RNA interference screening analyses that proves better performance than simple gene rankings by z-scores, by re-prioritizing genes and replacing them in their biological context, as shown by the three studies that we reanalyzed. Formatted input data for the three studied datasets, source code and web site for testing the system are available from the companion web site at http://htsnet.marseille.inserm.fr/. We also compared our program with existing algorithms (CARD and hotnet2). PMID:28949986

A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE).

PubMed

Stacey, R Greg; Skinnider, Michael A; Scott, Nichollas E; Foster, Leonard J

2017-10-23

An organism's protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome. Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE , where usage instructions can be found. An example dataset and output are also provided for testing purposes. PrInCE is the first fast and easy-to-use data analysis pipeline that predicts interactomes and protein complexes from co-elution data. PrInCE allows researchers without bioinformatics expertise to analyze high-throughput co-elution datasets.

ImmunemiR - A Database of Prioritized Immune miRNA Disease Associations and its Interactome.

PubMed

Prabahar, Archana; Natarajan, Jeyakumar

2017-01-01

MicroRNAs are the key regulators of gene expression and their abnormal expression in the immune system may be associated with several human diseases such as inflammation, cancer and autoimmune diseases. Elucidation of miRNA disease association through the interactome will deepen the understanding of its disease mechanisms. A specialized database for immune miRNAs is highly desirable to demonstrate the immune miRNA disease associations in the interactome. miRNAs specific to immune related diseases were retrieved from curated databases such as HMDD, miR2disease and PubMed literature based on MeSH classification of immune system diseases. The additional data such as miRNA target genes, genes coding protein-protein interaction information were compiled from related resources. Further, miRNAs were prioritized to specific immune diseases using random walk ranking algorithm. In total 245 immune miRNAs associated with 92 OMIM disease categories were identified from external databases. The resultant data were compiled as ImmunemiR, a database of prioritized immune miRNA disease associations. This database provides both text based annotation information and network visualization of its interactome. To our knowledge, ImmunemiR is the first available database to provide a comprehensive repository of human immune disease associated miRNAs with network visualization options of its target genes, protein-protein interactions (PPI) and its disease associations. It is freely available at http://www.biominingbu.org/immunemir/. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Reconstruction of the experimentally supported human protein interactome: what can we learn?

PubMed

Klapa, Maria I; Tsafou, Kalliopi; Theodoridis, Evangelos; Tsakalidis, Athanasios; Moschonas, Nicholas K

2013-10-02

Understanding the topology and dynamics of the human protein-protein interaction (PPI) network will significantly contribute to biomedical research, therefore its systematic reconstruction is required. Several meta-databases integrate source PPI datasets, but the protein node sets of their networks vary depending on the PPI data combined. Due to this inherent heterogeneity, the way in which the human PPI network expands via multiple dataset integration has not been comprehensively analyzed. We aim at assembling the human interactome in a global structured way and exploring it to gain insights of biological relevance. First, we defined the UniProtKB manually reviewed human "complete" proteome as the reference protein-node set and then we mined five major source PPI datasets for direct PPIs exclusively between the reference proteins. We updated the protein and publication identifiers and normalized all PPIs to the UniProt identifier level. The reconstructed interactome covers approximately 60% of the human proteome and has a scale-free structure. No apparent differentiating gene functional classification characteristics were identified for the unrepresented proteins. The source dataset integration augments the network mainly in PPIs. Polyubiquitin emerged as the highest-degree node, but the inclusion of most of its identified PPIs may be reconsidered. The high number (>300) of connections of the subsequent fifteen proteins correlates well with their essential biological role. According to the power-law network structure, the unrepresented proteins should mainly have up to four connections with equally poorly-connected interactors. Reconstructing the human interactome based on the a priori definition of the protein nodes enabled us to identify the currently included part of the human "complete" proteome, and discuss the role of the proteins within the network topology with respect to their function. As the network expansion has to comply with the scale-free theory, we suggest that the core of the human interactome has essentially emerged. Thus, it could be employed in systems biology and biomedical research, despite the considerable number of currently unrepresented proteins. The latter are probably involved in specialized physiological conditions, justifying the scarcity of related PPI information, and their identification can assist in designing relevant functional experiments and targeted text mining algorithms.

Developing and applying a gene functional association network for anti-angiogenic kinase inhibitor activity assessment in an angiogenesis co-culture model

PubMed Central

Chen, Yuefeng; Wei, Tao; Yan, Lei; Lawrence, Frank; Qian, Hui-Rong; Burkholder, Timothy P; Starling, James J; Yingling, Jonathan M; Shou, Jianyong

2008-01-01

Background Tumor angiogenesis is a highly regulated process involving intercellular communication as well as the interactions of multiple downstream signal transduction pathways. Disrupting one or even a few angiogenesis pathways is often insufficient to achieve sustained therapeutic benefits due to the complexity of angiogenesis. Targeting multiple angiogenic pathways has been increasingly recognized as a viable strategy. However, translation of the polypharmacology of a given compound to its antiangiogenic efficacy remains a major technical challenge. Developing a global functional association network among angiogenesis-related genes is much needed to facilitate holistic understanding of angiogenesis and to aid the development of more effective anti-angiogenesis therapeutics. Results We constructed a comprehensive gene functional association network or interactome by transcript profiling an in vitro angiogenesis model, in which human umbilical vein endothelial cells (HUVECs) formed capillary structures when co-cultured with normal human dermal fibroblasts (NHDFs). HUVEC competence and NHDF supportiveness of cord formation were found to be highly cell-passage dependent. An enrichment test of Biological Processes (BP) of differentially expressed genes (DEG) revealed that angiogenesis related BP categories significantly changed with cell passages. Built upon 2012 DEGs identified from two microarray studies, the resulting interactome captured 17226 functional gene associations and displayed characteristics of a scale-free network. The interactome includes the involvement of oncogenes and tumor suppressor genes in angiogenesis. We developed a network walking algorithm to extract connectivity information from the interactome and applied it to simulate the level of network perturbation by three multi-targeted anti-angiogenic kinase inhibitors. Simulated network perturbation correlated with observed anti-angiogenesis activity in a cord formation bioassay. Conclusion We established a comprehensive gene functional association network to model in vitro angiogenesis regulation. The present study provided a proof-of-concept pilot of applying network perturbation analysis to drug phenotypic activity assessment. PMID:18518970

Interactome Analysis of Microtubule-targeting Agents Reveals Cytotoxicity Bases in Normal Cells.

PubMed

Gutiérrez-Escobar, Andrés Julián; Méndez-Callejas, Gina

2017-12-01

Cancer causes millions of deaths annually and microtubule-targeting agents (MTAs) are the most commonly-used anti-cancer drugs. However, the high toxicity of MTAs on normal cells raises great concern. Due to the non-selectivity of MTA targets, we analyzed the interaction network in a non-cancerous human cell. Subnetworks of fourteen MTAs were reconstructed and the merged network was compared against a randomized network to evaluate the functional richness. We found that 71.4% of the MTA interactome nodes are shared, which affects cellular processes such as apoptosis, cell differentiation, cell cycle control, stress response, and regulation of energy metabolism. Additionally, possible secondary targets were identified as client proteins of interphase microtubules. MTAs affect apoptosis signaling pathways by interacting with client proteins of interphase microtubules, suggesting that their primary targets are non-tumor cells. The paclitaxel and doxorubicin networks share essential topological axes, suggesting synergistic effects. This may explain the exacerbated toxicity observed when paclitaxel and doxorubicin are used in combination for cancer treatment. Copyright © 2017 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Network topological analysis reveals the functional cohesiveness for the newly discovered links by Yeast 2 Hybrid approach

NASA Astrophysics Data System (ADS)

Ghiassian, Susan; Pevzner, Sam; Rolland, Thomas; Tassan, Murat; Barabasi, Albert Laszlo; Vidal, Mark; CCNR, Northeastern University Collaboration; Dana Farber Cancer Institute Collaboration

2014-03-01

Protein-protein interaction maps and interactomes are the blueprint of Network Medicine and systems biology and are being experimentally studied by different groups. Despite the wide usage of Literature Curated Interactome (LCI), these sources are biased towards different parameters such as highly studied proteins. Yeast two hybrid method is a high throughput experimental setup which screens proteins in an unbiased fashion. Current knowledge of protein interactions is far from complete. In fact the previous offered data from Y2H method (2005), is estimated to offer only 5% of all potential protein interactions. Currently this coverage has increased to 20% of what is known as reference HI In this work we study the topological properties of Y2H protein-protein interactions network with LCI and show although they both agree on some properties, LCI shows a clear unbiased nature of interaction selections. Most importantly, we assess the properties of PPI as it evolves with increasing the coverage. We show that, the newly discovered interactions tend to connect proteins that have been closer than average in the previous PPI release. reinforcing the modular structure of PPI. Furthermore, we show, some unseen effects on PPI (as opposed to LCI) can be explained by its incompleteness.

AIM: a comprehensive Arabidopsis interactome module database and related interologs in plants.

PubMed

Wang, Yi; Thilmony, Roger; Zhao, Yunjun; Chen, Guoping; Gu, Yong Q

2014-01-01

Systems biology analysis of protein modules is important for understanding the functional relationships between proteins in the interactome. Here, we present a comprehensive database named AIM for Arabidopsis (Arabidopsis thaliana) interactome modules. The database contains almost 250,000 modules that were generated using multiple analysis methods and integration of microarray expression data. All the modules in AIM are well annotated using multiple gene function knowledge databases. AIM provides a user-friendly interface for different types of searches and offers a powerful graphical viewer for displaying module networks linked to the enrichment annotation terms. Both interactive Venn diagram and power graph viewer are integrated into the database for easy comparison of modules. In addition, predicted interologs from other plant species (homologous proteins from different species that share a conserved interaction module) are available for each Arabidopsis module. AIM is a powerful systems biology platform for obtaining valuable insights into the function of proteins in Arabidopsis and other plants using the modules of the Arabidopsis interactome. Database URL:http://probes.pw.usda.gov/AIM Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

The OncoPPi network of cancer-focused protein-protein interactions to inform biological insights and therapeutic strategies* | Office of Cancer Genomics

Cancer.gov

As genomics advances reveal the cancer gene landscape, a daunting task is to understand how these genes contribute to dysregulated oncogenic pathways. Integration of cancer genes into networks offers opportunities to reveal protein–protein interactions (PPIs) with functional and therapeutic significance. Here, we report the generation of a cancer-focused PPI network, termed OncoPPi, and identification of >260 cancer-associated PPIs not in other large-scale interactomes.

High-Confidence Interactome for RNF41 Built on Multiple Orthogonal Assays.

PubMed

Masschaele, Delphine; Wauman, Joris; Vandemoortele, Giel; De Sutter, Delphine; De Ceuninck, Leentje; Eyckerman, Sven; Tavernier, Jan

2018-04-06

Ring finger protein 41 (RNF41) is an E3 ubiquitin ligase involved in the ubiquitination and degradation of many proteins including ErbB3 receptors, BIRC6, and parkin. Next to this, RNF41 regulates the intracellular trafficking of certain JAK2-associated cytokine receptors by ubiquitinating and suppressing USP8, which, in turn, destabilizes the ESCRT-0 complex. To further elucidate the function of RNF41 we used different orthogonal approaches to reveal the RNF41 protein complex: affinity purification-mass spectrometry, BioID, and Virotrap. We combined these results with known data sets for RNF41 obtained with microarray MAPPIT and Y2H screens. This way, we establish a comprehensive high-resolution interactome network comprising 175 candidate protein partners. To remove potential methodological artifacts from this network, we distilled the data into a high-confidence interactome map by retaining a total of 19 protein hits identified in two or more of the orthogonal methods. AP2S1, a novel RNF41 interaction partner, was selected from this high-confidence interactome for further functional validation. We reveal a role for AP2S1 in leptin and LIF receptor signaling and show that RNF41 stabilizes and relocates AP2S1.

Crowd Sourcing a New Paradigm for Interactome Driven Drug Target Identification in Mycobacterium tuberculosis

PubMed Central

Rohira, Harsha; Bhat, Ashwini G.; Passi, Anurag; Mukherjee, Keya; Choudhary, Kumari Sonal; Kumar, Vikas; Arora, Anshula; Munusamy, Prabhakaran; Subramanian, Ahalyaa; Venkatachalam, Aparna; S, Gayathri; Raj, Sweety; Chitra, Vijaya; Verma, Kaveri; Zaheer, Salman; J, Balaganesh; Gurusamy, Malarvizhi; Razeeth, Mohammed; Raja, Ilamathi; Thandapani, Madhumohan; Mevada, Vishal; Soni, Raviraj; Rana, Shruti; Ramanna, Girish Muthagadhalli; Raghavan, Swetha; Subramanya, Sunil N.; Kholia, Trupti; Patel, Rajesh; Bhavnani, Varsha; Chiranjeevi, Lakavath; Sengupta, Soumi; Singh, Pankaj Kumar; Atray, Naresh; Gandhi, Swati; Avasthi, Tiruvayipati Suma; Nisthar, Shefin; Anurag, Meenakshi; Sharma, Pratibha; Hasija, Yasha; Dash, Debasis; Sharma, Arun; Scaria, Vinod; Thomas, Zakir; Chandra, Nagasuma; Brahmachari, Samir K.; Bhardwaj, Anshu

2012-01-01

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ‘Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ‘interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach. PMID:22808064

A comparative study of disease genes and drug targets in the human protein interactome

PubMed Central

2015-01-01

Background Disease genes cause or contribute genetically to the development of the most complex diseases. Drugs are the major approaches to treat the complex disease through interacting with their targets. Thus, drug targets are critical for treatment efficacy. However, the interrelationship between the disease genes and drug targets is not clear. Results In this study, we comprehensively compared the network properties of disease genes and drug targets for five major disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease). We first collected disease genes from genome-wide association studies (GWAS) for five disease categories and collected their corresponding drugs based on drugs' Anatomical Therapeutic Chemical (ATC) classification. Then, we obtained the drug targets for these five different disease categories. We found that, though the intersections between disease genes and drug targets were small, disease genes were significantly enriched in targets compared to their enrichment in human protein-coding genes. We further compared network properties of the proteins encoded by disease genes and drug targets in human protein-protein interaction networks (interactome). The results showed that the drug targets tended to have higher degree, higher betweenness, and lower clustering coefficient in cancer Furthermore, we observed a clear fraction increase of disease proteins or drug targets in the near neighborhood compared with the randomized genes. Conclusions The study presents the first comprehensive comparison of the disease genes and drug targets in the context of interactome. The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing. PMID:25861037

A comparative study of disease genes and drug targets in the human protein interactome.

PubMed

Sun, Jingchun; Zhu, Kevin; Zheng, W; Xu, Hua

2015-01-01

Disease genes cause or contribute genetically to the development of the most complex diseases. Drugs are the major approaches to treat the complex disease through interacting with their targets. Thus, drug targets are critical for treatment efficacy. However, the interrelationship between the disease genes and drug targets is not clear. In this study, we comprehensively compared the network properties of disease genes and drug targets for five major disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease). We first collected disease genes from genome-wide association studies (GWAS) for five disease categories and collected their corresponding drugs based on drugs' Anatomical Therapeutic Chemical (ATC) classification. Then, we obtained the drug targets for these five different disease categories. We found that, though the intersections between disease genes and drug targets were small, disease genes were significantly enriched in targets compared to their enrichment in human protein-coding genes. We further compared network properties of the proteins encoded by disease genes and drug targets in human protein-protein interaction networks (interactome). The results showed that the drug targets tended to have higher degree, higher betweenness, and lower clustering coefficient in cancer Furthermore, we observed a clear fraction increase of disease proteins or drug targets in the near neighborhood compared with the randomized genes. The study presents the first comprehensive comparison of the disease genes and drug targets in the context of interactome. The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing.

Analysis of the interactome of the Ser/Thr Protein Phosphatase type 1 in Plasmodium falciparum.

PubMed

Hollin, Thomas; De Witte, Caroline; Lenne, Astrid; Pierrot, Christine; Khalife, Jamal

2016-03-17

Protein Phosphatase 1 (PP1) is an enzyme essential to cell viability in the malaria parasite Plasmodium falciparum (Pf). The activity of PP1 is regulated by the binding of regulatory subunits, of which there are up to 200 in humans, but only 3 have been so far reported for the parasite. To better understand the P. falciparum PP1 (PfPP1) regulatory network, we here report the use of three strategies to characterize the PfPP1 interactome: co-affinity purified proteins identified by mass spectrometry, yeast two-hybrid (Y2H) screening and in silico analysis of the P. falciparum predicted proteome. Co-affinity purification followed by MS analysis identified 6 PfPP1 interacting proteins (Pips) of which 3 contained the RVxF consensus binding, 2 with a Fxx[RK]x[RK] motif, also shown to be a PP1 binding motif and one with both binding motifs. The Y2H screens identified 134 proteins of which 30 present the RVxF binding motif and 20 have the Fxx[RK]x[RK] binding motif. The in silico screen of the Pf predicted proteome using a consensus RVxF motif as template revealed the presence of 55 potential Pips. As further demonstration, 35 candidate proteins were validated as PfPP1 interacting proteins in an ELISA-based assay. To the best of our knowledge, this is the first study on PfPP1 interactome. The data reports several conserved PP1 interacting proteins as well as a high number of specific interactors to PfPP1. Their analysis indicates a high diversity of biological functions for PP1 in Plasmodium. Based on the present data and on an earlier study of the Pf interactome, a potential implication of Pips in protein folding/proteolysis, transcription and pathogenicity networks is proposed. The present work provides a starting point for further studies on the structural basis of these interactions and their functions in P. falciparum.

Distinctive Behaviors of Druggable Proteins in Cellular Networks

PubMed Central

Workman, Paul; Al-Lazikani, Bissan

2015-01-01

The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/. PMID:26699810

The amyloid interactome: Exploring protein aggregation

PubMed Central

Mastrokalou, Chara V.; Hamodrakas, Stavros J.

2017-01-01

Protein-protein interactions are the quintessence of physiological activities, but also participate in pathological conditions. Amyloid formation, an abnormal protein-protein interaction process, is a widespread phenomenon in divergent proteins and peptides, resulting in a variety of aggregation disorders. The complexity of the mechanisms underlying amyloid formation/amyloidogenicity is a matter of great scientific interest, since their revelation will provide important insight on principles governing protein misfolding, self-assembly and aggregation. The implication of more than one protein in the progression of different aggregation disorders, together with the cited synergistic occurrence between amyloidogenic proteins, highlights the necessity for a more universal approach, during the study of these proteins. In an attempt to address this pivotal need we constructed and analyzed the human amyloid interactome, a protein-protein interaction network of amyloidogenic proteins and their experimentally verified interactors. This network assembled known interconnections between well-characterized amyloidogenic proteins and proteins related to amyloid fibril formation. The consecutive extended computational analysis revealed significant topological characteristics and unraveled the functional roles of all constituent elements. This study introduces a detailed protein map of amyloidogenicity that will aid immensely towards separate intervention strategies, specifically targeting sub-networks of significant nodes, in an attempt to design possible novel therapeutics for aggregation disorders. PMID:28249044

Determination of Protein Interactome of Transcription Factor Sox2 in Embryonic Stem Cells Engineered for Inducible Expression of Four Reprogramming Factors*

PubMed Central

Gao, Zhiguang; Cox, Jesse L.; Gilmore, Joshua M.; Ormsbee, Briana D.; Mallanna, Sunil K.; Washburn, Michael P.; Rizzino, Angie

2012-01-01

Unbiased proteomic screens provide a powerful tool for defining protein-protein interaction networks. Previous studies employed multidimensional protein identification technology to identify the Sox2-interactome in embryonic stem cells (ESC) undergoing differentiation in response to a small increase in the expression of epitope-tagged Sox2. Thus far the Sox2-interactome in ESC has not been determined. To identify the Sox2-interactome in ESC, we engineered ESC for inducible expression of different combinations of epitope-tagged Sox2 along with Oct4, Klf4, and c-Myc. Epitope-tagged Sox2 was used to circumvent the lack of suitable Sox2 antibodies needed to perform an unbiased proteomic screen of Sox2-associated proteins. Although i-OS-ESC differentiate when both Oct4 and Sox2 are elevated, i-OSKM-ESC do not differentiate even when the levels of the four transcription factors are coordinately elevated ∼2–3-fold. Our findings with i-OS-ESC and i-OSKM-ESC provide new insights into the reasons why ESC undergo differentiation when Sox2 and Oct4 are elevated in ESC. Importantly, the use of i-OSKM-ESC enabled us to identify the Sox2-interactome in undifferentiated ESC. Using multidimensional protein identification technology, we identified >70 proteins that associate with Sox2 in ESC. We extended these findings by testing the function of the Sox2-assoicated protein Smarcd1 and demonstrate that knockdown of Smarcd1 disrupts the self-renewal of ESC and induces their differentiation. Together, our work provides the first description of the Sox2-interactome in ESC and indicates that Sox2 along with other master regulators is part of a highly integrated protein-protein interaction landscape in ESC. PMID:22334693

Towards Personalized Medicine Mediated by in Vitro Virus-Based Interactome Approaches

PubMed Central

Ohashi, Hiroyuki; Miyamoto-Sato, Etsuko

2014-01-01

We have developed a simple in vitro virus (IVV) selection system based on cell-free co-translation, using a highly stable and efficient mRNA display method. The IVV system is applicable to the high-throughput and comprehensive analysis of proteins and protein–ligand interactions. Huge amounts of genomic sequence data have been generated over the last decade. The accumulated genetic alterations and the interactome networks identified within cells represent a universal feature of a disease, and knowledge of these aspects can help to determine the optimal therapy for the disease. The concept of the “integrome” has been developed as a means of integrating large amounts of data. We have developed an interactome analysis method aimed at providing individually-targeted health care. We also consider future prospects for this system. PMID:24756093

Reconstruction of the experimentally supported human protein interactome: what can we learn?

PubMed Central

2013-01-01

Background Understanding the topology and dynamics of the human protein-protein interaction (PPI) network will significantly contribute to biomedical research, therefore its systematic reconstruction is required. Several meta-databases integrate source PPI datasets, but the protein node sets of their networks vary depending on the PPI data combined. Due to this inherent heterogeneity, the way in which the human PPI network expands via multiple dataset integration has not been comprehensively analyzed. We aim at assembling the human interactome in a global structured way and exploring it to gain insights of biological relevance. Results First, we defined the UniProtKB manually reviewed human “complete” proteome as the reference protein-node set and then we mined five major source PPI datasets for direct PPIs exclusively between the reference proteins. We updated the protein and publication identifiers and normalized all PPIs to the UniProt identifier level. The reconstructed interactome covers approximately 60% of the human proteome and has a scale-free structure. No apparent differentiating gene functional classification characteristics were identified for the unrepresented proteins. The source dataset integration augments the network mainly in PPIs. Polyubiquitin emerged as the highest-degree node, but the inclusion of most of its identified PPIs may be reconsidered. The high number (>300) of connections of the subsequent fifteen proteins correlates well with their essential biological role. According to the power-law network structure, the unrepresented proteins should mainly have up to four connections with equally poorly-connected interactors. Conclusions Reconstructing the human interactome based on the a priori definition of the protein nodes enabled us to identify the currently included part of the human “complete” proteome, and discuss the role of the proteins within the network topology with respect to their function. As the network expansion has to comply with the scale-free theory, we suggest that the core of the human interactome has essentially emerged. Thus, it could be employed in systems biology and biomedical research, despite the considerable number of currently unrepresented proteins. The latter are probably involved in specialized physiological conditions, justifying the scarcity of related PPI information, and their identification can assist in designing relevant functional experiments and targeted text mining algorithms. PMID:24088582

The role of the interactome in the maintenance of deleterious variability in human populations

PubMed Central

Garcia-Alonso, Luz; Jiménez-Almazán, Jorge; Carbonell-Caballero, Jose; Vela-Boza, Alicia; Santoyo-López, Javier; Antiñolo, Guillermo; Dopazo, Joaquin

2014-01-01

Recent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins. PMID:25261458

The role of the interactome in the maintenance of deleterious variability in human populations.

PubMed

Garcia-Alonso, Luz; Jiménez-Almazán, Jorge; Carbonell-Caballero, Jose; Vela-Boza, Alicia; Santoyo-López, Javier; Antiñolo, Guillermo; Dopazo, Joaquin

2014-09-26

Recent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins. © 2014 The Authors. Published under the terms of the CC BY 4.0 license.

A protein interaction network analysis for yeast integral membrane protein.

PubMed

Shi, Ming-Guang; Huang, De-Shuang; Li, Xue-Ling

2008-01-01

Although the yeast Saccharomyces cerevisiae is the best exemplified single-celled eukaryote, the vast number of protein-protein interactions of integral membrane proteins of Saccharomyces cerevisiae have not been characterized by experiments. Here, based on the kernel method of Greedy Kernel Principal Component analysis plus Linear Discriminant Analysis, we identify 300 protein-protein interactions involving 189 membrane proteins and get the outcome of a highly connected protein-protein interactions network. Furthermore, we study the global topological features of integral membrane proteins network of Saccharomyces cerevisiae. These results give the comprehensive description of protein-protein interactions of integral membrane proteins and reveal global topological and robustness of the interactome network at a system level. This work represents an important step towards a comprehensive understanding of yeast protein interactions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shatsky, Maxim; Allen, Simon; Gold, Barbara

Numerous affinity purification – mass-spectrometry (AP-MS) and yeast two hybrid (Y2H) screens have each defined thousands of pairwise protein-protein interactions (PPIs), most between functionally unrelated proteins. The accuracy of these networks, however, is under debate. Here we present an AP-MS survey of the bacterium Desulfovibrio vulgaris together with a critical reanalysis of nine published bacterial Y2H and AP-MS screens. We have identified 459 high confidence PPIs from D. vulgaris and 391 from Escherichia coli. Compared to the nine published interactomes, our two networks are smaller; are much less highly connected; have significantly lower false discovery rates; and are much moremore » enriched in protein pairs that are encoded in the same operon, have similar functions, and are reproducibly detected in other physical interaction assays. Lastly, our work establishes more stringent benchmarks for the properties of protein interactomes and suggests that bona fide PPIs much more frequently involve protein partners that are annotated with similar functions or that can be validated in independent assays than earlier studies suggested.« less

Bcl2-associated Athanogene 3 Interactome Analysis Reveals a New Role in Modulating Proteasome Activity*

PubMed Central

Chen, Ying; Yang, Li-Na; Cheng, Li; Tu, Shun; Guo, Shu-Juan; Le, Huang-Ying; Xiong, Qian; Mo, Ran; Li, Chong-Yang; Jeong, Jun-Seop; Jiang, Lizhi; Blackshaw, Seth; Bi, Li-Jun; Zhu, Heng; Tao, Sheng-Ce; Ge, Feng

2013-01-01

Bcl2-associated athanogene 3 (BAG3), a member of the BAG family of co-chaperones, plays a critical role in regulating apoptosis, development, cell motility, autophagy, and tumor metastasis and in mediating cell adaptive responses to stressful stimuli. BAG3 carries a BAG domain, a WW domain, and a proline-rich repeat (PXXP), all of which mediate binding to different partners. To elucidate BAG3's interaction network at the molecular level, we employed quantitative immunoprecipitation combined with knockdown and human proteome microarrays to comprehensively profile the BAG3 interactome in humans. We identified a total of 382 BAG3-interacting proteins with diverse functions, including transferase activity, nucleic acid binding, transcription factors, proteases, and chaperones, suggesting that BAG3 is a critical regulator of diverse cellular functions. In addition, we characterized interactions between BAG3 and some of its newly identified partners in greater detail. In particular, bioinformatic analysis revealed that the BAG3 interactome is strongly enriched in proteins functioning within the proteasome-ubiquitination process and that compose the proteasome complex itself, suggesting that a critical biological function of BAG3 is associated with the proteasome. Functional studies demonstrated that BAG3 indeed interacts with the proteasome and modulates its activity, sustaining cell survival and underlying resistance to therapy through the down-modulation of apoptosis. Taken as a whole, this study expands our knowledge of the BAG3 interactome, provides a valuable resource for understanding how BAG3 affects different cellular functions, and demonstrates that biologically relevant data can be harvested using this kind of integrated approach. PMID:23824909

Visualisation and graph-theoretic analysis of a large-scale protein structural interactome

PubMed Central

Bolser, Dan; Dafas, Panos; Harrington, Richard; Park, Jong; Schroeder, Michael

2003-01-01

Background Large-scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in the PDB. PSIMAP incorporates both functional and evolutionary information into a single network. Results We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. Conclusions Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level. PMID:14531933

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa).

PubMed

Liu, Shiwei; Liu, Yihui; Zhao, Jiawei; Cai, Shitao; Qian, Hongmei; Zuo, Kaijing; Zhao, Lingxia; Zhang, Lida

2017-04-01

Rice (Oryza sativa) is one of the most important staple foods for more than half of the global population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome-wide rice protein-protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet) using machine learning with structural relationship and functional information. RicePPINet contained 708 819 predicted interactions for 16 895 non-transposable element related proteins. The power of the network for discovering novel protein interactions was demonstrated through comparison with other publicly available protein-protein interaction (PPI) prediction methods, and by experimentally determined PPI data sets. Furthermore, global analysis of domain-mediated interactions revealed RicePPINet accurately reflects PPIs at the domain level. Our studies showed the efficiency of the RicePPINet-based method in prioritizing candidate genes involved in complex agronomic traits, such as disease resistance and drought tolerance, was approximately 2-11 times better than random prediction. RicePPINet provides an expanded landscape of computational interactome for the genetic dissection of agronomically important traits in rice. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

CASTIN: a system for comprehensive analysis of cancer-stromal interactome.

PubMed

Komura, Daisuke; Isagawa, Takayuki; Kishi, Kazuki; Suzuki, Ryohei; Sato, Reiko; Tanaka, Mariko; Katoh, Hiroto; Yamamoto, Shogo; Tatsuno, Kenji; Fukayama, Masashi; Aburatani, Hiroyuki; Ishikawa, Shumpei

2016-11-09

Cancer microenvironment plays a vital role in cancer development and progression, and cancer-stromal interactions have been recognized as important targets for cancer therapy. However, identifying relevant and druggable cancer-stromal interactions is challenging due to the lack of quantitative methods to analyze whole cancer-stromal interactome. We present CASTIN (CAncer-STromal INteractome analysis), a novel framework for the evaluation of cancer-stromal interactome from RNA-Seq data using cancer xenograft models. For each ligand-receptor interaction which is derived from curated protein-protein interaction database, CASTIN summarizes gene expression profiles of cancer and stroma into three evaluation indices. These indices provide quantitative evaluation and comprehensive visualization of interactome, and thus enable to identify critical cancer-microenvironment interactions, which would be potential drug targets. We applied CASTIN to the dataset of pancreas ductal adenocarcinoma, and successfully characterized the individual cancer in terms of cancer-stromal relationships, and identified both well-known and less-characterized druggable interactions. CASTIN provides comprehensive view of cancer-stromal interactome and is useful to identify critical interactions which may serve as potential drug targets in cancer-microenvironment. CASTIN is available at: http://github.com/tmd-gpat/CASTIN .

A Systems Biology Approach to Reveal Putative Host-Derived Biomarkers of Periodontitis by Network Topology Characterization of MMP-REDOX/NO and Apoptosis Integrated Pathways.

PubMed

Zeidán-Chuliá, Fares; Gürsoy, Mervi; Neves de Oliveira, Ben-Hur; Özdemir, Vural; Könönen, Eija; Gürsoy, Ulvi K

2015-01-01

Periodontitis, a formidable global health burden, is a common chronic disease that destroys tooth-supporting tissues. Biomarkers of the early phase of this progressive disease are of utmost importance for global health. In this context, saliva represents a non-invasive biosample. By using systems biology tools, we aimed to (1) identify an integrated interactome between matrix metalloproteinase (MMP)-REDOX/nitric oxide (NO) and apoptosis upstream pathways of periodontal inflammation, and (2) characterize the attendant topological network properties to uncover putative biomarkers to be tested in saliva from patients with periodontitis. Hence, we first generated a protein-protein network model of interactions ("BIOMARK" interactome) by using the STRING 10 database, a search tool for the retrieval of interacting genes/proteins, with "Experiments" and "Databases" as input options and a confidence score of 0.400. Second, we determined the centrality values (closeness, stress, degree or connectivity, and betweenness) for the "BIOMARK" members by using the Cytoscape software. We found Ubiquitin C (UBC), Jun proto-oncogene (JUN), and matrix metalloproteinase-14 (MMP14) as the most central hub- and non-hub-bottlenecks among the 211 genes/proteins of the whole interactome. We conclude that UBC, JUN, and MMP14 are likely an optimal candidate group of host-derived biomarkers, in combination with oral pathogenic bacteria-derived proteins, for detecting periodontitis at its early phase by using salivary samples from patients. These findings therefore have broader relevance for systems medicine in global health as well.

Identification of the PAK4 interactome reveals PAK4 phosphorylation of N-WASP and promotion of Arp2/3-dependent actin polymerization.

PubMed

Zhao, Miao; Spiess, Matthias; Johansson, Henrik J; Olofsson, Helene; Hu, Jianjiang; Lehtiö, Janne; Strömblad, Staffan

2017-09-29

p21-activated kinase 4 (PAK4) regulates cell proliferation, apoptosis, cell motility and F-actin remodeling, but the PAK4 interactome has not been systematically analyzed. Here, we comprehensively characterized the human PAK4 interactome by iTRAQ quantitative mass spectrometry of PAK4-immunoprecipitations. Consistent with its multiple reported functions, the PAK4 interactome was enriched in diverse protein networks, including the 14-3-3, proteasome, replication fork, CCT and Arp2/3 complexes. Because PAK4 co-immunoprecipitated most subunits of the Arp2/3 complex, we hypothesized that PAK4 may play a role in Arp2/3 dependent actin regulation. Indeed, we found that PAK4 interacts with and phosphorylates the nucleation promoting factor N-WASP at Ser484/Ser485 and promotes Arp2/3-dependent actin polymerization in vitro. Also, PAK4 ablation in vivo reduced N-WASP Ser484/Ser485 phosphorylation and altered the cellular balance between G- and F-actin as well as the actin organization. By presenting the PAK4 interactome, we here provide a powerful resource for further investigations and as proof of principle, we also indicate a novel mechanism by which PAK4 regulates actin cytoskeleton remodeling.

Searching for cellular partners of hantaviral nonstructural protein NSs: Y2H screening of mouse cDNA library and analysis of cellular interactome.

PubMed

Rönnberg, Tuomas; Jääskeläinen, Kirsi; Blot, Guillaume; Parviainen, Ville; Vaheri, Antti; Renkonen, Risto; Bouloy, Michele; Plyusnin, Alexander

2012-01-01

Hantaviruses (Bunyaviridae) are negative-strand RNA viruses with a tripartite genome. The small (S) segment encodes the nucleocapsid protein and, in some hantaviruses, also the nonstructural protein (NSs). The aim of this study was to find potential cellular partners for the hantaviral NSs protein. Toward this aim, yeast two-hybrid (Y2H) screening of mouse cDNA library was performed followed by a search for potential NSs protein counterparts via analyzing a cellular interactome. The resulting interaction network was shown to form logical, clustered structures. Furthermore, several potential binding partners for the NSs protein, for instance ACBD3, were identified and, to prove the principle, interaction between NSs and ACBD3 proteins was demonstrated biochemically.

Interactome INSIDER: a structural interactome browser for genomic studies.

PubMed

Meyer, Michael J; Beltrán, Juan Felipe; Liang, Siqi; Fragoza, Robert; Rumack, Aaron; Liang, Jin; Wei, Xiaomu; Yu, Haiyuan

2018-01-01

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

The cardiac TBX5 interactome reveals a chromatin remodeling network essential for cardiac septation

PubMed Central

Waldron, Lauren; Steimle, Jeffrey D.; Greco, Todd M.; Gomez, Nicholas C.; Dorr, Kerry M.; Kweon, Junghun; Temple, Brenda; Yang, Xinan Holly; Wilczewski, Caralynn M.; Davis, Ian J.; Cristea, Ileana M.; Moskowitz, Ivan P.; Conlon, Frank L.

2016-01-01

SUMMARY Human mutations in the cardiac transcription factor gene TBX5 cause Congenital Heart Disease (CHD), however the underlying mechanism is unknown. We report characterization of the endogenous TBX5 cardiac interactome and demonstrate that TBX5, long considered a transcriptional activator, interacts biochemically and genetically with the Nucleosome Remodeling and Deacetylase (NuRD) repressor complex. Incompatible gene programs are repressed by TBX5 in the developing heart. CHD missense mutations that disrupt the TBX5-NuRD interaction cause depression of a subset of repressed genes. Furthermore, the TBX5-NuRD interaction is required for heart development. Phylogenetic analysis showed that the TBX5-NuRD interaction domain evolved during early diversification of vertebrates, simultaneous with the evolution of cardiac septation. Collectively, this work defines a TBX5-NuRD interaction essential to cardiac development and the evolution of the mammalian heart, and when altered may contribute to human CHD. PMID:26859351

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network.

PubMed

Al-Harazi, Olfat; Al Insaif, Sadiq; Al-Ajlan, Monirah A; Kaya, Namik; Dzimiri, Nduna; Colak, Dilek

2016-06-20

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field. Copyright © 2015 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Ltd. All rights reserved.

The protein interactome of collapsin response mediator protein-2 (CRMP2/DPYSL2) reveals novel partner proteins in brain tissue.

PubMed

Martins-de-Souza, Daniel; Cassoli, Juliana S; Nascimento, Juliana M; Hensley, Kenneth; Guest, Paul C; Pinzon-Velasco, Andres M; Turck, Christoph W

2015-10-01

Collapsin response mediator protein-2 (CRMP2) is a CNS protein involved in neuronal development, axonal and neuronal growth, cell migration, and protein trafficking. Recent studies have linked perturbations in CRMP2 function to neurodegenerative disorders such as Alzheimer's disease, neuropathic pain, and Batten disease, and to psychiatric disorders such as schizophrenia. Like most proteins, CRMP2 functions though interactions with a molecular network of proteins and other molecules. Here, we have attempted to identify additional proteins of the CRMP2 interactome to provide further leads about its roles in neurological functions. We used a combined co-immunoprecipitation and shotgun proteomic approach in order to identify CRMP2 protein partners. We identified 78 CRMP2 protein partners not previously reported in public protein interaction databases. These were involved in seven biological processes, which included cell signaling, growth, metabolism, trafficking, and immune function, according to Gene Ontology classifications. Furthermore, 32 different molecular functions were found to be associated with these proteins, such as RNA binding, ribosomal functions, transporter activity, receptor activity, serine/threonine phosphatase activity, cell adhesion, cytoskeletal protein binding and catalytic activity. In silico pathway interactome construction revealed a highly connected network with the most overrepresented functions corresponding to semaphorin interactions, along with axon guidance and WNT5A signaling. Taken together, these findings suggest that the CRMP2 pathway is critical for regulating neuronal and synaptic architecture. Further studies along these lines might uncover novel biomarkers and drug targets for use in drug discovery. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Systematic Differences in Signal Emitting and Receiving Revealed by PageRank Analysis of a Human Protein Interactome

PubMed Central

Li, Xiu-Qing

2012-01-01

Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A) What is the general difference between signal emitting and receiving in a protein interactome? B) Which proteins are among the top ranked in directional ranking? C) Are high ranked proteins more evolutionarily conserved than low ranked ones? D) Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell. PMID:23028653

A network biology-based approach to evaluating the effect of environmental contaminants on human interactome and diseases.

PubMed

Iida, M; Takemoto, K

2018-09-30

Environmental contaminant exposure can pose significant risks to human health. Therefore, evaluating the impact of this exposure is of great importance; however, it is often difficult because both the molecular mechanism of disease and the mode of action of the contaminants are complex. We used network biology techniques to quantitatively assess the impact of environmental contaminants on the human interactome and diseases with a particular focus on seven major contaminant categories: persistent organic pollutants (POPs), dioxins, polycyclic aromatic hydrocarbons (PAHs), pesticides, perfluorochemicals (PFCs), metals, and pharmaceutical and personal care products (PPCPs). We integrated publicly available data on toxicogenomics, the diseasome, protein-protein interactions (PPIs), and gene essentiality and found that a few contaminants were targeted to many genes, and a few genes were targeted by many contaminants. The contaminant targets were hub proteins in the human PPI network, whereas the target proteins in most categories did not contain abundant essential proteins. Generally, contaminant targets and disease-associated proteins were closely associated with the PPI network, and the closeness of the associations depended on the disease type and chemical category. Network biology techniques were used to identify environmental contaminants with broad effects on the human interactome and contaminant-sensitive biomarkers. Moreover, this method enabled us to quantify the relationship between environmental contaminants and human diseases, which was supported by epidemiological and experimental evidence. These methods and findings have facilitated the elucidation of the complex relationship between environmental exposure and adverse health outcomes. Copyright © 2018 Elsevier Inc. All rights reserved.

The distinctive cell division interactome of Neisseria gonorrhoeae.

PubMed

Zou, Yinan; Li, Yan; Dillon, Jo-Anne R

2017-12-12

Bacterial cell division is an essential process driven by the formation of a Z-ring structure, as a cytoskeletal scaffold at the mid-cell, followed by the recruitment of various proteins which form the divisome. The cell division interactome reflects the complement of different interactions between all divisome proteins. To date, only two cell division interactomes have been characterized, in Escherichia coli and in Streptococcus pneumoniae. The cell divison proteins encoded by Neisseria gonorrhoeae include FtsZ, FtsA, ZipA, FtsK, FtsQ, FtsI, FtsW, and FtsN. The purpose of the present study was to characterize the cell division interactome of N. gonorrhoeae using several different methods to identify protein-protein interactions. We also characterized the specific subdomains of FtsA implicated in interactions with FtsZ, FtsQ, FtsN and FtsW. Using a combination of bacterial two-hybrid (B2H), glutathione S-transferase (GST) pull-down assays, and surface plasmon resonance (SPR), nine interactions were observed among the eight gonococcal cell division proteins tested. ZipA did not interact with any other cell division proteins. Comparisons of the N. gonorrhoeae cell division interactome with the published interactomes from E. coli and S. pneumoniae indicated that FtsA-FtsZ and FtsZ-FtsK interactions were common to all three species. FtsA-FtsW and FtsK-FtsN interactions were only present in N. gonorrhoeae. The 2A and 2B subdomains of FtsA Ng were involved in interactions with FtsQ, FtsZ, and FtsN, and the 2A subdomain was involved in interaction with FtsW. Results from this research indicate that N. gonorrhoeae has a distinctive cell division interactome as compared with other microorganisms.

The interactome of the copper transporter ATP7A belongs to a network of neurodevelopmental and neurodegeneration factors

PubMed Central

Comstra, Heather S; McArthy, Jacob; Rudin-Rush, Samantha; Hartwig, Cortnie; Gokhale, Avanti; Zlatic, Stephanie A; Blackburn, Jessica B; Werner, Erica; Petris, Michael; D’Souza, Priya; Panuwet, Parinya; Barr, Dana Boyd; Lupashin, Vladimir; Vrailas-Mortimer, Alysia; Faundez, Victor

2017-01-01

Genetic and environmental factors, such as metals, interact to determine neurological traits. We reasoned that interactomes of molecules handling metals in neurons should include novel metal homeostasis pathways. We focused on copper and its transporter ATP7A because ATP7A null mutations cause neurodegeneration. We performed ATP7A immunoaffinity chromatography and identified 541 proteins co-isolating with ATP7A. The ATP7A interactome concentrated gene products implicated in neurodegeneration and neurodevelopmental disorders, including subunits of the Golgi-localized conserved oligomeric Golgi (COG) complex. COG null cells possess altered content and subcellular localization of ATP7A and CTR1 (SLC31A1), the transporter required for copper uptake, as well as decreased total cellular copper, and impaired copper-dependent metabolic responses. Changes in the expression of ATP7A and COG subunits in Drosophila neurons altered synapse development in larvae and copper-induced mortality of adult flies. We conclude that the ATP7A interactome encompasses a novel COG-dependent mechanism to specify neuronal development and survival. DOI: http://dx.doi.org/10.7554/eLife.24722.001 PMID:28355134

Vascular Endothelial Growth Factor (VEGF) Promotes Assembly of the p130Cas Interactome to Drive Endothelial Chemotactic Signaling and Angiogenesis.

PubMed

Evans, Ian M; Kennedy, Susan A; Paliashvili, Ketevan; Santra, Tapesh; Yamaji, Maiko; Lovering, Ruth C; Britton, Gary; Frankel, Paul; Kolch, Walter; Zachary, Ian C

2017-02-01

p130Cas is a polyvalent adapter protein essential for cardiovascular development, and with a key role in cell movement. In order to identify the pathways by which p130Cas exerts its biological functions in endothelial cells we mapped the p130Cas interactome and its dynamic changes in response to VEGF using high-resolution mass spectrometry and reconstruction of protein interaction (PPI) networks with the aid of multiple PPI databases. VEGF enriched the p130Cas interactome in proteins involved in actin cytoskeletal dynamics and cell movement, including actin-binding proteins, small GTPases and regulators or binders of GTPases. Detailed studies showed that p130Cas association of the GTPase-binding scaffold protein, IQGAP1, plays a key role in VEGF chemotactic signaling, endothelial polarization, VEGF-induced cell migration, and endothelial tube formation. These findings indicate a cardinal role for assembly of the p130Cas interactome in mediating the cell migratory response to VEGF in angiogenesis, and provide a basis for further studies of p130Cas in cell movement. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Competing endogenous RNA and interactome bioinformatic analyses on human telomerase.

PubMed

Arancio, Walter; Pizzolanti, Giuseppe; Genovese, Swonild Ilenia; Baiamonte, Concetta; Giordano, Carla

2014-04-01

We present a classic interactome bioinformatic analysis and a study on competing endogenous (ce) RNAs for hTERT. The hTERT gene codes for the catalytic subunit and limiting component of the human telomerase complex. Human telomerase reverse transcriptase (hTERT) is essential for the integrity of telomeres. Telomere dysfunctions have been widely reported to be involved in aging, cancer, and cellular senescence. The hTERT gene network has been analyzed using the BioGRID interaction database (http://thebiogrid.org/) and related analysis tools such as Osprey (http://biodata.mshri.on.ca/osprey/servlet/Index) and GeneMANIA (http://genemania.org/). The network of interaction of hTERT transcripts has been further analyzed following the competing endogenous (ce) RNA hypotheses (messenger [m] RNAs cross-talk via micro [mi] RNAs) using the miRWalk database and tools (www.ma.uni-heidelberg.de/apps/zmf/mirwalk/). These analyses suggest a role for Akt, nuclear factor-κB (NF-κB), heat shock protein 90 (HSP90), p70/p80 autoantigen, 14-3-3 proteins, and dynein in telomere functions. Roles for histone acetylation/deacetylation and proteoglycan metabolism are also proposed.

A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize

PubMed Central

Musungu, Bryan; Bhatnagar, Deepak; Brown, Robert L.; Fakhoury, Ahmad M.; Geisler, Matt

2015-01-01

Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM) is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs) that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize. PMID:26089837

BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference.

PubMed

Garcia-Garcia, Javier; Schleker, Sylvia; Klein-Seetharaman, Judith; Oliva, Baldo

2012-07-01

Protein-protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

Centrality in the host-pathogen interactome is associated with pathogen fitness during infection.

PubMed

Crua Asensio, Núria; Muñoz Giner, Elisabet; de Groot, Natalia Sánchez; Torrent Burgas, Marc

2017-01-16

To perform their functions proteins must interact with each other, but how these interactions influence bacterial infection remains elusive. Here we demonstrate that connectivity in the host-pathogen interactome is directly related to pathogen fitness during infection. Using Y. pestis as a model organism, we show that the centrality-lethality rule holds for pathogen fitness during infection but only when the host-pathogen interactome is considered. Our results suggest that the importance of pathogen proteins during infection is directly related to their number of interactions with the host. We also show that pathogen proteins causing an extensive rewiring of the host interactome have a higher impact in pathogen fitness during infection. Hence, we conclude that hubs in the host-pathogen interactome should be explored as promising targets for antimicrobial drug design.

Centrality in the host-pathogen interactome is associated with pathogen fitness during infection

NASA Astrophysics Data System (ADS)

Crua Asensio, Núria; Muñoz Giner, Elisabet; de Groot, Natalia Sánchez; Torrent Burgas, Marc

2017-01-01

To perform their functions proteins must interact with each other, but how these interactions influence bacterial infection remains elusive. Here we demonstrate that connectivity in the host-pathogen interactome is directly related to pathogen fitness during infection. Using Y. pestis as a model organism, we show that the centrality-lethality rule holds for pathogen fitness during infection but only when the host-pathogen interactome is considered. Our results suggest that the importance of pathogen proteins during infection is directly related to their number of interactions with the host. We also show that pathogen proteins causing an extensive rewiring of the host interactome have a higher impact in pathogen fitness during infection. Hence, we conclude that hubs in the host-pathogen interactome should be explored as promising targets for antimicrobial drug design.

Elucidation of the 14-3-3ζ interactome reveals critical roles of RNA-splicing factors during adipogenesis.

PubMed

Mugabo, Yves; Sadeghi, Mina; Fang, Nancy N; Mayor, Thibault; Lim, Gareth E

2018-05-04

Adipogenesis involves a complex signaling network requiring strict temporal and spatial organization of effector molecules. Molecular scaffolds, such as 14-3-3 proteins, facilitate such organization, and we have previously identified 14-3-3ζ as an essential scaffold in adipocyte differentiation. The interactome of 14-3-3ζ is large and diverse, and it is possible that novel adipogenic factors may be present within it, but this possibility has not yet been tested. Herein, we generated mouse embryonic fibroblasts from mice overexpressing a tandem affinity purification (TAP) epitope-tagged 14-3-3ζ molecule. After inducing adipogenesis, TAP-14-3-3ζ complexes were purified, followed by MS analysis to determine the 14-3-3ζ interactome. We observed more than 100 proteins that were unique to adipocyte differentiation, 56 of which were novel interacting partners. Among these, we were able to identify previously established regulators of adipogenesis ( i.e. Ptrf/Cavin1) within the 14-3-3ζ interactome, confirming the utility of this approach to detect adipogenic factors. We found that proteins related to RNA metabolism, processing, and splicing were enriched in the interactome. Analysis of transcriptomic data revealed that 14-3-3ζ depletion in 3T3-L1 cells affected alternative splicing of mRNA during adipocyte differentiation. siRNA-mediated depletion of RNA-splicing factors within the 14-3-3ζ interactome, that is, of Hnrpf, Hnrpk, Ddx6, and Sfpq, revealed that they have essential roles in adipogenesis and in the alternative splicing of Pparg and the adipogenesis-associated gene Lpin1 In summary, we have identified novel adipogenic factors within the 14-3-3ζ interactome. Further characterization of additional proteins within the 14-3-3ζ interactome may help identify novel targets to block obesity-associated expansion of adipose tissues. © 2018 by The American Society for Biochemistry and Molecular Biology, Inc.

The Interactomic Analysis Reveals Pathogenic Protein Networks in Phomopsis longicolla Underlying Seed Decay of Soybean.

PubMed

Li, Shuxian; Musungu, Bryan; Lightfoot, David; Ji, Pingsheng

2018-01-01

Phomopsis longicolla T. W. Hobbs (syn. Diaporthe longicolla ) is the primary cause of Phomopsis seed decay (PSD) in soybean, Glycine max (L.) Merrill. This disease results in poor seed quality and is one of the most economically important seed diseases in soybean. The objectives of this study were to infer protein-protein interactions (PPI) and to identify conserved global networks and pathogenicity subnetworks in P. longicolla including orthologous pathways for cell signaling and pathogenesis. The interlog method used in the study identified 215,255 unique PPIs among 3,868 proteins. There were 1,414 pathogenicity related genes in P. longicolla identified using the pathogen host interaction (PHI) database. Additionally, 149 plant cell wall degrading enzymes (PCWDE) were detected. The network captured five different classes of carbohydrate degrading enzymes, including the auxiliary activities, carbohydrate esterases, glycoside hydrolases, glycosyl transferases, and carbohydrate binding molecules. From the PPI analysis, novel interacting partners were determined for each of the PCWDE classes. The most predominant class of PCWDE was a group of 60 glycoside hydrolases proteins. The glycoside hydrolase subnetwork was found to be interacting with 1,442 proteins within the network and was among the largest clusters. The orthologous proteins FUS3, HOG, CYP1, SGE1, and the g5566t.1 gene identified in this study could play an important role in pathogenicity. Therefore, the P. longicolla protein interactome (PiPhom) generated in this study can lead to a better understanding of PPIs in soybean pathogens. Furthermore, the PPI may aid in targeting of genes and proteins for further studies of the pathogenicity mechanisms.

The Interactomic Analysis Reveals Pathogenic Protein Networks in Phomopsis longicolla Underlying Seed Decay of Soybean

PubMed Central

Li, Shuxian; Musungu, Bryan; Lightfoot, David; Ji, Pingsheng

2018-01-01

Phomopsis longicolla T. W. Hobbs (syn. Diaporthe longicolla) is the primary cause of Phomopsis seed decay (PSD) in soybean, Glycine max (L.) Merrill. This disease results in poor seed quality and is one of the most economically important seed diseases in soybean. The objectives of this study were to infer protein–protein interactions (PPI) and to identify conserved global networks and pathogenicity subnetworks in P. longicolla including orthologous pathways for cell signaling and pathogenesis. The interlog method used in the study identified 215,255 unique PPIs among 3,868 proteins. There were 1,414 pathogenicity related genes in P. longicolla identified using the pathogen host interaction (PHI) database. Additionally, 149 plant cell wall degrading enzymes (PCWDE) were detected. The network captured five different classes of carbohydrate degrading enzymes, including the auxiliary activities, carbohydrate esterases, glycoside hydrolases, glycosyl transferases, and carbohydrate binding molecules. From the PPI analysis, novel interacting partners were determined for each of the PCWDE classes. The most predominant class of PCWDE was a group of 60 glycoside hydrolases proteins. The glycoside hydrolase subnetwork was found to be interacting with 1,442 proteins within the network and was among the largest clusters. The orthologous proteins FUS3, HOG, CYP1, SGE1, and the g5566t.1 gene identified in this study could play an important role in pathogenicity. Therefore, the P. longicolla protein interactome (PiPhom) generated in this study can lead to a better understanding of PPIs in soybean pathogens. Furthermore, the PPI may aid in targeting of genes and proteins for further studies of the pathogenicity mechanisms. PMID:29666630

Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

PubMed

Brady, Arthur; Maxwell, Kyle; Daniels, Noah; Cowen, Lenore J

2009-01-01

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.

Fault Tolerance in Protein Interaction Networks: Stable Bipartite Subgraphs and Redundant Pathways

PubMed Central

Brady, Arthur; Maxwell, Kyle; Daniels, Noah; Cowen, Lenore J.

2009-01-01

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair. PMID:19399174

Identification of the Interactome of a Palmitoylated Membrane Protein, Phosphatidylinositol 4-Kinase Type II Alpha.

PubMed

Gokhale, Avanti; Ryder, Pearl V; Zlatic, Stephanie A; Faundez, Victor

2016-01-01

Phosphatidylinositol 4-kinases (PI4K) are enzymes responsible for the production of phosphatidylinositol 4-phosphates, important intermediates in several cell signaling pathways. PI4KIIα is the most abundant membrane-associated kinase in mammalian cells and is involved in a variety of essential cellular functions. However, the precise role(s) of PI4KIIα in the cell is not yet completely deciphered. Here we present an experimental protocol that uses a chemical cross-linker, DSP, combined with immunoprecipitation and immunoaffinity purification to identify novel PI4KIIα interactors. As predicted, PI4KIIα participates in transient, low-affinity interactions that are stabilized by the use of DSP. Using this optimized protocol we have successfully identified actin cytoskeleton regulators-the WASH complex and RhoGEF1, as major novel interactors of PI4KIIα. While this chapter focuses on the PI4KIIα interactome, this protocol can and has been used to generate other membrane interactome networks.

SteinerNet: a web server for integrating ‘omic’ data to discover hidden components of response pathways

PubMed Central

Tuncbag, Nurcan; McCallum, Scott; Huang, Shao-shan Carol; Fraenkel, Ernest

2012-01-01

High-throughput technologies including transcriptional profiling, proteomics and reverse genetics screens provide detailed molecular descriptions of cellular responses to perturbations. However, it is difficult to integrate these diverse data to reconstruct biologically meaningful signaling networks. Previously, we have established a framework for integrating transcriptional, proteomic and interactome data by searching for the solution to the prize-collecting Steiner tree problem. Here, we present a web server, SteinerNet, to make this method available in a user-friendly format for a broad range of users with data from any species. At a minimum, a user only needs to provide a set of experimentally detected proteins and/or genes and the server will search for connections among these data from the provided interactomes for yeast, human, mouse, Drosophila melanogaster and Caenorhabditis elegans. More advanced users can upload their own interactome data as well. The server provides interactive visualization of the resulting optimal network and downloadable files detailing the analysis and results. We believe that SteinerNet will be useful for researchers who would like to integrate their high-throughput data for a specific condition or cellular response and to find biologically meaningful pathways. SteinerNet is accessible at http://fraenkel.mit.edu/steinernet. PMID:22638579

The NRF2-related interactome and regulome contain multifunctional proteins and fine-tuned autoregulatory loops.

PubMed

Papp, Diána; Lenti, Katalin; Módos, Dezső; Fazekas, Dávid; Dúl, Zoltán; Türei, Dénes; Földvári-Nagy, László; Nussinov, Ruth; Csermely, Péter; Korcsmáros, Tamás

2012-06-21

NRF2 is a well-known, master transcription factor (TF) of oxidative and xenobiotic stress responses. Recent studies uncovered an even wider regulatory role of NRF2 influencing carcinogenesis, inflammation and neurodegeneration. Prompted by these advances here we present a systems-level resource for NRF2 interactome and regulome that includes 289 protein-protein, 7469 TF-DNA and 85 miRNA interactions. As systems-level examples of NRF2-related signaling we identified regulatory loops of NRF2 interacting proteins (e.g., JNK1 and CBP) and a fine-tuned regulatory system, where 35 TFs regulated by NRF2 influence 63 miRNAs that down-regulate NRF2. The presented network and the uncovered regulatory loops may facilitate the development of efficient, NRF2-based therapeutic agents. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Identification of the interactome between fish plasma proteins and Edwardsiella tarda reveals tissue-specific strategies against bacterial infection.

PubMed

Li, Hui; Huang, Xiaoyan; Zeng, Zaohai; Peng, Xuan-Xian; Peng, Bo

2016-09-01

Elucidating the complex pathogen-host interaction is essential for a comprehensive understanding of how these remarkable agents invade their hosts and how the hosts defend against these invaders. During the infection, pathogens interact intensively with host to enable their survival, which can be revealed through their interactome. Edwardsiella tarda is a Gram-negative bacterial pathogen causing huge economic loss in aquaculture and a spectrum of intestinal and extraintestinal diseases in humans. E. tarda is an ideal model for host-pathogen investigation as it infects fish in three distinct steps: entering the host, circulating through the blood and establishing infection. We adopted a previous established proteomic approach that inactivated E. tarda cells and covalent crosslink fish plasma proteins were used to capture plasma proteins and bacterial outer membrane proteins, respectively. By the combinatorial use of proteomic and biochemical approaches, six plasma proteins and seven outer membrane proteins (OMPs) were identified. Interactions among these proteins were validated with protein-array, far-Western blotting and co-immunoprecipitation. At last, seventeen plasma protein-bacteria protein-protein interaction were confirmed to be involved in the interaction network, forming a complex interactome. Compared to our previous results, different host proteins were detected, whereas some of the bacterial proteins were similar, which indicates that hosts adopt tissue-specific strategies to cope with the same pathogen during infection. Thus, our results provide a robust demonstration of both bacterial initiators and host receptors or interacting proteins to further explore infection and anti-infective mechanisms between hosts and microbes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mass Spectrometry Analysis of Spatial Protein Networks by Colocalization Analysis (COLA).

PubMed

Mardakheh, Faraz K

2017-01-01

A major challenge in systems biology is comprehensive mapping of protein interaction networks. Crucially, such interactions are often dynamic in nature, necessitating methods that can rapidly mine the interactome across varied conditions and treatments to reveal change in the interaction networks. Recently, we described a fast mass spectrometry-based method to reveal functional interactions in mammalian cells on a global scale, by revealing spatial colocalizations between proteins (COLA) (Mardakheh et al., Mol Biosyst 13:92-105, 2017). As protein localization and function are inherently linked, significant colocalization between two proteins is a strong indication for their functional interaction. COLA uses rapid complete subcellular fractionation, coupled with quantitative proteomics to generate a subcellular localization profile for each protein quantified by the mass spectrometer. Robust clustering is then applied to reveal significant similarities in protein localization profiles, indicative of colocalization.

Computational Methods to Predict Protein Interaction Partners

NASA Astrophysics Data System (ADS)

Valencia, Alfonso; Pazos, Florencio

In the new paradigm for studying biological phenomena represented by Systems Biology, cellular components are not considered in isolation but as forming complex networks of relationships. Protein interaction networks are among the first objects studied from this new point of view. Deciphering the interactome (the whole network of interactions for a given proteome) has been shown to be a very complex task. Computational techniques for detecting protein interactions have become standard tools for dealing with this problem, helping and complementing their experimental counterparts. Most of these techniques use genomic or sequence features intuitively related with protein interactions and are based on "first principles" in the sense that they do not involve training with examples. There are also other computational techniques that use other sources of information (i.e. structural information or even experimental data) or are based on training with examples.

The BioPlex Network: A Systematic Exploration of the Human Interactome.

PubMed

Huttlin, Edward L; Ting, Lily; Bruckner, Raphael J; Gebreab, Fana; Gygi, Melanie P; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Colby, Greg; Baltier, Kurt; Dong, Rui; Guarani, Virginia; Vaites, Laura Pontano; Ordureau, Alban; Rad, Ramin; Erickson, Brian K; Wühr, Martin; Chick, Joel; Zhai, Bo; Kolippakkam, Deepak; Mintseris, Julian; Obar, Robert A; Harris, Tim; Artavanis-Tsakonas, Spyros; Sowa, Mathew E; De Camilli, Pietro; Paulo, Joao A; Harper, J Wade; Gygi, Steven P

2015-07-16

Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors. Copyright © 2015 Elsevier Inc. All rights reserved.

The BioPlex Network: A Systematic Exploration of the Human Interactome

PubMed Central

Huttlin, Edward L.; Ting, Lily; Bruckner, Raphael J.; Gebreab, Fana; Gygi, Melanie P.; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Colby, Greg; Baltier, Kurt; Dong, Rui; Guarani, Virginia; Vaites, Laura Pontano; Ordureau, Alban; Rad, Ramin; Erickson, Brian K.; Wühr, Martin; Chick, Joel; Zhai, Bo; Kolippakkam, Deepak; Mintseris, Julian; Obar, Robert A.; Harris, Tim; Artavanis-Tsakonas, Spyros; Sowa, Mathew E.; DeCamilli, Pietro; Paulo, Joao A.; Harper, J. Wade; Gygi, Steven P.

2015-01-01

SUMMARY Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally-related proteins. Finally, BioPlex, in combination with other approaches can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial Amyotrophic Lateral Sclerosis perturb a defined community of interactors. PMID:26186194

Structural reducibility of multilayer networks

NASA Astrophysics Data System (ADS)

de Domenico, Manlio; Nicosia, Vincenzo; Arenas, Alexandre; Latora, Vito

2015-04-01

Many complex systems can be represented as networks consisting of distinct types of interactions, which can be categorized as links belonging to different layers. For example, a good description of the full protein-protein interactome requires, for some organisms, up to seven distinct network layers, accounting for different genetic and physical interactions, each containing thousands of protein-protein relationships. A fundamental open question is then how many layers are indeed necessary to accurately represent the structure of a multilayered complex system. Here we introduce a method based on quantum theory to reduce the number of layers to a minimum while maximizing the distinguishability between the multilayer network and the corresponding aggregated graph. We validate our approach on synthetic benchmarks and we show that the number of informative layers in some real multilayer networks of protein-genetic interactions, social, economical and transportation systems can be reduced by up to 75%.

Systems-level analysis of risk genes reveals the modular nature of schizophrenia.

PubMed

Liu, Jiewei; Li, Ming; Luo, Xiong-Jian; Su, Bing

2018-05-19

Schizophrenia (SCZ) is a complex mental disorder with high heritability. Genetic studies (especially recent genome-wide association studies) have identified many risk genes for schizophrenia. However, the physical interactions among the proteins encoded by schizophrenia risk genes remain elusive and it is not known whether the identified risk genes converge on common molecular networks or pathways. Here we systematically investigated the network characteristics of schizophrenia risk genes using the high-confidence protein-protein interactions (PPI) from the human interactome. We found that schizophrenia risk genes encode a densely interconnected PPI network (P = 4.15 × 10 -31 ). Compared with the background genes, the schizophrenia risk genes in the interactome have significantly higher degree (P = 5.39 × 10 -11 ), closeness centrality (P = 7.56 × 10 -11 ), betweeness centrality (P = 1.29 × 10 -11 ), clustering coefficient (P = 2.22 × 10 -2 ), and shorter average shortest path length (P = 7.56 × 10 -11 ). Based on the densely interconnected PPI network, we identified 48 hub genes and 4 modules formed by highly interconnected schizophrenia genes. We showed that the proteins encoded by schizophrenia hub genes have significantly more direct physical interactions. Gene ontology (GO) analysis revealed that cell adhesion, cell cycle, immune system response, and GABR-receptor complex categories were enriched in the modules formed by highly interconnected schizophrenia risk genes. Our study reveals that schizophrenia risk genes encode a densely interconnected molecular network and demonstrates the modular nature of schizophrenia. Copyright © 2018 Elsevier B.V. All rights reserved.

Split luciferase complementation assay to detect regulated protein-protein interactions in rice protoplasts in a large-scale format

PubMed Central

2014-01-01

Background The rice interactome, in which a network of protein-protein interactions has been elucidated in rice, is a useful resource to identify functional modules of rice signal transduction pathways. Protein-protein interactions occur in cells in two ways, constitutive and regulative. While a yeast-based high-throughput method has been widely used to identify the constitutive interactions, a method to detect the regulated interactions is rarely developed for a large-scale analysis. Results A split luciferase complementation assay was applied to detect the regulated interactions in rice. A transformation method of rice protoplasts in a 96-well plate was first established for a large-scale analysis. In addition, an antibody that specifically recognizes a carboxyl-terminal fragment of Renilla luciferase was newly developed. A pair of antibodies that recognize amino- and carboxyl- terminal fragments of Renilla luciferase, respectively, was then used to monitor quality and quantity of interacting recombinant-proteins accumulated in the cells. For a proof-of-concept, the method was applied to detect the gibberellin-dependent interaction between GIBBERELLIN INSENSITIVE DWARF1 and SLENDER RICE 1. Conclusions A method to detect regulated protein-protein interactions was developed towards establishment of the rice interactome. PMID:24987490

Structural Bioinformatics of the Interactome

PubMed Central

Petrey, Donald; Honig, Barry

2014-01-01

The last decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information, and to analyze this information so as to infer both the function of individual molecules and how they interact to modulate the behavior of biological systems. Here we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance the basic understanding of biological systems and their disregulation, and how they are being applied in drug development. PMID:24895853

Integration of multiple biological features yields high confidence human protein interactome.

PubMed

Karagoz, Kubra; Sevimoglu, Tuba; Arga, Kazim Yalcin

2016-08-21

The biological function of a protein is usually determined by its physical interaction with other proteins. Protein-protein interactions (PPIs) are identified through various experimental methods and are stored in curated databases. The noisiness of the existing PPI data is evident, and it is essential that a more reliable data is generated. Furthermore, the selection of a set of PPIs at different confidence levels might be necessary for many studies. Although different methodologies were introduced to evaluate the confidence scores for binary interactions, a highly reliable, almost complete PPI network of Homo sapiens is not proposed yet. The quality and coverage of human protein interactome need to be improved to be used in various disciplines, especially in biomedicine. In the present work, we propose an unsupervised statistical approach to assign confidence scores to PPIs of H. sapiens. To achieve this goal PPI data from six different databases were collected and a total of 295,288 non-redundant interactions between 15,950 proteins were acquired. The present scoring system included the context information that was assigned to PPIs derived from eight biological attributes. A high confidence network, which included 147,923 binary interactions between 13,213 proteins, had scores greater than the cutoff value of 0.80, for which sensitivity, specificity, and coverage were 94.5%, 80.9%, and 82.8%, respectively. We compared the present scoring method with others for evaluation. Reducing the noise inherent in experimental PPIs via our scoring scheme increased the accuracy significantly. As it was demonstrated through the assessment of process and cancer subnetworks, this study allows researchers to construct and analyze context-specific networks via valid PPI sets and one can easily achieve subnetworks around proteins of interest at a specified confidence level. Copyright © 2016 Elsevier Ltd. All rights reserved.

"Fuzziness" in the celular interactome: a historical perspective.

PubMed

Welch, G Rickey

2012-01-01

Some historical background is given for appreciating the impact of the empirical construct known as the cellular protein-protein interactome, which is a seemingly de novo entity that has arisen of late within the context of postgenomic systems biology. The approach here builds on a generalized principle of "fuzziness" in protein behavior, proposed by Tompa and Fuxreiter.(1) Recent controversies in the analysis and interpretation of the interactome studies are rationalized historically under the auspices of this concept. There is an extensive literature on protein-protein interactions, dating to the mid-1900s, which may help clarify the "fuzziness" in the interactome picture and, also, provide a basis for understanding the physiological importance of protein-protein interactions in vivo.

Photoreactive Stapled BH3 Peptides to Dissect the BCL-2 Family Interactome

PubMed Central

Braun, Craig R.; Mintseris, Julian; Gavathiotis, Evripidis; Bird, Gregory H.; Gygi, Steven P.; Walensky, Loren D.

2010-01-01

SUMMARY Defining protein interactions forms the basis for discovery of biological pathways, disease mechanisms, and opportunities for therapeutic intervention. To harness the robust binding affinity and selectivity of structured peptides for interactome discovery, we engineered photoreactive stapled BH3 peptide helices that covalently capture their physiologic BCL-2 family targets. The crosslinking α-helices covalently trap both static and dynamic protein interactors, and enable rapid identification of interaction sites, providing a critical link between interactome discovery and targeted drug design. PMID:21168768

Small Molecule Interactome Mapping by Photoaffinity Labeling Reveals Binding Site Hotspots for the NSAIDs.

PubMed

Gao, Jinxu; Mfuh, Adelphe; Amako, Yuka; Woo, Christina M

2018-03-28

Many therapeutics elicit cell-type specific polypharmacology that is executed by a network of molecular recognition events between a small molecule and the whole proteome. However, measurement of the structures that underpin the molecular associations between the proteome and even common therapeutics, such as the nonsteroidal anti-inflammatory drugs (NSAIDs), is limited by the inability to map the small molecule interactome. To address this gap, we developed a platform termed small molecule interactome mapping by photoaffinity labeling (SIM-PAL) and applied it to the in cellulo direct characterization of specific NSAID binding sites. SIM-PAL uses (1) photochemical conjugation of NSAID derivatives in the whole proteome and (2) enrichment and isotope-recoding of the conjugated peptides for (3) targeted mass spectrometry-based assignment. Using SIM-PAL, we identified the NSAID interactome consisting of over 1000 significantly enriched proteins and directly characterized nearly 200 conjugated peptides representing direct binding sites of the photo-NSAIDs with proteins from Jurkat and K562 cells. The enriched proteins were often identified as parts of complexes, including known targets of NSAID activity (e.g., NF-κB) and novel interactions (e.g., AP-2, proteasome). The conjugated peptides revealed direct NSAID binding sites from the cell surface to the nucleus and a specific binding site hotspot for the three photo-NSAIDs on histones H2A and H2B. NSAID binding stabilized COX-2 and histone H2A by cellular thermal shift assay. Since small molecule stabilization of protein complexes is a gain of function regulatory mechanism, it is conceivable that NSAIDs affect biological processes through these broader proteomic interactions. SIM-PAL enabled characterization of NSAID binding site hotspots and is amenable to map global binding sites for virtually any molecule of interest.

Global De Novo Protein-Protein Interactome Elucidates Interactions of Drought-Responsive Proteins in Horse Gram (Macrotyloma uniflorum).

PubMed

Bhardwaj, Jyoti; Gangwar, Indu; Panzade, Ganesh; Shankar, Ravi; Yadav, Sudesh Kumar

2016-06-03

Inspired by the availability of de novo transcriptome of horse gram (Macrotyloma uniflorum) and recent developments in systems biology studies, the first ever global protein-protein interactome (PPI) map was constructed for this highly drought-tolerant legume. Large-scale studies of PPIs and the constructed database would provide rationale behind the interplay at cascading translational levels for drought stress-adaptive mechanisms in horse gram. Using a bidirectional approach (interolog and domain-based), a high-confidence interactome map and database for horse gram was constructed. Available transcriptomic information for shoot and root tissues of a sensitive (M-191; genotype 1) and a drought-tolerant (M-249; genotype 2) genotype of horse gram was utilized to draw comparative PPI subnetworks under drought stress. High-confidence 6804 interactions were predicted among 1812 proteins covering about one-fourth of the horse gram proteome. The highest number of interactions (33.86%) in horse gram interactome matched with Arabidopsis PPI data. The top five hub nodes mostly included ubiquitin and heat-shock-related proteins. Higher numbers of PPIs were found to be responsive in shoot tissue (416) and root tissue (2228) of genotype 2 compared with shoot tissue (136) and root tissue (579) of genotype 1. Characterization of PPIs using gene ontology analysis revealed that kinase and transferase activities involved in signal transduction, cellular processes, nucleocytoplasmic transport, protein ubiquitination, and localization of molecules were most responsive to drought stress. Hence, these could be framed in stress adaptive mechanisms of horse gram. Being the first legume global PPI map, it would provide new insights into gene and protein regulatory networks for drought stress tolerance mechanisms in horse gram. Information compiled in the form of database (MauPIR) will provide the much needed high-confidence systems biology information for horse gram genes, proteins, and involved processes. This information would ease the effort and increase the efficacy for similar studies on other legumes. Public access is available at http://14.139.59.221/MauPIR/ .

Analysis of the STAT3 interactome using in-situ biotinylation and SILAC.

PubMed

Blumert, Conny; Kalkhof, Stefan; Brocke-Heidrich, Katja; Kohajda, Tibor; von Bergen, Martin; Horn, Friedemann

2013-12-06

Signal transducer and activator of transcription 3 (STAT3) is activated by a variety of cytokines and growth factors. To generate a comprehensive data set of proteins interacting specifically with STAT3, we applied stable isotope labeling with amino acids in cell culture (SILAC). For high-affinity pull-down using streptavidin, we fused STAT3 with a short peptide tag allowing biotinylation in situ (bio-tag), which did not affect STAT3 functions. By this approach, 3642 coprecipitated proteins were detected in human embryonic kidney-293 cells. Filtering using statistical and functional criteria finally extracted 136 proteins as putative interaction partners of STAT3. Both, a physical interaction network analysis and the enrichment of known and predicted interaction partners suggested that our filtering criteria successfully enriched true STAT3 interactors. Our approach identified numerous novel interactors, including ones previously predicted to associate with STAT3. By reciprocal coprecipitation, we were able to verify the physical association between STAT3 and selected interactors, including the novel interaction with TOX4, a member of the TOX high mobility group box family. Applying the same method, we next investigated the activation-dependency of the STAT3 interactome. Again, we identified both known and novel interactions. Thus, our approach allows to study protein-protein interaction effectively and comprehensively. The location, activity, function, degradation, and synthesis of proteins are significantly regulated by interactions of proteins with other proteins, biopolymers and small molecules. Thus, the comprehensive characterization of interactions of proteins in a given proteome is the next milestone on the path to understanding the biochemistry of the cell. In order to generate a comprehensive interactome dataset of proteins specifically interacting with a selected bait protein, we fused our bait protein STAT3 with a short peptide tag allowing biotinylation in situ (bio-tag). This bio-tag allows an affinity pull-down using streptavidin but affected neither the activation of STAT3 by tyrosine phosphorylation nor its transactivating potential. We combined SILAC for accurate relative protein quantification, subcellular fractionation to increase the coverage of interacting proteins, high-affinity pull-down and a stringent filtering method to successfully analyze the interactome of STAT3. With our approach we confirmed several already known and identified numerous novel STAT3 interactors. The approach applied provides a rapid and effective method, which is broadly applicable for studying protein-protein interactions and their dependency on post-translational modifications. © 2013. Published by Elsevier B.V. All rights reserved.

Columbia University: Computational Human High-grade Glioblastoma Multiforme Interactome - miRNA (Post-transcriptional) Layer | Office of Cancer Genomics

Cancer.gov

The Human High-Grade Glioma Interactome (HGi) contains a genome-wide complement of molecular interactions that are Glioblastoma Multiforme (GBM)-specific. HGi v3 contains the post-transcriptional layer of the HGi, which includes the miRNA-target (RNA-RNA) layer of the interactome. Read the Abstract

Mining protein-protein interaction networks: denoising effects

NASA Astrophysics Data System (ADS)

Marras, Elisabetta; Capobianco, Enrico

2009-01-01

A typical instrument to pursue analysis in complex network studies is the analysis of the statistical distributions. They are usually computed for measures which characterize network topology, and are aimed at capturing both structural and dynamics aspects. Protein-protein interaction networks (PPIN) have also been studied through several measures. It is in general observed that a power law is expected to characterize scale-free networks. However, mixing the original noise cover with outlying information and other system-dependent fluctuations makes the empirical detection of the power law a difficult task. As a result the uncertainty level increases when looking at the observed sample; in particular, one may wonder whether the computed features may be sufficient to explain the interactome. We then address noise problems by implementing both decomposition and denoising techniques that reduce the impact of factors known to affect the accuracy of power law detection.

Identification of host cellular proteins that interact with the M protein of a highly pathogenic porcine reproductive and respiratory syndrome virus vaccine strain.

PubMed

Wang, Qian; Li, Yanwei; Dong, Hong; Wang, Li; Peng, Jinmei; An, Tongqing; Yang, Xufu; Tian, Zhijun; Cai, Xuehui

2017-02-22

The highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) continues to pose one of the greatest threats to the swine industry. M protein is the most conserved and important structural protein of PRRSV. However, information about the host cellular proteins that interact with M protein remains limited. Host cellular proteins that interact with the M protein of HP-PRRSV were immunoprecipitated from MARC-145 cells infected with PRRSV HuN4-F112 using the M monoclonal antibody (mAb). The differentially expressed proteins were identified by LC-MS/MS. The screened proteins were used for bioinformatics analysis including Gene Ontology, the interaction network, and the enriched KEGG pathways. Some interested cellular proteins were validated to interact with M protein by CO-IP. The PRRSV HuN4-F112 infection group had 10 bands compared with the control group. The bands included 219 non-redundant cellular proteins that interact with M protein, which were identified by LC-MS/MS with high confidence. The gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) pathway bioinformatic analyses indicated that the identified proteins could be assigned to several different subcellular locations and functional classes. Functional analysis of the interactome profile highlighted cellular pathways associated with protein translation, infectious disease, and signal transduction. Two interested cellular proteins-nuclear factor of activated T cells 45 kDa (NF45) and proliferating cell nuclear antigen (PCNA)-that could interact with M protein were validated by Co-IP and confocal analyses. The interactome data between PRRSV M protein and cellular proteins were identified and contribute to the understanding of the roles of M protein in the replication and pathogenesis of PRRSV. The interactome of M protein will aid studies of virus/host interactions and provide means to decrease the threat of PRRSV to the swine industry in the future.

Sequential Elution Interactome Analysis of the Mind Bomb 1 Ubiquitin Ligase Reveals a Novel Role in Dendritic Spine Outgrowth*

PubMed Central

Mertz, Joseph; Tan, Haiyan; Pagala, Vishwajeeth; Bai, Bing; Chen, Ping-Chung; Li, Yuxin; Cho, Ji-Hoon; Shaw, Timothy; Wang, Xusheng; Peng, Junmin

2015-01-01

The mind bomb 1 (Mib1) ubiquitin ligase is essential for controlling metazoan development by Notch signaling and possibly the Wnt pathway. It is also expressed in postmitotic neurons and regulates neuronal morphogenesis and synaptic activity by mechanisms that are largely unknown. We sought to comprehensively characterize the Mib1 interactome and study its potential function in neuron development utilizing a novel sequential elution strategy for affinity purification, in which Mib1 binding proteins were eluted under different stringency and then quantified by the isobaric labeling method. The strategy identified the Mib1 interactome with both deep coverage and the ability to distinguish high-affinity partners from low-affinity partners. A total of 817 proteins were identified during the Mib1 affinity purification, including 56 high-affinity partners and 335 low-affinity partners, whereas the remaining 426 proteins are likely copurified contaminants or extremely weak binding proteins. The analysis detected all previously known Mib1-interacting proteins and revealed a large number of novel components involved in Notch and Wnt pathways, endocytosis and vesicle transport, the ubiquitin-proteasome system, cellular morphogenesis, and synaptic activities. Immunofluorescence studies further showed colocalization of Mib1 with five selected proteins: the Usp9x (FAM) deubiquitinating enzyme, alpha-, beta-, and delta-catenins, and CDKL5. Mutations of CDKL5 are associated with early infantile epileptic encephalopathy-2 (EIEE2), a severe form of mental retardation. We found that the expression of Mib1 down-regulated the protein level of CDKL5 by ubiquitination, and antagonized CDKL5 function during the formation of dendritic spines. Thus, the sequential elution strategy enables biochemical characterization of protein interactomes; and Mib1 analysis provides a comprehensive interactome for investigating its role in signaling networks and neuronal development. PMID:25931508

Mapping the Small Molecule Interactome by Mass Spectrometry.

PubMed

Flaxman, Hope A; Woo, Christina M

2018-01-16

Mapping small molecule interactions throughout the proteome provides the critical structural basis for functional analysis of their impact on biochemistry. However, translation of mass spectrometry-based proteomics methods to directly profile the interaction between a small molecule and the whole proteome is challenging because of the substoichiometric nature of many interactions, the diversity of covalent and noncovalent interactions involved, and the subsequent computational complexity associated with their spectral assignment. Recent advances in chemical proteomics have begun fill this gap to provide a structural basis for the breadth of small molecule-protein interactions in the whole proteome. Innovations enabling direct characterization of the small molecule interactome include faster, more sensitive instrumentation coupled to chemical conjugation, enrichment, and labeling methods that facilitate detection and assignment. These methods have started to measure molecular interaction hotspots due to inherent differences in local amino acid reactivity and binding affinity throughout the proteome. Measurement of the small molecule interactome is producing structural insights and methods for probing and engineering protein biochemistry. Direct structural characterization of the small molecule interactome is a rapidly emerging area pushing new frontiers in biochemistry at the interface of small molecules and the proteome.

A "candidate-interactome" aggregate analysis of genome-wide association data in multiple sclerosis.

PubMed

Mechelli, Rosella; Umeton, Renato; Policano, Claudia; Annibali, Viviana; Coarelli, Giulia; Ricigliano, Vito A G; Vittori, Danila; Fornasiero, Arianna; Buscarinu, Maria Chiara; Romano, Silvia; Salvetti, Marco; Ristori, Giovanni

2013-01-01

Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a "candidate interactome" (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8-Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms.

The extraction of drug-disease correlations based on module distance in incomplete human interactome.

PubMed

Yu, Liang; Wang, Bingbo; Ma, Xiaoke; Gao, Lin

2016-12-23

Extracting drug-disease correlations is crucial in unveiling disease mechanisms, as well as discovering new indications of available drugs, or drug repositioning. Both the interactome and the knowledge of disease-associated and drug-associated genes remain incomplete. We present a new method to predict the associations between drugs and diseases. Our method is based on a module distance, which is originally proposed to calculate distances between modules in incomplete human interactome. We first map all the disease genes and drug genes to a combined protein interaction network. Then based on the module distance, we calculate the distances between drug gene sets and disease gene sets, and take the distances as the relationships of drug-disease pairs. We also filter possible false positive drug-disease correlations by p-value. Finally, we validate the top-100 drug-disease associations related to six drugs in the predicted results. The overlapping between our predicted correlations with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways demonstrate our approach can not only effectively identify new drug indications, but also provide new insight into drug-disease discovery.

Interactomes, manufacturomes and relational biology: analogies between systems biology and manufacturing systems

PubMed Central

2011-01-01

Background We review and extend the work of Rosen and Casti who discuss category theory with regards to systems biology and manufacturing systems, respectively. Results We describe anticipatory systems, or long-range feed-forward chemical reaction chains, and compare them to open-loop manufacturing processes. We then close the loop by discussing metabolism-repair systems and describe the rationality of the self-referential equation f = f (f). This relationship is derived from some boundary conditions that, in molecular systems biology, can be stated as the cardinality of the following molecular sets must be about equal: metabolome, genome, proteome. We show that this conjecture is not likely correct so the problem of self-referential mappings for describing the boundary between living and nonliving systems remains an open question. We calculate a lower and upper bound for the number of edges in the molecular interaction network (the interactome) for two cellular organisms and for two manufacturomes for CMOS integrated circuit manufacturing. Conclusions We show that the relevant mapping relations may not be Abelian, and that these problems cannot yet be resolved because the interactomes and manufacturomes are incomplete. PMID:21689427

Interactome Mapping Guided by Tissue-Specific Phosphorylation in Age-Related Macular Degeneration

PubMed Central

Sripathi, Srinivas R.; He, Weilue; Prigge, Cameron L.; Sylvester, O’Donnell; Um, Ji-Yeon; Powell, Folami L.; Neksumi, Musa; Bernstein, Paul S.; Choo, Dong-Won; Bartoli, Manuela; Gutsaeva, Diana R.; Jahng, Wan Jin

2017-01-01

The current study aims to determine the molecular mechanisms of age-related macular degeneration (AMD) using the phosphorylation network. Specifically, we examined novel biomarkers for oxidative stress by protein interaction mapping using in vitro and in vivo models that mimic the complex and progressive characteristics of AMD. We hypothesized that the early apoptotic reactions could be initiated by protein phosphorylation in region-dependent (peripheral retina vs. macular) and tissue-dependent (retinal pigment epithelium vs. retina) manner under chronic oxidative stress. The analysis of protein interactome and oxidative biomarkers showed the presence of tissue- and region-specific post-translational mechanisms that contribute to AMD progression and suggested new therapeutic targets that include ubiquitin, erythropoietin, vitronectin, MMP2, crystalline, nitric oxide, and prohibitin. Phosphorylation of specific target proteins in RPE cells is a central regulatory mechanism as a survival tool under chronic oxidative imbalance. The current interactome map demonstrates a positive correlation between oxidative stress-mediated phosphorylation and AMD progression and provides a basis for understanding oxidative stress-induced cytoskeletal changes and the mechanism of aggregate formation induced by protein phosphorylation. This information could provide an effective therapeutic approach to treat age-related neurodegeneration. PMID:28580316

Interactome Mapping Guided by Tissue-Specific Phosphorylation in Age-Related Macular Degeneration.

PubMed

Sripathi, Srinivas R; He, Weilue; Prigge, Cameron L; Sylvester, O'Donnell; Um, Ji-Yeon; Powell, Folami L; Neksumi, Musa; Bernstein, Paul S; Choo, Dong-Won; Bartoli, Manuela; Gutsaeva, Diana R; Jahng, Wan Jin

2017-02-01

The current study aims to determine the molecular mechanisms of age-related macular degeneration (AMD) using the phosphorylation network. Specifically, we examined novel biomarkers for oxidative stress by protein interaction mapping using in vitro and in vivo models that mimic the complex and progressive characteristics of AMD. We hypothesized that the early apoptotic reactions could be initiated by protein phosphorylation in region-dependent (peripheral retina vs. macular) and tissue-dependent (retinal pigment epithelium vs. retina) manner under chronic oxidative stress. The analysis of protein interactome and oxidative biomarkers showed the presence of tissue- and region-specific post-translational mechanisms that contribute to AMD progression and suggested new therapeutic targets that include ubiquitin, erythropoietin, vitronectin, MMP2, crystalline, nitric oxide, and prohibitin. Phosphorylation of specific target proteins in RPE cells is a central regulatory mechanism as a survival tool under chronic oxidative imbalance. The current interactome map demonstrates a positive correlation between oxidative stress-mediated phosphorylation and AMD progression and provides a basis for understanding oxidative stress-induced cytoskeletal changes and the mechanism of aggregate formation induced by protein phosphorylation. This information could provide an effective therapeutic approach to treat age-related neurodegeneration.

Interacting Network of the Gap Junction (GJ) Protein Connexin43 (Cx43) is Modulated by Ischemia and Reperfusion in the Heart.

PubMed

Martins-Marques, Tania; Anjo, Sandra Isabel; Pereira, Paulo; Manadas, Bruno; Girão, Henrique

2015-11-01

The coordinated and synchronized cardiac muscle contraction relies on an efficient gap junction-mediated intercellular communication (GJIC) between cardiomyocytes, which involves the rapid anisotropic impulse propagation through connexin (Cx)-containing channels, namely of Cx43, the most abundant Cx in the heart. Expectedly, disturbing mechanisms that affect channel activity, localization and turnover of Cx43 have been implicated in several cardiomyopathies, such as myocardial ischemia. Besides gap junction-mediated intercellular communication, Cx43 has been associated with channel-independent functions, including modulation of cell adhesion, differentiation, proliferation and gene transcription. It has been suggested that the role played by Cx43 is dictated by the nature of the proteins that interact with Cx43. Therefore, the characterization of the Cx43-interacting network and its dynamics is vital to understand not only the molecular mechanisms underlying pathological malfunction of gap junction-mediated intercellular communication, but also to unveil novel and unanticipated biological functions of Cx43. In the present report, we applied a quantitative SWATH-MS approach to characterize the Cx43 interactome in rat hearts subjected to ischemia and ischemia-reperfusion. Our results demonstrate that, in the heart, Cx43 interacts with proteins related with various biological processes such as metabolism, signaling and trafficking. The interaction of Cx43 with proteins involved in gene transcription strengthens the emerging concept that Cx43 has a role in gene expression regulation. Importantly, our data shows that the interactome of Cx43 (Connexome) is differentially modulated in diseased hearts. Overall, the characterization of Cx43-interacting network may contribute to the establishment of new therapeutic targets to modulate cardiac function in physiological and pathological conditions. Data are available via ProteomeXchange with identifier PXD002331. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome.

PubMed

Li, Yiwei; Ilie, Lucian

2017-11-15

Proteins perform their functions usually by interacting with other proteins. Predicting which proteins interact is a fundamental problem. Experimental methods are slow, expensive, and have a high rate of error. Many computational methods have been proposed among which sequence-based ones are very promising. However, so far no such method is able to predict effectively the entire human interactome: they require too much time or memory. We present SPRINT (Scoring PRotein INTeractions), a new sequence-based algorithm and tool for predicting protein-protein interactions. We comprehensively compare SPRINT with state-of-the-art programs on seven most reliable human PPI datasets and show that it is more accurate while running orders of magnitude faster and using very little memory. SPRINT is the only sequence-based program that can effectively predict the entire human interactome: it requires between 15 and 100 min, depending on the dataset. Our goal is to transform the very challenging problem of predicting the entire human interactome into a routine task. The source code of SPRINT is freely available from https://github.com/lucian-ilie/SPRINT/ and the datasets and predicted PPIs from www.csd.uwo.ca/faculty/ilie/SPRINT/ .

Single Molecule Approaches in RNA-Protein Interactions.

PubMed

Serebrov, Victor; Moore, Melissa J

RNA-protein interactions govern every aspect of RNA metabolism, and aberrant RNA-binding proteins are the cause of hundreds of genetic diseases. Quantitative measurements of these interactions are necessary in order to understand mechanisms leading to diseases and to develop efficient therapies. Existing methods of RNA-protein interactome capture can afford a comprehensive snapshot of RNA-protein interaction networks but lack the ability to characterize the dynamics of these interactions. As all ensemble methods, their resolution is also limited by statistical averaging. Here we discuss recent advances in single molecule techniques that have the potential to tackle these challenges. We also provide a thorough overview of single molecule colocalization microscopy and the essential protein and RNA tagging and detection techniques.

Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases.

PubMed

Barshir, Ruth; Shwartz, Omer; Smoly, Ilan Y; Yeger-Lotem, Esti

2014-06-01

An open question in human genetics is what underlies the tissue-specific manifestation of hereditary diseases, which are caused by genomic aberrations that are present in cells across the human body. Here we analyzed this phenomenon for over 300 hereditary diseases by using comparative network analysis. We created an extensive resource of protein expression and interactions in 16 main human tissues, by integrating recent data of gene and protein expression across tissues with data of protein-protein interactions (PPIs). The resulting tissue interaction networks (interactomes) shared a large fraction of their proteins and PPIs, and only a small fraction of them were tissue-specific. Applying this resource to hereditary diseases, we first show that most of the disease-causing genes are widely expressed across tissues, yet, enigmatically, cause disease phenotypes in few tissues only. Upon testing for factors that could lead to tissue-specific vulnerability, we find that disease-causing genes tend to have elevated transcript levels and increased number of tissue-specific PPIs in their disease tissues compared to unaffected tissues. We demonstrate through several examples that these tissue-specific PPIs can highlight disease mechanisms, and thus, owing to their small number, provide a powerful filter for interrogating disease etiologies. As two thirds of the hereditary diseases are associated with these factors, comparative tissue analysis offers a meaningful and efficient framework for enhancing the understanding of the molecular basis of hereditary diseases.

From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks

PubMed Central

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

2013-01-01

Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial. PMID:23563395

From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

PubMed

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

2013-01-01

Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.

An "on-matrix" digestion procedure for AP-MS experiments dissects the interplay between complex-conserved and serotype-specific reactivities in Dengue virus-human plasma interactome.

PubMed

Ramos, Yassel; Huerta, Vivian; Martín, Dayron; Palomares, Sucel; Yero, Alexis; Pupo, Dianne; Gallien, Sebastien; Martín, Alejandro M; Pérez-Riverol, Yasset; Sarría, Mónica; Guirola, Osmany; Chinea, Glay; Domon, Bruno; González, Luis Javier

2017-07-13

The interactions between the four Dengue virus (DENV) serotypes and plasma proteins are crucial in the initial steps of viral infection to humans. Affinity purification combined with quantitative mass spectrometry analysis, has become one of the most powerful tools for the investigation on novel protein-protein interactions. Using this approach, we report here that a significant number of bait-interacting proteins do not dissociate under standard elution conditions, i.e. acid pH and chaotropic agents, and that this problem can be circumvented by using the "on-matrix" digestion procedure described here. This procedure enabled the identification of 16 human plasma proteins interacting with domain III from the envelope protein of DENV serotypes 1, 3 and 4 that would have not been detected otherwise and increased the known DIIIE interactors in human plasma to 59 proteins. Selected Reaction Monitoring analysis evidenced DENV interactome in human plasma is rather conserved although significant differences on the reactivity of viral serotypes with specific proteins do exist. A comparison between the serotype-dependent profile of reactivity and the conservation pattern of amino acid residues suggests an evolutionary selection of highly conserved interactions with the host and other interactions mediated for surface regions of higher variability. False negative results on the identification of interacting proteins in pull-down experiments compromise the subsequent interpretation of results and the formulation of a working hypothesis for the derived future work. In this study we demonstrate the presence of bait-interacting proteins reluctant to dissociate under elution conditions of acid pH and presence of chaotropics. We propose the direct proteolytic digestion of proteins while still bound to the affinity matrix ("on-matrix" digestion) and evaluate the impact of this methodology in the comparative study of the interactome of the four serotypes of Dengue virus mediated by the domain III of the viral envelope glycoprotein. Fifty nine proteins were identified as putative interaction partners of Dengue virus (IPs) either due to direct binding or by co-isolation with interacting proteins. Collectively the IPs identified from the pull-down with the recombinant domain III proteins representing the four viral serotypes, 29% were identified only after "on-matrix" digestion which demonstrate the usefulness of this method of recovering bait-bound proteins. Results highlight a particular importance of "on-matrix" digestion procedure for comparative studies where a stronger interaction with one of the interest baits could prevent a bound protein to elute under standard conditions thus leading to misinterpretation as absent in the interactome of this particular bait. The analysis of the Interaction Network indicates that Dengue virus interactome mediated by the domain III of the envelope protein is rather conserved in the viral complex suggesting a key role of these interactions for viral infection thus making candidates to explore for potential biomarkers of clinical outcome in DENV-caused disease. Interestingly, some particular IPs exhibit significant differences in the strength of the interaction with the viral serotypes representing interactions that involve more variable regions in the surface of the domain III. Since such variable regions are the consequence of the interaction with antibodies generated by human immune response; this result relates the interaction with proteins from human plasma with the interplay of the virus and the human immune system. Copyright © 2017 Elsevier B.V. All rights reserved.

Characterization of a Protein Interactome by Co-Immunoprecipitation and Shotgun Mass Spectrometry.

PubMed

Maccarrone, Giuseppina; Bonfiglio, Juan Jose; Silberstein, Susana; Turck, Christoph W; Martins-de-Souza, Daniel

2017-01-01

Identifying the partners of a given protein (the interactome) may provide leads about the protein's function and the molecular mechanisms in which it is involved. One of the alternative strategies used to characterize protein interactomes consists of co-immunoprecipitation (co-IP) followed by shotgun mass spectrometry. This enables the isolation and identification of a protein target in its native state and its interactome from cells or tissue lysates under physiological conditions. In this chapter, we describe a co-IP protocol for interactome studies that uses an antibody against a protein of interest bound to protein A/G plus agarose beads to isolate a protein complex. The interacting proteins may be further fractionated by SDS-PAGE, followed by in-gel tryptic digestion and nano liquid chromatography high-resolution tandem mass spectrometry (nLC ESI-MS/MS) for identification purposes. The computational tools, strategy for protein identification, and use of interactome databases also will be described.

Interactome of the hepatitis C virus: Literature mining with ANDSystem.

PubMed

Saik, Olga V; Ivanisenko, Timofey V; Demenkov, Pavel S; Ivanisenko, Vladimir A

2016-06-15

A study of the molecular genetics mechanisms of host-pathogen interactions is of paramount importance in developing drugs against viral diseases. Currently, the literature contains a huge amount of information that describes interactions between HCV and human proteins. In addition, there are many factual databases that contain experimentally verified data on HCV-host interactions. The sources of such data are the original data along with the data manually extracted from the literature. However, the manual analysis of scientific publications is time consuming and, because of this, databases created with such an approach often do not have complete information. One of the most promising methods to provide actualisation and completeness of information is text mining. Here, with the use of a previously developed method by the authors using ANDSystem, an automated extraction of information on the interactions between HCV and human proteins was conducted. As a data source for the text mining approach, PubMed abstracts and full text articles were used. Additionally, external factual databases were analyzed. On the basis of this analysis, a special version of ANDSystem, extended with the HCV interactome, was created. The HCV interactome contains information about the interactions between 969 human and 11 HCV proteins. Among the 969 proteins, 153 'new' proteins were found not previously referred to in any external databases of protein-protein interactions for HCV-host interactions. Thus, the extended ANDSystem possesses a more comprehensive detailing of HCV-host interactions versus other existing databases. It was interesting that HCV proteins more preferably interact with human proteins that were already involved in a large number of protein-protein interactions as well as those associated with many diseases. Among human proteins of the HCV interactome, there were a large number of proteins regulated by microRNAs. It turned out that the results obtained for protein-protein interactions and microRNA-regulation did not depend on how well the proteins were studied, while protein-disease interactions appeared to be dependent on the level of study. In particular, the mean number of diseases linked to well-studied proteins (proteins were considered well-studied if they were mentioned in 50 or more PubMed publications) from the HCV interactome was 20.8, significantly exceeding the mean number of associations with diseases (10.1) for the total set of well-studied human proteins present in ANDSystem. For proteins not highly poorly-studied investigated, proteins from the HCV interactome (each protein was referred to in less than 50 publications) distribution of the number of diseases associated with them had no statistically significant differences from the distribution of the number of diseases associated with poorly-studied proteins based on the total set of human proteins stored in ANDSystem. With this, the average number of associations with diseases for the HCV interactome and the total set of human proteins were 0.3 and 0.2, respectively. Thus, ANDSystem, extended with the HCV interactome, can be helpful in a wide range of issues related to analyzing HCV-host interactions in the search for anti-HCV drug targets. The demo version of the extended ANDSystem covered here containing only interactions between human proteins, genes, metabolites, diseases, miRNAs and molecular-genetic pathways, as well as interactions between human proteins/genes and HCV proteins, is freely available at the following web address: http://www-bionet.sscc.ru/psd/andhcv/. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network.

PubMed

Podder, Avijit; Jatana, Nidhi; Latha, N

2014-09-21

Dopamine receptors (DR) are one of the major neurotransmitter receptors present in human brain. Malfunctioning of these receptors is well established to trigger many neurological and psychiatric disorders. Taking into consideration that proteins function collectively in a network for most of the biological processes, the present study is aimed to depict the interactions between all dopamine receptors following a systems biology approach. To capture comprehensive interactions of candidate proteins associated with human dopamine receptors, we performed a protein-protein interaction network (PPIN) analysis of all five receptors and their protein partners by mapping them into human interactome and constructed a human Dopamine Receptors Interaction Network (DRIN). We explored the topology of dopamine receptors as molecular network, revealing their characteristics and the role of central network elements. More to the point, a sub-network analysis was done to determine major functional clusters in human DRIN that govern key neurological pathways. Besides, interacting proteins in a pathway were characterized and prioritized based on their affinity for utmost drug molecules. The vulnerability of different networks to the dysfunction of diverse combination of components was estimated under random and direct attack scenarios. To the best of our knowledge, the current study is unique to put all five dopamine receptors together in a common interaction network and to understand the functionality of interacting proteins collectively. Our study pinpointed distinctive topological and functional properties of human dopamine receptors that have helped in identifying potential therapeutic drug targets in the dopamine interaction network. Copyright © 2014 Elsevier Ltd. All rights reserved.

Network perturbation by recurrent regulatory variants in cancer

PubMed Central

Cho, Ara; Lee, Insuk; Choi, Jung Kyoon

2017-01-01

Cancer driving genes have been identified as recurrently affected by variants that alter protein-coding sequences. However, a majority of cancer variants arise in noncoding regions, and some of them are thought to play a critical role through transcriptional perturbation. Here we identified putative transcriptional driver genes based on combinatorial variant recurrence in cis-regulatory regions. The identified genes showed high connectivity in the cancer type-specific transcription regulatory network, with high outdegree and many downstream genes, highlighting their causative role during tumorigenesis. In the protein interactome, the identified transcriptional drivers were not as highly connected as coding driver genes but appeared to form a network module centered on the coding drivers. The coding and regulatory variants associated via these interactions between the coding and transcriptional drivers showed exclusive and complementary occurrence patterns across tumor samples. Transcriptional cancer drivers may act through an extensive perturbation of the regulatory network and by altering protein network modules through interactions with coding driver genes. PMID:28333928

A Viral-Human Interactome Based on Structural Motif-Domain Interactions Captures the Human Infectome

PubMed Central

Guo, Xianwu; Rodríguez-Pérez, Mario A.

2013-01-01

Protein interactions between a pathogen and its host are fundamental in the establishment of the pathogen and underline the infection mechanism. In the present work, we developed a single predictive model for building a host-viral interactome based on the identification of structural descriptors from motif-domain interactions of protein complexes deposited in the Protein Data Bank (PDB). The structural descriptors were used for searching, in a database of protein sequences of human and five clinically important viruses; therefore, viral and human proteins sharing a descriptor were predicted as interacting proteins. The analysis of the host-viral interactome allowed to identify a set of new interactions that further explain molecular mechanism associated with viral infections and showed that it was able to capture human proteins already associated to viral infections (human infectome) and non-infectious diseases (human diseasome). The analysis of human proteins targeted by viral proteins in the context of a human interactome showed that their neighbors are enriched in proteins reported with differential expression under infection and disease conditions. It is expected that the findings of this work will contribute to the development of systems biology for infectious diseases, and help guide the rational identification and prioritization of novel drug targets. PMID:23951184

Investigation of a protein complex network

NASA Astrophysics Data System (ADS)

Mashaghi, A. R.; Ramezanpour, A.; Karimipour, V.

2004-09-01

The budding yeast Saccharomyces cerevisiae is the first eukaryote whose genome has been completely sequenced. It is also the first eukaryotic cell whose proteome (the set of all proteins) and interactome (the network of all mutual interactions between proteins) has been analyzed. In this paper we study the structure of the yeast protein complex network in which weighted edges between complexes represent the number of shared proteins. It is found that the network of protein complexes is a small world network with scale free behavior for many of its distributions. However we find that there are no strong correlations between the weights and degrees of neighboring complexes. To reveal non-random features of the network we also compare it with a null model in which the complexes randomly select their proteins. Finally we propose a simple evolutionary model based on duplication and divergence of proteins.

RISE: a database of RNA interactome from sequencing experiments

PubMed Central

Gong, Jing; Shao, Di; Xu, Kui

2018-01-01

Abstract We present RISE (http://rise.zhanglab.net), a database of RNA Interactome from Sequencing Experiments. RNA-RNA interactions (RRIs) are essential for RNA regulation and function. RISE provides a comprehensive collection of RRIs that mainly come from recent transcriptome-wide sequencing-based experiments like PARIS, SPLASH, LIGR-seq, and MARIO, as well as targeted studies like RIA-seq, RAP-RNA and CLASH. It also includes interactions aggregated from other primary databases and publications. The RISE database currently contains 328,811 RNA-RNA interactions mainly in human, mouse and yeast. While most existing RNA databases mainly contain interactions of miRNA targeting, notably, more than half of the RRIs in RISE are among mRNA and long non-coding RNAs. We compared different RRI datasets in RISE and found limited overlaps in interactions resolved by different techniques and in different cell lines. It may suggest technology preference and also dynamic natures of RRIs. We also analyzed the basic features of the human and mouse RRI networks and found that they tend to be scale-free, small-world, hierarchical and modular. The analysis may nominate important RNAs or RRIs for further investigation. Finally, RISE provides a Circos plot and several table views for integrative visualization, with extensive molecular and functional annotations to facilitate exploration of biological functions for any RRI of interest. PMID:29040625

Interactome Analyses of Mature γ-Secretase Complexes Reveal Distinct Molecular Environments of Presenilin (PS) Paralogs and Preferential Binding of Signal Peptide Peptidase to PS2*

PubMed Central

Jeon, Amy Hye Won; Böhm, Christopher; Chen, Fusheng; Huo, Hairu; Ruan, Xueying; Ren, Carl He; Ho, Keith; Qamar, Seema; Mathews, Paul M.; Fraser, Paul E.; Mount, Howard T. J.; St George-Hyslop, Peter; Schmitt-Ulms, Gerold

2013-01-01

γ-Secretase plays a pivotal role in the production of neurotoxic amyloid β-peptides (Aβ) in Alzheimer disease (AD) and consists of a heterotetrameric core complex that includes the aspartyl intramembrane protease presenilin (PS). The human genome codes for two presenilin paralogs. To understand the causes for distinct phenotypes of PS paralog-deficient mice and elucidate whether PS mutations associated with early-onset AD affect the molecular environment of mature γ-secretase complexes, quantitative interactome comparisons were undertaken. Brains of mice engineered to express wild-type or mutant PS1, or HEK293 cells stably expressing PS paralogs with N-terminal tandem-affinity purification tags served as biological source materials. The analyses revealed novel interactions of the γ-secretase core complex with a molecular machinery that targets and fuses synaptic vesicles to cellular membranes and with the H+-transporting lysosomal ATPase macrocomplex but uncovered no differences in the interactomes of wild-type and mutant PS1. The catenin/cadherin network was almost exclusively found associated with PS1. Another intramembrane protease, signal peptide peptidase, predominantly co-purified with PS2-containing γ-secretase complexes and was observed to influence Aβ production. PMID:23589300

A side-effect free method for identifying cancer drug targets.

PubMed

Ashraf, Md Izhar; Ong, Seng-Kai; Mujawar, Shama; Pawar, Shrikant; More, Pallavi; Paul, Somnath; Lahiri, Chandrajit

2018-04-27

Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.

TrypsNetDB: An integrated framework for the functional characterization of trypanosomatid proteins

PubMed Central

Gazestani, Vahid H.; Yip, Chun Wai; Nikpour, Najmeh; Berghuis, Natasha

2017-01-01

Trypanosomatid parasites cause serious infections in humans and production losses in livestock. Due to the high divergence from other eukaryotes, such as humans and model organisms, the functional roles of many trypanosomatid proteins cannot be predicted by homology-based methods, rendering a significant portion of their proteins as uncharacterized. Recent technological advances have led to the availability of multiple systematic and genome-wide datasets on trypanosomatid parasites that are informative regarding the biological role(s) of their proteins. Here, we report TrypsNetDB (http://trypsNetDB.org), a web-based resource for the functional annotation of 16 different species/strains of trypanosomatid parasites. The database not only visualizes the network context of the queried protein(s) in an intuitive way but also examines the response of the represented network in more than 50 different biological contexts and its enrichment for various biological terms and pathways, protein sequence signatures, and potential RNA regulatory elements. The interactome core of the database, as of Jan 23, 2017, contains 101,187 interactions among 13,395 trypanosomatid proteins inferred from 97 genome-wide and focused studies on the interactome of these organisms. PMID:28158179

The Orphan Disease Networks

PubMed Central

Zhang, Minlu; Zhu, Cheng; Jacomy, Alexis; Lu, Long J.; Jegga, Anil G.

2011-01-01

The low prevalence rate of orphan diseases (OD) requires special combined efforts to improve diagnosis, prevention, and discovery of novel therapeutic strategies. To identify and investigate relationships based on shared genes or shared functional features, we have conducted a bioinformatic-based global analysis of all orphan diseases with known disease-causing mutant genes. Starting with a bipartite network of known OD and OD-causing mutant genes and using the human protein interactome, we first construct and topologically analyze three networks: the orphan disease network, the orphan disease-causing mutant gene network, and the orphan disease-causing mutant gene interactome. Our results demonstrate that in contrast to the common disease-causing mutant genes that are predominantly nonessential, a majority of orphan disease-causing mutant genes are essential. In confirmation of this finding, we found that OD-causing mutant genes are topologically important in the protein interactome and are ubiquitously expressed. Additionally, functional enrichment analysis of those genes in which mutations cause ODs shows that a majority result in premature death or are lethal in the orthologous mouse gene knockout models. To address the limitations of traditional gene-based disease networks, we also construct and analyze OD networks on the basis of shared enriched features (biological processes, cellular components, pathways, phenotypes, and literature citations). Analyzing these functionally-linked OD networks, we identified several additional OD-OD relations that are both phenotypically similar and phenotypically diverse. Surprisingly, we observed that the wiring of the gene-based and other feature-based OD networks are largely different; this suggests that the relationship between ODs cannot be fully captured by the gene-based network alone. PMID:21664998

A Novel Framework for the Comparative Analysis of Biological Networks

PubMed Central

Pache, Roland A.; Aloy, Patrick

2012-01-01

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes. PMID:22363585

Rapid, Optimized Interactomic Screening

PubMed Central

Hakhverdyan, Zhanna; Domanski, Michal; Hough, Loren; Oroskar, Asha A.; Oroskar, Anil R.; Keegan, Sarah; Dilworth, David J.; Molloy, Kelly R.; Sherman, Vadim; Aitchison, John D.; Fenyö, David; Chait, Brian T.; Jensen, Torben Heick; Rout, Michael P.; LaCava, John

2015-01-01

We must reliably map the interactomes of cellular macromolecular complexes in order to fully explore and understand biological systems. However, there are no methods to accurately predict how to capture a given macromolecular complex with its physiological binding partners. Here, we present a screen that comprehensively explores the parameters affecting the stability of interactions in affinity-captured complexes, enabling the discovery of physiological binding partners and the elucidation of their functional interactions in unparalleled detail. We have implemented this screen on several macromolecular complexes from a variety of organisms, revealing novel profiles even for well-studied proteins. Our approach is robust, economical and automatable, providing an inroad to the rigorous, systematic dissection of cellular interactomes. PMID:25938370

Diffany: an ontology-driven framework to infer, visualise and analyse differential molecular networks.

PubMed

Van Landeghem, Sofie; Van Parys, Thomas; Dubois, Marieke; Inzé, Dirk; Van de Peer, Yves

2016-01-05

Differential networks have recently been introduced as a powerful way to study the dynamic rewiring capabilities of an interactome in response to changing environmental conditions or stimuli. Currently, such differential networks are generated and visualised using ad hoc methods, and are often limited to the analysis of only one condition-specific response or one interaction type at a time. In this work, we present a generic, ontology-driven framework to infer, visualise and analyse an arbitrary set of condition-specific responses against one reference network. To this end, we have implemented novel ontology-based algorithms that can process highly heterogeneous networks, accounting for both physical interactions and regulatory associations, symmetric and directed edges, edge weights and negation. We propose this integrative framework as a standardised methodology that allows a unified view on differential networks and promotes comparability between differential network studies. As an illustrative application, we demonstrate its usefulness on a plant abiotic stress study and we experimentally confirmed a predicted regulator. Diffany is freely available as open-source java library and Cytoscape plugin from http://bioinformatics.psb.ugent.be/supplementary_data/solan/diffany/.

Pathway perturbations in signaling networks: Linking genotype to phenotype.

PubMed

Li, Yongsheng; McGrail, Daniel J; Latysheva, Natasha; Yi, Song; Babu, M Madan; Sahni, Nidhi

2018-05-10

Genes and gene products interact with each other to form signal transduction networks in the cell. The interactome networks are under intricate regulation in physiological conditions, but could go awry upon genome instability caused by genetic mutations. In the past decade with next-generation sequencing technologies, an increasing number of genomic mutations have been identified in a variety of disease patients and healthy individuals. As functional and systematic studies on these mutations leap forward, they begin to reveal insights into cellular homeostasis and disease mechanisms. In this review, we discuss recent advances in the field of network biology and signaling pathway perturbations upon genomic changes, and highlight the success of various omics datasets in unraveling genotype-to-phenotype relationships. Copyright © 2018 Elsevier Ltd. All rights reserved.

Systems Proteomics for Translational Network Medicine

PubMed Central

Arrell, D. Kent; Terzic, Andre

2012-01-01

Universal principles underlying network science, and their ever-increasing applications in biomedicine, underscore the unprecedented capacity of systems biology based strategies to synthesize and resolve massive high throughput generated datasets. Enabling previously unattainable comprehension of biological complexity, systems approaches have accelerated progress in elucidating disease prediction, progression, and outcome. Applied to the spectrum of states spanning health and disease, network proteomics establishes a collation, integration, and prioritization algorithm to guide mapping and decoding of proteome landscapes from large-scale raw data. Providing unparalleled deconvolution of protein lists into global interactomes, integrative systems proteomics enables objective, multi-modal interpretation at molecular, pathway, and network scales, merging individual molecular components, their plurality of interactions, and functional contributions for systems comprehension. As such, network systems approaches are increasingly exploited for objective interpretation of cardiovascular proteomics studies. Here, we highlight network systems proteomic analysis pipelines for integration and biological interpretation through protein cartography, ontological categorization, pathway and functional enrichment and complex network analysis. PMID:22896016

Inferring the Brassica rapa Interactome Using Protein–Protein Interaction Data from Arabidopsis thaliana

PubMed Central

Yang, Jianhua; Osman, Kim; Iqbal, Mudassar; Stekel, Dov J.; Luo, Zewei; Armstrong, Susan J.; Franklin, F. Chris H.

2013-01-01

Following successful completion of the Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein–protein interaction (PPI) data are available from the major PPI databases (DBs). It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct, and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain–domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability. PMID:23293649

Different adaptation strategies of two citrus scion/rootstock combinations in response to drought stress

PubMed Central

Coelho Filho, Mauricio Antônio; Morillon, Raphaël; Bonatto, Diego; da Silva Gesteira, Abelmon

2017-01-01

Scion/rootstock interaction is important for plant development and for breeding programs. In this context, polyploid rootstocks presented several advantages, mainly in relation to biotic and abiotic stresses. Here we analyzed the response to drought of two different scion/rootstock combinations presenting different polyploidy: the diploid (2x) and autotetraploid (4x) Rangpur lime (Citrus limonia, Osbeck) rootstocks grafted with 2x Valencia Delta sweet orange (Citrus sinensis) scions, named V/2xRL and V/4xRL, respectively. Based on previous gene expression data, we developed an interactomic approach to identify proteins involved in V/2xRL and V/4xRL response to drought. A main interactomic network containing 3,830 nodes and 97,652 edges was built from V/2xRL and V/4xRL data. Exclusive proteins of the V/2xRL and V/4xRL networks (2,056 and 1,001, respectively), as well as common to both networks (773) were identified. Functional clusters were obtained and two models of drought stress response for the V/2xRL and V/4xRL genotypes were designed. Even if the V/2xRL plant implement some tolerance mechanisms, the global plant response to drought was rapid and quickly exhaustive resulting in a general tendency to dehydration avoidance, which presented some advantage in short and strong drought stress conditions, but which, in long terms, does not allow the plant survival. At the contrary, the V/4xRL plants presented a response which strong impacts on development but that present some advantages in case of prolonged drought. Finally, some specific proteins, which presented high centrality on interactomic analysis were identified as good candidates for subsequent functional analysis of citrus genes related to drought response, as well as be good markers of one or another physiological mechanism implemented by the plants. PMID:28545114

Hub Protein Controversy: Taking a Closer Look at Plant Stress Response Hubs

PubMed Central

Vandereyken, Katy; Van Leene, Jelle; De Coninck, Barbara; Cammue, Bruno P. A.

2018-01-01

Plant stress responses involve numerous changes at the molecular and cellular level and are regulated by highly complex signaling pathways. Studying protein-protein interactions (PPIs) and the resulting networks is therefore becoming increasingly important in understanding these responses. Crucial in PPI networks are the so-called hubs or hub proteins, commonly defined as the most highly connected central proteins in scale-free PPI networks. However, despite their importance, a growing amount of confusion and controversy seems to exist regarding hub protein identification, characterization and classification. In order to highlight these inconsistencies and stimulate further clarification, this review critically analyses the current knowledge on hub proteins in the plant interactome field. We focus on current hub protein definitions, including the properties generally seen as hub-defining, and the challenges and approaches associated with hub protein identification. Furthermore, we give an overview of the most important large-scale plant PPI studies of the last decade that identified hub proteins, pointing out the lack of overlap between different studies. As such, it appears that although major advances are being made in the plant interactome field, defining hub proteins is still heavily dependent on the quality, origin and interpretation of the acquired PPI data. Nevertheless, many hub proteins seem to have a reported role in the plant stress response, including transcription factors, protein kinases and phosphatases, ubiquitin proteasome system related proteins, (co-)chaperones and redox signaling proteins. A significant number of identified plant stress hubs are however still functionally uncharacterized, making them interesting targets for future research. This review clearly shows the ongoing improvements in the plant interactome field but also calls attention to the need for a more comprehensive and precise identification of hub proteins, allowing a more efficient systems biology driven unraveling of complex processes, including those involved in stress responses. PMID:29922309

Quantitative Tagless Copurification: A Method to Validate and Identify Protein-Protein Interactions

DOE PAGES

Shatsky, Maxim; Dong, Ming; Liu, Haichuan; ...

2016-04-20

Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris. These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification ofmore » endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR.« less

Quantitative Tagless Copurification: A Method to Validate and Identify Protein-Protein Interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shatsky, Maxim; Dong, Ming; Liu, Haichuan

Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris. These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification ofmore » endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR.« less

Characterization of chromosomal architecture in Arabidopsis by chromosome conformation capture

PubMed Central

2013-01-01

Background The packaging of long chromatin fibers in the nucleus poses a major challenge, as it must fulfill both physical and functional requirements. Until recently, insights into the chromosomal architecture of plants were mainly provided by cytogenetic studies. Complementary to these analyses, chromosome conformation capture technologies promise to refine and improve our view on chromosomal architecture and to provide a more generalized description of nuclear organization. Results Employing circular chromosome conformation capture, this study describes chromosomal architecture in Arabidopsis nuclei from a genome-wide perspective. Surprisingly, the linear organization of chromosomes is reflected in the genome-wide interactome. In addition, we study the interplay of the interactome and epigenetic marks and report that the heterochromatic knob on the short arm of chromosome 4 maintains a pericentromere-like interaction profile and interactome despite its euchromatic surrounding. Conclusion Despite the extreme condensation that is necessary to pack the chromosomes into the nucleus, the Arabidopsis genome appears to be packed in a predictive manner, according to the following criteria: heterochromatin and euchromatin represent two distinct interactomes; interactions between chromosomes correlate with the linear position on the chromosome arm; and distal chromosome regions have a higher potential to interact with other chromosomes. PMID:24267747

Identification and functional analysis of the BIM interactome; new clues on its possible involvement in Epstein-Barr Virus-associated diseases.

PubMed

Rouka, Erasmia; Kyriakou, Despoina

2015-12-01

Epigenetic deregulation is a common feature in the pathogenesis of Epstein-Barr Virus (EBV)-related lymphomas and carcinomas. Previous studies have demonstrated a strong association between EBV latency in B-cells and epigenetic silencing of the tumor suppressor gene BIM. This study aimed to the construction and functional analysis of the BIM interactome in order to identify novel host genes that may be targeted by EBV. Fifty-nine unique interactors were found to compose the BIM gene network. Ontological analysis at the pathway level highlighted infectious diseases along with neuropathologies. These results underline the possible interplay between the BIM interactome and EBV-associated disorders.

Computational genomic analysis of PARK7 interactome reveals high BBS1 gene expression as a prognostic factor favoring survival in malignant pleural mesothelioma.

PubMed

Vavougios, Georgios D; Solenov, Evgeniy I; Hatzoglou, Chrissi; Baturina, Galina S; Katkova, Liubov E; Molyvdas, Paschalis Adam; Gourgoulianis, Konstantinos I; Zarogiannis, Sotirios G

2015-10-01

The aim of our study was to assess the differential gene expression of Parkinson protein 7 (PARK7) interactome in malignant pleural mesothelioma (MPM) using data mining techniques to identify novel candidate genes that may play a role in the pathogenicity of MPM. We constructed the PARK7 interactome using the ConsensusPathDB database. We then interrogated the Oncomine Cancer Microarray database using the Gordon Mesothelioma Study, for differential gene expression of the PARK7 interactome. In ConsensusPathDB, 38 protein interactors of PARK7 were identified. In the Gordon Mesothelioma Study, 34 of them were assessed out of which SUMO1, UBC3, KIAA0101, HDAC2, DAXX, RBBP4, BBS1, NONO, RBBP7, HTRA2, and STUB1 were significantly overexpressed whereas TRAF6 and MTA2 were significantly underexpressed in MPM patients (network 2). Furthermore, Kaplan-Meier analysis revealed that MPM patients with high BBS1 expression had a median overall survival of 16.5 vs. 8.7 mo of those that had low expression. For validation purposes, we performed a meta-analysis in Oncomine database in five sarcoma datasets. Eight network 2 genes (KIAA0101, HDAC2, SUMO1, RBBP4, NONO, RBBP7, HTRA2, and MTA2) were significantly differentially expressed in an array of 18 different sarcoma types. Finally, Gene Ontology annotation enrichment analysis revealed significant roles of the PARK7 interactome in NuRD, CHD, and SWI/SNF protein complexes. In conclusion, we identified 13 novel genes differentially expressed in MPM, never reported before. Among them, BBS1 emerged as a novel predictor of overall survival in MPM. Finally, we identified that PARK7 interactome is involved in novel pathways pertinent in MPM disease. Copyright © 2015 the American Physiological Society.

The cyclin D1-CDK4 oncogenic interactome enables identification of potential novel oncogenes and clinical prognosis

PubMed Central

Jirawatnotai, Siwanon; Sharma, Samanta; Michowski, Wojciech; Suktitipat, Bhoom; Geng, Yan; Quackenbush, John; Elias, Joshua E; Gygi, Steven P; Wang, Yaoyu E; Sicinski, Piotr

2014-01-01

Overexpression of cyclin D1 and its catalytic partner, CDK4, is frequently seen in human cancers. We constructed cyclin D1 and CDK4 protein interaction network in a human breast cancer cell line MCF7, and identified novel CDK4 protein partners. Among CDK4 interactors we observed several proteins functioning in protein folding and in complex assembly. One of the novel partners of CDK4 is FKBP5, which we found to be required to maintain CDK4 levels in cancer cells. An integrative analysis of the extended cyclin D1 cancer interactome and somatic copy number alterations in human cancers identified BAIAPL21 as a potential novel human oncogene. We observed that in several human tumor types BAIAPL21 is expressed at higher levels as compared to normal tissue. Forced overexpression of BAIAPL21 augmented anchorage independent growth, increased colony formation by cancer cells and strongly enhanced the ability of cells to form tumors in vivo. Lastly, we derived an Aggregate Expression Score (AES), which quantifies the expression of all cyclin D1 interactors in a given tumor. We observed that AES has a prognostic value among patients with ER-positive breast cancers. These studies illustrate the utility of analyzing the interactomes of proteins involved in cancer to uncover potential oncogenes, or to allow better cancer prognosis. PMID:25486477

Schizophrenia interactome with 504 novel protein–protein interactions

PubMed Central

Ganapathiraju, Madhavi K; Thahir, Mohamed; Handen, Adam; Sarkar, Saumendra N; Sweet, Robert A; Nimgaonkar, Vishwajit L; Loscher, Christine E; Bauer, Eileen M; Chaparala, Srilakshmi

2016-01-01

Genome-wide association studies of schizophrenia (GWAS) have revealed the role of rare and common genetic variants, but the functional effects of the risk variants remain to be understood. Protein interactome-based studies can facilitate the study of molecular mechanisms by which the risk genes relate to schizophrenia (SZ) genesis, but protein–protein interactions (PPIs) are unknown for many of the liability genes. We developed a computational model to discover PPIs, which is found to be highly accurate according to computational evaluations and experimental validations of selected PPIs. We present here, 365 novel PPIs of liability genes identified by the SZ Working Group of the Psychiatric Genomics Consortium (PGC). Seventeen genes that had no previously known interactions have 57 novel interactions by our method. Among the new interactors are 19 drug targets that are targeted by 130 drugs. In addition, we computed 147 novel PPIs of 25 candidate genes investigated in the pre-GWAS era. While there is little overlap between the GWAS genes and the pre-GWAS genes, the interactomes reveal that they largely belong to the same pathways, thus reconciling the apparent disparities between the GWAS and prior gene association studies. The interactome including 504 novel PPIs overall, could motivate other systems biology studies and trials with repurposed drugs. The PPIs are made available on a webserver, called Schizo-Pi at http://severus.dbmi.pitt.edu/schizo-pi with advanced search capabilities. PMID:27336055

M-Finder: Uncovering functionally associated proteins from interactome data integrated with GO annotations

PubMed Central

2013-01-01

Background Protein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. Gene Ontology (GO) and its annotations are a significant resource for functional characterization of proteins. Because of wide coverage, GO data have often been adopted as a benchmark for protein function prediction on the genomic scale. Results We propose a computational approach, called M-Finder, for functional association pattern mining. This method employs semantic analytics to integrate the genome-wide PPIs with GO data. We also introduce an interactive web application tool that visualizes a functional association network linked to a protein specified by a user. The proposed approach comprises two major components. First, the PPIs that have been generated by high-throughput methods are weighted in terms of their functional consistency using GO and its annotations. We assess two advanced semantic similarity metrics which quantify the functional association level of each interacting protein pair. We demonstrate that these measures outperform the other existing methods by evaluating their agreement to other biological features, such as sequence similarity, the presence of common Pfam domains, and core PPIs. Second, the information flow-based algorithm is employed to discover a set of proteins functionally associated with the protein in a query and their links efficiently. This algorithm reconstructs a functional association network of the query protein. The output network size can be flexibly determined by parameters. Conclusions M-Finder provides a useful framework to investigate functional association patterns with any protein. This software will also allow users to perform further systematic analysis of a set of proteins for any specific function. It is available online at http://bionet.ecs.baylor.edu/mfinder PMID:24565382

Principles of Protein Recognition and Properties of Protein-protein Interfaces

NASA Astrophysics Data System (ADS)

Keskin, Ozlem; Gursoy, Attila; Nussinov, Ruth

In this chapter we address two aspects - the static physical interactions which allow the information transfer for the function to be performed; and the dynamic, i.e. how the information is transmitted between the binding sites in the single protein molecule and in the network. We describe the single protein molecules and their complexes; and the analogy between protein folding and protein binding. Eventually, to fully understand the interactome and how it performs the essential cellular functions, we have to put all together - and hierarchically progress through the system.

RNA-Binding Proteins Revisited - The Emerging Arabidopsis mRNA Interactome.

PubMed

Köster, Tino; Marondedze, Claudius; Meyer, Katja; Staiger, Dorothee

2017-06-01

RNA-protein interaction is an important checkpoint to tune gene expression at the RNA level. Global identification of proteins binding in vivo to mRNA has been possible through interactome capture - where proteins are fixed to target RNAs by UV crosslinking and purified through affinity capture of polyadenylated RNA. In Arabidopsis over 500 RNA-binding proteins (RBPs) enriched in UV-crosslinked samples have been identified. As in mammals and yeast, the mRNA interactomes came with a few surprises. For example, a plethora of the proteins caught on RNA had not previously been linked to RNA-mediated processes, for example proteins of intermediary metabolism. Thus, the studies provide unprecedented insights into the composition of the mRNA interactome, highlighting the complexity of RNA-mediated processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks.

PubMed

Folador, Edson Luiz; de Carvalho, Paulo Vinícius Sanches Daltro; Silva, Wanderson Marques; Ferreira, Rafaela Salgado; Silva, Artur; Gromiha, Michael; Ghosh, Preetam; Barh, Debmalya; Azevedo, Vasco; Röttger, Richard

2016-11-04

Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.

Putative human sperm Interactome: a networks study.

PubMed

Ordinelli, Alessandra; Bernabò, Nicola; Orsini, Massimiliano; Mattioli, Mauro; Barboni, Barbara

2018-04-11

For over sixty years, it has been known that mammalian spermatozoa immediately after ejaculation are virtually infertile. They became able to fertilize only after they reside for long time (hours to days) within female genital tract where they complete their functional maturation, the capacitation. This process is finely regulated by the interaction with the female environment and involves, in spermatozoa, a myriad of molecules as messengers and target of signals. Since, to date, a model able to represent the molecular interaction that characterize sperm physiology does not exist, we realized the Human Sperm Interactme Network3.0 (HSIN3.0) and its main component (HSNI3.0_MC), starting from the pathway active in male germ cells. HSIN3.0 and HSIN3.0_MC are scale free networks, adherent to the Barabasi-Albert model, and are characterised by an ultra-small world topology. We found that they are resistant to random attacks and that are designed to respond quickly and specifically to external inputs. In addition, it has been possible to identify the most connected nodes (the hubs) and the bottlenecks nodes. This result allowed us to explore the control mechanisms active in driving sperm biochemical machinery and to verify the different levels of controls: party vs. date hubs and hubs vs. bottlenecks, thanks the availability of data from KO mice. Finally, we found that several key nodes represent molecules specifically involved in function that are thought to be not present or not active in sperm cells, such as control of cell cycle, proteins synthesis, nuclear trafficking, and immune response, thus potentially open new perspectives on the study of sperm biology. For the first time we present a network representing putative human sperm interactome. This result gives very intriguing biological information and could contribute to the knowledge of spermatozoa, either in physiological or pathological conditions.

Comprehensive interactome of Otx2 in the adult mouse neural retina.

PubMed

Fant, Bruno; Samuel, Alexander; Audebert, Stéphane; Couzon, Agnès; El Nagar, Salsabiel; Billon, Nathalie; Lamonerie, Thomas

2015-11-01

The Otx2 homeodomain transcription factor exerts multiple functions in specific developmental contexts, probably through the regulation of different sets of genes. Protein partners of Otx2 have been shown to modulate its activity. Therefore, the Otx2 interactome may play a key role in selecting a precise target-gene repertoire, hence determining its function in a specific tissue. To address the nature of Otx2 interactome, we generated a new recombinant Otx2(CTAP-tag) mouse line, designed for protein complexes purification. We validated this mouse line by establishing the Otx2 interactome in the adult neural retina. In this tissue, Otx2 is thought to have overlapping function with its paralog Crx. Our analysis revealed that, in contrary to Crx, Otx2 did not develop interactions with proteins that are known to regulate phototransduction genes but showed specific partnership with factors associated with retinal development. The relationship between Otx2 and Crx in the neural retina should therefore be considered as complementarity rather than redundancy. Furthermore, study of the Otx2 interactome revealed strong associations with RNA processing and translation machineries, suggesting unexpected roles for Otx2 in the regulation of selected target genes all along the transcription/translation pathway. The Otx2(CTAP-tag) line, therefore, appears suitable for a systematic approach to Otx2 protein-protein interactions. genesis 53:685-694, 2015. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

∆F508 CFTR interactome remodelling promotes rescue of cystic fibrosis.

PubMed

Pankow, Sandra; Bamberger, Casimir; Calzolari, Diego; Martínez-Bartolomé, Salvador; Lavallée-Adam, Mathieu; Balch, William E; Yates, John R

2015-12-24

Deletion of phenylalanine 508 of the cystic fibrosis transmembrane conductance regulator (∆F508 CFTR) is the major cause of cystic fibrosis, one of the most common inherited childhood diseases. The mutated CFTR anion channel is not fully glycosylated and shows minimal activity in bronchial epithelial cells of patients with cystic fibrosis. Low temperature or inhibition of histone deacetylases can partly rescue ∆F508 CFTR cellular processing defects and function. A favourable change of ∆F508 CFTR protein-protein interactions was proposed as a mechanism of rescue; however, CFTR interactome dynamics during temperature shift and inhibition of histone deacetylases are unknown. Here we report the first comprehensive analysis of the CFTR and ∆F508 CFTR interactome and its dynamics during temperature shift and inhibition of histone deacetylases. By using a novel deep proteomic analysis method, we identify 638 individual high-confidence CFTR interactors and discover a ∆F508 deletion-specific interactome, which is extensively remodelled upon rescue. Detailed analysis of the interactome remodelling identifies key novel interactors, whose loss promote ∆F508 CFTR channel function in primary cystic fibrosis epithelia or which are critical for CFTR biogenesis. Our results demonstrate that global remodelling of ∆F508 CFTR interactions is crucial for rescue, and provide comprehensive insight into the molecular disease mechanisms of cystic fibrosis caused by deletion of F508.

Clustering coefficients of protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Miller, Gerald A.; Shi, Yi Y.; Qian, Hong; Bomsztyk, Karol

2007-05-01

The properties of certain networks are determined by hidden variables that are not explicitly measured. The conditional probability (propagator) that a vertex with a given value of the hidden variable is connected to k other vertices determines all measurable properties. We study hidden variable models and find an averaging approximation that enables us to obtain a general analytical result for the propagator. Analytic results showing the validity of the approximation are obtained. We apply hidden variable models to protein-protein interaction networks (PINs) in which the hidden variable is the association free energy, determined by distributions that depend on biochemistry and evolution. We compute degree distributions as well as clustering coefficients of several PINs of different species; good agreement with measured data is obtained. For the human interactome two different parameter sets give the same degree distributions, but the computed clustering coefficients differ by a factor of about 2. This shows that degree distributions are not sufficient to determine the properties of PINs.

BioPlex Display: An Interactive Suite for Large-Scale AP-MS Protein-Protein Interaction Data.

PubMed

Schweppe, Devin K; Huttlin, Edward L; Harper, J Wade; Gygi, Steven P

2018-01-05

The development of large-scale data sets requires a new means to display and disseminate research studies to large audiences. Knowledge of protein-protein interaction (PPI) networks has become a principle interest of many groups within the field of proteomics. At the confluence of technologies, such as cross-linking mass spectrometry, yeast two-hybrid, protein cofractionation, and affinity purification mass spectrometry (AP-MS), detection of PPIs can uncover novel biological inferences at a high-throughput. Thus new platforms to provide community access to large data sets are necessary. To this end, we have developed a web application that enables exploration and dissemination of the growing BioPlex interaction network. BioPlex is a large-scale interactome data set based on AP-MS of baits from the human ORFeome. The latest BioPlex data set release (BioPlex 2.0) contains 56 553 interactions from 5891 AP-MS experiments. To improve community access to this vast compendium of interactions, we developed BioPlex Display, which integrates individual protein querying, access to empirical data, and on-the-fly annotation of networks within an easy-to-use and mobile web application. BioPlex Display enables rapid acquisition of data from BioPlex and development of hypotheses based on protein interactions.

When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases

PubMed Central

2011-01-01

Background Comprehensive understanding of molecular mechanisms underlying viral infection is a major challenge towards the discovery of new antiviral drugs and susceptibility factors of human diseases. New advances in the field are expected from systems-level modelling and integration of the incessant torrent of high-throughput "-omics" data. Results Here, we describe the Human Infectome protein interaction Network, a novel systems virology model of a virtual virus-infected human cell concerning 110 viruses. This in silico model was applied to comprehensively explore the molecular relationships between viruses and their associated diseases. This was done by merging virus-host and host-host physical protein-protein interactomes with the set of genes essential for viral replication and involved in human genetic diseases. This systems-level approach provides strong evidence that viral proteomes target a wide range of functional and inter-connected modules of proteins as well as highly central and bridging proteins within the human interactome. The high centrality of targeted proteins was correlated to their essentiality for viruses' lifecycle, using functional genomic RNAi data. A stealth-attack of viruses on proteins bridging cellular functions was demonstrated by simulation of cellular network perturbations, a property that could be essential in the molecular aetiology of some human diseases. Networking the Human Infectome and Diseasome unravels the connectivity of viruses to a wide range of diseases and profiled molecular basis of Hepatitis C Virus-induced diseases as well as 38 new candidate genetic predisposition factors involved in type 1 diabetes mellitus. Conclusions The Human Infectome and Diseasome Networks described here provide a unique gateway towards the comprehensive modelling and analysis of the systems level properties associated to viral infection as well as candidate genes potentially involved in the molecular aetiology of human diseases. PMID:21255393

The binary protein-protein interaction landscape of Escherichia coli

PubMed Central

Rajagopala, Seesandra V.; Vlasblom, James; Arnold, Roland; Franca-Koh, Jonathan; Pakala, Suman B.; Phanse, Sadhna; Ceol, Arnaud; Häuser, Roman; Siszler, Gabriella; Wuchty, Stefan; Emili, Andrew; Babu, Mohan; Aloy, Patrick; Pieper, Rembert; Uetz, Peter

2014-01-01

Efforts to map the Escherichia coli interactome have identified several hundred macromolecular complexes, but direct binary protein-protein interactions (PPIs) have not been surveyed on a large scale. Here we performed yeast two-hybrid screens of 3,305 baits against 3,606 preys (~70% of the E. coli proteome) in duplicate to generate a map of 2,234 interactions, approximately doubling the number of known binary PPIs in E. coli. Integration of binary PPIs and genetic interactions revealed functional dependencies among components involved in cellular processes, including envelope integrity, flagellum assembly and protein quality control. Many of the binary interactions that could be mapped within multi-protein complexes were informative regarding internal topology and indicated that interactions within complexes are significantly more conserved than those interactions connecting different complexes. This resource will be useful for inferring bacterial gene function and provides a draft reference of the basic physical wiring network of this evolutionarily significant model microbe. PMID:24561554

ZikaBase: An integrated ZIKV- Human Interactome Map database.

PubMed

Gurumayum, Sanathoi; Brahma, Rahul; Naorem, Leimarembi Devi; Muthaiyan, Mathavan; Gopal, Jeyakodi; Venkatesan, Amouda

2018-01-15

Re-emergence of ZIKV has caused infections in more than 1.5 million people. The molecular mechanism and pathogenesis of ZIKV is not well explored due to unavailability of adequate model and lack of publically accessible resources to provide information of ZIKV-Human protein interactome map till today. This study made an attempt to curate the ZIKV-Human interaction proteins from published literatures and RNA-Seq data. 11 direct interaction, 12 associated genes are retrieved from literatures and 3742 Differentially Expressed Genes (DEGs) are obtained from RNA-Seq analysis. The genes have been analyzed to construct the ZIKV-Human Interactome Map. The importance of the study has been illustrated by the enrichment analysis and observed that direct interaction and associated genes are enriched in viral entry into host cell. Also, ZIKV infection modulates 32% signal and 27% immune system pathways. The integrated database, ZikaBase has been developed to help the virology research community and accessible at https://test5.bicpu.edu.in. Copyright © 2017 Elsevier Inc. All rights reserved.

Comprehensive Identification of mRNA-Binding Proteins of Leishmania donovani by Interactome Capture.

PubMed

Nandan, Devki; Thomas, Sneha A; Nguyen, Anne; Moon, Kyung-Mee; Foster, Leonard J; Reiner, Neil E

2017-01-01

Leishmania are unicellular eukaryotes responsible for leishmaniasis in humans. Like other trypanosomatids, leishmania regulate protein coding gene expression almost exclusively at the post-transcriptional level with the help of RNA binding proteins (RBPs). Due to the presence of polycystronic transcription units, leishmania do not regulate RNA polymerase II-dependent transcription initiation. Recent evidence suggests that the main control points in gene expression are mRNA degradation and translation. Protein-RNA interactions are involved in every aspect of RNA biology, such as mRNA splicing, polyadenylation, localization, degradation, and translation. A detailed picture of these interactions would likely prove to be highly informative in understanding leishmania biology and virulence. We developed a strategy involving covalent UV cross-linking of RBPs to mRNA in vivo, followed by interactome capture using oligo(dT) magnetic beads to define comprehensively the mRNA interactome of growing L. donovani amastigotes. The protein mass spectrometry analysis of captured proteins identified 79 mRNA interacting proteins which withstood very stringent washing conditions. Strikingly, we found that 49 of these mRNA interacting proteins had no orthologs or homologs in the human genome. Consequently, these may represent high quality candidates for selective drug targeting leading to novel therapeutics. These results show that this unbiased, systematic strategy has the promise to be applicable to study the mRNA interactome during various biological settings such as metabolic changes, stress (low pH environment, oxidative stress and nutrient deprivation) or drug treatment.

Prediction of cassava protein interactome based on interolog method.

PubMed

Thanasomboon, Ratana; Kalapanulak, Saowalak; Netrphan, Supatcharee; Saithong, Treenut

2017-12-08

Cassava is a starchy root crop whose role in food security becomes more significant nowadays. Together with the industrial uses for versatile purposes, demand for cassava starch is continuously growing. However, in-depth study to uncover the mystery of cellular regulation, especially the interaction between proteins, is lacking. To reduce the knowledge gap in protein-protein interaction (PPI), genome-scale PPI network of cassava was constructed using interolog-based method (MePPI-In, available at http://bml.sbi.kmutt.ac.th/ppi ). The network was constructed from the information of seven template plants. The MePPI-In included 90,173 interactions from 7,209 proteins. At least, 39 percent of the total predictions were found with supports from gene/protein expression data, while further co-expression analysis yielded 16 highly promising PPIs. In addition, domain-domain interaction information was employed to increase reliability of the network and guide the search for more groups of promising PPIs. Moreover, the topology and functional content of MePPI-In was similar to the networks of Arabidopsis and rice. The potential contribution of MePPI-In for various applications, such as protein-complex formation and prediction of protein function, was discussed and exemplified. The insights provided by our MePPI-In would hopefully enable us to pursue precise trait improvement in cassava.

PROFEAT Update: A Protein Features Web Server with Added Facility to Compute Network Descriptors for Studying Omics-Derived Networks.

PubMed

Zhang, P; Tao, L; Zeng, X; Qin, C; Chen, S Y; Zhu, F; Yang, S Y; Li, Z R; Chen, W P; Chen, Y Z

2017-02-03

The studies of biological, disease, and pharmacological networks are facilitated by the systems-level investigations using computational tools. In particular, the network descriptors developed in other disciplines have found increasing applications in the study of the protein, gene regulatory, metabolic, disease, and drug-targeted networks. Facilities are provided by the public web servers for computing network descriptors, but many descriptors are not covered, including those used or useful for biological studies. We upgraded the PROFEAT web server http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi for computing up to 329 network descriptors and protein-protein interaction descriptors. PROFEAT network descriptors comprehensively describe the topological and connectivity characteristics of unweighted (uniform binding constants and molecular levels), edge-weighted (varying binding constants), node-weighted (varying molecular levels), edge-node-weighted (varying binding constants and molecular levels), and directed (oriented processes) networks. The usefulness of the network descriptors is illustrated by the literature-reported studies of the biological networks derived from the genome, interactome, transcriptome, metabolome, and diseasome profiles. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicted Arabidopsis Interactome Resource and Gene Set Linkage Analysis: A Transcriptomic Analysis Resource.

PubMed

Yao, Heng; Wang, Xiaoxuan; Chen, Pengcheng; Hai, Ling; Jin, Kang; Yao, Lixia; Mao, Chuanzao; Chen, Xin

2018-05-01

An advanced functional understanding of omics data is important for elucidating the design logic of physiological processes in plants and effectively controlling desired traits in plants. We present the latest versions of the Predicted Arabidopsis Interactome Resource (PAIR) and of the gene set linkage analysis (GSLA) tool, which enable the interpretation of an observed transcriptomic change (differentially expressed genes [DEGs]) in Arabidopsis ( Arabidopsis thaliana ) with respect to its functional impact for biological processes. PAIR version 5.0 integrates functional association data between genes in multiple forms and infers 335,301 putative functional interactions. GSLA relies on this high-confidence inferred functional association network to expand our perception of the functional impacts of an observed transcriptomic change. GSLA then interprets the biological significance of the observed DEGs using established biological concepts (annotation terms), describing not only the DEGs themselves but also their potential functional impacts. This unique analytical capability can help researchers gain deeper insights into their experimental results and highlight prospective directions for further investigation. We demonstrate the utility of GSLA with two case studies in which GSLA uncovered how molecular events may have caused physiological changes through their collective functional influence on biological processes. Furthermore, we showed that typical annotation-enrichment tools were unable to produce similar insights to PAIR/GSLA. The PAIR version 5.0-inferred interactome and GSLA Web tool both can be accessed at http://public.synergylab.cn/pair/. © 2018 American Society of Plant Biologists. All Rights Reserved.

Enhancing the role of veterinary vaccines reducing zoonotic diseases of humans: Linking systems biology with vaccine development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Leslie G.; Khare, Sangeeta; Lawhon, Sara D.

The aim of research on infectious diseases is their prevention, and brucellosis and salmonellosis as such are classic examples of worldwide zoonoses for application of a systems biology approach for enhanced rational vaccine development. When used optimally, vaccines prevent disease manifestations, reduce transmission of disease, decrease the need for pharmaceutical intervention, and improve the health and welfare of animals, as well as indirectly protecting against zoonotic diseases of people. Advances in the last decade or so using comprehensive systems biology approaches linking genomics, proteomics, bioinformatics, and biotechnology with immunology, pathogenesis and vaccine formulation and delivery are expected to enable enhancedmore » approaches to vaccine development. The goal of this paper is to evaluate the role of computational systems biology analysis of host:pathogen interactions (the interactome) as a tool for enhanced rational design of vaccines. Systems biology is bringing a new, more robust approach to veterinary vaccine design based upon a deeper understanding of the host pathogen interactions and its impact on the host's molecular network of the immune system. A computational systems biology method was utilized to create interactome models of the host responses to Brucella melitensis (BMEL), Mycobacterium avium paratuberculosis (MAP), Salmonella enterica Typhimurium (STM), and a Salmonella mutant (isogenic *sipA, sopABDE2) and linked to the basis for rational development of vaccines for brucellosis and salmonellosis as reviewed by Adams et al. and Ficht et al. [1,2]. A bovine ligated ileal loop biological model was established to capture the host gene expression response at multiple time points post infection. New methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct a comparative pathogenicity analysis of 219 signaling and metabolic pathways and 1620 gene ontology (GO) categories that defined the host's biosignatures to each infectious condition. Through this DBN computational approach, the method identified significantly perturbed pathways and GO category groups of genes that define the pathogenicity signatures of the infectious agent. Our preliminary results provide deeper understanding of the overall complexity of host innate immune response as well as the identification of host gene perturbations that defines a unique host temporal biosignature response to each pathogen. The application of advanced computational methods for developing interactome models based on DBNs has proven to be instrumental in elucidating novel host responses and improved functional biological insight into the host defensive mechanisms. Evaluating the unique differences in pathway and GO perturbations across pathogen conditions allowed the identification of plausible host pathogen interaction mechanisms. Accordingly, a systems biology approach to study molecular pathway gene expression profiles of host cellular responses to microbial pathogens holds great promise as a methodology to identify, model and predict the overall dynamics of the host pathogen interactome. Thus, we propose that such an approach has immediate application to the rational design of brucellosis and salmonellosis vaccines.« less

Enhancing the role of veterinary vaccines reducing zoonotic diseases of humans: linking systems biology with vaccine development.

PubMed

Adams, L Garry; Khare, Sangeeta; Lawhon, Sara D; Rossetti, Carlos A; Lewin, Harris A; Lipton, Mary S; Turse, Joshua E; Wylie, Dennis C; Bai, Yu; Drake, Kenneth L

2011-09-22

The aim of research on infectious diseases is their prevention, and brucellosis and salmonellosis as such are classic examples of worldwide zoonoses for application of a systems biology approach for enhanced rational vaccine development. When used optimally, vaccines prevent disease manifestations, reduce transmission of disease, decrease the need for pharmaceutical intervention, and improve the health and welfare of animals, as well as indirectly protecting against zoonotic diseases of people. Advances in the last decade or so using comprehensive systems biology approaches linking genomics, proteomics, bioinformatics, and biotechnology with immunology, pathogenesis and vaccine formulation and delivery are expected to enable enhanced approaches to vaccine development. The goal of this paper is to evaluate the role of computational systems biology analysis of host:pathogen interactions (the interactome) as a tool for enhanced rational design of vaccines. Systems biology is bringing a new, more robust approach to veterinary vaccine design based upon a deeper understanding of the host-pathogen interactions and its impact on the host's molecular network of the immune system. A computational systems biology method was utilized to create interactome models of the host responses to Brucella melitensis (BMEL), Mycobacterium avium paratuberculosis (MAP), Salmonella enterica Typhimurium (STM), and a Salmonella mutant (isogenic ΔsipA, sopABDE2) and linked to the basis for rational development of vaccines for brucellosis and salmonellosis as reviewed by Adams et al. and Ficht et al. [1,2]. A bovine ligated ileal loop biological model was established to capture the host gene expression response at multiple time points post infection. New methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct a comparative pathogenicity analysis of 219 signaling and metabolic pathways and 1620 gene ontology (GO) categories that defined the host's biosignatures to each infectious condition. Through this DBN computational approach, the method identified significantly perturbed pathways and GO category groups of genes that define the pathogenicity signatures of the infectious agent. Our preliminary results provide deeper understanding of the overall complexity of host innate immune response as well as the identification of host gene perturbations that defines a unique host temporal biosignature response to each pathogen. The application of advanced computational methods for developing interactome models based on DBNs has proven to be instrumental in elucidating novel host responses and improved functional biological insight into the host defensive mechanisms. Evaluating the unique differences in pathway and GO perturbations across pathogen conditions allowed the identification of plausible host-pathogen interaction mechanisms. Accordingly, a systems biology approach to study molecular pathway gene expression profiles of host cellular responses to microbial pathogens holds great promise as a methodology to identify, model and predict the overall dynamics of the host-pathogen interactome. Thus, we propose that such an approach has immediate application to the rational design of brucellosis and salmonellosis vaccines. Copyright © 2011 Elsevier Ltd. All rights reserved.

Interactome Screening Identifies the ER Luminal Chaperone Hsp47 as a Regulator of the Unfolded Protein Response Transducer IRE1α.

PubMed

Sepulveda, Denisse; Rojas-Rivera, Diego; Rodríguez, Diego A; Groenendyk, Jody; Köhler, Andres; Lebeaupin, Cynthia; Ito, Shinya; Urra, Hery; Carreras-Sureda, Amado; Hazari, Younis; Vasseur-Cognet, Mireille; Ali, Maruf M U; Chevet, Eric; Campos, Gisela; Godoy, Patricio; Vaisar, Tomas; Bailly-Maitre, Béatrice; Nagata, Kazuhiro; Michalak, Marek; Sierralta, Jimena; Hetz, Claudio

2018-01-18

Maintenance of endoplasmic reticulum (ER) proteostasis is controlled by a dynamic signaling network known as the unfolded protein response (UPR). IRE1α is a major UPR transducer, determining cell fate under ER stress. We used an interactome screening to unveil several regulators of the UPR, highlighting the ER chaperone Hsp47 as the major hit. Cellular and biochemical analysis indicated that Hsp47 instigates IRE1α signaling through a physical interaction. Hsp47 directly binds to the ER luminal domain of IRE1α with high affinity, displacing the negative regulator BiP from the complex to facilitate IRE1α oligomerization. The regulation of IRE1α signaling by Hsp47 is evolutionarily conserved as validated using fly and mouse models of ER stress. Hsp47 deficiency sensitized cells and animals to experimental ER stress, revealing the significance of Hsp47 to global proteostasis maintenance. We conclude that Hsp47 adjusts IRE1α signaling by fine-tuning the threshold to engage an adaptive UPR. Copyright © 2018 Elsevier Inc. All rights reserved.

mRNA interactome capture in mammalian cells.

PubMed

Kastelic, Nicolai; Landthaler, Markus

2017-08-15

Throughout their entire life cycle, mRNAs are associated with RNA-binding proteins (RBPs), forming ribonucleoprotein (RNP) complexes with highly dynamic compositions. Their interplay is one key to control gene regulatory mechanisms from mRNA synthesis to decay. To assay the global scope of RNA-protein interactions, we and others have published a method combining crosslinking with highly stringent oligo(dT) affinity purification to enrich proteins associated with polyadenylated RNA (poly(A)+ RNA). Identification of the poly(A)+ RNA-bound proteome (also: mRNA interactome capture) has by now been applied to a diversity of cell lines and model organisms, uncovering comprehensive repertoires of RBPs and hundreds of novel RBP candidates. In addition to determining the RBP catalog in a given biological system, mRNA interactome capture allows the examination of changes in protein-mRNA interactions in response to internal and external stimuli, altered cellular programs and disease. Copyright © 2017. Published by Elsevier Inc.

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.

PubMed

Heberle, Henry; Carazzolle, Marcelo Falsarella; Telles, Guilherme P; Meirelles, Gabriela Vaz; Minghim, Rosane

2017-09-13

The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction.

PubMed

Silva, Joana Vieira; Yoon, Sooyeon; Domingues, Sara; Guimarães, Sofia; Goltsev, Alexander V; da Cruz E Silva, Edgar Figueiredo; Mendes, José Fernando F; da Cruz E Silva, Odete Abreu Beirão; Fardilha, Margarida

2015-01-16

Amyloid precursor protein (APP) is widely recognized for playing a central role in Alzheimer's disease pathogenesis. Although APP is expressed in several tissues outside the human central nervous system, the functions of APP and its family members in other tissues are still poorly understood. APP is involved in several biological functions which might be potentially important for male fertility, such as cell adhesion, cell motility, signaling, and apoptosis. Furthermore, APP superfamily members are known to be associated with fertility. Knowledge on the protein networks of APP in human testis and spermatozoa will shed light on the function of APP in the male reproductive system. We performed a Yeast Two-Hybrid screen and a database search to study the interaction network of APP in human testis and sperm. To gain insights into the role of APP superfamily members in fertility, the study was extended to APP-like protein 2 (APLP2). We analyzed several topological properties of the APP interaction network and the biological and physiological properties of the proteins in the APP interaction network were also specified by gene ontologyand pathways analyses. We classified significant features related to the human male reproduction for the APP interacting proteins and identified modules of proteins with similar functional roles which may show cooperative behavior for male fertility. The present work provides the first report on the APP interactome in human testis. Our approach allowed the identification of novel interactions and recognition of key APP interacting proteins for male reproduction, particularly in sperm-oocyte interaction.

Architecture of the human interactome defines protein communities and disease networks

PubMed Central

Huttlin, Edward L.; Bruckner, Raphael J.; Paulo, Joao A.; Cannon, Joe R.; Ting, Lily; Baltier, Kurt; Colby, Greg; Gebreab, Fana; Gygi, Melanie P.; Parzen, Hannah; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Pontano-Vaites, Laura; Swarup, Sharan; White, Anne E.; Schweppe, Devin K.; Rad, Ramin; Erickson, Brian K.; Obar, Robert A.; Guruharsha, K.G.; Li, Kejie; Artavanis-Tsakonas, Spyros; Gygi, Steven P.; Harper, J. Wade

2017-01-01

The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidation of how genome variation contributes to disease1–3. Here, we present BioPlex 2.0 (Biophysical Interactions of ORFEOME-derived complexes), which employs robust affinity purification-mass spectrometry (AP-MS) methodology4 to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein coding genes from the human genome, and constitutes the largest such network to date. With >56,000 candidate interactions, BioPlex 2.0 contains >29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering (MCL)5 of interacting proteins identified more than 1300 protein communities representing diverse cellular activities. Genes essential for cell fitness6,7 are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization. PMID:28514442

Recent Coselection in Human Populations Revealed by Protein–Protein Interaction Network

PubMed Central

Qian, Wei; Zhou, Hang; Tang, Kun

2015-01-01

Genome-wide scans for signals of natural selection in human populations have identified a large number of candidate loci that underlie local adaptations. This is surprising given the relatively short evolutionary time since the divergence of the human population. One hypothesis that has not been formally examined is whether and how the recent human evolution may have been shaped by coselection in the context of complex molecular interactome. In this study, genome-wide signals of selection were scanned in East Asians, Europeans, and Africans using 1000 Genome data, and subsequently mapped onto the protein–protein interaction (PPI) network. We found that the candidate genes of recent positive selection localized significantly closer to each other on the PPI network than expected, revealing substantial clustering of selected genes. Furthermore, gene pairs of shorter PPI network distances showed higher similarities of their recent evolutionary paths than those further apart. Last, subnetworks enriched with recent coselection signals were identified, which are substantially overrepresented in biological pathways related to signal transduction, neurogenesis, and immune function. These results provide the first genome-wide evidence for association of recent selection signals with the PPI network, shedding light on the potential mechanisms of recent coselection in the human genome. PMID:25532814

Toward Repurposing Metformin as a Precision Anti-Cancer Therapy Using Structural Systems Pharmacology

PubMed Central

Hart, Thomas; Dider, Shihab; Han, Weiwei; Xu, Hua; Zhao, Zhongming; Xie, Lei

2016-01-01

Metformin, a drug prescribed to treat type-2 diabetes, exhibits anti-cancer effects in a portion of patients, but the direct molecular and genetic interactions leading to this pleiotropic effect have not yet been fully explored. To repurpose metformin as a precision anti-cancer therapy, we have developed a novel structural systems pharmacology approach to elucidate metformin’s molecular basis and genetic biomarkers of action. We integrated structural proteome-scale drug target identification with network biology analysis by combining structural genomic, functional genomic, and interactomic data. Through searching the human structural proteome, we identified twenty putative metformin binding targets and their interaction models. We experimentally verified the interactions between metformin and our top-ranked kinase targets. Notably, kinases, particularly SGK1 and EGFR were identified as key molecular targets of metformin. Subsequently, we linked these putative binding targets to genes that do not directly bind to metformin but whose expressions are altered by metformin through protein-protein interactions, and identified network biomarkers of phenotypic response of metformin. The molecular targets and the key nodes in genetic networks are largely consistent with the existing experimental evidence. Their interactions can be affected by the observed cancer mutations. This study will shed new light into repurposing metformin for safe, effective, personalized therapies. PMID:26841718

Prediction of GCRV virus-host protein interactome based on structural motif-domain interactions.

PubMed

Zhang, Aidi; He, Libo; Wang, Yaping

2017-03-02

Grass carp hemorrhagic disease, caused by grass carp reovirus (GCRV), is the most fatal causative agent in grass carp aquaculture. Protein-protein interactions between virus and host are one avenue through which GCRV can trigger infection and induce disease. Experimental approaches for the detection of host-virus interactome have many inherent limitations, and studies on protein-protein interactions between GCRV and its host remain rare. In this study, based on known motif-domain interaction information, we systematically predicted the GCRV virus-host protein interactome by using motif-domain interaction pair searching strategy. These proteins derived from different domain families and were predicted to interact with different motif patterns in GCRV. JAM-A protein was successfully predicted to interact with motifs of GCRV Sigma1-like protein, and shared the similar binding mode compared with orthoreovirus. Differentially expressed genes during GCRV infection process were extracted and mapped to our predicted interactome, the overlapped genes displayed different tissue expression distributions on the whole, the overall expression level in intestinal is higher than that of other three tissues, which may suggest that the functions of these genes are more active in intestinal. Function annotation and pathway enrichment analysis revealed that the host targets were largely involved in signaling pathway and immune pathway, such as interferon-gamma signaling pathway, VEGF signaling pathway, EGF receptor signaling pathway, B cell activation, and T cell activation. Although the predicted PPIs may contain some false positives due to limited data resource and poor research background in non-model species, the computational method still provide reasonable amount of interactions, which can be further validated by high throughput experiments. The findings of this work will contribute to the development of system biology for GCRV infectious diseases, and help guide the identification of novel receptors of GCRV in its host.

Network analysis of the genomic basis of the placebo effect

PubMed Central

Wang, Rui-Sheng; Hall, Kathryn T.; Giulianini, Franco; Passow, Dani; Kaptchuk, Ted J.

2017-01-01

The placebo effect is a phenomenon in which patients who are given an inactive treatment (e.g., inert pill) show a perceived or actual improvement in a medical condition. Placebo effects in clinical trials have been investigated for many years especially because placebo treatments often serve as the control arm of randomized clinical trial designs. Recent observations suggest that placebo effects may be modified by genetics. This observation has given rise to the term “placebome,” which refers to a group of genome-related mediators that affect an individual’s response to placebo treatments. In this study, we conduct a network analysis of the placebome and identify a placebome module in the comprehensive human interactome using a seed-connector algorithm. The placebome module is significantly enriched with neurotransmitter signaling pathways and brain-specific proteins. We validate the placebome module using a large cohort of the Women’s Genome Health Study (WGHS) trial and demonstrate that the placebome module is significantly enriched with genes whose SNPs modify the outcome in the placebo arm of the trial. To gain insights into placebo effects in different diseases and drug treatments, we use a network proximity measure to examine the closeness of the placebome module to different disease modules and drug target modules. The results demonstrate that the network proximity of the placebome module to disease modules in the interactome significantly correlates with the strength of the placebo effect in the corresponding diseases. The proximity of the placebome module to molecular pathways affected by certain drug classes indicates the existence of placebo-drug interactions. This study is helpful for understanding the molecular mechanisms mediating the placebo response, and sets the stage for minimizing its effects in clinical trials and for developing therapeutic strategies that intentionally engage it. PMID:28570268

A “Candidate-Interactome” Aggregate Analysis of Genome-Wide Association Data in Multiple Sclerosis

PubMed Central

Policano, Claudia; Annibali, Viviana; Coarelli, Giulia; Ricigliano, Vito A. G.; Vittori, Danila; Fornasiero, Arianna; Buscarinu, Maria Chiara; Romano, Silvia; Salvetti, Marco; Ristori, Giovanni

2013-01-01

Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a “candidate interactome” (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8-Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms. PMID:23696811

Net Venn - An integrated network analysis web platform for gene lists

USDA-ARS?s Scientific Manuscript database

Many lists containing biological identifiers such as gene lists have been generated in various genomics projects. Identifying the overlap among gene lists can enable us to understand the similarities and differences between the datasets. Here, we present an interactome network-based web application...

Functional Proteomics Identifies Acinus L as a Direct Insulin- and Amino Acid-Dependent Mammalian Target of Rapamycin Complex 1 (mTORC1) Substrate*

PubMed Central

Schwarz, Jennifer Jasmin; Wiese, Heike; Tölle, Regine Charlotte; Zarei, Mostafa; Dengjel, Jörn; Warscheid, Bettina; Thedieck, Kathrin

2015-01-01

The serine/threonine kinase mammalian target of rapamycin (mTOR) governs growth, metabolism, and aging in response to insulin and amino acids (aa), and is often activated in metabolic disorders and cancer. Much is known about the regulatory signaling network that encompasses mTOR, but surprisingly few direct mTOR substrates have been established to date. To tackle this gap in our knowledge, we took advantage of a combined quantitative phosphoproteomic and interactomic strategy. We analyzed the insulin- and aa-responsive phosphoproteome upon inhibition of the mTOR complex 1 (mTORC1) component raptor, and investigated in parallel the interactome of endogenous mTOR. By overlaying these two datasets, we identified acinus L as a potential novel mTORC1 target. We confirmed acinus L as a direct mTORC1 substrate by co-immunoprecipitation and MS-enhanced kinase assays. Our study delineates a triple proteomics strategy of combined phosphoproteomics, interactomics, and MS-enhanced kinase assays for the de novo-identification of mTOR network components, and provides a rich source of potential novel mTOR interactors and targets for future investigation. PMID:25907765

Native KCC2 interactome reveals PACSIN1 as a critical regulator of synaptic inhibition.

PubMed

Mahadevan, Vivek; Khademullah, C Sahara; Dargaei, Zahra; Chevrier, Jonah; Uvarov, Pavel; Kwan, Julian; Bagshaw, Richard D; Pawson, Tony; Emili, Andrew; De Koninck, Yves; Anggono, Victor; Airaksinen, Matti; Woodin, Melanie A

2017-10-13

KCC2 is a neuron-specific K + -Cl - cotransporter essential for establishing the Cl - gradient required for hyperpolarizing inhibition in the central nervous system (CNS). KCC2 is highly localized to excitatory synapses where it regulates spine morphogenesis and AMPA receptor confinement. Aberrant KCC2 function contributes to human neurological disorders including epilepsy and neuropathic pain. Using functional proteomics, we identified the KCC2-interactome in the mouse brain to determine KCC2-protein interactions that regulate KCC2 function. Our analysis revealed that KCC2 interacts with diverse proteins, and its most predominant interactors play important roles in postsynaptic receptor recycling. The most abundant KCC2 interactor is a neuronal endocytic regulatory protein termed PACSIN1 (SYNDAPIN1). We verified the PACSIN1-KCC2 interaction biochemically and demonstrated that shRNA knockdown of PACSIN1 in hippocampal neurons increases KCC2 expression and hyperpolarizes the reversal potential for Cl - . Overall, our global native-KCC2 interactome and subsequent characterization revealed PACSIN1 as a novel and potent negative regulator of KCC2.

Native KCC2 interactome reveals PACSIN1 as a critical regulator of synaptic inhibition

PubMed Central

Mahadevan, Vivek; Chevrier, Jonah; Uvarov, Pavel; Kwan, Julian; Bagshaw, Richard D; Pawson, Tony; Emili, Andrew; De Koninck, Yves; Anggono, Victor; Airaksinen, Matti

2017-01-01

KCC2 is a neuron-specific K+-Cl– cotransporter essential for establishing the Cl- gradient required for hyperpolarizing inhibition in the central nervous system (CNS). KCC2 is highly localized to excitatory synapses where it regulates spine morphogenesis and AMPA receptor confinement. Aberrant KCC2 function contributes to human neurological disorders including epilepsy and neuropathic pain. Using functional proteomics, we identified the KCC2-interactome in the mouse brain to determine KCC2-protein interactions that regulate KCC2 function. Our analysis revealed that KCC2 interacts with diverse proteins, and its most predominant interactors play important roles in postsynaptic receptor recycling. The most abundant KCC2 interactor is a neuronal endocytic regulatory protein termed PACSIN1 (SYNDAPIN1). We verified the PACSIN1-KCC2 interaction biochemically and demonstrated that shRNA knockdown of PACSIN1 in hippocampal neurons increases KCC2 expression and hyperpolarizes the reversal potential for Cl-. Overall, our global native-KCC2 interactome and subsequent characterization revealed PACSIN1 as a novel and potent negative regulator of KCC2. PMID:29028184

Dawn of the in vivo RNA structurome and interactome.

PubMed

Kwok, Chun Kit

2016-10-15

RNA is one of the most fascinating biomolecules in living systems given its structural versatility to fold into elaborate architectures for important biological functions such as gene regulation, catalysis, and information storage. Knowledge of RNA structures and interactions can provide deep insights into their functional roles in vivo For decades, RNA structural studies have been conducted on a transcript-by-transcript basis. The advent of next-generation sequencing (NGS) has enabled the development of transcriptome-wide structural probing methods to profile the global landscape of RNA structures and interactions, also known as the RNA structurome and interactome, which transformed our understanding of the RNA structure-function relationship on a transcriptomic scale. In this review, molecular tools and NGS methods used for RNA structure probing are presented, novel insights uncovered by RNA structurome and interactome studies are highlighted, and perspectives on current challenges and potential future directions are discussed. A more complete understanding of the RNA structures and interactions in vivo will help illuminate the novel roles of RNA in gene regulation, development, and diseases. © 2016 The Author(s); published by Portland Press Limited on behalf of the Biochemical Society.

Protein interactome analysis of 12 mitogen-activated protein kinase kinase kinase in rice using a yeast two-hybrid system.

PubMed

Singh, Raksha; Lee, Jae-Eun; Dangol, Sarmina; Choi, Jihyun; Yoo, Ran Hee; Moon, Jae Sun; Shim, Jae-Kyung; Rakwal, Randeep; Agrawal, Ganesh Kumar; Jwa, Nam-Soo

2014-01-01

The mitogen-activated protein kinase (MAPK) cascade is composed at least of MAP3K (for MAPK kinase kinase), MAP2K, and MAPK family modules. These components together play a central role in mediating extracellular signals to the cell and vice versa by interacting with their partner proteins. However, the MAP3K-interacting proteins remain poorly investigated in plants. Here, we utilized a yeast two-hybrid system and bimolecular fluorescence complementation in the model crop rice (Oryza sativa) to map MAP3K-interacting proteins. We identified 12 novel nonredundant interacting protein pairs (IPPs) representing 11 nonredundant interactors using 12 rice MAP3Ks (available as full-length cDNA in the rice KOME (http://cdna01.dna.affrc.go.jp/cDNA/) at the time of experimental design and execution) as bait and a rice seedling cDNA library as prey. Of the 12 MAP3Ks, only six had interacting protein partners. The established MAP3K interactome consisted of two kinases, three proteases, two forkhead-associated domain-containing proteins, two expressed proteins, one E3 ligase, one regulatory protein, and one retrotransposon protein. Notably, no MAP3K showed physical interaction with either MAP2K or MAPK. Seven IPPs (58.3%) were confirmed in vivo by bimolecular fluorescence complementation. Subcellular localization of 14 interactors, together involved in nine IPPs (75%) further provide prerequisite for biological significance of the IPPs. Furthermore, GO of identified interactors predicted their involvement in diverse physiological responses, which were supported by a literature survey. These findings increase our knowledge of the MAP3K-interacting proteins, help in proposing a model of MAPK modules, provide a valuable resource for developing a complete map of the rice MAPK interactome, and allow discussion for translating the interactome knowledge to rice crop improvement against environmental factors. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A convex optimization approach for identification of human tissue-specific interactomes.

PubMed

Mohammadi, Shahin; Grama, Ananth

2016-06-15

Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/tissue-selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes. We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state-of-the-art techniques. Finally, using case studies of Alzheimer's and Parkinson's diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets. http://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html mohammadi@purdue.edu. © The Author 2016. Published by Oxford University Press.

The multitasking organ: recent insights into skin immune function.

PubMed

Di Meglio, Paola; Perera, Gayathri K; Nestle, Frank O

2011-12-23

The skin provides the first line defense of the human body against injury and infection. By integrating recent findings in cutaneous immunology with fundamental concepts of skin biology, we portray the skin as a multitasking organ ensuring body homeostasis. Crosstalk between the skin and its microbial environment is also highlighted as influencing the response to injury, infection, and autoimmunity. The importance of the skin immune network is emphasized by the identification of several skin-resident cell subsets, each with its unique functions. Lessons learned from targeted therapy in inflammatory skin conditions, such as psoriasis, provide further insights into skin immune function. Finally, we look at the skin as an interacting network of immune signaling pathways exemplified by the development of a disease interactome for psoriasis. Copyright © 2011 Elsevier Inc. All rights reserved.

Network-based association of hypoxia-responsive genes with cardiovascular diseases

NASA Astrophysics Data System (ADS)

Wang, Rui-Sheng; Oldham, William M.; Loscalzo, Joseph

2014-10-01

Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiology of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct a hypoxia-CVD bipartite network and find several interesting hypoxia-CVD modules with significant gene ontology similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology. Based on these observations, we can predict novel genes that may be associated with CVD. This network-based association study gives us a broad view of the relationships between hypoxia and cardiovascular diseases and provides new insights into the role of hypoxia in cardiovascular biology.

Utilising the EGFR interactome to identify mechanisms of drug resistance in non-small cell lung cancer - Proof of concept towards a systems pharmacology approach.

PubMed

Saafan, Hisham; Foerster, Sarah; Parra-Guillen, Zinnia P; Hammer, Elke; Michaelis, Martin; Cinatl, Jindrich; Völker, Uwe; Fröhlich, Holger; Kloft, Charlotte; Ritter, Christoph A

2016-10-30

Drug treatment of epidermal growth factor receptor (EGFR) positive non-small cell lung cancer has improved substantially by targeting activating mutations within the receptor tyrosine kinase domain. However, the development of drug resistance still limits this approach. As root causes, large heterogeneity between tumour entities but also within tumour cells have been suggested. Therefore, approaches to identify these multitude and complex mechanisms are urgently required. Affinity purification coupled with high resolution mass spectrometry was applied to isolate and characterise the EGFR interactome from HCC4006 non-small cell lung cancer cells and their variant HCC4006 r ERLO 0.5 adapted to grow in the presence of therapeutically relevant concentrations of erlotinib. Bioinformatics analyses were carried out to identify proteins and their related molecular functions that interact differentially with EGFR in the untreated state or when incubated with erlotinib prior to EGFR activation. Across all experimental conditions 375 proteins were detected to participate in the EGFR interactome, 90% of which constituted a complex protein interaction network that was bioinformatically reconstructed from literature data. Treatment of HCC4006 r ERLO 0.5 cells carrying a resistance phenotype to erlotinib was associated with an increase of protein levels of members of the clathrin-associated adaptor protein family AP2 (AP2A1, AP2A2, AP2B1), structural proteins of cytoskeleton rearrangement as well as signalling molecules such as Shc. Validation experiments confirmed activation of the Ras-Raf-Mek-Erk (MAPK)-pathway, of which Shc is an initiating adaptor molecule, in HCC4006 r ERLO 0.5 cells. Taken together, differential proteins in the EGFR interactome of HCC4006 r ERLO 0.5 cells were identified that could be related to multiple resistance mechanisms including alterations in growth factor receptor expression, cellular remodelling processes suggesting epithelial-to-mesenchymal transition as well as alterations in downstream signalling. Knowledge of these mechanisms is a pivotal step to build an integrative model of drug resistance in a systems pharmacology manner and to be able to investigate the interplay of these mechanisms and ultimately recommend combinatorial treatment strategies to overcome drug resistance. Copyright © 2016 Elsevier B.V. All rights reserved.

Serial interactome capture of the human cell nucleus.

PubMed

Conrad, Thomas; Albrecht, Anne-Susann; de Melo Costa, Veronica Rodrigues; Sauer, Sascha; Meierhofer, David; Ørom, Ulf Andersson

2016-04-04

Novel RNA-guided cellular functions are paralleled by an increasing number of RNA-binding proteins (RBPs). Here we present 'serial RNA interactome capture' (serIC), a multiple purification procedure of ultraviolet-crosslinked poly(A)-RNA-protein complexes that enables global RBP detection with high specificity. We apply serIC to the nuclei of proliferating K562 cells to obtain the first human nuclear RNA interactome. The domain composition of the 382 identified nuclear RBPs markedly differs from previous IC experiments, including few factors without known RNA-binding domains that are in good agreement with computationally predicted RNA binding. serIC extends the number of DNA-RNA-binding proteins (DRBPs), and reveals a network of RBPs involved in p53 signalling and double-strand break repair. serIC is an effective tool to couple global RBP capture with additional selection or labelling steps for specific detection of highly purified RBPs.

LSU network hubs integrate abiotic and biotic stress responses via interaction with the superoxide dismutase FSD2

PubMed Central

Garcia-Molina, Antoni; Altmann, Melina; Alkofer, Angela; Epple, Petra M.; Dangl, Jeffery L.

2017-01-01

Abstract In natural environments, plants often experience different stresses simultaneously, and adverse abiotic conditions can weaken the plant immune system. Interactome mapping revealed that the LOW SULPHUR UPREGULATED (LSU) proteins are hubs in an Arabidopsis protein interaction network that are targeted by virulence effectors from evolutionarily diverse pathogens. Here we show that LSU proteins are up-regulated in several abiotic and biotic stress conditions, such as nutrient depletion or salt stress, by both transcriptional and post-translational mechanisms. Interference with LSU expression prevents chloroplastic reactive oxygen species (ROS) production and proper stomatal closure during sulphur stress. We demonstrate that LSU1 interacts with the chloroplastic superoxide dismutase FSD2 and stimulates its enzymatic activity in vivo and in vitro. Pseudomonas syringae virulence effectors interfere with this interaction and preclude re-localization of LSU1 to chloroplasts. We demonstrate that reduced LSU levels cause a moderately enhanced disease susceptibility in plants exposed to abiotic stresses such as nutrient deficiency, high salinity, or heavy metal toxicity, whereas LSU1 overexpression confers significant disease resistance in several of these conditions. Our data suggest that the network hub LSU1 plays an important role in co-ordinating plant immune responses across a spectrum of abiotic stress conditions. PMID:28207043

On the integration of protein-protein interaction networks with gene expression and 3D structural data: What can be gained?

NASA Astrophysics Data System (ADS)

Bertolazzi, Paola; Bock, Mary Ellen; Guerra, Concettina; Paci, Paola; Santoni, Daniele

2014-06-01

The biological role of proteins has been analyzed from different perspectives, initially by considering proteins as isolated biological entities, then as cooperating entities that perform their function by interacting with other molecules. There are other dimensions that are important for the complete understanding of the biological processes: time and location. However a protein is rarely annotated with temporal and spatial information. Experimental Protein-Proteins Interaction (PPI) data are static; furthermore they generally do not include transient interactions which are a considerable fraction of the interactome of many organisms. One way to incorporate temporal and condition information is to use other sources of information, such as gene expression data and 3D structural data. Here we review work done to understand the insight that can be gained by enriching PPI data with gene expression and 3D structural data. In particular, we address the following questions: Can the dynamics of a single protein or of an interaction be accurately derived from these data? Can the assembly-disassembly of protein complexes be traced over time? What type of topological changes occur in a PPI network architecture over time?

Caspofungin exposure alters the core septin AspB interactome of Aspergillus fumigatus.

PubMed

Vargas-Muñiz, José M; Renshaw, Hilary; Waitt, Greg; Soderblom, Erik J; Moseley, M Arthur; Palmer, Jonathan M; Juvvadi, Praveen R; Keller, Nancy P; Steinbach, William J

2017-04-01

Aspergillus fumigatus, the main etiological agent of invasive aspergillosis, is a leading cause of death in immunocompromised patients. Septins, a conserved family of GTP-binding proteins, serve as scaffolding proteins to recruit enzymes and key regulators to different cellular compartments. Deletion of the A. fumigatus septin aspB increases susceptibility to the echinocandin antifungal caspofungin. However, how AspB mediates this response to caspofungin is unknown. Here, we characterized the AspB interactome under basal conditions and after exposure to a clinically relevant concentration of caspofungin. While A. fumigatus AspB interacted with 334 proteins, including kinases, cell cycle regulators, and cell wall synthesis-related proteins under basal growth conditions, caspofungin exposure altered AspB interactions. A total of 69 of the basal interactants did not interact with AspB after exposure to caspofungin, and 54 new interactants were identified following caspofungin exposure. We generated A. fumigatus deletion strains for 3 proteins (ArpB, Cyp4, and PpoA) that only interacted with AspB following exposure to caspofungin that were previously annotated as induced after exposure to antifungal agents, yet only PpoA was implicated in the response to caspofungin. Taken together, we defined how the septin AspB interactome is altered in the presence of a clinically relevant antifungal. Copyright © 2017 Elsevier Inc. All rights reserved.

The Pleiotropic MET Receptor Network: Circuit Development and the Neural-Medical Interface of Autism

PubMed Central

Eagleson, Kathie L.; Xie, Zhihui; Levitt, Pat

2016-01-01

People with autism spectrum disorder (ASD) and other neurodevelopmental disorders (NDDs) are behaviorally and medically heterogeneous. The combination of polygenicity and gene pleiotropy - the influence of one gene on distinct phenotypes - raises questions of how specific genes and their protein products interact to contribute to NDDs. A preponderance of evidence supports developmental and pathophysiological roles for the MET receptor tyrosine kinase, a multi-functional receptor that mediates distinct biological responses depending upon cell context. MET influences neuron architecture and synapse maturation in the forebrain, and regulates homeostasis in gastrointestinal and immune systems, both commonly disrupted in NDDs. Peak expression of synapse-enriched MET is conserved across rodent and primate forebrain, yet regional differences in primate neocortex are pronounced, with enrichment in circuits that participate in social information processing. A functional risk allele in the MET promoter, enriched in subgroups of children with ASD, reduces transcription and disrupts socially-relevant neural circuits structurally and functionally. In mice, circuit-specific deletion of Met causes distinct atypical behaviors. MET activation increases dendritic complexity and nascent synapse number, but synapse maturation requires reductions in MET. MET mediates its specific biological effects through different intracellular signaling pathways, and has a complex protein interactome that is enriched in ASD and other NDD candidates. The interactome is co-regulated in developing human neocortex. We suggest that a gene as pleiotropic and highly regulated as MET, together with its interactome, is biologically relevant in normal and pathophysiological contexts, impacting central and peripheral phenotypes that contribute to NDD risk and clinical symptoms. PMID:27837921

Notch3 Interactome Analysis Identified WWP2 as a Negative Regulator of Notch3 Signaling in Ovarian Cancer

PubMed Central

Guan, Bin; Wu, Ren-Chin; Zhu, Heng; Blackshaw, Seth; Shih, Ie-Ming; Wang, Tian-Li

2014-01-01

The Notch3 signaling pathway is thought to play a critical role in cancer development, as evidenced by the Notch3 amplification and rearrangement observed in human cancers. However, the molecular mechanism by which Notch3 signaling contributes to tumorigenesis is largely unknown. In an effort to identify the molecular modulators of the Notch3 signaling pathway, we screened for Notch3-intracellular domain (N3-ICD) interacting proteins using a human proteome microarray. Pathway analysis of the Notch3 interactome demonstrated that ubiquitin C was the molecular hub of the top functional network, suggesting the involvement of ubiquitination in modulating Notch3 signaling. Thereby, we focused on functional characterization of an E3 ubiquitin-protein ligase, WWP2, a top candidate in the Notch3 interactome list. Co-immunoprecipitation experiments showed that WWP2 interacted with N3-ICD but not with intracellular domains from other Notch receptors. Wild-type WWP2 but not ligase-deficient mutant WWP2 increases mono-ubiquitination of the membrane-tethered Notch3 fragment, therefore attenuating Notch3 pathway activity in cancer cells and leading to cell cycle arrest. The mono-ubiquitination by WWP2 may target an endosomal/lysosomal degradation fate for Notch3 as suggested by the fact that the process could be suppressed by the endosomal/lysosomal inhibitor. Analysis of The Cancer Genome Atlas dataset showed that the majority of ovarian carcinomas harbored homozygous or heterozygous deletions in WWP2 locus, and there was an inverse correlation in the expression levels between WWP2 and Notch3 in ovarian carcinomas. Furthermore, ectopic expression of WWP2 decreased tumor development in a mouse xenograft model and suppressed the Notch3-induced phenotypes including increase in cancer stem cell-like cell population and platinum resistance. Taken together, our results provide evidence that WWP2 serves as a tumor suppressor by negatively regulating Notch3 signaling in ovarian cancer. PMID:25356737

Development and application of a recombination-based library versus library high- throughput yeast two-hybrid (RLL-Y2H) screening system.

PubMed

Yang, Fang; Lei, Yingying; Zhou, Meiling; Yao, Qili; Han, Yichao; Wu, Xiang; Zhong, Wanshun; Zhu, Chenghang; Xu, Weize; Tao, Ran; Chen, Xi; Lin, Da; Rahman, Khaista; Tyagi, Rohit; Habib, Zeshan; Xiao, Shaobo; Wang, Dang; Yu, Yang; Chen, Huanchun; Fu, Zhenfang; Cao, Gang

2018-02-16

Protein-protein interaction (PPI) network maintains proper function of all organisms. Simple high-throughput technologies are desperately needed to delineate the landscape of PPI networks. While recent state-of-the-art yeast two-hybrid (Y2H) systems improved screening efficiency, either individual colony isolation, library preparation arrays, gene barcoding or massive sequencing are still required. Here, we developed a recombination-based 'library vs library' Y2H system (RLL-Y2H), by which multi-library screening can be accomplished in a single pool without any individual treatment. This system is based on the phiC31 integrase-mediated integration between bait and prey plasmids. The integrated fragments were digested by MmeI and subjected to deep sequencing to decode the interaction matrix. We applied this system to decipher the trans-kingdom interactome between Mycobacterium tuberculosis and host cells and further identified Rv2427c interfering with the phagosome-lysosome fusion. This concept can also be applied to other systems to screen protein-RNA and protein-DNA interactions and delineate signaling landscape in cells.

Building protein-protein interaction networks for Leishmania species through protein structural information.

PubMed

Dos Santos Vasconcelos, Crhisllane Rafaele; de Lima Campos, Túlio; Rezende, Antonio Mauro

2018-03-06

Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability. The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.

Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling

PubMed Central

Huang, Shao-shan Carol; Clarke, David C.; Gosline, Sara J. C.; Labadorf, Adam; Chouinard, Candace R.; Gordon, William; Lauffenburger, Douglas A.; Fraenkel, Ernest

2013-01-01

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets. PMID:23408876

High-confidence prediction of global interactomes based on genome-wide coevolutionary networks

PubMed Central

Juan, David; Pazos, Florencio; Valencia, Alfonso

2008-01-01

Interacting or functionally related protein families tend to have similar phylogenetic trees. Based on this observation, techniques have been developed to predict interaction partners. The observed degree of similarity between the phylogenetic trees of two proteins is the result of many different factors besides the actual interaction or functional relationship between them. Such factors influence the performance of interaction predictions. One aspect that can influence this similarity is related to the fact that a given protein interacts with many others, and hence it must adapt to all of them. Accordingly, the interaction or coadaptation signal within its tree is a composite of the influence of all of the interactors. Here, we introduce a new estimator of coevolution to overcome this and other problems. Instead of relying on the individual value of tree similarity between two proteins, we use the whole network of similarities between all of the pairs of proteins within a genome to reassess the similarity of that pair, thereby taking into account its coevolutionary context. We show that this approach offers a substantial improvement in interaction prediction performance, providing a degree of accuracy/coverage comparable with, or in some cases better than, that of experimental techniques. Moreover, important information on the structure, function, and evolution of macromolecular complexes can be inferred with this methodology. PMID:18199838

High-confidence prediction of global interactomes based on genome-wide coevolutionary networks.

PubMed

Juan, David; Pazos, Florencio; Valencia, Alfonso

2008-01-22

Interacting or functionally related protein families tend to have similar phylogenetic trees. Based on this observation, techniques have been developed to predict interaction partners. The observed degree of similarity between the phylogenetic trees of two proteins is the result of many different factors besides the actual interaction or functional relationship between them. Such factors influence the performance of interaction predictions. One aspect that can influence this similarity is related to the fact that a given protein interacts with many others, and hence it must adapt to all of them. Accordingly, the interaction or coadaptation signal within its tree is a composite of the influence of all of the interactors. Here, we introduce a new estimator of coevolution to overcome this and other problems. Instead of relying on the individual value of tree similarity between two proteins, we use the whole network of similarities between all of the pairs of proteins within a genome to reassess the similarity of that pair, thereby taking into account its coevolutionary context. We show that this approach offers a substantial improvement in interaction prediction performance, providing a degree of accuracy/coverage comparable with, or in some cases better than, that of experimental techniques. Moreover, important information on the structure, function, and evolution of macromolecular complexes can be inferred with this methodology.

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online)

PubMed Central

Hsu, Chun-Nan; Lai, Jin-Mei; Liu, Chia-Hung; Tseng, Huei-Hun; Lin, Chih-Yun; Lin, Kuan-Ting; Yeh, Hsu-Hua; Sung, Ting-Yi; Hsu, Wen-Lian; Su, Li-Jen; Lee, Sheng-An; Chen, Chang-Han; Lee, Gen-Cher; Lee, DT; Shiue, Yow-Ling; Yeh, Chang-Wei; Chang, Chao-Hui; Kao, Cheng-Yan; Huang, Chi-Ying F

2007-01-01

Background The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. Results Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO , to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. Conclusion This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment. PMID:17326819

Approaches for assessing and discovering protein interactions in cancer

PubMed Central

Mohammed, Hisham; Carroll, Jason S.

2013-01-01

Significant insight into the function of proteins, can be delineated by discovering and characterising interacting proteins. There are numerous methods for the discovery of unknown associated protein networks, with purification of the bait (the protein of interest) followed by Mass Spectrometry (MS) as a common theme. In recent years, advances have permitted the purification of endogenous proteins and methods for scaling down starting material. As such, approaches for rapid, unbiased identification of protein interactomes are becoming a standard tool in the researchers toolbox, rather than a technique that is only available to specialists. This review will highlight some of the recent technical advances in proteomic based discovery approaches, the pros and cons of various methods and some of the key findings in cancer related systems. PMID:24072816

Mass Spectrometry-Based Screening Platform Reveals Orco Interactome in Drosophila melanogaster.

PubMed

Yu, Kate E; Kim, Do-Hyoung; Kim, Yong-In; Jones, Walton D; Lee, J Eugene

2018-02-28

Animals use their odorant receptors to receive chemical information from the environment. Insect odorant receptors differ from the G protein-coupled odorant receptors in vertebrates and nematodes, and very little is known about their protein-protein interactions. Here, we introduce a mass spectrometric platform designed for the large-scale analysis of insect odorant receptor protein-protein interactions. Using this platform, we obtained the first Orco interactome from Drosophila melanogaster . From a total of 1,186 identified proteins, we narrowed the interaction candidates to 226, of which only two-thirds have been named. These candidates include the known olfactory proteins Or92a and Obp51a. Around 90% of the proteins having published names likely function inside the cell, and nearly half of these intracellular proteins are associated with the endomembrane system. In a basic loss-of-function electrophysiological screen, we found that the disruption of eight (i.e., Rab5, CG32795, Mpcp, Tom70, Vir-1, CG30427, Eaat1, and CG2781) of 28 randomly selected candidates affects olfactory responses in vivo . Thus, because this Orco interactome includes physiologically meaningful candidates, we anticipate that our platform will help guide further research on the molecular mechanisms of the insect odorant receptor family.

High-throughput proteomics : optical approaches.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Davidson, George S.

2008-09-01

Realistic cell models could greatly accelerate our ability to engineer biochemical pathways and the production of valuable organic products, which would be of great use in the development of biofuels, pharmaceuticals, and the crops for the next green revolution. However, this level of engineering will require a great deal more knowledge about the mechanisms of life than is currently available. In particular, we need to understand the interactome (which proteins interact) as it is situated in the three dimensional geometry of the cell (i.e., a situated interactome), and the regulation/dynamics of these interactions. Methods for optical proteomics have become availablemore » that allow the monitoring and even disruption/control of interacting proteins in living cells. Here, a range of these methods is reviewed with respect to their role in elucidating the interactome and the relevant spatial localizations. Development of these technologies and their integration into the core competencies of research organizations can position whole institutions and teams of researchers to lead in both the fundamental science and the engineering applications of cellular biology. That leadership could be particularly important with respect to problems of national urgency centered around security, biofuels, and healthcare.« less

Comparative analysis of protein-protein interactions in the defense response of rice and wheat.

PubMed

Cantu, Dario; Yang, Baoju; Ruan, Randy; Li, Kun; Menzo, Virginia; Fu, Daolin; Chern, Mawsheng; Ronald, Pamela C; Dubcovsky, Jorge

2013-03-12

Despite the importance of wheat as a major staple crop and the negative impact of diseases on its production worldwide, the genetic mechanisms and gene interactions involved in the resistance response in wheat are still poorly understood. The complete sequence of the rice genome has provided an extremely useful parallel road map for genetic and genomics studies in wheat. The recent construction of a defense response interactome in rice has the potential to further enhance the translation of advances in rice to wheat and other grasses. The objective of this study was to determine the degree of conservation in the protein-protein interactions in the rice and wheat defense response interactomes. As entry points we selected proteins that serve as key regulators of the rice defense response: the RAR1/SGT1/HSP90 protein complex, NPR1, XA21, and XB12 (XA21 interacting protein 12). Using available wheat sequence databases and phylogenetic analyses we identified and cloned the wheat orthologs of these four rice proteins, including recently duplicated paralogs, and their known direct interactors and tested 86 binary protein interactions using yeast-two-hybrid (Y2H) assays. All interactions between wheat proteins were further tested using in planta bimolecular fluorescence complementation (BiFC). Eighty three percent of the known rice interactions were confirmed when wheat proteins were tested with rice interactors and 76% were confirmed using wheat protein pairs. All interactions in the RAR1/SGT1/ HSP90, NPR1 and XB12 nodes were confirmed for the identified orthologous wheat proteins, whereas only forty four percent of the interactions were confirmed in the interactome node centered on XA21. We hypothesize that this reduction may be associated with a different sub-functionalization history of the multiple duplications that occurred in this gene family after the divergence of the wheat and rice lineages. The observed high conservation of interactions between proteins that serve as key regulators of the rice defense response suggests that the existing rice interactome can be used to predict interactions in wheat. Such predictions are less reliable for nodes that have undergone a different history of duplications and sub-functionalization in the two lineages.

Integrative Analysis of Brain Region-specific Shank3 Interactomes for Understanding the Heterogeneity of Neuronal Pathophysiology Related to SHANK3 Mutations

PubMed Central

Lee, Yeunkum; Kang, Hyojin; Lee, Bokyoung; Zhang, Yinhua; Kim, Yoonhee; Kim, Shinhyun; Kim, Won-Ki; Han, Kihoon

2017-01-01

Recent molecular genetic studies have identified 100s of risk genes for various neurodevelopmental and neuropsychiatric disorders. As the number of risk genes increases, it is becoming clear that different mutations of a single gene could cause different types of disorders. One of the best examples of such a gene is SHANK3, which encodes a core scaffold protein of the neuronal excitatory post-synapse. Deletions, duplications, and point mutations of SHANK3 are associated with autism spectrum disorders, intellectual disability, schizophrenia, bipolar disorder, and attention deficit hyperactivity disorder. Nevertheless, how the different mutations of SHANK3 can lead to such phenotypic diversity remains largely unknown. In this study, we investigated whether Shank3 could form protein complexes in a brain region-specific manner, which might contribute to the heterogeneity of neuronal pathophysiology caused by SHANK3 mutations. To test this, we generated a medial prefrontal cortex (mPFC) Shank3 in vivo interactome consisting of 211 proteins, and compared this protein list with a Shank3 interactome previously generated from mixed hippocampal and striatal (HP+STR) tissues. Unexpectedly, we found that only 47 proteins (about 20%) were common between the two interactomes, while 164 and 208 proteins were specifically identified in the mPFC and HP+STR interactomes, respectively. Each of the mPFC- and HP+STR-specific Shank3 interactomes represents a highly interconnected network. Upon comparing the brain region-enriched proteomes, we found that the large difference between the mPFC and HP+STR Shank3 interactomes could not be explained by differential protein expression profiles among the brain regions. Importantly, bioinformatic pathway analysis revealed that the representative biological functions of the mPFC- and HP+STR-specific Shank3 interactomes were different, suggesting that these interactors could mediate the brain region-specific functions of Shank3. Meanwhile, the same analysis on the common Shank3 interactors, including Homer and GKAP/SAPAP proteins, suggested that they could mainly function as scaffolding proteins at the post-synaptic density. Lastly, we found that the mPFC- and HP+STR-specific Shank3 interactomes contained a significant number of proteins associated with neurodevelopmental and neuropsychiatric disorders. These results suggest that Shank3 can form protein complexes in a brain region-specific manner, which might contribute to the pathophysiological and phenotypic diversity of disorders related to SHANK3 mutations. PMID:28469556

Competition-cooperation relationship networks characterize the competition and cooperation between proteins

PubMed Central

Li, Hong; Zhou, Yuan; Zhang, Ziding

2015-01-01

By analyzing protein-protein interaction (PPI) networks, one can find that a protein may have multiple binding partners. However, it is difficult to determine whether the interactions with these partners occur simultaneously from binary PPIs alone. Here, we construct the yeast and human competition-cooperation relationship networks (CCRNs) based on protein structural interactomes to clearly exhibit the relationship (competition or cooperation) between two partners of the same protein. If two partners compete for the same interaction interface, they would be connected by a competitive edge; otherwise, they would be connected by a cooperative edge. The properties of three kinds of hubs (i.e., competitive, modest, and cooperative hubs) are analyzed in the CCRNs. Our results show that competitive hubs have higher clustering coefficients and form clusters in the human CCRN, but these tendencies are not observed in the yeast CCRN. We find that the human-specific proteins contribute significantly to these differences. Subsequently, we conduct a series of computational experiments to investigate the regulatory mechanisms that avoid competition between proteins. Our comprehensive analyses reveal that for most yeast and human protein competitors, transcriptional regulation plays an important role. Moreover, the human-specific proteins have a particular preference for other regulatory mechanisms, such as alternative splicing. PMID:26108281

Prediction of missing common genes for disease pairs using network based module separation on incomplete human interactome.

PubMed

Akram, Pakeeza; Liao, Li

2017-12-06

Identification of common genes associated with comorbid diseases can be critical in understanding their pathobiological mechanism. This work presents a novel method to predict missing common genes associated with a disease pair. Searching for missing common genes is formulated as an optimization problem to minimize network based module separation from two subgraphs produced by mapping genes associated with disease onto the interactome. Using cross validation on more than 600 disease pairs, our method achieves significantly higher average receiver operating characteristic ROC Score of 0.95 compared to a baseline ROC score 0.60 using randomized data. Missing common genes prediction is aimed to complete gene set associated with comorbid disease for better understanding of biological intervention. It will also be useful for gene targeted therapeutics related to comorbid diseases. This method can be further considered for prediction of missing edges to complete the subgraph associated with disease pair.

Interaction Network of Proteins Associated with Human Cytomegalovirus IE2-p86 Protein during Infection: A Proteomic Analysis

PubMed Central

Du, Guixin; Stinski, Mark F.

2013-01-01

Human cytomegalovirus protein IE2-p86 exerts its functions through interaction with other viral and cellular proteins. To further delineate its protein interaction network, we generated a recombinant virus expressing SG-tagged IE2-p86 and used tandem affinity purification coupled with mass spectrometry. A total of 9 viral proteins and 75 cellular proteins were found to associate with IE2-p86 protein during the first 48 hours of infection. The protein profile at 8, 24, and 48 h post infection revealed that UL84 tightly associated with IE2-p86, and more viral and cellular proteins came into association with IE2-p86 with the progression of virus infection. A computational analysis of the protein-protein interaction network indicated that all of the 9 viral proteins and most of the cellular proteins identified in the study are interconnected to varying degrees. Of the cellular proteins that were confirmed to associate with IE2-p86 by immunoprecipitation, C1QBP was further shown to be upregulated by HCMV infection and colocalized with IE2-p86, UL84 and UL44 in the virus replication compartment of the nucleus. The IE2-p86 interactome network demonstrated the temporal development of stable and abundant protein complexes that associate with IE2-p86 and provided a framework to benefit future studies of various protein complexes during HCMV infection. PMID:24358118

Exploring the interactions of the RAS family in the human protein network and their potential implications in RAS-directed therapies

PubMed Central

Bueno, Anibal; Morilla, Ian; Diez, Diego; Moya-Garcia, Aurelio A.; Lozano, José; Ranea, Juan A.G.

2016-01-01

RAS proteins are the founding members of the RAS superfamily of GTPases. They are involved in key signaling pathways regulating essential cellular functions such as cell growth and differentiation. As a result, their deregulation by inactivating mutations often results in aberrant cell proliferation and cancer. With the exception of the relatively well-known KRAS, HRAS and NRAS proteins, little is known about how the interactions of the other RAS human paralogs affect cancer evolution and response to treatment. In this study we performed a comprehensive analysis of the relationship between the phylogeny of RAS proteins and their location in the protein interaction network. This analysis was integrated with the structural analysis of conserved positions in available 3D structures of RAS complexes. Our results show that many RAS proteins with divergent sequences are found close together in the human interactome. We found specific conserved amino acid positions in this group that map to the binding sites of RAS with many of their signaling effectors, suggesting that these pairs could share interacting partners. These results underscore the potential relevance of cross-talking in the RAS signaling network, which should be taken into account when considering the inhibitory activity of drugs targeting specific RAS oncoproteins. This study broadens our understanding of the human RAS signaling network and stresses the importance of considering its potential cross-talk in future therapies. PMID:27713118

Differential Occurrence of Interactions and Interaction Domains in Proteins Containing Homopolymeric Amino Acid Repeats

PubMed Central

Pelassa, Ilaria; Fiumara, Ferdinando

2015-01-01

Homopolymeric amino acids repeats (AARs), which are widespread in proteomes, have often been viewed simply as spacers between protein domains, or even as “junk” sequences with no obvious function but with a potential to cause harm upon expansion as in genetic diseases associated with polyglutamine or polyalanine expansions, including Huntington disease and cleidocranial dysplasia. A growing body of evidence indicates however that at least some AARs can form organized, functional protein structures, and can regulate protein function. In particular, certain AARs can mediate protein-protein interactions, either through homotypic AAR-AAR contacts or through heterotypic contacts with other protein domains. It is still unclear however, whether AARs may have a generalized, proteome-wide role in shaping protein-protein interaction networks. Therefore, we have undertaken here a bioinformatics screening of the human proteome and interactome in search of quantitative evidence of such a role. We first identified the sets of proteins that contain repeats of any one of the 20 amino acids, as well as control sets of proteins chosen at random in the proteome. We then analyzed the connectivity between the proteins of the AAR-containing protein sets and we compared it with that observed in the corresponding control networks. We find evidence for different degrees of connectivity in the different AAR-containing protein networks. Indeed, networks of proteins containing polyglutamine, polyglutamate, polyproline, and other AARs show significantly increased levels of connectivity, whereas networks containing polyleucine and other hydrophobic repeats show lower degrees of connectivity. Furthermore, we observed that numerous protein-protein, -nucleic acid, and -lipid interaction domains are significantly enriched in specific AAR protein groups. These findings support the notion of a generalized, combinatorial role of AARs, together with conventional protein interaction domains, in shaping the interaction networks of the human proteome, and define proteome-wide knowledge that may guide the informed biological exploration of the role of AARs in protein interactions. PMID:26734058

Comprehensive Identification of RNA-Binding Proteins by RNA Interactome Capture.

PubMed

Castello, Alfredo; Horos, Rastislav; Strein, Claudia; Fischer, Bernd; Eichelbaum, Katrin; Steinmetz, Lars M; Krijgsveld, Jeroen; Hentze, Matthias W

2016-01-01

RNA associates with RNA-binding proteins (RBPs) from synthesis to decay, forming dynamic ribonucleoproteins (RNPs). In spite of the preeminent role of RBPs regulating RNA fate, the scope of cellular RBPs has remained largely unknown. We have recently developed a novel and comprehensive method to identify the repertoire of active RBPs of cultured cells, called RNA interactome capture. Using in vivo UV cross-linking on cultured cells, proteins are covalently bound to RNA if the contact between the two is direct ("zero distance"). Protein-RNA complexes are purified by poly(A) tail-dependent oligo(dT) capture and analyzed by quantitative mass spectrometry. Because UV irradiation is applied to living cells and purification is performed using highly stringent washes, RNA interactome capture identifies physiologic and direct protein-RNA interactions. Applied to HeLa cells, this protocol revealed the near-complete repertoire of RBPs, including hundreds of novel RNA binders. Apart from its RBP discovery capacity, quantitative and comparative RNA interactome capture can also be used to study the responses of the RBP repertoire to different physiological cues and processes, including metabolic stress, differentiation, development, or the response to drugs.

RNA/DNA Hybrid Interactome Identifies DXH9 as a Molecular Player in Transcriptional Termination and R-Loop-Associated DNA Damage.

PubMed

Cristini, Agnese; Groh, Matthias; Kristiansen, Maiken S; Gromak, Natalia

2018-05-08

R-loops comprise an RNA/DNA hybrid and displaced single-stranded DNA. They play important biological roles and are implicated in pathology. Even so, proteins recognizing these structures are largely undefined. Using affinity purification with the S9.6 antibody coupled to mass spectrometry, we defined the RNA/DNA hybrid interactome in HeLa cells. This consists of known R-loop-associated factors SRSF1, FACT, and Top1, and yet uncharacterized interactors, including helicases, RNA processing, DNA repair, and chromatin factors. We validate specific examples of these interactors and characterize their involvement in R-loop biology. A top candidate DHX9 helicase promotes R-loop suppression and transcriptional termination. DHX9 interacts with PARP1, and both proteins prevent R-loop-associated DNA damage. DHX9 and other interactome helicases are overexpressed in cancer, linking R-loop-mediated DNA damage and disease. Our RNA/DNA hybrid interactome provides a powerful resource to study R-loop biology in health and disease. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Rare disruptive variants in the DISC1 Interactome and Regulome: association with cognitive ability and schizophrenia.

PubMed

Teng, S; Thomson, P A; McCarthy, S; Kramer, M; Muller, S; Lihm, J; Morris, S; Soares, D C; Hennah, W; Harris, S; Camargo, L M; Malkov, V; McIntosh, A M; Millar, J K; Blackwood, D H; Evans, K L; Deary, I J; Porteous, D J; McCombie, W R

2018-05-01

Schizophrenia (SCZ), bipolar disorder (BD) and recurrent major depressive disorder (rMDD) are common psychiatric illnesses. All have been associated with lower cognitive ability, and show evidence of genetic overlap and substantial evidence of pleiotropy with cognitive function and neuroticism. Disrupted in schizophrenia 1 (DISC1) protein directly interacts with a large set of proteins (DISC1 Interactome) that are involved in brain development and signaling. Modulation of DISC1 expression alters the expression of a circumscribed set of genes (DISC1 Regulome) that are also implicated in brain biology and disorder. Here we report targeted sequencing of 59 DISC1 Interactome genes and 154 Regulome genes in 654 psychiatric patients and 889 cognitively-phenotyped control subjects, on whom we previously reported evidence for trait association from complete sequencing of the DISC1 locus. Burden analyses of rare and singleton variants predicted to be damaging were performed for psychiatric disorders, cognitive variables and personality traits. The DISC1 Interactome and Regulome showed differential association across the phenotypes tested. After family-wise error correction across all traits (FWER across ), an increased burden of singleton disruptive variants in the Regulome was associated with SCZ (FWER across P=0.0339). The burden of singleton disruptive variants in the DISC1 Interactome was associated with low cognitive ability at age 11 (FWER across P=0.0043). These results identify altered regulation of schizophrenia candidate genes by DISC1 and its core Interactome as an alternate pathway for schizophrenia risk, consistent with the emerging effects of rare copy number variants associated with intellectual disability.

Arabidopsis G-protein interactome reveals connections to cell wall carbohydrates and morphogenesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klopffleisch, Karsten; Phan, Nguyen; Chen, Jay

2011-01-01

The heterotrimeric G-protein complex is minimally composed of G{alpha}, G{beta}, and G{gamma} subunits. In the classic scenario, the G-protein complex is the nexus in signaling from the plasma membrane, where the heterotrimeric G-protein associates with heptahelical G-protein-coupled receptors (GPCRs), to cytoplasmic target proteins called effectors. Although a number of effectors are known in metazoans and fungi, none of these are predicted to exist in their canonical forms in plants. To identify ab initio plant G-protein effectors and scaffold proteins, we screened a set of proteins from the G-protein complex using two-hybrid complementation in yeast. After deep and exhaustive interrogation, wemore » detected 544 interactions between 434 proteins, of which 68 highly interconnected proteins form the core G-protein interactome. Within this core, over half of the interactions comprising two-thirds of the nodes were retested and validated as genuine in planta. Co-expression analysis in combination with phenotyping of loss-of-function mutations in a set of core interactome genes revealed a novel role for G-proteins in regulating cell wall modification.« less

Arabidopsis G-protein interactome reveals connections to cell wall carbohydrates and morphogenesis.

PubMed

Klopffleisch, Karsten; Phan, Nguyen; Augustin, Kelsey; Bayne, Robert S; Booker, Katherine S; Botella, Jose R; Carpita, Nicholas C; Carr, Tyrell; Chen, Jin-Gui; Cooke, Thomas Ryan; Frick-Cheng, Arwen; Friedman, Erin J; Fulk, Brandon; Hahn, Michael G; Jiang, Kun; Jorda, Lucia; Kruppe, Lydia; Liu, Chenggang; Lorek, Justine; McCann, Maureen C; Molina, Antonio; Moriyama, Etsuko N; Mukhtar, M Shahid; Mudgil, Yashwanti; Pattathil, Sivakumar; Schwarz, John; Seta, Steven; Tan, Matthew; Temp, Ulrike; Trusov, Yuri; Urano, Daisuke; Welter, Bastian; Yang, Jing; Panstruga, Ralph; Uhrig, Joachim F; Jones, Alan M

2011-09-27

The heterotrimeric G-protein complex is minimally composed of Gα, Gβ, and Gγ subunits. In the classic scenario, the G-protein complex is the nexus in signaling from the plasma membrane, where the heterotrimeric G-protein associates with heptahelical G-protein-coupled receptors (GPCRs), to cytoplasmic target proteins called effectors. Although a number of effectors are known in metazoans and fungi, none of these are predicted to exist in their canonical forms in plants. To identify ab initio plant G-protein effectors and scaffold proteins, we screened a set of proteins from the G-protein complex using two-hybrid complementation in yeast. After deep and exhaustive interrogation, we detected 544 interactions between 434 proteins, of which 68 highly interconnected proteins form the core G-protein interactome. Within this core, over half of the interactions comprising two-thirds of the nodes were retested and validated as genuine in planta. Co-expression analysis in combination with phenotyping of loss-of-function mutations in a set of core interactome genes revealed a novel role for G-proteins in regulating cell wall modification.

Arabidopsis G-protein interactome reveals connections to cell wall carbohydrates and morphogenesis

PubMed Central

Klopffleisch, Karsten; Phan, Nguyen; Augustin, Kelsey; Bayne, Robert S; Booker, Katherine S; Botella, Jose R; Carpita, Nicholas C; Carr, Tyrell; Chen, Jin-Gui; Cooke, Thomas Ryan; Frick-Cheng, Arwen; Friedman, Erin J; Fulk, Brandon; Hahn, Michael G; Jiang, Kun; Jorda, Lucia; Kruppe, Lydia; Liu, Chenggang; Lorek, Justine; McCann, Maureen C; Molina, Antonio; Moriyama, Etsuko N; Mukhtar, M Shahid; Mudgil, Yashwanti; Pattathil, Sivakumar; Schwarz, John; Seta, Steven; Tan, Matthew; Temp, Ulrike; Trusov, Yuri; Urano, Daisuke; Welter, Bastian; Yang, Jing; Panstruga, Ralph; Uhrig, Joachim F; Jones, Alan M

2011-01-01

The heterotrimeric G-protein complex is minimally composed of Gα, Gβ, and Gγ subunits. In the classic scenario, the G-protein complex is the nexus in signaling from the plasma membrane, where the heterotrimeric G-protein associates with heptahelical G-protein-coupled receptors (GPCRs), to cytoplasmic target proteins called effectors. Although a number of effectors are known in metazoans and fungi, none of these are predicted to exist in their canonical forms in plants. To identify ab initio plant G-protein effectors and scaffold proteins, we screened a set of proteins from the G-protein complex using two-hybrid complementation in yeast. After deep and exhaustive interrogation, we detected 544 interactions between 434 proteins, of which 68 highly interconnected proteins form the core G-protein interactome. Within this core, over half of the interactions comprising two-thirds of the nodes were retested and validated as genuine in planta. Co-expression analysis in combination with phenotyping of loss-of-function mutations in a set of core interactome genes revealed a novel role for G-proteins in regulating cell wall modification. PMID:21952135

Exploring the interactome: microfluidic isolation of proteins and interacting partners for quantitative analysis by electron microscopy.

PubMed

Giss, Dominic; Kemmerling, Simon; Dandey, Venkata; Stahlberg, Henning; Braun, Thomas

2014-05-20

Multimolecular protein complexes are important for many cellular processes. However, the stochastic nature of the cellular interactome makes the experimental detection of complex protein assemblies difficult and quantitative analysis at the single molecule level essential. Here, we present a fast and simple microfluidic method for (i) the quantitative isolation of endogenous levels of untagged protein complexes from minute volumes of cell lysates under close to physiological conditions and (ii) the labeling of specific components constituting these complexes. The method presented uses specific antibodies that are conjugated via a photocleavable linker to magnetic beads that are trapped in microcapillaries to immobilize the target proteins. Proteins are released by photocleavage, eluted, and subsequently analyzed by quantitative transmission electron microscopy at the single molecule level. Additionally, before photocleavage, immunogold can be employed to label proteins that interact with the primary target protein. Thus, the presented method provides a new way to study the interactome and, in combination with single molecule transmission electron microscopy, to structurally characterize the large, dynamic, heterogeneous multimolecular protein complexes formed.

The Pleiotropic MET Receptor Network: Circuit Development and the Neural-Medical Interface of Autism.

PubMed

Eagleson, Kathie L; Xie, Zhihui; Levitt, Pat

2017-03-01

People with autism spectrum disorder and other neurodevelopmental disorders (NDDs) are behaviorally and medically heterogeneous. The combination of polygenicity and gene pleiotropy-the influence of one gene on distinct phenotypes-raises questions of how specific genes and their protein products interact to contribute to NDDs. A preponderance of evidence supports developmental and pathophysiological roles for the MET receptor tyrosine kinase, a multifunctional receptor that mediates distinct biological responses depending upon cell context. MET influences neuron architecture and synapse maturation in the forebrain and regulates homeostasis in gastrointestinal and immune systems, both commonly disrupted in NDDs. Peak expression of synapse-enriched MET is conserved across rodent and primate forebrain, yet regional differences in primate neocortex are pronounced, with enrichment in circuits that participate in social information processing. A functional risk allele in the MET promoter, enriched in subgroups of children with autism spectrum disorder, reduces transcription and disrupts socially relevant neural circuits structurally and functionally. In mice, circuit-specific deletion of Met causes distinct atypical behaviors. MET activation increases dendritic complexity and nascent synapse number, but synapse maturation requires reductions in MET. MET mediates its specific biological effects through different intracellular signaling pathways and has a complex protein interactome that is enriched in autism spectrum disorder and other NDD candidates. The interactome is coregulated in developing human neocortex. We suggest that a gene as pleiotropic and highly regulated as MET, together with its interactome, is biologically relevant in normal and pathophysiological contexts, affecting central and peripheral phenotypes that contribute to NDD risk and clinical symptoms. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

LSU network hubs integrate abiotic and biotic stress responses via interaction with the superoxide dismutase FSD2.

PubMed

Garcia-Molina, Antoni; Altmann, Melina; Alkofer, Angela; Epple, Petra M; Dangl, Jeffery L; Falter-Braun, Pascal

2017-02-01

In natural environments, plants often experience different stresses simultaneously, and adverse abiotic conditions can weaken the plant immune system. Interactome mapping revealed that the LOW SULPHUR UPREGULATED (LSU) proteins are hubs in an Arabidopsis protein interaction network that are targeted by virulence effectors from evolutionarily diverse pathogens. Here we show that LSU proteins are up-regulated in several abiotic and biotic stress conditions, such as nutrient depletion or salt stress, by both transcriptional and post-translational mechanisms. Interference with LSU expression prevents chloroplastic reactive oxygen species (ROS) production and proper stomatal closure during sulphur stress. We demonstrate that LSU1 interacts with the chloroplastic superoxide dismutase FSD2 and stimulates its enzymatic activity in vivo and in vitro. Pseudomonas syringae virulence effectors interfere with this interaction and preclude re-localization of LSU1 to chloroplasts. We demonstrate that reduced LSU levels cause a moderately enhanced disease susceptibility in plants exposed to abiotic stresses such as nutrient deficiency, high salinity, or heavy metal toxicity, whereas LSU1 overexpression confers significant disease resistance in several of these conditions. Our data suggest that the network hub LSU1 plays an important role in co-ordinating plant immune responses across a spectrum of abiotic stress conditions. © The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Protein Network of the Pseudomonas aeruginosa Denitrification Apparatus

PubMed Central

Borrero-de Acuña, José Manuel; Rohde, Manfred; Wissing, Josef; Jänsch, Lothar; Schobert, Max; Molinari, Gabriella; Timmis, Kenneth N.

2016-01-01

ABSTRACT Oxidative phosphorylation using multiple-component, membrane-associated protein complexes is the most effective way for a cell to generate energy. Here, we systematically investigated the multiple protein-protein interactions of the denitrification apparatus of the pathogenic bacterium Pseudomonas aeruginosa. During denitrification, nitrate (Nar), nitrite (Nir), nitric oxide (Nor), and nitrous oxide (Nos) reductases catalyze the reaction cascade of NO3− → NO2− → NO → N2O → N2. Genetic experiments suggested that the nitric oxide reductase NorBC and the regulatory protein NosR are the nucleus of the denitrification protein network. We utilized membrane interactomics in combination with electron microscopy colocalization studies to elucidate the corresponding protein-protein interactions. The integral membrane proteins NorC, NorB, and NosR form the core assembly platform that binds the nitrate reductase NarGHI and the periplasmic nitrite reductase NirS via its maturation factor NirF. The periplasmic nitrous oxide reductase NosZ is linked via NosR. The nitrate transporter NarK2, the nitrate regulatory system NarXL, various nitrite reductase maturation proteins, NirEJMNQ, and the Nos assembly lipoproteins NosFL were also found to be attached. A number of proteins associated with energy generation, including electron-donating dehydrogenases, the complete ATP synthase, almost all enzymes of the tricarboxylic acid (TCA) cycle, and the Sec system of protein transport, among many other proteins, were found to interact with the denitrification proteins. This deduced nitrate respirasome is presumably only one part of an extensive cytoplasmic membrane-anchored protein network connecting cytoplasmic, inner membrane, and periplasmic proteins to mediate key activities occurring at the barrier/interface between the cytoplasm and the external environment. IMPORTANCE The processes of cellular energy generation are catalyzed by large multiprotein enzyme complexes. The molecular basis for the interaction of these complexes is poorly understood. We employed membrane interactomics and electron microscopy to determine the protein-protein interactions involved. The well-investigated enzyme complexes of denitrification of the pathogenic bacterium Pseudomonas aeruginosa served as a model. Denitrification is one essential step of the universal N cycle and provides the bacterium with an effective alternative to oxygen respiration. This process allows the bacterium to form biofilms, which create low-oxygen habitats and which are a key in the infection mechanism. Our results provide new insights into the molecular basis of respiration, as well as opening a new window into the infection strategies of this pathogen. PMID:26903416

The Functional Landscape of Hsp27 Reveals New Cellular Processes such as DNA Repair and Alternative Splicing and Proposes Novel Anticancer Targets*

PubMed Central

Katsogiannou, Maria; Andrieu, Claudia; Baylot, Virginie; Baudot, Anaïs; Dusetti, Nelson J.; Gayet, Odile; Finetti, Pascal; Garrido, Carmen; Birnbaum, Daniel; Bertucci, François; Brun, Christine; Rocchi, Palma

2014-01-01

Previously, we identified the stress-induced chaperone, Hsp27, as highly overexpressed in castration-resistant prostate cancer and developed an Hsp27 inhibitor (OGX-427) currently tested in phase I/II clinical trials as a chemosensitizing agent in different cancers. To better understand the Hsp27 poorly-defined cytoprotective functions in cancers and increase the OGX-427 pharmacological safety, we established the Hsp27-protein interaction network using a yeast two-hybrid approach and identified 226 interaction partners. As an example, we showed that targeting Hsp27 interaction with TCTP, a partner protein identified in our screen increases therapy sensitivity, opening a new promising field of research for therapeutic approaches that could decrease or abolish toxicity for normal cells. Results of an in-depth bioinformatics network analysis allying the Hsp27 interaction map into the human interactome underlined the multifunctional character of this protein. We identified interactions of Hsp27 with proteins involved in eight well known functions previously related to Hsp27 and uncovered 17 potential new ones, such as DNA repair and RNA splicing. Validation of Hsp27 involvement in both processes in human prostate cancer cells supports our system biology-predicted functions and provides new insights into Hsp27 roles in cancer cells. PMID:25277244

MitProNet: A Knowledgebase and Analysis Platform of Proteome, Interactome and Diseases for Mammalian Mitochondria

PubMed Central

Mao, Song; Chai, Xiaoqiang; Hu, Yuling; Hou, Xugang; Tang, Yiheng; Bi, Cheng; Li, Xiao

2014-01-01

Mitochondrion plays a central role in diverse biological processes in most eukaryotes, and its dysfunctions are critically involved in a large number of diseases and the aging process. A systematic identification of mitochondrial proteomes and characterization of functional linkages among mitochondrial proteins are fundamental in understanding the mechanisms underlying biological functions and human diseases associated with mitochondria. Here we present a database MitProNet which provides a comprehensive knowledgebase for mitochondrial proteome, interactome and human diseases. First an inventory of mammalian mitochondrial proteins was compiled by widely collecting proteomic datasets, and the proteins were classified by machine learning to achieve a high-confidence list of mitochondrial proteins. The current version of MitProNet covers 1124 high-confidence proteins, and the remainders were further classified as middle- or low-confidence. An organelle-specific network of functional linkages among mitochondrial proteins was then generated by integrating genomic features encoded by a wide range of datasets including genomic context, gene expression profiles, protein-protein interactions, functional similarity and metabolic pathways. The functional-linkage network should be a valuable resource for the study of biological functions of mitochondrial proteins and human mitochondrial diseases. Furthermore, we utilized the network to predict candidate genes for mitochondrial diseases using prioritization algorithms. All proteins, functional linkages and disease candidate genes in MitProNet were annotated according to the information collected from their original sources including GO, GEO, OMIM, KEGG, MIPS, HPRD and so on. MitProNet features a user-friendly graphic visualization interface to present functional analysis of linkage networks. As an up-to-date database and analysis platform, MitProNet should be particularly helpful in comprehensive studies of complicated biological mechanisms underlying mitochondrial functions and human mitochondrial diseases. MitProNet is freely accessible at http://bio.scu.edu.cn:8085/MitProNet. PMID:25347823

Big data mining powers fungal research: recent advances in fission yeast systems biology approaches.

PubMed

Wang, Zhe

2017-06-01

Biology research has entered into big data era. Systems biology approaches therefore become the powerful tools to obtain the whole landscape of how cell separate, grow, and resist the stresses. Fission yeast Schizosaccharomyces pombe is wonderful unicellular eukaryote model, especially studying its division and metabolism can facilitate to understanding the molecular mechanism of cancer and discovering anticancer agents. In this perspective, we discuss the recent advanced fission yeast systems biology tools, mainly focus on metabolomics profiling and metabolic modeling, protein-protein interactome and genetic interaction network, DNA sequencing and applications, and high-throughput phenotypic screening. We therefore hope this review can be useful for interested fungal researchers as well as bioformaticians.

Characterization of the zinc-induced Shank3 interactome of mouse synaptosome.

PubMed

Lee, Yeunkum; Ryu, Jae Ryun; Kang, Hyojin; Kim, Yoonhee; Kim, Shinhyun; Zhang, Yinhua; Jin, Chunmei; Cho, Hyo Min; Kim, Won-Ki; Sun, Woong; Han, Kihoon

2017-12-16

Variants of the SHANK3 gene, which encodes a core scaffold protein of the postsynaptic density of excitatory synapses, have been causally associated with numerous brain disorders. Shank3 proteins directly bind zinc ions through their C-terminal sterile α motif domain, which enhances the multimerization and synaptic localization of Shank3, to regulate excitatory synaptic strength. However, no studies have explored whether zinc affects the protein interactions of Shank3, which might contribute to the synaptic changes observed after zinc application. To examine this, we first purified Shank3 protein complexes from mouse brain synaptosomal lysates that were incubated with different concentrations of ZnCl 2 , and analyzed them with mass spectrometry. We used strict criteria to identify 71 proteins that specifically interacted with Shank3 when extra ZnCl 2 was added to the lysate. To characterize the zinc-induced Shank3 interactome, we performed various bioinformatic analyses that revealed significant associations of the interactome with subcellular compartments, including mitochondria, and brain disorders, such as bipolar disorder and schizophrenia. Together, our results showing that zinc affected the Shank3 protein interactions of in vitro mouse synaptosomes provided an additional link between zinc and core synaptic proteins that have been implicated in multiple brain disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Interactome analysis of transcriptional coactivator multiprotein bridging factor 1 unveils a yeast AP-1-like transcription factor involved in oxidation tolerance of mycopathogen Beauveria bassiana.

PubMed

Chu, Xin-Ling; Dong, Wei-Xia; Ding, Jin-Li; Feng, Ming-Guang; Ying, Sheng-Hua

2018-02-01

Oxidation tolerance is an important determinant to predict the virulence and biocontrol potential of Beauveria bassiana, a well-known entomopathogenic fungus. As a transcriptional coactivator, multiprotein bridging factor 1 mediates the activity of transcription factor in diverse physiological processes, and its homolog in B. bassiana (BbMBF1) contributes to fungal oxidation tolerance. In this study, the BbMBF1-interactomes under oxidative stress and normal growth condition were deciphered by mass spectrometry integrated with the immunoprecipitation. BbMBF1p factor has a broad interaction with proteins that are involved in various cellular processes, and this interaction is dynamically regulated by oxidative stress. Importantly, a B. bassiana homolog of yeast AP-1-like transcription factor (BbAP-1) was specifically associated with the BbMBF1-interactome under oxidation and significantly contributed to fungal oxidation tolerance. In addition, qPCR analysis revealed that several antioxidant genes are jointly controlled by BbAP-1 and BbMBF1. Conclusively, it is proposed that BbMBF1p protein mediates BbAP-1p factor to transcribe the downstream antioxidant genes in B. bassiana under oxidative stress. This study demonstrates for the first time a proteomic view of the MBF1-interactome in fungi, and presents an initial framework to probe the transcriptional mechanism involved in fungal response to oxidation, which will provide a new strategy to improve the biocontrol efficacy of B. bassiana.

Pharmacological targeting of the transcription factor SOX18 delays breast cancer in mice.

PubMed

Overman, Jeroen; Fontaine, Frank; Moustaqil, Mehdi; Mittal, Deepak; Sierecki, Emma; Sacilotto, Natalia; Zuegg, Johannes; Robertson, Avril Ab; Holmes, Kelly; Salim, Angela A; Mamidyala, Sreeman; Butler, Mark S; Robinson, Ashley S; Lesieur, Emmanuelle; Johnston, Wayne; Alexandrov, Kirill; Black, Brian L; Hogan, Benjamin M; De Val, Sarah; Capon, Robert J; Carroll, Jason S; Bailey, Timothy L; Koopman, Peter; Jauch, Ralf; Smyth, Mark J; Cooper, Matthew A; Gambin, Yann; Francois, Mathias

2017-01-31

Pharmacological targeting of transcription factors holds great promise for the development of new therapeutics, but strategies based on blockade of DNA binding, nuclear shuttling, or individual protein partner recruitment have yielded limited success to date. Transcription factors typically engage in complex interaction networks, likely masking the effects of specifically inhibiting single protein-protein interactions. Here, we used a combination of genomic, proteomic and biophysical methods to discover a suite of protein-protein interactions involving the SOX18 transcription factor, a known regulator of vascular development and disease. We describe a small-molecule that is able to disrupt a discrete subset of SOX18-dependent interactions. This compound selectively suppressed SOX18 transcriptional outputs in vitro and interfered with vascular development in zebrafish larvae. In a mouse pre-clinical model of breast cancer, treatment with this inhibitor significantly improved survival by reducing tumour vascular density and metastatic spread. Our studies validate an interactome-based molecular strategy to interfere with transcription factor activity, for the development of novel disease therapeutics.

Altered Protein Interactions of the Endogenous Interactome of PTPIP51 towards MAPK Signaling

PubMed Central

Brobeil, Alexander; Chehab, Rajaa; Dietel, Eric; Gattenlöhner, Stefan; Wimmer, Monika

2017-01-01

Protein–protein interactions play a pivotal role in normal cellular functions as well as in carcinogenesis. The protein–protein interactions form functional clusters during signal transduction. To elucidate the fine calibration of the protein–protein interactions of protein tyrosine phosphatase interacting protein 51 (PTPIP51) a small molecule drug, namely LDC-3, directly targeting PTPIP51 is now available. Therefore, LDC-3 allows for the studying of the regulation of the endogenous interactome by modulating PTPIP51 binding capacity. Small interfering ribonucleic acid (siRNA) experiments show that the modification in PTPIP51 binding capacity is induced by LDC-3. Application of LDC-3 annuls the known regulatory phosphorylation mechanisms for PTPIP51 and consequently, significantly alters the assembly of the PTPIP51 associated protein complexes. The treatment of human keratinocytes (HaCaT cells) with LDC-3 induces an altered protein–protein interaction profile of the endogenous interactome of PTPIP51. In addition, LDC-3 stabilizes PTPIP51 within a mitogen activated protein kinase (MAPK) complex composed of Raf-1 and the scaffold protein 14-3-3, independent of the phosphorylation status of PTPIP51. Of note, under LDC-3 treatment the regulatory function of the PTP1B on PTPIP51 fails to impact the PTPIP51 interaction characteristics, as reported for the HaCaT cell line. In summary, LDC-3 gives the unique opportunity to directly modulate PTPIP51 in malignant cells, thus targeting potential dysregulated signal transduction pathways such as the MAPK cascade. The provided data give critical insights in the therapeutic potential of PTPIP51 protein interactions and thus are basic for possible targeted therapy regimens. PMID:28754031

Factors affecting interactome-based prediction of human genes associated with clinical signs.

PubMed

González-Pérez, Sara; Pazos, Florencio; Chagoyen, Mónica

2017-07-17

Clinical signs are a fundamental aspect of human pathologies. While disease diagnosis is problematic or impossible in many cases, signs are easier to perceive and categorize. Clinical signs are increasingly used, together with molecular networks, to prioritize detected variants in clinical genomics pipelines, even if the patient is still undiagnosed. Here we analyze the ability of these network-based methods to predict genes that underlie clinical signs from the human interactome. Our analysis reveals that these approaches can locate genes associated with clinical signs with variable performance that depends on the sign and associated disease. We analyzed several clinical and biological factors that explain these variable results, including number of genes involved (mono- vs. oligogenic diseases), mode of inheritance, type of clinical sign and gene product function. Our results indicate that the characteristics of the clinical signs and their related diseases should be considered for interpreting the results of network-prediction methods, such as those aimed at discovering disease-related genes and variants. These results are important due the increasing use of clinical signs as an alternative to diseases for studying the molecular basis of human pathologies.

Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities.

PubMed

Lapek, John D; Greninger, Patricia; Morris, Robert; Amzallag, Arnaud; Pruteanu-Malinici, Iulian; Benes, Cyril H; Haas, Wilhelm

2017-10-01

The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein-protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein-protein associations affect the phenotype of biological systems.

Triangular Alignment (TAME). A Tensor-based Approach for Higher-order Network Alignment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mohammadi, Shahin; Gleich, David F.; Kolda, Tamara G.

2015-11-01

Network alignment is an important tool with extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel formulation of the network alignment problem that extends topological similarity to higher-order structures and provide a new objective function that maximizes the number of aligned substructures. This objective function corresponds to an integer programming problem, which is NP-hard. Consequently, we approximate this objective function as a surrogate function whose maximization results in a tensor eigenvalue problem. Based on this formulation, we present an algorithm called Triangularmore » AlignMEnt (TAME), which attempts to maximize the number of aligned triangles across networks. We focus on alignment of triangles because of their enrichment in complex networks; however, our formulation and resulting algorithms can be applied to general motifs. Using a case study on the NAPABench dataset, we show that TAME is capable of producing alignments with up to 99% accuracy in terms of aligned nodes. We further evaluate our method by aligning yeast and human interactomes. Our results indicate that TAME outperforms the state-of-art alignment methods both in terms of biological and topological quality of the alignments.« less

Recent coselection in human populations revealed by protein-protein interaction network.

PubMed

Qian, Wei; Zhou, Hang; Tang, Kun

2014-12-21

Genome-wide scans for signals of natural selection in human populations have identified a large number of candidate loci that underlie local adaptations. This is surprising given the relatively short evolutionary time since the divergence of the human population. One hypothesis that has not been formally examined is whether and how the recent human evolution may have been shaped by coselection in the context of complex molecular interactome. In this study, genome-wide signals of selection were scanned in East Asians, Europeans, and Africans using 1000 Genome data, and subsequently mapped onto the protein-protein interaction (PPI) network. We found that the candidate genes of recent positive selection localized significantly closer to each other on the PPI network than expected, revealing substantial clustering of selected genes. Furthermore, gene pairs of shorter PPI network distances showed higher similarities of their recent evolutionary paths than those further apart. Last, subnetworks enriched with recent coselection signals were identified, which are substantially overrepresented in biological pathways related to signal transduction, neurogenesis, and immune function. These results provide the first genome-wide evidence for association of recent selection signals with the PPI network, shedding light on the potential mechanisms of recent coselection in the human genome. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

The SSU processome interactome in Saccharomyces cerevisiae reveals novel protein subcomplexes.

PubMed

Vincent, Nicholas G; Charette, J Michael; Baserga, Susan J

2018-01-01

Ribosome assembly is an evolutionarily conserved and energy intensive process required for cellular growth, proliferation, and maintenance. In yeast, assembly of the small ribosomal subunit (SSU) requires approximately 75 assembly factors that act in coordination to form the SSU processome, a 6 MDa ribonucleoprotein complex. The SSU processome is required for processing, modifying, and folding the preribosomal RNA (rRNA) to prepare it for incorporation into the mature SSU. Although the protein composition of the SSU processome has been known for some time, the interaction network of the proteins required for its assembly has remained poorly defined. Here, we have used a semi-high-throughput yeast two-hybrid (Y2H) assay and coimmunoprecipitation validation method to produce a high-confidence interactome of SSU processome assembly factors (SPAFs), providing essential insight into SSU assembly and ribosome biogenesis. Further, we used glycerol density-gradient sedimentation to reveal the presence of protein subcomplexes that have not previously been observed. Our work not only provides essential insight into SSU assembly and ribosome biogenesis, but also serves as an important resource for future investigations into how defects in biogenesis and assembly cause congenital disorders of ribosomes known as ribosomopathies. © 2018 Vincent et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Towards Inferring Protein Interactions: Challenges and Solutions

NASA Astrophysics Data System (ADS)

Zhang, Ya; Zha, Hongyuan; Chu, Chao-Hsien; Ji, Xiang

2006-12-01

Discovering interacting proteins has been an essential part of functional genomics. However, existing experimental techniques only uncover a small portion of any interactome. Furthermore, these data often have a very high false rate. By conceptualizing the interactions at domain level, we provide a more abstract representation of interactome, which also facilitates the discovery of unobserved protein-protein interactions. Although several domain-based approaches have been proposed to predict protein-protein interactions, they usually assume that domain interactions are independent on each other for the convenience of computational modeling. A new framework to predict protein interactions is proposed in this paper, where no assumption is made about domain interactions. Protein interactions may be the result of multiple domain interactions which are dependent on each other. A conjunctive norm form representation is used to capture the relationships between protein interactions and domain interactions. The problem of interaction inference is then modeled as a constraint satisfiability problem and solved via linear programing. Experimental results on a combined yeast data set have demonstrated the robustness and the accuracy of the proposed algorithm. Moreover, we also map some predicted interacting domains to three-dimensional structures of protein complexes to show the validity of our predictions.

Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters.

PubMed

Javierre, Biola M; Burren, Oliver S; Wilder, Steven P; Kreuzhuber, Roman; Hill, Steven M; Sewitz, Sven; Cairns, Jonathan; Wingett, Steven W; Várnai, Csilla; Thiecke, Michiel J; Burden, Frances; Farrow, Samantha; Cutler, Antony J; Rehnström, Karola; Downes, Kate; Grassi, Luigi; Kostadima, Myrto; Freire-Pritchett, Paula; Wang, Fan; Stunnenberg, Hendrik G; Todd, John A; Zerbino, Daniel R; Stegle, Oliver; Ouwehand, Willem H; Frontini, Mattia; Wallace, Chris; Spivakov, Mikhail; Fraser, Peter

2016-11-17

Long-range interactions between regulatory elements and gene promoters play key roles in transcriptional regulation. The vast majority of interactions are uncharted, constituting a major missing link in understanding genome control. Here, we use promoter capture Hi-C to identify interacting regions of 31,253 promoters in 17 human primary hematopoietic cell types. We show that promoter interactions are highly cell type specific and enriched for links between active promoters and epigenetically marked enhancers. Promoter interactomes reflect lineage relationships of the hematopoietic tree, consistent with dynamic remodeling of nuclear architecture during differentiation. Interacting regions are enriched in genetic variants linked with altered expression of genes they contact, highlighting their functional role. We exploit this rich resource to connect non-coding disease variants to putative target promoters, prioritizing thousands of disease-candidate genes and implicating disease pathways. Our results demonstrate the power of primary cell promoter interactomes to reveal insights into genomic regulatory mechanisms underlying common diseases. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Host cell interactome of PA protein of H5N1 influenza A virus in chicken cells.

PubMed

Wang, Qiao; Li, Qinghe; Liu, Ranran; Zheng, Maiqing; Wen, Jie; Zhao, Guiping

2016-03-16

Influenza A virus (IAV) heavily depends on viral-host protein interactions in order to replicate and spread. Identification of host factors that interact with viral proteins plays crucial roles in understanding the mechanism of IAV infection. Here we report the interaction landscape of H5N1 IAV PA protein in chicken cells through the use of affinity purification and mass spectrometry. PA protein was expressed in chicken cells and PA interacting complexes were captured by co-immunoprecipitation and analyzed by mass spectrometry. A total of 134 proteins were identified as PA-host interacting factors. Protein complexes including the minichromosome maintenance complex (MCM), 26S proteasome and the coat protein I (COPI) complex associated with PA in chicken cells, indicating the essential roles of these functional protein complexes during the course of IAV infection. Gene Ontology and pathway enrichment analysis both showed strong enrichment of PA interacting proteins in the category of DNA replication, covering genes such as PCNA, MCM2, MCM3, MCM4, MCM5 and MCM7. This study has uncovered the comprehensive interactome of H5N1 IAV PA protein in its chicken host and helps to establish the foundation for further investigation into the newly identified viral-host interactions. Influenza A virus (IAV) is a great threat to public health and avian production. However, the manner in which avian IAV recruits the host cellular machinery for replication and how the host antagonizes the IAV infection was previously poorly understood. Here we present the viral-host interactome of the H5N1 IAV PA protein and reveal the comprehensive association of host factors with PA. Copyright © 2016 Elsevier B.V. All rights reserved.

An in vivo proteomic analysis of the Me31B interactome in Drosophila germ granules.

PubMed

DeHaan, Hunter; McCambridge, Aidan; Armstrong, Brittany; Cruse, Carlie; Solanki, Dhruv; Trinidad, Jonathan C; Arkov, Alexey L; Gao, Ming

2017-11-01

Drosophila Me31B is a conserved protein of germ granules, ribonucleoprotein complexes essential for germ cell development. Me31B post-transcriptionally regulates mRNAs by interacting with other germ granule proteins. However, a Me31B interactome is lacking. Here, we use an in vivo proteomics approach to show that the Me31B interactome contains polypeptides from four functional groups: RNA regulatory proteins, glycolytic enzymes, cytoskeleton/motor proteins, and germ plasm components. We further show that Me31B likely colocalizes with the germ plasm components Tudor (Tud), Vasa, and Aubergine in the nuage and germ plasm and provide evidence that Me31B may directly bind to Tud in a symmetrically dimethylated arginine-dependent manner. Our study supports the role of Me31B in RNA regulation and suggests its novel roles in germ granule assembly and function. © 2017 Federation of European Biochemical Societies.

Systems Biology-Based Investigation of Cellular Antiviral Drug Targets Identified by Gene-Trap Insertional Mutagenesis.

PubMed

Cheng, Feixiong; Murray, James L; Zhao, Junfei; Sheng, Jinsong; Zhao, Zhongming; Rubin, Donald H

2016-09-01

Viruses require host cellular factors for successful replication. A comprehensive systems-level investigation of the virus-host interactome is critical for understanding the roles of host factors with the end goal of discovering new druggable antiviral targets. Gene-trap insertional mutagenesis is a high-throughput forward genetics approach to randomly disrupt (trap) host genes and discover host genes that are essential for viral replication, but not for host cell survival. In this study, we used libraries of randomly mutagenized cells to discover cellular genes that are essential for the replication of 10 distinct cytotoxic mammalian viruses, 1 gram-negative bacterium, and 5 toxins. We herein reported 712 candidate cellular genes, characterizing distinct topological network and evolutionary signatures, and occupying central hubs in the human interactome. Cell cycle phase-specific network analysis showed that host cell cycle programs played critical roles during viral replication (e.g. MYC and TAF4 regulating G0/1 phase). Moreover, the viral perturbation of host cellular networks reflected disease etiology in that host genes (e.g. CTCF, RHOA, and CDKN1B) identified were frequently essential and significantly associated with Mendelian and orphan diseases, or somatic mutations in cancer. Computational drug repositioning framework via incorporating drug-gene signatures from the Connectivity Map into the virus-host interactome identified 110 putative druggable antiviral targets and prioritized several existing drugs (e.g. ajmaline) that may be potential for antiviral indication (e.g. anti-Ebola). In summary, this work provides a powerful methodology with a tight integration of gene-trap insertional mutagenesis testing and systems biology to identify new antiviral targets and drugs for the development of broadly acting and targeted clinical antiviral therapeutics.

NR4A nuclear receptors are orphans but not lonesome.

PubMed

Kurakula, Kondababu; Koenis, Duco S; van Tiel, Claudia M; de Vries, Carlie J M

2014-11-01

The NR4A subfamily of nuclear receptors consists of three mammalian members: Nur77, Nurr1, and NOR-1. The NR4A receptors are involved in essential physiological processes such as adaptive and innate immune cell differentiation, metabolism and brain function. They act as transcription factors that directly modulate gene expression, but can also form trans-repressive complexes with other transcription factors. In contrast to steroid hormone nuclear receptors such as the estrogen receptor or the glucocorticoid receptor, no ligands have been described for the NR4A receptors. This lack of known ligands might be explained by the structure of the ligand-binding domain of NR4A receptors, which shows an active conformation and a ligand-binding pocket that is filled with bulky amino acid side-chains. Other mechanisms, such as transcriptional control, post-translational modifications and protein-protein interactions therefore seem to be more important in regulating the activity of the NR4A receptors. For Nur77, over 80 interacting proteins (the interactome) have been identified so far, and roughly half of these interactions has been studied in more detail. Although the NR4As show some overlap in interacting proteins, less information is available on the interactome of Nurr1 and NOR-1. Therefore, the present review will describe the current knowledge on the interactomes of all three NR4A nuclear receptors with emphasis on Nur77. Copyright © 2014 Elsevier B.V. All rights reserved.

Dengue-2 structural proteins associate with human proteins to produce a coagulation and innate immune response biased interactome.

PubMed

Folly, Brenda B; Weffort-Santos, Almeriane M; Fathman, C G; Soares, Luis R B

2011-01-31

Dengue virus infection is a public health threat to hundreds of millions of individuals in the tropical regions of the globe. Although Dengue infection usually manifests itself in its mildest, though often debilitating clinical form, dengue fever, life-threatening complications commonly arise in the form of hemorrhagic shock and encephalitis. The etiological basis for the virus-induced pathology in general, and the different clinical manifestations in particular, are not well understood. We reasoned that a detailed knowledge of the global biological processes affected by virus entry into a cell might help shed new light on this long-standing problem. A bacterial two-hybrid screen using DENV2 structural proteins as bait was performed, and the results were used to feed a manually curated, global dengue-human protein interaction network. Gene ontology and pathway enrichment, along with network topology and microarray meta-analysis, were used to generate hypothesis regarding dengue disease biology. Combining bioinformatic tools with two-hybrid technology, we screened human cDNA libraries to catalogue proteins physically interacting with the DENV2 virus structural proteins, Env, cap and PrM. We identified 31 interacting human proteins representing distinct biological processes that are closely related to the major clinical diagnostic feature of dengue infection: haemostatic imbalance. In addition, we found dengue-binding human proteins involved with additional key aspects, previously described as fundamental for virus entry into cells and the innate immune response to infection. Construction of a DENV2-human global protein interaction network revealed interesting biological properties suggested by simple network topology analysis. Our experimental strategy revealed that dengue structural proteins interact with human protein targets involved in the maintenance of blood coagulation and innate anti-viral response processes, and predicts that the interaction of dengue proteins with a proposed human protein interaction network produces a modified biological outcome that may be behind the hallmark pathologies of dengue infection.

The cell-cycle interactome: a source of growth regulators?

PubMed

Blomme, Jonas; Inzé, Dirk; Gonzalez, Nathalie

2014-06-01

When plants develop, cell proliferation and cell expansion are tightly controlled in order to generate organs with a determinate final size such as leaves. Several studies have demonstrated the importance of the cell proliferation phase for leaf growth, illustrating that cell-cycle regulation is crucial for correct leaf development. A large and complex set of interacting proteins that constitute the cell-cycle interactome controls the transition from one cell-cycle phase to another. Here, we review the current knowledge on cell-cycle regulators from this interactome affecting final leaf size when their expression is altered, mainly in Arabidopsis. In addition to the description of mutants of CYCLIN-DEPENDENT KINASES (CDKs), CYCLINS (CYCs), and their transcriptional and post-translational regulators, a phenotypic analysis of gain- and loss-of-function mutants for 27 genes encoding proteins that interact with cell-cycle proteins is presented. This compilation of information shows that when cell-cycle-related genes are mis-expressed, leaf growth is often altered and that, seemingly, three main trends appear to be crucial in the regulation of final organ size by cell-cycle-related genes: (i) cellular compensation; (ii) gene dosage; and (iii) correct transition through the G2/M phase by ANAPHASE PROMOTING COMPLEX/CYCLOSOME (APC/C) activation. In conclusion, this meta-analysis shows that the cell-cycle interactome is enriched in leaf growth regulators, and illustrates the potential to identify new leaf growth regulators among putative new cell-cycle regulators. © The Author 2013. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Modeling microbial communities: current, developing, and future technologies for predicting microbial community interaction.

PubMed

Larsen, Peter; Hamada, Yuki; Gilbert, Jack

2012-07-31

Never has there been a greater opportunity for investigating microbial communities. Not only are the profound effects of microbial ecology on every aspect of Earth's geochemical cycles beginning to be understood, but also the analytical and computational tools for investigating microbial Earth are undergoing a rapid revolution. This environmental microbial interactome, the system of interactions between the microbiome and the environment, has shaped the planet's past and will undoubtedly continue to do so in the future. We review recent approaches for modeling microbial community structures and the interactions of microbial populations with their environments. Different modeling approaches consider the environmental microbial interactome from different aspects, and each provides insights to different facets of microbial ecology. We discuss the challenges and opportunities for the future of microbial modeling and describe recent advances in microbial community modeling that are extending current descriptive technologies into a predictive science. Copyright © 2012 Elsevier B.V. All rights reserved.

Mapping the local protein interactome of the NuA3 histone acetyltransferase

PubMed Central

Smart, Sherri K; Mackintosh, Samuel G; Edmondson, Ricky D; Taverna, Sean D; Tackett, Alan J

2009-01-01

Protein–protein interactions modulate cellular functions ranging from the activity of enzymes to signal transduction cascades. A technology termed transient isotopic differentiation of interactions as random or targeted (transient I-DIRT) is described for the identification of stable and transient protein–protein interactions in vivo. The procedure combines mild in vivo chemical cross-linking and non-stringent affinity purification to isolate low abundance chromatin-associated protein complexes. Using isotopic labeling and mass spectrometric readout, purified proteins are categorized with respect to the protein ‘bait’ as stable, transient, or contaminant. Here we characterize the local interactome of the chromatin-associated NuA3 histone lysine-acetyltransferase protein complex. We describe transient associations with the yFACT nucleosome assembly complex, RSC chromatin remodeling complex and a nucleosome assembly protein. These novel, physical associations with yFACT, RSC, and Nap1 provide insight into the mechanism of NuA3-associated transcription and chromatin regulation. PMID:19621382

SCOWLP classification: Structural comparison and analysis of protein binding regions

PubMed Central

Teyra, Joan; Paszkowski-Rogacz, Maciej; Anders, Gerd; Pisabarro, M Teresa

2008-01-01

Background Detailed information about protein interactions is critical for our understanding of the principles governing protein recognition mechanisms. The structures of many proteins have been experimentally determined in complex with different ligands bound either in the same or different binding regions. Thus, the structural interactome requires the development of tools to classify protein binding regions. A proper classification may provide a general view of the regions that a protein uses to bind others and also facilitate a detailed comparative analysis of the interacting information for specific protein binding regions at atomic level. Such classification might be of potential use for deciphering protein interaction networks, understanding protein function, rational engineering and design. Description Protein binding regions (PBRs) might be ideally described as well-defined separated regions that share no interacting residues one another. However, PBRs are often irregular, discontinuous and can share a wide range of interacting residues among them. The criteria to define an individual binding region can be often arbitrary and may differ from other binding regions within a protein family. Therefore, the rational behind protein interface classification should aim to fulfil the requirements of the analysis to be performed. We extract detailed interaction information of protein domains, peptides and interfacial solvent from the SCOWLP database and we classify the PBRs of each domain family. For this purpose, we define a similarity index based on the overlapping of interacting residues mapped in pair-wise structural alignments. We perform our classification with agglomerative hierarchical clustering using the complete-linkage method. Our classification is calculated at different similarity cut-offs to allow flexibility in the analysis of PBRs, feature especially interesting for those protein families with conflictive binding regions. The hierarchical classification of PBRs is implemented into the SCOWLP database and extends the SCOP classification with three additional family sub-levels: Binding Region, Interface and Contacting Domains. SCOWLP contains 9,334 binding regions distributed within 2,561 families. In 65% of the cases we observe families containing more than one binding region. Besides, 22% of the regions are forming complex with more than one different protein family. Conclusion The current SCOWLP classification and its web application represent a framework for the study of protein interfaces and comparative analysis of protein family binding regions. This comparison can be performed at atomic level and allows the user to study interactome conservation and variability. The new SCOWLP classification may be of great utility for reconstruction of protein complexes, understanding protein networks and ligand design. SCOWLP will be updated with every SCOP release. The web application is available at . PMID:18182098

The application of modular protein domains in proteomics

PubMed Central

Jadwin, Joshua A.; Ogiue-Ikeda, Mari; Machida, Kazuya

2012-01-01

The ability of modular protein domains to independently fold and bind short peptide ligands both in vivo and in vitro has allowed a significant number of protein-protein interaction studies to take advantage of them as affinity and detection reagents. Here, we refer to modular domain based proteomics as “domainomics” to draw attention to the potential of using domains and their motifs as tools in proteomics. In this review we describe core concepts of domainomics, established and emerging technologies, and recent studies by functional category. Accumulation of domain-motif binding data should ultimately provide the foundation for domain-specific interactomes, which will likely reveal the underlying substructure of protein networks as well as the selectivity and plasticity of signal transduction. PMID:22710164

Chemical Synthesis of Hydrocarbon-Stapled Peptides for Protein Interaction Research and Therapeutic Targeting

PubMed Central

Bird, Gregory H.; Crannell, W. Christian; Walensky, Loren D.

2016-01-01

The peptide alpha-helix represents one of Nature’s most featured protein shapes and is employed in a diversity of protein architectures, spanning the very cytoskeletal infrastructure of the cell to the most intimate contact points between crucial signaling proteins. By installing an all-hydrocarbon crosslink into native sequences, we recapitulate the shape and biological activity of natural peptide alpha-helices, yielding a chemical toolbox to both interrogate the protein interactome and modulate interaction networks for potential therapeutic benefit. Here, we describe our latest approach to synthesizing Stabilized Alpha-Helices (SAH) corresponding to key protein interaction domains. We emphasize a stepwise approach to the production of crosslinking non-natural amino acids, their incorporation into peptide templates, and the application of ruthenium-catalyzed ring closing metathesis to generate hydrocarbon-stapled peptides. Through facile derivatization and functionalization steps, SAHs can be tailored for a broad range of applications in biochemical, structural, proteomic, cellular and in vivo studies. PMID:23801563

The Hsp90 co-chaperone p23 of Toxoplasma gondii: Identification, functional analysis and dynamic interactome determination

PubMed Central

Echeverria, Pablo C.; Figueras, Maria J.; Vogler, Malvina; Kriehuber, Thomas; de Miguel, Natalia; Deng, Bin; Dalmasso, Maria C.; Matthews, Dwight E.; Matrajt, Mariana; Haslbeck, Martin; Buchner, Johannes; Angel, Sergio O.

2010-01-01

Toxoplasma gondii is among the most successful parasites, with nearly half of the human population chronically infected. Recently a link between the T. gondii Hsp90 chaperone machinery and parasite development was observed. Here, the T. gondii Hsp90 co-chaperones p23 and Hip were identified mining the Toxoplasma- database (www.toxodb.org). Their identity was confirmed by domain structure and blast analysis. Additionally, analysis of the secondary structure and studies on the chaperone function of the purified protein verified the p23 identity. Studies of co-immunoprecipitation (co-IP) identified two different types of complexes, one comprising at least Hip-Hsp70-Hsp90 and another containing at least p23-Hsp90. Indirect immunofluorescence assays showed that Hip is localized in the cytoplasm in tachyzoites and as well in bradyzoites. For p23 in contrast, a solely cytoplasmic localization was only observed in the tachyzoite stage whereas nuclear and cytosolic distribution and colocalization with Hsp90 was observed in bradyzoites. These results indicate that the T. gondii Hsp90-heterocomplex cycle is similar to the one proposed for higher eukaryotes, further highlighting the implication of the Hsp90/p23 in parasite development. Furthermore, co-IP experiments of tachyzoite/bradyzoite lysates with anti-p23 antiserum and identification of the complexed proteins together with the use of the curated interaction data available from different source (orthologs and Plasmodium databases) allowed us to construct an interaction network (interactome) covering the dynamics of the Hsp90 chaperone machinery. PMID:20403389

iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D

PubMed Central

Liluashvili, Vaja; Kalayci, Selim; Fluder, Eugene; Wilson, Manda; Gabow, Aaron

2017-01-01

Abstract Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field. PMID:28814063

iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D.

PubMed

Liluashvili, Vaja; Kalayci, Selim; Fluder, Eugene; Wilson, Manda; Gabow, Aaron; Gümüs, Zeynep H

2017-08-01

Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field. © The Authors 2017. Published by Oxford University Press.

Proteomic Analysis of the Mediator Complex Interactome in Saccharomyces cerevisiae.

PubMed

Uthe, Henriette; Vanselow, Jens T; Schlosser, Andreas

2017-02-27

Here we present the most comprehensive analysis of the yeast Mediator complex interactome to date. Particularly gentle cell lysis and co-immunopurification conditions allowed us to preserve even transient protein-protein interactions and to comprehensively probe the molecular environment of the Mediator complex in the cell. Metabolic 15 N-labeling thereby enabled stringent discrimination between bona fide interaction partners and nonspecifically captured proteins. Our data indicates a functional role for Mediator beyond transcription initiation. We identified a large number of Mediator-interacting proteins and protein complexes, such as RNA polymerase II, general transcription factors, a large number of transcriptional activators, the SAGA complex, chromatin remodeling complexes, histone chaperones, highly acetylated histones, as well as proteins playing a role in co-transcriptional processes, such as splicing, mRNA decapping and mRNA decay. Moreover, our data provides clear evidence, that the Mediator complex interacts not only with RNA polymerase II, but also with RNA polymerases I and III, and indicates a functional role of the Mediator complex in rRNA processing and ribosome biogenesis.

The functional landscape of Hsp27 reveals new cellular processes such as DNA repair and alternative splicing and proposes novel anticancer targets.

PubMed

Katsogiannou, Maria; Andrieu, Claudia; Baylot, Virginie; Baudot, Anaïs; Dusetti, Nelson J; Gayet, Odile; Finetti, Pascal; Garrido, Carmen; Birnbaum, Daniel; Bertucci, François; Brun, Christine; Rocchi, Palma

2014-12-01

Previously, we identified the stress-induced chaperone, Hsp27, as highly overexpressed in castration-resistant prostate cancer and developed an Hsp27 inhibitor (OGX-427) currently tested in phase I/II clinical trials as a chemosensitizing agent in different cancers. To better understand the Hsp27 poorly-defined cytoprotective functions in cancers and increase the OGX-427 pharmacological safety, we established the Hsp27-protein interaction network using a yeast two-hybrid approach and identified 226 interaction partners. As an example, we showed that targeting Hsp27 interaction with TCTP, a partner protein identified in our screen increases therapy sensitivity, opening a new promising field of research for therapeutic approaches that could decrease or abolish toxicity for normal cells. Results of an in-depth bioinformatics network analysis allying the Hsp27 interaction map into the human interactome underlined the multifunctional character of this protein. We identified interactions of Hsp27 with proteins involved in eight well known functions previously related to Hsp27 and uncovered 17 potential new ones, such as DNA repair and RNA splicing. Validation of Hsp27 involvement in both processes in human prostate cancer cells supports our system biology-predicted functions and provides new insights into Hsp27 roles in cancer cells. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network.

PubMed

Alvarez-Ponce, David; Feyertag, Felix; Chakraborty, Sandip

2017-06-01

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein-protein interaction data set and the human signal transduction network-a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Enhanced Prediction of Src Homology 2 (SH2) Domain Binding Potentials Using a Fluorescence Polarization-derived c-Met, c-Kit, ErbB, and Androgen Receptor Interactome*

PubMed Central

Leung, Kin K.; Hause, Ronald J.; Barkinge, John L.; Ciaccio, Mark F.; Chuu, Chih-Pin; Jones, Richard B.

2014-01-01

Many human diseases are associated with aberrant regulation of phosphoprotein signaling networks. Src homology 2 (SH2) domains represent the major class of protein domains in metazoans that interact with proteins phosphorylated on the amino acid residue tyrosine. Although current SH2 domain prediction algorithms perform well at predicting the sequences of phosphorylated peptides that are likely to result in the highest possible interaction affinity in the context of random peptide library screens, these algorithms do poorly at predicting the interaction potential of SH2 domains with physiologically derived protein sequences. We employed a high throughput interaction assay system to empirically determine the affinity between 93 human SH2 domains and phosphopeptides abstracted from several receptor tyrosine kinases and signaling proteins. The resulting interaction experiments revealed over 1000 novel peptide-protein interactions and provided a glimpse into the common and specific interaction potentials of c-Met, c-Kit, GAB1, and the human androgen receptor. We used these data to build a permutation-based logistic regression classifier that performed considerably better than existing algorithms for predicting the interaction potential of several SH2 domains. PMID:24728074

Proteomics profiling of interactome dynamics by colocalisation analysis (COLA).

PubMed

Mardakheh, Faraz K; Sailem, Heba Z; Kümper, Sandra; Tape, Christopher J; McCully, Ryan R; Paul, Angela; Anjomani-Virmouni, Sara; Jørgensen, Claus; Poulogiannis, George; Marshall, Christopher J; Bakal, Chris

2016-12-20

Localisation and protein function are intimately linked in eukaryotes, as proteins are localised to specific compartments where they come into proximity of other functionally relevant proteins. Significant co-localisation of two proteins can therefore be indicative of their functional association. We here present COLA, a proteomics based strategy coupled with a bioinformatics framework to detect protein-protein co-localisations on a global scale. COLA reveals functional interactions by matching proteins with significant similarity in their subcellular localisation signatures. The rapid nature of COLA allows mapping of interactome dynamics across different conditions or treatments with high precision.

Estimation of Global Network Statistics from Incomplete Data

PubMed Central

Bliss, Catherine A.; Danforth, Christopher M.; Dodds, Peter Sheridan

2014-01-01

Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar's hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week. PMID:25338183

An Integrated Systems Biology Approach Identifies TRIM25 as a Key Determinant of Breast Cancer Metastasis.

PubMed

Walsh, Logan A; Alvarez, Mariano J; Sabio, Erich Y; Reyngold, Marsha; Makarov, Vladimir; Mukherjee, Suranjit; Lee, Ken-Wing; Desrichard, Alexis; Turcan, Şevin; Dalin, Martin G; Rajasekhar, Vinagolu K; Chen, Shuibing; Vahdat, Linda T; Califano, Andrea; Chan, Timothy A

2017-08-15

At the root of most fatal malignancies are aberrantly activated transcriptional networks that drive metastatic dissemination. Although individual metastasis-associated genes have been described, the complex regulatory networks presiding over the initiation and maintenance of metastatic tumors are still poorly understood. There is untapped value in identifying therapeutic targets that broadly govern coordinated transcriptional modules dictating metastatic progression. Here, we reverse engineered and interrogated a breast cancer-specific transcriptional interaction network (interactome) to define transcriptional control structures causally responsible for regulating genetic programs underlying breast cancer metastasis in individual patients. Our analyses confirmed established pro-metastatic transcription factors, and they uncovered TRIM25 as a key regulator of metastasis-related transcriptional programs. Further, in vivo analyses established TRIM25 as a potent regulator of metastatic disease and poor survival outcome. Our findings suggest that identifying and targeting keystone proteins, like TRIM25, can effectively collapse transcriptional hierarchies necessary for metastasis formation, thus representing an innovative cancer intervention strategy. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

PubMed Central

Li, Jingjing; Shi, Minyi; Ma, Zhihai; Zhao, Shuchun; Euskirchen, Ghia; Ziskin, Jennifer; Urban, Alexander; Hallmayer, Joachim; Snyder, Michael

2014-01-01

Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology. PMID:25549968

The protein-protein interaction network of eyestalk, Y-organ and hepatopancreas in Chinese mitten crab Eriocheir sinensis.

PubMed

Hao, Tong; Zeng, Zheng; Wang, Bin; Zhang, Yichen; Liu, Yichen; Geng, Xuyun; Sun, Jinsheng

2014-03-27

The protein-protein interaction network (PIN) is an effective information tool for understanding the complex biological processes inside the cell and solving many biological problems such as signaling pathway identification and prediction of protein functions. Eriocheir sinensis is a highly-commercial aquaculture species with an unclear proteome background which hinders the construction and development of PIN for E. sinensis. However, in recent years, the development of next-generation deep-sequencing techniques makes it possible to get high throughput data of E. sinensis tanscriptome and subsequently obtain a systematic overview of the protein-protein interaction system. In this work we sequenced the transcriptional RNA of eyestalk, Y-organ and hepatopancreas in E. sinensis and generated a PIN of E. sinensis which included 3,223 proteins and 35,787 interactions. Each protein-protein interaction in the network was scored according to the homology and genetic relationship. The signaling sub-network, representing the signal transduction pathways in E. sinensis, was extracted from the global network, which depicted a global view of the signaling systems in E. sinensis. Seven basic signal transduction pathways were identified in E. sinensis. By investigating the evolution paths of the seven pathways, we found that these pathways got mature in different evolutionary stages. Moreover, the functions of unclassified proteins and unigenes in the PIN of E. sinensis were predicted. Specifically, the functions of 549 unclassified proteins related to 864 unclassified unigenes were assigned, which respectively covered 76% and 73% of all the unclassified proteins and unigenes in the network. The PIN generated in this work is the first large-scale PIN of aquatic crustacean, thereby providing a paradigmatic blueprint of the aquatic crustacean interactome. Signaling sub-network extracted from the global PIN depicts the interaction of different signaling proteins and the evolutionary paths of the identified signal transduction pathways. Furthermore, the function assignment of unclassified proteins based on the PIN offers a new reference in protein function exploration. More importantly, the construction of the E. sinensis PIN provides necessary experience for the exploration of PINs in other aquatic crustacean species.

Re-analysis of protein data reveals the germination pathway and up accumulation mechanism of cell wall hydrolases during the radicle protrusion step of seed germination in Podophyllum hexandrum- a high altitude plant

PubMed Central

Dogra, Vivek; Bagler, Ganesh; Sreenivasulu, Yelam

2015-01-01

Podophyllum hexandrum Royle is an important high-altitude plant of Himalayas with immense medicinal value. Earlier, it was reported that the cell wall hydrolases were up accumulated during radicle protrusion step of Podophyllum seed germination. In the present study, Podophyllum seed Germination protein interaction Network (PGN) was constructed by using the differentially accumulated protein (DAP) data set of Podophyllum during the radicle protrusion step of seed germination, with reference to Arabidopsis protein–protein interaction network (AtPIN). The developed PGN is comprised of a giant cluster with 1028 proteins having 10,519 interactions and a few small clusters with relevant gene ontological signatures. In this analysis, a germination pathway related cluster which is also central to the topology and information dynamics of PGN was obtained with a set of 60 key proteins. Among these, eight proteins which are known to be involved in signaling, metabolism, protein modification, cell wall modification, and cell cycle regulation processes were found commonly highlighted in both the proteomic and interactome analysis. The systems-level analysis of PGN identified the key proteins involved in radicle protrusion step of seed germination in Podophyllum. PMID:26579141

Dissecting the chromatin interactome of microRNA genes.

PubMed

Chen, Dijun; Fu, Liang-Yu; Zhang, Zhao; Li, Guoliang; Zhang, Hang; Jiang, Li; Harrison, Andrew P; Shanahan, Hugh P; Klukas, Christian; Zhang, Hong-Yu; Ruan, Yijun; Chen, Ling-Ling; Chen, Ming

2014-03-01

Our knowledge of the role of higher-order chromatin structures in transcription of microRNA genes (MIRs) is evolving rapidly. Here we investigate the effect of 3D architecture of chromatin on the transcriptional regulation of MIRs. We demonstrate that MIRs have transcriptional features that are similar to protein-coding genes. RNA polymerase II-associated ChIA-PET data reveal that many groups of MIRs and protein-coding genes are organized into functionally compartmentalized chromatin communities and undergo coordinated expression when their genomic loci are spatially colocated. We observe that MIRs display widespread communication in those transcriptionally active communities. Moreover, miRNA-target interactions are significantly enriched among communities with functional homogeneity while depleted from the same community from which they originated, suggesting MIRs coordinating function-related pathways at posttranscriptional level. Further investigation demonstrates the existence of spatial MIR-MIR chromatin interacting networks. We show that groups of spatially coordinated MIRs are frequently from the same family and involved in the same disease category. The spatial interaction network possesses both common and cell-specific subnetwork modules that result from the spatial organization of chromatin within different cell types. Together, our study unveils an entirely unexplored layer of MIR regulation throughout the human genome that links the spatial coordination of MIRs to their co-expression and function.

Pharmacological targeting of the transcription factor SOX18 delays breast cancer in mice

PubMed Central

Overman, Jeroen; Fontaine, Frank; Moustaqil, Mehdi; Mittal, Deepak; Sierecki, Emma; Sacilotto, Natalia; Zuegg, Johannes; Robertson, Avril AB; Holmes, Kelly; Salim, Angela A; Mamidyala, Sreeman; Butler, Mark S; Robinson, Ashley S; Lesieur, Emmanuelle; Johnston, Wayne; Alexandrov, Kirill; Black, Brian L; Hogan, Benjamin M; De Val, Sarah; Capon, Robert J; Carroll, Jason S; Bailey, Timothy L; Koopman, Peter; Jauch, Ralf; Smyth, Mark J; Cooper, Matthew A; Gambin, Yann; Francois, Mathias

2017-01-01

Pharmacological targeting of transcription factors holds great promise for the development of new therapeutics, but strategies based on blockade of DNA binding, nuclear shuttling, or individual protein partner recruitment have yielded limited success to date. Transcription factors typically engage in complex interaction networks, likely masking the effects of specifically inhibiting single protein-protein interactions. Here, we used a combination of genomic, proteomic and biophysical methods to discover a suite of protein-protein interactions involving the SOX18 transcription factor, a known regulator of vascular development and disease. We describe a small-molecule that is able to disrupt a discrete subset of SOX18-dependent interactions. This compound selectively suppressed SOX18 transcriptional outputs in vitro and interfered with vascular development in zebrafish larvae. In a mouse pre-clinical model of breast cancer, treatment with this inhibitor significantly improved survival by reducing tumour vascular density and metastatic spread. Our studies validate an interactome-based molecular strategy to interfere with transcription factor activity, for the development of novel disease therapeutics. DOI: http://dx.doi.org/10.7554/eLife.21221.001 PMID:28137359

The functional interactome landscape of the human histone deacetylase family

PubMed Central

Joshi, Preeti; Greco, Todd M; Guise, Amanda J; Luo, Yang; Yu, Fang; Nesvizhskii, Alexey I; Cristea, Ileana M

2013-01-01

Histone deacetylases (HDACs) are a diverse family of essential transcriptional regulatory enzymes, that function through the spatial and temporal recruitment of protein complexes. As the composition and regulation of HDAC complexes are only partially characterized, we built the first global protein interaction network for all 11 human HDACs in T cells. Integrating fluorescence microscopy, immunoaffinity purifications, quantitative mass spectrometry, and bioinformatics, we identified over 200 unreported interactions for both well-characterized and lesser-studied HDACs, a subset of which were validated by orthogonal approaches. We establish HDAC11 as a member of the survival of motor neuron complex and pinpoint a functional role in mRNA splicing. We designed a complementary label-free and metabolic-labeling mass spectrometry-based proteomics strategy for profiling interaction stability among different HDAC classes, revealing that HDAC1 interactions within chromatin-remodeling complexes are largely stable, while transcription factors preferentially exist in rapid equilibrium. Overall, this study represents a valuable resource for investigating HDAC functions in health and disease, encompassing emerging themes of HDAC regulation in cell cycle and RNA processing and a deeper functional understanding of HDAC complex stability. PMID:23752268

System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks.

PubMed

Gyurko, David M; Soti, Csaba; Stetak, Attila; Csermely, Peter

2014-05-01

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides ' learning-competent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

Insights into the polerovirus-plant interactome revealed by co-immunoprecipitation and mass spectrometry

USDA-ARS?s Scientific Manuscript database

The identification of host proteins that interact with virus proteins is a major challenge for the field of virology. Phloem-limited viruses pose extraordinary challenges for in vivo protein interaction experiments because these viruses are localized in very few and highly specialized host cells. ...

The Sharpin interactome reveals a role for Sharpin in lamellipodium formation via the Arp2/3 complex.

PubMed

Khan, Meraj H; Salomaa, Siiri I; Jacquemet, Guillaume; Butt, Umar; Miihkinen, Mitro; Deguchi, Takahiro; Kremneva, Elena; Lappalainen, Pekka; Humphries, Martin J; Pouwels, Jeroen

2017-09-15

Sharpin, a multifunctional adaptor protein, regulates several signalling pathways. For example, Sharpin enhances signal-induced NF-κB signalling as part of the linear ubiquitin assembly complex (LUBAC) and inhibits integrins, the T cell receptor, caspase 1 and PTEN. However, despite recent insights into Sharpin and LUBAC function, a systematic approach to identify the signalling pathways regulated by Sharpin has not been reported. Here, we present the first 'Sharpin interactome', which identifies a large number of novel potential Sharpin interactors in addition to several known ones. These data suggest that Sharpin and LUBAC might regulate a larger number of biological processes than previously identified, such as endosomal trafficking, RNA processing, metabolism and cytoskeleton regulation. Importantly, using the Sharpin interactome, we have identified a novel role for Sharpin in lamellipodium formation. We demonstrate that Sharpin interacts with Arp2/3, a protein complex that catalyses actin filament branching. We have identified the Arp2/3-binding site in Sharpin and demonstrate using a specific Arp2/3-binding deficient mutant that the Sharpin-Arp2/3 interaction promotes lamellipodium formation in a LUBAC-independent fashion.This article has an associated First Person interview with the first author of the paper. © 2017. Published by The Company of Biologists Ltd.

HIV–host interactome revealed directly from infected cells

PubMed Central

Luo, Yang; Jacobs, Erica Y.; Greco, Todd M.; Mohammed, Kevin D.; Tong, Tommy; Keegan, Sarah; Binley, James M.; Cristea, Ileana M.; Fenyö, David; Rout, Michael P.; Chait, Brian T.; Muesing, Mark A.

2016-01-01

Although genetically compact, HIV-1 commandeers vast arrays of cellular machinery to sustain and protect it during cycles of viral outgrowth. Transposon-mediated saturation linker scanning mutagenesis was used to isolate fully replication-competent viruses harbouring a potent foreign epitope tag. Using these viral isolates, we performed differential isotopic labelling and affinity-capture mass spectrometric analyses on samples obtained from cultures of human lymphocytes to classify the vicinal interactomes of the viral Env and Vif proteins as they occur during natural infection. Importantly, interacting proteins were recovered without bias, regardless of their potential for positive, negative or neutral impact on viral replication. We identified specific host associations made with trimerized Env during its biosynthesis, at virological synapses, with innate immune effectors (such as HLA-E) and with certain cellular signalling pathways (for example, Notch1). We also defined Vif associations with host proteins involved in the control of nuclear transcription and nucleoside biosynthesis as well as those interacting stably or transiently with the cytoplasmic protein degradation apparatus. Our approach is broadly applicable to elucidating pathogen–host interactomes, providing high-certainty identification of interactors by their direct access during cycling infection. Understanding the pathophysiological consequences of these associations is likely to provide strategic targets for antiviral intervention. PMID:27375898

A systems immunology approach identifies the collective impact of 5 miRs in Th2 inflammation.

PubMed

Kılıç, Ayşe; Santolini, Marc; Nakano, Taiji; Schiller, Matthias; Teranishi, Mizue; Gellert, Pascal; Ponomareva, Yuliya; Braun, Thomas; Uchida, Shizuka; Weiss, Scott T; Sharma, Amitabh; Renz, Harald

2018-06-07

Allergic asthma is a chronic inflammatory disease dominated by a CD4+ T helper 2 (Th2) cell signature. The immune response amplifies in self-enforcing loops, promoting Th2-driven cellular immunity and leaving the host unable to terminate inflammation. Posttranscriptional mechanisms, including microRNAs (miRs), are pivotal in maintaining immune homeostasis. Since an altered expression of various miRs has been associated with T cell-driven diseases, including asthma, we hypothesized that miRs control mechanisms ensuring Th2 stability and maintenance in the lung. We isolated murine CD4+ Th2 cells from allergic inflamed lungs and profiled gene and miR expression. Instead of focusing on the magnitude of miR differential expression, here we addressed the secondary consequences for the set of molecular interactions in the cell, the interactome. We developed the Impact of Differential Expression Across Layers, a network-based algorithm to prioritize disease-relevant miRs based on the central role of their targets in the molecular interactome. This method identified 5 Th2-related miRs (mir27b, mir206, mir106b, mir203, and mir23b) whose antagonization led to a sharp reduction of the Th2 phenotype. Overall, a systems biology tool was developed and validated, highlighting the role of miRs in Th2-driven immune response. This result offers potentially novel approaches for therapeutic interventions.

HiQuant: Rapid Postquantification Analysis of Large-Scale MS-Generated Proteomics Data.

PubMed

Bryan, Kenneth; Jarboui, Mohamed-Ali; Raso, Cinzia; Bernal-Llinares, Manuel; McCann, Brendan; Rauch, Jens; Boldt, Karsten; Lynn, David J

2016-06-03

Recent advances in mass-spectrometry-based proteomics are now facilitating ambitious large-scale investigations of the spatial and temporal dynamics of the proteome; however, the increasing size and complexity of these data sets is overwhelming current downstream computational methods, specifically those that support the postquantification analysis pipeline. Here we present HiQuant, a novel application that enables the design and execution of a postquantification workflow, including common data-processing steps, such as assay normalization and grouping, and experimental replicate quality control and statistical analysis. HiQuant also enables the interpretation of results generated from large-scale data sets by supporting interactive heatmap analysis and also the direct export to Cytoscape and Gephi, two leading network analysis platforms. HiQuant may be run via a user-friendly graphical interface and also supports complete one-touch automation via a command-line mode. We evaluate HiQuant's performance by analyzing a large-scale, complex interactome mapping data set and demonstrate a 200-fold improvement in the execution time over current methods. We also demonstrate HiQuant's general utility by analyzing proteome-wide quantification data generated from both a large-scale public tyrosine kinase siRNA knock-down study and an in-house investigation into the temporal dynamics of the KSR1 and KSR2 interactomes. Download HiQuant, sample data sets, and supporting documentation at http://hiquant.primesdb.eu .

Essential protein discovery based on a combination of modularity and conservatism.

PubMed

Zhao, Bihai; Wang, Jianxin; Li, Xueyong; Wu, Fang-Xiang

2016-11-01

Essential proteins are indispensable for the survival of a living organism and play important roles in the emerging field of synthetic biology. Many computational methods have been proposed to identify essential proteins by using the topological features of interactome networks. However, most of these methods ignored intrinsic biological meaning of proteins. Researches show that essentiality is tied not only to the protein or gene itself, but also to the molecular modules to which that protein belongs. The results of this study reveal the modularity of essential proteins. On the other hand, essential proteins are more evolutionarily conserved than nonessential proteins and frequently bind each other. That is to say, conservatism is another important feature of essential proteins. Multiple networks are constructed by integrating protein-protein interaction (PPI) networks, time course gene expression data and protein domain information. Based on these networks, a new essential protein identification method is proposed based on a combination of modularity and conservatism of proteins. Experimental results show that the proposed method outperforms other essential protein identification methods in terms of a number essential protein out of top ranked candidates. Copyright © 2016. Published by Elsevier Inc.

Serum Amyloid P Component (SAP) Interactome in Human Plasma Containing Physiological Calcium Levels.

PubMed

Poulsen, Ebbe Toftgaard; Pedersen, Kata Wolff; Marzeda, Anna Maria; Enghild, Jan J

2017-02-14

The pentraxin serum amyloid P component (SAP) is secreted by the liver and found in plasma at a concentration of approximately 30 mg/L. SAP is a 25 kDa homopentamer known to bind both protein and nonprotein ligands, all in a calcium-dependent manner. The function of SAP is unclear but likely involves the humoral innate immune system spanning the complement system, inflammation, and coagulation. Also, SAP is known to bind to the generic structure of amyloid deposits and possibly to protect them against proteolysis. In this study, we have characterized the SAP interactome in human plasma containing the physiological Ca 2+ concentration using SAP affinity pull-down and co-immunoprecipitation experiments followed by mass spectrometry analyses. The analyses resulted in the identification of 33 proteins, of which 24 were direct or indirect interaction partners not previously reported. The SAP interactome can be divided into categories that include apolipoproteins, the complement system, coagulation, and proteolytic regulation.

Characterization of the Cardiac Overexpression of HSPB2 Reveals Mitochondrial and Myogenic Roles Supported by a Cardiac HspB2 Interactome.

PubMed

Grose, Julianne H; Langston, Kelsey; Wang, Xiaohui; Squires, Shayne; Mustafi, Soumyajit Banerjee; Hayes, Whitney; Neubert, Jonathan; Fischer, Susan K; Fasano, Matthew; Saunders, Gina Moore; Dai, Qiang; Christians, Elisabeth; Lewandowski, E Douglas; Ping, Peipei; Benjamin, Ivor J

2015-01-01

Small Heat Shock Proteins (sHSPs) are molecular chaperones that transiently interact with other proteins, thereby assisting with quality control of proper protein folding and/or degradation. They are also recruited to protect cells from a variety of stresses in response to extreme heat, heavy metals, and oxidative-reductive stress. Although ten human sHSPs have been identified, their likely diverse biological functions remain an enigma in health and disease, and much less is known about non-redundant roles in selective cells and tissues. Herein, we set out to comprehensively characterize the cardiac-restricted Heat Shock Protein B-2 (HspB2), which exhibited ischemic cardioprotection in transgenic overexpressing mice including reduced infarct size and maintenance of ATP levels. Global yeast two-hybrid analysis using HspB2 (bait) and a human cardiac library (prey) coupled with co-immunoprecipitation studies for mitochondrial target validation revealed the first HspB2 "cardiac interactome" to contain many myofibril and mitochondrial-binding partners consistent with the overexpression phenotype. This interactome has been submitted to the Biological General Repository for Interaction Datasets (BioGRID). A related sHSP chaperone HspB5 had only partially overlapping binding partners, supporting specificity of the interactome as well as non-redundant roles reported for these sHSPs. Evidence that the cardiac yeast two-hybrid HspB2 interactome targets resident mitochondrial client proteins is consistent with the role of HspB2 in maintaining ATP levels and suggests new chaperone-dependent functions for metabolic homeostasis. One of the HspB2 targets, glyceraldehyde 3-phosphate dehydrogenase (GAPDH), has reported roles in HspB2 associated phenotypes including cardiac ATP production, mitochondrial function, and apoptosis, and was validated as a potential client protein of HspB2 through chaperone assays. From the clientele and phenotypes identified herein, it is tempting to speculate that small molecule activators of HspB2 might be deployed to mitigate mitochondrial related diseases such as cardiomyopathy and neurodegenerative disease.

NRF2-ome: an integrated web resource to discover protein interaction and regulatory networks of NRF2.

PubMed

Türei, Dénes; Papp, Diána; Fazekas, Dávid; Földvári-Nagy, László; Módos, Dezső; Lenti, Katalin; Csermely, Péter; Korcsmáros, Tamás

2013-01-01

NRF2 is the master transcriptional regulator of oxidative and xenobiotic stress responses. NRF2 has important roles in carcinogenesis, inflammation, and neurodegenerative diseases. We developed an online resource, NRF2-ome, to provide an integrated and systems-level database for NRF2. The database contains manually curated and predicted interactions of NRF2 as well as data from external interaction databases. We integrated NRF2 interactome with NRF2 target genes, NRF2 regulating TFs, and miRNAs. We connected NRF2-ome to signaling pathways to allow mapping upstream NRF2 regulatory components that could directly or indirectly influence NRF2 activity totaling 35,967 protein-protein and signaling interactions. The user-friendly website allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. We illustrated the applicability of the website by suggesting a posttranscriptional negative feedback of NRF2 by MAFG protein and raised the possibility of a connection between NRF2 and the JAK/STAT pathway through STAT1 and STAT3. NRF2-ome can also be used as an evaluation tool to help researchers and drug developers to understand the hidden regulatory mechanisms in the complex network of NRF2.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein–Protein Interaction Network

PubMed Central

Feyertag, Felix; Chakraborty, Sandip

2017-01-01

Abstract The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein–protein interaction data set and the human signal transduction network—a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets. PMID:28854629

Differential Regulation of Cryptic Genetic Variation Shapes the Genetic Interactome Underlying Complex Traits.

PubMed

Yadav, Anupama; Dhole, Kaustubh; Sinha, Himanshu

2016-12-01

Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets.

Differential Regulation of Cryptic Genetic Variation Shapes the Genetic Interactome Underlying Complex Traits

PubMed Central

Yadav, Anupama; Dhole, Kaustubh

2016-01-01

Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets. PMID:28172852

Exosomes and Homeostatic Synaptic Plasticity Are Linked to Each other and to Huntington's, Parkinson's, and Other Neurodegenerative Diseases by Database-Enabled Analyses of Comprehensively Curated Datasets

PubMed Central

Wang, James K. T.; Langfelder, Peter; Horvath, Steve; Palazzolo, Michael J.

2017-01-01

Huntington's disease (HD) is a progressive and autosomal dominant neurodegeneration caused by CAG expansion in the huntingtin gene (HTT), but the pathophysiological mechanism of mutant HTT (mHTT) remains unclear. To study HD using systems biological methodologies on all published data, we undertook the first comprehensive curation of two key PubMed HD datasets: perturbation genes that impact mHTT-driven endpoints and therefore are putatively linked causally to pathogenic mechanisms, and the protein interactome of HTT that reflects its biology. We perused PubMed articles containing co-citation of gene IDs and MeSH terms of interest to generate mechanistic gene sets for iterative enrichment analyses and rank ordering. The HD Perturbation database of 1,218 genes highly overlaps the HTT Interactome of 1,619 genes, suggesting links between normal HTT biology and mHTT pathology. These two HD datasets are enriched for protein networks of key genes underlying two mechanisms not previously implicated in HD nor in each other: exosome synaptic functions and homeostatic synaptic plasticity. Moreover, proteins, possibly including HTT, and miRNA detected in exosomes from a wide variety of sources also highly overlap the HD datasets, suggesting both mechanistic and biomarker links. Finally, the HTT Interactome highly intersects protein networks of pathogenic genes underlying Parkinson's, Alzheimer's and eight non-HD polyglutamine diseases, ALS, and spinal muscular atrophy. These protein networks in turn highly overlap the exosome and homeostatic synaptic plasticity gene sets. Thus, we hypothesize that HTT and other neurodegeneration pathogenic genes form a large interlocking protein network involved in exosome and homeostatic synaptic functions, particularly where the two mechanisms intersect. Mutant pathogenic proteins cause dysfunctions at distinct points in this network, each altering the two mechanisms in specific fashion that contributes to distinct disease pathologies, depending on the gene mutation and the cellular and biological context. This protein network is rich with drug targets, and exosomes may provide disease biomarkers, thus enabling drug discovery. All the curated datasets are made available for other investigators. Elucidating the roles of pathogenic neurodegeneration genes in exosome and homeostatic synaptic functions may provide a unifying framework for the age-dependent, progressive and tissue selective nature of multiple neurodegenerative diseases. PMID:28611571

Exosomes and Homeostatic Synaptic Plasticity Are Linked to Each other and to Huntington's, Parkinson's, and Other Neurodegenerative Diseases by Database-Enabled Analyses of Comprehensively Curated Datasets.

PubMed

Wang, James K T; Langfelder, Peter; Horvath, Steve; Palazzolo, Michael J

2017-01-01

Huntington's disease (HD) is a progressive and autosomal dominant neurodegeneration caused by CAG expansion in the huntingtin gene ( HTT ), but the pathophysiological mechanism of mutant HTT (mHTT) remains unclear. To study HD using systems biological methodologies on all published data, we undertook the first comprehensive curation of two key PubMed HD datasets: perturbation genes that impact mHTT-driven endpoints and therefore are putatively linked causally to pathogenic mechanisms, and the protein interactome of HTT that reflects its biology. We perused PubMed articles containing co-citation of gene IDs and MeSH terms of interest to generate mechanistic gene sets for iterative enrichment analyses and rank ordering. The HD Perturbation database of 1,218 genes highly overlaps the HTT Interactome of 1,619 genes, suggesting links between normal HTT biology and mHTT pathology. These two HD datasets are enriched for protein networks of key genes underlying two mechanisms not previously implicated in HD nor in each other: exosome synaptic functions and homeostatic synaptic plasticity. Moreover, proteins, possibly including HTT, and miRNA detected in exosomes from a wide variety of sources also highly overlap the HD datasets, suggesting both mechanistic and biomarker links. Finally, the HTT Interactome highly intersects protein networks of pathogenic genes underlying Parkinson's, Alzheimer's and eight non-HD polyglutamine diseases, ALS, and spinal muscular atrophy. These protein networks in turn highly overlap the exosome and homeostatic synaptic plasticity gene sets. Thus, we hypothesize that HTT and other neurodegeneration pathogenic genes form a large interlocking protein network involved in exosome and homeostatic synaptic functions, particularly where the two mechanisms intersect. Mutant pathogenic proteins cause dysfunctions at distinct points in this network, each altering the two mechanisms in specific fashion that contributes to distinct disease pathologies, depending on the gene mutation and the cellular and biological context. This protein network is rich with drug targets, and exosomes may provide disease biomarkers, thus enabling drug discovery. All the curated datasets are made available for other investigators. Elucidating the roles of pathogenic neurodegeneration genes in exosome and homeostatic synaptic functions may provide a unifying framework for the age-dependent, progressive and tissue selective nature of multiple neurodegenerative diseases.

Lysine methylation modulates the protein-protein interactions of yeast cytochrome C Cyc1p.

PubMed

Winter, Daniel L; Abeygunawardena, Dhanushi; Hart-Smith, Gene; Erce, Melissa A; Wilkins, Marc R

2015-07-01

In recent years, protein methylation has been established as a major intracellular PTM. It has also been proposed to modulate protein-protein interactions (PPIs) in the interactome. To investigate the effect of PTMs on PPIs, we recently developed the conditional two-hybrid (C2H) system. With this, we demonstrated that arginine methylation can modulate PPIs in the yeast interactome. Here, we used the C2H system to investigate the effect of lysine methylation. Specifically, we asked whether Ctm1p-mediated trimethylation of yeast cytochrome c Cyc1p, on lysine 78, modulates its interactions with Erv1p, Ccp1p, Cyc2p and Cyc3p. We show that the interactions between Cyc1p and Erv1p, and between Cyc1p and Cyc3p, are significantly increased upon trimethylation of lysine 78. This increase of interaction helps explain the reported facilitation of Cyc1p import into the mitochondrial intermembrane space upon methylation. This first application of the C2H system to the study of methyllysine-modulated interactions further confirms its robustness and flexibility. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A scalable double-barcode sequencing platform for characterization of dynamic protein-protein interactions.

PubMed

Schlecht, Ulrich; Liu, Zhimin; Blundell, Jamie R; St Onge, Robert P; Levy, Sasha F

2017-05-25

Several large-scale efforts have systematically catalogued protein-protein interactions (PPIs) of a cell in a single environment. However, little is known about how the protein interactome changes across environmental perturbations. Current technologies, which assay one PPI at a time, are too low throughput to make it practical to study protein interactome dynamics. Here, we develop a highly parallel protein-protein interaction sequencing (PPiSeq) platform that uses a novel double barcoding system in conjunction with the dihydrofolate reductase protein-fragment complementation assay in Saccharomyces cerevisiae. PPiSeq detects PPIs at a rate that is on par with current assays and, in contrast with current methods, quantitatively scores PPIs with enough accuracy and sensitivity to detect changes across environments. Both PPI scoring and the bulk of strain construction can be performed with cell pools, making the assay scalable and easily reproduced across environments. PPiSeq is therefore a powerful new tool for large-scale investigations of dynamic PPIs.

Merging in-silico and in vitro salivary protein complex partners using the STRING database: A tutorial.

PubMed

Crosara, Karla Tonelli Bicalho; Moffa, Eduardo Buozi; Xiao, Yizhi; Siqueira, Walter Luiz

2018-01-16

Protein-protein interaction is a common physiological mechanism for protection and actions of proteins in an organism. The identification and characterization of protein-protein interactions in different organisms is necessary to better understand their physiology and to determine their efficacy. In a previous in vitro study using mass spectrometry, we identified 43 proteins that interact with histatin 1. Six previously documented interactors were confirmed and 37 novel partners were identified. In this tutorial, we aimed to demonstrate the usefulness of the STRING database for studying protein-protein interactions. We used an in-silico approach along with the STRING database (http://string-db.org/) and successfully performed a fast simulation of a novel constructed histatin 1 protein-protein network, including both the previously known and the predicted interactors, along with our newly identified interactors. Our study highlights the advantages and importance of applying bioinformatics tools to merge in-silico tactics with experimental in vitro findings for rapid advancement of our knowledge about protein-protein interactions. Our findings also indicate that bioinformatics tools such as the STRING protein network database can help predict potential interactions between proteins and thus serve as a guide for future steps in our exploration of the Human Interactome. Our study highlights the usefulness of the STRING protein database for studying protein-protein interactions. The STRING database can collect and integrate data about known and predicted protein-protein associations from many organisms, including both direct (physical) and indirect (functional) interactions, in an easy-to-use interface. Copyright © 2017 Elsevier B.V. All rights reserved.

The interactomic analysis reveals pathogenic protein networks in Phomopsis longicolla underlying seed decay of soybean

USDA-ARS?s Scientific Manuscript database

Phomopsis longicolla T. W. Hobbs (syn. Diaporthe longicolla) is the primary cause of Phomopsis seed decay (PSD) in soybean, Glycine max (L.) Merrill. This disease causes poor seed quality and is one of the most economically important diseases in soybean. The objectives of this study were to perform ...

Proteomic Analysis of the Mediator Complex Interactome in Saccharomyces cerevisiae

PubMed Central

Uthe, Henriette; Vanselow, Jens T.; Schlosser, Andreas

2017-01-01

Here we present the most comprehensive analysis of the yeast Mediator complex interactome to date. Particularly gentle cell lysis and co-immunopurification conditions allowed us to preserve even transient protein-protein interactions and to comprehensively probe the molecular environment of the Mediator complex in the cell. Metabolic 15N-labeling thereby enabled stringent discrimination between bona fide interaction partners and nonspecifically captured proteins. Our data indicates a functional role for Mediator beyond transcription initiation. We identified a large number of Mediator-interacting proteins and protein complexes, such as RNA polymerase II, general transcription factors, a large number of transcriptional activators, the SAGA complex, chromatin remodeling complexes, histone chaperones, highly acetylated histones, as well as proteins playing a role in co-transcriptional processes, such as splicing, mRNA decapping and mRNA decay. Moreover, our data provides clear evidence, that the Mediator complex interacts not only with RNA polymerase II, but also with RNA polymerases I and III, and indicates a functional role of the Mediator complex in rRNA processing and ribosome biogenesis. PMID:28240253

Expression of DISC1-interactome members correlates with cognitive phenotypes related to schizophrenia.

PubMed

Rampino, Antonio; Walker, Rosie May; Torrance, Helen Scott; Anderson, Susan Maguire; Fazio, Leonardo; Di Giorgio, Annabella; Taurisano, Paolo; Gelao, Barbara; Romano, Raffaella; Masellis, Rita; Ursini, Gianluca; Caforio, Grazia; Blasi, Giuseppe; Millar, J Kirsty; Porteous, David John; Thomson, Pippa Ann; Bertolino, Alessandro; Evans, Kathryn Louise

2014-01-01

Cognitive dysfunction is central to the schizophrenia phenotype. Genetic and functional studies have implicated Disrupted-in-Schizophrenia 1 (DISC1), a leading candidate gene for schizophrenia and related psychiatric conditions, in cognitive function. Altered expression of DISC1 and DISC1-interactors has been identified in schizophrenia. Dysregulated expression of DISC1-interactome genes might, therefore, contribute to schizophrenia susceptibility via disruption of molecular systems required for normal cognitive function. Here, the blood RNA expression levels of DISC1 and DISC1-interacting proteins were measured in 63 control subjects. Cognitive function was assessed using neuropsychiatric tests and functional magnetic resonance imaging was used to assess the activity of prefrontal cortical regions during the N-back working memory task, which is abnormal in schizophrenia. Pairwise correlations between gene expression levels and the relationship between gene expression levels and cognitive function and N-back-elicited brain activity were assessed. Finally, the expression levels of DISC1, AKAP9, FEZ1, NDEL1 and PCM1 were compared between 63 controls and 69 schizophrenic subjects. We found that DISC1-interactome genes showed correlated expression in the blood of healthy individuals. The expression levels of several interactome members were correlated with cognitive performance and N-back-elicited activity in the prefrontal cortex. In addition, DISC1 and NDEL1 showed decreased expression in schizophrenic subjects compared to healthy controls. Our findings highlight the importance of the coordinated expression of DISC1-interactome genes for normal cognitive function and suggest that dysregulated DISC1 and NDEL1 expression might, in part, contribute to susceptibility for schizophrenia via disruption of prefrontal cortex-dependent cognitive functions.

Expression of DISC1-Interactome Members Correlates with Cognitive Phenotypes Related to Schizophrenia

PubMed Central

Rampino, Antonio; Walker, Rosie May; Torrance, Helen Scott; Anderson, Susan Maguire; Fazio, Leonardo; Di Giorgio, Annabella; Taurisano, Paolo; Gelao, Barbara; Romano, Raffaella; Masellis, Rita; Ursini, Gianluca; Caforio, Grazia; Blasi, Giuseppe; Millar, J. Kirsty; Porteous, David John; Thomson, Pippa Ann; Bertolino, Alessandro; Evans, Kathryn Louise

2014-01-01

Cognitive dysfunction is central to the schizophrenia phenotype. Genetic and functional studies have implicated Disrupted-in-Schizophrenia 1 (DISC1), a leading candidate gene for schizophrenia and related psychiatric conditions, in cognitive function. Altered expression of DISC1 and DISC1-interactors has been identified in schizophrenia. Dysregulated expression of DISC1-interactome genes might, therefore, contribute to schizophrenia susceptibility via disruption of molecular systems required for normal cognitive function. Here, the blood RNA expression levels of DISC1 and DISC1-interacting proteins were measured in 63 control subjects. Cognitive function was assessed using neuropsychiatric tests and functional magnetic resonance imaging was used to assess the activity of prefrontal cortical regions during the N-back working memory task, which is abnormal in schizophrenia. Pairwise correlations between gene expression levels and the relationship between gene expression levels and cognitive function and N-back-elicited brain activity were assessed. Finally, the expression levels of DISC1, AKAP9, FEZ1, NDEL1 and PCM1 were compared between 63 controls and 69 schizophrenic subjects. We found that DISC1-interactome genes showed correlated expression in the blood of healthy individuals. The expression levels of several interactome members were correlated with cognitive performance and N-back-elicited activity in the prefrontal cortex. In addition, DISC1 and NDEL1 showed decreased expression in schizophrenic subjects compared to healthy controls. Our findings highlight the importance of the coordinated expression of DISC1-interactome genes for normal cognitive function and suggest that dysregulated DISC1 and NDEL1 expression might, in part, contribute to susceptibility for schizophrenia via disruption of prefrontal cortex-dependent cognitive functions. PMID:24940743

Oncoprotein AEG-1 is an endoplasmic reticulum RNA-binding protein whose interactome is enriched in organelle resident protein-encoding mRNAs.

PubMed

Hsu, Jack C-C; Reid, David W; Hoffman, Alyson M; Sarkar, Devanand; Nicchitta, Christopher V

2018-05-01

Astrocyte elevated gene-1 (AEG-1), an oncogene whose overexpression promotes tumor cell proliferation, angiogenesis, invasion, and enhanced chemoresistance, is thought to function primarily as a scaffolding protein, regulating PI3K/Akt and Wnt/β-catenin signaling pathways. Here we report that AEG-1 is an endoplasmic reticulum (ER) resident integral membrane RNA-binding protein (RBP). Examination of the AEG-1 RNA interactome by HITS-CLIP and PAR-CLIP methodologies revealed a high enrichment for endomembrane organelle-encoding transcripts, most prominently those encoding ER resident proteins, and within this cohort, for integral membrane protein-encoding RNAs. Cluster mapping of the AEG-1/RNA interaction sites demonstrated a normalized rank order interaction of coding sequence >5' untranslated region, with 3' untranslated region interactions only weakly represented. Intriguingly, AEG-1/membrane protein mRNA interaction sites clustered downstream from encoded transmembrane domains, suggestive of a role in membrane protein biogenesis. Secretory and cytosolic protein-encoding mRNAs were also represented in the AEG-1 RNA interactome, with the latter category notably enriched in genes functioning in mRNA localization, translational regulation, and RNA quality control. Bioinformatic analyses of RNA-binding motifs and predicted secondary structure characteristics indicate that AEG-1 lacks established RNA-binding sites though shares the property of high intrinsic disorder commonly seen in RBPs. These data implicate AEG-1 in the localization and regulation of secretory and membrane protein-encoding mRNAs and provide a framework for understanding AEG-1 function in health and disease. © 2018 Hsu et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

DenHunt - A Comprehensive Database of the Intricate Network of Dengue-Human Interactions

PubMed Central

Arjunan, Selvam; Sastri, Narayan P.; Chandra, Nagasuma

2016-01-01

Dengue virus (DENV) is a human pathogen and its etiology has been widely established. There are many interactions between DENV and human proteins that have been reported in literature. However, no publicly accessible resource for efficiently retrieving the information is yet available. In this study, we mined all publicly available dengue–human interactions that have been reported in the literature into a database called DenHunt. We retrieved 682 direct interactions of human proteins with dengue viral components, 382 indirect interactions and 4120 differentially expressed human genes in dengue infected cell lines and patients. We have illustrated the importance of DenHunt by mapping the dengue–human interactions on to the host interactome and observed that the virus targets multiple host functional complexes of important cellular processes such as metabolism, immune system and signaling pathways suggesting a potential role of these interactions in viral pathogenesis. We also observed that 7 percent of the dengue virus interacting human proteins are also associated with other infectious and non-infectious diseases. Finally, the understanding that comes from such analyses could be used to design better strategies to counteract the diseases caused by dengue virus. The whole dataset has been catalogued in a searchable database, called DenHunt (http://proline.biochem.iisc.ernet.in/DenHunt/). PMID:27618709

DenHunt - A Comprehensive Database of the Intricate Network of Dengue-Human Interactions.

PubMed

Karyala, Prashanthi; Metri, Rahul; Bathula, Christopher; Yelamanchi, Syam K; Sahoo, Lipika; Arjunan, Selvam; Sastri, Narayan P; Chandra, Nagasuma

2016-09-01

Dengue virus (DENV) is a human pathogen and its etiology has been widely established. There are many interactions between DENV and human proteins that have been reported in literature. However, no publicly accessible resource for efficiently retrieving the information is yet available. In this study, we mined all publicly available dengue-human interactions that have been reported in the literature into a database called DenHunt. We retrieved 682 direct interactions of human proteins with dengue viral components, 382 indirect interactions and 4120 differentially expressed human genes in dengue infected cell lines and patients. We have illustrated the importance of DenHunt by mapping the dengue-human interactions on to the host interactome and observed that the virus targets multiple host functional complexes of important cellular processes such as metabolism, immune system and signaling pathways suggesting a potential role of these interactions in viral pathogenesis. We also observed that 7 percent of the dengue virus interacting human proteins are also associated with other infectious and non-infectious diseases. Finally, the understanding that comes from such analyses could be used to design better strategies to counteract the diseases caused by dengue virus. The whole dataset has been catalogued in a searchable database, called DenHunt (http://proline.biochem.iisc.ernet.in/DenHunt/).

A Novel Hybrid Yeast-Human Network Analysis Reveals an Essential Role for FNBP1L in Antibacterial Autophagy1

PubMed Central

Huett, Alan; Ng, Aylwin; Cao, Zhifang; Kuballa, Petric; Komatsu, Masaaki; Daly, Mark J.; Podolsky, Daniel K.; Xavier, Ramnik J.

2009-01-01

Autophagy is a conserved cellular process required for the removal of defective organelles, protein aggregates, and intracellular pathogens. We used a network analysis strategy to identify novel human autophagy components based upon the yeast interactome centered on the core yeast autophagy proteins. This revealed the potential involvement of 14 novel mammalian genes in autophagy, several of which have known or predicted roles in membrane organization or dynamics. We selected one of these membrane interactors, FNBP1L (formin binding protein 1-like), an F-BAR-containing protein (also termed Toca-1), for further study based upon a predicted interaction with ATG3. We confirmed the FNBP1L/ATG3 interaction biochemically and mapped the FNBP1L domains responsible. Using a functional RNA interference approach, we determined that FNBP1L is essential for autophagy of the intracellular pathogen Salmonella enterica serovar Typhimurium and show that the autophagy process serves to restrict the growth of intracellular bacteria. However, FNBP1L appears dispensable for other forms of autophagy induced by serum starvation or rapamycin. We present a model where FNBP1L is essential for autophagy of intracellular pathogens and identify FNBP1L as a differentially used molecule in specific autophagic contexts. By using network biology to derive functional biological information, we demonstrate the utility of integrated genomics to novel molecule discovery in autophagy. PMID:19342671

Etiologic Field Effect: Reappraisal of the Field Effect Concept in Cancer Predisposition and Progression

PubMed Central

Lochhead, Paul; Chan, Andrew T; Nishihara, Reiko; Fuchs, Charles S; Beck, Andrew H; Giovannucci, Edward; Ogino, Shuji

2014-01-01

The term “field effect” (also known as field defect, field cancerization, or field carcinogenesis) has been used to describe a field of cellular and molecular alteration, which predisposes to the development of neoplasms within that territory. We explore an expanded, integrative concept, “etiologic field effect”, which asserts that various etiologic factors (the exposome including dietary, lifestyle, environmental, microbial, hormonal, and genetic factors) and their interactions (the interactome) contribute to a tissue microenvironmental milieu that constitutes a “field of susceptibility” to neoplasia initiation, evolution, and progression. Importantly, etiological fields predate the acquisition of molecular aberrations commonly considered to indicate presence of filed effect. Inspired by molecular pathological epidemiology (MPE) research, which examines the influence of etiologic factors on cellular and molecular alterations during disease course, an etiologically-focused approach to field effect can: 1) broaden the horizons of our inquiry into cancer susceptibility and progression at molecular, cellular, and environmental levels, during all stages of tumor evolution; 2) embrace host-environment-tumor interactions (including gene-environment interactions) occurring in the tumor microenvironment; and, 3) help explain intriguing observations, such as shared molecular features between bilateral primary breast carcinomas, and between synchronous colorectal cancers, where similar molecular changes are absent from intervening normal colon. MPE research has identified a number of endogenous and environmental exposures which can influence not only molecular signatures in the genome, epigenome, transcriptome, proteome, metabolome and interactome, but also host immunity and tumor behavior. We anticipate that future technological advances will allow the development of in vivo biosensors capable of detecting and quantifying “etiologic field effect” as abnormal network pathology patterns of cellular and microenvironmental responses to endogenous and exogenous exposures. Through an “etiologic field effect” paradigm, and holistic systems pathology (systems biology) approaches to cancer biology, we can improve personalized prevention and treatment strategies for precision medicine. PMID:24925058

Interactome-transcriptome analysis discovers signatures complementary to GWAS Loci of Type 2 Diabetes

PubMed Central

Li, Jing-Woei; Lee, Heung-Man; Wang, Ying; Tong, Amy Hin-Yan; Yip, Kevin Y.; Tsui, Stephen Kwok-Wing; Lok, Si; Ozaki, Risa; Luk, Andrea O; Kong, Alice P. S.; So, Wing-Yee; Ma, Ronald C. W.; Chan, Juliana C. N.; Chan, Ting-Fung

2016-01-01

Protein interactions play significant roles in complex diseases. We analyzed peripheral blood mononuclear cells (PBMC) transcriptome using a multi-method strategy. We constructed a tissue-specific interactome (T2Di) and identified 420 molecular signatures associated with T2D-related comorbidity and symptoms, mainly implicated in inflammation, adipogenesis, protein phosphorylation and hormonal secretion. Apart from explaining the residual associations within the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study, the T2Di signatures were enriched in pathogenic cell type-specific regulatory elements related to fetal development, immunity and expression quantitative trait loci (eQTL). The T2Di revealed a novel locus near a well-established GWAS loci AChE, in which SRRT interacts with JAZF1, a T2D-GWAS gene implicated in pancreatic function. The T2Di also included known anti-diabetic drug targets (e.g. PPARD, MAOB) and identified possible druggable targets (e.g. NCOR2, PDGFR). These T2Di signatures were validated by an independent computational method, and by expression data of pancreatic islet, muscle and liver with some of the signatures (CEBPB, SREBF1, MLST8, SRF, SRRT and SLC12A9) confirmed in PBMC from an independent cohort of 66 T2D and 66 control subjects. By combining prior knowledge and transcriptome analysis, we have constructed an interactome to explain the multi-layered regulatory pathways in T2D. PMID:27752041

Global analysis of host-pathogen interactions that regulate early stage HIV-1 replication

PubMed Central

König, Renate; Zhou, Yingyao; Elleder, Daniel; Diamond, Tracy L.; Bonamy, Ghislain M.C.; Irelan, Jeffrey T.; Chiang, Chih-yuan; Tu, Buu P.; De Jesus, Paul D.; Lilley, Caroline E.; Seidel, Shannon; Opaluch, Amanda M.; Caldwell, Jeremy S.; Weitzman, Matthew D.; Kuhen, Kelli L.; Bandyopadhyay, Sourav; Ideker, Trey; Orth, Anthony P.; Miraglia, Loren J.; Bushman, Frederic D.; Young, John A.; Chanda, Sumit K.

2008-01-01

Human Immunodeficiency Viruses (HIV-1 and HIV-2) rely upon host-encoded proteins to facilitate their replication. Here we combined genome-wide siRNA analyses with interrogation of human interactome databases to assemble a host-pathogen biochemical network containing 213 confirmed host cellular factors and 11 HIV-1-encoded proteins. Protein complexes that regulate ubiquitin conjugation, proteolysis, DNA damage response and RNA splicing were identified as important modulators of early stage HIV-1 infection. Additionally, over 40 new factors were shown to specifically influence initiation and/or kinetics of HIV-1 DNA synthesis, including cytoskeletal regulatory proteins, modulators of post-translational modification, and nucleic acid binding proteins. Finally, fifteen proteins with diverse functional roles, including nuclear transport, prostaglandin synthesis, ubiquitination, and transcription, were found to influence nuclear import or viral DNA integration. Taken together, the multi-scale approach described here has uncovered multiprotein virus-host interactions that likely act in concert to facilitate early steps of HIV-1 infection. PMID:18854154

Insights into the polerovirus-plant interactome revealed by coimmunoprecipitation and mass spectrometry.

PubMed

DeBlasio, Stacy L; Johnson, Richard; Mahoney, Jaclyn; Karasev, Alexander; Gray, Stewart M; MacCoss, Michael J; Cilia, Michelle

2015-04-01

Identification of host proteins interacting with the aphidborne Potato leafroll virus (PLRV) from the genus Polerovirus, family Luteoviridae, is a critical step toward understanding how PLRV and related viruses infect plants. However, the tight spatial distribution of PLRV to phloem tissues poses challenges. A polyclonal antibody raised against purified PLRV virions was used to coimmunoprecipitate virus-host protein complexes from Nicotiana benthamiana tissue inoculated with an infectious PLRV cDNA clone using Agrobacterium tumefaciens. A. tumefaciens-mediated delivery of PLRV enabled infection and production of assembled, insect-transmissible virus in most leaf cells, overcoming the dynamic range constraint posed by a systemically infected host. Isolated protein complexes were characterized using high-resolution mass spectrometry and consisted of host proteins interacting directly or indirectly with virions, as well as the nonincorporated readthrough protein (RTP) and three phosphorylated positional isomers of the RTP. A bioinformatics analysis using ClueGO and STRING showed that plant proteins in the PLRV protein interaction network regulate key biochemical processes, including carbon fixation, amino acid biosynthesis, ion transport, protein folding, and trafficking.

Interactome analysis of longitudinal pharyngeal infection of cynomolgus macaques by group A Streptococcus.

PubMed

Shea, Patrick R; Virtaneva, Kimmo; Kupko, John J; Porcella, Stephen F; Barry, William T; Wright, Fred A; Kobayashi, Scott D; Carmody, Aaron; Ireland, Robin M; Sturdevant, Daniel E; Ricklefs, Stacy M; Babar, Imran; Johnson, Claire A; Graham, Morag R; Gardner, Donald J; Bailey, John R; Parnell, Michael J; Deleo, Frank R; Musser, James M

2010-03-09

Relatively little is understood about the dynamics of global host-pathogen transcriptome changes that occur during bacterial infection of mucosal surfaces. To test the hypothesis that group A Streptococcus (GAS) infection of the oropharynx provokes a distinct host transcriptome response, we performed genome-wide transcriptome analysis using a nonhuman primate model of experimental pharyngitis. We also identified host and pathogen biological processes and individual host and pathogen gene pairs with correlated patterns of expression, suggesting interaction. For this study, 509 host genes and seven biological pathways were differentially expressed throughout the entire 32-day infection cycle. GAS infection produced an initial widespread significant decrease in expression of many host genes, including those involved in cytokine production, vesicle formation, metabolism, and signal transduction. This repression lasted until day 4, at which time a large increase in expression of host genes was observed, including those involved in protein translation, antigen presentation, and GTP-mediated signaling. The interactome analysis identified 73 host and pathogen gene pairs with correlated expression levels. We discovered significant correlations between transcripts of GAS genes involved in hyaluronic capsule production and host endocytic vesicle formation, GAS GTPases and host fibrinolytic genes, and GAS response to interaction with neutrophils. We also identified a strong signal, suggesting interaction between host gammadelta T cells and genes in the GAS mevalonic acid synthesis pathway responsible for production of isopentenyl-pyrophosphate, a short-chain phospholipid that stimulates these T cells. Taken together, our results are unique in providing a comprehensive understanding of the host-pathogen interactome during mucosal infection by a bacterial pathogen.

The MYO6 interactome reveals adaptor complexes coordinating early endosome and cytoskeletal dynamics.

PubMed

O'Loughlin, Thomas; Masters, Thomas A; Buss, Folma

2018-04-01

The intracellular functions of myosin motors requires a number of adaptor molecules, which control cargo attachment, but also fine-tune motor activity in time and space. These motor-adaptor-cargo interactions are often weak, transient or highly regulated. To overcome these problems, we use a proximity labelling-based proteomics strategy to map the interactome of the unique minus end-directed actin motor MYO6. Detailed biochemical and functional analysis identified several distinct MYO6-adaptor modules including two complexes containing RhoGEFs: the LIFT (LARG-Induced F-actin for Tethering) complex that controls endosome positioning and motility through RHO-driven actin polymerisation; and the DISP (DOCK7-Induced Septin disPlacement) complex, a novel regulator of the septin cytoskeleton. These complexes emphasise the role of MYO6 in coordinating endosome dynamics and cytoskeletal architecture. This study provides the first in vivo interactome of a myosin motor protein and highlights the power of this approach in uncovering dynamic and functionally diverse myosin motor complexes. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

Shifted Transversal Design smart-pooling for high coverage interactome mapping

PubMed Central

Xin, Xiaofeng; Rual, Jean-François; Hirozane-Kishikawa, Tomoko; Hill, David E.; Vidal, Marc; Boone, Charles; Thierry-Mieg, Nicolas

2009-01-01

“Smart-pooling,” in which test reagents are multiplexed in a highly redundant manner, is a promising strategy for achieving high efficiency, sensitivity, and specificity in systems-level projects. However, previous applications relied on low redundancy designs that do not leverage the full potential of smart-pooling, and more powerful theoretical constructions, such as the Shifted Transversal Design (STD), lack experimental validation. Here we evaluate STD smart-pooling in yeast two-hybrid (Y2H) interactome mapping. We employed two STD designs and two established methods to perform ORFeome-wide Y2H screens with 12 baits. We found that STD pooling achieves similar levels of sensitivity and specificity as one-on-one array-based Y2H, while the costs and workloads are divided by three. The screening-sequencing approach is the most cost- and labor-efficient, yet STD identifies about twofold more interactions. Screening-sequencing remains an appropriate method for quickly producing low-coverage interactomes, while STD pooling appears as the method of choice for obtaining maps with higher coverage. PMID:19447967

The Activation-Induced Assembly of an RNA/Protein Interactome Centered on the Splicing Factor U2AF2 Regulates Gene Expression in Human CD4 T Cells.

PubMed

Whisenant, Thomas C; Peralta, Eigen R; Aarreberg, Lauren D; Gao, Nina J; Head, Steven R; Ordoukhanian, Phillip; Williamson, Jamie R; Salomon, Daniel R

2015-01-01

Activation of CD4 T cells is a reaction to challenges such as microbial pathogens, cancer and toxins that defines adaptive immune responses. The roles of T cell receptor crosslinking, intracellular signaling, and transcription factor activation are well described, but the importance of post-transcriptional regulation by RNA-binding proteins (RBPs) has not been considered in depth. We describe a new model expanding and activating primary human CD4 T cells and applied this to characterizing activation-induced assembly of splicing factors centered on U2AF2. We immunoprecipitated U2AF2 to identify what mRNA transcripts were bound as a function of activation by TCR crosslinking and costimulation. In parallel, mass spectrometry revealed the proteins incorporated into the U2AF2-centered RNA/protein interactome. Molecules that retained interaction with the U2AF2 complex after RNAse treatment were designated as "central" interactome members (CIMs). Mass spectrometry also identified a second class of activation-induced proteins, "peripheral" interactome members (PIMs), that bound to the same transcripts but were not in physical association with U2AF2 or its partners. siRNA knockdown of two CIMs and two PIMs caused changes in activation marker expression, cytokine secretion, and gene expression that were unique to each protein and mapped to pathways associated with key aspects of T cell activation. While knocking down the PIM, SYNCRIP, impacts a limited but immunologically important set of U2AF2-bound transcripts, knockdown of U2AF1 significantly impairs assembly of the majority of protein and mRNA components in the activation-induced interactome. These results demonstrated that CIMs and PIMs, either directly or indirectly through RNA, assembled into activation-induced U2AF2 complexes and play roles in post-transcriptional regulation of genes related to cytokine secretion. These data suggest an additional layer of regulation mediated by the activation-induced assembly of RNA splicing interactomes that is important for understanding T cell activation.

The Activation-Induced Assembly of an RNA/Protein Interactome Centered on the Splicing Factor U2AF2 Regulates Gene Expression in Human CD4 T Cells

PubMed Central

Aarreberg, Lauren D.; Gao, Nina J.; Head, Steven R.; Ordoukhanian, Phillip; Williamson, Jamie R.; Salomon, Daniel R.

2015-01-01

Activation of CD4 T cells is a reaction to challenges such as microbial pathogens, cancer and toxins that defines adaptive immune responses. The roles of T cell receptor crosslinking, intracellular signaling, and transcription factor activation are well described, but the importance of post-transcriptional regulation by RNA-binding proteins (RBPs) has not been considered in depth. We describe a new model expanding and activating primary human CD4 T cells and applied this to characterizing activation-induced assembly of splicing factors centered on U2AF2. We immunoprecipitated U2AF2 to identify what mRNA transcripts were bound as a function of activation by TCR crosslinking and costimulation. In parallel, mass spectrometry revealed the proteins incorporated into the U2AF2-centered RNA/protein interactome. Molecules that retained interaction with the U2AF2 complex after RNAse treatment were designated as “central” interactome members (CIMs). Mass spectrometry also identified a second class of activation-induced proteins, “peripheral” interactome members (PIMs), that bound to the same transcripts but were not in physical association with U2AF2 or its partners. siRNA knockdown of two CIMs and two PIMs caused changes in activation marker expression, cytokine secretion, and gene expression that were unique to each protein and mapped to pathways associated with key aspects of T cell activation. While knocking down the PIM, SYNCRIP, impacts a limited but immunologically important set of U2AF2-bound transcripts, knockdown of U2AF1 significantly impairs assembly of the majority of protein and mRNA components in the activation-induced interactome. These results demonstrated that CIMs and PIMs, either directly or indirectly through RNA, assembled into activation-induced U2AF2 complexes and play roles in post-transcriptional regulation of genes related to cytokine secretion. These data suggest an additional layer of regulation mediated by the activation-induced assembly of RNA splicing interactomes that is important for understanding T cell activation. PMID:26641092

Modeling gene regulatory networks: A network simplification algorithm

NASA Astrophysics Data System (ADS)

Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

2016-12-01

Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

AF4 and AF4N protein complexes: recruitment of P-TEFb kinase, their interactome and potential functions

PubMed Central

Scholz, Bastian; Kowarz, Eric; Rössler, Tanja; Ahmad, Khalil; Steinhilber, Dieter; Marschalek, Rolf

2015-01-01

AF4/AFF1 and AF5/AFF4 are the molecular backbone to assemble “super-elongation complexes” (SECs) that have two main functions: (1) control of transcriptional elongation by recruiting the positive transcription elongation factor b (P-TEFb = CyclinT1/CDK9) that is usually stored in inhibitory 7SK RNPs; (2) binding of different histone methyltransferases, like DOT1L, NSD1 and CARM1. This way, transcribed genes obtain specific histone signatures (e.g. H3K79me2/3, H3K36me2) to generate a transcriptional memory system. Here we addressed several questions: how is P-TEFb recruited into SEC, how is the AF4 interactome composed, and what is the function of the naturally occuring AF4N protein variant which exhibits only the first 360 amino acids of the AF4 full-length protein. Noteworthy, shorter protein variants are a specific feature of all AFF protein family members. Here, we demonstrate that full-length AF4 and AF4N are both catalyzing the transition of P-TEFb from 7SK RNP to their N-terminal domain. We have also mapped the protein-protein interaction network within both complexes. In addition, we have first evidence that the AF4N protein also recruits TFIIH and the tumor suppressor MEN1. This indicate that AF4N may have additional functions in transcriptional initiation and in MEN1-dependend transcriptional processes. PMID:26171280

Reconstruction of the temporal signaling network in Salmonella-infected human cells.

PubMed

Budak, Gungor; Eren Ozsoy, Oyku; Aydin Son, Yesim; Can, Tolga; Tuncbag, Nurcan

2015-01-01

Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Given that the bacterial infection modifies the response network of the host, a more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic dataset. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches, such as the one presented here, have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections.

Elucidation of the Ebola virus VP24 cellular interactome and disruption of virus biology through targeted inhibition of host-cell protein function.

PubMed

García-Dorival, Isabel; Wu, Weining; Dowall, Stuart; Armstrong, Stuart; Touzelet, Olivier; Wastling, Jonathan; Barr, John N; Matthews, David; Carroll, Miles; Hewson, Roger; Hiscox, Julian A

2014-11-07

Viral pathogenesis in the infected cell is a balance between antiviral responses and subversion of host-cell processes. Many viral proteins specifically interact with host-cell proteins to promote virus biology. Understanding these interactions can lead to knowledge gains about infection and provide potential targets for antiviral therapy. One such virus is Ebola, which has profound consequences for human health and causes viral hemorrhagic fever where case fatality rates can approach 90%. The Ebola virus VP24 protein plays a critical role in the evasion of the host immune response and is likely to interact with multiple cellular proteins. To map these interactions and better understand the potential functions of VP24, label-free quantitative proteomics was used to identify cellular proteins that had a high probability of forming the VP24 cellular interactome. Several known interactions were confirmed, thus placing confidence in the technique, but new interactions were also discovered including one with ATP1A1, which is involved in osmoregulation and cell signaling. Disrupting the activity of ATP1A1 in Ebola-virus-infected cells with a small molecule inhibitor resulted in a decrease in progeny virus, thus illustrating how quantitative proteomics can be used to identify potential therapeutic targets.

A meta-analysis to evaluate the cellular processes regulated by the interactome of endogenous and over-expressed estrogen receptor alpha.

PubMed

Simões, Joana; Amado, Francisco M; Vitorino, Rui; Helguero, Luisa A

2015-01-01

The nature of the proteins complexes that regulate ERα subcellular localization and activity is still an open question in breast cancer biology. Identification of such complexes will help understand development of endocrine resistance in ER+ breast cancer. Mass spectrometry (MS) has allowed comprehensive analysis of the ERα interactome. We have compared six published works analyzing the ERα interactome of MCF-7 and HeLa cells in order to identify a shared or different pathway-related fingerprint. Overall, 806 ERα interacting proteins were identified. The cellular processes were differentially represented according to the ERα purification methodology, indicating that the methodologies used are complementary. While in MCF-7 cells, the interactome of endogenous and over-expressed ERα essentially represents the same biological processes and cellular components, the proteins identified were not over-lapping; thus, suggesting that the biological response may differ as the regulatory/participating proteins in these complexes are different. Interestingly, biological processes uniquely associated to ERα over-expressed in HeLa cell line included L-serine biosynthetic process, cellular amino acid biosynthetic process and cell redox homeostasis. In summary, all the approaches analyzed in this meta-analysis are valid and complementary; in particular, for those cases where the processes occur at low frequency with normal ERα levels, and can be identified when the receptor is over-expressed. However special effort should be put into validating these findings in cells expressing physiological ERα levels.

Combining affinity proteomics and network context to identify new phosphatase substrates and adapters in growth pathways

PubMed Central

Sacco, Francesca; Boldt, Karsten; Calderone, Alberto; Panni, Simona; Paoluzi, Serena; Castagnoli, Luisa; Ueffing, Marius; Cesareni, Gianni

2014-01-01

Protein phosphorylation homoeostasis is tightly controlled and pathological conditions are caused by subtle alterations of the cell phosphorylation profile. Altered levels of kinase activities have already been associated to specific diseases. Less is known about the impact of phosphatases, the enzymes that down-regulate phosphorylation by removing the phosphate groups. This is partly due to our poor understanding of the phosphatase-substrate network. Much of phosphatase substrate specificity is not based on intrinsic enzyme specificity with the catalytic pocket recognizing the sequence/structure context of the phosphorylated residue. In addition many phosphatase catalytic subunits do not form a stable complex with their substrates. This makes the inference and validation of phosphatase substrates a non-trivial task. Here, we present a novel approach that builds on the observation that much of phosphatase substrate selection is based on the network of physical interactions linking the phosphatase to the substrate. We first used affinity proteomics coupled to quantitative mass spectrometry to saturate the interactome of eight phosphatases whose down regulations was shown to affect the activation of the RAS-PI3K pathway. By integrating information from functional siRNA with protein interaction information, we develop a strategy that aims at inferring phosphatase physiological substrates. Graph analysis is used to identify protein scaffolds that may link the catalytic subunits to their substrates. By this approach we rediscover several previously described phosphatase substrate interactions and characterize two new protein scaffolds that promote the dephosphorylation of PTPN11 and ERK by DUSP18 and DUSP26, respectively. PMID:24847354

Characterization of hampin/MSL1 as a node in the nuclear interactome

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dmitriev, Ruslan I.; Korneenko, Tatyana V.; Department of Physiology, Pharmacology, Metabolism, and Cardiovascular Sciences, University of Toledo College of Medicine, Toledo, OH 43614

2007-04-20

Hampin, homolog of Drosophila MSL1, is a partner of histone acetyltransferase MYST1/MOF. Functions of these proteins remain poorly understood beyond their participation in chromatin remodeling complex MSL. In order to identify new proteins interacting with hampin, we screened a mouse cDNA library in yeast two-hybrid system with mouse hampin as bait and found five high-confidence interactors: MYST1, TPR proteins TTC4 and KIAA0103, NOP17 (homolog of a yeast nucleolar protein), and transcription factor GC BP. Subsequently, all these proteins were used as baits in library screenings and more new interactions were found: tumor suppressor RASSF1C and spliceosome component PRP3 for KIAA0103,more » ring finger RNF10 for RASSF1C, and RNA polymerase II regulator NELF-C for MYST1. The majority of the observed interactions was confirmed in vitro by pull-down of bacterially expressed proteins. Reconstruction of a fragment of mammalian interactome suggests that hampin may be linked to diverse regulatory processes in the nucleus.« less

Proteins interacting with cloning scars: a source of false positive protein-protein interactions.

PubMed

Banks, Charles A S; Boanca, Gina; Lee, Zachary T; Florens, Laurence; Washburn, Michael P

2015-02-23

A common approach for exploring the interactome, the network of protein-protein interactions in cells, uses a commercially available ORF library to express affinity tagged bait proteins; these can be expressed in cells and endogenous cellular proteins that copurify with the bait can be identified as putative interacting proteins using mass spectrometry. Control experiments can be used to limit false-positive results, but in many cases, there are still a surprising number of prey proteins that appear to copurify specifically with the bait. Here, we have identified one source of false-positive interactions in such studies. We have found that a combination of: 1) the variable sequence of the C-terminus of the bait with 2) a C-terminal valine "cloning scar" present in a commercially available ORF library, can in some cases create a peptide motif that results in the aberrant co-purification of endogenous cellular proteins. Control experiments may not identify false positives resulting from such artificial motifs, as aberrant binding depends on sequences that vary from one bait to another. It is possible that such cryptic protein binding might occur in other systems using affinity tagged proteins; this study highlights the importance of conducting careful follow-up studies where novel protein-protein interactions are suspected.

Proteins interacting with cloning scars: a source of false positive protein-protein interactions

PubMed Central

Banks, Charles A. S.; Boanca, Gina; Lee, Zachary T.; Florens, Laurence; Washburn, Michael P.

2015-01-01

A common approach for exploring the interactome, the network of protein-protein interactions in cells, uses a commercially available ORF library to express affinity tagged bait proteins; these can be expressed in cells and endogenous cellular proteins that copurify with the bait can be identified as putative interacting proteins using mass spectrometry. Control experiments can be used to limit false-positive results, but in many cases, there are still a surprising number of prey proteins that appear to copurify specifically with the bait. Here, we have identified one source of false-positive interactions in such studies. We have found that a combination of: 1) the variable sequence of the C-terminus of the bait with 2) a C-terminal valine “cloning scar” present in a commercially available ORF library, can in some cases create a peptide motif that results in the aberrant co-purification of endogenous cellular proteins. Control experiments may not identify false positives resulting from such artificial motifs, as aberrant binding depends on sequences that vary from one bait to another. It is possible that such cryptic protein binding might occur in other systems using affinity tagged proteins; this study highlights the importance of conducting careful follow-up studies where novel protein-protein interactions are suspected. PMID:25704442

Plant peptides in defense and signaling.

PubMed

Marmiroli, Nelson; Maestri, Elena

2014-06-01

This review focuses on plant peptides involved in defense against pathogen infection and those involved in the regulation of growth and development. Defense peptides, defensins, cyclotides and anti-microbial peptides are compared and contrasted. Signaling peptides are classified according to their major sites of activity. Finally, a network approach to creating an interactomic peptide map is described. Copyright © 2014 Elsevier Inc. All rights reserved.

System-wide identification of RNA-binding proteins by interactome capture.

PubMed

Castello, Alfredo; Horos, Rastislav; Strein, Claudia; Fischer, Bernd; Eichelbaum, Katrin; Steinmetz, Lars M; Krijgsveld, Jeroen; Hentze, Matthias W

2013-03-01

Owing to their preeminent biological functions, the repertoire of expressed RNA-binding proteins (RBPs) and their activity states are highly informative about cellular systems. We have developed a novel and unbiased technique, called interactome capture, for identifying the active RBPs of cultured cells. By making use of in vivo UV cross-linking of RBPs to polyadenylated RNAs, covalently bound proteins are captured with oligo(dT) magnetic beads. After stringent washes, the mRNA interactome is determined by quantitative mass spectrometry (MS). The protocol takes 3 working days for analysis of single proteins by western blotting, and about 2 weeks for the determination of complete cellular mRNA interactomes by MS. The most important advantage of interactome capture over other in vitro and in silico approaches is that only RBPs bound to RNA in a physiological environment are identified. When applied to HeLa cells, interactome capture revealed hundreds of novel RBPs. Interactome capture can also be broadly used to compare different biological states, including metabolic stress, cell cycle, differentiation, development or the response to drugs.

Proteomics profiling of interactome dynamics by colocalisation analysis (COLA)† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6mb00701e Click here for additional data file. Click here for additional data file.

PubMed Central

Sailem, Heba Z.; Kümper, Sandra; Tape, Christopher J.; McCully, Ryan R.; Paul, Angela; Anjomani-Virmouni, Sara; Jørgensen, Claus; Poulogiannis, George; Marshall, Christopher J.

2017-01-01

Localisation and protein function are intimately linked in eukaryotes, as proteins are localised to specific compartments where they come into proximity of other functionally relevant proteins. Significant co-localisation of two proteins can therefore be indicative of their functional association. We here present COLA, a proteomics based strategy coupled with a bioinformatics framework to detect protein–protein co-localisations on a global scale. COLA reveals functional interactions by matching proteins with significant similarity in their subcellular localisation signatures. The rapid nature of COLA allows mapping of interactome dynamics across different conditions or treatments with high precision. PMID:27824369

Interactome analysis reveals ZNF804A, a schizophrenia risk gene, as a novel component of protein translational machinery critical for embryonic neurodevelopment

PubMed Central

Zhou, Y; Dong, F; Lanz, T A; Reinhart, V; Li, M; Liu, L; Zou, J; Xi, H S; Mao, Y

2018-01-01

Recent genome-wide association studies identified over 100 genetic loci that significantly associate with schizophrenia (SZ). A top candidate gene, ZNF804A, was robustly replicated in different populations. However, its neural functions are largely unknown. Here we show in mouse that ZFP804A, the homolog of ZNF804A, is required for normal progenitor proliferation and neuronal migration. Using a yeast two-hybrid genome-wide screen, we identified novel interacting proteins of ZNF804A. Rather than transcriptional factors, genes involved in mRNA translation are highly represented in our interactome result. ZNF804A co-fractionates with translational machinery and modulates the translational efficiency as well as the mTOR pathway. The ribosomal protein RPSA interacts with ZNF804A and rescues the migration and translational defects caused by ZNF804A knockdown. RNA immunoprecipitation–RNAseq (RIP-Seq) identified transcripts bound to ZFP804A. Consistently, ZFP804A associates with many short transcripts involved in translational and mitochondrial regulation. Moreover, among the transcripts associated with ZFP804A, a SZ risk gene, neurogranin (NRGN), is one of ZFP804A targets. Interestingly, downregulation of ZFP804A decreases NRGN expression and overexpression of NRGN can ameliorate ZFP804A-mediated migration defect. To verify the downstream targets of ZNF804A, a Duolink in situ interaction assay confirmed genes from our RIP-Seq data as the ZNF804A targets. Thus, our work uncovered a novel mechanistic link of a SZ risk gene to neurodevelopment and translational control. The interactome-driven approach here is an effective way for translating genome-wide association findings into novel biological insights of human diseases. PMID:28924186

Characterization of the Human NEK7 Interactome Suggests Catalytic and Regulatory Properties Distinct from Those of NEK6

PubMed Central

2015-01-01

Human NEK7 is a regulator of cell division and plays an important role in growth and survival of mammalian cells. Human NEK6 and NEK7 are closely related, consisting of a conserved C-terminal catalytic domain and a nonconserved and disordered N-terminal regulatory domain, crucial to mediate the interactions with their respective proteins. Here, in order to better understand NEK7 cellular functions, we characterize the NEK7 interactome by two screening approaches: one using a yeast two-hybrid system and the other based on immunoprecipitation followed by mass spectrometry analysis. These approaches led to the identification of 61 NEK7 interactors that contribute to a variety of biological processes, including cell division. Combining additional interaction and phosphorylation assays from yeast two-hybrid screens, we validated CC2D1A, TUBB2B, MNAT1, and NEK9 proteins as potential NEK7 interactors and substrates. Notably, endogenous RGS2, TUBB, MNAT1, NEK9, and PLEKHA8 localized with NEK7 at key sites throughout the cell cycle, especially during mitosis and cytokinesis. Furthermore, we obtained evidence that the closely related kinases NEK6 and NEK7 do not share common interactors, with the exception of NEK9, and display different modes of protein interaction, depending on their N- and C-terminal regions, in distinct fashions. In summary, our work shows for the first time a comprehensive NEK7 interactome that, combined with functional in vitro and in vivo assays, suggests that NEK7 is a multifunctional kinase acting in different cellular processes in concert with cell division signaling and independently of NEK6. PMID:25093993

Hypothesis: NDL proteins function in stress responses by regulating microtubule organization

PubMed Central

Khatri, Nisha; Mudgil, Yashwanti

2015-01-01

N-MYC DOWNREGULATED-LIKE proteins (NDL), members of the alpha/beta hydrolase superfamily were recently rediscovered as interactors of G-protein signaling in Arabidopsis thaliana. Although the precise molecular function of NDL proteins is still elusive, in animals these proteins play protective role in hypoxia and expression is induced by hypoxia and nickel, indicating role in stress. Homology of NDL1 with animal counterpart N-MYC DOWNREGULATED GENE (NDRG) suggests similar functions in animals and plants. It is well established that stress responses leads to the microtubule depolymerization and reorganization which is crucial for stress tolerance. NDRG is a microtubule-associated protein which mediates the microtubule organization in animals by causing acetylation and increases the stability of α-tubulin. As NDL1 is highly homologous to NDRG, involvement of NDL1 in the microtubule organization during plant stress can also be expected. Discovery of interaction of NDL with protein kinesin light chain- related 1, enodomembrane family protein 70, syntaxin-23, tubulin alpha-2 chain, as a part of G protein interactome initiative encourages us to postulate microtubule stabilizing functions for NDL family in plants. Our search for NDL interactors in G protein interactome also predicts the role of NDL proteins in abiotic stress tolerance management. Based on published report in animals and predicted interacting partners for NDL in G protein interactome lead us to hypothesize involvement of NDL in the microtubule organization during abiotic stress management in plants. PMID:26583023

Hypothesis: NDL proteins function in stress responses by regulating microtubule organization.

PubMed

Khatri, Nisha; Mudgil, Yashwanti

2015-01-01

N-MYC DOWNREGULATED-LIKE proteins (NDL), members of the alpha/beta hydrolase superfamily were recently rediscovered as interactors of G-protein signaling in Arabidopsis thaliana. Although the precise molecular function of NDL proteins is still elusive, in animals these proteins play protective role in hypoxia and expression is induced by hypoxia and nickel, indicating role in stress. Homology of NDL1 with animal counterpart N-MYC DOWNREGULATED GENE (NDRG) suggests similar functions in animals and plants. It is well established that stress responses leads to the microtubule depolymerization and reorganization which is crucial for stress tolerance. NDRG is a microtubule-associated protein which mediates the microtubule organization in animals by causing acetylation and increases the stability of α-tubulin. As NDL1 is highly homologous to NDRG, involvement of NDL1 in the microtubule organization during plant stress can also be expected. Discovery of interaction of NDL with protein kinesin light chain- related 1, enodomembrane family protein 70, syntaxin-23, tubulin alpha-2 chain, as a part of G protein interactome initiative encourages us to postulate microtubule stabilizing functions for NDL family in plants. Our search for NDL interactors in G protein interactome also predicts the role of NDL proteins in abiotic stress tolerance management. Based on published report in animals and predicted interacting partners for NDL in G protein interactome lead us to hypothesize involvement of NDL in the microtubule organization during abiotic stress management in plants.

Making connections for life: an in vivo map of the yeast interactome.

PubMed

Kast, Juergen

2008-10-01

Proteins are the true workhorses of any cell. To carry out specific tasks, they frequently bind other molecules in their surroundings. Due to their structural complexity and flexibility, the most diverse array of interactions is seen with other proteins. The different geometries and affinities available for such interactions typically bestow specific functions on proteins. Having available a map of protein-protein interactions is therefore of enormous importance for any researcher interested in gaining insight into biological systems at the level of cells and organisms. In a recent report, a novel approach has been employed that relies on the spontaneous folding of complementary enzyme fragments fused to two different proteins to test whether these interact in their actual cellular context [Tarassov et al., Science 320, 1465-1470 (2008)]. Genome-wide application of this protein-fragment complementation assay has resulted in the first map of the in vivo interactome of Saccharomyces cerevisiae. The current data show striking similarities but also significant differences to those obtained using other large-scale approaches for the same task. This warrants a general discussion of the current state of affairs of protein-protein interaction studies and foreseeable future trends, highlighting their significance for a variety of applications and their potential to revolutionize our understanding of the architecture and dynamics of biological systems.

Making connections for life: an in vivo map of the yeast interactome

PubMed Central

Kast, Juergen

2008-01-01

Proteins are the true workhorses of any cell. To carry out specific tasks, they frequently bind other molecules in their surroundings. Due to their structural complexity and flexibility, the most diverse array of interactions is seen with other proteins. The different geometries and affinities available for such interactions typically bestow specific functions on proteins. Having available a map of protein–protein interactions is therefore of enormous importance for any researcher interested in gaining insight into biological systems at the level of cells and organisms. In a recent report, a novel approach has been employed that relies on the spontaneous folding of complementary enzyme fragments fused to two different proteins to test whether these interact in their actual cellular context [Tarassov et al., Science 320, 1465–1470 (2008)]. Genome-wide application of this protein-fragment complementation assay has resulted in the first map of the in vivo interactome of Saccharomyces cerevisiae. The current data show striking similarities but also significant differences to those obtained using other large-scale approaches for the same task. This warrants a general discussion of the current state of affairs of protein–protein interaction studies and foreseeable future trends, highlighting their significance for a variety of applications and their potential to revolutionize our understanding of the architecture and dynamics of biological systems. PMID:19404434

Bacteriophage Protein–Protein Interactions

PubMed Central

Häuser, Roman; Blasche, Sonja; Dokland, Terje; Haggård-Ljungquist, Elisabeth; von Brunn, Albrecht; Salas, Margarita; Casjens, Sherwood; Molineux, Ian

2012-01-01

Bacteriophages T7, λ, P22, and P2/P4 (from Escherichia coli), as well as ϕ29 (from Bacillus subtilis), are among the best-studied bacterial viruses. This chapter summarizes published protein interaction data of intraviral protein interactions, as well as known phage–host protein interactions of these phages retrieved from the literature. We also review the published results of comprehensive protein interaction analyses of Pneumococcus phages Dp-1 and Cp-1, as well as coliphages λ and T7. For example, the ≈55 proteins encoded by the T7 genome are connected by ≈43 interactions with another ≈15 between the phage and its host. The chapter compiles published interactions for the well-studied phages λ (33 intra-phage/22 phage-host), P22 (38/9), P2/P4 (14/3), and ϕ29 (20/2). We discuss whether different interaction patterns reflect different phage lifestyles or whether they may be artifacts of sampling. Phages that infect the same host can interact with different host target proteins, as exemplified by E. coli phage λ and T7. Despite decades of intensive investigation, only a fraction of these phage interactomes are known. Technical limitations and a lack of depth in many studies explain the gaps in our knowledge. Strategies to complete current interactome maps are described. Although limited space precludes detailed overviews of phage molecular biology, this compilation will allow future studies to put interaction data into the context of phage biology. PMID:22748812

Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression

PubMed Central

Bolgar, Bence; Deakin, Bill

2017-01-01

Comorbidity patterns have become a major source of information to explore shared mechanisms of pathogenesis between disorders. In hypothesis-free exploration of comorbid conditions, disease-disease networks are usually identified by pairwise methods. However, interpretation of the results is hindered by several confounders. In particular a very large number of pairwise associations can arise indirectly through other comorbidity associations and they increase exponentially with the increasing breadth of the investigated diseases. To investigate and filter this effect, we computed and compared pairwise approaches with a systems-based method, which constructs a sparse Bayesian direct multimorbidity map (BDMM) by systematically eliminating disease-mediated comorbidity relations. Additionally, focusing on depression-related parts of the BDMM, we evaluated correspondence with results from logistic regression, text-mining and molecular-level measures for comorbidities such as genetic overlap and the interactome-based association score. We used a subset of the UK Biobank Resource, a cross-sectional dataset including 247 diseases and 117,392 participants who filled out a detailed questionnaire about mental health. The sparse comorbidity map confirmed that depressed patients frequently suffer from both psychiatric and somatic comorbid disorders. Notably, anxiety and obesity show strong and direct relationships with depression. The BDMM identified further directly co-morbid somatic disorders, e.g. irritable bowel syndrome, fibromyalgia, or migraine. Using the subnetwork of depression and metabolic disorders for functional analysis, the interactome-based system-level score showed the best agreement with the sparse disease network. This indicates that these epidemiologically strong disease-disease relations have improved correspondence with expected molecular-level mechanisms. The substantially fewer number of comorbidity relations in the BDMM compared to pairwise methods implies that biologically meaningful comorbid relations may be less frequent than earlier pairwise methods suggested. The computed interactive comprehensive multimorbidity views over the diseasome are available on the web at Co=MorNet: bioinformatics.mit.bme.hu/UKBNetworks. PMID:28644851

Immunological network signatures of cancer progression and survival

PubMed Central

2011-01-01

Background The immune contribution to cancer progression is complex and difficult to characterize. For example in tumors, immune gene expression is detected from the combination of normal, tumor and immune cells in the tumor microenvironment. Profiling the immune component of tumors may facilitate the characterization of the poorly understood roles immunity plays in cancer progression. However, the current approaches to analyze the immune component of a tumor rely on incomplete identification of immune factors. Methods To facilitate a more comprehensive approach, we created a ranked immunological relevance score for all human genes, developed using a novel strategy that combines text mining and information theory. We used this score to assign an immunological grade to gene expression profiles, and thereby quantify the immunological component of tumors. This immunological relevance score was benchmarked against existing manually curated immune resources as well as high-throughput studies. To further characterize immunological relevance for genes, the relevance score was charted against both the human interactome and cancer information, forming an expanded interactome landscape of tumor immunity. We applied this approach to expression profiles in melanomas, thus identifying and grading their immunological components, followed by identification of their associated protein interactions. Results The power of this strategy was demonstrated by the observation of early activation of the adaptive immune response and the diversity of the immune component during melanoma progression. Furthermore, the genome-wide immunological relevance score classified melanoma patient groups, whose immunological grade correlated with clinical features, such as immune phenotypes and survival. Conclusions The assignment of a ranked immunological relevance score to all human genes extends the content of existing immune gene resources and enriches our understanding of immune involvement in complex biological networks. The application of this approach to tumor immunity represents an automated systems strategy that quantifies the immunological component in complex disease. In so doing, it stratifies patients according to their immune profiles, which may lead to effective computational prognostic and clinical guides. PMID:21453479

Evidence That a Psychopathology Interactome Has Diagnostic Value, Predicting Clinical Needs: An Experience Sampling Study

PubMed Central

van Os, Jim; Lataster, Tineke; Delespaul, Philippe; Wichers, Marieke; Myin-Germeys, Inez

2014-01-01

Background For the purpose of diagnosis, psychopathology can be represented as categories of mental disorder, symptom dimensions or symptom networks. Also, psychopathology can be assessed at different levels of temporal resolution (monthly episodes, daily fluctuating symptoms, momentary fluctuating mental states). We tested the diagnostic value, in terms of prediction of treatment needs, of the combination of symptom networks and momentary assessment level. Method Fifty-seven patients with a psychotic disorder participated in an ESM study, capturing psychotic experiences, emotions and circumstances at 10 semi-random moments in the flow of daily life over a period of 6 days. Symptoms were assessed by interview with the Positive and Negative Syndrome Scale (PANSS); treatment needs were assessed using the Camberwell Assessment of Need (CAN). Results Psychotic symptoms assessed with the PANSS (Clinical Psychotic Symptoms) were strongly associated with psychotic experiences assessed with ESM (Momentary Psychotic Experiences). However, the degree to which Momentary Psychotic Experiences manifested as Clinical Psychotic Symptoms was determined by level of momentary negative affect (higher levels increasing probability of Momentary Psychotic Experiences manifesting as Clinical Psychotic Symptoms), momentary positive affect (higher levels decreasing probability of Clinical Psychotic Symptoms), greater persistence of Momentary Psychotic Experiences (persistence predicting increased probability of Clinical Psychotic Symptoms) and momentary environmental stress associated with events and activities (higher levels increasing probability of Clinical Psychotic Symptoms). Similarly, the degree to which momentary visual or auditory hallucinations manifested as Clinical Psychotic Symptoms was strongly contingent on the level of accompanying momentary paranoid delusional ideation. Momentary Psychotic Experiences were associated with CAN unmet treatment needs, over and above PANSS measures of psychopathology, similarly moderated by momentary interactions with emotions and context. Conclusion The results suggest that psychopathology, represented as an interactome at the momentary level of temporal resolution, is informative in diagnosing clinical needs, over and above traditional symptom measures. PMID:24466189

Molecular characterization and interactome analysis of Trypanosoma cruzi tryparedoxin II.

PubMed

Arias, Diego G; Piñeyro, María Dolores; Iglesias, Alberto A; Guerrero, Sergio A; Robello, Carlos

2015-04-29

Trypanosoma cruzi, the causative agent of Chagas disease, possesses two tryparedoxins (TcTXNI and TcTXNII), belonging to the thioredoxin superfamily. TXNs are oxidoreductases which mediate electron transfer between trypanothione and peroxiredoxins. This constitutes a difference with the host cells, in which these activities are mediated by thioredoxins. These differences make TXNs an attractive target for drug development. In a previous work we characterized TcTXNI, including the redox interactome. In this work we extend the study to TcTXNII. We demonstrate that TcTXNII is a transmembrane protein anchored to the surface of the mitochondria and endoplasmic reticulum, with a cytoplasmatic orientation of the redox domain. It would be expressed during the metacyclogenesis process. In order to continue with the characterization of the redox interactome of T. cruzi, we designed an active site mutant TcTXNII lacking the resolving cysteine, and through the expression of this mutant protein and incubation with T. cruzi proteins, heterodisulfide complexes were isolated by affinity chromatography and identified by mass spectrometry. This allowed us to identify sixteen TcTXNII interacting proteins, which are involved in a wide range of cellular processes, indicating the relevance of TcTXNII, and contributing to our understanding of the redox interactome of T. cruzi. T. cruzi, the causative agent of Chagas disease, constitutes a major sanitary problem in Latin America. The number of estimated infected persons is ca. 8 million, 28 million people are at risk of infection and ~20,000 deaths occur per year in endemic regions. No vaccines are available at present, and most drugs currently in use were developed decades ago and show variable efficacy with undesirable side effects. The parasite is able to live and prolipherate inside macrophage phagosomes, where it is exposed to cytotoxic reactive oxygen and nitrogen species, derived from macrophage activation. Therefore, T. cruzi antioxidant mechanisms constitute an active field of investigation, since they could provide the basis for a rational drug development. Peroxide detoxification in this parasite is achieved by ascorbate peroxidase and different thiol-dependent peroxidases. Among them, both mitochondrial and cytosolic tryparedoxin peroxidases, typical two-cysteine peroxiredoxins, were found to be important for hydrogen peroxide and peroxynitrite detoxification and their expression levels correlated with parasite infectivity and virulence. In trypanosomes tryparedoxins and not thioredoxins act as peroxiredoxin reductases, suggesting that these enzymes substitute thioredoxins in these parasites. T. cruzi possesses two tryparedoxin genes, TcTXNI and TcTXN II. Since thioredoxins are proteins with several targets actively participating of complex redox networks, we have previously investigated if this is the case also for TcTXNI, for which we described relevant partners (J Proteomics. 2011;74(9):1683-92). In this manuscript we investigated the interactions of TcTXNII. We have designed an active site mutant tryparedoxin II lacking the resolving cysteine and, through the expression of this mutant protein and its incubation with T. cruzi proteins, hetero disulfide complexes were isolated by affinity chromatography purification and identified by electrophoresis separation and MS identification. This allowed us to identify sixteen TcTXNII interacting proteins which are involved in different and relevant cellular processes. Moreover, we demonstrate that TcTXNII is a transmembrane protein anchored to the surface of the mitochondria and endoplasmic reticulum. Copyright © 2015 Elsevier B.V. All rights reserved.

Proteomic Analysis of Virus-Host Interactions in an Infectious Context Using Recombinant Viruses*

PubMed Central

Komarova, Anastassia V.; Combredet, Chantal; Meyniel-Schicklin, Laurène; Chapelle, Manuel; Caignard, Grégory; Camadro, Jean-Michel; Lotteau, Vincent; Vidalain, Pierre-Olivier; Tangy, Frédéric

2011-01-01

RNA viruses exhibit small-sized genomes encoding few proteins, but still establish complex networks of interactions with host cell components to achieve replication and spreading. Ideally, these virus-host protein interactions should be mapped directly in infected cell culture, but such a high standard is often difficult to reach when using conventional approaches. We thus developed a new strategy based on recombinant viruses expressing tagged viral proteins to capture both direct and indirect physical binding partners during infection. As a proof of concept, we engineered a recombinant measles virus (MV) expressing one of its virulence factors, the MV-V protein, with a One-STrEP amino-terminal tag. This allowed virus-host protein complex analysis directly from infected cells by combining modified tandem affinity chromatography and mass spectrometry analysis. Using this approach, we established a prosperous list of 245 cellular proteins interacting either directly or indirectly with MV-V, and including four of the nine already known partners of this viral factor. These interactions were highly specific of MV-V because they were not recovered when the nucleoprotein MV-N, instead of MV-V, was tagged. Besides key components of the antiviral response, cellular proteins from mitochondria, ribosomes, endoplasmic reticulum, protein phosphatase 2A, and histone deacetylase complex were identified for the first time as prominent targets of MV-V and the critical role of the later protein family in MV replication was addressed. Most interestingly, MV-V showed some preferential attachment to essential proteins in the human interactome network, as assessed by centrality and interconnectivity measures. Furthermore, the list of MV-V interactors also showed a massive enrichment for well-known targets of other viruses. Altogether, this clearly supports our approach based on reverse genetics of viruses combined with high-throughput proteomics to probe the interaction network that viruses establish in infected cells. PMID:21911578

Characterization of host proteins interacting with the lymphocytic choriomeningitis virus L protein.

PubMed

Khamina, Kseniya; Lercher, Alexander; Caldera, Michael; Schliehe, Christopher; Vilagos, Bojan; Sahin, Mehmet; Kosack, Lindsay; Bhattacharya, Anannya; Májek, Peter; Stukalov, Alexey; Sacco, Roberto; James, Leo C; Pinschewer, Daniel D; Bennett, Keiryn L; Menche, Jörg; Bergthaler, Andreas

2017-12-01

RNA-dependent RNA polymerases (RdRps) play a key role in the life cycle of RNA viruses and impact their immunobiology. The arenavirus lymphocytic choriomeningitis virus (LCMV) strain Clone 13 provides a benchmark model for studying chronic infection. A major genetic determinant for its ability to persist maps to a single amino acid exchange in the viral L protein, which exhibits RdRp activity, yet its functional consequences remain elusive. To unravel the L protein interactions with the host proteome, we engineered infectious L protein-tagged LCMV virions by reverse genetics. A subsequent mass-spectrometric analysis of L protein pulldowns from infected human cells revealed a comprehensive network of interacting host proteins. The obtained LCMV L protein interactome was bioinformatically integrated with known host protein interactors of RdRps from other RNA viruses, emphasizing interconnected modules of human proteins. Functional characterization of selected interactors highlighted proviral (DDX3X) as well as antiviral (NKRF, TRIM21) host factors. To corroborate these findings, we infected Trim21-/- mice with LCMV and found impaired virus control in chronic infection. These results provide insights into the complex interactions of the arenavirus LCMV and other viral RdRps with the host proteome and contribute to a better molecular understanding of how chronic viruses interact with their host.

p53 inhibits autophagy by interacting with the human ortholog of yeast Atg17, RB1CC1/FIP200.

PubMed

Morselli, Eugenia; Shen, Shensi; Ruckenstuhl, Christoph; Bauer, Maria Anna; Mariño, Guillermo; Galluzzi, Lorenzo; Criollo, Alfredo; Michaud, Mickael; Maiuri, Maria Chiara; Chano, Tokuhiro; Madeo, Frank; Kroemer, Guido

2011-08-15

The tumor suppressor protein p53 tonically suppresses autophagy when it is present in the cytoplasm. This effect is phylogenetically conserved from mammals to nematodes, and human p53 can inhibit autophagy in yeast, as we show here. Bioinformatic investigations of the p53 interactome in relationship to the autophagy-relevant protein network underscored the possible relevance of a direct molecular interaction between p53 and the mammalian ortholog of the essential yeast autophagy protein Atg17, namely RB1-inducible coiled-coil protein 1 (RB1CC1), also called FAK family kinase-interacting protein of 200 KDa (FIP200). Mutational analyses revealed that a single point mutation in p53 (K382R) abolished its capacity to inhibit autophagy upon transfection into p53-deficient human colon cancer or yeast cells. In conditions in which wild-type p53 co-immunoprecipitated with RB1CC1/FIP200, p53 (K382R) failed to do so, underscoring the importance of the physical interaction between these proteins for the control of autophagy. In conclusion, p53 regulates autophagy through a direct molecular interaction with RB1CC1/FIP200, a protein that is essential for the very apical step of autophagy initiation.

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

PubMed Central

Nesvizhskii, Alexey I.

2013-01-01

Analysis of protein interaction networks and protein complexes using affinity purification and mass spectrometry (AP/MS) is among most commonly used and successful applications of proteomics technologies. One of the foremost challenges of AP/MS data is a large number of false positive protein interactions present in unfiltered datasets. Here we review computational and informatics strategies for detecting specific protein interaction partners in AP/MS experiments, with a focus on incomplete (as opposite to genome-wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or integrated peptide intensities that can be extracted from AP/MS data. We also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. Computational approaches for benchmarking of scoring methods are discussed, and the need for generation of reference AP/MS datasets is highlighted. Finally, we discuss the possibility of more extended modeling of experimental AP/MS data, including integration with external information such as protein interaction predictions based on functional genomics data. PMID:22611043

The Ties That Bind: Mapping the Dynamic Enhancer-Promoter Interactome

DOE PAGES

Spurrell, Cailyn H.; Dickel, Diane E.; Visel, Axel

2016-11-17

Coupling chromosome conformation capture to molecular enrichment for promoter-containing DNA fragments enables the systematic mapping of interactions between individual distal regulatory sequences and their target genes. Here in this Minireview, we describe recent progress in the application of this technique and related complementary approaches to gain insight into the lineage- and cell-type-specific dynamics of interactions between regulators and gene promoters.

Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform.

PubMed

Dubovenko, Alexey; Nikolsky, Yuri; Rakhmatulin, Eugene; Nikolskaya, Tatiana

2017-01-01

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

A human XPC protein interactome--a resource.

PubMed

Lubin, Abigail; Zhang, Ling; Chen, Hua; White, Victoria M; Gong, Feng

2013-12-23

Global genome nucleotide excision repair (GG-NER) is responsible for identifying and removing bulky adducts from non-transcribed DNA that result from damaging agents such as UV radiation and cisplatin. Xeroderma pigmentosum complementation group C (XPC) is one of the essential damage recognition proteins of the GG-NER pathway and its dysfunction results in xeroderma pigmentosum (XP), a disorder involving photosensitivity and a predisposition to cancer. To better understand the identification of DNA damage by XPC in the context of chromatin and the role of XPC in the pathogenesis of XP, we characterized the interactome of XPC using a high throughput yeast two-hybrid screening. Our screening showed 49 novel interactors of XPC involved in DNA repair and replication, proteolysis and post-translational modifications, transcription regulation, signal transduction, and metabolism. Importantly, we validated the XPC-OTUD4 interaction by co-IP and provided evidence that OTUD4 knockdown in human cells indeed affects the levels of ubiquitinated XPC, supporting a hypothesis that the OTUD4 deubiquitinase is involved in XPC recycling by cleaving the ubiquitin moiety. This high-throughput characterization of the XPC interactome provides a resource for future exploration and suggests that XPC may have many uncharacterized cellular functions.

AIM: A comprehensive Arabidopsis Interactome Module database and related interologs in plants

USDA-ARS?s Scientific Manuscript database

Systems biology analysis of protein modules is important for understanding the functional relationships between proteins in the interactome. Here, we present a comprehensive database named AIM for Arabidopsis (Arabidopsis thaliana) interactome modules. The database contains almost 250,000 modules th...

The Porphyromonas gingivalis/Host Interactome Shows Enrichment in GWASdb Genes Related to Alzheimer's Disease, Diabetes and Cardiovascular Diseases

PubMed Central

Carter, Chris J.; France, James; Crean, StJohn; Singhrao, Sim K.

2017-01-01

Periodontal disease is of established etiology in which polymicrobial synergistic ecology has become dysbiotic under the influence of Porphyromonas gingivalis. Following breakdown of the host's protective oral tissue barriers, P. gingivalis migrates to developing inflammatory pathologies that associate with Alzheimer's disease (AD). Periodontal disease is a risk factor for cardiovascular disorders (CVD), type II diabetes mellitus (T2DM), AD and other chronic diseases, whilst T2DM exacerbates periodontitis. This study analyzed the relationship between the P. gingivalis/host interactome and the genes identified in genome-wide association studies (GWAS) for the aforementioned conditions using data from GWASdb (P < 1E-03) and, in some cases, from the NCBI/EBI GWAS database (P < 1E-05). Gene expression data from periodontitis or P. gingivalis microarray was compared to microarray datasets from the AD hippocampus and/or from carotid artery plaques. The results demonstrated that the host genes of the P. gingivalis interactome were significantly enriched in genes deposited in GWASdb genes related to cognitive disorders, AD and dementia, and its co-morbid conditions T2DM, obesity, and CVD. The P. gingivalis/host interactome was also enriched in GWAS genes from the more stringent NCBI-EBI database for AD, atherosclerosis and T2DM. The misregulated genes in periodontitis tissue or P. gingivalis infected macrophages also matched those in the AD hippocampus or atherosclerotic plaques. Together, these data suggest important gene/environment interactions between P. gingivalis and susceptibility genes or gene expression changes in conditions where periodontal disease is a contributory factor. PMID:29311898

The Porphyromonas gingivalis/Host Interactome Shows Enrichment in GWASdb Genes Related to Alzheimer's Disease, Diabetes and Cardiovascular Diseases.

PubMed

Carter, Chris J; France, James; Crean, StJohn; Singhrao, Sim K

2017-01-01

Periodontal disease is of established etiology in which polymicrobial synergistic ecology has become dysbiotic under the influence of Porphyromonas gingivalis . Following breakdown of the host's protective oral tissue barriers, P. gingivalis migrates to developing inflammatory pathologies that associate with Alzheimer's disease (AD). Periodontal disease is a risk factor for cardiovascular disorders (CVD), type II diabetes mellitus (T2DM), AD and other chronic diseases, whilst T2DM exacerbates periodontitis. This study analyzed the relationship between the P. gingivalis /host interactome and the genes identified in genome-wide association studies (GWAS) for the aforementioned conditions using data from GWASdb ( P < 1E-03) and, in some cases, from the NCBI/EBI GWAS database ( P < 1E-05). Gene expression data from periodontitis or P. gingivalis microarray was compared to microarray datasets from the AD hippocampus and/or from carotid artery plaques. The results demonstrated that the host genes of the P. gingivalis interactome were significantly enriched in genes deposited in GWASdb genes related to cognitive disorders, AD and dementia, and its co-morbid conditions T2DM, obesity, and CVD. The P. gingivalis /host interactome was also enriched in GWAS genes from the more stringent NCBI-EBI database for AD, atherosclerosis and T2DM. The misregulated genes in periodontitis tissue or P. gingivalis infected macrophages also matched those in the AD hippocampus or atherosclerotic plaques. Together, these data suggest important gene/environment interactions between P. gingivalis and susceptibility genes or gene expression changes in conditions where periodontal disease is a contributory factor.

High-density functional-RNA arrays as a versatile platform for studying RNA-based interactions.

PubMed

Phillips, Jack O; Butt, Louise E; Henderson, Charlotte A; Devonshire, Martin; Healy, Jess; Conway, Stuart J; Locker, Nicolas; Pickford, Andrew R; Vincent, Helen A; Callaghan, Anastasia J

2018-05-28

We are just beginning to unravel the myriad of interactions in which non-coding RNAs participate. The intricate RNA interactome is the foundation of many biological processes, including bacterial virulence and human disease, and represents unexploited resources for the development of potential therapeutic interventions. However, identifying specific associations of a given RNA from the multitude of possible binding partners within the cell requires robust high-throughput systems for their rapid screening. Here, we present the first demonstration of functional-RNA arrays as a novel platform technology designed for the study of such interactions using immobilized, active RNAs. We have generated high-density RNA arrays by an innovative method involving surface-capture of in vitro transcribed RNAs. This approach has significant advantages over existing technologies, particularly in its versatility in regards to binding partner character. Indeed, proof-of-principle application of RNA arrays to both RNA-small molecule and RNA-RNA pairings is demonstrated, highlighting their potential as a platform technology for mapping RNA-based networks and for pharmaceutical screening. Furthermore, the simplicity of the method supports greater user-accessibility over currently available technologies. We anticipate that functional-RNA arrays will find broad utility in the expanding field of RNA characterization.

The Reactive Species Interactome: Evolutionary Emergence, Biological Significance, and Opportunities for Redox Metabolomics and Personalized Medicine.

PubMed

Cortese-Krott, Miriam M; Koning, Anne; Kuhnle, Gunter G C; Nagy, Peter; Bianco, Christopher L; Pasch, Andreas; Wink, David A; Fukuto, Jon M; Jackson, Alan A; van Goor, Harry; Olson, Kenneth R; Feelisch, Martin

2017-10-01

Oxidative stress is thought to account for aberrant redox homeostasis and contribute to aging and disease. However, more often than not, administration of antioxidants is ineffective, suggesting that our current understanding of the underlying regulatory processes is incomplete. Recent Advances: Similar to reactive oxygen species and reactive nitrogen species, reactive sulfur species are now emerging as important signaling molecules, targeting regulatory cysteine redox switches in proteins, affecting gene regulation, ion transport, intermediary metabolism, and mitochondrial function. To rationalize the complexity of chemical interactions of reactive species with themselves and their targets and help define their role in systemic metabolic control, we here introduce a novel integrative concept defined as the reactive species interactome (RSI). The RSI is a primeval multilevel redox regulatory system whose architecture, together with the physicochemical characteristics of its constituents, allows efficient sensing and rapid adaptation to environmental changes and various other stressors to enhance fitness and resilience at the local and whole-organism level. To better characterize the RSI-related processes that determine fluxes through specific pathways and enable integration, it is necessary to disentangle the chemical biology and activity of reactive species (including precursors and reaction products), their targets, communication systems, and effects on cellular, organ, and whole-organism bioenergetics using system-level/network analyses. Understanding the mechanisms through which the RSI operates will enable a better appreciation of the possibilities to modulate the entire biological system; moreover, unveiling molecular signatures that characterize specific environmental challenges or other forms of stress will provide new prevention/intervention opportunities for personalized medicine. Antioxid. Redox Signal. 00, 000-000.

Emory University: "LC-MS analysis of PRAS40 protein-protein interactions" | Office of Cancer Genomics

Cancer.gov

This study focuses on subcellular localization and interactome of nuclear PRAS40 in HeLa cells.  Read the abstract.  Experimental Approaches Read the detailed Experimental Approaches. If you cannot access the manuscript, or if you have additional questions, please email Andrei Ivanov.

Interactome analyses identify ties of PrP and its mammalian paralogs to oligomannosidic N-glycans and endoplasmic reticulum-derived chaperones.

PubMed

Watts, Joel C; Huo, Hairu; Bai, Yu; Ehsani, Sepehr; Jeon, Amy Hye Won; Won, Amy Hye; Shi, Tujin; Daude, Nathalie; Lau, Agnes; Young, Rebecca; Xu, Lei; Carlson, George A; Williams, David; Westaway, David; Schmitt-Ulms, Gerold

2009-10-01

The physiological environment which hosts the conformational conversion of the cellular prion protein (PrP(C)) to disease-associated isoforms has remained enigmatic. A quantitative investigation of the PrP(C) interactome was conducted in a cell culture model permissive to prion replication. To facilitate recognition of relevant interactors, the study was extended to Doppel (Prnd) and Shadoo (Sprn), two mammalian PrP(C) paralogs. Interestingly, this work not only established a similar physiological environment for the three prion protein family members in neuroblastoma cells, but also suggested direct interactions amongst them. Furthermore, multiple interactions between PrP(C) and the neural cell adhesion molecule, the laminin receptor precursor, Na/K ATPases and protein disulfide isomerases (PDI) were confirmed, thereby reconciling previously separate findings. Subsequent validation experiments established that interactions of PrP(C) with PDIs may extend beyond the endoplasmic reticulum and may play a hitherto unrecognized role in the accumulation of PrP(Sc). A simple hypothesis is presented which accounts for the majority of interactions observed in uninfected cells and suggests that PrP(C) organizes its molecular environment on account of its ability to bind to adhesion molecules harboring immunoglobulin-like domains, which in turn recognize oligomannose-bearing membrane proteins.

An in vivo imaging-based assay for detecting protein interactions over a wide range of binding affinities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fowlkes, Jason Davidson; Owens, Elizabeth T; Standaert, Robert F

2009-01-01

Identifying and characterizing protein interactions are fundamental steps towards understanding and modeling biological networks. Methods that detect protein interactions in intact cells rather than buffered solutions are likely more relevant to natural systems since molecular crowding events in the cytosol can influence the diffusion and reactivity of individual proteins. One in vivo, imaging-based method relies on the co-localization of two proteins of interest fused to DivIVA, a cell division protein from Bacillus subtilis, and green fluorescent protein (GFP). We have modified this imaging-based assay to facilitate rapid cloning by constructing new vectors encoding N- and C-terminal DivIVA or GFP molecularmore » tag fusions based on site-specific recombination technology. The sensitivity of the assay was defined using a well-characterized protein interaction system involving the eukaryotic nuclear import receptor subunit, Importin (Imp ) and variant nuclear localization signals (NLS) representing a range of binding affinities. These data demonstrate that the modified co-localization assay is sensitive enough to detect protein interactions with Kd values that span over four orders of magnitude (1nM to 15 M). Lastly, this assay was used to confirm numerous protein interactions identified from mass spectrometry-based analyses of affinity isolates as part of an interactome mapping project in Rhodopseudomonas palustris« less

A survey of the interactome of Kaposi's sarcoma-associated herpesvirus ORF45 revealed its binding to viral ORF33 and cellular USP7, resulting in stabilization of ORF33 that is required for production of progeny viruses.

PubMed

Gillen, Joseph; Li, Wenwei; Liang, Qiming; Avey, Denis; Wu, Jianjun; Wu, Fayi; Myoung, JinJong; Zhu, Fanxiu

2015-05-01

The ORF45 protein of Kaposi's sarcoma-associated herpesvirus (KSHV) is a gammaherpesvirus-specific immediate-early tegument protein. Our previous studies have revealed its crucial roles in both early and late stages of KSHV infection. In this study, we surveyed the interactome of ORF45 using a panel of monoclonal antibodies. In addition to the previously identified extracellular regulated kinase (ERK) and p90 ribosomal S6 kinase (RSK) proteins, we found several other copurified proteins, including prominent ones of ∼38 kDa and ∼130 kDa. Mass spectrometry revealed that the 38-kDa protein is viral ORF33 and the 130-kDa protein is cellular USP7 (ubiquitin-specific protease 7). We mapped the ORF33-binding domain to the highly conserved carboxyl-terminal 19 amino acids (aa) of ORF45 and the USP7-binding domain to the reported consensus motif in the central region of ORF45. Using immunofluorescence staining, we observed colocalization of ORF45 with ORF33 or USP7 both under transfected conditions and in KSHV-infected cells. Moreover, we noticed ORF45-dependent relocalization of a portion of ORF33/USP7 from the nucleus to the cytoplasm. We found that ORF45 caused an increase in ORF33 protein accumulation that was abolished if either the ORF33- or USP7-binding domain in ORF45 was deleted. Furthermore, deletion of the conserved carboxyl terminus of ORF45 in the KSHV genome drastically reduced the level of ORF33 protein in KSHV-infected cells and abolished production of progeny virions. Collectively, our results not only reveal new components of the ORF45 interactome, but also demonstrate that the interactions among these proteins are crucial for KSHV lytic replication. Kaposi's sarcoma-associated herpesvirus (KSHV) is the causative agent of several human cancers. KSHV ORF45 is a multifunctional protein that is required for KSHV lytic replication, but the exact mechanisms by which ORF45 performs its critical functions are unclear. Our previous studies revealed that all ORF45 protein in cells exists in high-molecular-weight complexes. We therefore sought to characterize the interactome of ORF45 to provide insights into its roles during lytic replication. Using a panel of monoclonal antibodies, we surveyed the ORF45 interactome in KSHV-infected cells. We identified two new binding partners of ORF45: the viral protein ORF33 and cellular ubiquitin-specific protease 7 (USP7). We further demonstrate that the interaction between ORF45 and ORF33 is crucial for the efficient production of KSHV viral particles, suggesting that the targeted interference with this interaction may represent a novel strategy to inhibit KSHV lytic replication. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

The NMR contribution to protein-protein networking in Fe-S protein maturation.

PubMed

Banci, Lucia; Camponeschi, Francesca; Ciofi-Baffoni, Simone; Piccioli, Mario

2018-03-22

Iron-sulfur proteins were among the first class of metalloproteins that were actively studied using NMR spectroscopy tailored to paramagnetic systems. The hyperfine shifts, their temperature dependencies and the relaxation rates of nuclei of cluster-bound residues are an efficient fingerprint of the nature and the oxidation state of the Fe-S cluster. NMR significantly contributed to the analysis of the magnetic coupling patterns and to the understanding of the electronic structure occurring in [2Fe-2S], [3Fe-4S] and [4Fe-4S] clusters bound to proteins. After the first NMR structure of a paramagnetic protein was obtained for the reduced E. halophila HiPIP I, many NMR structures were determined for several Fe-S proteins in different oxidation states. It was found that differences in chemical shifts, in patterns of unobserved residues, in internal mobility and in thermodynamic stability are suitable data to map subtle changes between the two different oxidation states of the protein. Recently, the interaction networks responsible for maturing human mitochondrial and cytosolic Fe-S proteins have been largely characterized by combining solution NMR standard experiments with those tailored to paramagnetic systems. We show here the contribution of solution NMR in providing a detailed molecular view of "Fe-S interactomics". This contribution was particularly effective when protein-protein interactions are weak and transient, and thus difficult to be characterized at high resolution with other methodologies.

Characterization of clinical signs in the human interactome.

PubMed

Chagoyen, Monica; Pazos, Florencio

2016-06-15

Many diseases are related by shared associated molecules and pathways, exhibiting comorbidities and common phenotypes, an indication of the continuous nature of the human pathological landscape. Although it is continuous, this landscape is always partitioned into discrete diseases when studied at the molecular level. Clinical signs are also important phenotypic descriptors that can reveal the molecular mechanisms that underlie pathological states, but have seldom been the subject of systemic research. Here, we quantify the modular nature of the clinical signs associated with genetic diseases in the human interactome. We found that clinical signs are reflected as modules at the molecular network level, to at least to the same extent as diseases. They can thus serve as a valid complementary partition of the human pathological landscape, with implications for etiology research, diagnosis and treatment. monica.chagoyen@cnb.csic.es Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The RNA-binding protein repertoire of embryonic stem cells.

PubMed

Kwon, S Chul; Yi, Hyerim; Eichelbaum, Katrin; Föhr, Sophia; Fischer, Bernd; You, Kwon Tae; Castello, Alfredo; Krijgsveld, Jeroen; Hentze, Matthias W; Kim, V Narry

2013-09-01

RNA-binding proteins (RBPs) have essential roles in RNA-mediated gene regulation, and yet annotation of RBPs is limited mainly to those with known RNA-binding domains. To systematically identify the RBPs of embryonic stem cells (ESCs), we here employ interactome capture, which combines UV cross-linking of RBP to RNA in living cells, oligo(dT) capture and MS. From mouse ESCs (mESCs), we have defined 555 proteins constituting the mESC mRNA interactome, including 283 proteins not previously annotated as RBPs. Of these, 68 new RBP candidates are highly expressed in ESCs compared to differentiated cells, implicating a role in stem-cell physiology. Two well-known E3 ubiquitin ligases, Trim25 (also called Efp) and Trim71 (also called Lin41), are validated as RBPs, revealing a potential link between RNA biology and protein-modification pathways. Our study confirms and expands the atlas of RBPs, providing a useful resource for the study of the RNA-RBP network in stem cells.

The Ties That Bind: Mapping the Dynamic Enhancer-Promoter Interactome.

PubMed

Spurrell, Cailyn H; Dickel, Diane E; Visel, Axel

2016-11-17

Coupling chromosome conformation capture to molecular enrichment for promoter-containing DNA fragments enables the systematic mapping of interactions between individual distal regulatory sequences and their target genes. In this Minireview, we describe recent progress in the application of this technique and related complementary approaches to gain insight into the lineage- and cell-type-specific dynamics of interactions between regulators and gene promoters. Copyright © 2016 Elsevier Inc. All rights reserved.

Emory University: "LC-MS analysis of PRAS40 protein-protein interactions" | Office of Cancer Genomics

Cancer.gov

This study focuses on subcellular localization and interactome of nuclear PRAS40 in HeLa cells.  Read the abstract.  Experimental Approaches Read the detailed Experimental Approaches. If you cannot access the manuscript, or if you have additional questions, please email Andrei Ivanov. Data

Use of The Yeast Two-Hybrid System to Identify Targets of Fungal Effectors

USDA-ARS?s Scientific Manuscript database

The yeast-two hybrid (Y2H) system is a binary method widely used to determine direct interactions between paired proteins. Although having certain limitations, this method has become one of the two main systemic tools (along with affinity purification/mass spectrometry) for interactome mapping in mo...

Toxoplasmosis and Polygenic Disease Susceptibility Genes: Extensive Toxoplasma gondii Host/Pathogen Interactome Enrichment in Nine Psychiatric or Neurological Disorders.

PubMed

Carter, C J

2013-01-01

Toxoplasma gondii is not only implicated in schizophrenia and related disorders, but also in Alzheimer's or Parkinson's disease, cancer, cardiac myopathies, and autoimmune disorders. During its life cycle, the pathogen interacts with ~3000 host genes or proteins. Susceptibility genes for multiple sclerosis, Alzheimer's disease, schizophrenia, bipolar disorder, depression, childhood obesity, Parkinson's disease, attention deficit hyperactivity disorder (P  from  8.01E - 05  (ADHD)  to  1.22E - 71) (multiple sclerosis), and autism (P = 0.013), but not anorexia or chronic fatigue are highly enriched in the human arm of this interactome and 18 (ADHD) to 33% (MS) of the susceptibility genes relate to it. The signalling pathways involved in the susceptibility gene/interactome overlaps are relatively specific and relevant to each disease suggesting a means whereby susceptibility genes could orient the attentions of a single pathogen towards disruption of the specific pathways that together contribute (positively or negatively) to the endophenotypes of different diseases. Conditional protein knockdown, orchestrated by T. gondii proteins or antibodies binding to those of the host (pathogen derived autoimmunity) and metabolite exchange, may contribute to this disruption. Susceptibility genes may thus be related to the causes and influencers of disease, rather than (and as well as) to the disease itself.

The nuclear DEK interactome supports multi-functionality.

PubMed

Smith, Eric A; Krumpelbeck, Eric F; Jegga, Anil G; Prell, Malte; Matrka, Marie M; Kappes, Ferdinand; Greis, Kenneth D; Ali, Abdullah M; Meetei, Amom R; Wells, Susanne I

2018-01-01

DEK is an oncoprotein that is overexpressed in many forms of cancer and participates in numerous cellular pathways. Of these different pathways, relevant interacting partners and functions of DEK are well described in regard to the regulation of chromatin structure, epigenetic marks, and transcription. Most of this understanding was derived by investigating DNA-binding and chromatin processing capabilities of the oncoprotein. To facilitate the generation of mechanism-driven hypotheses regarding DEK activities in underexplored areas, we have developed the first DEK interactome model using tandem-affinity purification and mass spectrometry. With this approach, we identify IMPDH2, DDX21, and RPL7a as novel DEK binding partners, hinting at new roles for the oncogene in de novo nucleotide biosynthesis and ribosome formation. Additionally, a hydroxyurea-specific interaction with replication protein A (RPA) was observed, suggesting that a DEK-RPA complex may form in response to DNA replication fork stalling. Taken together, these findings highlight diverse activities for DEK across cellular pathways and support a model wherein this molecule performs a plethora of functions. © 2017 Wiley Periodicals, Inc.

Polyubiquitin-Photoactivatable Crosslinking Reagents for Mapping Ubiquitin Interactome Identify Rpn1 as a Proteasome Ubiquitin-Associating Subunit.

PubMed

Chojnacki, Michal; Mansour, Wissam; Hameed, Dharjath S; Singh, Rajesh K; El Oualid, Farid; Rosenzweig, Rina; Nakasone, Mark A; Yu, Zanlin; Glaser, Fabian; Kay, Lewis E; Fushman, David; Ovaa, Huib; Glickman, Michael H

2017-04-20

Ubiquitin (Ub) signaling is a diverse group of processes controlled by covalent attachment of small protein Ub and polyUb chains to a range of cellular protein targets. The best documented Ub signaling pathway is the one that delivers polyUb proteins to the 26S proteasome for degradation. However, studies of molecular interactions involved in this process have been hampered by the transient and hydrophobic nature of these interactions and the lack of tools to study them. Here, we develop Ub-phototrap (Ub PT ), a synthetic Ub variant containing a photoactivatable crosslinking side chain. Enzymatic polymerization into chains of defined lengths and linkage types provided a set of reagents that led to identification of Rpn1 as a third proteasome ubiquitin-associating subunit that coordinates docking of substrate shuttles, unloading of substrates, and anchoring of polyUb conjugates. Our work demonstrates the value of Ub PT , and we expect that its future uses will help define and investigate the ubiquitin interactome. Copyright © 2017 Elsevier Ltd. All rights reserved.

Scaffold protein harmonin (USH1C) provides molecular links between Usher syndrome type 1 and type 2.

PubMed

Reiners, Jan; van Wijk, Erwin; Märker, Tina; Zimmermann, Ulrike; Jürgens, Karin; te Brinke, Heleen; Overlack, Nora; Roepman, Ronald; Knipper, Marlies; Kremer, Hannie; Wolfrum, Uwe

2005-12-15

Usher syndrome (USH) is the most frequent cause of combined deaf-blindness in man. USH is clinically and genetically heterogeneous with at least 11 chromosomal loci assigned to the three USH types (USH1A-G, USH2A-C, USH3A). Although the different USH types exhibit almost the same phenotype in human, the identified USH genes encode for proteins which belong to very different protein classes and families. We and others recently reported that the scaffold protein harmonin (USH1C-gene product) integrates all identified USH1 molecules in a USH1-protein network. Here, we investigated the relationship between the USH2 molecules and this USH1-protein network. We show a molecular interaction between the scaffold protein harmonin (USH1C) and the USH2A protein, VLGR1 (USH2C) and the candidate for USH2B, NBC3. We pinpoint these interactions to interactions between the PDZ1 domain of harmonin and the PDZ-binding motifs at the C-termini of the USH2 proteins and NBC3. We demonstrate that USH2A, VLGR1 and NBC3 are co-expressed with the USH1-protein harmonin in the synaptic terminals of both retinal photoreceptors and inner ear hair cells. In hair cells, these USH proteins are also localized in the signal uptaking stereocilia. Our data indicate that the USH2 proteins and NBC3 are further partners in the supramolecular USH-protein network in the retina and inner ear which shed new light on the function of USH2 proteins and the entire USH-protein network. These findings provide first evidence for a molecular linkage between the pathophysiology in USH1 and USH2. The organization of USH molecules in a mutual 'interactome' related to the disease can explain the common phenotype in USH.

Transcription Factor NRF2 as a Therapeutic Target for Chronic Diseases: A Systems Medicine Approach.

PubMed

Cuadrado, Antonio; Manda, Gina; Hassan, Ahmed; Alcaraz, María José; Barbas, Coral; Daiber, Andreas; Ghezzi, Pietro; León, Rafael; López, Manuela G; Oliva, Baldo; Pajares, Marta; Rojo, Ana I; Robledinos-Antón, Natalia; Valverde, Angela M; Guney, Emre; Schmidt, Harald H H W

2018-04-01

Systems medicine has a mechanism-based rather than a symptom- or organ-based approach to disease and identifies therapeutic targets in a nonhypothesis-driven manner. In this work, we apply this to transcription factor nuclear factor (erythroid-derived 2)-like 2 (NRF2) by cross-validating its position in a protein-protein interaction network (the NRF2 interactome) functionally linked to cytoprotection in low-grade stress, chronic inflammation, metabolic alterations, and reactive oxygen species formation. Multiscale network analysis of these molecular profiles suggests alterations of NRF2 expression and activity as a common mechanism in a subnetwork of diseases (the NRF2 diseasome). This network joins apparently heterogeneous phenotypes such as autoimmune, respiratory, digestive, cardiovascular, metabolic, and neurodegenerative diseases, along with cancer. Importantly, this approach matches and confirms in silico several applications for NRF2-modulating drugs validated in vivo at different phases of clinical development. Pharmacologically, their profile is as diverse as electrophilic dimethyl fumarate, synthetic triterpenoids like bardoxolone methyl and sulforaphane, protein-protein or DNA-protein interaction inhibitors, and even registered drugs such as metformin and statins, which activate NRF2 and may be repurposed for indications within the NRF2 cluster of disease phenotypes. Thus, NRF2 represents one of the first targets fully embraced by classic and systems medicine approaches to facilitate both drug development and drug repurposing by focusing on a set of disease phenotypes that appear to be mechanistically linked. The resulting NRF2 drugome may therefore rapidly advance several surprising clinical options for this subset of chronic diseases. Copyright © 2018 by The Author(s).

Completing sparse and disconnected protein-protein network by deep learning.

PubMed

Huang, Lei; Liao, Li; Wu, Cathy H

2018-03-22

Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge. In this work, we developed a novel PPI prediction method based on deep learning neural network and regularized Laplacian kernel. We use a neural network with an autoencoder-like architecture to implicitly simulate the evolutionary processes of a PPI network. Neurons of the output layer correspond to proteins and are labeled with values (1 for interaction and 0 for otherwise) from the adjacency matrix of a sparse disconnected training PPI network. Unlike autoencoder, neurons at the input layer are given all zero input, reflecting an assumption of no a priori knowledge about PPIs, and hidden layers of smaller sizes mimic ancient interactome at different times during evolution. After the training step, an evolved PPI network whose rows are outputs of the neural network can be obtained. We then predict PPIs by applying the regularized Laplacian kernel to the transition matrix that is built upon the evolved PPI network. The results from cross-validation experiments show that the PPI prediction accuracies for yeast data and human data measured as AUC are increased by up to 8.4 and 14.9% respectively, as compared to the baseline. Moreover, the evolved PPI network can also help us leverage complementary information from the disconnected training network and multiple heterogeneous data sources. Tested by the yeast data with six heterogeneous feature kernels, the results show our method can further improve the prediction performance by up to 2%, which is very close to an upper bound that is obtained by an Approximate Bayesian Computation based sampling method. The proposed evolution deep neural network, coupled with regularized Laplacian kernel, is an effective tool in completing sparse and disconnected PPI networks and in facilitating integration of heterogeneous data sources.

Detection of candidate biomarkers of prostate cancer progression in serum: a depletion-free 3D LC/MS quantitative proteomics pilot study.

PubMed

Larkin, S E T; Johnston, H E; Jackson, T R; Jamieson, D G; Roumeliotis, T I; Mockridge, C I; Michael, A; Manousopoulou, A; Papachristou, E K; Brown, M D; Clarke, N W; Pandha, H; Aukim-Hastie, C L; Cragg, M S; Garbis, S D; Townsend, P A

2016-10-25

Prostate cancer (PCa) is the most common male cancer in the United Kingdom and we aimed to identify clinically relevant biomarkers corresponding to stage progression of the disease. We used enhanced proteomic profiling of PCa progression using iTRAQ 3D LC mass spectrometry on high-quality serum samples to identify biomarkers of PCa. We identified >1000 proteins. Following specific inclusion/exclusion criteria we targeted seven proteins of which two were validated by ELISA and six potentially interacted forming an 'interactome' with only a single protein linking each marker. This network also includes accepted cancer markers, such as TNF, STAT3, NF-κB and IL6. Our linked and interrelated biomarker network highlights the potential utility of six of our seven markers as a panel for diagnosing PCa and, critically, in determining the stage of the disease. Our validation analysis of the MS-identified proteins found that SAA alongside KLK3 may improve categorisation of PCa than by KLK3 alone, and that TSR1, although not significant in this model, might also be a clinically relevant biomarker.

MaxiK channel interactome reveals its interaction with GABA transporter 3 and heat shock protein 60 in the mammalian brain.

PubMed

Singh, H; Li, M; Hall, L; Chen, S; Sukur, S; Lu, R; Caputo, A; Meredith, A L; Stefani, E; Toro, L

2016-03-11

Large conductance voltage and calcium-activated potassium (MaxiK) channels are activated by membrane depolarization and elevated cytosolic Ca(2+). In the brain, they localize to neurons and astrocytes, where they play roles such as resetting the membrane potential during an action potential, neurotransmitter release, and neurovascular coupling. MaxiK channels are known to associate with several modulatory proteins and accessory subunits, and each of these interactions can have distinct physiological consequences. To uncover new players in MaxiK channel brain physiology, we applied a directed proteomic approach and obtained MaxiK channel pore-forming α subunit brain interactome using specific antibodies. Controls included immunoprecipitations with rabbit immunoglobulin G (IgG) and with anti-MaxiK antibodies in wild type and MaxiK channel knockout mice (Kcnma1(-/-)), respectively. We have found known and unreported interactive partners that localize to the plasma membrane, extracellular space, cytosol and intracellular organelles including mitochondria, nucleus, endoplasmic reticulum and Golgi apparatus. Localization of MaxiK channel to mitochondria was further confirmed using purified brain mitochondria colabeled with MitoTracker. Independent proof of MaxiK channel interaction with previously unidentified partners is given for GABA transporter 3 (GAT3) and heat shock protein 60 (HSP60). In human embryonic kidney 293 cells containing SV40 T-antigen (HEK293T) cells, both GAT3 and HSP60 coimmunoprecipitated and colocalized with MaxiK channel; colabeling was observed mainly at the cell periphery with GAT3 and intracellularly with HSP60 with protein proximity indices of ∼ 0.6 and ∼ 0.4, respectively. In rat primary hippocampal neurons, colocalization index was identical for GAT3 (∼ 0.6) and slightly higher for HSP60 (∼ 0.5) association with MaxiK channel. The results of this study provide a complete interactome of MaxiK channel the mouse brain, further establish the localization of MaxiK channel in the mouse brain mitochondria and demonstrate the interaction of MaxiK channel with GAT3 and HSP60 in neurons. The interaction of MaxiK channel with GAT3 opens the possibility of a role of MaxiK channel in GABA homeostasis and signaling. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Viral Perturbations of Host Networks Reflect Disease Etiology

PubMed Central

Dricot, Amélie; Padi, Megha; Byrdsong, Danielle; Franchi, Rachel; Lee, Deok-Sun; Rozenblatt-Rosen, Orit; Mar, Jessica C.; Calderwood, Michael A.; Baldwin, Amy; Zhao, Bo; Santhanam, Balaji; Braun, Pascal; Simonis, Nicolas; Huh, Kyung-Won; Hellner, Karin; Grace, Miranda; Chen, Alyce; Rubio, Renee; Marto, Jarrod A.; Christakis, Nicholas A.; Kieff, Elliott; Roth, Frederick P.; Roecklein-Canfield, Jennifer; DeCaprio, James A.; Cusick, Michael E.; Quackenbush, John; Hill, David E.; Münger, Karl; Vidal, Marc; Barabási, Albert-László

2012-01-01

Many human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia. PMID:22761553

Chemokine interactome mapping enables tailored intervention in acute and chronic inflammation.

PubMed

von Hundelshausen, Philipp; Agten, Stijn M; Eckardt, Veit; Blanchet, Xavier; Schmitt, Martin M; Ippel, Hans; Neideck, Carlos; Bidzhekov, Kiril; Leberzammer, Julian; Wichapong, Kanin; Faussner, Alexander; Drechsler, Maik; Grommes, Jochen; van Geffen, Johanna P; Li, He; Ortega-Gomez, Almudena; Megens, Remco T A; Naumann, Ronald; Dijkgraaf, Ingrid; Nicolaes, Gerry A F; Döring, Yvonne; Soehnlein, Oliver; Lutgens, Esther; Heemskerk, Johan W M; Koenen, Rory R; Mayo, Kevin H; Hackeng, Tilman M; Weber, Christian

2017-04-05

Chemokines orchestrate leukocyte trafficking and function in health and disease. Heterophilic interactions between chemokines in a given microenvironment may amplify, inhibit, or modulate their activity; however, a systematic evaluation of the chemokine interactome has not been performed. We used immunoligand blotting and surface plasmon resonance to obtain a comprehensive map of chemokine-chemokine interactions and to confirm their specificity. Structure-function analyses revealed that chemokine activity can be enhanced by CC-type heterodimers but inhibited by CXC-type heterodimers. Functional synergism was achieved through receptor heteromerization induced by CCL5-CCL17 or receptor retention at the cell surface via auxiliary proteoglycan binding of CCL5-CXCL4. In contrast, inhibitory activity relied on conformational changes (in CXCL12), affecting receptor signaling. Obligate CC-type heterodimers showed high efficacy and potency and drove acute lung injury and atherosclerosis, processes abrogated by specific CCL5-derived peptide inhibitors or knock-in of an interaction-deficient CXCL4 variant. Atheroprotective effects of CCL17 deficiency were phenocopied by a CCL5-derived peptide disrupting CCL5-CCL17 heterodimers, whereas a CCL5 α-helix peptide mimicked inhibitory effects on CXCL12-driven platelet aggregation. Thus, formation of specific chemokine heterodimers differentially dictates functional activity and can be exploited for therapeutic targeting. Copyright © 2017, American Association for the Advancement of Science.

Proteomic analyses of signalling complexes associated with receptor tyrosine kinase identify novel members of fibroblast growth factor receptor 3 interactome.

PubMed

Balek, Lukas; Nemec, Pavel; Konik, Peter; Kunova Bosakova, Michaela; Varecha, Miroslav; Gudernova, Iva; Medalova, Jirina; Krakow, Deborah; Krejci, Pavel

2018-01-01

Receptor tyrosine kinases (RTKs) form multiprotein complexes that initiate and propagate intracellular signals and determine the RTK-specific signalling patterns. Unravelling the full complexity of protein interactions within the RTK-associated complexes is essential for understanding of RTK functions, yet it remains an understudied area of cell biology. We describe a comprehensive approach to characterize RTK interactome. A single tag immunoprecipitation and phosphotyrosine protein isolation followed by mass-spectrometry was used to identify proteins interacting with fibroblast growth factor receptor 3 (FGFR3). A total of 32 experiments were carried out in two different cell types and identified 66 proteins out of which only 20 (30.3%) proteins were already known FGFR interactors. Using co-immunoprecipitations, we validated FGFR3 interaction with adapter protein STAM1, transcriptional regulator SHOX2, translation elongation factor eEF1A1, serine/threonine kinases ICK, MAK and CCRK, and inositol phosphatase SHIP2. We show that unappreciated signalling mediators exist for well-studied RTKs, such as FGFR3, and may be identified via proteomic approaches described here. These approaches are easily adaptable to other RTKs, enabling identification of novel signalling mediators for majority of the known human RTKs. Copyright © 2017 Elsevier Inc. All rights reserved.

The Reactive Species Interactome: Evolutionary Emergence, Biological Significance, and Opportunities for Redox Metabolomics and Personalized Medicine

PubMed Central

Koning, Anne; Kuhnle, Gunter G.C.; Nagy, Peter; Bianco, Christopher L.; Pasch, Andreas; Wink, David A.; Fukuto, Jon M.; Jackson, Alan A.; van Goor, Harry; Olson, Kenneth R.

2017-01-01

Abstract Significance: Oxidative stress is thought to account for aberrant redox homeostasis and contribute to aging and disease. However, more often than not, administration of antioxidants is ineffective, suggesting that our current understanding of the underlying regulatory processes is incomplete. Recent Advances: Similar to reactive oxygen species and reactive nitrogen species, reactive sulfur species are now emerging as important signaling molecules, targeting regulatory cysteine redox switches in proteins, affecting gene regulation, ion transport, intermediary metabolism, and mitochondrial function. To rationalize the complexity of chemical interactions of reactive species with themselves and their targets and help define their role in systemic metabolic control, we here introduce a novel integrative concept defined as the reactive species interactome (RSI). The RSI is a primeval multilevel redox regulatory system whose architecture, together with the physicochemical characteristics of its constituents, allows efficient sensing and rapid adaptation to environmental changes and various other stressors to enhance fitness and resilience at the local and whole-organism level. Critical Issues: To better characterize the RSI-related processes that determine fluxes through specific pathways and enable integration, it is necessary to disentangle the chemical biology and activity of reactive species (including precursors and reaction products), their targets, communication systems, and effects on cellular, organ, and whole-organism bioenergetics using system-level/network analyses. Future Directions: Understanding the mechanisms through which the RSI operates will enable a better appreciation of the possibilities to modulate the entire biological system; moreover, unveiling molecular signatures that characterize specific environmental challenges or other forms of stress will provide new prevention/intervention opportunities for personalized medicine. Antioxid. Redox Signal. 27, 684–712. PMID:28398072

Co-evolutionary Analysis of Domains in Interacting Proteins Reveals Insights into Domain–Domain Interactions Mediating Protein–Protein Interactions

PubMed Central

Jothi, Raja; Cherukuri, Praveen F.; Tasneem, Asba; Przytycka, Teresa M.

2006-01-01

Recent advances in functional genomics have helped generate large-scale high-throughput protein interaction data. Such networks, though extremely valuable towards molecular level understanding of cells, do not provide any direct information about the regions (domains) in the proteins that mediate the interaction. Here, we performed co-evolutionary analysis of domains in interacting proteins in order to understand the degree of co-evolution of interacting and non-interacting domains. Using a combination of sequence and structural analysis, we analyzed protein–protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the noninteracting domain pairs. Motivated by this finding, we developed a computational method to test the generality of the observed trend, and to predict large-scale domain–domain interactions. Given a protein–protein interaction, the proposed method predicts the domain pair(s) that is most likely to mediate the protein interaction. We applied this method on the yeast interactome to predict domain–domain interactions, and used known domain–domain interactions found in PDB crystal structures to validate our predictions. Our results show that the prediction accuracy of the proposed method is statistically significant. Comparison of our prediction results with those from two other methods reveals that only a fraction of predictions are shared by all the three methods, indicating that the proposed method can detect known interactions missed by other methods. We believe that the proposed method can be used with other methods to help identify previously unrecognized domain–domain interactions on a genome scale, and could potentially help reduce the search space for identifying interaction sites. PMID:16949097

Discovery and targeted LC-MS/MS of purified polerovirus reveals differences in the virus-host interactome associated with altered aphid transmission

USDA-ARS?s Scientific Manuscript database

The transmission of viruses in the Luteoviridae, such as Cereal yellow dwarf virus (CYDV), requires a series of precisely orchestrated interactions between virus proteins, plant proteins, and aphid proteins. These viruses are retained in the phloem for aphid acquisition and are transmitted by aphids...

Escherichia coli O157:H7 and rectoanal junction cell interactome

USDA-ARS?s Scientific Manuscript database

Introduction. Cattle are the primary E. coli O157 (O157) reservoir and principal source of human infection. The anatomical site of O157 persistence is the bovine recto-anal (RAJ) junction; hence, an in-depth understanding of O157-RAJ interactions will help develop novel modalities to limit O157 in c...

Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

PubMed Central

Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Tabatabaei, Seyyed Mohammad; Namaki, Saeed

2016-01-01

Background The involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein–protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease. Methods Gene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MS vs. control) and PBMCs (relapse vs. remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression. Results The networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapse vs. remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MS vs. control) and CDC37, MAP3K3, MYC genes in PBMCs (relapse vs. remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways. Discussion This study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets. PMID:28028462

Systematic detection of positive selection in the human-pathogen interactome and lasting effects on infectious disease susceptibility.

PubMed

Corona, Erik; Wang, Liuyang; Ko, Dennis; Patel, Chirag J

2018-01-01

Infectious disease has shaped the natural genetic diversity of humans throughout the world. A new approach to capture positive selection driven by pathogens would provide information regarding pathogen exposure in distinct human populations and the constantly evolving arms race between host and disease-causing agents. We created a human pathogen interaction database and used the integrated haplotype score (iHS) to detect recent positive selection in genes that interact with proteins from 26 different pathogens. We used the Human Genome Diversity Panel to identify specific populations harboring pathogen-interacting genes that have undergone positive selection. We found that human genes that interact with 9 pathogen species show evidence of recent positive selection. These pathogens are Yersenia pestis, human immunodeficiency virus (HIV) 1, Zaire ebolavirus, Francisella tularensis, dengue virus, human respiratory syncytial virus, measles virus, Rubella virus, and Bacillus anthracis. For HIV-1, GWAS demonstrate that some naturally selected variants in the host-pathogen protein interaction networks continue to have functional consequences for susceptibility to these pathogens. We show that selected human genes were enriched for HIV susceptibility variants (identified through GWAS), providing further support for the hypothesis that ancient humans were exposed to lentivirus pandemics. Human genes in the Italian, Miao, and Biaka Pygmy populations that interact with Y. pestis show significant signs of selection. These results reveal some of the genetic footprints created by pathogens in the human genome that may have left lasting marks on susceptibility to infectious disease.

Integration of Proteomic, Transcriptional, and Interactome Data Reveals Hidden Signaling Components

PubMed Central

Huang, Shao-shan Carol; Fraenkel, Ernest

2009-01-01

Cellular signaling and regulatory networks underlie fundamental biological processes such as growth, differentiation, and response to the environment. Although there are now various high-throughput methods for studying these processes, knowledge of them remains fragmentary. Typically, the vast majority of hits identified by transcriptional, proteomic, and genetic assays lie outside of the expected pathways. These unexpected components of the cellular response are often the most interesting, because they can provide new insights into biological processes and potentially reveal new therapeutic approaches. However, they are also the most difficult to interpret. We present a technique, based on the Steiner tree problem, that uses previously reported protein-protein and protein-DNA interactions to determine how these hits are organized into functionally coherent pathways, revealing many components of the cellular response that are not readily apparent in the original data. Applied simultaneously to phosphoproteomic and transcriptional data for the yeast pheromone response, it identifies changes in diverse cellular processes that extend far beyond the expected pathways. PMID:19638617

Proteomic analysis of sweet algerian apricot kernels (Prunus armeniaca L.) by combinatorial peptide ligand libraries and LC-MS/MS.

PubMed

Ghorab, Hamida; Lammi, Carmen; Arnoldi, Anna; Kabouche, Zahia; Aiello, Gilda

2018-01-15

An investigation on the proteome of the sweet kernel of apricot, based on equalisation with combinatorial peptide ligand libraries (CPLLs), SDS-PAGE, nLC-ESI-MS/MS, and database search, permitted identifying 175 proteins. Gene ontology analysis indicated that their main molecular functions are in nucleotide binding (20.9%), hydrolase activities (10.6%), kinase activities (7%), and catalytic activity (5.6%). A protein-protein association network analysis using STRING software permitted to build an interactomic map of all detected proteins, characterised by 34 interactions. In order to forecast the potential health benefits deriving from the consumption of these proteins, the two most abundant, i.e. Prunin 1 and 2, were enzymatically digested in silico predicting 10 and 14 peptides, respectively. Searching their sequences in the database BIOPEP, it was possible to suggest a variety of bioactivities, including dipeptidyl peptidase-IV (DPP-IV) and angiotensin converting enzyme I (ACE) inhibition, glucose uptake stimulation and antioxidant properties. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computer applications making rapid advances in high throughput microbial proteomics (HTMP).

PubMed

Anandkumar, Balakrishna; Haga, Steve W; Wu, Hui-Fen

2014-02-01

The last few decades have seen the rise of widely-available proteomics tools. From new data acquisition devices, such as MALDI-MS and 2DE to new database searching softwares, these new products have paved the way for high throughput microbial proteomics (HTMP). These tools are enabling researchers to gain new insights into microbial metabolism, and are opening up new areas of study, such as protein-protein interactions (interactomics) discovery. Computer software is a key part of these emerging fields. This current review considers: 1) software tools for identifying the proteome, such as MASCOT or PDQuest, 2) online databases of proteomes, such as SWISS-PROT, Proteome Web, or the Proteomics Facility of the Pathogen Functional Genomics Resource Center, and 3) software tools for applying proteomic data, such as PSI-BLAST or VESPA. These tools allow for research in network biology, protein identification, functional annotation, target identification/validation, protein expression, protein structural analysis, metabolic pathway engineering and drug discovery.

The expanding universe of ribonucleoproteins: of novel RNA-binding proteins and unconventional interactions.

PubMed

Beckmann, Benedikt M; Castello, Alfredo; Medenbach, Jan

2016-06-01

Post-transcriptional regulation of gene expression plays a critical role in almost all cellular processes. Regulation occurs mostly by RNA-binding proteins (RBPs) that recognise RNA elements and form ribonucleoproteins (RNPs) to control RNA metabolism from synthesis to decay. Recently, the repertoire of RBPs was significantly expanded owing to methodological advances such as RNA interactome capture. The newly identified RNA binders are involved in diverse biological processes and belong to a broad spectrum of protein families, many of them exhibiting enzymatic activities. This suggests the existence of an extensive crosstalk between RNA biology and other, in principle unrelated, cell functions such as intermediary metabolism. Unexpectedly, hundreds of new RBPs do not contain identifiable RNA-binding domains (RBDs), raising the question of how they interact with RNA. Despite the many functions that have been attributed to RNA, our understanding of RNPs is still mostly governed by a rather protein-centric view, leading to the idea that proteins have evolved to bind to and regulate RNA and not vice versa. However, RNPs formed by an RNA-driven interaction mechanism (RNA-determined RNPs) are abundant and offer an alternative explanation for the surprising lack of classical RBDs in many RNA-interacting proteins. Moreover, RNAs can act as scaffolds to orchestrate and organise protein networks and directly control their activity, suggesting that nucleic acids might play an important regulatory role in many cellular processes, including metabolism.

Systems Biomedicine of Rabies Delineates the Affected Signaling Pathways.

PubMed

Azimzadeh Jamalkandi, Sadegh; Mozhgani, Sayed-Hamidreza; Gholami Pourbadie, Hamid; Mirzaie, Mehdi; Noorbakhsh, Farshid; Vaziri, Behrouz; Gholami, Alireza; Ansari-Pour, Naser; Jafari, Mohieddin

2016-01-01

The prototypical neurotropic virus, rabies, is a member of the Rhabdoviridae family that causes lethal encephalomyelitis. Although there have been a plethora of studies investigating the etiological mechanism of the rabies virus and many precautionary methods have been implemented to avert the disease outbreak over the last century, the disease has surprisingly no definite remedy at its late stages. The psychological symptoms and the underlying etiology, as well as the rare survival rate from rabies encephalitis, has still remained a mystery. We, therefore, undertook a systems biomedicine approach to identify the network of gene products implicated in rabies. This was done by meta-analyzing whole-transcriptome microarray datasets of the CNS infected by strain CVS-11, and integrating them with interactome data using computational and statistical methods. We first determined the differentially expressed genes (DEGs) in each study and horizontally integrated the results at the mRNA and microRNA levels separately. A total of 61 seed genes involved in signal propagation system were obtained by means of unifying mRNA and microRNA detected integrated DEGs. We then reconstructed a refined protein-protein interaction network (PPIN) of infected cells to elucidate the rabies-implicated signal transduction network (RISN). To validate our findings, we confirmed differential expression of randomly selected genes in the network using Real-time PCR. In conclusion, the identification of seed genes and their network neighborhood within the refined PPIN can be useful for demonstrating signaling pathways including interferon circumvent, toward proliferation and survival, and neuropathological clue, explaining the intricate underlying molecular neuropathology of rabies infection and thus rendered a molecular framework for predicting potential drug targets.

Comprehensively Characterizing the Thioredoxin Interactome In Vivo Highlights the Central Role Played by This Ubiquitous Oxidoreductase in Redox Control*

PubMed Central

Arts, Isabelle S.; Vertommen, Didier; Baldin, Francesca; Laloux, Géraldine; Collet, Jean-François

2016-01-01

Thioredoxin (Trx) is a ubiquitous oxidoreductase maintaining protein-bound cysteine residues in the reduced thiol state. Here, we combined a well-established method to trap Trx substrates with the power of bacterial genetics to comprehensively characterize the in vivo Trx redox interactome in the model bacterium Escherichia coli. Using strains engineered to optimize trapping, we report the identification of a total 268 Trx substrates, including 201 that had never been reported to depend on Trx for reduction. The newly identified Trx substrates are involved in a variety of cellular processes, ranging from energy metabolism to amino acid synthesis and transcription. The interaction between Trx and two of its newly identified substrates, a protein required for the import of most carbohydrates, PtsI, and the bacterial actin homolog MreB was studied in detail. We provide direct evidence that PtsI and MreB contain cysteine residues that are susceptible to oxidation and that participate in the formation of an intermolecular disulfide with Trx. By considerably expanding the number of Trx targets, our work highlights the role played by this major oxidoreductase in a variety of cellular processes. Moreover, as the dependence on Trx for reduction is often conserved across species, it also provides insightful information on the interactome of Trx in organisms other than E. coli. PMID:27081212

Toxoplasmosis and Polygenic Disease Susceptibility Genes: Extensive Toxoplasma gondii Host/Pathogen Interactome Enrichment in Nine Psychiatric or Neurological Disorders

PubMed Central

Carter, C. J.

2013-01-01

Toxoplasma gondii is not only implicated in schizophrenia and related disorders, but also in Alzheimer's or Parkinson's disease, cancer, cardiac myopathies, and autoimmune disorders. During its life cycle, the pathogen interacts with ~3000 host genes or proteins. Susceptibility genes for multiple sclerosis, Alzheimer's disease, schizophrenia, bipolar disorder, depression, childhood obesity, Parkinson's disease, attention deficit hyperactivity disorder (P  from  8.01E − 05  (ADHD)  to  1.22E − 71) (multiple sclerosis), and autism (P = 0.013), but not anorexia or chronic fatigue are highly enriched in the human arm of this interactome and 18 (ADHD) to 33% (MS) of the susceptibility genes relate to it. The signalling pathways involved in the susceptibility gene/interactome overlaps are relatively specific and relevant to each disease suggesting a means whereby susceptibility genes could orient the attentions of a single pathogen towards disruption of the specific pathways that together contribute (positively or negatively) to the endophenotypes of different diseases. Conditional protein knockdown, orchestrated by T. gondii proteins or antibodies binding to those of the host (pathogen derived autoimmunity) and metabolite exchange, may contribute to this disruption. Susceptibility genes may thus be related to the causes and influencers of disease, rather than (and as well as) to the disease itself. PMID:23533776

Challenges in structural approaches to cell modeling

PubMed Central

Im, Wonpil; Liang, Jie; Olson, Arthur; Zhou, Huan-Xiang; Vajda, Sandor; Vakser, Ilya A.

2016-01-01

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field. PMID:27255863

Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.

PubMed

Hu, Yan-Shi; Xin, Juncai; Hu, Ying; Zhang, Lei; Wang, Ju

2017-04-27

Our understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease. In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm. We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified. By means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.

Network-based function prediction and interactomics: the case for metabolic enzymes.

PubMed

Janga, S C; Díaz-Mejía, J Javier; Moreno-Hagelsieb, G

2011-01-01

As sequencing technologies increase in power, determining the functions of unknown proteins encoded by the DNA sequences so produced becomes a major challenge. Functional annotation is commonly done on the basis of amino-acid sequence similarity alone. Long after sequence similarity becomes undetectable by pair-wise comparison, profile-based identification of homologs can often succeed due to the conservation of position-specific patterns, important for a protein's three dimensional folding and function. Nevertheless, prediction of protein function from homology-driven approaches is not without problems. Homologous proteins might evolve different functions and the power of homology detection has already started to reach its maximum. Computational methods for inferring protein function, which exploit the context of a protein in cellular networks, have come to be built on top of homology-based approaches. These network-based functional inference techniques provide both a first hand hint into a proteins' functional role and offer complementary insights to traditional methods for understanding the function of uncharacterized proteins. Most recent network-based approaches aim to integrate diverse kinds of functional interactions to boost both coverage and confidence level. These techniques not only promise to solve the moonlighting aspect of proteins by annotating proteins with multiple functions, but also increase our understanding on the interplay between different functional classes in a cell. In this article we review the state of the art in network-based function prediction and describe some of the underlying difficulties and successes. Given the volume of high-throughput data that is being reported the time is ripe to employ these network-based approaches, which can be used to unravel the functions of the uncharacterized proteins accumulating in the genomic databases. © 2010 Elsevier Inc. All rights reserved.

SYSTEMS BIOLOGY ANALYSES OF GENE EXPRESSION AND GENOME WIDE ASSOCIATION STUDY DATA IN OBSTRUCTIVE SLEEP APNEA

PubMed Central

LIU, YU; PATEL, SANJAY; NIBBE, ROD; MAXWELL, SEAN; CHOWDHURY, SALIM A.; KOYUTURK, MEHMET; ZHU, XIAOFENG; LARKIN, EMMA K.; BUXBAUM, SARAH G; PUNJABI, NARESH M.; GHARIB, SINA A.; REDLINE, SUSAN; CHANCE, MARK R.

2015-01-01

The precise molecular etiology of obstructive sleep apnea (OSA) is unknown; however recent research indicates that several interconnected aberrant pathways and molecular abnormalities are contributors to OSA. Identifying the genes and pathways associated with OSA can help to expand our understanding of the risk factors for the disease as well as provide new avenues for potential treatment. Towards these goals, we have integrated relevant high dimensional data from various sources, such as genome-wide expression data (microarray), protein-protein interaction (PPI) data and results from genome-wide association studies (GWAS) in order to define sub-network elements that connect some of the known pathways related to the disease as well as define novel regulatory modules related to OSA. Two distinct approaches are applied to identify sub-networks significantly associated with OSA. In the first case we used a biased approach based on sixty genes/proteins with known associations with sleep disorders and/or metabolic disease to seed a search using commercial software to discover networks associated with disease followed by information theoretic (mutual information) scoring of the sub-networks. In the second case we used an unbiased approach and generated an interactome constructed from publicly available gene expression profiles and PPI databases, followed by scoring of the network with p-values from GWAS data derived from OSA patients to uncover sub-networks significant for the disease phenotype. A comparison of the approaches reveals a number of proteins that have been previously known to be associated with OSA or sleep. In addition, our results indicate a novel association of Phosphoinositide 3-kinase, the STAT family of proteins and its related pathways with OSA. PMID:21121029

Seeding for pervasively overlapping communities

NASA Astrophysics Data System (ADS)

Lee, Conrad; Reid, Fergal; McDaid, Aaron; Hurley, Neil

2011-06-01

In some social and biological networks, the majority of nodes belong to multiple communities. It has recently been shown that a number of the algorithms specifically designed to detect overlapping communities do not perform well in such highly overlapping settings. Here, we consider one class of these algorithms, those which optimize a local fitness measure, typically by using a greedy heuristic to expand a seed into a community. We perform synthetic benchmarks which indicate that an appropriate seeding strategy becomes more important as the extent of community overlap increases. We find that distinct cliques provide the best seeds. We find further support for this seeding strategy with benchmarks on a Facebook network and the yeast interactome.

Identification and characterization of moonlighting long non-coding RNAs based on RNA and protein interactome.

PubMed

Cheng, Lixin; Leung, Kwong-Sak

2018-05-16

Moonlighting proteins are a class of proteins having multiple distinct functions, which play essential roles in a variety of cellular and enzymatic functioning systems. Although there have long been calls for computational algorithms for the identification of moonlighting proteins, research on approaches to identify moonlighting long non-coding RNAs (lncRNAs) has never been undertaken. Here, we introduce a novel methodology, MoonFinder, for the identification of moonlighting lncRNAs. MoonFinder is a statistical algorithm identifying moonlighting lncRNAs without a priori knowledge through the integration of protein interactome, RNA-protein interactions, and functional annotation of proteins. We identify 155 moonlighting lncRNA candidates and uncover that they are a distinct class of lncRNAs characterized by specific sequence and cellular localization features. The non-coding genes that transcript moonlighting lncRNAs tend to have shorter but more exons and the moonlighting lncRNAs have a variable localization pattern with a high chance of residing in the cytoplasmic compartment in comparison to the other lncRNAs. Moreover, moonlighting lncRNAs and moonlighting proteins are rather mutually exclusive in terms of both their direct interactions and interacting partners. Our results also shed light on how the moonlighting candidates and their interacting proteins implicated in the formation and development of cancers and other diseases. The code implementing MoonFinder is supplied as an R package in the supplementary material. lxcheng@cse.cuhk.edu.hk or ksleung@cse.cuhk.edu.hk. Supplementary data are available at Bioinformatics online.

MEGADOCK: An All-to-All Protein-Protein Interaction Prediction System Using Tertiary Structure Data

PubMed Central

Ohue, Masahito; Matsuzaki, Yuri; Uchikoga, Nobuyuki; Ishida, Takashi; Akiyama, Yutaka

2014-01-01

The elucidation of protein-protein interaction (PPI) networks is important for understanding cellular structure and function and structure-based drug design. However, the development of an effective method to conduct exhaustive PPI screening represents a computational challenge. We have been investigating a protein docking approach based on shape complementarity and physicochemical properties. We describe here the development of the protein-protein docking software package “MEGADOCK” that samples an extremely large number of protein dockings at high speed. MEGADOCK reduces the calculation time required for docking by using several techniques such as a novel scoring function called the real Pairwise Shape Complementarity (rPSC) score. We showed that MEGADOCK is capable of exhaustive PPI screening by completing docking calculations 7.5 times faster than the conventional docking software, ZDOCK, while maintaining an acceptable level of accuracy. When MEGADOCK was applied to a subset of a general benchmark dataset to predict 120 relevant interacting pairs from 120 x 120 = 14,400 combinations of proteins, an F-measure value of 0.231 was obtained. Further, we showed that MEGADOCK can be applied to a large-scale protein-protein interaction-screening problem with accuracy better than random. When our approach is combined with parallel high-performance computing systems, it is now feasible to search and analyze protein-protein interactions while taking into account three-dimensional structures at the interactome scale. MEGADOCK is freely available at http://www.bi.cs.titech.ac.jp/megadock. PMID:23855673

Magnetic nanoparticles to recover cellular organelles and study the time resolved nanoparticle-cell interactome throughout uptake.

PubMed

Bertoli, Filippo; Davies, Gemma-Louise; Monopoli, Marco P; Moloney, Micheal; Gun'ko, Yurii K; Salvati, Anna; Dawson, Kenneth A

2014-08-27

Nanoparticles in contact with cells and living organisms generate quite novel interactions at the interface between the nanoparticle surface and the surrounding biological environment. However, a detailed time resolved molecular level description of the evolving interactions as nanoparticles are internalized and trafficked within the cellular environment is still missing and will certainly be required for the emerging arena of nanoparticle-cell interactions to mature. In this paper promising methodologies to map out the time resolved nanoparticle-cell interactome for nanoparticle uptake are discussed. Thus silica coated magnetite nanoparticles are presented to cells and their magnetic properties used to isolate, in a time resolved manner, the organelles containing the nanoparticles. Characterization of the recovered fractions shows that different cell compartments are isolated at different times, in agreement with imaging results on nanoparticle intracellular location. Subsequently the internalized nanoparticles can be further isolated from the recovered organelles, allowing the study of the most tightly nanoparticle-bound biomolecules, analogous to the 'hard corona' that so far has mostly been characterized in extracellular environments. Preliminary data on the recovered nanoparticles suggest that significant portion of the original corona (derived from the serum in which particles are presented to the cells) is preserved as nanoparticles are trafficked through the cells. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SInCRe—structural interactome computational resource for Mycobacterium tuberculosis

PubMed Central

Metri, Rahul; Hariharaputran, Sridhar; Ramakrishnan, Gayatri; Anand, Praveen; Raghavender, Upadhyayula S.; Ochoa-Montaño, Bernardo; Higueruelo, Alicia P.; Sowdhamini, Ramanathan; Chandra, Nagasuma R.; Blundell, Tom L.; Srinivasan, Narayanaswamy

2015-01-01

We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein–protein and protein–small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein–protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host–pathogen protein–protein interactions. Together they provide prerequisites for identification of off-target binding. Database URL: http://proline.biochem.iisc.ernet.in/sincre PMID:26130660

High-throughput proteomic characterization of plasma rich in growth factors (PRGF-Endoret)-derived fibrin clot interactome.

PubMed

Anitua, Eduardo; Prado, Roberto; Azkargorta, Mikel; Rodriguez-Suárez, Eva; Iloro, Ibon; Casado-Vela, Juan; Elortza, Felix; Orive, Gorka

2015-11-01

Plasma rich in growth factors (PRGF®-Endoret®) is an autologous technology that contains a set of proteins specifically addressed to wound healing and tissue regeneration. The scaffold formed by using this technology is a clot mainly composed of fibrin protein, forming a three-dimensional (3D) macroscopic network. This biomaterial is easily obtained by biotechnological means from blood and can be used in a range of situations to help wound healing and tissue regeneration. Although the main constituent of this clot is the fibrin scaffold, little is known about other proteins interacting in this clot that may act as adjuvants in the healing process. The aim of this study was to characterize the proteins enclosed by PRGF-Endoret scaffold, using a double-proteomic approach that combines 1D-SDS-PAGE approach followed by LC-MS/MS, and 2-DE followed by MALDI-TOF/TOF. The results presented here provide a description of the catalogue of key proteins in close contact with the fibrin scaffold. The obtained lists of proteins were grouped into families and networks according to gene ontology. Taken together, an enrichment of both proteins and protein families specifically involved in tissue regeneration and wound healing has been found. Copyright © 2013 John Wiley & Sons, Ltd.

Bioinformatics Analysis Reveals Distinct Molecular Characteristics of Hepatitis B-Related Hepatocellular Carcinomas from Very Early to Advanced Barcelona Clinic Liver Cancer Stages.

PubMed

Kong, Fan-Yun; Wei, Xiao; Zhou, Kai; Hu, Wei; Kou, Yan-Bo; You, Hong-Juan; Liu, Xiao-Mei; Zheng, Kui-Yang; Tang, Ren-Xian

2016-01-01

Hepatocellular carcinoma (HCC)is the fifth most common malignancy associated with high mortality. One of the risk factors for HCC is chronic hepatitis B virus (HBV) infection. The treatment strategy for the disease is dependent on the stage of HCC, and the Barcelona clinic liver cancer (BCLC) staging system is used in most HCC cases. However, the molecular characteristics of HBV-related HCC in different BCLC stages are still unknown. Using GSE14520 microarray data from HBV-related HCC cases with BCLC stages from 0 (very early stage) to C (advanced stage) in the gene expression omnibus (GEO) database, differentially expressed genes (DEGs), including common DEGs and unique DEGs in different BCLC stages, were identified. These DEGs were located on different chromosomes. The molecular functions and biology pathways of DEGs were identified by gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and the interactome networks of DEGs were constructed using the NetVenn online tool. The results revealed that both common DEGs and stage-specific DEGs were associated with various molecular functions and were involved in special biological pathways. In addition, several hub genes were found in the interactome networks of DEGs. The identified DEGs and hub genes promote our understanding of the molecular mechanisms underlying the development of HBV-related HCC through the different BCLC stages, and might be used as staging biomarkers or molecular targets for the treatment of HCC with HBV infection.

Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing

NASA Astrophysics Data System (ADS)

Piñero, Janet; Berenstein, Ariel; Gonzalez-Perez, Abel; Chernomoretz, Ariel; Furlong, Laura I.

2016-04-01

Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules.

Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing

PubMed Central

Piñero, Janet; Berenstein, Ariel; Gonzalez-Perez, Abel; Chernomoretz, Ariel; Furlong, Laura I.

2016-01-01

Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules. PMID:27080396

Triangle network motifs predict complexes by complementing high-error interactomes with structural information.

PubMed

Andreopoulos, Bill; Winter, Christof; Labudde, Dirk; Schroeder, Michael

2009-06-27

A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles. We find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes. Given high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN.

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

PubMed Central

Andreopoulos, Bill; Winter, Christof; Labudde, Dirk; Schroeder, Michael

2009-01-01

Background A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles. Results We find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes. Conclusion Given high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN. PMID:19558694

hPDI: a database of experimental human protein-DNA interactions.

PubMed

Xie, Zhi; Hu, Shaohui; Blackshaw, Seth; Zhu, Heng; Qian, Jiang

2010-01-15

The human protein DNA Interactome (hPDI) database holds experimental protein-DNA interaction data for humans identified by protein microarray assays. The unique characteristics of hPDI are that it contains consensus DNA-binding sequences not only for nearly 500 human transcription factors but also for >500 unconventional DNA-binding proteins, which are completely uncharacterized previously. Users can browse, search and download a subset or the entire data via a web interface. This database is freely accessible for any academic purposes. http://bioinfo.wilmer.jhu.edu/PDI/.

MCL-CAw: a refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

PubMed Central

2010-01-01

Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw. PMID:20939868

Next-Generation Technologies for Multiomics Approaches Including Interactome Sequencing

PubMed Central

Ohashi, Hiroyuki; Miyamoto-Sato, Etsuko

2015-01-01

The development of high-speed analytical techniques such as next-generation sequencing and microarrays allows high-throughput analysis of biological information at a low cost. These techniques contribute to medical and bioscience advancements and provide new avenues for scientific research. Here, we outline a variety of new innovative techniques and discuss their use in omics research (e.g., genomics, transcriptomics, metabolomics, proteomics, and interactomics). We also discuss the possible applications of these methods, including an interactome sequencing technology that we developed, in future medical and life science research. PMID:25649523

SONAR Discovers RNA-Binding Proteins from Analysis of Large-Scale Protein-Protein Interactomes.

PubMed

Brannan, Kristopher W; Jin, Wenhao; Huelga, Stephanie C; Banks, Charles A S; Gilmore, Joshua M; Florens, Laurence; Washburn, Michael P; Van Nostrand, Eric L; Pratt, Gabriel A; Schwinn, Marie K; Daniels, Danette L; Yeo, Gene W

2016-10-20

RNA metabolism is controlled by an expanding, yet incomplete, catalog of RNA-binding proteins (RBPs), many of which lack characterized RNA binding domains. Approaches to expand the RBP repertoire to discover non-canonical RBPs are currently needed. Here, HaloTag fusion pull down of 12 nuclear and cytoplasmic RBPs followed by quantitative mass spectrometry (MS) demonstrates that proteins interacting with multiple RBPs in an RNA-dependent manner are enriched for RBPs. This motivated SONAR, a computational approach that predicts RNA binding activity by analyzing large-scale affinity precipitation-MS protein-protein interactomes. Without relying on sequence or structure information, SONAR identifies 1,923 human, 489 fly, and 745 yeast RBPs, including over 100 human candidate RBPs that contain zinc finger domains. Enhanced CLIP confirms RNA binding activity and identifies transcriptome-wide RNA binding sites for SONAR-predicted RBPs, revealing unexpected RNA binding activity for disease-relevant proteins and DNA binding proteins. Copyright © 2016 Elsevier Inc. All rights reserved.

Identifying biological pathways that underlie primordial short stature using network analysis.

PubMed

Hanson, Dan; Stevens, Adam; Murray, Philip G; Black, Graeme C M; Clayton, Peter E

2014-06-01

Mutations in CUL7, OBSL1 and CCDC8, leading to disordered ubiquitination, cause one of the commonest primordial growth disorders, 3-M syndrome. This condition is associated with i) abnormal p53 function, ii) GH and/or IGF1 resistance, which may relate to failure to recycle signalling molecules, and iii) cellular IGF2 deficiency. However the exact molecular mechanisms that may link these abnormalities generating growth restriction remain undefined. In this study, we have used immunoprecipitation/mass spectrometry and transcriptomic studies to generate a 3-M 'interactome', to define key cellular pathways and biological functions associated with growth failure seen in 3-M. We identified 189 proteins which interacted with CUL7, OBSL1 and CCDC8, from which a network including 176 of these proteins was generated. To strengthen the association to 3-M syndrome, these proteins were compared with an inferred network generated from the genes that were differentially expressed in 3-M fibroblasts compared with controls. This resulted in a final 3-M network of 131 proteins, with the most significant biological pathway within the network being mRNA splicing/processing. We have shown using an exogenous insulin receptor (INSR) minigene system that alternative splicing of exon 11 is significantly changed in HEK293 cells with altered expression of CUL7, OBSL1 and CCDC8 and in 3-M fibroblasts. The net result is a reduction in the expression of the mitogenic INSR isoform in 3-M syndrome. From these preliminary data, we hypothesise that disordered ubiquitination could result in aberrant mRNA splicing in 3-M; however, further investigation is required to determine whether this contributes to growth failure. © 2014 The authors.

Systems Biomedicine of Rabies Delineates the Affected Signaling Pathways

PubMed Central

Azimzadeh Jamalkandi, Sadegh; Mozhgani, Sayed-Hamidreza; Gholami Pourbadie, Hamid; Mirzaie, Mehdi; Noorbakhsh, Farshid; Vaziri, Behrouz; Gholami, Alireza; Ansari-Pour, Naser; Jafari, Mohieddin

2016-01-01

The prototypical neurotropic virus, rabies, is a member of the Rhabdoviridae family that causes lethal encephalomyelitis. Although there have been a plethora of studies investigating the etiological mechanism of the rabies virus and many precautionary methods have been implemented to avert the disease outbreak over the last century, the disease has surprisingly no definite remedy at its late stages. The psychological symptoms and the underlying etiology, as well as the rare survival rate from rabies encephalitis, has still remained a mystery. We, therefore, undertook a systems biomedicine approach to identify the network of gene products implicated in rabies. This was done by meta-analyzing whole-transcriptome microarray datasets of the CNS infected by strain CVS-11, and integrating them with interactome data using computational and statistical methods. We first determined the differentially expressed genes (DEGs) in each study and horizontally integrated the results at the mRNA and microRNA levels separately. A total of 61 seed genes involved in signal propagation system were obtained by means of unifying mRNA and microRNA detected integrated DEGs. We then reconstructed a refined protein–protein interaction network (PPIN) of infected cells to elucidate the rabies-implicated signal transduction network (RISN). To validate our findings, we confirmed differential expression of randomly selected genes in the network using Real-time PCR. In conclusion, the identification of seed genes and their network neighborhood within the refined PPIN can be useful for demonstrating signaling pathways including interferon circumvent, toward proliferation and survival, and neuropathological clue, explaining the intricate underlying molecular neuropathology of rabies infection and thus rendered a molecular framework for predicting potential drug targets. PMID:27872612

Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications.

PubMed

Wang, Rui-Sheng; Loscalzo, Joseph

2018-05-20

Understanding the genetic basis of complex diseases is challenging. Prior work shows that disease-related proteins do not typically function in isolation. Rather, they often interact with each other to form a network module that underlies dysfunctional mechanistic pathways. Identifying such disease modules will provide insights into a systems-level understanding of molecular mechanisms of diseases. Owing to the incompleteness of our knowledge of disease proteins and limited information on the biological mediators of pathobiological processes, the key proteins (seed proteins) for many diseases appear scattered over the human protein-protein interactome and form a few small branches, rather than coherent network modules. In this paper, we develop a network-based algorithm, called the Seed Connector algorithm (SCA), to pinpoint disease modules by adding as few additional linking proteins (seed connectors) to the seed protein pool as possible. Such seed connectors are hidden disease module elements that are critical for interpreting the functional context of disease proteins. The SCA aims to connect seed disease proteins so that disease mechanisms and pathways can be decoded based on predicted coherent network modules. We validate the algorithm using a large corpus of 70 complex diseases and binding targets of over 200 drugs, and demonstrate the biological relevance of the seed connectors. Lastly, as a specific proof of concept, we apply the SCA to a set of seed proteins for coronary artery disease derived from a meta-analysis of large-scale genome-wide association studies and obtain a coronary artery disease module enriched with important disease-related signaling pathways and drug targets not previously recognized. Copyright © 2018 Elsevier Ltd. All rights reserved.

A QUICK Screen for Lrrk2 Interaction Partners – Leucine-rich Repeat Kinase 2 is Involved in Actin Cytoskeleton Dynamics*

PubMed Central

Meixner, Andrea; Boldt, Karsten; Van Troys, Marleen; Askenazi, Manor; Gloeckner, Christian J.; Bauer, Matthias; Marto, Jarrod A.; Ampe, Christophe; Kinkl, Norbert; Ueffing, Marius

2011-01-01

Mutations in human leucine-rich repeat kinase 2 (Lrrk2), a protein of yet unknown function, are linked to Parkinson's disease caused by degeneration of midbrain dopaminergic neurons. The protein comprises several domains including a GTPase and a kinase domain both affected by several pathogenic mutations. To elucidate the molecular interaction network of endogenous Lrrk2 under stoichiometric constraints, we applied QUICK (quantitative immunoprecipitation combined with knockdown) in NIH3T3 cells. The identified interactome reveals actin isoforms as well as actin-associated proteins involved in actin filament assembly, organization, rearrangement, and maintenance, suggesting that the biological function of Lrrk2 is linked to cytoskeletal dynamics. In fact, we demonstrate Lrrk2 de novo binding to F-actin and its ability to modulate its assembly in vitro. When tested in intact cells, knockdown of Lrrk2 causes morphological alterations in NIH3T3 cells. In developing dopaminergic midbrain primary neurons, Lrrk2 knockdown results in shortened neurite processes, indicating a physiological role of Lrrk2 in cytoskeletal organization and dynamics of dopaminergic neurons. Hence, our results demonstrate that molecular interactions as well as the physiological function of Lrrk2 are closely related to the organization of the actin-based cytoskeleton, a crucial feature of neuronal development and neuron function. PMID:20876399

Elucidating the genotype-phenotype relationships and network perturbations of human shared and specific disease genes from an evolutionary perspective.

PubMed

Begum, Tina; Ghosh, Tapash Chandra

2014-10-05

To date, numerous studies have been attempted to determine the extent of variation in evolutionary rates between human disease and nondisease (ND) genes. In our present study, we have considered human autosomal monogenic (Mendelian) disease genes, which were classified into two groups according to the number of phenotypic defects, that is, specific disease (SPD) gene (one gene: one defect) and shared disease (SHD) gene (one gene: multiple defects). Here, we have compared the evolutionary rates of these two groups of genes, that is, SPD genes and SHD genes with respect to ND genes. We observed that the average evolutionary rates are slow in SHD group, intermediate in SPD group, and fast in ND group. Group-to-group evolutionary rate differences remain statistically significant regardless of their gene expression levels and number of defects. We demonstrated that disease genes are under strong selective constraint if they emerge through edgetic perturbation or drug-induced perturbation of the interactome network, show tissue-restricted expression, and are involved in transmembrane transport. Among all the factors, our regression analyses interestingly suggest the independent effects of 1) drug-induced perturbation and 2) the interaction term of expression breadth and transmembrane transport on protein evolutionary rates. We reasoned that the drug-induced network disruption is a combination of several edgetic perturbations and, thus, has more severe effect on gene phenotypes. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

The Co-Metabolism within the Gut-Brain Metabolic Interaction: Potential Targets for Drug Treatment and Design.

PubMed

Obrenovich, Mark; Flückiger, Rudolf; Sykes, Lorraine; Donskey, Curtis

2016-01-01

We know that within the complex mammalian gut is any number of metabolic biomes. The gut has been sometimes called the "second brain" within the "gut-brain axis". A more informative term would be the gut-brain metabolic interactome, which is coined here to underscore the relationship between the digestive system and cognitive function or dysfunction as the case may be. Co-metabolism between the host and the intestinal microbiota is essential for life's processes. How diet, lifestyle, antibiotics and other factors shape the gut microbiome constitutes a rapidly growing area of research. Conversely, the gut microbiome also affects mammalian systems. Metabolites of the gut-brain axis are potential targets for treatment and drug design since the interaction or biochemical interplay results in net metabolite production or end-products with either positive or negative effects on human health. This review explores the gut-brain metabolic interactome, with particular emphasis on drug design and treatment strategies and how commensal bacteria or their disruption lead to dysbiosis and the effect this has on neurochemistry. Increasing data indicate that the intestinal microbiome can affect neurobiology, from mental and even behavioral health to memory, depression, mood, anxiety, obesity, cravings and even the creation and maintenance of the blood brain barrier.

Challenges in structural approaches to cell modeling.

PubMed

Im, Wonpil; Liang, Jie; Olson, Arthur; Zhou, Huan-Xiang; Vajda, Sandor; Vakser, Ilya A

2016-07-31

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field. Copyright © 2016 Elsevier Ltd. All rights reserved.

Transcriptional atlas of cardiogenesis maps congenital heart disease interactome.

PubMed

Li, Xing; Martinez-Fernandez, Almudena; Hartjes, Katherine A; Kocher, Jean-Pierre A; Olson, Timothy M; Terzic, Andre; Nelson, Timothy J

2014-07-01

Mammalian heart development is built on highly conserved molecular mechanisms with polygenetic perturbations resulting in a spectrum of congenital heart diseases (CHD). However, knowledge of cardiogenic ontogeny that regulates proper cardiogenesis remains largely based on candidate-gene approaches. Mapping the dynamic transcriptional landscape of cardiogenesis from a genomic perspective is essential to integrate the knowledge of heart development into translational applications that accelerate disease discovery efforts toward mechanistic-based treatment strategies. Herein, we designed a time-course transcriptome analysis to investigate the genome-wide dynamic expression landscape of innate murine cardiogenesis ranging from embryonic stem cells to adult cardiac structures. This comprehensive analysis generated temporal and spatial expression profiles, revealed stage-specific gene functions, and mapped the dynamic transcriptome of cardiogenesis to curated pathways. Reconciling known genetic underpinnings of CHD, we deconstructed a disease-centric dynamic interactome encoded within this cardiogenic atlas to identify stage-specific developmental disturbances clustered on regulation of epithelial-to-mesenchymal transition (EMT), BMP signaling, NF-AT signaling, TGFb-dependent EMT, and Notch signaling. Collectively, this cardiogenic transcriptional landscape defines the time-dependent expression of cardiac ontogeny and prioritizes regulatory networks at the interface between health and disease. Copyright © 2014 the American Physiological Society.

Inferring Mechanisms of Compensation from E-MAP and SGA Data Using Local Search Algorithms for Max Cut

NASA Astrophysics Data System (ADS)

Leiserson, Mark D. M.; Tatar, Diana; Cowen, Lenore J.; Hescott, Benjamin J.

A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome.

Inferring mechanisms of compensation from E-MAP and SGA data using local search algorithms for max cut.

PubMed

Leiserson, Mark D M; Tatar, Diana; Cowen, Lenore J; Hescott, Benjamin J

2011-11-01

A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods, which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome.

Grouping annotations on the subcellular layered interactome demonstrates enhanced autophagy activity in a recurrent experimental autoimmune uveitis T cell line.

PubMed

Jia, Xiuzhi; Li, Jingbo; Shi, Dejing; Zhao, Yu; Dong, Yucui; Ju, Huanyu; Yang, Jinfeng; Sun, Jianhua; Li, Xia; Ren, Huan

2014-01-01

Human uveitis is a type of T cell-mediated autoimmune disease that often shows relapse-remitting courses affecting multiple biological processes. As a cytoplasmic process, autophagy has been seen as an adaptive response to cell death and survival, yet the link between autophagy and T cell-mediated autoimmunity is not certain. In this study, based on the differentially expressed genes (GSE19652) between the recurrent versus monophasic T cell lines, whose adoptive transfer to susceptible animals may result in respective recurrent or monophasic uveitis, we proposed grouping annotations on a subcellular layered interactome framework to analyze the specific bioprocesses that are linked to the recurrence of T cell autoimmunity. That is, the subcellular layered interactome was established by the Cytoscape and Cerebral plugin based on differential expression, global interactome, and subcellular localization information. Then, the layered interactomes were grouping annotated by the ClueGO plugin based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. The analysis showed that significant bioprocesses with autophagy were orchestrated in the cytoplasmic layered interactome and that mTOR may have a regulatory role in it. Furthermore, by setting up recurrent and monophasic uveitis in Lewis rats, we confirmed by transmission electron microscopy that, in comparison to the monophasic disease, recurrent uveitis in vivo showed significantly increased autophagy activity and extended lymphocyte infiltration to the affected retina. In summary, our framework methodology is a useful tool to disclose specific bioprocesses and molecular targets that can be attributed to a certain disease. Our results indicated that targeted inhibition of autophagy pathways may perturb the recurrence of uveitis.

Dynamic Interactions Between Cancer Stem Cells And Their Stromal Partners.

PubMed

Park, Tea Soon; Donnenberg, Vera S; Donnenberg, Albert D; Zambidis, Elias T; Zimmerlin, Ludovic

2014-03-01

The cancer stem cell (CSC) paradigm presumes the existence of self-renewing cancer cells capable of regenerating all tumor compartments and exhibiting stem cell-associated phenotypes. Recent interpretations of the CSC hypothesis envision stemness as a dynamic trait of tumor-initiating cells rather than a defined and unique cell type. Bidirectional crosstalk between the tumor microenvironment and the cancer bulk is well described in the literature and the tumor-associated stroma, vasculature and immune infiltrate have all been implicated as direct contributors to tumor development. These non-neoplastic cell types have also been shown to organize specific niches within the tumor bulk where they can control the intra-tumor CSC content and alter the fate of CSCs and tumor progenitors during tumorigenesis to acquire phenotypic features for invasion, metastasis and dormancy. Despite the complexity of the tumor-stroma interactome, novel therapeutic approaches envision combining tumor-ablative treatment with manipulation of the tumor microenvironment. We will review the currently available literature that provides clues about the complex cellular network that regulate the CSC phenotype and its niches during tumor progression.

Prion-like domains in RNA binding proteins are essential for building subnuclear paraspeckles

PubMed Central

Hennig, Sven; Kong, Geraldine; Mannen, Taro; Sadowska, Agata; Kobelke, Simon; Blythe, Amanda; Knott, Gavin J.; Iyer, K. Swaminathan; Ho, Diwei; Newcombe, Estella A.; Hosoki, Kana; Goshima, Naoki; Kawaguchi, Tetsuya; Hatters, Danny; Trinkle-Mulcahy, Laura; Hirose, Tetsuro; Bond, Charles S.

2015-01-01

Prion-like domains (PLDs) are low complexity sequences found in RNA binding proteins associated with the neurodegenerative disorder amyotrophic lateral sclerosis. Recently, PLDs have been implicated in mediating gene regulation via liquid-phase transitions that drive ribonucleoprotein granule assembly. In this paper, we report many PLDs in proteins associated with paraspeckles, subnuclear bodies that form around long noncoding RNA. We mapped the interactome network of paraspeckle proteins, finding enrichment of PLDs. We show that one protein, RBM14, connects key paraspeckle subcomplexes via interactions mediated by its PLD. We further show that the RBM14 PLD, as well as the PLD of another essential paraspeckle protein, FUS, is required to rescue paraspeckle formation in cells in which their endogenous counterpart has been knocked down. Similar to FUS, the RBM14 PLD also forms hydrogels with amyloid-like properties. These results suggest a role for PLD-mediated liquid-phase transitions in paraspeckle formation, highlighting this nuclear body as an excellent model system for understanding the perturbation of such processes in neurodegeneration. PMID:26283796

Protein Interactome of Muscle Invasive Bladder Cancer

PubMed Central

Bhat, Akshay; Heinzel, Andreas; Mayer, Bernd; Perco, Paul; Mühlberger, Irmgard; Husi, Holger; Merseburger, Axel S.; Zoidakis, Jerome; Vlahou, Antonia; Schanstra, Joost P.; Mischak, Harald; Jankowski, Vera

2015-01-01

Muscle invasive bladder carcinoma is a complex, multifactorial disease caused by disruptions and alterations of several molecular pathways that result in heterogeneous phenotypes and variable disease outcome. Combining this disparate knowledge may offer insights for deciphering relevant molecular processes regarding targeted therapeutic approaches guided by molecular signatures allowing improved phenotype profiling. The aim of the study is to characterize muscle invasive bladder carcinoma on a molecular level by incorporating scientific literature screening and signatures from omics profiling. Public domain omics signatures together with molecular features associated with muscle invasive bladder cancer were derived from literature mining to provide 286 unique protein-coding genes. These were integrated in a protein-interaction network to obtain a molecular functional map of the phenotype. This feature map educated on three novel disease-associated pathways with plausible involvement in bladder cancer, namely Regulation of actin cytoskeleton, Neurotrophin signalling pathway and Endocytosis. Systematic integration approaches allow to study the molecular context of individual features reported as associated with a clinical phenotype and could potentially help to improve the molecular mechanistic description of the disorder. PMID:25569276

Evaluation of a Bead-Free Coimmunoprecipitation Technique for Identification of Virus-Host Protein Interactions Using High-Resolution Mass Spectrometry.

PubMed

DeBlasio, Stacy L; Bereman, Michael S; Mahoney, Jaclyn; Thannhauser, Theodore W; Gray, Stewart M; MacCoss, Michael J; Cilia Heck, Michelle

2017-09-01

Protein interactions between virus and host are essential for viral propagation and movement, as viruses lack most of the proteins required to thrive on their own. Precision methods aimed at disrupting virus-host interactions represent new approaches to disease management but require in-depth knowledge of the identity and binding specificity of host proteins within these interaction networks. Protein coimmunoprecipitation (co-IP) coupled with mass spectrometry (MS) provides a high-throughput way to characterize virus-host interactomes in a single experiment. Common co-IP methods use antibodies immobilized on agarose or magnetic beads to isolate virus-host complexes in solutions of host tissue homogenate. Although these workflows are well established, they can be fairly laborious and expensive. Therefore, we evaluated the feasibility of using antibody-coated microtiter plates coupled with MS analysis as an easy, less expensive way to identify host proteins that interact with Potato leafroll virus (PLRV), an insect-borne RNA virus that infects potatoes. With the use of the bead-free platform, we were able to detect 36 plant and 1 nonstructural viral protein significantly coimmunoprecipitating with PLRV. Two of these proteins, a 14-3-3 signal transduction protein and malate dehydrogenase 2 (mMDH2), were detected as having a weakened or lost association with a structural mutant of the virus, demonstrating that the bead-free method is sensitive enough to detect quantitative differences that can be used to pin-point domains of interaction. Collectively, our analysis shows that the bead-free platform is a low-cost alternative that can be used by core facilities and other investigators to identify plant and viral proteins interacting with virions and/or the viral structural proteins.

CREDO: a structural interactomics database for drug discovery

PubMed Central

Schreyer, Adrian M.; Blundell, Tom L.

2013-01-01

CREDO is a unique relational database storing all pairwise atomic interactions of inter- as well as intra-molecular contacts between small molecules and macromolecules found in experimentally determined structures from the Protein Data Bank. These interactions are integrated with further chemical and biological data. The database implements useful data structures and algorithms such as cheminformatics routines to create a comprehensive analysis platform for drug discovery. The database can be accessed through a web-based interface, downloads of data sets and web services at http://www-cryst.bioc.cam.ac.uk/credo. Database URL: http://www-cryst.bioc.cam.ac.uk/credo PMID:23868908

Rearrangement of the Protein Phosphatase 1 Interactome During Heart Failure Progression.

PubMed

Chiang, David Y; Alsina, Katherina M; Corradini, Eleonora; Fitzpatrick, Martin; Ni, Li; Lahiri, Satadru K; Reynolds, Julia; Pan, Xiaolu; Scott, Larry; Heck, Albert J R; Wehrens, Xander H

2018-04-18

Background -Heart failure (HF) is a complex disease with a rising prevalence despite advances in treatment. Protein phosphatase 1 (PP1) has long been implicated in HF pathogenesis but its exact role is both unclear and controversial. Most previous studies measured only the PP1 catalytic subunit (PP1c) without investigating its diverse set of interactors, which confer localization and substrate specificity to the holoenzyme. In this study we define the PP1 interactome in cardiac tissue and test the hypothesis that this interactome becomes rearranged during HF progression at the level of specific PP1c interactors. Methods -Mice were subjected to transverse aortic constriction and grouped based on ejection fraction (EF) into sham, hypertrophy, moderate HF (EF 30-40%), and severe HF (EF<30%). Cardiac lysates were subjected to affinity-purification using anti-PP1c antibodies followed by high-resolution mass spectrometry. Ppp1r7 was knocked down in mouse cardiomyocytes and HeLa cells using adeno-associated virus serotype 9 (AAV9) and siRNA, respectively. Calcium imaging was performed on isolated ventricular myocytes. Results -Seventy-one and 98 PP1c interactors were quantified from mouse cardiac and HeLa lysates, respectively, including many novel interactors and protein complexes. This represents the largest reproducible PP1 interactome dataset ever captured from any tissue, including both primary and secondary/tertiary interactors. Nine PP1c interactors with changes in their binding to PP1c were strongly associated with HF progression including two known (Ppp1r7, Ppp1r18) and 7 novel interactors. Within the entire cardiac PP1 interactome, Ppp1r7 had the highest binding to PP1c. Cardiac-specific knockdown in mice led to cardiac dysfunction and disruption of calcium release from the sarcoplasmic reticulum. Conclusions -PP1 is best studied at the level of its interactome, which undergoes significant rearrangement during HF progression. The nine key interactors that are associated with HF progression may represent potential targets in HF therapy. In particular, Ppp1r7 may play a central role in regulating the PP1 interactome by acting as a competitive molecular "sponge" of PP1c.

Functional proteomic and interactome analysis of proteins associated with beef tenderness in angus cattle

USDA-ARS?s Scientific Manuscript database

Beef is a source of high quality protein for the human population, and beef tenderness has significant influence on beef palatability, consumer expectation and industry profitability. To further elucidate the factors affecting beef tenderness, functional proteomics and bioinformatics interactome ana...

RNAseq and Proteomics for Analysing Complex Oomycete Plant Interactions.

PubMed

Burra, Dharani D; Vetukuri, Ramesh R; Resjö, Svante; Grenville-Briggs, Laura J; Andreasson, Erik

2016-01-01

The oomycetes include some of the most devastating plant pathogens. In this review we discuss the latest results from oomycete and plant studies with emphasis on interaction studies. We focus on the outcomes of RNAseq and proteomics studies and some pitfalls of these approaches. Both pathogenic interactions and biological control are discussed. We underline the usefulness of studies at several levels of complexity from studies of one organism, up to two or more and within agricultural fields (managed settings) up to wild ecosystems. Finally we identify areas of future interest such as detailed interactome studies, dual RNAseq studies, peptide modification studies and population/meta omics with or without biological control agents.

Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana.

PubMed

Van Leene, Jelle; Hollunder, Jens; Eeckhout, Dominique; Persiau, Geert; Van De Slijke, Eveline; Stals, Hilde; Van Isterdael, Gert; Verkest, Aurine; Neirynck, Sandy; Buffel, Yelle; De Bodt, Stefanie; Maere, Steven; Laukens, Kris; Pharazyn, Anne; Ferreira, Paulo C G; Eloy, Nubia; Renne, Charlotte; Meyer, Christian; Faure, Jean-Denis; Steinbrenner, Jens; Beynon, Jim; Larkin, John C; Van de Peer, Yves; Hilson, Pierre; Kuiper, Martin; De Veylder, Lieven; Van Onckelen, Harry; Inzé, Dirk; Witters, Erwin; De Jaeger, Geert

2010-08-10

Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)-cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK-cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants.

3DIANA: 3D Domain Interaction Analysis: A Toolbox for Quaternary Structure Modeling

PubMed Central

Segura, Joan; Sanchez-Garcia, Ruben; Tabas-Madrid, Daniel; Cuenca-Alba, Jesus; Sorzano, Carlos Oscar S.; Carazo, Jose Maria

2016-01-01

Electron microscopy (EM) is experiencing a revolution with the advent of a new generation of Direct Electron Detectors, enabling a broad range of large and flexible structures to be resolved well below 1 nm resolution. Although EM techniques are evolving to the point of directly obtaining structural data at near-atomic resolution, for many molecules the attainable resolution might not be enough to propose high-resolution structural models. However, accessing information on atomic coordinates is a necessary step toward a deeper understanding of the molecular mechanisms that allow proteins to perform specific tasks. For that reason, methods for the integration of EM three-dimensional maps with x-ray and NMR structural data are being developed, a modeling task that is normally referred to as fitting, resulting in the so called hybrid models. In this work, we present a novel application—3DIANA—specially targeted to those cases in which the EM map resolution is medium or low and additional experimental structural information is scarce or even lacking. In this way, 3DIANA statistically evaluates proposed/potential contacts between protein domains, presents a complete catalog of both structurally resolved and predicted interacting regions involving these domains and, finally, suggests structural templates to model the interaction between them. The evaluation of the proposed interactions is computed with DIMERO, a new method that scores physical binding sites based on the topology of protein interaction networks, which has recently shown the capability to increase by 200% the number of domain-domain interactions predicted in interactomes as compared to previous approaches. The new application displays the information at a sequence and structural level and is accessible through a web browser or as a Chimera plugin at http://3diana.cnb.csic.es. PMID:26772592

Systematic Determination of Human Cyclin Dependent Kinase (CDK)-9 Interactome Identifies Novel Functions in RNA Splicing Mediated by the DEAD Box (DDX)-5/17 RNA Helicases.

PubMed

Yang, Jun; Zhao, Yingxin; Kalita, Mridul; Li, Xueling; Jamaluddin, Mohammad; Tian, Bing; Edeh, Chukwudi B; Wiktorowicz, John E; Kudlicki, Andrzej; Brasier, Allan R

2015-10-01

Inducible transcriptional elongation is a rapid, stereotypic mechanism for activating immediate early immune defense genes by the epithelium in response to viral pathogens. Here, the recruitment of a multifunctional complex containing the cyclin dependent kinase 9 (CDK9) triggers the process of transcriptional elongation activating resting RNA polymerase engaged with innate immune response (IIR) genes. To identify additional functional activity of the CDK9 complex, we conducted immunoprecipitation (IP) enrichment-stable isotope labeling LC-MS/MS of the CDK9 complex in unstimulated cells and from cells activated by a synthetic dsRNA, polyinosinic/polycytidylic acid [poly (I:C)]. 245 CDK9 interacting proteins were identified with high confidence in the basal state and 20 proteins in four functional classes were validated by IP-SRM-MS. These data identified that CDK9 interacts with DDX 5/17, a family of ATP-dependent RNA helicases, important in alternative RNA splicing of NFAT5, and mH2A1 mRNA two proteins controlling redox signaling. A direct comparison of the basal versus activated state was performed using stable isotope labeling and validated by IP-SRM-MS. Recruited into the CDK9 interactome in response to poly(I:C) stimulation are HSPB1, DNA dependent kinases, and cytoskeletal myosin proteins that exchange with 60S ribosomal structural proteins. An integrated human CDK9 interactome map was developed containing all known human CDK9- interacting proteins. These data were used to develop a probabilistic global map of CDK9-dependent target genes that predicted two functional states controlling distinct cellular functions, one important in immune and stress responses. The CDK9-DDX5/17 complex was shown to be functionally important by shRNA-mediated knockdown, where differential accumulation of alternatively spliced NFAT5 and mH2A1 transcripts and alterations in downstream redox signaling were seen. The requirement of CDK9 for DDX5 recruitment to NFAT5 and mH2A1 chromatin target was further demonstrated using chromatin immunoprecipitation (ChIP). These data indicate that CDK9 is a dynamic multifunctional enzyme complex mediating not only transcriptional elongation, but also alternative RNA splicing and potentially translational control. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

A bioinformatic and mechanistic study elicits the antifibrotic effect of ursolic acid through the attenuation of oxidative stress with the involvement of ERK, PI3K/Akt, and p38 MAPK signaling pathways in human hepatic stellate cells and rat liver

PubMed Central

He, Wenhua; Shi, Feng; Zhou, Zhi-Wei; Li, Bimin; Zhang, Kunhe; Zhang, Xinhua; Ouyang, Canhui; Zhou, Shu-Feng; Zhu, Xuan

2015-01-01

NADPH oxidases (NOXs) are a predominant mediator of redox homeostasis in hepatic stellate cells (HSCs), and oxidative stress plays an important role in the pathogenesis of liver fibrosis. Ursolic acid (UA) is a pentacyclic triterpenoid with various pharmacological activities, but the molecular targets and underlying mechanisms for its antifibrotic effect in the liver remain elusive. This study aimed to computationally predict the molecular interactome and mechanistically investigate the antifibrotic effect of UA on oxidative stress, with a focus on NOX4 activity and cross-linked signaling pathways in human HSCs and rat liver. Drug–drug interaction via chemical–protein interactome tool, a server that can predict drug–drug interaction via chemical–protein interactome, was used to predict the molecular targets of UA, and Database for Annotation, Visualization, and Integrated Discovery was employed to analyze the signaling pathways of the predicted targets of UA. The bioinformatic data showed that there were 611 molecular proteins possibly interacting with UA and that there were over 49 functional clusters responding to UA. The subsequential benchmarking data showed that UA significantly reduced the accumulation of type I collagen in HSCs in rat liver, increased the expression level of MMP-1, but decreased the expression level of TIMP-1 in HSC-T6 cells. UA also remarkably reduced the gene expression level of type I collagen in HSC-T6 cells. Furthermore, UA remarkably attenuated oxidative stress via negative regulation of NOX4 activity and expression in HSC-T6 cells. The employment of specific chemical inhibitors, SB203580, LY294002, PD98059, and AG490, demonstrated the involvement of ERK, PI3K/Akt, and p38 MAPK signaling pathways in the regulatory effect of UA on NOX4 activity and expression. Collectively, the antifibrotic effect of UA is partially due to the oxidative stress attenuating effect through manipulating NOX4 activity and expression. The results suggest that UA may act as a promising antifibrotic agent. More studies are warranted to evaluate the safety and efficacy of UA in the treatment of liver fibrosis. PMID:26347199

Comparative proteomic analysis of normal and collagen IX null mouse cartilage reveals altered extracellular matrix composition and novel components of the collagen IX interactome.

PubMed

Brachvogel, Bent; Zaucke, Frank; Dave, Keyur; Norris, Emma L; Stermann, Jacek; Dayakli, Münire; Koch, Manuel; Gorman, Jeffrey J; Bateman, John F; Wilson, Richard

2013-05-10

Collagen IX is an integral cartilage extracellular matrix component important in skeletal development and joint function. Proteomic analysis and validation studies revealed novel alterations in collagen IX null cartilage. Matrilin-4, collagen XII, thrombospondin-4, fibronectin, βig-h3, and epiphycan are components of the in vivo collagen IX interactome. We applied a proteomics approach to advance our understanding of collagen IX ablation in cartilage. The cartilage extracellular matrix is essential for endochondral bone development and joint function. In addition to the major aggrecan/collagen II framework, the interacting complex of collagen IX, matrilin-3, and cartilage oligomeric matrix protein (COMP) is essential for cartilage matrix stability, as mutations in Col9a1, Col9a2, Col9a3, Comp, and Matn3 genes cause multiple epiphyseal dysplasia, in which patients develop early onset osteoarthritis. In mice, collagen IX ablation results in severely disturbed growth plate organization, hypocellular regions, and abnormal chondrocyte shape. This abnormal differentiation is likely to involve altered cell-matrix interactions but the mechanism is not known. To investigate the molecular basis of the collagen IX null phenotype we analyzed global differences in protein abundance between wild-type and knock-out femoral head cartilage by capillary HPLC tandem mass spectrometry. We identified 297 proteins in 3-day cartilage and 397 proteins in 21-day cartilage. Components that were differentially abundant between wild-type and collagen IX-deficient cartilage included 15 extracellular matrix proteins. Collagen IX ablation was associated with dramatically reduced COMP and matrilin-3, consistent with known interactions. Matrilin-1, matrilin-4, epiphycan, and thrombospondin-4 levels were reduced in collagen IX null cartilage, providing the first in vivo evidence for these proteins belonging to the collagen IX interactome. Thrombospondin-4 expression was reduced at the mRNA level, whereas matrilin-4 was verified as a novel collagen IX-binding protein. Furthermore, changes in TGFβ-induced protein βig-h3 and fibronectin abundance were found in the collagen IX knock-out but not associated with COMP ablation, indicating specific involvement in the abnormal collagen IX null cartilage. In addition, the more widespread expression of collagen XII in the collagen IX-deficient cartilage suggests an attempted compensatory response to the absence of collagen IX. Our differential proteomic analysis of cartilage is a novel approach to identify candidate matrix protein interactions in vivo, underpinning further analysis of mutant cartilage lacking other matrix components or harboring disease-causing mutations.

Interactome Analysis of NS1 Protein Encoded by Influenza A H7N9 Virus Reveals an Inhibitory Role of NS1 in Host mRNA Maturation.

PubMed

Kuo, Rei-Lin; Chen, Chi-Jene; Tam, Ee-Hong; Huang, Chung-Guei; Li, Li-Hsin; Li, Zong-Hua; Su, Pei-Chia; Liu, Hao-Ping; Wu, Chih-Ching

2018-04-06

Influenza A virus infections can result in severe respiratory diseases. The H7N9 subtype of avian influenza A virus has been transmitted to humans and caused severe disease and death. Nonstructural protein 1 (NS1) of influenza A virus is a virulence determinant during viral infection. To elucidate the functions of the NS1 encoded by influenza A H7N9 virus (H7N9 NS1), interaction partners of H7N9 NS1 in human cells were identified with immunoprecipitation followed by SDS-PAGE coupled with liquid chromatography-tandem mass spectrometry (GeLC-MS/MS). We identified 36 cellular proteins as the interacting partners of the H7N9 NS1, and they are involved in RNA processing, mRNA splicing via spliceosome, and the mRNA surveillance pathway. Two of the interacting partners, cleavage and polyadenylation specificity factor subunit 2 (CPSF2) and CPSF7, were confirmed to interact with H7N9 NS1 using coimmunoprecipitation and immunoblotting based on the previous finding that the two proteins are involved in pre-mRNA polyadenylation machinery. Furthermore, we illustrate that overexpression of H7N9 NS1, as well as infection by the influenza A H7N9 virus, interfered with pre-mRNA polyadenylation in host cells. This study comprehensively profiled the interactome of H7N9 NS1 in host cells, and the results demonstrate a novel endotype for H7N9 NS1 in inhibiting host mRNA maturation.

Networks and games for precision medicine.

PubMed

Biane, Célia; Delaplace, Franck; Klaudel, Hanna

2016-12-01

Recent advances in omics technologies provide the leverage for the emergence of precision medicine that aims at personalizing therapy to patient. In this undertaking, computational methods play a central role for assisting physicians in their clinical decision-making by combining data analysis and systems biology modelling. Complex diseases such as cancer or diabetes arise from the intricate interplay of various biological molecules. Therefore, assessing drug efficiency requires to study the effects of elementary perturbations caused by diseases on relevant biological networks. In this paper, we propose a computational framework called Network-Action Game applied to best drug selection problem combining Game Theory and discrete models of dynamics (Boolean networks). Decision-making is modelled using Game Theory that defines the process of drug selection among alternative possibilities, while Boolean networks are used to model the effects of the interplay between disease and drugs actions on the patient's molecular system. The actions/strategies of disease and drugs are focused on arc alterations of the interactome. The efficiency of this framework has been evaluated for drug prediction on a model of breast cancer signalling. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Inferring Mechanisms of Compensation from E-MAP and SGA Data Using Local Search Algorithms for Max Cut

PubMed Central

Leiserson, Mark D.M.; Tatar, Diana; Cowen, Lenore J.

2011-01-01

Abstract A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods, which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome. PMID:21882903

TCTEX1D4 Interactome in Human Testis: Unraveling the Function of Dynein Light Chain in Spermatozoa

PubMed Central

Freitas, Maria João; Korrodi-Gregório, Luís; Morais-Santos, Filipa; da Cruz e Silva, Edgar

2014-01-01

Abstract Studies were designed to identify the TCTEX1D4 interactome in human testis, with the purpose of unraveling putative protein complexes essential to male reproduction and thus novel TCTEX1D4 functions. TCTEX1D4 is a dynein light chain that belongs to the DYNT1/TCTEX1 family. In spermatozoa, it appears to be important to sperm motility, intraflagellar transport, and acrosome reaction. To contribute to the knowledge on TCTEX1D4 function in testis and spermatozoa, a yeast two-hybrid assay was performed in testis, which allowed the identification of 40 novel TCTEX1D4 interactors. Curiously, another dynein light chain, TCTEX1D2, was identified and its existence demonstrated for the first time in human spermatozoa. Immunofluorescence studies proved that TCTEX1D2 is an intra-acrosomal protein also present in the midpiece, suggesting a role in cargo movement in human spermatozoa. Further, an in silico profile of TCTEX1D4 revealed that most TCTEX1D4 interacting proteins were not previously characterized and the ones described present a very broad nature. This reinforces TCTEX1D4 as a dynein light chain that is capable of interacting with a variety of functionally different proteins. These observations collectively contribute to a deeper molecular understanding of the human spermatozoa function. PMID:24606217

Visualization of protein interaction networks: problems and solutions

PubMed Central

2013-01-01

Background Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI) are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN) and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins) and edges (interactions), the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology) that enriches the PINs with semantic information, but complicates their visualization. Methods In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i) technology, i.e. availability/license of the software and supported OS (Operating System) platforms; (ii) interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii) visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv) analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the possibility to interact with external databases. Results Currently, many tools are available and it is not easy for the users choosing one of them. Some tools offer sophisticated 2D and 3D network visualization making available many layout algorithms, others tools are more data-oriented and support integration of interaction data coming from different sources and data annotation. Finally, some specialistic tools are dedicated to the analysis of pathways and cellular processes and are oriented toward systems biology studies, where the dynamic aspects of the processes being studied are central. Conclusion A current trend is the deployment of open, extensible visualization tools (e.g. Cytoscape), that may be incrementally enriched by the interactomics community with novel and more powerful functions for PIN analysis, through the development of plug-ins. On the other hand, another emerging trend regards the efficient and parallel implementation of the visualization engine that may provide high interactivity and near real-time response time, as in NAViGaTOR. From a technological point of view, open-source, free and extensible tools, like Cytoscape, guarantee a long term sustainability due to the largeness of the developers and users communities, and provide a great flexibility since new functions are continuously added by the developer community through new plug-ins, but the emerging parallel, often closed-source tools like NAViGaTOR, can offer near real-time response time also in the analysis of very huge PINs. PMID:23368786

Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations.

PubMed

Barradas-Bautista, Didier; Fernández-Recio, Juan

2017-01-01

Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level.

Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations

PubMed Central

2017-01-01

Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level. PMID:28841721

A systems biology perspective on the role of WRKY transcription factors in drought responses in plants.

PubMed

Tripathi, Prateek; Rabara, Roel C; Rushton, Paul J

2014-02-01

Drought is one of the major challenges affecting crop productivity and yield. However, water stress responses are notoriously multigenic and quantitative with strong environmental effects on phenotypes. It is also clear that water stress often does not occur alone under field conditions but rather in conjunction with other abiotic stresses such as high temperature and high light intensities. A multidisciplinary approach with successful integration of a whole range of -omics technologies will not only define the system, but also provide new gene targets for both transgenic approaches and marker-assisted selection. Transcription factors are major players in water stress signaling and some constitute major hubs in the signaling webs. The main transcription factors in this network include MYB, bHLH, bZIP, ERF, NAC, and WRKY transcription factors. The role of WRKY transcription factors in abiotic stress signaling networks is just becoming apparent and systems biology approaches are starting to define their places in the signaling network. Using systems biology approaches, there are now many transcriptomic analyses and promoter analyses that concern WRKY transcription factors. In addition, reports on nuclear proteomics have identified WRKY proteins that are up-regulated at the protein level by water stress. Interactomics has started to identify different classes of WRKY-interacting proteins. What are often lacking are connections between metabolomics, WRKY transcription factors, promoters, biosynthetic pathways, fluxes and downstream responses. As more levels of the system are characterized, a more detailed understanding of the roles of WRKY transcription factors in drought responses in crops will be obtained.

The Endogenous GRP78 Interactome in Human Head and Neck Cancers: A Deterministic Role of Cell Surface GRP78 in Cancer Stemness.

PubMed

Chen, Hsin-Ying; Chang, Joseph Tung-Chieh; Chien, Kun-Yi; Lee, Yun-Shien; You, Guo-Rung; Cheng, Ann-Joy

2018-01-11

Cell surface glucose regulated protein 78 (GRP78), an endoplasmic reticulum (ER) chaperone, was suggested to be a cancer stem cell marker, but the influence of this molecule on cancer stemness is poorly characterized. In this study, we developed a mass spectrometry platform to detect the endogenous interactome of GRP78 and investigated its role in cancer stemness. The interactome results showed that cell surface GRP78 associates with multiple molecules. The influence of cell population heterogeneity of head and neck cancer cell lines (OECM1, FaDu, and BM2) according to the cell surface expression levels of GRP78 and the GRP78 interactome protein, Progranulin, was investigated. The four sorted cell groups exhibited distinct cell cycle distributions, asymmetric/symmetric cell divisions, and different relative expression levels of stemness markers. Our results demonstrate that cell surface GRP78 promotes cancer stemness, whereas drives cells toward a non-stemlike phenotype when it chaperones Progranulin. We conclude that cell surface GRP78 is a chaperone exerting a deterministic influence on cancer stemness.

Prediction of scaffold proteins based on protein interaction and domain architectures.

PubMed

Oh, Kimin; Yi, Gwan-Su

2016-07-28

Scaffold proteins are known for being crucial regulators of various cellular functions by assembling multiple proteins involved in signaling and metabolic pathways. Identification of scaffold proteins and the study of their molecular mechanisms can open a new aspect of cellular systemic regulation and the results can be applied in the field of medicine and engineering. Despite being highlighted as the regulatory roles of dozens of scaffold proteins, there was only one known computational approach carried out so far to find scaffold proteins from interactomes. However, there were limitations in finding diverse types of scaffold proteins because their criteria were restricted to the classical scaffold proteins. In this paper, we will suggest a systematic approach to predict massive scaffold proteins from interactomes and to characterize the roles of scaffold proteins comprehensively. From a total of 10,419 basic scaffold protein candidates in protein interactomes, we classified them into three classes according to the structural evidences for scaffolding, such as domain architectures, domain interactions and protein complexes. Finally, we could define 2716 highly reliable scaffold protein candidates and their characterized functional features. To assess the accuracy of our prediction, the gold standard positive and negative data sets were constructed. We prepared 158 gold standard positive data and 844 gold standard negative data based on the functional information from Gene Ontology consortium. The precision, sensitivity and specificity of our testing was 80.3, 51.0, and 98.5 % respectively. Through the function enrichment analysis of highly reliable scaffold proteins, we could confirm the significantly enriched functions that are related to scaffold protein binding. We also identified functional association between scaffold proteins and their recruited proteins. Furthermore, we checked that the disease association of scaffold proteins is higher than kinases. In conclusion, we could predict larger volume of scaffold proteins and analyzed their functional characteristics. Deeper understandings about the roles of scaffold proteins from this study will provide a higher opportunity to find therapeutic or engineering applications of scaffold proteins using their functional characteristics.

Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature.

PubMed

Poornima, Paramasivan; Kumar, Jothi Dinesh; Zhao, Qiaoli; Blunder, Martina; Efferth, Thomas

2016-09-01

Despite massive investments in drug research and development, the significant decline in the number of new drugs approved or translated to clinical use raises the question, whether single targeted drug discovery is the right approach. To combat complex systemic diseases that harbour robust biological networks such as cancer, single target intervention is proved to be ineffective. In such cases, network pharmacology approaches are highly useful, because they differ from conventional drug discovery by addressing the ability of drugs to target numerous proteins or networks involved in a disease. Pleiotropic natural products are one of the promising strategies due to their multi-targeting and due to lower side effects. In this review, we discuss the application of network pharmacology for cancer drug discovery. We provide an overview of the current state of knowledge on network pharmacology, focus on different technical approaches and implications for cancer therapy (e.g. polypharmacology and synthetic lethality), and illustrate the therapeutic potential with selected examples green tea polyphenolics, Eleutherococcus senticosus, Rhodiola rosea, and Schisandra chinensis). Finally, we present future perspectives on their plausible applications for diagnosis and therapy of cancer. Copyright © 2016 Elsevier Ltd. All rights reserved.

DNAJC17 is localized in nuclear speckles and interacts with splicing machinery components.

PubMed

Pascarella, A; Ferrandino, G; Credendino, S C; Moccia, C; D'Angelo, F; Miranda, B; D'Ambrosio, C; Bielli, P; Spadaro, O; Ceccarelli, M; Scaloni, A; Sette, C; De Felice, M; De Vita, G; Amendola, E

2018-05-17

DNAJC17 is a heat shock protein (HSP40) family member, identified in mouse as susceptibility gene for congenital hypothyroidism. DNAJC17 knockout mouse embryos die prior to implantation. In humans, germline homozygous mutations in DNAJC17 have been found in syndromic retinal dystrophy patients, while heterozygous mutations represent candidate pathogenic events for myeloproliferative disorders. Despite widespread expression and involvement in human diseases, DNAJC17 function is still poorly understood. Herein, we have investigated its function through high-throughput transcriptomic and proteomic approaches. DNAJC17-depleted cells transcriptome highlighted genes involved in general functional categories, mainly related to gene expression. Conversely, DNAJC17 interactome can be classified in very specific functional networks, with the most enriched one including proteins involved in splicing. Furthermore, several splicing-related interactors, were independently validated by co-immunoprecipitation and in vivo co-localization. Accordingly, co-localization of DNAJC17 with SC35, a marker of nuclear speckles, further supported its interaction with spliceosomal components. Lastly, DNAJC17 up-regulation enhanced splicing efficiency of minigene reporter in live cells, while its knockdown induced perturbations of splicing efficiency at whole genome level, as demonstrated by specific analysis of RNAseq data. In conclusion, our study strongly suggests a role of DNAJC17 in splicing-related processes and provides support to its recognized essential function in early development.

Dissecting the Gene Network of Dietary Restriction to Identify Evolutionarily Conserved Pathways and New Functional Genes

PubMed Central

Wuttke, Daniel; Connor, Richard; Vora, Chintan; Craig, Thomas; Li, Yang; Wood, Shona; Vasieva, Olga; Shmookler Reis, Robert; Tang, Fusheng; de Magalhães, João Pedro

2012-01-01

Dietary restriction (DR), limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR–essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/). To dissect the interactions of DR–essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR–essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR–essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2) had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR–induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of multiple organisms led us to suggest that DR commonly suppresses translation, while stimulating an ancient reproduction-related process. PMID:22912585

Discovery Proteomics Identifies a Molecular Link between the Coatomer Protein Complex I and Androgen Receptor-dependent Transcription*

PubMed Central

Hsiao, Jordy J.; Smits, Melinda M.; Ng, Brandon H.; Lee, Jinhee; Wright, Michael E.

2016-01-01

Aberrant androgen receptor (AR)-dependent transcription is a hallmark of human prostate cancers. At the molecular level, ligand-mediated AR activation is coordinated through spatial and temporal protein-protein interactions involving AR-interacting proteins, which we designate the “AR-interactome.” Despite many years of research, the ligand-sensitive protein complexes involved in ligand-mediated AR activation in prostate tumor cells have not been clearly defined. Here, we describe the development, characterization, and utilization of a novel human LNCaP prostate tumor cell line, N-AR, which stably expresses wild-type AR tagged at its N terminus with the streptavidin-binding peptide epitope (streptavidin-binding peptide-tagged wild-type androgen receptor; SBP-AR). A bioanalytical workflow involving streptavidin chromatography and label-free quantitative mass spectrometry was used to identify SBP-AR and associated ligand-sensitive cytosolic proteins/protein complexes linked to AR activation in prostate tumor cells. Functional studies verified that ligand-sensitive proteins identified in the proteomic screen encoded modulators of AR-mediated transcription, suggesting that these novel proteins were putative SBP-AR-interacting proteins in N-AR cells. This was supported by biochemical associations between recombinant SBP-AR and the ligand-sensitive coatomer protein complex I (COPI) retrograde trafficking complex in vitro. Extensive biochemical and molecular experiments showed that the COPI retrograde complex regulates ligand-mediated AR transcriptional activation, which correlated with the mobilization of the Golgi-localized ARA160 coactivator to the nuclear compartment of prostate tumor cells. Collectively, this study provides a bioanalytical strategy to validate the AR-interactome and define novel AR-interacting proteins involved in ligand-mediated AR activation in prostate tumor cells. Moreover, we describe a cellular system to study how compartment-specific AR-interacting proteins influence AR activation and contribute to aberrant AR-dependent transcription that underlies the majority of human prostate cancers. PMID:27365400

Expanding the Interactome of TES by Exploiting TES Modules with Different Subcellular Localizations.

PubMed

Sala, Stefano; Van Troys, Marleen; Medves, Sandrine; Catillon, Marie; Timmerman, Evy; Staes, An; Schaffner-Reckinger, Elisabeth; Gevaert, Kris; Ampe, Christophe

2017-05-05

The multimodular nature of many eukaryotic proteins underlies their temporal or spatial engagement in a range of protein cocomplexes. Using the multimodule protein testin (TES), we here report a proteomics approach to increase insight in cocomplex diversity. The LIM-domain containing and tumor suppressor protein TES is present at different actin cytoskeleton adhesion structures in cells and influences cell migration, adhesion and spreading. TES module accessibility has been proposed to vary due to conformational switching and variants of TES lacking specific domains target to different subcellular locations. By applying iMixPro AP-MS ("intelligent Mixing of Proteomes"-affinity purification-mass spectrometry) to a set of tagged-TES modular variants, we identified proteins residing in module-specific cocomplexes. The obtained distinct module-specific interactomes combine to a global TES interactome that becomes more extensive and richer in information. Applying pathway analysis to the module interactomes revealed expected actin-related canonical pathways and also less expected pathways. We validated two new TES cocomplex partners: TGFB1I1 and a short form of the glucocorticoid receptor. TES and TGFB1I1 are shown to oppositely affect cell spreading providing biological validity for their copresence in complexes since they act in similar processes.

Proteomic characterization of hempseed (Cannabis sativa L.).

PubMed

Aiello, Gilda; Fasoli, Elisa; Boschin, Giovanna; Lammi, Carmen; Zanoni, Chiara; Citterio, Attilio; Arnoldi, Anna

2016-09-16

This paper presents an investigation on hempseed proteome. The experimental approach, based on combinatorial peptide ligand libraries (CPLLs), SDS-PAGE separation, nLC-ESI-MS/MS identification, and database search, permitted identifying in total 181 expressed proteins. This very large number of identifications was achieved by searching in two databases: Cannabis sativa L. (56 gene products identified) and Arabidopsis thaliana (125 gene products identified). By performing a protein-protein association network analysis using the STRING software, it was possible to build the first interactomic map of all detected proteins, characterized by 137 nodes and 410 interactions. Finally, a Gene Ontology analysis of the identified species permitted to classify their molecular functions: the great majority is involved in the seed metabolic processes (41%), responses to stimulus (8%), and biological process (7%). Hempseed is an underexploited non-legume protein-rich seed. Although its protein is well known for its digestibility, essential amino acid composition, and useful techno-functional properties, a comprehensive proteome characterization is still lacking. The objective of this work was to fill this knowledge gap and provide information useful for a better exploitation of this seed in different food products. Copyright © 2016 Elsevier B.V. All rights reserved.

GRID-seq reveals the global RNA-chromatin interactome

PubMed Central

Li, Xiao; Zhou, Bing; Chen, Liang; Gou, Lan-Tao; Li, Hairi; Fu, Xiang-Dong

2017-01-01

Higher eukaryotic genomes are bound by a large number of coding and non-coding RNAs, but approaches to comprehensively map the identity and binding sites of these RNAs are lacking. Here we report a method to in situ capture global RNA interactions with DNA by deep sequencing (GRID-seq), which enables the comprehensive identification of the entire repertoire of chromatin-interacting RNAs and their respective binding sites. In human, mouse and Drosophila cells, we detected a large set of tissue-specific coding and non-coding RNAs that are bound to active promoters and enhancers, especially super-enhancers. Assuming that most mRNA-chromatin interactions indicate the physical proximity of a promoter and an enhancer, we constructed a three-dimensional global connectivity map of promoters and enhancers, revealing transcription activity-linked genomic interactions in the nucleus. PMID:28922346

Defining the human deubiquitinating enzyme interaction landscape.

PubMed

Sowa, Mathew E; Bennett, Eric J; Gygi, Steven P; Harper, J Wade

2009-07-23

Deubiquitinating enzymes (Dubs) function to remove covalently attached ubiquitin from proteins, thereby controlling substrate activity and/or abundance. For most Dubs, their functions, targets, and regulation are poorly understood. To systematically investigate Dub function, we initiated a global proteomic analysis of Dubs and their associated protein complexes. This was accomplished through the development of a software platform called CompPASS, which uses unbiased metrics to assign confidence measurements to interactions from parallel nonreciprocal proteomic data sets. We identified 774 candidate interacting proteins associated with 75 Dubs. Using Gene Ontology, interactome topology classification, subcellular localization, and functional studies, we link Dubs to diverse processes, including protein turnover, transcription, RNA processing, DNA damage, and endoplasmic reticulum-associated degradation. This work provides the first glimpse into the Dub interaction landscape, places previously unstudied Dubs within putative biological pathways, and identifies previously unknown interactions and protein complexes involved in this increasingly important arm of the ubiquitin-proteasome pathway.

Defining the Human Deubiquitinating Enzyme Interaction Landscape

PubMed Central

Sowa, Mathew E.; Bennett, Eric J.; Gygi, Steven P.; Harper, J. Wade

2009-01-01

Summary Deubiquitinating enzymes (Dubs) function to remove covalently attached ubiquitin from proteins, thereby controlling substrate activity and/or abundance. For most Dubs, their functions, targets, and regulation are poorly understood. To systematically investigate Dub function, we initiated a global proteomic analysis of Dubs and their associated protein complexes. This was accomplished through the development of a software platform, called CompPASS, which uses unbiased metrics to assign confidence measurements to interactions from parallel non-reciprocal proteomic datasets. We identified 774 candidate interacting proteins associated with 75 Dubs. Using Gene Ontology, interactome topology classification, sub-cellular localization and functional studies, we link Dubs to diverse processes, including protein turnover, transcription, RNA processing, DNA damage, and endoplasmic reticulum-associated degradation. This work provides the first glimpse into the Dub interaction landscape, places previously unstudied Dubs within putative biological pathways, and identifies previously unknown interactions and protein complexes involved in this increasingly important arm of the ubiquitin-proteasome pathway. PMID:19615732

Comprehensive Characterization of Minichromosome Maintenance Complex (MCM) Protein Interactions Using Affinity and Proximity Purifications Coupled to Mass Spectrometry.

PubMed

Dubois, Marie-Line; Bastin, Charlotte; Lévesque, Dominique; Boisvert, François-Michel

2016-09-02

The extensive identification of protein-protein interactions under different conditions is an important challenge to understand the cellular functions of proteins. Here we use and compare different approaches including affinity purification and purification by proximity coupled to mass spectrometry to identify protein complexes. We explore the complete interactome of the minichromosome maintenance (MCM) complex by using both approaches for all of the different MCM proteins. Overall, our analysis identified unique and shared interaction partners and proteins enriched for distinct biological processes including DNA replication, DNA repair, and cell cycle regulation. Furthermore, we mapped the changes in protein interactions of the MCM complex in response to DNA damage, identifying a new role for this complex in DNA repair. In summary, we demonstrate the complementarity of these approaches for the characterization of protein interactions within the MCM complex.

Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana

PubMed Central

Van Leene, Jelle; Hollunder, Jens; Eeckhout, Dominique; Persiau, Geert; Van De Slijke, Eveline; Stals, Hilde; Van Isterdael, Gert; Verkest, Aurine; Neirynck, Sandy; Buffel, Yelle; De Bodt, Stefanie; Maere, Steven; Laukens, Kris; Pharazyn, Anne; Ferreira, Paulo C G; Eloy, Nubia; Renne, Charlotte; Meyer, Christian; Faure, Jean-Denis; Steinbrenner, Jens; Beynon, Jim; Larkin, John C; Van de Peer, Yves; Hilson, Pierre; Kuiper, Martin; De Veylder, Lieven; Van Onckelen, Harry; Inzé, Dirk; Witters, Erwin; De Jaeger, Geert

2010-01-01

Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)–cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK–cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants. PMID:20706207

The Two-pore channel (TPC) interactome unmasks isoform-specific roles for TPCs in endolysosomal morphology and cell pigmentation

PubMed Central

Lin-Moshier, Yaping; Keebler, Michael V.; Hooper, Robert; Boulware, Michael J.; Liu, Xiaolong; Churamani, Dev; Abood, Mary E.; Walseth, Timothy F.; Brailoiu, Eugen; Patel, Sandip; Marchant, Jonathan S.

2014-01-01

The two-pore channels (TPC1 and TPC2) belong to an ancient family of intracellular ion channels expressed in the endolysosomal system. Little is known about how regulatory inputs converge to modulate TPC activity, and proposed activation mechanisms are controversial. Here, we compiled a proteomic characterization of the human TPC interactome, which revealed that TPCs complex with many proteins involved in Ca2+ homeostasis, trafficking, and membrane organization. Among these interactors, TPCs were resolved to scaffold Rab GTPases and regulate endomembrane dynamics in an isoform-specific manner. TPC2, but not TPC1, caused a proliferation of endolysosomal structures, dysregulating intracellular trafficking, and cellular pigmentation. These outcomes required both TPC2 and Rab activity, as well as their interactivity, because TPC2 mutants that were inactive, or rerouted away from their endogenous expression locale, or deficient in Rab binding, failed to replicate these outcomes. Nicotinic acid adenine dinucleotide phosphate (NAADP)-evoked Ca2+ release was also impaired using either a Rab binding-defective TPC2 mutant or a Rab inhibitor. These data suggest a fundamental role for the ancient TPC complex in trafficking that holds relevance for lysosomal proliferative scenarios observed in disease. PMID:25157141

The Two-pore channel (TPC) interactome unmasks isoform-specific roles for TPCs in endolysosomal morphology and cell pigmentation.

PubMed

Lin-Moshier, Yaping; Keebler, Michael V; Hooper, Robert; Boulware, Michael J; Liu, Xiaolong; Churamani, Dev; Abood, Mary E; Walseth, Timothy F; Brailoiu, Eugen; Patel, Sandip; Marchant, Jonathan S

2014-09-09

The two-pore channels (TPC1 and TPC2) belong to an ancient family of intracellular ion channels expressed in the endolysosomal system. Little is known about how regulatory inputs converge to modulate TPC activity, and proposed activation mechanisms are controversial. Here, we compiled a proteomic characterization of the human TPC interactome, which revealed that TPCs complex with many proteins involved in Ca(2+) homeostasis, trafficking, and membrane organization. Among these interactors, TPCs were resolved to scaffold Rab GTPases and regulate endomembrane dynamics in an isoform-specific manner. TPC2, but not TPC1, caused a proliferation of endolysosomal structures, dysregulating intracellular trafficking, and cellular pigmentation. These outcomes required both TPC2 and Rab activity, as well as their interactivity, because TPC2 mutants that were inactive, or rerouted away from their endogenous expression locale, or deficient in Rab binding, failed to replicate these outcomes. Nicotinic acid adenine dinucleotide phosphate (NAADP)-evoked Ca(2+) release was also impaired using either a Rab binding-defective TPC2 mutant or a Rab inhibitor. These data suggest a fundamental role for the ancient TPC complex in trafficking that holds relevance for lysosomal proliferative scenarios observed in disease.

An HDAC3-PROX1 corepressor module acts on HNF4α to control hepatic triglycerides.

PubMed

Armour, Sean M; Remsberg, Jarrett R; Damle, Manashree; Sidoli, Simone; Ho, Wesley Y; Li, Zhenghui; Garcia, Benjamin A; Lazar, Mitchell A

2017-09-15

The histone deacetylase HDAC3 is a critical mediator of hepatic lipid metabolism, and liver-specific deletion of HDAC3 leads to fatty liver. To elucidate the underlying mechanism, here we report a method of cross-linking followed by mass spectrometry to define a high-confidence HDAC3 interactome in vivo that includes the canonical NCoR-HDAC3 complex as well as Prospero-related homeobox 1 protein (PROX1). HDAC3 and PROX1 co-localize extensively on the mouse liver genome, and are co-recruited by hepatocyte nuclear factor 4α (HNF4α). The HDAC3-PROX1 module controls the expression of a gene program regulating lipid homeostasis, and hepatic-specific ablation of either component increases triglyceride content in liver. These findings underscore the importance of specific combinations of transcription factors and coregulators in the fine tuning of organismal metabolism.HDAC3 is a critical mediator of hepatic lipid metabolism and its loss leads to fatty liver. Here, the authors characterize the liver HDAC3 interactome in vivo, provide evidence that HDAC3 interacts with PROX1, and show that HDAC3 and PROX1 control expression of genes regulating lipid homeostasis.

The nuclear F-actin interactome of Xenopus oocytes reveals an actin-bundling kinesin that is essential for meiotic cytokinesis

PubMed Central

Samwer, Matthias; Dehne, Heinz-Jürgen; Spira, Felix; Kollmar, Martin; Gerlich, Daniel W; Urlaub, Henning; Görlich, Dirk

2013-01-01

Nuclei of Xenopus laevis oocytes grow 100 000-fold larger in volume than a typical somatic nucleus and require an unusual intranuclear F-actin scaffold for mechanical stability. We now developed a method for mapping F-actin interactomes and identified a comprehensive set of F-actin binders from the oocyte nuclei. Unexpectedly, the most prominent interactor was a novel kinesin termed NabKin (Nuclear and meiotic actin-bundling Kinesin). NabKin not only binds microtubules but also F-actin structures, such as the intranuclear actin bundles in prophase and the contractile actomyosin ring during cytokinesis. The interaction between NabKin and F-actin is negatively regulated by Importin-β and is responsive to spatial information provided by RanGTP. Disconnecting NabKin from F-actin during meiosis caused cytokinesis failure and egg polyploidy. We also found actin-bundling activity in Nabkin's somatic paralogue KIF14, which was previously shown to be essential for somatic cell division. Our data are consistent with the notion that NabKin/KIF14 directly link microtubules with F-actin and that such link is essential for cytokinesis. PMID:23727888

PREFACE: Physics approaches to protein interactions and gene regulation Physics approaches to protein interactions and gene regulation

NASA Astrophysics Data System (ADS)

Nussinov, Ruth; Panchenko, Anna R.; Przytycka, Teresa

2011-06-01

Physics approaches focus on uncovering, modeling and quantitating the general principles governing the micro and macro universe. This has always been an important component of biological research, however recent advances in experimental techniques and the accumulation of unprecedented genome-scale experimental data produced by these novel technologies now allow for addressing fundamental questions on a large scale. These relate to molecular interactions, principles of bimolecular recognition, and mechanisms of signal propagation. The functioning of a cell requires a variety of intermolecular interactions including protein-protein, protein-DNA, protein-RNA, hormones, peptides, small molecules, lipids and more. Biomolecules work together to provide specific functions and perturbations in intermolecular communication channels often lead to cellular malfunction and disease. A full understanding of the interactome requires an in-depth grasp of the biophysical principles underlying individual interactions as well as their organization in cellular networks. Phenomena can be described at different levels of abstraction. Computational and systems biology strive to model cellular processes by integrating and analyzing complex data from multiple experimental sources using interdisciplinary tools. As a result, both the causal relationships between the variables and the general features of the system can be discovered, which even without knowing the details of the underlying mechanisms allow for putting forth hypotheses and predicting the behavior of the systems in response to perturbation. And here lies the strength of in silico models which provide control and predictive power. At the same time, the complexity of individual elements and molecules can be addressed by the fields of molecular biophysics, physical biology and structural biology, which focus on the underlying physico-chemical principles and may explain the molecular mechanisms of cellular function. In this issue we have assembled a representative set of papers written by experts with diverse scientific backgrounds, each offering a unique viewpoint on using computational and physics methods to study biological systems at different levels of organization. We start with studies that aim to decipher the mechanisms of molecular recognition using biophysics methods and then expand our scale, concluding the issue with studies of interaction networks at cellular and population levels. Biomolecules interact with each other in a highly specific manner and selectively recognize their partners among hundreds of thousands of other molecules. As the paper by Zhang et al points out, this recognition process should be fast and guided by long-range electrostatic forces that select and bring the interacting partners together. The authors show that the increase of salt concentration leads to destabilization of protein complexes, suggesting an optimization of the charge-charge interactions across the protein binding interfaces. The following paper by Berezovsky further explores the balance of different interactions in protein complexes and uses physical concepts to explain the entire spectrum of protein structural classes, from intrinsically disordered to hyperthermostable proteins. The author describes highly unstructured viral proteins at one end of the spectrum and discusses the balance of stabilizing interactions in protein complexes from thermophilic organisms at the other. Recently accumulated evidence has indicated that native proteins do not necessarily require a unique structure to be biologically active, and in some cases structural disorder or intrinsic flexibility can be a prerequisite for their function. From the physical point of view, these disordered/flexible proteins exist in dynamic equilibrium between different conformational states, some of which could be selected upon binding to another partner. Such a property allows disordered proteins to achieve specific binding and at the same time reversibility and diversity in their interactions. Interestingly, as is shown in the paper by Mészáros et al, even though some disordered regions and proteins have a tendency to fold upon binding, the structures of their complexes still reveal their inherent flexibility. Indeed, disordered proteins and their complexes have certain properties which distinguish them from proteins with well-defined structures. This is evident from the papers by Lobanov and Galzitskaya, and Mészáros et al, which show that such characteristic features of disordered proteins allow their successful computational prediction from the sequence alone. Computational prediction of protein disorder has been used in another study by Takeda et al where the authors investigate the role of disorder in the function of a specific actin capping protein. The paper presents normal mode analysis with the elastic network model to examine the mechanisms of intrinsic flexibility and its biological role in actin function. Analysis of the underlying mechanisms and key factors in protein recognition might be essential for the prediction of protein-protein interactions. The papers by Tuncbag et al and Hashimoto et al demonstrate how incorporating the physico-chemical properties of binding interfaces and their atomic details obtained from protein crystal structures might be used to increase the accuracy of predicted protein-protein interactions and provide data on relative orientations of interacting proteins and on the locations of binding sites. Moreover, analysis of protein-protein interactions might require further fine-tuning for different types of assemblies, like that shown in the example of homooligomers by Hashimoto et al. Studies of protein-protein interactions at the molecular level have contributed considerably to understanding the principles of large-scale organization of the cellular interactome. Using graph theory as a unifying language, many characteristic properties of bimolecular networks have been identified, including scale free distribution of the vertex degree, network motifs, and modularity, to name a few. These studies of network organization require the network to be as complete as possible, which given the limitations of experimental techniques is not currently the case. Therefore, experimental procedures for detecting biomolecular interactions should be complemented by computational approaches. The paper by Lees et al provides a review of computational methods, integrating multiple independent sources of data to infer physical and functional protein-protein interaction networks. One of the important aspects of protein interactions that should be accounted for in the prediction of protein interaction networks is that many proteins are composed of distinct domains. Protein domains may mediate protein interactions while proteins and their interaction networks may gain complexity through gene duplication and expansion of existing domain architectures via domain rearrangements. The latter mechanisms have been explored in detail in the paper by Cohen-Gihon et al. Protein-protein interactions are not the only component of the cell's interactome. Regulation of cell activity can be achieved at the level of transcription and involve a transcription factor—DNA binding which typically requires recognition of a specific DNA sequence motif. Chip-Chip and the more recent Chip-Seq technologies allow in vivo identification of DNA binding sites and, together with novel in vitro approaches, provide data necessary for deciphering the corresponding binding motifs. Such information, complemented by structures of protein-DNA complexes and knowledge of the differences in binding sites among homologs, opens the door to constructing predictive binding models. The paper by Persikov and Singh provides an example of such a model in the Cys2His2 zinc finger family. Recent studies have indicated that the presence of such binding motifs is, however, neither necessary nor sufficient for transcription factor activity. Transcription regulation is a complex and still not fully understood process involving, in addition to protein-DNA binding, other factors such as epigenetic modifications and three-dimensional DNA organization. In this issue, Levens and Benham discuss another important mechanism which is likely to contribute to overall gene regulation—changes of DNA secondary structure in response to supercoiling-induced stress. Pointing out that DNA is "more than a cipher", they argue that the DNA structural transitions driven by negative supercoiling may have profound consequences for the cell and have to be accounted for in detailed models. There is considerable progress in physical modeling of DNA dynamics in response to stress. Such efforts, supported by experimental data, will bring us closer to an understanding of the role of supercoiling in gene regulation. Large-scale biomolecular interaction networks not only provide a system-level view of cellular processes, but are also increasingly used to model communications between molecules. The lack of sufficient biochemical data and the gigantic scale of the network prevented detailed modeling of network dynamics and have stimulated the development of simplified models such as the information flow approach described by Kim et al in this issue. Importantly, despite their simplicity, such models proved to be extremely useful for identifying network modules, essential nodes, and molecular pathways which are dysregulated in complex diseases such as cancer. Finally, moving from studies of single cells towards populations, one has to recognize the heterogeneity present within a population of cells. In the context of protein abundance, such cell-to-cell variation within clonal populations of cells, referred to as expression noise, has recently become a focus of intense cross-disciplinary research. Concerted efforts of experimentalists, physicists and mathematicians have brought us closer to understanding the source, potential drawbacks and benefits of noise for cell function. Differences in protein expression levels are even more pronounced in samples from mixed cell populations. How does such a mixture of cell populations affect the measurements of total gene expression? This question is addressed by Hebenstreit and Teichmann who show that decomposing a signal coming from a mixture of cellular populations requires insights from theoretical modeling. Recent technological advancements permitting genome-wide scale measurements of diverse molecular properties and consequently higher levels of quantitative reasoning are attracting physicists, mathematicians and computer scientists to the study of biological systems. Building on the synergy between these fields, we are entering an exciting era where physics methods are used in conjunction with these disciplines which, combined with statistical methods, provide quantitative descriptions of biology. Acknowledgments This project was funded with federal funds from the National Cancer Institute, National Institutes of Health, under contract number HHSN261200800001E. This research was supported by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research and the National Library of Medicine at National Institutes of Health/DHHS. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government.

Mapping protein-protein interactions using yeast two-hybrid assays.

PubMed

Mehla, Jitender; Caufield, J Harry; Uetz, Peter

2015-05-01

Yeast two-hybrid (Y2H) screens are an efficient system for mapping protein-protein interactions and whole interactomes. The screens can be performed using random libraries or collections of defined open reading frames (ORFs) called ORFeomes. This protocol describes both library and array-based Y2H screening, with an emphasis on array-based assays. Array-based Y2H is commonly used to test a number of "prey" proteins for interactions with a single "bait" (target) protein or pool of proteins. The advantage of this approach is the direct identification of interacting protein pairs without further downstream experiments: The identity of the preys is known and does not require further confirmation. In contrast, constructing and screening a random prey library requires identification of individual prey clones and systematic retesting. Retesting is typically performed in an array format. © 2015 Cold Spring Harbor Laboratory Press.

Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes

PubMed Central

2013-01-01

Background MicroRNAs (miRNAs) are important post-transcriptional regulators that have been demonstrated to play an important role in human diseases. Elucidating the associations between miRNAs and diseases at the systematic level will deepen our understanding of the molecular mechanisms of diseases. However, miRNA-disease associations identified by previous computational methods are far from completeness and more effort is needed. Results We developed a computational framework to identify miRNA-disease associations by performing random walk analysis, and focused on the functional link between miRNA targets and disease genes in protein-protein interaction (PPI) networks. Furthermore, a bipartite miRNA-disease network was constructed, from which several miRNA-disease co-regulated modules were identified by hierarchical clustering analysis. Our approach achieved satisfactory performance in identifying known cancer-related miRNAs for nine human cancers with an area under the ROC curve (AUC) ranging from 71.3% to 91.3%. By systematically analyzing the global properties of the miRNA-disease network, we found that only a small number of miRNAs regulated genes involved in various diseases, genes associated with neurological diseases were preferentially regulated by miRNAs and some immunological diseases were associated with several specific miRNAs. We also observed that most diseases in the same co-regulated module tended to belong to the same disease category, indicating that these diseases might share similar miRNA regulatory mechanisms. Conclusions In this study, we present a computational framework to identify miRNA-disease associations, and further construct a bipartite miRNA-disease network for systematically analyzing the global properties of miRNA regulation of disease genes. Our findings provide a broad perspective on the relationships between miRNAs and diseases and could potentially aid future research efforts concerning miRNA involvement in disease pathogenesis. PMID:24103777

circlncRNAnet: an integrated web-based resource for mapping functional networks of long or circular forms of noncoding RNAs.

PubMed

Wu, Shao-Min; Liu, Hsuan; Huang, Po-Jung; Chang, Ian Yi-Feng; Lee, Chi-Ching; Yang, Chia-Yu; Tsai, Wen-Sy; Tan, Bertrand Chin-Ming

2018-01-01

Despite their lack of protein-coding potential, long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) have emerged as key determinants in gene regulation, acting to fine-tune transcriptional and signaling output. These noncoding RNA transcripts are known to affect expression of messenger RNAs (mRNAs) via epigenetic and post-transcriptional regulation. Given their widespread target spectrum, as well as extensive modes of action, a complete understanding of their biological relevance will depend on integrative analyses of systems data at various levels. While a handful of publicly available databases have been reported, existing tools do not fully capture, from a network perspective, the functional implications of lncRNAs or circRNAs of interest. Through an integrated and streamlined design, circlncRNAnet aims to broaden the understanding of ncRNA candidates by testing in silico several hypotheses of ncRNA-based functions, on the basis of large-scale RNA-seq data. This web server is implemented with several features that represent advances in the bioinformatics of ncRNAs: (1) a flexible framework that accepts and processes user-defined next-generation sequencing-based expression data; (2) multiple analytic modules that assign and productively assess the regulatory networks of user-selected ncRNAs by cross-referencing extensively curated databases; (3) an all-purpose, information-rich workflow design that is tailored to all types of ncRNAs. Outputs on expression profiles, co-expression networks and pathways, and molecular interactomes, are dynamically and interactively displayed according to user-defined criteria. In short, users may apply circlncRNAnet to obtain, in real time, multiple lines of functionally relevant information on circRNAs/lncRNAs of their interest. In summary, circlncRNAnet provides a "one-stop" resource for in-depth analyses of ncRNA biology. circlncRNAnet is freely available at http://app.cgu.edu.tw/circlnc/. © The Authors 2017. Published by Oxford University Press.

A network medicine approach to quantify distance between hereditary disease modules on the interactome

NASA Astrophysics Data System (ADS)

Caniza, Horacio; Romero, Alfonso E.; Paccanaro, Alberto

2015-12-01

We introduce a MeSH-based method that accurately quantifies similarity between heritable diseases at molecular level. This method effectively brings together the existing information about diseases that is scattered across the vast corpus of biomedical literature. We prove that sets of MeSH terms provide a highly descriptive representation of heritable disease and that the structure of MeSH provides a natural way of combining individual MeSH vocabularies. We show that our measure can be used effectively in the prediction of candidate disease genes. We developed a web application to query more than 28.5 million relationships between 7,574 hereditary diseases (96% of OMIM) based on our similarity measure.

Experimental Methods for Protein Interaction Identification and Characterization

NASA Astrophysics Data System (ADS)

Uetz, Peter; Titz, Björn; Cagney, Gerard

There are dozens of methods for the detection of protein-protein interactions but they fall into a few broad categories. Fragment complementation assays such as the yeast two-hybrid (Y2H) system are based on split proteins that are functionally reconstituted by fusions of interacting proteins. Biophysical methods include structure determination and mass spectrometric (MS) identification of proteins in complexes. Biochemical methods include methods such as far western blotting and peptide arrays. Only the Y2H and protein complex purification combined with MS have been used on a larger scale. Due to the lack of data it is still difficult to compare these methods with respect to their efficiency and error rates. Current data does not favor any particular method and thus multiple experimental approaches are necessary to maximally cover the interactome of any target cell or organism.

Coupling an EML4-ALK centric interactome with RNA interference identifies sensitizers to ALK inhibitors

PubMed Central

Zhang, Guolin; Scarborough, Hannah; Kim, Jihye; Rozhok, Andrii I.; Chen, Y. Ann; Zhang, Xiaohui; Song, Lanxi; Bai, Yun; Fang, Bin; Liu, Richard Z.; Koomen, John; Tan, Aik Choon; Degregori, James; Haura, Eric B.

2017-01-01

Patients with lung cancers harboring anaplastic lymphoma kinase (ALK) gene fusions benefit from treatment with ALK kinase inhibitors but acquired resistance inevitably arises. A better understanding of proximal ALK signaling mechanisms may identify sensitizers to ALK inhibitors that disrupt the balance between pro-survival and pro-apoptotic effector signals. Using affinity purification coupled with mass spectrometry in an ALK fusion lung cancer cell line (H3122), we generated an ALK signaling network and investigated signaling activity using tyrosine phosphoproteomics. We identified a network of 464 proteins composed of subnetworks with differential response to ALK inhibitors. A small hairpin RNA screen targeting 407 proteins in this network revealed 64 and 9 proteins whose loss sensitized cells to crizotinib and alectinib, respectively. Among these, knocking down fibroblast growth factor receptor substrate 2 (FRS2) or coiled-coil and C2 domain-containing protein 1A (CC2D1A, both scaffolding proteins, sensitized multiple ALK fusion cell lines to the ALK inhibitors crizotinib and alectinib. Collectively, our data provides a resource that enhances our understanding of signaling and drug resistance networks consequent to ALK fusions, and identifies potential targets to improve the efficacy of ALK inhibitors in patients. PMID:27811184

SPR Biosensors in Direct Molecular Fishing: Implications for Protein Interactomics.

PubMed

Florinskaya, Anna; Ershov, Pavel; Mezentsev, Yuri; Kaluzhskiy, Leonid; Yablokov, Evgeniy; Medvedev, Alexei; Ivanov, Alexis

2018-05-18

We have developed an original experimental approach based on the use of surface plasmon resonance (SPR) biosensors, applicable for investigation of potential partners involved in protein⁻protein interactions (PPI) as well as protein⁻peptide or protein⁻small molecule interactions. It is based on combining a SPR biosensor, size exclusion chromatography (SEC), mass spectrometric identification of proteins (LC-MS/MS) and direct molecular fishing employing principles of affinity chromatography for isolation of potential partner proteins from the total lysate of biological samples using immobilized target proteins (or small non-peptide compounds) as ligands. Applicability of this approach has been demonstrated within the frame of the Human Proteome Project (HPP) and PPI regulation by a small non-peptide biologically active compound, isatin.

Toxic Diatom Aldehydes Affect Defence Gene Networks in Sea Urchins

PubMed Central

Varrella, Stefano; Ruocco, Nadia; Ianora, Adrianna; Bentley, Matt G.; Costantini, Maria

2016-01-01

Marine organisms possess a series of cellular strategies to counteract the negative effects of toxic compounds, including the massive reorganization of gene expression networks. Here we report the modulated dose-dependent response of activated genes by diatom polyunsaturated aldehydes (PUAs) in the sea urchin Paracentrotus lividus. PUAs are secondary metabolites deriving from the oxidation of fatty acids, inducing deleterious effects on the reproduction and development of planktonic and benthic organisms that feed on these unicellular algae and with anti-cancer activity. Our previous results showed that PUAs target several genes, implicated in different functional processes in this sea urchin. Using interactomic Ingenuity Pathway Analysis we now show that the genes targeted by PUAs are correlated with four HUB genes, NF-κB, p53, δ-2-catenin and HIF1A, which have not been previously reported for P. lividus. We propose a working model describing hypothetical pathways potentially involved in toxic aldehyde stress response in sea urchins. This represents the first report on gene networks affected by PUAs, opening new perspectives in understanding the cellular mechanisms underlying the response of benthic organisms to diatom exposure. PMID:26914213

Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm. | Office of Cancer Genomics

Cancer.gov

We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined.

The RNA-binding protein LARP1 is a post-transcriptional regulator of survival and tumorigenesis in ovarian cancer

PubMed Central

Hopkins, Thomas G.; Mura, Manuela; Al-Ashtal, Hiba A.; Lahr, Roni M.; Abd-Latip, Normala; Sweeney, Katrina; Lu, Haonan; Weir, Justin; El-Bahrawy, Mona; Steel, Jennifer H.; Ghaem-Maghami, Sadaf; Aboagye, Eric O.; Berman, Andrea J.; Blagden, Sarah P.

2016-01-01

RNA-binding proteins (RBPs) are increasingly identified as post-transcriptional drivers of cancer progression. The RBP LARP1 is an mRNA stability regulator, and elevated expression of the protein in hepatocellular and lung cancers is correlated with adverse prognosis. LARP1 associates with an mRNA interactome that is enriched for oncogenic transcripts. Here we explore the role of LARP1 in epithelial ovarian cancer, a disease characterized by the rapid acquisition of resistance to chemotherapy through the induction of pro-survival signalling. We show, using ovarian cell lines and xenografts, that LARP1 is required for cancer cell survival and chemotherapy resistance. LARP1 promotes tumour formation in vivo and maintains cancer stem cell-like populations. Using transcriptomic analysis following LARP1 knockdown, cross-referenced against the LARP1 interactome, we identify BCL2 and BIK as LARP1 mRNA targets. We demonstrate that, through an interaction with the 3′ untranslated regions (3′ UTRs) of BCL2 and BIK, LARP1 stabilizes BCL2 but destabilizes BIK with the net effect of resisting apoptosis. Together, our data indicate that by differentially regulating the stability of a selection of mRNAs, LARP1 promotes ovarian cancer progression and chemotherapy resistance. PMID:26717985

DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome.

PubMed

Luo, Heng; Zhang, Ping; Cao, Xi Hang; Du, Dizheng; Ye, Hao; Huang, Hui; Li, Can; Qin, Shengying; Wan, Chunling; Shi, Leming; He, Lin; Yang, Lun

2016-11-02

The cost of developing a new drug has increased sharply over the past years. To ensure a reasonable return-on-investment, it is useful for drug discovery researchers in both industry and academia to identify all the possible indications for early pipeline molecules. For the first time, we propose the term computational "drug candidate positioning" or "drug positioning", to describe the above process. It is distinct from drug repositioning, which identifies new uses for existing drugs and maximizes their value. Since many therapeutic effects are mediated by unexpected drug-protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles to predict indications. Here we introduce the server DPDR-CPI, which can make real-time predictions based only on the structure of the small molecule. When a user submits a molecule, the server will dock it across 611 human proteins, generating a CPI profile of features that can be used for predictions. It can suggest the likelihood of relevance of the input molecule towards ~1,000 human diseases with top predictions listed. DPDR-CPI achieved an overall AUROC of 0.78 during 10-fold cross-validations and AUROC of 0.76 for the independent validation. The server is freely accessible via http://cpi.bio-x.cn/dpdr/.

Examining the Interactome of Huperzine A by Magnetic Biopanning

PubMed Central

Guo, Wei; Liu, Shupeng; Peng, Jinliang; Wei, Xiaohui; Sun, Ye; Qiu, Yangsheng; Gao, Guangwei; Wang, Peng; Xu, Yuhong

2012-01-01

Huperzine A is a bioactive compound derived from traditional Chinese medicine plant Qian Ceng Ta (Huperzia serrata), and was found to have multiple neuroprotective effects. In addition to being a potent acetylcholinesterase inhibitor, it was thought to act through other mechanisms such as antioxidation, antiapoptosis, etc. However, the molecular targets involved with these mechanisms were not identified. In this study, we attempted to exam the interactome of Huperzine A using a cDNA phage display library and also mammalian brain tissue extracts. The drugs were chemically linked on the surface of magnetic particles and the interactive phages or proteins were collected and analyzed. Among the various cDNA expressing phages selected, one was identified to encode the mitochondria NADH dehydrogenase subunit 1. Specific bindings between the drug and the target phages and target proteins were confirmed. Another enriched phage clone was identified as mitochondria ATP synthase, which was also panned out from the proteome of mouse brain tissue lysate. These data indicated the possible involvement of mitochondrial respiratory chain matrix enzymes in Huperzine A's pharmacological effects. Such involvement had been suggested by previous studies based on enzyme activity changes. Our data supported the new mechanism. Overall we demonstrated the feasibility of using magnetic biopanning as a simple and viable method for investigating the complex molecular mechanisms of bioactive molecules. PMID:22615909

APP is cleaved by Bace1 in pre-synaptic vesicles and establishes a pre-synaptic interactome, via its intracellular domain, with molecular complexes that regulate pre-synaptic vesicles functions.

PubMed

Del Prete, Dolores; Lombino, Franco; Liu, Xinran; D'Adamio, Luciano

2014-01-01

Amyloid Precursor Protein (APP) is a type I membrane protein that undergoes extensive processing by secretases, including BACE1. Although mutations in APP and genes that regulate processing of APP, such as PSENs and BRI2/ITM2B, cause dementias, the normal function of APP in synaptic transmission, synaptic plasticity and memory formation is poorly understood. To grasp the biochemical mechanisms underlying the function of APP in the central nervous system, it is important to first define the sub-cellular localization of APP in synapses and the synaptic interactome of APP. Using biochemical and electron microscopy approaches, we have found that APP is localized in pre-synaptic vesicles, where it is processed by Bace1. By means of a proteomic approach, we have characterized the synaptic interactome of the APP intracellular domain. We focused on this region of APP because in vivo data underline the central functional and pathological role of the intracellular domain of APP. Consistent with the expression of APP in pre-synaptic vesicles, the synaptic APP intracellular domain interactome is predominantly constituted by pre-synaptic, rather than post-synaptic, proteins. This pre-synaptic interactome of the APP intracellular domain includes proteins expressed on pre-synaptic vesicles such as the vesicular SNARE Vamp2/Vamp1 and the Ca2+ sensors Synaptotagmin-1/Synaptotagmin-2, and non-vesicular pre-synaptic proteins that regulate exocytosis, endocytosis and recycling of pre-synaptic vesicles, such as target-membrane-SNAREs (Syntaxin-1b, Syntaxin-1a, Snap25 and Snap47), Munc-18, Nsf, α/β/γ-Snaps and complexin. These data are consistent with a functional role for APP, via its carboxyl-terminal domain, in exocytosis, endocytosis and/or recycling of pre-synaptic vesicles.

Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition.

PubMed

Lambert, Jean-Philippe; Ivosev, Gordana; Couzens, Amber L; Larsen, Brett; Taipale, Mikko; Lin, Zhen-Yuan; Zhong, Quan; Lindquist, Susan; Vidal, Marc; Aebersold, Ruedi; Pawson, Tony; Bonner, Ron; Tate, Stephen; Gingras, Anne-Claude

2013-12-01

Characterizing changes in protein-protein interactions associated with sequence variants (e.g., disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies in which cost and time are major considerations. We have coupled AP to data-independent mass spectrometric acquisition (sequential window acquisition of all theoretical spectra, SWATH) and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. We used AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes and propose a scalable pipeline for systems biology studies.

Usher syndrome: molecular links of pathogenesis, proteins and pathways.

PubMed

Kremer, Hannie; van Wijk, Erwin; Märker, Tina; Wolfrum, Uwe; Roepman, Ronald

2006-10-15

Usher syndrome is the most common form of deaf-blindness. The syndrome is both clinically and genetically heterogeneous, and to date, eight causative genes have been identified. The proteins encoded by these genes are part of a dynamic protein complex that is present in hair cells of the inner ear and in photoreceptor cells of the retina. The localization of the Usher proteins and the phenotype in animal models indicate that the Usher protein complex is essential in the morphogenesis of the stereocilia bundle in hair cells and in the calycal processes of photoreceptor cells. In addition, the Usher proteins are important in the synaptic processes of both cell types. The association of other proteins with the complex indicates functional links to a number of basic cell-biological processes. Prominently present is the connection to the dynamics of the actin cytoskeleton, involved in cellular morphology, cell polarity and cell-cell interactions. The Usher protein complex can also be linked to the cadherins/catenins in the adherens junction-associated protein complexes, suggesting a role in cell polarity and tissue organization. A third link can be established to the integrin transmembrane signaling network. The Usher interactome, as outlined in this review, participates in pathways common in inner ear and retina that are disrupted in the Usher syndrome.

Assembling a protein-protein interaction map of the SSU processome from existing datasets.

PubMed

Lim, Young H; Charette, J Michael; Baserga, Susan J

2011-03-10

The small subunit (SSU) processome is a large ribonucleoprotein complex involved in small ribosomal subunit assembly. It consists of the U3 snoRNA and ∼72 proteins. While most of its components have been identified, the protein-protein interactions (PPIs) among them remain largely unknown, and thus the assembly, architecture and function of the SSU processome remains unclear. We queried PPI databases for SSU processome proteins to quantify the degree to which the three genome-wide high-throughput yeast two-hybrid (HT-Y2H) studies, the genome-wide protein fragment complementation assay (PCA) and the literature-curated (LC) datasets cover the SSU processome interactome. We find that coverage of the SSU processome PPI network is remarkably sparse. Two of the three HT-Y2H studies each account for four and six PPIs between only six of the 72 proteins, while the third study accounts for as little as one PPI and two proteins. The PCA dataset has the highest coverage among the genome-wide studies with 27 PPIs between 25 proteins. The LC dataset was the most extensive, accounting for 34 proteins and 38 PPIs, many of which were validated by independent methods, thereby further increasing their reliability. When the collected data were merged, we found that at least 70% of the predicted PPIs have yet to be determined and 26 proteins (36%) have no known partners. Since the SSU processome is conserved in all Eukaryotes, we also queried HT-Y2H datasets from six additional model organisms, but only four orthologues and three previously known interologous interactions were found. This provides a starting point for further work on SSU processome assembly, and spotlights the need for a more complete genome-wide Y2H analysis.

Assembling a Protein-Protein Interaction Map of the SSU Processome from Existing Datasets

PubMed Central

Baserga, Susan J.

2011-01-01

Background The small subunit (SSU) processome is a large ribonucleoprotein complex involved in small ribosomal subunit assembly. It consists of the U3 snoRNA and ∼72 proteins. While most of its components have been identified, the protein-protein interactions (PPIs) among them remain largely unknown, and thus the assembly, architecture and function of the SSU processome remains unclear. Methodology We queried PPI databases for SSU processome proteins to quantify the degree to which the three genome-wide high-throughput yeast two-hybrid (HT-Y2H) studies, the genome-wide protein fragment complementation assay (PCA) and the literature-curated (LC) datasets cover the SSU processome interactome. Conclusions We find that coverage of the SSU processome PPI network is remarkably sparse. Two of the three HT-Y2H studies each account for four and six PPIs between only six of the 72 proteins, while the third study accounts for as little as one PPI and two proteins. The PCA dataset has the highest coverage among the genome-wide studies with 27 PPIs between 25 proteins. The LC dataset was the most extensive, accounting for 34 proteins and 38 PPIs, many of which were validated by independent methods, thereby further increasing their reliability. When the collected data were merged, we found that at least 70% of the predicted PPIs have yet to be determined and 26 proteins (36%) have no known partners. Since the SSU processome is conserved in all Eukaryotes, we also queried HT-Y2H datasets from six additional model organisms, but only four orthologues and three previously known interologous interactions were found. This provides a starting point for further work on SSU processome assembly, and spotlights the need for a more complete genome-wide Y2H analysis. PMID:21423703

Definition of a RACK1 Interaction Network in Drosophila melanogaster Using SWATH-MS.

PubMed

Kuhn, Lauriane; Majzoub, Karim; Einhorn, Evelyne; Chicher, Johana; Pompon, Julien; Imler, Jean-Luc; Hammann, Philippe; Meignin, Carine

2017-07-05

Receptor for Activated protein C kinase 1 (RACK1) is a scaffold protein that has been found in association with several signaling complexes, and with the 40S subunit of the ribosome. Using the model organism Drosophila melanogaster , we recently showed that RACK1 is required at the ribosome for internal ribosome entry site (IRES)-mediated translation of viruses. Here, we report a proteomic characterization of the interactome of RACK1 in Drosophila S2 cells. We carried out Label-Free quantitation using both Data-Dependent and Data-Independent Acquisition (DDA and DIA, respectively) and observed a significant advantage for the Sequential Window Acquisition of all THeoretical fragment-ion spectra (SWATH) method, both in terms of identification of interactants and quantification of low abundance proteins. These data represent the first SWATH spectral library available for Drosophila and will be a useful resource for the community. A total of 52 interacting proteins were identified, including several molecules involved in translation such as structural components of the ribosome, factors regulating translation initiation or elongation, and RNA binding proteins. Among these 52 proteins, 15 were identified as partners by the SWATH strategy only. Interestingly, these 15 proteins are significantly enriched for the functions translation and nucleic acid binding. This enrichment reflects the engagement of RACK1 at the ribosome and highlights the added value of SWATH analysis. A functional screen did not reveal any protein sharing the interesting properties of RACK1, which is required for IRES-dependent translation and not essential for cell viability. Intriguingly however, 10 of the RACK1 partners identified restrict replication of Cricket paralysis virus (CrPV), an IRES-containing virus. Copyright © 2017 Kuhn et al.

O-GlcNAcylation is a key modulator of skeletal muscle sarcomeric morphometry associated to modulation of protein-protein interactions.

PubMed

Lambert, Matthias; Richard, Elodie; Duban-Deweer, Sophie; Krzewinski, Frederic; Deracinois, Barbara; Dupont, Erwan; Bastide, Bruno; Cieniewski-Bernard, Caroline

2016-09-01

The sarcomere structure of skeletal muscle is determined through multiple protein-protein interactions within an intricate sarcomeric cytoskeleton network. The molecular mechanisms involved in the regulation of this sarcomeric organization, essential to muscle function, remain unclear. O-GlcNAcylation, a post-translational modification modifying several key structural proteins and previously described as a modulator of the contractile activity, was never considered to date in the sarcomeric organization. C2C12 skeletal myotubes were treated with Thiamet-G (OGA inhibitor) in order to increase the global O-GlcNAcylation level. Our data clearly showed a modulation of the O-GlcNAc level more sensitive and dynamic in the myofilament-enriched fraction than total proteome. This fine O-GlcNAc level modulation was closely related to changes of the sarcomeric morphometry. Indeed, the dark-band and M-line widths increased, while the I-band width and the sarcomere length decreased according to the myofilament O-GlcNAc level. Some structural proteins of the sarcomere such as desmin, αB-crystallin, α-actinin, moesin and filamin-C have been identified within modulated protein complexes through O-GlcNAc level variations. Their interactions seemed to be changed, especially for desmin and αB-crystallin. For the first time, our findings clearly demonstrate that O-GlcNAcylation, through dynamic regulations of the structural interactome, could be an important modulator of the sarcomeric structure and may provide new insights in the understanding of molecular mechanisms of neuromuscular diseases characterized by a disorganization of the sarcomeric structure. In the present study, we demonstrated a role of O-GlcNAcylation in the sarcomeric structure modulation. Copyright © 2016 Elsevier B.V. All rights reserved.

Dissection of the BCR-ABL signaling network using highly specific monobody inhibitors to the SHP2 SH2 domains.

PubMed

Sha, Fern; Gencer, Emel Basak; Georgeon, Sandrine; Koide, Akiko; Yasui, Norihisa; Koide, Shohei; Hantschel, Oliver

2013-09-10

The dysregulated tyrosine kinase BCR-ABL causes chronic myelogenous leukemia in humans and forms a large multiprotein complex that includes the Src-homology 2 (SH2) domain-containing phosphatase 2 (SHP2). The expression of SHP2 is necessary for BCR-ABL-dependent oncogenic transformation, but the precise signaling mechanisms of SHP2 are not well understood. We have developed binding proteins, termed monobodies, for the N- and C-terminal SH2 domains of SHP2. Intracellular expression followed by interactome analysis showed that the monobodies are essentially monospecific to SHP2. Two crystal structures revealed that the monobodies occupy the phosphopeptide-binding sites of the SH2 domains and thus can serve as competitors of SH2-phosphotyrosine interactions. Surprisingly, the segments of both monobodies that bind to the peptide-binding grooves run in the opposite direction to that of canonical phosphotyrosine peptides, which may contribute to their exquisite specificity. When expressed in cells, monobodies targeting the N-SH2 domain disrupted the interaction of SHP2 with its upstream activator, the Grb2-associated binder 2 adaptor protein, suggesting decoupling of SHP2 from the BCR-ABL protein complex. Inhibition of either N-SH2 or C-SH2 was sufficient to inhibit two tyrosine phosphorylation events that are critical for SHP2 catalytic activity and to block ERK activation. In contrast, targeting the N-SH2 or C-SH2 revealed distinct roles of the two SH2 domains in downstream signaling, such as the phosphorylation of paxillin and signal transducer and activator of transcription 5. Our results delineate a hierarchy of function for the SH2 domains of SHP2 and validate monobodies as potent and specific antagonists of protein-protein interactions in cancer cells.