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

Regulatory networks and connected components of the neutral space. A look at functional islands

NASA Astrophysics Data System (ADS)

Boldhaus, G.; Klemm, K.

2010-09-01

The functioning of a living cell is largely determined by the structure of its regulatory network, comprising non-linear interactions between regulatory genes. An important factor for the stability and evolvability of such regulatory systems is neutrality - typically a large number of alternative network structures give rise to the necessary dynamics. Here we study the discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004] and the set of networks capable of reproducing it, which we call functional. Among these, the empirical yeast wildtype network is close to optimal with respect to sparse wiring. Under point mutations, which establish or delete single interactions, the neutral space of functional networks is fragmented into ≈ 4.7 × 108 components. One of the smaller ones contains the wildtype network. On average, functional networks reachable from the wildtype by mutations are sparser, have higher noise resilience and fewer fixed point attractors as compared with networks outside of this wildtype component.

Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast

PubMed Central

Ye, Ping; Peyser, Brian D; Spencer, Forrest A; Bader, Joel S

2005-01-01

Background In a genetic interaction, the phenotype of a double mutant differs from the combined phenotypes of the underlying single mutants. When the single mutants have no growth defect, but the double mutant is lethal or exhibits slow growth, the interaction is termed synthetic lethality or synthetic fitness. These genetic interactions reveal gene redundancy and compensating pathways. Recently available large-scale data sets of genetic interactions and protein interactions in Saccharomyces cerevisiae provide a unique opportunity to elucidate the topological structure of biological pathways and how genes function in these pathways. Results We have defined congruent genes as pairs of genes with similar sets of genetic interaction partners and constructed a genetic congruence network by linking congruent genes. By comparing path lengths in three types of networks (genetic interaction, genetic congruence, and protein interaction), we discovered that high genetic congruence not only exhibits correlation with direct protein interaction linkage but also exhibits commensurate distance with the protein interaction network. However, consistent distances were not observed between genetic and protein interaction networks. We also demonstrated that congruence and protein networks are enriched with motifs that indicate network transitivity, while the genetic network has both transitive (triangle) and intransitive (square) types of motifs. These results suggest that robustness of yeast cells to gene deletions is due in part to two complementary pathways (square motif) or three complementary pathways, any two of which are required for viability (triangle motif). Conclusion Genetic congruence is superior to genetic interaction in prediction of protein interactions and function associations. Genetically interacting pairs usually belong to parallel compensatory pathways, which can generate transitive motifs (any two of three pathways needed) or intransitive motifs (either of two pathways needed). PMID:16283923

Statistical indicators of collective behavior and functional clusters in gene networks of yeast

NASA Astrophysics Data System (ADS)

Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

2006-03-01

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

PubMed

Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

2017-05-01

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

The Protein Interaction Network of Bacteriophage Lambda with Its Host, Escherichia coli

PubMed Central

Blasche, Sonja; Wuchty, Stefan; Rajagopala, Seesandra V.

2013-01-01

Although most of the 73 open reading frames (ORFs) in bacteriophage λ have been investigated intensively, the function of many genes in host-phage interactions remains poorly understood. Using yeast two-hybrid screens of all lambda ORFs for interactions with its host Escherichia coli, we determined a raw data set of 631 host-phage interactions resulting in a set of 62 high-confidence interactions after multiple rounds of retesting. These links suggest novel regulatory interactions between the E. coli transcriptional network and lambda proteins. Targeted host proteins and genes required for lambda infection are enriched among highly connected proteins, suggesting that bacteriophages resemble interaction patterns of human viruses. Lambda tail proteins interact with both bacterial fimbrial proteins and E. coli proteins homologous to other phage proteins. Lambda appears to dramatically differ from other phages, such as T7, because of its unusually large number of modified and processed proteins, which reduces the number of host-virus interactions detectable by yeast two-hybrid screens. PMID:24049175

Predicting Physical Interactions between Protein Complexes*

PubMed Central

Clancy, Trevor; Rødland, Einar Andreas; Nygard, Ståle; Hovig, Eivind

2013-01-01

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships. PMID:23438732

Evolutionary model selection and parameter estimation for protein-protein interaction network based on differential evolution algorithm

PubMed Central

Huang, Lei; Liao, Li; Wu, Cathy H.

2016-01-01

Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273

Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

PubMed Central

Boucher, Benjamin; Lee, Anna Y.; Hallett, Michael; Jenna, Sarah

2016-01-01

A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution. PMID:26871911

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

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

Graph theoretic analysis of protein interaction networks of eukaryotes

NASA Astrophysics Data System (ADS)

Goh, K.-I.; Kahng, B.; Kim, D.

2005-11-01

Owing to the recent progress in high-throughput experimental techniques, the datasets of large-scale protein interactions of prototypical multicellular species, the nematode worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster, have been assayed. The datasets are obtained mainly by using the yeast hybrid method, which contains false-positive and false-negative simultaneously. Accordingly, while it is desirable to test such datasets through further wet experiments, here we invoke recent developed network theory to test such high-throughput datasets in a simple way. Based on the fact that the key biological processes indispensable to maintaining life are conserved across eukaryotic species, and the comparison of structural properties of the protein interaction networks (PINs) of the two species with those of the yeast PIN, we find that while the worm and yeast PIN datasets exhibit similar structural properties, the current fly dataset, though most comprehensively screened ever, does not reflect generic structural properties correctly as it is. The modularity is suppressed and the connectivity correlation is lacking. Addition of interologs to the current fly dataset increases the modularity and enhances the occurrence of triangular motifs as well. The connectivity correlation function of the fly, however, remains distinct under such interolog additions, for which we present a possible scenario through an in silico modeling.

Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis

PubMed Central

2018-01-01

Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that ‘leftover’ proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module allows cells to tune where endocytosis occurs, providing sensitive control over cargo uptake via clathrin-coated vesicles. PMID:29518071

Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis.

PubMed

Holland, David O; Johnson, Margaret E

2018-03-01

Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that 'leftover' proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module allows cells to tune where endocytosis occurs, providing sensitive control over cargo uptake via clathrin-coated vesicles.

Combined zebrafish-yeast chemical-genetic screens reveal gene-copper-nutrition interactions that modulate melanocyte pigmentation.

PubMed

Ishizaki, Hironori; Spitzer, Michaela; Wildenhain, Jan; Anastasaki, Corina; Zeng, Zhiqiang; Dolma, Sonam; Shaw, Michael; Madsen, Erik; Gitlin, Jonathan; Marais, Richard; Tyers, Mike; Patton, E Elizabeth

2010-01-01

Hypopigmentation is a feature of copper deficiency in humans, as caused by mutation of the copper (Cu(2+)) transporter ATP7A in Menkes disease, or an inability to absorb copper after gastric surgery. However, many causes of copper deficiency are unknown, and genetic polymorphisms might underlie sensitivity to suboptimal environmental copper conditions. Here, we combined phenotypic screens in zebrafish for compounds that affect copper metabolism with yeast chemical-genetic profiles to identify pathways that are sensitive to copper depletion. Yeast chemical-genetic interactions revealed that defects in intracellular trafficking pathways cause sensitivity to low-copper conditions; partial knockdown of the analogous Ap3s1 and Ap1s1 trafficking components in zebrafish sensitized developing melanocytes to hypopigmentation in low-copper environmental conditions. Because trafficking pathways are essential for copper loading into cuproproteins, our results suggest that hypomorphic alleles of trafficking components might underlie sensitivity to reduced-copper nutrient conditions. In addition, we used zebrafish-yeast screening to identify a novel target pathway in copper metabolism for the small-molecule MEK kinase inhibitor U0126. The zebrafish-yeast screening method combines the power of zebrafish as a disease model with facile genome-scale identification of chemical-genetic interactions in yeast to enable the discovery and dissection of complex multigenic interactions in disease-gene networks.

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

PubMed Central

2010-01-01

Background In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships. Results The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches. Conclusions The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms. PMID:21172053

Adding biological meaning to human protein-protein interactions identified by yeast two-hybrid screenings: A guide through bioinformatics tools.

PubMed

Felgueiras, Juliana; Silva, Joana Vieira; Fardilha, Margarida

2018-01-16

"A man is known by the company he keeps" is a popular expression that perfectly fits proteins. A common approach to characterize the function of a target protein is to identify its interacting partners and thus infer its roles based on the known functions of the interactors. Protein-protein interaction networks (PPINs) have been created for several organisms, including humans, primarily as results of high-throughput screenings, such as yeast two-hybrid (Y2H). Their unequivocal use to understand events underlying human pathophysiology is promising in identifying genes and proteins associated with diseases. Therefore, numerous opportunities have emerged for PPINs as tools for clinical management of diseases: network-based disease classification systems, discovery of biomarkers and identification of therapeutic targets. Despite the great advantages of PPINs, their use is still unrecognised by several researchers who generate high-throughput data to generally characterize interactions in a certain model or to select an interaction to study in detail. We strongly believe that both approaches are not exclusive and that we can use PPINs as a complementary methodology and rich-source of information to the initial study proposal. Here, we suggest a pipeline to deal with Y2H results using bioinformatics tools freely available for academics. Yeast two-hybrid is widely-used to identify protein-protein interactions. Conventionally, the positive clones that result from a yeast two-hybrid screening are sequenced to identify the interactors of the protein of interest (also known as bait protein), and few interactions, thought as potentially relevant for the model in study, are selected for further validation using biochemical methods (e.g. co-immunoprecipitation and co-localization). The huge amount of data that is potentially lost during this conservative approach motivated us to write this tutorial-like review, so that researchers feel encouraged to take advantage of bioinformatics tools to their full potential to analyse protein-protein interactions as a comprehensive network. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Novel insights into the architecture and protein interaction network of yeast eIF3.

PubMed

Khoshnevis, Sohail; Hauer, Florian; Milón, Pohl; Stark, Holger; Ficner, Ralf

2012-12-01

Translation initiation in eukaryotes is a multistep process requiring the orchestrated interaction of several eukaryotic initiation factors (eIFs). The largest of these factors, eIF3, forms the scaffold for other initiation factors, promoting their binding to the 40S ribosomal subunit. Biochemical and structural studies on eIF3 need highly pure eIF3. However, natively purified eIF3 comprise complexes containing other proteins such as eIF5. Therefore we have established in vitro reconstitution protocols for Saccharomyces cerevisiae eIF3 using its five recombinantly expressed and purified subunits. This reconstituted eIF3 complex (eIF3(rec)) exhibits the same size and activity as the natively purified eIF3 (eIF3(nat)). The homogeneity and stoichiometry of eIF3(rec) and eIF3(nat) were confirmed by analytical size exclusion chromatography, mass spectrometry, and multi-angle light scattering, demonstrating the presence of one copy of each subunit in the eIF3 complex. The reconstituted and native eIF3 complexes were compared by single-particle electron microscopy showing a high degree of structural conservation. The interaction network between eIF3 proteins was studied by means of limited proteolysis, analytical size exclusion chromatography, in vitro binding assays, and isothermal titration calorimetry, unveiling distinct protein domains and subcomplexes that are critical for the integrity of the protein network in yeast eIF3. Taken together, the data presented here provide a novel procedure to obtain highly pure yeast eIF3, suitable for biochemical and structural analysis, in addition to a detailed picture of the network of protein interactions within this complex.

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.

The autophagy interaction network of the aging model Podospora anserina.

PubMed

Philipp, Oliver; Hamann, Andrea; Osiewacz, Heinz D; Koch, Ina

2017-03-27

Autophagy is a conserved molecular pathway involved in the degradation and recycling of cellular components. It is active either as response to starvation or molecular damage. Evidence is emerging that autophagy plays a key role in the degradation of damaged cellular components and thereby affects aging and lifespan control. In earlier studies, it was found that autophagy in the aging model Podospora anserina acts as a longevity assurance mechanism. However, only little is known about the individual components controlling autophagy in this aging model. Here, we report a biochemical and bioinformatics study to detect the protein-protein interaction (PPI) network of P. anserina combining experimental and theoretical methods. We constructed the PPI network of autophagy in P. anserina based on the corresponding networks of yeast and human. We integrated PaATG8 interaction partners identified in an own yeast two-hybrid analysis using ATG8 of P. anserina as bait. Additionally, we included age-dependent transcriptome data. The resulting network consists of 89 proteins involved in 186 interactions. We applied bioinformatics approaches to analyze the network topology and to prove that the network is not random, but exhibits biologically meaningful properties. We identified hub proteins which play an essential role in the network as well as seven putative sub-pathways, and interactions which are likely to be evolutionary conserved amongst species. We confirmed that autophagy-associated genes are significantly often up-regulated and co-expressed during aging of P. anserina. With the present study, we provide a comprehensive biological network of the autophagy pathway in P. anserina comprising PPI and gene expression data. It is based on computational prediction as well as experimental data. We identified sub-pathways, important hub proteins, and evolutionary conserved interactions. The network clearly illustrates the relation of autophagy to aging processes and enables further specific studies to understand autophagy and aging in P. anserina as well as in other systems.

Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator

PubMed Central

Usaite, Renata; Jewett, Michael C; Oliveira, Ana Paula; Yates, John R; Olsson, Lisbeth; Nielsen, Jens

2009-01-01

Highly conserved among eukaryotic cells, the AMP-activated kinase (AMPK) is a central regulator of carbon metabolism. To map the complete network of interactions around AMPK in yeast (Snf1) and to evaluate the role of its regulatory subunit Snf4, we measured global mRNA, protein and metabolite levels in wild type, Δsnf1, Δsnf4, and Δsnf1Δsnf4 knockout strains. Using four newly developed computational tools, including novel DOGMA sub-network analysis, we showed the benefits of three-level ome-data integration to uncover the global Snf1 kinase role in yeast. We for the first time identified Snf1's global regulation on gene and protein expression levels, and showed that yeast Snf1 has a far more extensive function in controlling energy metabolism than reported earlier. Additionally, we identified complementary roles of Snf1 and Snf4. Similar to the function of AMPK in humans, our findings showed that Snf1 is a low-energy checkpoint and that yeast can be used more extensively as a model system for studying the molecular mechanisms underlying the global regulation of AMPK in mammals, failure of which leads to metabolic diseases. PMID:19888214

"Omics" of Selenium Biology: A Prospective Study of Plasma Proteome Network Before and After Selenized-Yeast Supplementation in Healthy Men.

PubMed

Sinha, Indu; Karagoz, Kubra; Fogle, Rachel L; Hollenbeak, Christopher S; Zea, Arnold H; Arga, Kazim Y; Stanley, Anne E; Hawkes, Wayne C; Sinha, Raghu

2016-04-01

Low selenium levels have been linked to a higher incidence of cancer and other diseases, including Keshan, Chagas, and Kashin-Beck, and insulin resistance. Additionally, muscle and cardiovascular disorders, immune dysfunction, cancer, neurological disorders, and endocrine function have been associated with mutations in genes encoding for selenoproteins. Selenium biology is complex, and a systems biology approach to study global metabolomics, genomics, and/or proteomics may provide important clues to examining selenium-responsive markers in circulation. In the current investigation, we applied a global proteomics approach on plasma samples collected from a previously conducted, double-blinded placebo controlled clinical study, where men were supplemented with selenized-yeast (Se-Yeast; 300 μg/day, 3.8 μmol/day) or placebo-yeast for 48 weeks. Proteomic analysis was performed by iTRAQ on 8 plasma samples from each arm at baseline and 48 weeks. A total of 161 plasma proteins were identified in both arms. Twenty-two proteins were significantly altered following Se-Yeast supplementation and thirteen proteins were significantly changed after placebo-yeast supplementation in healthy men. The differentially expressed proteins were involved in complement and coagulation pathways, immune functions, lipid metabolism, and insulin resistance. Reconstruction and analysis of protein-protein interaction network around selected proteins revealed several hub proteins. One of the interactions suggested by our analysis, PHLD-APOA4, which is involved in insulin resistance, was subsequently validated by Western blot analysis. Our systems approach illustrates a viable platform for investigating responsive proteomic profile in 'before and after' condition following Se-Yeast supplementation. The nature of proteins identified suggests that selenium may play an important role in complement and coagulation pathways, and insulin resistance.

A protein interaction map for cell polarity development

PubMed Central

Drees, Becky L.; Sundin, Bryan; Brazeau, Elizabeth; Caviston, Juliane P.; Chen, Guang-Chao; Guo, Wei; Kozminski, Keith G.; Lau, Michelle W.; Moskow, John J.; Tong, Amy; Schenkman, Laura R.; McKenzie, Amos; Brennwald, Patrick; Longtine, Mark; Bi, Erfei; Chan, Clarence; Novick, Peter; Boone, Charles; Pringle, John R.; Davis, Trisha N.; Fields, Stanley; Drubin, David G.

2001-01-01

Many genes required for cell polarity development in budding yeast have been identified and arranged into a functional hierarchy. Core elements of the hierarchy are widely conserved, underlying cell polarity development in diverse eukaryotes. To enumerate more fully the protein–protein interactions that mediate cell polarity development, and to uncover novel mechanisms that coordinate the numerous events involved, we carried out a large-scale two-hybrid experiment. 68 Gal4 DNA binding domain fusions of yeast proteins associated with the actin cytoskeleton, septins, the secretory apparatus, and Rho-type GTPases were used to screen an array of yeast transformants that express ∼90% of the predicted Saccharomyces cerevisiae open reading frames as Gal4 activation domain fusions. 191 protein–protein interactions were detected, of which 128 had not been described previously. 44 interactions implicated 20 previously uncharacterized proteins in cell polarity development. Further insights into possible roles of 13 of these proteins were revealed by their multiple two-hybrid interactions and by subcellular localization. Included in the interaction network were associations of Cdc42 and Rho1 pathways with proteins involved in exocytosis, septin organization, actin assembly, microtubule organization, autophagy, cytokinesis, and cell wall synthesis. Other interactions suggested direct connections between Rho1- and Cdc42-regulated pathways; the secretory apparatus and regulators of polarity establishment; actin assembly and the morphogenesis checkpoint; and the exocytic and endocytic machinery. In total, a network of interactions that provide an integrated response of signaling proteins, the cytoskeleton, and organelles to the spatial cues that direct polarity development was revealed. PMID:11489916

Discrete dynamical system modelling for gene regulatory networks of 5-hydroxymethylfurfural tolerance for ethanologenic yeast.

PubMed

Song, M; Ouyang, Z; Liu, Z L

2009-05-01

Composed of linear difference equations, a discrete dynamical system (DDS) model was designed to reconstruct transcriptional regulations in gene regulatory networks (GRNs) for ethanologenic yeast Saccharomyces cerevisiae in response to 5-hydroxymethylfurfural (HMF), a bioethanol conversion inhibitor. The modelling aims at identification of a system of linear difference equations to represent temporal interactions among significantly expressed genes. Power stability is imposed on a system model under the normal condition in the absence of the inhibitor. Non-uniform sampling, typical in a time-course experimental design, is addressed by a log-time domain interpolation. A statistically significant DDS model of the yeast GRN derived from time-course gene expression measurements by exposure to HMF, revealed several verified transcriptional regulation events. These events implicate Yap1 and Pdr3, transcription factors consistently known for their regulatory roles by other studies or postulated by independent sequence motif analysis, suggesting their involvement in yeast tolerance and detoxification of the inhibitor.

Synthetic Genetic Arrays: Automation of Yeast Genetics.

PubMed

Kuzmin, Elena; Costanzo, Michael; Andrews, Brenda; Boone, Charles

2016-04-01

Genome-sequencing efforts have led to great strides in the annotation of protein-coding genes and other genomic elements. The current challenge is to understand the functional role of each gene and how genes work together to modulate cellular processes. Genetic interactions define phenotypic relationships between genes and reveal the functional organization of a cell. Synthetic genetic array (SGA) methodology automates yeast genetics and enables large-scale and systematic mapping of genetic interaction networks in the budding yeast,Saccharomyces cerevisiae SGA facilitates construction of an output array of double mutants from an input array of single mutants through a series of replica pinning steps. Subsequent analysis of genetic interactions from SGA-derived mutants relies on accurate quantification of colony size, which serves as a proxy for fitness. Since its development, SGA has given rise to a variety of other experimental approaches for functional profiling of the yeast genome and has been applied in a multitude of other contexts, such as genome-wide screens for synthetic dosage lethality and integration with high-content screening for systematic assessment of morphology defects. SGA-like strategies can also be implemented similarly in a number of other cell types and organisms, includingSchizosaccharomyces pombe,Escherichia coli, Caenorhabditis elegans, and human cancer cell lines. The genetic networks emerging from these studies not only generate functional wiring diagrams but may also play a key role in our understanding of the complex relationship between genotype and phenotype. © 2016 Cold Spring Harbor Laboratory Press.

Construction of reliable protein-protein interaction networks with a new interaction generality measure.

PubMed

Saito, Rintaro; Suzuki, Harukazu; Hayashizaki, Yoshihide

2003-04-12

Recent screening techniques have made large amounts of protein-protein interaction data available, from which biologically important information such as the function of uncharacterized proteins, the existence of novel protein complexes, and novel signal-transduction pathways can be discovered. However, experimental data on protein interactions contain many false positives, making these discoveries difficult. Therefore computational methods of assessing the reliability of each candidate protein-protein interaction are urgently needed. We developed a new 'interaction generality' measure (IG2) to assess the reliability of protein-protein interactions using only the topological properties of their interaction-network structure. Using yeast protein-protein interaction data, we showed that reliable protein-protein interactions had significantly lower IG2 values than less-reliable interactions, suggesting that IG2 values can be used to evaluate and filter interaction data to enable the construction of reliable protein-protein interaction networks.

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.

Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks.

PubMed

Nishtala, Sneha; Neelamraju, Yaseswini; Janga, Sarath Chandra

2016-05-10

RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks

NASA Astrophysics Data System (ADS)

Nishtala, Sneha; Neelamraju, Yaseswini; Janga, Sarath Chandra

2016-05-01

RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

Fluctuations in Mass-Action Equilibrium of Protein Binding Networks

NASA Astrophysics Data System (ADS)

Yan, Koon-Kiu; Walker, Dylan; Maslov, Sergei

2008-12-01

We consider two types of fluctuations in the mass-action equilibrium in protein binding networks. The first type is driven by slow changes in total concentrations of interacting proteins. The second type (spontaneous) is caused by quickly decaying thermodynamic deviations away from equilibrium. We investigate the effects of network connectivity on fluctuations by comparing them to scenarios in which the interacting pair is isolated from the network and analytically derives bounds on fluctuations. Collective effects are shown to sometimes lead to large amplification of spontaneous fluctuations. The strength of both types of fluctuations is positively correlated with the complex connectivity and negatively correlated with complex concentration. Our general findings are illustrated using a curated network of protein interactions and multiprotein complexes in baker’s yeast, with empirical protein concentrations.

BIND: the Biomolecular Interaction Network Database

PubMed Central

Bader, Gary D.; Betel, Doron; Hogue, Christopher W. V.

2003-01-01

The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD. PMID:12519993

Mass-action equilibrium and non-specific interactions in protein binding networks

NASA Astrophysics Data System (ADS)

Maslov, Sergei

2009-03-01

Large-scale protein binding networks serve as a paradigm of complex properties of living cells. These networks are naturally weighted with edges characterized by binding strength and protein-nodes -- by their concentrations. However, the state-of-the-art high-throughput experimental techniques generate just a binary (yes or no) information about individual interactions. As a result, most of the previous research concentrated just on topology of these networks. In a series of recent publications [1-4] my collaborators and I went beyond purely topological studies and calculated the mass-action equilibrium of a genome-wide binding network using experimentally determined protein concentrations, localizations, and reliable binding interactions in baker's yeast. We then studied how this equilibrium responds to large perturbations [1-2] and noise [3] in concentrations of proteins. We demonstrated that the change in the equilibrium concentration of a protein exponentially decays (and sign-alternates) with its network distance away from the perturbed node. This explains why, despite a globally connected topology, individual functional modules in such networks are able to operate fairly independently. In a separate study [4] we quantified the interplay between specific and non-specific binding interactions under crowded conditions inside living cells. We show how the need to limit the waste of resources constrains the number of types and concentrations of proteins that are present at the same time and at the same place in yeast cells. [1] S Maslov, I. Ispolatov, PNAS 104:13655 (2007). [2] S. Maslov, K. Sneppen, I. Ispolatov, New J. of Phys. 9: 273 (2007). [3] K-K. Yan, D. Walker, S. Maslov, PRL accepted (2008). [4] J. Zhang, S. Maslov, and E. I. Shakhnovich, Mol Syst Biol 4, 210 (2008).

Quantitative maps of genetic interactions in yeast - comparative evaluation and integrative analysis.

PubMed

Lindén, Rolf O; Eronen, Ville-Pekka; Aittokallio, Tero

2011-03-24

High-throughput genetic screening approaches have enabled systematic means to study how interactions among gene mutations contribute to quantitative fitness phenotypes, with the aim of providing insights into the functional wiring diagrams of genetic interaction networks on a global scale. However, it is poorly known how well these quantitative interaction measurements agree across the screening approaches, which hinders their integrated use toward improving the coverage and quality of the genetic interaction maps in yeast and other organisms. Using large-scale data matrices from epistatic miniarray profiling (E-MAP), genetic interaction mapping (GIM), and synthetic genetic array (SGA) approaches, we carried out here a systematic comparative evaluation among these quantitative maps of genetic interactions in yeast. The relatively low association between the original interaction measurements or their customized scores could be improved using a matrix-based modelling framework, which enables the use of single- and double-mutant fitness estimates and measurements, respectively, when scoring genetic interactions. Toward an integrative analysis, we show how the detections from the different screening approaches can be combined to suggest novel positive and negative interactions which are complementary to those obtained using any single screening approach alone. The matrix approximation procedure has been made available to support the design and analysis of the future screening studies. We have shown here that even if the correlation between the currently available quantitative genetic interaction maps in yeast is relatively low, their comparability can be improved by means of our computational matrix approximation procedure, which will enable integrative analysis and detection of a wider spectrum of genetic interactions using data from the complementary screening approaches.

Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

PubMed

Wang, Jian; Xie, Dong; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

2012-06-21

Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

Influence of Protein Abundance on High-Throughput Protein-Protein Interaction Detection

DTIC Science & Technology

2009-06-05

the interaction data sets we determined, via comparisons with strict randomized simulations , the propensity for essential proteins to selectively...and analysis of high- quality PPI data sets. Materials and Methods We analyzed protein interaction networks for yeast and E. coli determined from Y2H...we reinvestigated the centrality-lethality rule, which implies that proteins having more interactions are more likely to be essential. From analysis

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

Systematic analysis of Ca2+ homeostasis in Saccharomyces cerevisiae based on chemical-genetic interaction profiles

PubMed Central

Ghanegolmohammadi, Farzan; Yoshida, Mitsunori; Ohnuki, Shinsuke; Sukegawa, Yuko; Okada, Hiroki; Obara, Keisuke; Kihara, Akio; Suzuki, Kuninori; Kojima, Tetsuya; Yachie, Nozomu; Hirata, Dai; Ohya, Yoshikazu

2017-01-01

We investigated the global landscape of Ca2+ homeostasis in budding yeast based on high-dimensional chemical-genetic interaction profiles. The morphological responses of 62 Ca2+-sensitive (cls) mutants were quantitatively analyzed with the image processing program CalMorph after exposure to a high concentration of Ca2+. After a generalized linear model was applied, an analysis of covariance model was used to detect significant Ca2+–cls interactions. We found that high-dimensional, morphological Ca2+–cls interactions were mixed with positive (86%) and negative (14%) chemical-genetic interactions, whereas one-dimensional fitness Ca2+–cls interactions were all negative in principle. Clustering analysis with the interaction profiles revealed nine distinct gene groups, six of which were functionally associated. In addition, characterization of Ca2+–cls interactions revealed that morphology-based negative interactions are unique signatures of sensitized cellular processes and pathways. Principal component analysis was used to discriminate between suppression and enhancement of the Ca2+-sensitive phenotypes triggered by inactivation of calcineurin, a Ca2+-dependent phosphatase. Finally, similarity of the interaction profiles was used to reveal a connected network among the Ca2+ homeostasis units acting in different cellular compartments. Our analyses of high-dimensional chemical-genetic interaction profiles provide novel insights into the intracellular network of yeast Ca2+ homeostasis. PMID:28566553

Exploring the Yeast Acetylome Using Functional Genomics

PubMed Central

Duffy, Supipi Kaluarachchi; Friesen, Helena; Baryshnikova, Anastasia; Lambert, Jean-Philippe; Chong, Yolanda T.; Figeys, Daniel; Andrews, Brenda

2014-01-01

SUMMARY Lysine acetylation is a dynamic posttranslational modification with a well-defined role in regulating histones. The impact of acetylation on other cellular functions remains relatively uncharacterized. We explored the budding yeast acetylome with a functional genomics approach, assessing the effects of gene overexpression in the absence of lysine deacetylases (KDACs). We generated a network of 463 synthetic dosage lethal (SDL) interactions involving class I and II KDACs, revealing many cellular pathways regulated by different KDACs. A biochemical survey of genes interacting with the KDAC RPD3 identified 72 proteins acetylated in vivo. In-depth analysis of one of these proteins, Swi4, revealed a role for acetylation in G1-specific gene expression. Acetylation of Swi4 regulates interaction with its partner Swi6, both components of the SBF transcription factor. This study expands our view of the yeast acetylome, demonstrates the utility of functional genomic screens for exploring enzymatic pathways, and provides functional information that can be mined for future studies. PMID:22579291

Plasticity of genetic interactions in metabolic networks of yeast.

PubMed

Harrison, Richard; Papp, Balázs; Pál, Csaba; Oliver, Stephen G; Delneri, Daniela

2007-02-13

Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence of compensatory mechanisms (mutational robustness), or both. Here, we analyze the interaction between these two forces by exploring the condition-dependence of synthetic genetic interactions that define redundant functions and alternative pathways. We performed systems-level flux balance analysis of the yeast (Saccharomyces cerevisiae) metabolic network to identify genetic interactions and then tested the model's predictions with in vivo gene-deletion studies. We found that the majority of synthetic genetic interactions are restricted to certain environmental conditions, partly because of the lack of compensation under some (but not all) nutrient conditions. Moreover, the phylogenetic cooccurrence of synthetically interacting pairs is not significantly different from random expectation. These findings suggest that these gene pairs have at least partially independent functions, and, hence, compensation is only a byproduct of their evolutionary history. Experimental analyses that used multiple gene deletion strains not only confirmed predictions of the model but also showed that investigation of false predictions may both improve functional annotation within the model and also lead to the discovery of higher-order genetic interactions. Our work supports the view that functional redundancy may be more apparent than real, and it offers a unified framework for the evolution of environmental adaptation and mutational robustness.

Pathway connectivity and signaling coordination in the yeast stress-activated signaling network

PubMed Central

Chasman, Deborah; Ho, Yi-Hsuan; Berry, David B; Nemec, Corey M; MacGilvray, Matthew E; Hose, James; Merrill, Anna E; Lee, M Violet; Will, Jessica L; Coon, Joshua J; Ansari, Aseem Z; Craven, Mark; Gasch, Audrey P

2014-01-01

Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet knowledge of the complete stress-activated regulatory network as well as design principles for signal integration remains incomplete. We developed an experimental and computational approach to integrate available protein interaction data with gene fitness contributions, mutant transcriptome profiles, and phospho-proteome changes in cells responding to salt stress, to infer the salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and pointing to previously unknown ‘hubs’ of signal integration. We exploited these predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related transcripts. We find that the orthologous human network is enriched for cancer-causing genes, underscoring the importance of the subnetwork's predictions in understanding stress biology. PMID:25411400

s-core network decomposition: A generalization of k-core analysis to weighted networks

NASA Astrophysics Data System (ADS)

Eidsaa, Marius; Almaas, Eivind

2013-12-01

A broad range of systems spanning biology, technology, and social phenomena may be represented and analyzed as complex networks. Recent studies of such networks using k-core decomposition have uncovered groups of nodes that play important roles. Here, we present s-core analysis, a generalization of k-core (or k-shell) analysis to complex networks where the links have different strengths or weights. We demonstrate the s-core decomposition approach on two random networks (ER and configuration model with scale-free degree distribution) where the link weights are (i) random, (ii) correlated, and (iii) anticorrelated with the node degrees. Finally, we apply the s-core decomposition approach to the protein-interaction network of the yeast Saccharomyces cerevisiae in the context of two gene-expression experiments: oxidative stress in response to cumene hydroperoxide (CHP), and fermentation stress response (FSR). We find that the innermost s-cores are (i) different from innermost k-cores, (ii) different for the two stress conditions CHP and FSR, and (iii) enriched with proteins whose biological functions give insight into how yeast manages these specific stresses.

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.

MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action.

PubMed

Nelson, Justin; Simpkins, Scott W; Safizadeh, Hamid; Li, Sheena C; Piotrowski, Jeff S; Hirano, Hiroyuki; Yashiroda, Yoko; Osada, Hiroyuki; Yoshida, Minoru; Boone, Charles; Myers, Chad L

2018-04-01

Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks. MOSAIC is available at http://mosaic.cs.umn.edu. hisyo@riken.jp, yoshidam@riken.jp, charlie.boone@utoronto.ca or chadm@umn.edu. Supplementary data are available at Bioinformatics online.

Version 6 of the consensus yeast metabolic network refines biochemical coverage and improves model performance

PubMed Central

Heavner, Benjamin D.; Smallbone, Kieran; Price, Nathan D.; Walker, Larry P.

2013-01-01

Updates to maintain a state-of-the art reconstruction of the yeast metabolic network are essential to reflect our understanding of yeast metabolism and functional organization, to eliminate any inaccuracies identified in earlier iterations, to improve predictive accuracy and to continue to expand into novel subsystems to extend the comprehensiveness of the model. Here, we present version 6 of the consensus yeast metabolic network (Yeast 6) as an update to the community effort to computationally reconstruct the genome-scale metabolic network of Saccharomyces cerevisiae S288c. Yeast 6 comprises 1458 metabolites participating in 1888 reactions, which are annotated with 900 yeast genes encoding the catalyzing enzymes. Compared with Yeast 5, Yeast 6 demonstrates improved sensitivity, specificity and positive and negative predictive values for predicting gene essentiality in glucose-limited aerobic conditions when analyzed with flux balance analysis. Additionally, Yeast 6 improves the accuracy of predicting the likelihood that a mutation will cause auxotrophy. The network reconstruction is available as a Systems Biology Markup Language (SBML) file enriched with Minimium Information Requested in the Annotation of Biochemical Models (MIRIAM)-compliant annotations. Small- and macromolecules in the network are referenced to authoritative databases such as Uniprot or ChEBI. Molecules and reactions are also annotated with appropriate publications that contain supporting evidence. Yeast 6 is freely available at http://yeast.sf.net/ as three separate SBML files: a model using the SBML level 3 Flux Balance Constraint package, a model compatible with the MATLAB® COBRA Toolbox for backward compatibility and a reconstruction containing only reactions for which there is experimental evidence (without the non-biological reactions necessary for simulating growth). Database URL: http://yeast.sf.net/ PMID:23935056

Network Compression as a Quality Measure for Protein Interaction Networks

PubMed Central

Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

2012-01-01

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

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.

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

Construction of ontology augmented networks for protein complex prediction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

2013-01-01

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

Passing messages between biological networks to refine predicted interactions.

PubMed

Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng

2013-01-01

Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

Process-driven inference of biological network structure: feasibility, minimality, and multiplicity

NASA Astrophysics Data System (ADS)

Zeng, Chen

2012-02-01

For a given dynamic process, identifying the putative interaction networks to achieve it is the inference problem. In this talk, we address the computational complexity of inference problem in the context of Boolean networks under dominant inhibition condition. The first is a proof that the feasibility problem (is there a network that explains the dynamics?) can be solved in polynomial-time. Second, while the minimality problem (what is the smallest network that explains the dynamics?) is shown to be NP-hard, a simple polynomial-time heuristic is shown to produce near-minimal solutions, as demonstrated by simulation. Third, the theoretical framework also leads to a fast polynomial-time heuristic to estimate the number of network solutions with reasonable accuracy. We will apply these approaches to two simplified Boolean network models for the cell cycle process of budding yeast (Li 2004) and fission yeast (Davidich 2008). Our results demonstrate that each of these networks contains a giant backbone motif spanning all the network nodes that provides the desired main functionality, while the remaining edges in the network form smaller motifs whose role is to confer stability properties rather than provide function. Moreover, we show that the bioprocesses of these two cell cycle models differ considerably from a typically generated process and are intrinsically cascade-like.

Scale-space measures for graph topology link protein network architecture to function.

PubMed

Hulsman, Marc; Dimitrakopoulos, Christos; de Ridder, Jeroen

2014-06-15

The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale). In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions-genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture. Matlab(TM) code to calculate the scale-aware topological measures (STMs) is available at http://bioinformatics.tudelft.nl/TSSA © The Author 2014. Published by Oxford University Press.

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

PubMed Central

Reguly, Teresa; Breitkreutz, Ashton; Boucher, Lorrie; Breitkreutz, Bobby-Joe; Hon, Gary C; Myers, Chad L; Parsons, Ainslie; Friesen, Helena; Oughtred, Rose; Tong, Amy; Stark, Chris; Ho, Yuen; Botstein, David; Andrews, Brenda; Boone, Charles; Troyanskya, Olga G; Ideker, Trey; Dolinski, Kara; Batada, Nizar N; Tyers, Mike

2006-01-01

Background The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. Results We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID () and SGD () databases. Conclusion Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks. PMID:16762047

Impact of observational incompleteness on the structural properties of protein interaction networks

NASA Astrophysics Data System (ADS)

Kuhnt, Mathias; Glauche, Ingmar; Greiner, Martin

2007-01-01

The observed structure of protein interaction networks is corrupted by many false positive/negative links. This observational incompleteness is abstracted as random link removal and a specific, experimentally motivated (spoke) link rearrangement. Their impact on the structural properties of gene-duplication-and-mutation network models is studied. For the degree distribution a curve collapse is found, showing no sensitive dependence on the link removal/rearrangement strengths and disallowing a quantitative extraction of model parameters. The spoke link rearrangement process moves other structural observables, like degree correlations, cluster coefficient and motif frequencies, closer to their counterparts extracted from the yeast data. This underlines the importance to take a precise modeling of the observational incompleteness into account when network structure models are to be quantitatively compared to data.

Transcriptional Regulatory Networks in Saccharomyces cerevisiae

NASA Astrophysics Data System (ADS)

Lee, Tong Ihn; Rinaldi, Nicola J.; Robert, François; Odom, Duncan T.; Bar-Joseph, Ziv; Gerber, Georg K.; Hannett, Nancy M.; Harbison, Christopher T.; Thompson, Craig M.; Simon, Itamar; Zeitlinger, Julia; Jennings, Ezra G.; Murray, Heather L.; Gordon, D. Benjamin; Ren, Bing; Wyrick, John J.; Tagne, Jean-Bosco; Volkert, Thomas L.; Fraenkel, Ernest; Gifford, David K.; Young, Richard A.

2002-10-01

We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.

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

Delineating functional principles of the bow tie structure of a kinase-phosphatase network in the budding yeast.

PubMed

Abd-Rabbo, Diala; Michnick, Stephen W

2017-03-16

Kinases and phosphatases (KP) form complex self-regulating networks essential for cellular signal processing. In spite of having a wealth of data about interactions among KPs and their substrates, we have very limited models of the structures of the directed networks they form and consequently our ability to formulate hypotheses about how their structure determines the flow of information in these networks is restricted. We assembled and studied the largest bona fide kinase-phosphatase network (KP-Net) known to date for the yeast Saccharomyces cerevisiae. Application of the vertex sort (VS) algorithm on the KP-Net allowed us to elucidate its hierarchical structure in which nodes are sorted into top, core and bottom layers, forming a bow tie structure with a strongly connected core layer. Surprisingly, phosphatases tend to sort into the top layer, implying they are less regulated by phosphorylation than kinases. Superposition of the widest range of KP biological properties over the KP-Net hierarchy shows that core layer KPs: (i), receive the largest number of inputs; (ii), form bottlenecks implicated in multiple pathways and in decision-making; (iii), and are among the most regulated KPs both temporally and spatially. Moreover, top layer KPs are more abundant and less noisy than those in the bottom layer. Finally, we showed that the VS algorithm depends on node degrees without biasing the biological results of the sorted network. The VS algorithm is available as an R package ( https://cran.r-project.org/web/packages/VertexSort/index.html ). The KP-Net model we propose possesses a bow tie hierarchical structure in which the top layer appears to ensure highest fidelity and the core layer appears to mediate signal integration and cell state-dependent signal interpretation. Our model of the yeast KP-Net provides both functional insight into its organization as we understand today and a framework for future investigation of information processing in yeast and eukaryotes in general.

Theory for the Emergence of Modularity in Complex Systems

NASA Astrophysics Data System (ADS)

Deem, Michael; Park, Jeong-Man

2013-03-01

Biological systems are modular, and this modularity evolves over time and in different environments. A number of observations have been made of increased modularity in biological systems under increased environmental pressure. We here develop a theory for the dynamics of modularity in these systems. We find a principle of least action for the evolved modularity at long times. In addition, we find a fluctuation dissipation relation for the rate of change of modularity at short times. We discuss a number of biological and social systems that can be understood with this framework. The modularity of the protein-protein interaction network increases when yeast are exposed to heat shock, and the modularity of the protein-protein networks in both yeast and E. coli appears to have increased over evolutionary time. Food webs in low-energy, stressful environments are more modular than those in plentiful environments, arid ecologies are more modular during droughts, and foraging of sea otters is more modular when food is limiting. The modularity of social networks changes over time: stock brokers instant messaging networks are more modular under stressful market conditions, criminal networks are more modular under increased police pressure, and world trade network modularity has decreased

Engineering microbial phenotypes through rewiring of genetic networks

PubMed Central

Rodrigues, Rui T.L.; Lee, Sangjin; Haines, Matthew

2017-01-01

Abstract The ability to program cellular behaviour is a major goal of synthetic biology, with applications in health, agriculture and chemicals production. Despite efforts to build ‘orthogonal’ systems, interactions between engineered genetic circuits and the endogenous regulatory network of a host cell can have a significant impact on desired functionality. We have developed a strategy to rewire the endogenous cellular regulatory network of yeast to enhance compatibility with synthetic protein and metabolite production. We found that introducing novel connections in the cellular regulatory network enabled us to increase the production of heterologous proteins and metabolites. This strategy is demonstrated in yeast strains that show significantly enhanced heterologous protein expression and higher titers of terpenoid production. Specifically, we found that the addition of transcriptional regulation between free radical induced signalling and nitrogen regulation provided robust improvement of protein production. Assessment of rewired networks revealed the importance of key topological features such as high betweenness centrality. The generation of rewired transcriptional networks, selection for specific phenotypes, and analysis of resulting library members is a powerful tool for engineering cellular behavior and may enable improved integration of heterologous protein and metabolite pathways. PMID:28369627

Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network

PubMed Central

2012-01-01

Background Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature. Results Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3. Conclusions Yeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file 4 and Additional file 5 and at http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible. PMID:22663945

Flavivirus NS3 and NS5 proteins interaction network: a high-throughput yeast two-hybrid screen

PubMed Central

2011-01-01

Background The genus Flavivirus encompasses more than 50 distinct species of arthropod-borne viruses, including several major human pathogens, such as West Nile virus, yellow fever virus, Japanese encephalitis virus and the four serotypes of dengue viruses (DENV type 1-4). Each year, flaviviruses cause more than 100 million infections worldwide, some of which lead to life-threatening conditions such as encephalitis or haemorrhagic fever. Among the viral proteins, NS3 and NS5 proteins constitute the major enzymatic components of the viral replication complex and are essential to the flavivirus life cycle. Results We report here the results of a high-throughput yeast two-hybrid screen to identify the interactions between human host proteins and the flavivirus NS3 and NS5 proteins. Using our screen results and literature curation, we performed a global analysis of the NS3 and NS5 cellular targets based on functional annotation with the Gene Ontology features. We finally created the first flavivirus NS3 and NS5 proteins interaction network and analysed the topological features of this network. Our proteome mapping screen identified 108 human proteins interacting with NS3 or NS5 proteins or both. The global analysis of the cellular targets revealed the enrichment of host proteins involved in RNA binding, transcription regulation, vesicular transport or innate immune response regulation. Conclusions We proposed that the selective disruption of these newly identified host/virus interactions could represent a novel and attractive therapeutic strategy in treating flavivirus infections. Our virus-host interaction map provides a basis to unravel fundamental processes about flavivirus subversion of the host replication machinery and/or immune defence strategy. PMID:22014111

Flavivirus NS3 and NS5 proteins interaction network: a high-throughput yeast two-hybrid screen.

PubMed

Le Breton, Marc; Meyniel-Schicklin, Laurène; Deloire, Alexandre; Coutard, Bruno; Canard, Bruno; de Lamballerie, Xavier; Andre, Patrice; Rabourdin-Combe, Chantal; Lotteau, Vincent; Davoust, Nathalie

2011-10-20

The genus Flavivirus encompasses more than 50 distinct species of arthropod-borne viruses, including several major human pathogens, such as West Nile virus, yellow fever virus, Japanese encephalitis virus and the four serotypes of dengue viruses (DENV type 1-4). Each year, flaviviruses cause more than 100 million infections worldwide, some of which lead to life-threatening conditions such as encephalitis or haemorrhagic fever. Among the viral proteins, NS3 and NS5 proteins constitute the major enzymatic components of the viral replication complex and are essential to the flavivirus life cycle. We report here the results of a high-throughput yeast two-hybrid screen to identify the interactions between human host proteins and the flavivirus NS3 and NS5 proteins. Using our screen results and literature curation, we performed a global analysis of the NS3 and NS5 cellular targets based on functional annotation with the Gene Ontology features. We finally created the first flavivirus NS3 and NS5 proteins interaction network and analysed the topological features of this network. Our proteome mapping screen identified 108 human proteins interacting with NS3 or NS5 proteins or both. The global analysis of the cellular targets revealed the enrichment of host proteins involved in RNA binding, transcription regulation, vesicular transport or innate immune response regulation. We proposed that the selective disruption of these newly identified host/virus interactions could represent a novel and attractive therapeutic strategy in treating flavivirus infections. Our virus-host interaction map provides a basis to unravel fundamental processes about flavivirus subversion of the host replication machinery and/or immune defence strategy.

Cellular Ubc2/Rad6 E2 ubiquitin-conjugating enzyme facilitates tombusvirus replication in yeast and plants

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

Imura, Yoshiyuki, E-mail: imura@brs.nihon-u.ac.jp; Molho, Melissa; Chuang, Chingkai

Mono- and multi-ubiquitination alters the functions and subcellular localization of many cellular and viral proteins. Viruses can co-opt or actively manipulate the ubiquitin network to support viral processes or suppress innate immunity. Using yeast (Saccharomyces cerevisiae) model host, we show that the yeast Rad6p (radiation sensitive 6) E2 ubiquitin-conjugating enzyme and its plant ortholog, AtUbc2, interact with two tombusviral replication proteins and these E2 ubiquitin-conjugating enzymes could be co-purified with the tombusvirus replicase. We demonstrate that TBSV RNA replication and the mono- and bi-ubiquitination level of p33 is decreased in rad6Δ yeast. However, plasmid-based expression of AtUbc2p could complement bothmore » defects in rad6Δ yeast. Knockdown of UBC2 expression in plants also decreases tombusvirus accumulation and reduces symptom severity, suggesting that Ubc2p is critical for virus replication in plants. We provide evidence that Rad6p is involved in promoting the subversion of Vps23p and Vps4p ESCRT proteins for viral replicase complex assembly. - Highlights: • Tombusvirus p33 replication protein interacts with cellular RAD6/Ubc2 E2 enzymes. • Deletion of RAD6 reduces tombusvirus replication in yeast. • Silencing of UBC2 in plants inhibits tombusvirus replication. • Mono- and bi-ubiquitination of p33 replication protein in yeast and in vitro. • Rad6p promotes the recruitment of cellular ESCRT proteins into the tombusvirus replicase.« less

The Topology of the Growing Human Interactome Data.

PubMed

Janjić, Vuk; Pržulj, Nataša

2014-06-01

We have long moved past the one-gene-one-function concept originally proposed by Beadle and Tatum back in 1941; but the full understanding of genotype-phenotype relations still largely relies on the analysis of static, snapshot-like, interaction data sets. Here, we look at what global patterns can be uncovered if we simply trace back the human interactome network over the last decade of protein-protein interaction (PPI) screening. We take a purely topological approach and find that as the human interactome is getting denser, it is not only gaining in structure (in terms of now being better fit by structured network models than before), but also there are patterns in the way in which it is growing: (a) newly added proteins tend to get linked to existing proteins in the interactome that are not know to interact; and (b) new proteins tend to link to already well connected proteins. Moreover, the alignment between human and yeast interactomes spanning over 40% of yeast's proteins - that are involved in regulation of transcription, RNA splicing and other cellcycle- related processes-suggests the existence of a part of the interactome which remains topologically and functionally unaffected through evolution. Furthermore, we find a small sub-network, specific to the "core" of the human interactome and involved in regulation of transcription and cancer development, whose wiring has not changed within the human interactome over the last 10 years of interacome data acquisition. Finally, we introduce a generalisation of the clustering coefficient of a network as a new measure called the cycle coefficient, and use it to show that PPI networks of human and model organisms are wired in a tight way which forbids the occurrence large cycles.

The topology of the growing human interactome data.

PubMed

Janjić, Vuk; Pržulj, Nataša

2014-06-23

We have long moved past the one-gene–one-function concept originally proposed by Beadle and Tatum back in 1941; but the full understanding of genotype–phenotype relations still largely relies on the analysis of static, snapshot-like, interaction data sets. Here, we look at what global patterns can be uncovered if we simply trace back the human interactome network over the last decade of protein- protein interaction (PPI) screening. We take a purely topological approach and find that as the human interactome is getting denser, it is not only gaining in structure (in terms of now being better fit by structured network models than before), but also there are patterns in the way in which it is growing: (a) newly added proteins tend to get linked to existing proteins in the interactome that are not know to interact; and (b) new proteins tend to link to already well connected proteins. Moreover, the alignment between human and yeast interactomes spanning over 40% of yeast’s proteins — that are involved in regulation of transcription, RNA splicing and other cellcycle-related processes—suggests the existence of a part of the interactome which remains topologically and functionally unaffected through evolution. Furthermore, we find a small sub-network, specific to the “core” of the human interactome and involved in regulation of transcription and cancer development, whose wiring has not changed within the human interactome over the last 10 years of interacome data acquisition. Finally, we introduce a generalisation of the clustering coefficient of a network as a new measure called the cycle coefficient, and use it to show that PPI networks of human and model organisms are wired in a tight way which forbids the occurrence large cycles.

The control of translational accuracy is a determinant of healthy ageing in yeast

PubMed Central

Leadsham, Jane E.; Sauvadet, Aimie; Tarrant, Daniel; Adam, Ilectra S.; Saromi, Kofo; Laun, Peter; Rinnerthaler, Mark; Breitenbach-Koller, Hannelore; Breitenbach, Michael; Tuite, Mick F.; Gourlay, Campbell W.

2017-01-01

Life requires the maintenance of molecular function in the face of stochastic processes that tend to adversely affect macromolecular integrity. This is particularly relevant during ageing, as many cellular functions decline with age, including growth, mitochondrial function and energy metabolism. Protein synthesis must deliver functional proteins at all times, implying that the effects of protein synthesis errors like amino acid misincorporation and stop-codon read-through must be minimized during ageing. Here we show that loss of translational accuracy accelerates the loss of viability in stationary phase yeast. Since reduced translational accuracy also reduces the folding competence of at least some proteins, we hypothesize that negative interactions between translational errors and age-related protein damage together overwhelm the cellular chaperone network. We further show that multiple cellular signalling networks control basal error rates in yeast cells, including a ROS signal controlled by mitochondrial activity, and the Ras pathway. Together, our findings indicate that signalling pathways regulating growth, protein homeostasis and energy metabolism may jointly safeguard accurate protein synthesis during healthy ageing. PMID:28100667

Integrating high-throughput genetic interaction mapping and high-content screening to explore yeast spindle morphogenesis

PubMed Central

Vizeacoumar, Franco J.; van Dyk, Nydia; S.Vizeacoumar, Frederick; Cheung, Vincent; Li, Jingjing; Sydorskyy, Yaroslav; Case, Nicolle; Li, Zhijian; Datti, Alessandro; Nislow, Corey; Raught, Brian; Zhang, Zhaolei; Frey, Brendan; Bloom, Kerry

2010-01-01

We describe the application of a novel screening approach that combines automated yeast genetics, synthetic genetic array (SGA) analysis, and a high-content screening (HCS) system to examine mitotic spindle morphogenesis. We measured numerous spindle and cellular morphological parameters in thousands of single mutants and corresponding sensitized double mutants lacking genes known to be involved in spindle function. We focused on a subset of genes that appear to define a highly conserved mitotic spindle disassembly pathway, which is known to involve Ipl1p, the yeast aurora B kinase, as well as the cell cycle regulatory networks mitotic exit network (MEN) and fourteen early anaphase release (FEAR). We also dissected the function of the kinetochore protein Mcm21p, showing that sumoylation of Mcm21p regulates the enrichment of Ipl1p and other chromosomal passenger proteins to the spindle midzone to mediate spindle disassembly. Although we focused on spindle disassembly in a proof-of-principle study, our integrated HCS-SGA method can be applied to virtually any pathway, making it a powerful means for identifying specific cellular functions. PMID:20065090

The control of translational accuracy is a determinant of healthy ageing in yeast.

PubMed

von der Haar, Tobias; Leadsham, Jane E; Sauvadet, Aimie; Tarrant, Daniel; Adam, Ilectra S; Saromi, Kofo; Laun, Peter; Rinnerthaler, Mark; Breitenbach-Koller, Hannelore; Breitenbach, Michael; Tuite, Mick F; Gourlay, Campbell W

2017-01-01

Life requires the maintenance of molecular function in the face of stochastic processes that tend to adversely affect macromolecular integrity. This is particularly relevant during ageing, as many cellular functions decline with age, including growth, mitochondrial function and energy metabolism. Protein synthesis must deliver functional proteins at all times, implying that the effects of protein synthesis errors like amino acid misincorporation and stop-codon read-through must be minimized during ageing. Here we show that loss of translational accuracy accelerates the loss of viability in stationary phase yeast. Since reduced translational accuracy also reduces the folding competence of at least some proteins, we hypothesize that negative interactions between translational errors and age-related protein damage together overwhelm the cellular chaperone network. We further show that multiple cellular signalling networks control basal error rates in yeast cells, including a ROS signal controlled by mitochondrial activity, and the Ras pathway. Together, our findings indicate that signalling pathways regulating growth, protein homeostasis and energy metabolism may jointly safeguard accurate protein synthesis during healthy ageing. © 2017 The Authors.

Environmental and Genetic Determinants of Colony Morphology in Yeast

PubMed Central

Granek, Joshua A.; Magwene, Paul M.

2010-01-01

Nutrient stresses trigger a variety of developmental switches in the budding yeast Saccharomyces cerevisiae. One of the least understood of such responses is the development of complex colony morphology, characterized by intricate, organized, and strain-specific patterns of colony growth and architecture. The genetic bases of this phenotype and the key environmental signals involved in its induction have heretofore remained poorly understood. By surveying multiple strain backgrounds and a large number of growth conditions, we show that limitation for fermentable carbon sources coupled with a rich nitrogen source is the primary trigger for the colony morphology response in budding yeast. Using knockout mutants and transposon-mediated mutagenesis, we demonstrate that two key signaling networks regulating this response are the filamentous growth MAP kinase cascade and the Ras-cAMP-PKA pathway. We further show synergistic epistasis between Rim15, a kinase involved in integration of nutrient signals, and other genes in these pathways. Ploidy, mating-type, and genotype-by-environment interactions also appear to play a role in the controlling colony morphology. Our study highlights the high degree of network reuse in this model eukaryote; yeast use the same core signaling pathways in multiple contexts to integrate information about environmental and physiological states and generate diverse developmental outputs. PMID:20107600

An integrated approach to characterize genetic interaction networks in yeast metabolism

PubMed Central

Szappanos, Balázs; Kovács, Károly; Szamecz, Béla; Honti, Frantisek; Costanzo, Michael; Baryshnikova, Anastasia; Gelius-Dietrich, Gabriel; Lercher, Martin J.; Jelasity, Márk; Myers, Chad L.; Andrews, Brenda J.; Boone, Charles; Oliver, Stephen G.; Pál, Csaba; Papp, Balázs

2011-01-01

Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii) superpose the data on a detailed systems biology model of metabolism, and iii) introduce a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigate the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability. Last, we demonstrate the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments. PMID:21623372

Direct TFIIA-TFIID Protein Contacts Drive Budding Yeast Ribosomal Protein Gene Transcription*

PubMed Central

Layer, Justin H.; Weil, P. Anthony

2013-01-01

We have previously shown that yeast TFIID provides coactivator function on the promoters of ribosomal protein-encoding genes (RPGs) by making direct contact with the transactivator repressor activator protein 1 (Rap1). Further, our structural studies of assemblies generated with purified Rap1, TFIID, and TFIIA on RPG enhancer-promoter DNA indicate that Rap1-TFIID interaction induces dramatic conformational rearrangements of enhancer-promoter DNA and TFIID-bound TFIIA. These data indicate a previously unknown yet critical role for yeast TFIIA in the integration of activator-TFIID contacts with promoter conformation and downstream preinitiation complex formation and/or function. Here we describe the use of systematic mutagenesis to define how specific TFIIA contacts contribute to these processes. We have verified that TFIIA is required for RPG transcription in vivo and in vitro, consistent with the existence of a critical Rap1-TFIIA-TFIID interaction network. We also identified essential points of contact for TFIIA and Rap1 within the Rap1 binding domain of the Taf4 subunit of TFIID. These data suggest a mechanism for how interactions between TFIID, TFIIA, and Rap1 contribute to the high rate of transcription initiation seen on RPGs in vivo. PMID:23814059

Passing Messages between Biological Networks to Refine Predicted Interactions

PubMed Central

Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng

2013-01-01

Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net. PMID:23741402

From the Cover: Toward a protein-protein interaction map of the budding yeast: A comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins

NASA Astrophysics Data System (ADS)

Ito, Takashi; Tashiro, Kosuke; Muta, Shigeru; Ozawa, Ritsuko; Chiba, Tomoko; Nishizawa, Mayumi; Yamamoto, Kiyoshi; Kuhara, Satoru; Sakaki, Yoshiyuki

2000-02-01

Protein-protein interactions play pivotal roles in various aspects of the structural and functional organization of the cell, and their complete description is indispensable to thorough understanding of the cell. As an approach toward this goal, here we report a comprehensive system to examine two-hybrid interactions in all of the possible combinations between proteins of Saccharomyces cerevisiae. We cloned all of the yeast ORFs individually as a DNA-binding domain fusion ("bait") in a MATa strain and as an activation domain fusion ("prey") in a MATα strain, and subsequently divided them into pools, each containing 96 clones. These bait and prey clone pools were systematically mated with each other, and the transformants were subjected to strict selection for the activation of three reporter genes followed by sequence tagging. Our initial examination of ≈4 × 106 different combinations, constituting ≈10% of the total to be tested, has revealed 183 independent two-hybrid interactions, more than half of which are entirely novel. Notably, the obtained binary data allow us to extract more complex interaction networks, including the one that may explain a currently unsolved mechanism for the connection between distinct steps of vesicular transport. The approach described here thus will provide many leads for integration of various cellular functions and serve as a major driving force in the completion of the protein-protein interaction map.

Protein complex prediction in large ontology attributed protein-protein interaction networks.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

2013-01-01

Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

Identifying protein complexes in PPI network using non-cooperative sequential game.

PubMed

Maulik, Ujjwal; Basu, Srinka; Ray, Sumanta

2017-08-21

Identifying protein complexes from protein-protein interaction (PPI) network is an important and challenging task in computational biology as it helps in better understanding of cellular mechanisms in various organisms. In this paper we propose a noncooperative sequential game based model for protein complex detection from PPI network. The key hypothesis is that protein complex formation is driven by mechanism that eventually optimizes the number of interactions within the complex leading to dense subgraph. The hypothesis is drawn from the observed network property named small world. The proposed multi-player game model translates the hypothesis into the game strategies. The Nash equilibrium of the game corresponds to a network partition where each protein either belong to a complex or form a singleton cluster. We further propose an algorithm to find the Nash equilibrium of the sequential game. The exhaustive experiment on synthetic benchmark and real life yeast networks evaluates the structural as well as biological significance of the network partitions.

An automated method for finding molecular complexes in large protein interaction networks

PubMed Central

Bader, Gary D; Hogue, Christopher WV

2003-01-01

Background Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. Results This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Conclusion Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from . PMID:12525261

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

PubMed Central

Roy, Sushmita; Martinez, Diego; Platero, Harriett; Lane, Terran; Werner-Washburne, Margaret

2009-01-01

Background Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information. Results AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins. Conclusion AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains. PMID:19936254

Modular synchronization in complex networks.

PubMed

Oh, E; Rho, K; Hong, H; Kahng, B

2005-10-01

We study the synchronization transition (ST) of a modified Kuramoto model on two different types of modular complex networks. It is found that the ST depends on the type of intermodular connections. For the network with decentralized (centralized) intermodular connections, the ST occurs at finite coupling constant (behaves abnormally). Such distinct features are found in the yeast protein interaction network and the Internet, respectively. Moreover, by applying the finite-size scaling analysis to an artificial network with decentralized intermodular connections, we obtain the exponent associated with the order parameter of the ST to be beta approximately 1 different from beta(MF) approximately 1/2 obtained from the scale-free network with the same degree distribution but the absence of modular structure, corresponding to the mean field value.

Spreading out of perturbations in reversible reaction networks

NASA Astrophysics Data System (ADS)

Maslov, Sergei; Sneppen, Kim; Ispolatov, I.

2007-08-01

Using an example of physical interactions between proteins, we study how a perturbation propagates in the equilibrium of a network of reversible reactions governed by the law of mass action. We introduce a matrix formalism to describe the linear response of all equilibrium concentrations to shifts in total abundances of individual reactants, and reveal its heuristic analogy to the flow of electric current in a network of resistors. Our main conclusion is that, on average, the induced changes in equilibrium concentrations decay exponentially as a function of network distance from the source of perturbation. We analyze how this decay is influenced by such factors as the topology of a network, binding strength, and correlations between concentrations of neighboring nodes. We find that the minimal branching of the network, small values of dissociation constants, and low equilibrium free (unbound) concentrations of reacting substances all decrease the decay constant and thus increase the range of propagation. Exact analytic expressions for the decay constant are obtained for the case of equally strong interactions and uniform as well as oscillating concentrations on the Bethe lattice. Our general findings are illustrated using a real network of protein-protein interactions in baker's yeast with experimentally determined protein concentrations.

A brewing understanding of the regulation of Bax function by Bcl-xL and Bcl-2.

PubMed

Renault, Thibaud T; Dejean, Laurent M; Manon, Stéphen

2017-01-01

Bcl-2 family members form a network of protein-protein interactions that regulate apoptosis through permeabilization of the mitochondrial outer membrane. Deciphering this intricate network requires streamlined experimental models, including the heterologous expression in yeast. This approach had previously enabled researchers to identify domains and residues that underlie the conformational changes driving the translocation, the insertion and the oligomerization of the pro-apoptotic protein Bax at the level of the mitochondrial outer membrane. Recent studies that combine experiments in yeast and in mammalian cells have shown the unexpected effect of the anti-apoptotic protein Bcl-xL on the priming of Bax. As demonstrated with the BH3-mimetic molecule ABT-737, this property of Bcl-xL, and of Bcl-2, is crucial to elaborate about how apoptosis could be reactivated in tumoral cells. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

On the Growth of Scientific Knowledge: Yeast Biology as a Case Study

PubMed Central

He, Xionglei; Zhang, Jianzhi

2009-01-01

The tempo and mode of human knowledge expansion is an enduring yet poorly understood topic. Through a temporal network analysis of three decades of discoveries of protein interactions and genetic interactions in baker's yeast, we show that the growth of scientific knowledge is exponential over time and that important subjects tend to be studied earlier. However, expansions of different domains of knowledge are highly heterogeneous and episodic such that the temporal turnover of knowledge hubs is much greater than expected by chance. Familiar subjects are preferentially studied over new subjects, leading to a reduced pace of innovation. While research is increasingly done in teams, the number of discoveries per researcher is greater in smaller teams. These findings reveal collective human behaviors in scientific research and help design better strategies in future knowledge exploration. PMID:19300476

On the growth of scientific knowledge: yeast biology as a case study.

PubMed

He, Xionglei; Zhang, Jianzhi

2009-03-01

The tempo and mode of human knowledge expansion is an enduring yet poorly understood topic. Through a temporal network analysis of three decades of discoveries of protein interactions and genetic interactions in baker's yeast, we show that the growth of scientific knowledge is exponential over time and that important subjects tend to be studied earlier. However, expansions of different domains of knowledge are highly heterogeneous and episodic such that the temporal turnover of knowledge hubs is much greater than expected by chance. Familiar subjects are preferentially studied over new subjects, leading to a reduced pace of innovation. While research is increasingly done in teams, the number of discoveries per researcher is greater in smaller teams. These findings reveal collective human behaviors in scientific research and help design better strategies in future knowledge exploration.

Comparing Networks from a Data Analysis Perspective

NASA Astrophysics Data System (ADS)

Li, Wei; Yang, Jing-Yu

To probe network characteristics, two predominant ways of network comparison are global property statistics and subgraph enumeration. However, they suffer from limited information and exhaustible computing. Here, we present an approach to compare networks from the perspective of data analysis. Initially, the approach projects each node of original network as a high-dimensional data point, and the network is seen as clouds of data points. Then the dispersion information of the principal component analysis (PCA) projection of the generated data clouds can be used to distinguish networks. We applied this node projection method to the yeast protein-protein interaction networks and the Internet Autonomous System networks, two types of networks with several similar higher properties. The method can efficiently distinguish one from the other. The identical result of different datasets from independent sources also indicated that the method is a robust and universal framework.

Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses.

PubMed

Mo, Chunyan; Wan, Shumin; Xia, Youquan; Ren, Ning; Zhou, Yang; Jiang, Xingyu

2018-01-01

Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL) protein and CBL-interacting protein kinase (CIPK) is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL ( MeCBL ) and 26 CIPK ( MeCIPK ) genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBL s and CIPK s. Reverse-transcriptase polymerase chain reaction (RT-PCR) analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR), and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10 , and Na + /H + antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses.

Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses

PubMed Central

Mo, Chunyan; Wan, Shumin; Xia, Youquan; Ren, Ning; Zhou, Yang; Jiang, Xingyu

2018-01-01

Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL) protein and CBL-interacting protein kinase (CIPK) is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL (MeCBL) and 26 CIPK (MeCIPK) genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBLs and CIPKs. Reverse-transcriptase polymerase chain reaction (RT-PCR) analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR), and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10, and Na+/H+ antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses. PMID:29552024

Network-assisted target identification for haploinsufficiency and homozygous profiling screens

PubMed Central

Wang, Sheng

2017-01-01

Chemical genomic screens have recently emerged as a systematic approach to drug discovery on a genome-wide scale. Drug target identification and elucidation of the mechanism of action (MoA) of hits from these noisy high-throughput screens remain difficult. Here, we present GIT (Genetic Interaction Network-Assisted Target Identification), a network analysis method for drug target identification in haploinsufficiency profiling (HIP) and homozygous profiling (HOP) screens. With the drug-induced phenotypic fitness defect of the deletion of a gene, GIT also incorporates the fitness defects of the gene’s neighbors in the genetic interaction network. On three genome-scale yeast chemical genomic screens, GIT substantially outperforms previous scoring methods on target identification on HIP and HOP assays, respectively. Finally, we showed that by combining HIP and HOP assays, GIT further boosts target identification and reveals potential drug’s mechanism of action. PMID:28574983

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases.

PubMed

Berger, Seth I; Posner, Jeremy M; Ma'ayan, Avi

2007-10-04

In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica.

PubMed

Trébulle, Pauline; Nicaud, Jean-Marc; Leplat, Christophe; Elati, Mohamed

2017-01-01

Complex phenotypes, such as lipid accumulation, result from cooperativity between regulators and the integration of multiscale information. However, the elucidation of such regulatory programs by experimental approaches may be challenging, particularly in context-specific conditions. In particular, we know very little about the regulators of lipid accumulation in the oleaginous yeast of industrial interest Yarrowia lipolytica . This lack of knowledge limits the development of this yeast as an industrial platform, due to the time-consuming and costly laboratory efforts required to design strains with the desired phenotypes. In this study, we aimed to identify context-specific regulators and mechanisms, to guide explorations of the regulation of lipid accumulation in Y. lipolytica . Using gene regulatory network inference, and considering the expression of 6539 genes over 26 time points from GSE35447 for biolipid production and a list of 151 transcription factors, we reconstructed a gene regulatory network comprising 111 transcription factors, 4451 target genes and 17048 regulatory interactions (YL-GRN-1) supported by evidence of protein-protein interactions. This study, based on network interrogation and wet laboratory validation (a) highlights the relevance of our proposed measure, the transcription factors influence, for identifying phases corresponding to changes in physiological state without prior knowledge (b) suggests new potential regulators and drivers of lipid accumulation and (c) experimentally validates the impact of six of the nine regulators identified on lipid accumulation, with variations in lipid content from +43.2% to -31.2% on glucose or glycerol.

The PathoYeastract database: an information system for the analysis of gene and genomic transcription regulation in pathogenic yeasts.

PubMed

Monteiro, Pedro Tiago; Pais, Pedro; Costa, Catarina; Manna, Sauvagya; Sá-Correia, Isabel; Teixeira, Miguel Cacho

2017-01-04

We present the PATHOgenic YEAst Search for Transcriptional Regulators And Consensus Tracking (PathoYeastract - http://pathoyeastract.org) database, a tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in the pathogenic yeasts Candida albicans and C. glabrata Upon data retrieval from hundreds of publications, followed by curation, the database currently includes 28 000 unique documented regulatory associations between transcription factors (TF) and target genes and 107 DNA binding sites, considering 134 TFs in both species. Following the structure used for the YEASTRACT database, PathoYeastract makes available bioinformatics tools that enable the user to exploit the existing information to predict the TFs involved in the regulation of a gene or genome-wide transcriptional response, while ranking those TFs in order of their relative importance. Each search can be filtered based on the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. Promoter analysis tools and interactive visualization tools for the representation of TF regulatory networks are also provided. The PathoYeastract database further provides simple tools for the prediction of gene and genomic regulation based on orthologous regulatory associations described for other yeast species, a comparative genomics setup for the study of cross-species evolution of regulatory networks. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

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

Microbial Interactions within a Cheese Microbial Community▿ †

PubMed Central

Mounier, Jérôme; Monnet, Christophe; Vallaeys, Tatiana; Arditi, Roger; Sarthou, Anne-Sophie; Hélias, Arnaud; Irlinger, Françoise

2008-01-01

The interactions that occur during the ripening of smear cheeses are not well understood. Yeast-yeast interactions and yeast-bacterium interactions were investigated within a microbial community composed of three yeasts and six bacteria found in cheese. The growth dynamics of this community was precisely described during the ripening of a model cheese, and the Lotka-Volterra model was used to evaluate species interactions. Subsequently, the effects on ecosystem functioning of yeast omissions in the microbial community were evaluated. It was found both in the Lotka-Volterra model and in the omission study that negative interactions occurred between yeasts. Yarrowia lipolytica inhibited mycelial expansion of Geotrichum candidum, whereas Y. lipolytica and G. candidum inhibited Debaryomyces hansenii cell viability during the stationary phase. However, the mechanisms involved in these interactions remain unclear. It was also shown that yeast-bacterium interactions played a significant role in the establishment of this multispecies ecosystem on the cheese surface. Yeasts were key species in bacterial development, but their influences on the bacteria differed. It appeared that the growth of Arthrobacter arilaitensis or Hafnia alvei relied less on a specific yeast function because these species dominated the bacterial flora, regardless of which yeasts were present in the ecosystem. For other bacteria, such as Leucobacter sp. or Brevibacterium aurantiacum, growth relied on a specific yeast, i.e., G. candidum. Furthermore, B. aurantiacum, Corynebacterium casei, and Staphylococcus xylosus showed reduced colonization capacities in comparison with the other bacteria in this model cheese. Bacterium-bacterium interactions could not be clearly identified. PMID:17981942

Integrated inference and evaluation of host–fungi interaction networks

PubMed Central

Remmele, Christian W.; Luther, Christian H.; Balkenhol, Johannes; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus T.

2015-01-01

Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host–pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host–fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen–host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi–human and fungi–mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host–fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host–fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host–fungi transcriptome and proteome data. PMID:26300851

A Type-2 fuzzy data fusion approach for building reliable weighted protein interaction networks with application in protein complex detection.

PubMed

Mehranfar, Adele; Ghadiri, Nasser; Kouhsar, Morteza; Golshani, Ashkan

2017-09-01

Detecting the protein complexes is an important task in analyzing the protein interaction networks. Although many algorithms predict protein complexes in different ways, surveys on the interaction networks indicate that about 50% of detected interactions are false positives. Consequently, the accuracy of existing methods needs to be improved. In this paper we propose a novel algorithm to detect the protein complexes in 'noisy' protein interaction data. First, we integrate several biological data sources to determine the reliability of each interaction and determine more accurate weights for the interactions. A data fusion component is used for this step, based on the interval type-2 fuzzy voter that provides an efficient combination of the information sources. This fusion component detects the errors and diminishes their effect on the detection protein complexes. So in the first step, the reliability scores have been assigned for every interaction in the network. In the second step, we have proposed a general protein complex detection algorithm by exploiting and adopting the strong points of other algorithms and existing hypotheses regarding real complexes. Finally, the proposed method has been applied for the yeast interaction datasets for predicting the interactions. The results show that our framework has a better performance regarding precision and F-measure than the existing approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

PubMed

Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

2011-11-01

A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

Where Have All the Interactions Gone? Estimating the Coverage of Two-Hybrid Protein Interaction Maps

PubMed Central

Huang, Hailiang; Jedynak, Bruno M; Bader, Joel S

2007-01-01

Yeast two-hybrid screens are an important method for mapping pairwise physical interactions between proteins. The fraction of interactions detected in independent screens can be very small, and an outstanding challenge is to determine the reason for the low overlap. Low overlap can arise from either a high false-discovery rate (interaction sets have low overlap because each set is contaminated by a large number of stochastic false-positive interactions) or a high false-negative rate (interaction sets have low overlap because each misses many true interactions). We extend capture–recapture theory to provide the first unified model for false-positive and false-negative rates for two-hybrid screens. Analysis of yeast, worm, and fly data indicates that 25% to 45% of the reported interactions are likely false positives. Membrane proteins have higher false-discovery rates on average, and signal transduction proteins have lower rates. The overall false-negative rate ranges from 75% for worm to 90% for fly, which arises from a roughly 50% false-negative rate due to statistical undersampling and a 55% to 85% false-negative rate due to proteins that appear to be systematically lost from the assays. Finally, statistical model selection conclusively rejects the Erdös-Rényi network model in favor of the power law model for yeast and the truncated power law for worm and fly degree distributions. Much as genome sequencing coverage estimates were essential for planning the human genome sequencing project, the coverage estimates developed here will be valuable for guiding future proteomic screens. All software and datasets are available in Datasets S1 and S2, Figures S1–S5, and Tables S1−S6, and are also available from our Web site, http://www.baderzone.org. PMID:18039026

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.

The informational architecture of the cell.

PubMed

Walker, Sara Imari; Kim, Hyunju; Davies, Paul C W

2016-03-13

We compare the informational architecture of biological and random networks to identify informational features that may distinguish biological networks from random. The study presented here focuses on the Boolean network model for regulation of the cell cycle of the fission yeast Schizosaccharomyces pombe. We compare calculated values of local and global information measures for the fission yeast cell cycle to the same measures as applied to two different classes of random networks: Erdös-Rényi and scale-free. We report patterns in local information processing and storage that do indeed distinguish biological from random, associated with control nodes that regulate the function of the fission yeast cell-cycle network. Conversely, we find that integrated information, which serves as a global measure of 'emergent' information processing, does not differ from random for the case presented. We discuss implications for our understanding of the informational architecture of the fission yeast cell-cycle network in particular, and more generally for illuminating any distinctive physics that may be operative in life. © 2016 The Author(s).

Global Alignment of Pairwise Protein Interaction Networks for Maximal Common Conserved Patterns

DOE PAGES

Tian, Wenhong; Samatova, Nagiza F.

2013-01-01

A number of tools for the alignment of protein-protein interaction (PPI) networks have laid the foundation for PPI network analysis. Most of alignment tools focus on finding conserved interaction regions across the PPI networks through either local or global mapping of similar sequences. Researchers are still trying to improve the speed, scalability, and accuracy of network alignment. In view of this, we introduce a connected-components based fast algorithm, HopeMap, for network alignment. Observing that the size of true orthologs across species is small comparing to the total number of proteins in all species, we take a different approach based onmore » a precompiled list of homologs identified by KO terms. Applying this approach to S. cerevisiae (yeast) and D. melanogaster (fly), E. coli K12 and S. typhimurium , E. coli K12 and C. crescenttus , we analyze all clusters identified in the alignment. The results are evaluated through up-to-date known gene annotations, gene ontology (GO), and KEGG ortholog groups (KO). Comparing to existing tools, our approach is fast with linear computational cost, highly accurate in terms of KO and GO terms specificity and sensitivity, and can be extended to multiple alignments easily.« less

Development and application of a DNA microarray-based yeast two-hybrid system

PubMed Central

Suter, Bernhard; Fontaine, Jean-Fred; Yildirimman, Reha; Raskó, Tamás; Schaefer, Martin H.; Rasche, Axel; Porras, Pablo; Vázquez-Álvarez, Blanca M.; Russ, Jenny; Rau, Kirstin; Foulle, Raphaele; Zenkner, Martina; Saar, Kathrin; Herwig, Ralf; Andrade-Navarro, Miguel A.; Wanker, Erich E.

2013-01-01

The yeast two-hybrid (Y2H) system is the most widely applied methodology for systematic protein–protein interaction (PPI) screening and the generation of comprehensive interaction networks. We developed a novel Y2H interaction screening procedure using DNA microarrays for high-throughput quantitative PPI detection. Applying a global pooling and selection scheme to a large collection of human open reading frames, proof-of-principle Y2H interaction screens were performed for the human neurodegenerative disease proteins huntingtin and ataxin-1. Using systematic controls for unspecific Y2H results and quantitative benchmarking, we identified and scored a large number of known and novel partner proteins for both huntingtin and ataxin-1. Moreover, we show that this parallelized screening procedure and the global inspection of Y2H interaction data are uniquely suited to define specific PPI patterns and their alteration by disease-causing mutations in huntingtin and ataxin-1. This approach takes advantage of the specificity and flexibility of DNA microarrays and of the existence of solid-related statistical methods for the analysis of DNA microarray data, and allows a quantitative approach toward interaction screens in human and in model organisms. PMID:23275563

Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism

PubMed Central

Wildenhain, Jan; Spitzer, Michaela; Dolma, Sonam; Jarvik, Nick; White, Rachel; Roy, Marcia; Griffiths, Emma; Bellows, David S.; Wright, Gerard D.; Tyers, Mike

2016-01-01

The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug discovery. PMID:27874849

Increased copper toxicity in Saccharomyces cerevisiae lacking VPS35, a component of the retromer and monogenic Parkinson disease gene in humans.

PubMed

Sowada, Nadine; Stiller, Barbara; Kubisch, Christian

2016-08-05

The Saccharomyces cerevisiae gene VPS35 encodes a component of the retromer complex which is involved in vesicle transport from endosomes to the trans-Golgi network. Yeast and human VPS35 orthologs are highly conserved and mutations in human VPS35 cause an autosomal dominant form of late-onset Parkinson disease (PD). We now show that deletion of VPS35 in yeast (vps35Δ) leads to a dose-dependent growth defect towards copper. This increased sensitivity could be rescued by transformation with yeast wild-type VPS35 but not by the expression of a construct harboring the yeast equivalent (i.e. D686N) of the most commonly identified VPS35-associated PD mutation, p.D620N. In addition, we show that expression of one copy of α-synuclein, which is known to directly interact with copper, leads to a pronounced aggravation of copper toxicity in vps35Δ cells, thereby linking the regulation of copper homeostasis by Vps35p in yeast to one of the key molecules in PD pathophysiology. Copyright © 2016 Elsevier Inc. 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.

Categorizing Biases in High-Confidence High-Throughput Protein-Protein Interaction Data Sets*

PubMed Central

Yu, Xueping; Ivanic, Joseph; Memišević, Vesna; Wallqvist, Anders; Reifman, Jaques

2011-01-01

We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not necessarily imply that one detection methodology was better or worse, but rather that, to a large extent, the insights reflected the methodological biases themselves. Consequently, interpreting the protein interaction data within their experimental or cellular context provided the best avenue for overcoming biases and inferring biological knowledge. PMID:21876202

Saccharomyces cerevisiae Gle2/Rae1 is involved in septin organization, essential for cell cycle progression.

PubMed

Zander, Gesa; Kramer, Wilfried; Seel, Anika; Krebber, Heike

2017-11-01

Gle2/Rae1 is highly conserved from yeast to humans and has been described as an mRNA export factor. Additionally, it is implicated in the anaphase-promoting complex-mediated cell cycle regulation in higher eukaryotes. Here we identify an involvement for Saccharomyces cerevisiae Gle2 in septin organization, which is crucial for cell cycle progression and cell division. Gle2 genetically and physically interacts with components of the septin ring. Importantly, deletion of GLE2 leads to elongated buds, severe defects in septin-assembly and their cellular mislocalization. Septin-ring formation is triggered by the septin-regulating GTPase Cdc42, which establishes and maintains cell polarity. Additionally, activity of the master cell cycle regulator Cdc28 (Cdk1) is needed, which is, besides other functions, also required for G 2 /M-transition, and in yeast particularly responsible for initiating the apical-isotropic switch. We show genetic and physical interactions of Gle2 with both Cdc42 and Cdc28. Most importantly, we find that gle2∆ severely mislocalizes Cdc42, leading to defects in septin-complex formation and cell division. Thus, our findings suggest that Gle2 participates in the efficient organization of the septin assembly network, where it might act as a scaffold protein. © 2017 The Authors. Yeast published by John Wiley & Sons, Ltd. © 2017 The Authors. Yeast published by John Wiley & Sons, Ltd.

Interactions of Condensed Tannins with Saccharomyces cerevisiae Yeast Cells and Cell Walls: Tannin Location by Microscopy.

PubMed

Mekoue Nguela, Julie; Vernhet, Aude; Sieczkowski, Nathalie; Brillouet, Jean-Marc

2015-09-02

Interactions between grape tannins/red wine polyphenols and yeast cells/cell walls was previously studied within the framework of red wine aging and the use of yeast-derived products as an alternative to aging on lees. Results evidenced a quite different behavior between whole cells (biomass grown to elaborate yeast-derived products, inactivated yeast, and yeast inactivated after autolysis) and yeast cell walls (obtained from mechanical disruption of the biomass). Briefly, whole cells exhibited a high capacity to irreversibly adsorb grape and wine tannins, whereas only weak interactions were observed for cell walls. This last point was quite unexpected considering the literature and called into question the real role of cell walls in yeasts' ability to fix tannins. In the present work, tannin location after interactions between grape and wine tannins and yeast cells and cell walls was studied by means of transmission electron microscopy, light epifluorescence, and confocal microscopy. Microscopy observations evidenced that if tannins interact with cell walls, and especially cell wall mannoproteins, they also diffuse freely through the walls of dead cells to interact with their plasma membrane and cytoplasmic components.

Hsp90 interaction with Cdc2 and Plo1 kinases contributes to actomyosin ring condensation in fission yeast.

PubMed

Santino, Andrea; Tallada, Victor A; Jimenez, Juan; Garzón, Andrés

2012-08-01

In Schizosaccharomyces pombe, cytokinesis occurs by ordered recruitment of actomyosin components at the division site, followed by lateral condensation to produce a ring-like structure early in anaphase, which eventually matures and contracts at the end of mitosis. We found that in temperature-sensitive hsp90-w1 mutant cells, encoding an Hsp90 mutant protein, ring components were recruited to form a cortical network at the division site, but this network failed to condense into a compact ring, suggesting a role for Hsp90 in this particular step. hsp90-w1 mutant shows strong genetic interaction with specific mutant alleles of the fission yeast cdc2, such as cdc2-33. Interestingly, actomyosin ring defects in hsp90-w1 cdc2-33 mutant cells resembled that of hsp90-w1 single mutant at restrictive temperature. Noteworthy, similar genetic interaction was found with a mutant allele of polo-like kinase, plo1-ts4, suggesting that Hsp90 collaborates with Cdc2 and Plo1 cell cycle kinases to condense medial ring components. In vitro analyses suggested that Cdc2 and Plo1 physically interact with Hsp90. Association of Cdc2 to Hsp90 was ATP independent, while Plo1 binds to this chaperone in an ATP-dependent manner, indicating that these two kinases interact with different Hsp90 complexes. Overall, our analyses of hsp90-w1 reveal a possible role for this chaperone in medial ring condensation in association with Cdc2 and Plo1 kinases.

Water-network percolation transitions in hydrated yeast

NASA Astrophysics Data System (ADS)

Sokołowska, Dagmara; Król-Otwinowska, Agnieszka; Mościcki, Józef K.

2004-11-01

We discovered two percolation processes in succession in dc conductivity of bulk baker’s yeast in the course of dehydration. Critical exponents characteristic for the three-dimensional network for heavily hydrated system, and two dimensions in the light hydration limit, evidenced a dramatic change of the water network dimensionality in the dehydration process.

A Prize-Collecting Steiner Tree Approach for Transduction Network Inference

NASA Astrophysics Data System (ADS)

Bailly-Bechet, Marc; Braunstein, Alfredo; Zecchina, Riccardo

Into the cell, information from the environment is mainly propagated via signaling pathways which form a transduction network. Here we propose a new algorithm to infer transduction networks from heterogeneous data, using both the protein interaction network and expression datasets. We formulate the inference problem as an optimization task, and develop a message-passing, probabilistic and distributed formalism to solve it. We apply our algorithm to the pheromone response in the baker’s yeast S. cerevisiae. We are able to find the backbone of the known structure of the MAPK cascade of pheromone response, validating our algorithm. More importantly, we make biological predictions about some proteins whose role could be at the interface between pheromone response and other cellular functions.

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response.

PubMed

MacGilvray, Matthew E; Shishkova, Evgenia; Chasman, Deborah; Place, Michael; Gitter, Anthony; Coon, Joshua J; Gasch, Audrey P

2018-05-01

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

Statistical mechanics of scale-free gene expression networks

NASA Astrophysics Data System (ADS)

Gross, Eitan

2012-12-01

The gene co-expression networks of many organisms including bacteria, mice and man exhibit scale-free distribution. This heterogeneous distribution of connections decreases the vulnerability of the network to random attacks and thus may confer the genetic replication machinery an intrinsic resilience to such attacks, triggered by changing environmental conditions that the organism may be subject to during evolution. This resilience to random attacks comes at an energetic cost, however, reflected by the lower entropy of the scale-free distribution compared to the more homogenous, random network. In this study we found that the cell cycle-regulated gene expression pattern of the yeast Saccharomyces cerevisiae obeys a power-law distribution with an exponent α = 2.1 and an entropy of 1.58. The latter is very close to the maximal value of 1.65 obtained from linear optimization of the entropy function under the constraint of a constant cost function, determined by the average degree connectivity . We further show that the yeast's gene expression network can achieve scale-free distribution in a process that does not involve growth but rather via re-wiring of the connections between nodes of an ordered network. Our results support the idea of an evolutionary selection, which acts at the level of the protein sequence, and is compatible with the notion of greater biological importance of highly connected nodes in the protein interaction network. Our constrained re-wiring model provides a theoretical framework for a putative thermodynamically driven evolutionary selection process.

L-GRAAL: Lagrangian graphlet-based network aligner.

PubMed

Malod-Dognin, Noël; Pržulj, Nataša

2015-07-01

Discovering and understanding patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. A few methods have been proposed for global PPI network alignments, but because of NP-completeness of underlying sub-graph isomorphism problem, producing topologically and biologically accurate alignments remains a challenge. We introduce a novel global network alignment tool, Lagrangian GRAphlet-based ALigner (L-GRAAL), which directly optimizes both the protein and the interaction functional conservations, using a novel alignment search heuristic based on integer programming and Lagrangian relaxation. We compare L-GRAAL with the state-of-the-art network aligners on the largest available PPI networks from BioGRID and observe that L-GRAAL uncovers the largest common sub-graphs between the networks, as measured by edge-correctness and symmetric sub-structures scores, which allow transferring more functional information across networks. We assess the biological quality of the protein mappings using the semantic similarity of their Gene Ontology annotations and observe that L-GRAAL best uncovers functionally conserved proteins. Furthermore, we introduce for the first time a measure of the semantic similarity of the mapped interactions and show that L-GRAAL also uncovers best functionally conserved interactions. In addition, we illustrate on the PPI networks of baker's yeast and human the ability of L-GRAAL to predict new PPIs. Finally, L-GRAAL's results are the first to show that topological information is more important than sequence information for uncovering functionally conserved interactions. L-GRAAL is coded in C++. Software is available at: http://bio-nets.doc.ic.ac.uk/L-GRAAL/. n.malod-dognin@imperial.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Surface Aggregation of Candida albicans on Glass in the Absence and Presence of Adhering Streptococcus gordonii in a Parallel-Plate Flow Chamber: A Surface Thermodynamical Analysis Based on Acid-Base Interactions.

PubMed

Millsap; Bos; Busscher; van der Mei HC

1999-04-15

Adhesive interactions between yeasts and bacteria are important in the maintenance of infectious mixed biofilms on natural and biomaterial surfaces in the human body. In this study, the extended DLVO (Derjaguin-Landau-Verwey-Overbeek) approach has been applied to explain adhesive interactions between C. albicans ATCC 10261 and S. gordonii NCTC 7869 adhering on glass. Contact angles with different liquids and the zeta potentials of both the yeasts and bacteria were determined and their adhesive interactions were measured in a parallel-plate flow chamber.Streptococci were first allowed to adhere to the bottom glass plate of the flow chamber to different seeding densities, and subsequently deposition of yeasts was monitored with an image analysis system, yielding the degree of initial surface aggregation of the adhering yeasts and their spatial arrangement in a stationary end point. Irrespective of growth temperature, the yeast cells appeared uncharged in TNMC buffer, but yeasts grown at 37 degrees C were intrinsically more hydrophilic and had an increased electron-donating character than cells grown at 30 degrees C. All yeasts showed surface aggregation due to attractive Lifshitz-van der Waals forces. In addition, acid-base interactions between yeasts, yeasts and the glass substratum, and yeasts and the streptococci were attractive for yeasts grown at 30 degrees C, but yeasts grown at 37 degrees C only had favorable acid-base interactions with the bacteria, explaining the positive relationship between the surface coverage of the glass by streptococci and the surface aggregation of the yeasts. Copyright 1999 Academic Press.

Zea mays Taxilin Protein Negatively Regulates Opaque-2 Transcriptional Activity by Causing a Change in Its Sub-Cellular Distribution

PubMed Central

Zhang, Nan; Qiao, Zhenyi; Liang, Zheng; Mei, Bing; Xu, Zhengkai; Song, Rentao

2012-01-01

Zea mays (maize) Opaque-2 (ZmO2) protein is an important bZIP transcription factor that regulates the expression of major storage proteins (22-kD zeins) and other important genes during maize seed development. ZmO2 is subject to functional regulation through protein-protein interactions. To unveil the potential regulatory network associated with ZmO2, a protein-protein interaction study was carried out using the truncated version of ZmO2 (O2-2) as bait in a yeast two-hybrid screen with a maize seed cDNA library. A protein with homology to Taxilin was found to have stable interaction with ZmO2 in yeast and was designated as ZmTaxilin. Sequence analysis indicated that ZmTaxilin has a long coiled-coil domain containing three conserved zipper motifs. Each of the three zipper motifs is individually able to interact with ZmO2 in yeast. A GST pull-down assay demonstrated the interaction between GST-fused ZmTaxilin and ZmO2 extracted from developing maize seeds. Using onion epidermal cells as in vivo assay system, we found that ZmTaxilin could change the sub-cellular distribution of ZmO2. We also demonstrated that this change significantly repressed the transcriptional activity of ZmO2 on the 22-kD zein promoter. Our study suggests that a Taxilin-mediated change in sub-cellular distribution of ZmO2 may have important functional consequences for ZmO2 activity. PMID:22937104

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.

Topology and Control of the Cell-Cycle-Regulated Transcriptional Circuitry

PubMed Central

Haase, Steven B.; Wittenberg, Curt

2014-01-01

Nearly 20% of the budding yeast genome is transcribed periodically during the cell division cycle. The precise temporal execution of this large transcriptional program is controlled by a large interacting network of transcriptional regulators, kinases, and ubiquitin ligases. Historically, this network has been viewed as a collection of four coregulated gene clusters that are associated with each phase of the cell cycle. Although the broad outlines of these gene clusters were described nearly 20 years ago, new technologies have enabled major advances in our understanding of the genes comprising those clusters, their regulation, and the complex regulatory interplay between clusters. More recently, advances are being made in understanding the roles of chromatin in the control of the transcriptional program. We are also beginning to discover important regulatory interactions between the cell-cycle transcriptional program and other cell-cycle regulatory mechanisms such as checkpoints and metabolic networks. Here we review recent advances and contemporary models of the transcriptional network and consider these models in the context of eukaryotic cell-cycle controls. PMID:24395825

Yeast Modulation of Human Dendritic Cell Cytokine Secretion: An In Vitro Study

PubMed Central

Smith, Ida M.; Christensen, Jeffrey E.; Arneborg, Nils; Jespersen, Lene

2014-01-01

Probiotics are live microorganisms which when administered in adequate amounts confer a health benefit on the host. The concept of individual microorganisms influencing the makeup of T cell subsets via interactions with intestinal dendritic cells (DCs) appears to constitute the foundation for immunoregulatory effects of probiotics, and several studies have reported probiotic strains resulting in reduction of intestinal inflammation through modulation of DC function. Consequent to a focus on Saccharomyces boulardii as the fundamental probiotic yeast, very little is known about hundreds of non-Saccharomyces yeasts in terms of their interaction with the human gastrointestinal immune system. The aim of the present study was to evaluate 170 yeast strains representing 75 diverse species for modulation of inflammatory cytokine secretion by human DCs in vitro, as compared to cytokine responses induced by a S. boulardii reference strain with probiotic properties documented in clinical trials. Furthermore, we investigated whether cytokine inducing interactions between yeasts and human DCs are dependent upon yeast viability or rather a product of membrane interactions regardless of yeast metabolic function. We demonstrate high diversity in yeast induced cytokine profiles and employ multivariate data analysis to reveal distinct clustering of yeasts inducing similar cytokine profiles in DCs, highlighting clear species distinction within specific yeast genera. The observed differences in induced DC cytokine profiles add to the currently very limited knowledge of the cross-talk between yeasts and human immune cells and provide a foundation for selecting yeast strains for further characterization and development toward potentially novel yeast probiotics. Additionally, we present data to support a hypothesis that the interaction between yeasts and human DCs does not solely depend on yeast viability, a concept which may suggest a need for further classifications beyond the current definition of a probiotic. PMID:24816850

Motif structure and cooperation in real-world complex networks

NASA Astrophysics Data System (ADS)

Salehi, Mostafa; Rabiee, Hamid R.; Jalili, Mahdi

2010-12-01

Networks of dynamical nodes serve as generic models for real-world systems in many branches of science ranging from mathematics to physics, technology, sociology and biology. Collective behavior of agents interacting over complex networks is important in many applications. The cooperation between selfish individuals is one of the most interesting collective phenomena. In this paper we address the interplay between the motifs’ cooperation properties and their abundance in a number of real-world networks including yeast protein-protein interaction, human brain, protein structure, email communication, dolphins’ social interaction, Zachary karate club and Net-science coauthorship networks. First, the amount of cooperativity for all possible undirected subgraphs with three to six nodes is calculated. To this end, the evolutionary dynamics of the Prisoner’s Dilemma game is considered and the cooperativity of each subgraph is calculated as the percentage of cooperating agents at the end of the simulation time. Then, the three- to six-node motifs are extracted for each network. The significance of the abundance of a motif, represented by a Z-value, is obtained by comparing them with some properly randomized versions of the original network. We found that there is always a group of motifs showing a significant inverse correlation between their cooperativity amount and Z-value, i.e. the more the Z-value the less the amount of cooperativity. This suggests that networks composed of well-structured units do not have good cooperativity properties.

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

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.

Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

PubMed Central

Martínez, Verónica S.; Quek, Lake-Ee; Nielsen, Lars K.

2014-01-01

Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. PMID:25028891

Probing the Extent of Randomness in Protein Interaction Networks

DTIC Science & Technology

2008-07-11

other [54]. This characteristic is exemplified in Video S1, which contains a three-dimensional animation of the HT E . coli PPI network determined by...experiment using either yeast two-hybrid (Y2H) or tandem affinity purification (TAP) methodology: C. jejuni [18], E . coli (HT1) [12], E . coli (HT2...DCDWa C. jejuni [18] 1331 11664 0.095 0.095 2.91 2.85 E . coli (HT1) [12] 1289 5420 0.083 0.089 3.60 3.29 (HT2) [13] 3047 11477 0.064 0.085 3.37 3.27 C

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.

A Clb/Cdk1-mediated regulation of Fkh2 synchronizes CLB expression in the budding yeast cell cycle.

PubMed

Linke, Christian; Chasapi, Anastasia; González-Novo, Alberto; Al Sawad, Istabrak; Tognetti, Silvia; Klipp, Edda; Loog, Mart; Krobitsch, Sylvia; Posas, Francesc; Xenarios, Ioannis; Barberis, Matteo

2017-01-01

Precise timing of cell division is achieved by coupling waves of cyclin-dependent kinase (Cdk) activity with a transcriptional oscillator throughout cell cycle progression. Although details of transcription of cyclin genes are known, it is unclear which is the transcriptional cascade that modulates their expression in a timely fashion. Here, we demonstrate that a Clb/Cdk1-mediated regulation of the Fkh2 transcription factor synchronizes the temporal mitotic CLB expression in budding yeast. A simplified kinetic model of the cyclin/Cdk network predicts a linear cascade where a Clb/Cdk1-mediated regulation of an activator molecule drives CLB3 and CLB2 expression. Experimental validation highlights Fkh2 as modulator of CLB3 transcript levels, besides its role in regulating CLB2 expression. A Boolean model based on the minimal number of interactions needed to capture the information flow of the Clb/Cdk1 network supports the role of an activator molecule in the sequential activation, and oscillatory behavior, of mitotic Clb cyclins. This work illustrates how transcription and phosphorylation networks can be coupled by a Clb/Cdk1-mediated regulation that synchronizes them.

Identifying protein complexes based on brainstorming strategy.

PubMed

Shen, Xianjun; Zhou, Jin; Yi, Li; Hu, Xiaohua; He, Tingting; Yang, Jincai

2016-11-01

Protein complexes comprising of interacting proteins in protein-protein interaction network (PPI network) play a central role in driving biological processes within cells. Recently, more and more swarm intelligence based algorithms to detect protein complexes have been emerging, which have become the research hotspot in proteomics field. In this paper, we propose a novel algorithm for identifying protein complexes based on brainstorming strategy (IPC-BSS), which is integrated into the main idea of swarm intelligence optimization and the improved K-means algorithm. Distance between the nodes in PPI network is defined by combining the network topology and gene ontology (GO) information. Inspired by human brainstorming process, IPC-BSS algorithm firstly selects the clustering center nodes, and then they are separately consolidated with the other nodes with short distance to form initial clusters. Finally, we put forward two ways of updating the initial clusters to search optimal results. Experimental results show that our IPC-BSS algorithm outperforms the other classic algorithms on yeast and human PPI networks, and it obtains many predicted protein complexes with biological significance. Copyright © 2016 Elsevier Inc. All rights reserved.

The BioGRID Interaction Database: 2011 update

PubMed Central

Stark, Chris; Breitkreutz, Bobby-Joe; Chatr-aryamontri, Andrew; Boucher, Lorrie; Oughtred, Rose; Livstone, Michael S.; Nixon, Julie; Van Auken, Kimberly; Wang, Xiaodong; Shi, Xiaoqi; Reguly, Teresa; Rust, Jennifer M.; Winter, Andrew; Dolinski, Kara; Tyers, Mike

2011-01-01

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein interaction data from model organisms and humans (http://www.thebiogrid.org). BioGRID currently holds 347 966 interactions (170 162 genetic, 177 804 protein) curated from both high-throughput data sets and individual focused studies, as derived from over 23 000 publications in the primary literature. Complete coverage of the entire literature is maintained for budding yeast (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe) and thale cress (Arabidopsis thaliana), and efforts to expand curation across multiple metazoan species are underway. The BioGRID houses 48 831 human protein interactions that have been curated from 10 247 publications. Current curation drives are focused on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health. The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. An automated Interaction Management System (IMS) is used to prioritize, coordinate and track curation across international sites and projects. BioGRID provides interaction data to several model organism databases, resources such as Entrez-Gene and other interaction meta-databases. The entire BioGRID 3.0 data collection may be downloaded in multiple file formats, including PSI MI XML. Source code for BioGRID 3.0 is freely available without any restrictions. PMID:21071413

Training set expansion: an approach to improving the reconstruction of biological networks from limited and uneven reliable interactions

PubMed Central

Yip, Kevin Y.; Gerstein, Mark

2009-01-01

Motivation: An important problem in systems biology is reconstructing complete networks of interactions between biological objects by extrapolating from a few known interactions as examples. While there are many computational techniques proposed for this network reconstruction task, their accuracy is consistently limited by the small number of high-confidence examples, and the uneven distribution of these examples across the potential interaction space, with some objects having many known interactions and others few. Results: To address this issue, we propose two computational methods based on the concept of training set expansion. They work particularly effectively in conjunction with kernel approaches, which are a popular class of approaches for fusing together many disparate types of features. Both our methods are based on semi-supervised learning and involve augmenting the limited number of gold-standard training instances with carefully chosen and highly confident auxiliary examples. The first method, prediction propagation, propagates highly confident predictions of one local model to another as the auxiliary examples, thus learning from information-rich regions of the training network to help predict the information-poor regions. The second method, kernel initialization, takes the most similar and most dissimilar objects of each object in a global kernel as the auxiliary examples. Using several sets of experimentally verified protein–protein interactions from yeast, we show that training set expansion gives a measurable performance gain over a number of representative, state-of-the-art network reconstruction methods, and it can correctly identify some interactions that are ranked low by other methods due to the lack of training examples of the involved proteins. Contact: mark.gerstein@yale.edu Availability: The datasets and additional materials can be found at http://networks.gersteinlab.org/tse. PMID:19015141

Global study of holistic morphological effectors in the budding yeast Saccharomyces cerevisiae.

PubMed

Suzuki, Godai; Wang, Yang; Kubo, Karen; Hirata, Eri; Ohnuki, Shinsuke; Ohya, Yoshikazu

2018-02-20

The size of the phenotypic effect of a gene has been thoroughly investigated in terms of fitness and specific morphological traits in the budding yeast Saccharomyces cerevisiae, but little is known about gross morphological abnormalities. We identified 1126 holistic morphological effectors that cause severe gross morphological abnormality when deleted, and 2241 specific morphological effectors with weak holistic effects but distinctive effects on yeast morphology. Holistic effectors fell into many gene function categories and acted as network hubs, affecting a large number of morphological traits, interacting with a large number of genes, and facilitating high protein expression. Holistic morphological abnormality was useful for estimating the importance of a gene to morphology. The contribution of gene importance to fitness and morphology could be used to efficiently classify genes into functional groups. Holistic morphological abnormality can be used as a reproducible and reliable gene feature for high-dimensional morphological phenotyping. It can be used in many functional genomic applications.

Eukaryotic genes of archaebacterial origin are more important than the more numerous eubacterial genes, irrespective of function.

PubMed

Cotton, James A; McInerney, James O

2010-10-05

The traditional tree of life shows eukaryotes as a distinct lineage of living things, but many studies have suggested that the first eukaryotic cells were chimeric, descended from both Eubacteria (through the mitochondrion) and Archaebacteria. Eukaryote nuclei thus contain genes of both eubacterial and archaebacterial origins, and these genes have different functions within eukaryotic cells. Here we report that archaebacterium-derived genes are significantly more likely to be essential to yeast viability, are more highly expressed, and are significantly more highly connected and more central in the yeast protein interaction network. These findings hold irrespective of whether the genes have an informational or operational function, so that many features of eukaryotic genes with prokaryotic homologs can be explained by their origin, rather than their function. Taken together, our results show that genes of archaebacterial origin are in some senses more important to yeast metabolism than genes of eubacterial origin. This importance reflects these genes' origin as the ancestral nuclear component of the eukaryotic genome.

Locating overlapping dense subgraphs in gene (protein) association networks and predicting novel protein functional groups among these subgraphs

NASA Astrophysics Data System (ADS)

Palla, Gergely; Derenyi, Imre; Farkas, Illes J.; Vicsek, Tamas

2006-03-01

Most tasks in a cell are performed not by individual proteins, but by functional groups of proteins (either physically interacting with each other or associated in other ways). In gene (protein) association networks these groups show up as sets of densely connected nodes. In the yeast, Saccharomyces cerevisiae, known physically interacting groups of proteins (called protein complexes) strongly overlap: the total number of proteins contained by these complexes by far underestimates the sum of their sizes (2750 vs. 8932). Thus, most functional groups of proteins, both physically interacting and other, are likely to share many of their members with other groups. However, current algorithms searching for dense groups of nodes in networks usually exclude overlaps. With the aim to discover both novel functions of individual proteins and novel protein functional groups we combine in protein association networks (i) a search for overlapping dense subgraphs based on the Clique Percolation Method (CPM) (Palla, G., et.al. Nature 435, 814-818 (2005), http://angel.elte.hu/clustering), which explicitly allows for overlaps among the groups, and (ii) a verification and characterization of the identified groups of nodes (proteins) with the help of standard annotation databases listing known functions.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization.

PubMed

Tuikkala, Johannes; Vähämaa, Heidi; Salmela, Pekka; Nevalainen, Olli S; Aittokallio, Tero

2012-03-26

Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

Network thermodynamic curation of human and yeast genome-scale metabolic models.

PubMed

Martínez, Verónica S; Quek, Lake-Ee; Nielsen, Lars K

2014-07-15

Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Interactions between Brettanomyces bruxellensis and other yeast species during the initial stages of winemaking.

PubMed

Renouf, V; Falcou, M; Miot-Sertier, C; Perello, M C; De Revel, G; Lonvaud-Funel, A

2006-06-01

Wine is the product of complex interactions between yeasts and bacteria in grape must. Amongst yeast populations, two groups can be distinguished. The first, named non-Saccharomyces (NS), colonizes, with many other micro-organisms, the surface of grape berries. In the past, NS yeasts were primarily considered as spoilage micro-organisms. However, recent studies have established a positive contribution of certain NS yeasts to wine quality. Amongst the group of NS yeasts, Brettanomyces bruxellensis, which is not prevalent on wine grapes, plays an important part in the evolution of wine aroma. Some of their secondary metabolites, namely volatile phenols, are responsible for wine spoilage. The other group contributing to wine aroma, which is also the main agent of alcoholic fermentation (AF), is composed of Saccharomyces species. The fermenting must is a complex microbial ecosystem where numerous yeast strains grow and die according to their adaptation to the medium. Yeast-yeast interactions occur during winemaking right from the onset of AF. The aim of this study was to describe the interactions between B. bruxellensis, other NS and Saccharomyces cerevisiae during laboratory and practical scale winemaking. Molecular methods such as internal transcribed spacer-restriction fragment length polymorphism and polymerase chain reaction and denaturing gradient gel electrophoresis were used in laboratory scale experiments and cellar observations. The influence of different oenological practices, like the level of sulphiting at harvest time, cold maceration preceding AF, addition of commercial active dry yeasts on B. bruxellensis and other yeast interactions and their evolution during the initial stages of winemaking have been studied. Brettanomyces bruxellensis was the most adapted NS yeast at the beginning of AF, and towards the end of AF it appeared to be more resistant than S. cerevisiae to the conditions of increased alcohol and sugar limitation. Among all NS yeast species, B. bruxellensis is better adapted than other wild yeasts to resist in must and during AF. Moreover, B. bruxellensis appeared to be more tolerant to ethanol stress than S. cerevisiae and after AF B. bruxellensis was the main yeast species in wine. Brettanomyces bruxellensis interacts with other yeast species and adapts to the wine medium as the dominant yeast species at the end of AF. Contamination of B. bruxellensis might take place at the beginning of malolactic fermentation, which is a critical stage in winemaking.

Architecture of the Human and Yeast General Transcription and DNA Repair Factor TFIIH.

PubMed

Luo, Jie; Cimermancic, Peter; Viswanath, Shruthi; Ebmeier, Christopher C; Kim, Bong; Dehecq, Marine; Raman, Vishnu; Greenberg, Charles H; Pellarin, Riccardo; Sali, Andrej; Taatjes, Dylan J; Hahn, Steven; Ranish, Jeff

2015-09-03

TFIIH is essential for both RNA polymerase II transcription and DNA repair, and mutations in TFIIH can result in human disease. Here, we determine the molecular architecture of human and yeast TFIIH by an integrative approach using chemical crosslinking/mass spectrometry (CXMS) data, biochemical analyses, and previously published electron microscopy maps. We identified four new conserved "topological regions" that function as hubs for TFIIH assembly and more than 35 conserved topological features within TFIIH, illuminating a network of interactions involved in TFIIH assembly and regulation of its activities. We show that one of these conserved regions, the p62/Tfb1 Anchor region, directly interacts with the DNA helicase subunit XPD/Rad3 in native TFIIH and is required for the integrity and function of TFIIH. We also reveal the structural basis for defects in patients with xeroderma pigmentosum and trichothiodystrophy, with mutations found at the interface between the p62 Anchor region and the XPD subunit. Copyright © 2015 Elsevier Inc. All rights reserved.

Architecture of the human and yeast general transcription and DNA repair factor TFIIH

PubMed Central

Luo, Jie; Cimermancic, Peter; Viswanath, Shruthi; Ebmeier, Christopher C.; Kim, Bong; Dehecq, Marine; Raman, Vishnu; Greenberg, Charles H.; Pellarin, Riccardo; Sali, Andrej; Taatjes, Dylan J.; Hahn, Steven; Ranish, Jeff

2015-01-01

Summary TFIIH is essential for both RNA polymerase II transcription and DNA repair, and mutations in TFIIH can result in human disease. Here, we determine the molecular architecture of human and yeast TFIIH by an integrative approach using chemical crosslinking/mass spectrometry (CXMS) data, biochemical analyses, and previously published electron microscopy maps. We identified four new conserved “topological regions” that function as hubs for TFIIH assembly and more than 35 conserved topological features within TFIIH, illuminating a network of interactions involved in TFIIH assembly and regulation of its activities. We show that one of these conserved regions, the p62/Tfb1 Anchor region, directly interacts with the DNA helicase subunit XPD/Rad3 in native TFIIH and is required for the integrity and function of TFIIH. We also reveal the structural basis for defects in patients with Xeroderma pigmentosum and Trichothiodystrophy, with mutations found at the interface between the p62 Anchor region and the XPD subunit. PMID:26340423

Interactions between Drosophila and its natural yeast symbionts—Is Saccharomyces cerevisiae a good model for studying the fly-yeast relationship?

PubMed Central

Hoang, Don; Kopp, Artyom

2015-01-01

Yeasts play an important role in the biology of the fruit fly, Drosophila melanogaster. In addition to being a valuable source of nutrition, yeasts affect D. melanogaster behavior and interact with the host immune system. Most experiments investigating the role of yeasts in D. melanogaster biology use the baker’s yeast, Saccharomyces cerevisiae. However, S. cerevisiae is rarely found with natural populations of D. melanogaster or other Drosophila species. Moreover, the strain of S. cerevisiae used most often in D. melanogaster experiments is a commercially and industrially important strain that, to the best of our knowledge, was not isolated from flies. Since disrupting natural host–microbe interactions can have profound effects on host biology, the results from D. melanogaster–S. cerevisiae laboratory experiments may not be fully representative of host–microbe interactions in nature. In this study, we explore the D. melanogaster-yeast relationship using five different strains of yeast that were isolated from wild Drosophila populations. Ingested live yeasts have variable persistence in the D. melanogaster gastrointestinal tract. For example, Hanseniaspora occidentalis persists relative to S. cerevisiae, while Brettanomyces naardenensis is removed. Despite these differences in persistence relative to S. cerevisiae, we find that all yeasts decrease in total abundance over time. Reactive oxygen species (ROS) are an important component of the D. melanogaster anti-microbial response and can inhibit S. cerevisiae growth in the intestine. To determine if sensitivity to ROS explains the differences in yeast persistence, we measured yeast growth in the presence and absence of hydrogen peroxide. We find that B. naardenesis is completely inhibited by hydrogen peroxide, while H. occidentalis is not, which is consistent with yeast sensitivity to ROS affecting persistence within the D. melanogaster gastrointestinal tract. We also compared the feeding preference of D. melanogaster when given the choice between a naturally associated yeast and S. cerevisiae. We do not find a correlation between preferred yeasts and those that persist in the intestine. Notably, in no instances is S. cerevisiae preferred over the naturally associated strains. Overall, our results show that D. melanogaster-yeast interactions are more complex than might be revealed in experiments that use only S. cerevisiae. We propose that future research utilize other yeasts, and especially those that are naturally associated with Drosophila, to more fully understand the role of yeasts in Drosophila biology. Since the genetic basis of host–microbe interactions is shared across taxa and since many of these genes are initially discovered in D. melanogaster, a more realistic fly-yeast model system will benefit our understanding of host–microbe interactions throughout the animal kingdom. PMID:26336636

Interactions between Drosophila and its natural yeast symbionts-Is Saccharomyces cerevisiae a good model for studying the fly-yeast relationship?

PubMed

Hoang, Don; Kopp, Artyom; Chandler, James Angus

2015-01-01

Yeasts play an important role in the biology of the fruit fly, Drosophila melanogaster. In addition to being a valuable source of nutrition, yeasts affect D. melanogaster behavior and interact with the host immune system. Most experiments investigating the role of yeasts in D. melanogaster biology use the baker's yeast, Saccharomyces cerevisiae. However, S. cerevisiae is rarely found with natural populations of D. melanogaster or other Drosophila species. Moreover, the strain of S. cerevisiae used most often in D. melanogaster experiments is a commercially and industrially important strain that, to the best of our knowledge, was not isolated from flies. Since disrupting natural host-microbe interactions can have profound effects on host biology, the results from D. melanogaster-S. cerevisiae laboratory experiments may not be fully representative of host-microbe interactions in nature. In this study, we explore the D. melanogaster-yeast relationship using five different strains of yeast that were isolated from wild Drosophila populations. Ingested live yeasts have variable persistence in the D. melanogaster gastrointestinal tract. For example, Hanseniaspora occidentalis persists relative to S. cerevisiae, while Brettanomyces naardenensis is removed. Despite these differences in persistence relative to S. cerevisiae, we find that all yeasts decrease in total abundance over time. Reactive oxygen species (ROS) are an important component of the D. melanogaster anti-microbial response and can inhibit S. cerevisiae growth in the intestine. To determine if sensitivity to ROS explains the differences in yeast persistence, we measured yeast growth in the presence and absence of hydrogen peroxide. We find that B. naardenesis is completely inhibited by hydrogen peroxide, while H. occidentalis is not, which is consistent with yeast sensitivity to ROS affecting persistence within the D. melanogaster gastrointestinal tract. We also compared the feeding preference of D. melanogaster when given the choice between a naturally associated yeast and S. cerevisiae. We do not find a correlation between preferred yeasts and those that persist in the intestine. Notably, in no instances is S. cerevisiae preferred over the naturally associated strains. Overall, our results show that D. melanogaster-yeast interactions are more complex than might be revealed in experiments that use only S. cerevisiae. We propose that future research utilize other yeasts, and especially those that are naturally associated with Drosophila, to more fully understand the role of yeasts in Drosophila biology. Since the genetic basis of host-microbe interactions is shared across taxa and since many of these genes are initially discovered in D. melanogaster, a more realistic fly-yeast model system will benefit our understanding of host-microbe interactions throughout the animal kingdom.

Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach.

PubMed

Peng, Jiajie; Zhang, Xuanshuo; Hui, Weiwei; Lu, Junya; Li, Qianqian; Liu, Shuhui; Shang, Xuequn

2018-03-19

Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and gene annotations. We present NETSIM2, a new network-based method that allows researchers to measure GO-based gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based gene functional similarity. Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global gene-to-gene interactions in the co-functional network, especially on the species that gene annotations in GO are far from complete.

Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods

PubMed Central

2014-01-01

Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods. PMID:25374614

Neural network analysis of electrodynamic activity of yeast cells around 1 kHz

NASA Astrophysics Data System (ADS)

Janca, R.

2011-12-01

This paper deals with data analysis of electrodynamic activity of two mutants of yeast cells, cell cycle of which is synchronized and non-synchronized, respectively. We used data already published by Jelinek et al. and treat them with data mining method based on the multilayer neural network. Intersection of data mining and statistical distribution of the noise shows significant difference between synchronized and non-synchronized yeasts not only in total power, but also discrete frequencies.

Chromatin dynamics in plants.

PubMed

Fransz, Paul F; de Jong, J Hans

2002-12-01

Recent studies in yeast, animals and plants have provided major breakthroughs in unraveling the molecular mechanism of higher-order gene regulation. In conjunction with the DNA code, proteins that are involved in chromatin remodeling, histone modification and epigenetic imprinting form a large network of interactions that control the nuclear programming of cell identity. New insight into how chromatin conformations are regulated in plants sheds light on the relationships between chromosome function, cell differentiation and developmental patterns.

Functional conservation of the yeast and Arabidopsis RAD54-like genes.

PubMed

Klutstein, Michael; Shaked, Hezi; Sherman, Amir; Avivi-Ragolsky, Naomi; Shema, Efrat; Zenvirth, Drora; Levy, Avraham A; Simchen, Giora

2008-04-01

The Saccharomyces cerevisiae RAD54 gene has critical roles in DNA double-strand break repair, homologous recombination, and gene targeting. Previous results show that the yeast gene enhances gene targeting when expressed in Arabidopsis thaliana. In this work we address the trans-species compatibility of Rad54 functions. We show that overexpression of yeast RAD54 in Arabidopsis enhances DNA damage resistance severalfold. Thus, the yeast gene is active in the Arabidopsis homologous-recombination repair system. Moreover, we have identified an A. thaliana ortholog of yeast RAD54, named AtRAD54. This gene, with close sequence similarity to RAD54, complements methylmethane sulfonate (MMS) sensitivity but not UV sensitivity or gene targeting defects of rad54Delta mutant yeast cells. Overexpression of AtRAD54 in Arabidopsis leads to enhanced resistance to DNA damage. This gene's assignment as a RAD54 ortholog is further supported by the interaction of AtRad54 with AtRad51 and the interactions between alien proteins (i.e., yeast Rad54 with AtRAD51 and yeast Rad51 with AtRad54) in a yeast two-hybrid experiment. These interactions hint at the molecular nature of this interkingdom complementation, although the stronger effect of the yeast Rad54 in plants than AtRad54 in yeast might be explained by an ability of the Rad54 protein to act alone, independently of its interaction with Rad51.

Modular biological function is most effectively captured by combining molecular interaction data types.

PubMed

Ames, Ryan M; Macpherson, Jamie I; Pinney, John W; Lovell, Simon C; Robertson, David L

2013-01-01

Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.

Niche construction initiates the evolution of mutualistic interactions.

PubMed

Buser, Claudia C; Newcomb, Richard D; Gaskett, Anne C; Goddard, Matthew R

2014-10-01

Niche construction theory explains how organisms' niche modifications may feed back to affect their evolutionary trajectories. In theory, the evolution of other species accessing the same modified niche may also be affected. We propose that this niche construction may be a general mechanism driving the evolution of mutualisms. Drosophilid flies benefit from accessing yeast-infested fruits, but the consequences of this interaction for yeasts are unknown. We reveal high levels of variation among strains of Saccharomyces cerevisiae in their ability to modify fruits and attract Drosophila simulans. More attractive yeasts are dispersed more frequently, both in the lab and in the field, and flies associated with more attractive yeasts have higher fecundity. Although there may be multiple natural yeast and fly species interactions, our controlled assays in the lab and field provide evidence of a mutualistic interaction, facilitated by the yeast's niche modification. © 2014 John Wiley & Sons Ltd/CNRS.

Effect of memory in non-Markovian Boolean networks illustrated with a case study: A cell cycling process

NASA Astrophysics Data System (ADS)

Ebadi, H.; Saeedian, M.; Ausloos, M.; Jafari, G. R.

2016-11-01

The Boolean network is one successful model to investigate discrete complex systems such as the gene interacting phenomenon. The dynamics of a Boolean network, controlled with Boolean functions, is usually considered to be a Markovian (memory-less) process. However, both self-organizing features of biological phenomena and their intelligent nature should raise some doubt about ignoring the history of their time evolution. Here, we extend the Boolean network Markovian approach: we involve the effect of memory on the dynamics. This can be explored by modifying Boolean functions into non-Markovian functions, for example, by investigating the usual non-Markovian threshold function —one of the most applied Boolean functions. By applying the non-Markovian threshold function on the dynamical process of the yeast cell cycle network, we discover a power-law-like memory with a more robust dynamics than the Markovian dynamics.

Characterization of the interaction of yeast enolase with polynucleotides.

PubMed

al-Giery, A G; Brewer, J M

1992-09-23

Yeast enolase is inhibited under certain conditions by DNA. The enzyme binds to single-stranded DNA-cellulose. Inhibition was used for routine characterization of the interaction. The presence of the substrate 2-phospho-D-glycerate reduces inhibition and binding. Both yeast enolase isozymes behave similarly. Impure yeast enolase was purified by adsorption onto a single-stranded DNA-cellulose column followed by elution with substrate. Interaction with RNA, double-stranded DNA, or degraded DNA results in less inhibition, suggesting that yeast enolase preferentially binds single-stranded DNA. However, yeast enolase is not a DNA-unwinding protein. The enzyme is inhibited by the short synthetic oligodeoxynucleotides G6, G8 and G10 but not T8 or T6, suggesting some base specificity in the interaction. The interaction is stronger at more acid pH values, with an apparent pK of 5.6. The interaction is prevented by 0.3 M KCl, suggesting that electrostatic factors are important. Histidine or lysine reverse the inhibition at lower concentrations, while phosphate is still more effective. Binding of single-stranded DNA to enolase reduces the reaction of protein histidyl residues with diethylpyrocarbonate. The inhibition of yeast enolase by single-stranded DNA is not total, and suggests the active site is not directly involved in the interaction. Binding of substrate may induce a conformational change in the enzyme that interferes with DNA binding and vice versa.

Analyzing and interpreting genome data at the network level with ConsensusPathDB.

PubMed

Herwig, Ralf; Hardt, Christopher; Lienhard, Matthias; Kamburov, Atanas

2016-10-01

ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB (http://consensuspathdb.org) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user's input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high-throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales.

Prediction of C. elegans Longevity Genes by Human and Worm Longevity Networks

PubMed Central

de Magalhães, João Pedro; Ruvkun, Gary; Fraifeld, Vadim E.; Curran, Sean P.

2012-01-01

Intricate and interconnected pathways modulate longevity, but screens to identify the components of these pathways have not been saturating. Because biological processes are often executed by protein complexes and fine-tuned by regulatory factors, the first-order protein-protein interactors of known longevity genes are likely to participate in the regulation of longevity. Data-rich maps of protein interactions have been established for many cardinal organisms such as yeast, worms, and humans. We propose that these interaction maps could be mined for the identification of new putative regulators of longevity. For this purpose, we have constructed longevity networks in both humans and worms. We reasoned that the essential first-order interactors of known longevity-associated genes in these networks are more likely to have longevity phenotypes than randomly chosen genes. We have used C. elegans to determine whether post-developmental inactivation of these essential genes modulates lifespan. Our results suggest that the worm and human longevity networks are functionally relevant and possess a high predictive power for identifying new longevity regulators. PMID:23144747

Evolution of synthetic signaling scaffolds by recombination of modular protein domains.

PubMed

Lai, Andicus; Sato, Paloma M; Peisajovich, Sergio G

2015-06-19

Signaling scaffolds are proteins that interact via modular domains with multiple partners, regulating signaling networks in space and time and providing an ideal platform from which to alter signaling functions. However, to better exploit scaffolds for signaling engineering, it is necessary to understand the full extent of their modularity. We used a directed evolution approach to identify, from a large library of randomly shuffled protein interaction domains, variants capable of rescuing the signaling defect of a yeast strain in which Ste5, the scaffold in the mating pathway, had been deleted. After a single round of selection, we identified multiple synthetic scaffold variants with diverse domain architectures, able to mediate mating pathway activation in a pheromone-dependent manner. The facility with which this signaling network accommodates changes in scaffold architecture suggests that the mating signaling complex does not possess a single, precisely defined geometry into which the scaffold has to fit. These relaxed geometric constraints may facilitate the evolution of signaling networks, as well as their engineering for applications in synthetic biology.

Multiple interactions amongst floral homeotic MADS box proteins.

PubMed Central

Davies, B; Egea-Cortines, M; de Andrade Silva, E; Saedler, H; Sommer, H

1996-01-01

Most known floral homeotic genes belong to the MADS box family and their products act in combination to specify floral organ identity by an unknown mechanism. We have used a yeast two-hybrid system to investigate the network of interactions between the Antirrhinum organ identity gene products. Selective heterodimerization is observed between MADS box factors. Exclusive interactions are detected between two factors, DEFICIENS (DEF) and GLOBOSA (GLO), previously known to heterodimerize and control development of petals and stamens. In contrast, a third factor, PLENA (PLE), which is required for reproductive organ development, can interact with the products of MADS box genes expressed at early, intermediate and late stages. We also demonstrate that heterodimerization of DEF and GLO requires the K box, a domain not found in non-plant MADS box factors, indicating that the plant MADS box factors may have different criteria for interaction. The association of PLENA and the temporally intermediate MADS box factors suggests that part of their function in mediating between the meristem and organ identity genes is accomplished through direct interaction. These data reveal an unexpectedly complex network of interactions between the factors controlling flower development and have implications for the determination of organ identity. Images PMID:8861961

Molecular partners of hNOT/ALG3, the human counterpart of the Drosophila NOT and yeast ALG3 gene, suggest its involvement in distinct cellular processes relevant to congenital disorders of glycosylation, cancer, neurodegeneration and a variety of further pathologies.

PubMed

Hacker, Benedikt; Schultheiß, Christoph; Döring, Michael; Kurzik-Dumke, Ursula

2018-06-01

This study provides first insights into the involvement of hNOT/ALG3, the human counterpart of the Drosophila Neighbour of TID and yeast ALG3 gene, in various putative molecular networks. HNOT/ALG3 encodes two translated transcripts encoding precursor proteins differing in their N-terminus and showing 33% identity with the yeast asparagine-linked glycosylation 3 (ALG3) protein. Experimental evidence for the functional homology of the proteins of fly and man in the N-glycosylation has still to be provided. In this study, using the yeast two-hybrid technique we identify 17 molecular partners of hNOT-1/ALG3-1. We disclose the building of hNOT/ALG3 homodimers and provide experimental evidence for its in vivo interaction with the functionally linked proteins OSBP, OSBPL9 and LRP1, the SYPL1 protein and the transcription factor CREB3. Regarding the latter, we show that the 55 kDa N-glycosylated hNOT-1/ALG3-1 molecule binds the N-glycosylated CREB3 precursor but does not interact with CREB3's proteolytic products specific to the endoplasmic reticulum and to the nucleus. The interaction between the two partners is a prerequisite for the proteolytic activation of CREB3. In case of the further binding partners, our data suggest that hNOT-1/ALG3-1 interacts with both OSBPs and with their direct targets LRP1 and VAMP/VAP-A. Moreover, our results show that various partners of hNOT-1/ALG3-1 interact with its diverse post translationally processed products destined to distinct cellular compartments. Generally, our data suggest the involvement of hNOT-1/ALG3-1 in various molecular contexts determining essential processes associated with distinct cellular machineries and related to various pathologies, such as cancer, viral infections, neuronal and immunological disorders and CDG.

Protein–protein interactions and selection: yeast-based approaches that exploit guanine nucleotide-binding protein signaling.

PubMed

Ishii, Jun; Fukuda, Nobuo; Tanaka, Tsutomu; Ogino, Chiaki; Kondo, Akihiko

2010-05-01

For elucidating protein–protein interactions, many methodologies have been developed during the past two decades. For investigation of interactions inside cells under physiological conditions, yeast is an attractive organism with which to quickly screen for hopeful candidates using versatile genetic technologies, and various types of approaches are now available.Among them, a variety of unique systems using the guanine nucleotide-binding protein (G-protein) signaling pathway in yeast have been established to investigate the interactions of proteins for biological study and pharmaceutical research. G-proteins involved in various cellular processes are mainly divided into two groups: small monomeric G-proteins,and heterotrimeric G-proteins. In this minireview, we summarize the basic principles and applications of yeast-based screening systems, using these two types of G-protein, which are typically used for elucidating biological protein interactions but are differentiated from traditional yeast two-hybrid systems.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

PubMed Central

2012-01-01

Background Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. Methods We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. Results The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. Conclusions By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications. PMID:22448851

Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

PubMed

Wuchty, Stefan

2006-05-23

While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions by utilizing expectation scores of single domain interactions.

Protein complex prediction for large protein protein interaction networks with the Core&Peel method.

PubMed

Pellegrini, Marco; Baglioni, Miriam; Geraci, Filippo

2016-11-08

Biological networks play an increasingly important role in the exploration of functional modularity and cellular organization at a systemic level. Quite often the first tools used to analyze these networks are clustering algorithms. We concentrate here on the specific task of predicting protein complexes (PC) in large protein-protein interaction networks (PPIN). Currently, many state-of-the-art algorithms work well for networks of small or moderate size. However, their performance on much larger networks, which are becoming increasingly common in modern proteome-wise studies, needs to be re-assessed. We present a new fast algorithm for clustering large sparse networks: Core&Peel, which runs essentially in time and storage O(a(G)m+n) for a network G of n nodes and m arcs, where a(G) is the arboricity of G (which is roughly proportional to the maximum average degree of any induced subgraph in G). We evaluated Core&Peel on five PPI networks of large size and one of medium size from both yeast and homo sapiens, comparing its performance against those of ten state-of-the-art methods. We demonstrate that Core&Peel consistently outperforms the ten competitors in its ability to identify known protein complexes and in the functional coherence of its predictions. Our method is remarkably robust, being quite insensible to the injection of random interactions. Core&Peel is also empirically efficient attaining the second best running time over large networks among the tested algorithms. Our algorithm Core&Peel pushes forward the state-of the-art in PPIN clustering providing an algorithmic solution with polynomial running time that attains experimentally demonstrable good output quality and speed on challenging large real networks.

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

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

Synchronization of glycolytic oscillations in a yeast cell population.

PubMed

Danø, S; Hynne, F; De Monte, S; d'Ovidio, F; Sørensen, P G; Westerhoff, H

2001-01-01

The mechanism of active phase synchronization in a suspension of oscillatory yeast cells has remained a puzzle for almost half a century. The difficulty of the problem stems from the fact that the synchronization phenomenon involves the entire metabolic network of glycolysis and fermentation, and consequently it cannot be addressed at the level of a single enzyme or a single chemical species. In this paper it is shown how this system in a CSTR (continuous flow stirred tank reactor) can be modelled quantitatively as a population of Stuart-Landau oscillators interacting by exchange of metabolites through the extracellular medium, thus reducing the complexity of the problem without sacrificing the biochemical realism. The parameters of the model can be derived by a systematic expansion from any full-scale model of the yeast cell kinetics with a supercritical Hopf bifurcation. Some parameter values can also be obtained directly from analysis of perturbation experiments. In the mean-field limit, equations for the study of populations having a distribution of frequencies are used to simulate the effect of the inherent variations between cells.

Representing perturbed dynamics in biological network models

NASA Astrophysics Data System (ADS)

Stoll, Gautier; Rougemont, Jacques; Naef, Felix

2007-07-01

We study the dynamics of gene activities in relatively small size biological networks (up to a few tens of nodes), e.g., the activities of cell-cycle proteins during the mitotic cell-cycle progression. Using the framework of deterministic discrete dynamical models, we characterize the dynamical modifications in response to structural perturbations in the network connectivities. In particular, we focus on how perturbations affect the set of fixed points and sizes of the basins of attraction. Our approach uses two analytical measures: the basin entropy H and the perturbation size Δ , a quantity that reflects the distance between the set of fixed points of the perturbed network and that of the unperturbed network. Applying our approach to the yeast-cell-cycle network introduced by Li [Proc. Natl. Acad. Sci. U.S.A. 101, 4781 (2004)] provides a low-dimensional and informative fingerprint of network behavior under large classes of perturbations. We identify interactions that are crucial for proper network function, and also pinpoint functionally redundant network connections. Selected perturbations exemplify the breadth of dynamical responses in this cell-cycle model.

Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information.

PubMed

Li, Min; Li, Wenkai; Wu, Fang-Xiang; Pan, Yi; Wang, Jianxin

2018-06-14

Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automated adaptive inference of phenomenological dynamical models.

PubMed

Daniels, Bryan C; Nemenman, Ilya

2015-08-21

Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved.

In-vivo detection of binary PKA network interactions upon activation of endogenous GPCRs

PubMed Central

Röck, Ruth; Bachmann, Verena; Bhang, Hyo-eun C; Malleshaiah, Mohan; Raffeiner, Philipp; Mayrhofer, Johanna E; Tschaikner, Philipp M; Bister, Klaus; Aanstad, Pia; Pomper, Martin G; Michnick, Stephen W; Stefan, Eduard

2015-01-01

Membrane receptor-sensed input signals affect and modulate intracellular protein-protein interactions (PPIs). Consequent changes occur to the compositions of protein complexes, protein localization and intermolecular binding affinities. Alterations of compartmentalized PPIs emanating from certain deregulated kinases are implicated in the manifestation of diseases such as cancer. Here we describe the application of a genetically encoded Protein-fragment Complementation Assay (PCA) based on the Renilla Luciferase (Rluc) enzyme to compare binary PPIs of the spatially and temporally controlled protein kinase A (PKA) network in diverse eukaryotic model systems. The simplicity and sensitivity of this cell-based reporter allows for real-time recordings of mutually exclusive PPIs of PKA upon activation of selected endogenous G protein-coupled receptors (GPCRs) in cancer cells, xenografts of mice, budding yeast, and zebrafish embryos. This extends the application spectrum of Rluc PCA for the quantification of PPI-based receptor-effector relationships in physiological and pathological model systems. PMID:26099953

Automated adaptive inference of phenomenological dynamical models

PubMed Central

Daniels, Bryan C.; Nemenman, Ilya

2015-01-01

Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved. PMID:26293508

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity. deepNF is freely available at: https://github.com/VGligorijevic/deepNF. vgligorijevic@flatironinstitute.org, rb133@nyu.edu. Supplementary data are available at Bioinformatics online.

Autonomous Modeling, Statistical Complexity and Semi-annealed Treatment of Boolean Networks

NASA Astrophysics Data System (ADS)

Gong, Xinwei

This dissertation presents three studies on Boolean networks. Boolean networks are a class of mathematical systems consisting of interacting elements with binary state variables. Each element is a node with a Boolean logic gate, and the presence of interactions between any two nodes is represented by directed links. Boolean networks that implement the logic structures of real systems are studied as coarse-grained models of the real systems. Large random Boolean networks are studied with mean field approximations and used to provide a baseline of possible behaviors of large real systems. This dissertation presents one study of the former type, concerning the stable oscillation of a yeast cell-cycle oscillator, and two studies of the latter type, respectively concerning the statistical complexity of large random Boolean networks and an extension of traditional mean field techniques that accounts for the presence of short loops. In the cell-cycle oscillator study, a novel autonomous update scheme is introduced to study the stability of oscillations in small networks. A motif that corrects pulse-growing perturbations and a motif that grows pulses are identified. A combination of the two motifs is capable of sustaining stable oscillations. Examining a Boolean model of the yeast cell-cycle oscillator using an autonomous update scheme yields evidence that it is endowed with such a combination. Random Boolean networks are classified as ordered, critical or disordered based on their response to small perturbations. In the second study, random Boolean networks are taken as prototypical cases for the evaluation of two measures of complexity based on a criterion for optimal statistical prediction. One measure, defined for homogeneous systems, does not distinguish between the static spatial inhomogeneity in the ordered phase and the dynamical inhomogeneity in the disordered phase. A modification in which complexities of individual nodes are calculated yields vanishing complexity values for networks in the ordered and critical phases and for highly disordered networks, peaking somewhere in the disordered phase. Individual nodes with high complexity have, on average, a larger influence on the system dynamics. Lastly, a semi-annealed approximation that preserves the correlation between states at neighboring nodes is introduced to study a social game-inspired network model in which all links are bidirectional and all nodes have a self-input. The technique developed here is shown to yield accurate predictions of distribution of players' states, and accounts for some nontrivial collective behavior of game theoretic interest.

Yeast ribonuclease III uses a network of multiple hydrogen bonds for RNA binding and cleavage.

PubMed

Lavoie, Mathieu; Abou Elela, Sherif

2008-08-19

Members of the bacterial RNase III family recognize a variety of short structured RNAs with few common features. It is not clear how this group of enzymes supports high cleavage fidelity while maintaining a broad base of substrates. Here we show that the yeast orthologue of RNase III (Rnt1p) uses a network of 2'-OH-dependent interactions to recognize substrates with different structures. We designed a series of bipartite substrates permitting the distinction between binding and cleavage defects. Each substrate was engineered to carry a single or multiple 2'- O-methyl or 2'-fluoro ribonucleotide substitutions to prevent the formation of hydrogen bonds with a specific nucleotide or group of nucleotides. Interestingly, introduction of 2'- O-methyl ribonucleotides near the cleavage site increased the rate of catalysis, indicating that 2'-OH are not required for cleavage. Substitution of nucleotides in known Rnt1p binding site with 2'- O-methyl ribonucleotides inhibited cleavage while single 2'-fluoro ribonucleotide substitutions did not. This indicates that while no single 2'-OH is essential for Rnt1p cleavage, small changes in the substrate structure are not tolerated. Strikingly, several nucleotide substitutions greatly increased the substrate dissociation constant with little or no effect on the Michaelis-Menten constant or rate of catalysis. Together, the results indicate that Rnt1p uses a network of nucleotide interactions to identify its substrate and support two distinct modes of binding. One mode is primarily mediated by the dsRNA binding domain and leads to the formation of stable RNA/protein complex, while the other requires the presence of the nuclease and N-terminal domains and leads to RNA cleavage.

Control of the Ability of Profilin to Bind and Facilitate Nucleotide Exchange from G-actin*

PubMed Central

Wen, Kuo-Kuang; McKane, Melissa; Houtman, Jon C. D.; Rubenstein, Peter A.

2008-01-01

A major factor in profilin regulation of actin cytoskeletal dynamics is its facilitation of G-actin nucleotide exchange. However, the mechanism of this facilitation is unknown. We studied the interaction of yeast (YPF) and human profilin 1 (HPF1) with yeast and mammalian skeletal muscle actins. Homologous pairs (YPF and yeast actin, HPF1 and muscle actin) bound more tightly to one another than heterologous pairs. However, with saturating profilin, HPF1 caused a faster etheno-ATP exchange with both yeast and muscle actins than did YPF. Based on the -fold change in ATP exchange rate/Kd, however, the homologous pairs are more efficient than the heterologous pairs. Thus, strength of binding of profilin to actin and nucleotide exchange rate are not tightly coupled. Actin/HPF interactions were entropically driven, whereas YPF interactions were enthalpically driven. Hybrid yeast actins containing subdomain 1 (sub1) or subdomain 1 and 2 (sub12) muscle actin residues bound more weakly to YPF than did yeast actin (Kd = 2 μm versus 0.6 μm). These hybrids bound even more weakly to HPF than did yeast actin (Kd = 5 μm versus 3.2 μm). sub1/YPF interactions were entropically driven, whereas the sub12/YPF binding was enthalpically driven. Compared with WT yeast actin, YPF binding to sub1 occurred with a 5 times faster koff and a 2 times faster kon. sub12 bound with a 3 times faster koff and a 1.5 times slower kon. Profilin controls the energetics of its interaction with nonhybrid actin, but interactions between actin subdomains 1 and 2 affect the topography of the profilin binding site. PMID:18223293

Structure-Templated Predictions of Novel Protein Interactions from Sequence Information

PubMed Central

Betel, Doron; Breitkreuz, Kevin E; Isserlin, Ruth; Dewar-Darch, Danielle; Tyers, Mike; Hogue, Christopher W. V

2007-01-01

The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain–motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information. PMID:17892321

Detection of Protein Interactions in T3S Systems Using Yeast Two-Hybrid Analysis.

PubMed

Nilles, Matthew L

2017-01-01

Two-hybrid systems, sometimes termed interaction traps, are genetic systems designed to find and analyze interactions between proteins. The most common systems are yeast based (commonly Saccharomyces cerevisae) and rely on the functional reconstitution of the GAL4 transcriptional activator. Reporter genes, such as the lacZ gene of Escherichia coli (encodes β-galactosidase), are placed under GAL4-dependent transcriptional control to provide quick and reliable detection of protein interactions. In this method the use of a yeast-based two-hybrid system is described to study protein interactions between components of type III secretion systems.

Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

PubMed

Colak, Recep; Moser, Flavia; Chu, Jeffrey Shih-Chieh; Schönhuth, Alexander; Chen, Nansheng; Ester, Martin

2010-10-25

Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense) regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB), by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely available large-scale datasets. Software and data sets are available at http://www.sfu.ca/~ester/software/DECOB.zip.

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

PubMed Central

Kilicoglu, Halil; Shin, Dongwook; Rindflesch, Thomas C.

2014-01-01

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology. PMID:24921649

Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference.

PubMed

Chen, Guocai; Cairelli, Michael J; Kilicoglu, Halil; Shin, Dongwook; Rindflesch, Thomas C

2014-06-01

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

Mapping of Chikungunya Virus Interactions with Host Proteins Identified nsP2 as a Highly Connected Viral Component

PubMed Central

Bouraï, Mehdi; Lucas-Hourani, Marianne; Gad, Hans Henrik; Drosten, Christian; Jacob, Yves; Tafforeau, Lionel; Cassonnet, Patricia; Jones, Louis M.; Judith, Delphine; Couderc, Thérèse; Lecuit, Marc; André, Patrice; Kümmerer, Beate Mareike; Lotteau, Vincent; Desprès, Philippe; Vidalain, Pierre-Olivier

2012-01-01

Chikungunya virus (CHIKV) is a mosquito-transmitted alphavirus that has been responsible for an epidemic outbreak of unprecedented magnitude in recent years. Since then, significant efforts have been made to better understand the biology of this virus, but we still have poor knowledge of CHIKV interactions with host cell components at the molecular level. Here we describe the extensive use of high-throughput yeast two-hybrid (HT-Y2H) assays to characterize interactions between CHIKV and human proteins. A total of 22 high-confidence interactions, which essentially involved the viral nonstructural protein nsP2, were identified and further validated in protein complementation assay (PCA). These results were integrated to a larger network obtained by extensive mining of the literature for reports on alphavirus-host interactions. To investigate the role of cellular proteins interacting with nsP2, gene silencing experiments were performed in cells infected by a recombinant CHIKV expressing Renilla luciferase as a reporter. Collected data showed that heterogeneous nuclear ribonucleoprotein K (hnRNP-K) and ubiquilin 4 (UBQLN4) participate in CHIKV replication in vitro. In addition, we showed that CHIKV nsP2 induces a cellular shutoff, as previously reported for other Old World alphaviruses, and determined that among binding partners identified by yeast two-hybrid methods, the tetratricopeptide repeat protein 7B (TTC7B) plays a significant role in this activity. Altogether, this report provides the first interaction map between CHIKV and human proteins and describes new host cell proteins involved in the replication cycle of this virus. PMID:22258240

Yeast-yeast interactions revealed by aromatic profile analysis of Sauvignon Blanc wine fermented by single or co-culture of non-Saccharomyces and Saccharomyces yeasts.

PubMed

Sadoudi, Mohand; Tourdot-Maréchal, Raphaëlle; Rousseaux, Sandrine; Steyer, Damien; Gallardo-Chacón, Joan-Josep; Ballester, Jordi; Vichi, Stefania; Guérin-Schneider, Rémi; Caixach, Josep; Alexandre, Hervé

2012-12-01

There has been increasing interest in the use of selected non-Saccharomyces yeasts in co-culture with Saccharomyces cerevisiae. The main reason is that the multistarter fermentation process is thought to simulate indigenous fermentation, thus increasing wine aroma complexity while avoiding the risks linked to natural fermentation. However, multistarter fermentation is characterised by complex and largely unknown interactions between yeasts. Consequently the resulting wine quality is rather unpredictable. In order to better understand the interactions that take place between non-Saccharomyces and Saccharomyces yeasts during alcoholic fermentation, we analysed the volatile profiles of several mono-culture and co-cultures. Candida zemplinina, Torulaspora delbrueckii and Metschnikowia pulcherrima were used to conduct fermentations either in mono-culture or in co-culture with S. cerevisiae. Up to 48 volatile compounds belonging to different chemical families were quantified. For the first time, we show that C. zemplinina is a strong producer of terpenes and lactones. We demonstrate by means of multivariate analysis that different interactions exist between the co-cultures studied. We observed a synergistic effect on aromatic compound production when M. pulcherrima was in co-culture with S. cerevisiae. However a negative interaction was observed between C. zemplinina and S. cerevisiae, which resulted in a decrease in terpene and lactone content. These interactions are independent of biomass production. The aromatic profiles of T. delbrueckii and S. cerevisiae in mono-culture and in co-culture are very close, and are biomass-dependent, reflecting a neutral interaction. This study reveals that a whole family of compounds could be altered by such interactions. These results suggest that the entire metabolic pathway is affected by these interactions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Yeast casein kinase 2 governs morphology, biofilm formation, cell wall integrity, and host cell damage of Candida albicans.

PubMed

Jung, Sook-In; Rodriguez, Natalie; Irrizary, Jihyun; Liboro, Karl; Bogarin, Thania; Macias, Marlene; Eivers, Edward; Porter, Edith; Filler, Scott G; Park, Hyunsook

2017-01-01

The regulatory networks governing morphogenesis of a pleomorphic fungus, Candida albicans are extremely complex and remain to be completely elucidated. This study investigated the function of C. albicans yeast casein kinase 2 (CaYck2p). The yck2Δ/yck2Δ strain displayed constitutive pseudohyphae in both yeast and hyphal growth conditions, and formed enhanced biofilm under non-biofilm inducing condition. This finding was further supported by gene expression analysis of the yck2Δ/yck2Δ strain which showed significant upregulation of UME6, a key transcriptional regulator of hyphal transition and biofilm formation, and cell wall protein genes ALS3, HWP1, and SUN41, all of which are associated with morphogenesis and biofilm architecture. The yck2Δ/yck2Δ strain was hypersensitive to cell wall damaging agents and had increased compensatory chitin deposition in the cell wall accompanied by an upregulation of the expression of the chitin synthase genes, CHS2, CHS3, and CHS8. Absence of CaYck2p also affected fungal-host interaction; the yck2Δ/yck2Δ strain had significantly reduced ability to damage host cells. However, the yck2Δ/yck2Δ strain had wild-type susceptibility to cyclosporine and FK506, suggesting that CaYck2p functions independently from the Ca+/calcineurin pathway. Thus, in C. albicans, Yck2p is a multifunctional kinase that governs morphogenesis, biofilm formation, cell wall integrity, and host cell interactions.

Interactions between yeast lees and wine polyphenols during simulation of wine aging: I. Analysis of remnant polyphenolic compounds in the resulting wines.

PubMed

Mazauric, Jean-Paul; Salmon, Jean-Michel

2005-07-13

Wine aging on yeast lees is a traditional enological practice used during the manufacture of wines. This technique has increased in popularity in recent years for the aging of red wines. Although wine polyphenols interact with yeast lees to a limited extent, such interactions have a large effect on the reactivity toward oxygen of wine polyphenolic compounds and yeast lees. Various domains of the yeast cell wall are protected by wine polyphenols from the action of extracellular hydrolytic enzymatic activities. Polysaccharides released during autolysis are thought to exert a significant effect on the sensory qualities of wine. We studied the chemical composition of polyphenolic compounds remaining in solution or adsorbed on yeast lees after various contact times during the simulation of wine aging. The analysis of the remnant polyphenols in the wine indicated that wine polyphenols adsorption on yeast lees follows biphasic kinetics. An initial and rapid fixation is followed by a slow, constant, and saturating fixation that reaches its maximum after about 1 week. Only very few monomeric phenolic compounds remained adsorbed on yeast lees, and no preferential adsorption of low or high polymeric size tannins occurred. The remnant condensed tannins in the wine contained fewer epigallocatechin units than the initial tannins, indicating that polar condensed tannins were preferentially adsorbed on yeast lees. Conversely, the efficiency of anthocyanin adsorption on yeast lees was unrelated to its polarity.

The YeastGenome app: the Saccharomyces Genome Database at your fingertips.

PubMed

Wong, Edith D; Karra, Kalpana; Hitz, Benjamin C; Hong, Eurie L; Cherry, J Michael

2013-01-01

The Saccharomyces Genome Database (SGD) is a scientific database that provides researchers with high-quality curated data about the genes and gene products of Saccharomyces cerevisiae. To provide instant and easy access to this information on mobile devices, we have developed YeastGenome, a native application for the Apple iPhone and iPad. YeastGenome can be used to quickly find basic information about S. cerevisiae genes and chromosomal features regardless of internet connectivity. With or without network access, you can view basic information and Gene Ontology annotations about a gene of interest by searching gene names and gene descriptions or by browsing the database within the app to find the gene of interest. With internet access, the app provides more detailed information about the gene, including mutant phenotypes, references and protein and genetic interactions, as well as provides hyperlinks to retrieve detailed information by showing SGD pages and views of the genome browser. SGD provides online help describing basic ways to navigate the mobile version of SGD, highlights key features and answers frequently asked questions related to the app. The app is available from iTunes (http://itunes.com/apps/yeastgenome). The YeastGenome app is provided freely as a service to our community, as part of SGD's mission to provide free and open access to all its data and annotations.

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

The protein interaction map of bacteriophage lambda

PubMed Central

2011-01-01

Background Bacteriophage lambda is a model phage for most other dsDNA phages and has been studied for over 60 years. Although it is probably the best-characterized phage there are still about 20 poorly understood open reading frames in its 48-kb genome. For a complete understanding we need to know all interactions among its proteins. We have manually curated the lambda literature and compiled a total of 33 interactions that have been found among lambda proteins. We set out to find out how many protein-protein interactions remain to be found in this phage. Results In order to map lambda's interactions, we have cloned 68 out of 73 lambda open reading frames (the "ORFeome") into Gateway vectors and systematically tested all proteins for interactions using exhaustive array-based yeast two-hybrid screens. These screens identified 97 interactions. We found 16 out of 30 previously published interactions (53%). We have also found at least 18 new plausible interactions among functionally related proteins. All previously found and new interactions are combined into structural and network models of phage lambda. Conclusions Phage lambda serves as a benchmark for future studies of protein interactions among phage, viruses in general, or large protein assemblies. We conclude that we could not find all the known interactions because they require chaperones, post-translational modifications, or multiple proteins for their interactions. The lambda protein network connects 12 proteins of unknown function with well characterized proteins, which should shed light on the functional associations of these uncharacterized proteins. PMID:21943085

On construction of stochastic genetic networks based on gene expression sequences.

PubMed

Ching, Wai-Ki; Ng, Michael M; Fung, Eric S; Akutsu, Tatsuya

2005-08-01

Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n2) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model.

Turning gold into ‘junk’: transposable elements utilize central proteins of cellular networks

PubMed Central

Abrusán, György; Szilágyi, András; Zhang, Yang; Papp, Balázs

2013-01-01

The numerous discovered cases of domesticated transposable element (TE) proteins led to the recognition that TEs are a significant source of evolutionary innovation. However, much less is known about the reverse process, whether and to what degree the evolution of TEs is influenced by the genome of their hosts. We addressed this issue by searching for cases of incorporation of host genes into the sequence of TEs and examined the systems-level properties of these genes using the Saccharomyces cerevisiae and Drosophila melanogaster genomes. We identified 51 cases where the evolutionary scenario was the incorporation of a host gene fragment into a TE consensus sequence, and we show that both the yeast and fly homologues of the incorporated protein sequences have central positions in the cellular networks. An analysis of selective pressure (Ka/Ks ratio) detected significant selection in 37% of the cases. Recent research on retrovirus-host interactions shows that virus proteins preferentially target hubs of the host interaction networks enabling them to take over the host cell using only a few proteins. We propose that TEs face a similar evolutionary pressure to evolve proteins with high interacting capacities and take some of the necessary protein domains directly from their hosts. PMID:23341038

A New Method, "Reverse Yeast Two-Hybrid Array" (RYTHA), Identifies Mutants that Dissociate the Physical Interaction Between Elg1 and Slx5.

PubMed

Lev, Ifat; Shemesh, Keren; Volpe, Marina; Sau, Soumitra; Levinton, Nelly; Molco, Maya; Singh, Shivani; Liefshitz, Batia; Ben Aroya, Shay; Kupiec, Martin

2017-07-01

The vast majority of processes within the cell are carried out by proteins working in conjunction. The Yeast Two-Hybrid (Y2H) methodology allows the detection of physical interactions between any two interacting proteins. Here, we describe a novel systematic genetic methodology, "Reverse Yeast Two-Hybrid Array" (RYTHA), that allows the identification of proteins required for modulating the physical interaction between two given proteins. Our assay starts with a yeast strain in which the physical interaction of interest can be detected by growth on media lacking histidine, in the context of the Y2H methodology. By combining the synthetic genetic array technology, we can systematically screen mutant libraries of the yeast Saccharomyces cerevisiae to identify trans -acting mutations that disrupt the physical interaction of interest. We apply this novel method in a screen for mutants that disrupt the interaction between the N-terminus of Elg1 and the Slx5 protein. Elg1 is part of an alternative replication factor C-like complex that unloads PCNA during DNA replication and repair. Slx5 forms, together with Slx8, a SUMO-targeted ubiquitin ligase (STUbL) believed to send proteins to degradation. Our results show that the interaction requires both the STUbL activity and the PCNA unloading by Elg1, and identify topoisomerase I DNA-protein cross-links as a major factor in separating the two activities. Thus, we demonstrate that RYTHA can be applied to gain insights about particular pathways in yeast, by uncovering the connection between the proteasomal ubiquitin-dependent degradation pathway, DNA replication, and repair machinery, which can be separated by the topoisomerase-mediated cross-links to DNA. Copyright © 2017 by the Genetics Society of America.

Spatial organization of the budding yeast genome in the cell nucleus and identification of specific chromatin interactions from multi-chromosome constrained chromatin model.

PubMed

Gürsoy, Gamze; Xu, Yun; Liang, Jie

2017-07-01

Nuclear landmarks and biochemical factors play important roles in the organization of the yeast genome. The interaction pattern of budding yeast as measured from genome-wide 3C studies are largely recapitulated by model polymer genomes subject to landmark constraints. However, the origin of inter-chromosomal interactions, specific roles of individual landmarks, and the roles of biochemical factors in yeast genome organization remain unclear. Here we describe a multi-chromosome constrained self-avoiding chromatin model (mC-SAC) to gain understanding of the budding yeast genome organization. With significantly improved sampling of genome structures, both intra- and inter-chromosomal interaction patterns from genome-wide 3C studies are accurately captured in our model at higher resolution than previous studies. We show that nuclear confinement is a key determinant of the intra-chromosomal interactions, and centromere tethering is responsible for the inter-chromosomal interactions. In addition, important genomic elements such as fragile sites and tRNA genes are found to be clustered spatially, largely due to centromere tethering. We uncovered previously unknown interactions that were not captured by genome-wide 3C studies, which are found to be enriched with tRNA genes, RNAPIII and TFIIS binding. Moreover, we identified specific high-frequency genome-wide 3C interactions that are unaccounted for by polymer effects under landmark constraints. These interactions are enriched with important genes and likely play biological roles.

Genomic signatures of adaptation to wine biological ageing conditions in biofilm-forming flor yeasts.

PubMed

Coi, A L; Bigey, F; Mallet, S; Marsit, S; Zara, G; Gladieux, P; Galeote, V; Budroni, M; Dequin, S; Legras, J L

2017-04-01

The molecular and evolutionary processes underlying fungal domestication remain largely unknown despite the importance of fungi to bioindustry and for comparative adaptation genomics in eukaryotes. Wine fermentation and biological ageing are performed by strains of S. cerevisiae with, respectively, pelagic fermentative growth on glucose and biofilm aerobic growth utilizing ethanol. Here, we use environmental samples of wine and flor yeasts to investigate the genomic basis of yeast adaptation to contrasted anthropogenic environments. Phylogenetic inference and population structure analysis based on single nucleotide polymorphisms revealed a group of flor yeasts separated from wine yeasts. A combination of methods revealed several highly differentiated regions between wine and flor yeasts, and analyses using codon-substitution models for detecting molecular adaptation identified sites under positive selection in the high-affinity transporter gene ZRT1. The cross-population composite likelihood ratio revealed selective sweeps at three regions, including in the hexose transporter gene HXT7, the yapsin gene YPS6 and the membrane protein coding gene MTS27. Our analyses also revealed that the biological ageing environment has led to the accumulation of numerous mutations in proteins from several networks, including Flo11 regulation and divalent metal transport. Together, our findings suggest that the tuning of FLO11 expression and zinc transport networks are a distinctive feature of the genetic changes underlying the domestication of flor yeasts. Our study highlights the multiplicity of genomic changes underlying yeast adaptation to man-made habitats and reveals that flor/wine yeast lineage can serve as a useful model for studying the genomics of adaptive divergence. © 2017 John Wiley & Sons Ltd.

CBL-CIPK network for calcium signaling in higher plants

NASA Astrophysics Data System (ADS)

Luan, Sheng

Plants sense their environment by signaling mechanisms involving calcium. Calcium signals are encoded by a complex set of parameters and decoded by a large number of proteins including the more recently discovered CBL-CIPK network. The calcium-binding CBL proteins specifi-cally interact with a family of protein kinases CIPKs and regulate the activity and subcellular localization of these kinases, leading to the modification of kinase substrates. This represents a paradigm shift as compared to a calcium signaling mechanism from yeast and animals. One example of CBL-CIPK signaling pathways is the low-potassium response of Arabidopsis roots. When grown in low-K medium, plants develop stronger K-uptake capacity adapting to the low-K condition. Recent studies show that the increased K-uptake is caused by activation of a specific K-channel by the CBL-CIPK network. A working model for this regulatory pathway will be discussed in the context of calcium coding and decoding processes.

Genetic interaction networks: better understand to better predict

PubMed Central

Boucher, Benjamin; Jenna, Sarah

2013-01-01

A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances. PMID:24381582

A single determinant dominates the rate of yeast protein evolution.

PubMed

Drummond, D Allan; Raval, Alpan; Wilke, Claus O

2006-02-01

A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network) previously reported to have independent influences on protein evolutionary rates. Strikingly, our analysis reveals a single dominant variable linked to the number of translation events which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single major determinant among the seven predictors. The dominant variable explains nearly half the variation in the rate of synonymous and protein evolution. We show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. We overcome these difficulties by employing principal component regression, a multivariate regression of evolutionary rate against the principal components of the predictor variables. Our results support the hypothesis that translational selection governs the rate of synonymous and protein sequence evolution in yeast.

Transcriptional Regulation and the Diversification of Metabolism in Wine Yeast Strains

PubMed Central

Rossouw, Debra; Jacobson, Dan; Bauer, Florian F.

2012-01-01

Transcription factors and their binding sites have been proposed as primary targets of evolutionary adaptation because changes to single transcription factors can lead to far-reaching changes in gene expression patterns. Nevertheless, there is very little concrete evidence for such evolutionary changes. Industrial wine yeast strains, of the species Saccharomyces cerevisiae, are a geno- and phenotypically diverse group of organisms that have adapted to the ecological niches of industrial winemaking environments and have been selected to produce specific styles of wine. Variation in transcriptional regulation among wine yeast strains may be responsible for many of the observed differences and specific adaptations to different fermentative conditions in the context of commercial winemaking. We analyzed gene expression profiles of wine yeast strains to assess the impact of transcription factor expression on metabolic networks. The data provide new insights into the molecular basis of variations in gene expression in industrial strains and their consequent effects on metabolic networks important to wine fermentation. We show that the metabolic phenotype of a strain can be shifted in a relatively predictable manner by changing expression levels of individual transcription factors, opening opportunities to modify transcription networks to achieve desirable outcomes. PMID:22042577

Computational architecture of the yeast regulatory network

NASA Astrophysics Data System (ADS)

Maslov, Sergei; Sneppen, Kim

2005-12-01

The topology of regulatory networks contains clues to their overall design principles and evolutionary history. We find that while in- and out-degrees of a given protein in the regulatory network are not correlated with each other, there exists a strong negative correlation between the out-degree of a regulatory protein and in-degrees of its targets. Such correlation positions large regulatory modules on the periphery of the network and makes them rather well separated from each other. We also address the question of relative importance of different classes of proteins quantified by the lethality of null-mutants lacking one of them as well as by the level of their evolutionary conservation. It was found that in the yeast regulatory network highly connected proteins are in fact less important than their low-connected counterparts.

Arranging eukaryotic nuclear DNA polymerases for replication: Specific interactions with accessory proteins arrange Pols α, δ, and ϵ in the replisome for leading-strand and lagging-strand DNA replication.

PubMed

Kunkel, Thomas A; Burgers, Peter M J

2017-08-01

Biochemical and cryo-electron microscopy studies have just been published revealing interactions among proteins of the yeast replisome that are important for highly coordinated synthesis of the two DNA strands of the nuclear genome. These studies reveal key interactions important for arranging DNA polymerases α, δ, and ϵ for leading and lagging strand replication. The CMG (Mcm2-7, Cdc45, GINS) helicase is central to this interaction network. These are but the latest examples of elegant studies performed in the recent past that lead to a much better understanding of how the eukaryotic replication fork achieves efficient DNA replication that is accurate enough to prevent diseases yet allows evolution. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

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

Integrated cellular network of transcription regulations and protein-protein interactions

PubMed Central

2010-01-01

Background With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway. Results In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated. Conclusions We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology. PMID:20211003

Integrated cellular network of transcription regulations and protein-protein interactions.

PubMed

Wang, Yu-Chao; Chen, Bor-Sen

2010-03-08

With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway. In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated. We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.

A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks.

PubMed

Laomettachit, Teeraphan; Chen, Katherine C; Baumann, William T; Tyson, John J

2016-01-01

To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a "standard component" modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with "standard components" can capture in quantitative detail many essential properties of cell cycle control in budding yeast.

A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks

PubMed Central

Laomettachit, Teeraphan; Chen, Katherine C.; Baumann, William T.

2016-01-01

To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a “standard component” modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with “standard components” can capture in quantitative detail many essential properties of cell cycle control in budding yeast. PMID:27187804

A Temperature-Responsive Network Links Cell Shape and Virulence Traits in a Primary Fungal Pathogen

PubMed Central

Beyhan, Sinem; Gutierrez, Matias; Voorhies, Mark; Sil, Anita

2013-01-01

Survival at host temperature is a critical trait for pathogenic microbes of humans. Thermally dimorphic fungal pathogens, including Histoplasma capsulatum, are soil fungi that undergo dramatic changes in cell shape and virulence gene expression in response to host temperature. How these organisms link changes in temperature to both morphologic development and expression of virulence traits is unknown. Here we elucidate a temperature-responsive transcriptional network in H. capsulatum, which switches from a filamentous form in the environment to a pathogenic yeast form at body temperature. The circuit is driven by three highly conserved factors, Ryp1, Ryp2, and Ryp3, that are required for yeast-phase growth at 37°C. Ryp factors belong to distinct families of proteins that control developmental transitions in fungi: Ryp1 is a member of the WOPR family of transcription factors, and Ryp2 and Ryp3 are both members of the Velvet family of proteins whose molecular function is unknown. Here we provide the first evidence that these WOPR and Velvet proteins interact, and that Velvet proteins associate with DNA to drive gene expression. Using genome-wide chromatin immunoprecipitation studies, we determine that Ryp1, Ryp2, and Ryp3 associate with a large common set of genomic loci that includes known virulence genes, indicating that the Ryp factors directly control genes required for pathogenicity in addition to their role in regulating cell morphology. We further dissect the Ryp regulatory circuit by determining that a fourth transcription factor, which we name Ryp4, is required for yeast-phase growth and gene expression, associates with DNA, and displays interdependent regulation with Ryp1, Ryp2, and Ryp3. Finally, we define cis-acting motifs that recruit the Ryp factors to their interwoven network of temperature-responsive target genes. Taken together, our results reveal a positive feedback circuit that directs a broad transcriptional switch between environmental and pathogenic states in response to temperature. PMID:23935449

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

Investigating Conservation of the Cell-Cycle-Regulated Transcriptional Program in the Fungal Pathogen, Cryptococcus neoformans

PubMed Central

Sierra, Crystal S.; Haase, Steven B.

2016-01-01

The pathogenic yeast Cryptococcus neoformans causes fungal meningitis in immune-compromised patients. Cell proliferation in the budding yeast form is required for C. neoformans to infect human hosts, and virulence factors such as capsule formation and melanin production are affected by cell-cycle perturbation. Thus, understanding cell-cycle regulation is critical for a full understanding of virulence factors for disease. Our group and others have demonstrated that a large fraction of genes in Saccharomyces cerevisiae is expressed periodically during the cell cycle, and that proper regulation of this transcriptional program is important for proper cell division. Despite the evolutionary divergence of the two budding yeasts, we found that a similar percentage of all genes (~20%) is periodically expressed during the cell cycle in both yeasts. However, the temporal ordering of periodic expression has diverged for some orthologous cell-cycle genes, especially those related to bud emergence and bud growth. Genes regulating DNA replication and mitosis exhibited a conserved ordering in both yeasts, suggesting that essential cell-cycle processes are conserved in periodicity and in timing of expression (i.e. duplication before division). In S. cerevisiae cells, we have proposed that an interconnected network of periodic transcription factors (TFs) controls the bulk of the cell-cycle transcriptional program. We found that temporal ordering of orthologous network TFs was not always maintained; however, the TF network topology at cell-cycle commitment appears to be conserved in C. neoformans. During the C. neoformans cell cycle, DNA replication genes, mitosis genes, and 40 genes involved in virulence are periodically expressed. Future work toward understanding the gene regulatory network that controls cell-cycle genes is critical for developing novel antifungals to inhibit pathogen proliferation. PMID:27918582

Yeast diversity and native vigor for flavor phenotypes.

PubMed

Carrau, Francisco; Gaggero, Carina; Aguilar, Pablo S

2015-03-01

Saccharomyces cerevisiae, the yeast used widely for beer, bread, cider, and wine production, is the most resourceful eukaryotic model used for genetic engineering. A typical concern about using engineered yeasts for food production might be negative consumer perception of genetically modified organisms. However, we believe the true pitfall of using genetically modified yeasts is their limited capacity to either refine or improve the sensory properties of fermented foods under real production conditions. Alternatively, yeast diversity screening to improve the aroma and flavors could offer groundbreaking opportunities in food biotechnology. We propose a 'Yeast Flavor Diversity Screening' strategy which integrates knowledge from sensory analysis and natural whole-genome evolution with information about flavor metabolic networks and their regulation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Hippo Signaling in Mitosis: An Updated View in Light of the MEN Pathway.

PubMed

Hergovich, Alexander

2017-01-01

The Hippo pathway is an essential tumor suppressor signaling network that coordinates cell proliferation, death, and differentiation in higher eukaryotes. Intriguingly, the core components of the Hippo pathway are conserved from yeast to man, with the yeast analogs of mammalian MST1/2 (fly Hippo), MOB1 (fly Mats), LATS1/2 (fly Warts), and NDR1/2 (fly Tricornered) functioning as essential components of the mitotic exit network (MEN). Here, we update our previous summary of mitotic functions of Hippo core components in Drosophila melanogaster and mammals, with particular emphasis on similarities between the yeast MEN pathway and mitotic Hippo signaling. Mitotic functions of YAP and TAZ, the two main effectors of Hippo signaling, are also discussed.

Gateway Vectors for Efficient Artificial Gene Assembly In Vitro and Expression in Yeast Saccharomyces cerevisiae

PubMed Central

Giuraniuc, Claudiu V.; MacPherson, Murray; Saka, Yasushi

2013-01-01

Construction of synthetic genetic networks requires the assembly of DNA fragments encoding functional biological parts in a defined order. Yet this may become a time-consuming procedure. To address this technical bottleneck, we have created a series of Gateway shuttle vectors and an integration vector, which facilitate the assembly of artificial genes and their expression in the budding yeast Saccharomyces cerevisiae. Our method enables the rapid construction of an artificial gene from a promoter and an open reading frame (ORF) cassette by one-step recombination reaction in vitro. Furthermore, the plasmid thus created can readily be introduced into yeast cells to test the assembled gene’s functionality. As flexible regulatory components of a synthetic genetic network, we also created new versions of the tetracycline-regulated transactivators tTA and rtTA by fusing them to the auxin-inducible degron (AID). Using our gene assembly approach, we made yeast expression vectors of these engineered transactivators, AIDtTA and AIDrtTA and then tested their functions in yeast. We showed that these factors can be regulated by doxycycline and degraded rapidly after addition of auxin to the medium. Taken together, the method for combinatorial gene assembly described here is versatile and would be a valuable tool for yeast synthetic biology. PMID:23675537

Genome-wide expression analyses of the stationary phase model of ageing in yeast.

PubMed

Wanichthanarak, Kwanjeera; Wongtosrad, Nutvadee; Petranovic, Dina

2015-07-01

Ageing processes involved in replicative lifespan (RLS) and chronological lifespan (CLS) have been found to be conserved among many organisms, including in unicellular Eukarya such as yeast Saccharomyces cerevisiae. Here we performed an integrated approach of genome wide expression profiles of yeast at different time points, during growth and starvation. The aim of the study was to identify transcriptional changes in those conditions by using several different computational analyses in order to propose transcription factors, biological networks and metabolic pathways that seem to be relevant during the process of chronological ageing in yeast. Specifically, we performed differential gene expression analysis, gene-set enrichment analysis and network-based analysis, and we identified pathways affected in the stationary phase and specific transcription factors driving transcriptional adaptations. The results indicate signal propagation from G protein-coupled receptors through signaling pathway components and other stress and nutrient-induced transcription factors resulting in adaptation of yeast cells to the lack of nutrients by activating metabolism associated with aerobic metabolism of carbon sources such as ethanol, glycerol and fatty acids. In addition, we found STE12, XBP1 and TOS8 as highly connected nodes in the subnetworks of ageing yeast. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Improving protein–protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model

PubMed Central

An, Ji‐Yong; Meng, Fan‐Rong; Chen, Xing; Yan, Gui‐Ying; Hu, Ji‐Pu

2016-01-01

Abstract Predicting protein–protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high‐throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM‐BiGP that combines the relevance vector machine (RVM) model and Bi‐gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi‐gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five‐fold cross‐validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state‐of‐the‐art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM‐BiGP method is significantly better than the SVM‐based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM‐BiGP‐PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/BiGP/. PMID:27452983

Interaction between the plant ApDef1 defensin and Saccharomyces cerevisiae results in yeast death through a cell cycle- and caspase-dependent process occurring via uncontrolled oxidative stress.

PubMed

Soares, Júlia Ribeiro; José Tenório de Melo, Edésio; da Cunha, Maura; Fernandes, Kátia Valevski Sales; Taveira, Gabriel Bonan; da Silva Pereira, Lidia; Pimenta, Samy; Trindade, Fernanda Gomes; Regente, Mariana; Pinedo, Marcela; de la Canal, Laura; Gomes, Valdirene Moreira; de Oliveira Carvalho, André

2017-01-01

Plant defensins were discovered at beginning of the 90s'; however, their precise mechanism of action is still unknown. Herein, we studied ApDef 1 -Saccharomyces cerevisiae interaction. ApDef 1 -S. cerevisiae interaction was studied by determining the MIC, viability and death kinetic assays. Viability assay was repeated with hydroxyurea synchronized-yeast and pretreated with CCCP. Plasma membrane permeabilization, ROS induction, chromatin condensation, and caspase activation analyses were assessed through Sytox green, DAB, DAPI and FITC-VAD-FMK, respectively. Viability assay was done in presence of ascorbic acid and Z-VAD-FMK. Ultrastructural analysis was done by electron microscopy. ApDef 1 caused S. cerevisiae cell death and MIC was 7.8μM. Whole cell population died after 18h of ApDef 1 interaction. After 3h, 98.76% of synchronized cell population died. Pretreatment with CCCP protected yeast from ApDef 1 induced death. ApDef 1 -S. cerevisiae interaction resulted in membrane permeabilization, H 2 O 2 increased production, chromatin condensation and caspase activation. Ascorbic acid prevented yeast cell death and membrane permeabilization. Z-VAD-FMK prevented yeast cell death. ApDef 1 -S. cerevisiae interaction caused cell death through cell cycle dependentprocess which requires preserved membrane potential. After interaction, yeast went through uncontrolled ROS production and accumulation, which led to plasma membrane permeabilization, chromatin condensation and, ultimately, cell death by activation of caspase-dependent apoptosis via. We show novel requirements for the interaction between plant defensin and fungi cells, i.e. cell cycle phase and membrane potential, and we indicate that membrane permeabilization is probably caused by ROS and therefore, it would be an indirect event of the ApDef 1 -S. cerevisiae interaction. Copyright © 2016 Elsevier B.V. All rights reserved.

Enological Qualities and Interactions Between Native Yeast and Lactic Acid Bacteria from Queretaro, Mexico.

PubMed

Miranda-Castilleja, Dalia E; Martínez-Peniche, Ramón Á; Nadal Roquet-Jalmar, Montserrat; Aldrete-Tapia, J Alejandro; Arvizu-Medrano, Sofía M

2018-06-15

Despite the importance of strain compatibility, most of the enological strain selection studies are carried out separately on yeasts and lactic acid bacteria (LAB). In this study, the enological traits and interactions between native yeasts and LAB were studied. The H 2 S and acetic acid production, growth rates at 8 °C, killer phenotypes, flocculation, and tolerance to must and wine inhibitors were determined for 25 Saccharomyces yeasts. The ability to grow under two wine-like conditions was also determined in 37 LAB (Oenococcus oeni and Lactobacillus plantarum). The yeast-LAB compatibility of selected strains was tested in a sequential scheme. Finally, microvinification trials were performed using two strains from each group to determine the efficiencies and quality parameters. The phenotypic characterization by the K-means and hierarchical clusters indicated a correlation between flocculation and optical density increase in simulated must and wine medium (r = -0.415) and grouped the prominent yeasts SR19, SR26, and N05 as moderately flocculent, killer, acid producing, and highly tolerant strains. Among the LAB, L. plantarum FU39 grew 230% more than the rest. With regard to interactions, LAB growth stimulation (14-fold on average) due to the previous action of yeasts, particularly of SR19, was observed. The final quality of all wines was similar, but yeast SR19 performed a faster and more efficient fermentation than did N05, Also L. plantarum FU39 fermented faster than did O. oeni VC32. The use of quantitative data, and multivariate analyses allowed an integrative approach to the selection of a compatible and efficient pair of enological yeast-LAB strains. An alternative scheme is proposed for the joint selection of yeast and lactic acid bacteria strains, which allows us to foresee the interactions that may occur between them during winemaking. The kinetic parameters, turbidimetrically measured and analyzed by multivariate methods, simplify the detection of outstanding selectable microorganisms. This methodology can be implemented at any cellar or even any fermentative industry that aims to select compatible yeast and lactic acid bacteria. © 2018 Institute of Food Technologists®.

Forces in yeast flocculation

NASA Astrophysics Data System (ADS)

El-Kirat-Chatel, Sofiane; Beaussart, Audrey; Vincent, Stéphane P.; Abellán Flos, Marta; Hols, Pascal; Lipke, Peter N.; Dufrêne, Yves F.

2015-01-01

In the baker's yeast Saccharomyces cerevisiae, cell-cell adhesion (``flocculation'') is conferred by a family of lectin-like proteins known as the flocculin (Flo) proteins. Knowledge of the adhesive and mechanical properties of flocculins is important for understanding the mechanisms of yeast adhesion, and may help controlling yeast behaviour in biotechnology. We use single-molecule and single-cell atomic force microscopy (AFM) to explore the nanoscale forces engaged in yeast flocculation, focusing on the role of Flo1 as a prototype of flocculins. Using AFM tips labelled with mannose, we detect single flocculins on Flo1-expressing cells, showing they are widely exposed on the cell surface. When subjected to force, individual Flo1 proteins display two distinct force responses, i.e. weak lectin binding forces and strong unfolding forces reflecting the force-induced extension of hydrophobic tandem repeats. We demonstrate that cell-cell adhesion bonds also involve multiple weak lectin interactions together with strong unfolding forces, both associated with Flo1 molecules. Single-molecule and single-cell data correlate with microscale cell adhesion behaviour, suggesting strongly that Flo1 mechanics is critical for yeast flocculation. These results favour a model in which not only weak lectin-sugar interactions are involved in yeast flocculation but also strong hydrophobic interactions resulting from protein unfolding.

A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

PubMed

Zhang, Xiaomeng; Shao, Bin; Wu, Yangle; Qi, Ouyang

2013-01-01

One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.

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.

Constraints imposed by non-functional protein–protein interactions on gene expression and proteome size

PubMed Central

Zhang, Jingshan; Maslov, Sergei; Shakhnovich, Eugene I

2008-01-01

Crowded intracellular environments present a challenge for proteins to form functional specific complexes while reducing non-functional interactions with promiscuous non-functional partners. Here we show how the need to minimize the waste of resources to non-functional interactions limits the proteome diversity and the average concentration of co-expressed and co-localized proteins. Using the results of high-throughput Yeast 2-Hybrid experiments, we estimate the characteristic strength of non-functional protein–protein interactions. By combining these data with the strengths of specific interactions, we assess the fraction of time proteins spend tied up in non-functional interactions as a function of their overall concentration. This allows us to sketch the phase diagram for baker's yeast cells using the experimentally measured concentrations and subcellular localization of their proteins. The positions of yeast compartments on the phase diagram are consistent with our hypothesis that the yeast proteome has evolved to operate closely to the upper limit of its size, whereas keeping individual protein concentrations sufficiently low to reduce non-functional interactions. These findings have implication for conceptual understanding of intracellular compartmentalization, multicellularity and differentiation. PMID:18682700

Comparative study on the freeze stability of yeast and chemical leavened steamed bread dough.

PubMed

Wang, Pei; Yang, Runqiang; Gu, Zhenxin; Xu, Xueming; Jin, Zhengyu

2017-04-15

The present study comparatively evaluated the evolution of yeast and chemical leavened steamed bread dough (YLD/CLD) quality during freeze/thaw (FT) cycles. The steamed bread quality of CLD was more freeze-stable than that of the YLD after 3 FT cycles. Decreased yeast viability contributed to the loss of gassing power in YLD while no significant differences were observed for CLD during FT cycles. However, faster gas release rate in frozen CLD indicated gas retention loss due to the distortion of gluten network. Glutenin macropolymers (GMP) depolymerization via breakage of inter-chain disulfide (SS) bonds and conversions of α-helix and β-turn to β-sheet structures were the main indicators of gluten deterioration. Gluten network was more vulnerable in frozen YLD, resulting in detectable loss of viscoelasticity. The results suggested that supplement of chemical leavener contributed to a more freeze-tolerant gluten network besides its stable gassing power. Copyright © 2016 Elsevier Ltd. All rights reserved.

Nectar-living yeasts of a tropical host plant community: diversity and effects on community-wide floral nectar traits

PubMed Central

2017-01-01

We characterize the diversity of nectar-living yeasts of a tropical host plant community at different hierarchical sampling levels, measure the associations between yeasts and nectariferous plants, and measure the effect of yeasts on nectar traits. Using a series of hierarchically nested sampling units, we extracted nectar from an assemblage of host plants that were representative of the diversity of life forms, flower shapes, and pollinator types in the tropical area of Yucatan, Mexico. Yeasts were isolated from single nectar samples; their DNA was identified, the yeast cell density was estimated, and the sugar composition and concentration of nectar were quantified using HPLC. In contrast to previous studies from temperate regions, the diversity of nectar-living yeasts in the plant community was characterized by a relatively high number of equally common species with low dominance. Analyses predict highly diverse nectar yeast communities in a relatively narrow range of tropical vegetation, suggesting that the diversity of yeasts will increase as the number of sampling units increases at the level of the species, genera, and botanical families of the hosts. Significant associations between specific yeast species and host plants were also detected; the interaction between yeasts and host plants impacted the effect of yeast cell density on nectar sugars. This study provides an overall picture of the diversity of nectar-living yeasts in tropical host plants and suggests that the key factor that affects the community-wide patterns of nectar traits is not nectar chemistry, but rather the type of yeasts interacting with host plants. PMID:28717591

Yeast casein kinase 2 governs morphology, biofilm formation, cell wall integrity, and host cell damage of Candida albicans

PubMed Central

Irrizary, Jihyun; Liboro, Karl; Bogarin, Thania; Macias, Marlene; Eivers, Edward; Porter, Edith; Filler, Scott G.

2017-01-01

The regulatory networks governing morphogenesis of a pleomorphic fungus, Candida albicans are extremely complex and remain to be completely elucidated. This study investigated the function of C. albicans yeast casein kinase 2 (CaYck2p). The yck2Δ/yck2Δ strain displayed constitutive pseudohyphae in both yeast and hyphal growth conditions, and formed enhanced biofilm under non-biofilm inducing condition. This finding was further supported by gene expression analysis of the yck2Δ/yck2Δ strain which showed significant upregulation of UME6, a key transcriptional regulator of hyphal transition and biofilm formation, and cell wall protein genes ALS3, HWP1, and SUN41, all of which are associated with morphogenesis and biofilm architecture. The yck2Δ/yck2Δ strain was hypersensitive to cell wall damaging agents and had increased compensatory chitin deposition in the cell wall accompanied by an upregulation of the expression of the chitin synthase genes, CHS2, CHS3, and CHS8. Absence of CaYck2p also affected fungal-host interaction; the yck2Δ/yck2Δ strain had significantly reduced ability to damage host cells. However, the yck2Δ/yck2Δ strain had wild-type susceptibility to cyclosporine and FK506, suggesting that CaYck2p functions independently from the Ca+/calcineurin pathway. Thus, in C. albicans, Yck2p is a multifunctional kinase that governs morphogenesis, biofilm formation, cell wall integrity, and host cell interactions. PMID:29107946

Diverse RNA-binding proteins interact with functionally related sets of RNAs, suggesting an extensive regulatory system.

PubMed

Hogan, Daniel J; Riordan, Daniel P; Gerber, André P; Herschlag, Daniel; Brown, Patrick O

2008-10-28

RNA-binding proteins (RBPs) have roles in the regulation of many post-transcriptional steps in gene expression, but relatively few RBPs have been systematically studied. We searched for the RNA targets of 40 proteins in the yeast Saccharomyces cerevisiae: a selective sample of the approximately 600 annotated and predicted RBPs, as well as several proteins not annotated as RBPs. At least 33 of these 40 proteins, including three of the four proteins that were not previously known or predicted to be RBPs, were reproducibly associated with specific sets of a few to several hundred RNAs. Remarkably, many of the RBPs we studied bound mRNAs whose protein products share identifiable functional or cytotopic features. We identified specific sequences or predicted structures significantly enriched in target mRNAs of 16 RBPs. These potential RNA-recognition elements were diverse in sequence, structure, and location: some were found predominantly in 3'-untranslated regions, others in 5'-untranslated regions, some in coding sequences, and many in two or more of these features. Although this study only examined a small fraction of the universe of yeast RBPs, 70% of the mRNA transcriptome had significant associations with at least one of these RBPs, and on average, each distinct yeast mRNA interacted with three of the RBPs, suggesting the potential for a rich, multidimensional network of regulation. These results strongly suggest that combinatorial binding of RBPs to specific recognition elements in mRNAs is a pervasive mechanism for multi-dimensional regulation of their post-transcriptional fate.

Ethylene-responsive element-binding factor 5, ERF5, is involved in chitin-induced innate immunity response.

PubMed

Son, Geon Hui; Wan, Jinrong; Kim, Hye Jin; Nguyen, Xuan Canh; Chung, Woo Sik; Hong, Jong Chan; Stacey, Gary

2012-01-01

Our recent work demonstrated that chitin treatment modulated the expression of 118 transcription factor (TF) genes in Arabidopsis. To investigate the potential roles of these TF in chitin signaling and plant defense, we initiated an interaction study among these TF proteins, as well as two chitin-activated mitogen-activated protein kinases (MPK3 and MPK6), using a yeast two-hybrid system. This study revealed interactions among the following proteins: three ethylene-responsive element-binding factors (ERF), five WRKY transcription factors, one scarecrow-like (SCL), and the two MPK, in addition to many other interactions, reflecting a complex TF interaction network. Most of these interactions were subsequently validated by other methods, such as pull-down and in planta bimolecular fluorescence complementation assays. The key node ERF5 was shown to interact with multiple proteins in the network, such as ERF6, ERF8, and SCL13, as well as MPK3 and MPK6. Interestingly, ERF5 appeared to negatively regulate chitin signaling and plant defense against the fungal pathogen Alternaria brassicicola and positively regulate salicylic acid signaling and plant defense against the bacterial pathogen Pseudomonas syringae pv. tomato DC3000. Therefore, ERF5 may play an important role in plant innate immunity, likely through coordinating chitin and other defense pathways in plants in response to different pathogens.

Apple FLOWERING LOCUS T proteins interact with transcription factors implicated in cell growth and organ development.

PubMed

Mimida, Naozumi; Kidou, Shin-Ichiro; Iwanami, Hiroshi; Moriya, Shigeki; Abe, Kazuyuki; Voogd, Charlotte; Varkonyi-Gasic, Erika; Kotoda, Nobuhiro

2011-05-01

Understanding the flowering process in apple (Malus × domestica Borkh.) is essential for developing methods to shorten the breeding period and regulate fruit yield. It is known that FLOWERING LOCUS T (FT) acts as a transmissible floral inducer in the Arabidopsis flowering network system. To clarify the molecular network of two apple FT orthologues, MdFT1 and MdFT2, we performed a yeast two-hybrid screen to identify proteins that interact with MdFT1. We identified several transcription factors, including two members of the TCP (TEOSINTE BRANCHED1, CYCLOIDEA and PROLIFERATING CELL FACTORs) family, designated MdTCP2 and MdTCP4, and an Arabidopsis thaliana VOZ1 (Vascular plant One Zinc finger protein1)-like protein, designated MdVOZ1. MdTCP2 and MdVOZ1 also interacted with MdFT2 in yeast. The expression domain of MdTCP2 and MdVOZ1 partially overlapped with that of MdFT1 and MdFT2, most strikingly in apple fruit tissue, further suggesting a potential interaction in vivo. Constitutive expression of MdTCP2, MdTCP4 and MdVOZ1 in Arabidopsis affected plant size, leaf morphology and the formation of leaf primordia on the adaxial side of cotyledons. On the other hand, chimeric MdTCP2, MdTCP4 and MdVOZ1 repressors that included the ethylene-responsive transcription factors (ERF)-associated amphiphilic repression (EAR) domain motif influenced reproduction and inflorescence architecture in transgenic Arabidopsis. These results suggest that MdFT1 and/or MdFT2 might be involved in the regulation of cellular proliferation and the formation of new tissues and that they might affect leaf and fruit development by interacting with TCP- and VOZ-family proteins. DDBJ accession nos. AB531019 (MdTCP2a mRNA), AB531020 (MdTCP2b mRNA), AB531021 (MdTCP4a mRNA), AB531022 (MdTCP4b mRNA) and AB531023 (MdVOZ1a mRNA). © The Author 2011. Published by Oxford University Press. All rights reserved.

Mitochondrial inheritance in budding yeasts: towards an integrated understanding.

PubMed

Solieri, Lisa

2010-11-01

Recent advances in yeast mitogenomics have significantly contributed to our understanding of the diversity of organization, structure and topology in the mitochondrial genome of budding yeasts. In parallel, new insights on mitochondrial DNA (mtDNA) inheritance in the model organism Saccharomyces cerevisiae highlighted an integrated scenario where recombination, replication and segregation of mtDNA are intricately linked to mitochondrial nucleoid (mt-nucleoid) structure and organelle sorting. In addition to this, recent discoveries of bifunctional roles of some mitochondrial proteins have interesting implications on mito-nuclear genome interactions and the relationship between mtDNA inheritance, yeast fitness and speciation. This review summarizes the current knowledge on yeast mitogenomics, mtDNA inheritance with regard to mt-nucleoid structure and organelle dynamics, and mito-nuclear genome interactions. Copyright © 2010 Elsevier Ltd. All rights reserved.

Quantitative analysis of chaperone network throughput in budding yeast

PubMed Central

Brownridge, Philip; Lawless, Craig; Payapilly, Aishwarya B; Lanthaler, Karin; Holman, Stephen W; Harman, Victoria M; Grant, Christopher M; Beynon, Robert J; Hubbard, Simon J

2013-01-01

The network of molecular chaperones mediates the folding and translocation of the many proteins encoded in the genome of eukaryotic organisms, as well as a response to stress. It has been particularly well characterised in the budding yeast, Saccharomyces cerevisiae, where 63 known chaperones have been annotated and recent affinity purification and MS/MS experiments have helped characterise the attendant network of chaperone targets to a high degree. In this study, we apply our QconCAT methodology to directly quantify the set of yeast chaperones in absolute terms (copies per cell) via SRM MS. Firstly, we compare these to existing quantitative estimates of these yeast proteins, highlighting differences between approaches. Secondly, we cast the results into the context of the chaperone target network and show a distinct relationship between abundance of individual chaperones and their targets. This allows us to characterise the ‘throughput’ of protein molecules passing through individual chaperones and their groups on a proteome-wide scale in an unstressed model eukaryote for the first time. The results demonstrate specialisations of the chaperone classes, which display different overall workloads, efficiencies and preference for the sub-cellular localisation of their targets. The novel integration of the interactome data with quantification supports re-estimates of the level of protein throughout going through molecular chaperones. Additionally, although chaperones target fewer than 40% of annotated proteins we show that they mediate the folding of the majority of protein molecules (∼62% of the total protein flux in the cell), highlighting their importance. PMID:23420633

Evolutionarily engineered ethanologenic yeast detoxifies lignocellulosic biomass conversion inhibitors by reprogrammed pathways

PubMed Central

Ma, Menggen; Song, Mingzhou

2010-01-01

Lignocellulosic biomass conversion inhibitors, furfural and HMF, inhibit microbial growth and interfere with subsequent fermentation of ethanol, posing significant challenges for a sustainable cellulosic ethanol conversion industry. Numerous yeast genes were found to be associated with the inhibitor tolerance. However, limited knowledge is available about mechanisms of the tolerance and the detoxification of the biomass conversion inhibitors. Using a robust standard for absolute mRNA quantification assay and a recently developed tolerant ethanologenic yeast Saccharomyces cerevisiae NRRL Y-50049, we investigate pathway-based transcription profiles relevant to the yeast tolerance and the inhibitor detoxification. Under the synergistic inhibitory challenges by furfural and HMF, Y-50049 was able to withstand the inhibitor stress, in situ detoxify furfural and HMF, and produce ethanol, while its parental control Y-12632 failed to function till 65 h after incubation. The tolerant strain Y-50049 displayed enriched genetic background with significantly higher abundant of transcripts for at least 16 genes than a non-tolerant parental strain Y-12632. The enhanced expression of ZWF1 appeared to drive glucose metabolism in favor of pentose phosphate pathway over glycolysis at earlier steps of glucose metabolisms. Cofactor NAD(P)H generation steps were likely accelerated by enzymes encoded by ZWF1, GND1, GND2, TDH1, and ALD4. NAD(P)H-dependent aldehyde reductions including conversion of furfural and HMF, in return, provided sufficient NAD(P)+ for NAD(P)H regeneration in the yeast detoxification pathways. Enriched genetic background and a well maintained redox balance through reprogrammed expression responses of Y-50049 were accountable for the acquired tolerance and detoxification of furfural to furan methanol and HMF to furan dimethanol. We present significant gene interactions and regulatory networks involved in NAD(P)H regenerations and functional aldehyde reductions under the inhibitor stress. PMID:19517136

Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

PubMed Central

Franke, Lude; Bakel, Harm van; Fokkens, Like; de Jong, Edwin D.; Egmont-Petersen, Michael; Wijmenga, Cisca

2006-01-01

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray coexpressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown. PMID:16685651

A Genome-Wide Analysis Reveals No Nuclear Dobzhansky-Muller Pairs of Determinants of Speciation between S. cerevisiae and S. paradoxus, but Suggests More Complex Incompatibilities

PubMed Central

Kao, Katy C.; Schwartz, Katja; Sherlock, Gavin

2010-01-01

The Dobzhansky-Muller (D-M) model of speciation by genic incompatibility is widely accepted as the primary cause of interspecific postzygotic isolation. Since the introduction of this model, there have been theoretical and experimental data supporting the existence of such incompatibilities. However, speciation genes have been largely elusive, with only a handful of candidate genes identified in a few organisms. The Saccharomyces sensu stricto yeasts, which have small genomes and can mate interspecifically to produce sterile hybrids, are thus an ideal model for studying postzygotic isolation. Among them, only a single D-M pair, comprising a mitochondrially targeted product of a nuclear gene and a mitochondrially encoded locus, has been found. Thus far, no D-M pair of nuclear genes has been identified between any sensu stricto yeasts. We report here the first detailed genome-wide analysis of rare meiotic products from an otherwise sterile hybrid and show that no classic D-M pairs of speciation genes exist between the nuclear genomes of the closely related yeasts S. cerevisiae and S. paradoxus. Instead, our analyses suggest that more complex interactions, likely involving multiple loci having weak effects, may be responsible for their post-zygotic separation. The lack of a nuclear encoded classic D-M pair between these two yeasts, yet the existence of multiple loci that may each exert a small effect through complex interactions suggests that initial speciation events might not always be mediated by D-M pairs. An alternative explanation may be that the accumulation of polymorphisms leads to gamete inviability due to the activities of anti-recombination mechanisms and/or incompatibilities between the species' transcriptional and metabolic networks, with no single pair at least initially being responsible for the incompatibility. After such a speciation event, it is possible that one or more D-M pairs might subsequently arise following isolation. PMID:20686707

Yeast for virus research

PubMed Central

Zhao, Richard Yuqi

2017-01-01

Budding yeast (Saccharomyces cerevisiae) and fission yeast (Schizosaccharomyces pombe) are two popular model organisms for virus research. They are natural hosts for viruses as they carry their own indigenous viruses. Both yeasts have been used for studies of plant, animal and human viruses. Many positive sense (+) RNA viruses and some DNA viruses replicate with various levels in yeasts, thus allowing study of those viral activities during viral life cycle. Yeasts are single cell eukaryotic organisms. Hence, many of the fundamental cellular functions such as cell cycle regulation or programed cell death are highly conserved from yeasts to higher eukaryotes. Therefore, they are particularly suited to study the impact of those viral activities on related cellular activities during virus-host interactions. Yeasts present many unique advantages in virus research over high eukaryotes. Yeast cells are easy to maintain in the laboratory with relative short doubling time. They are non-biohazardous, genetically amendable with small genomes that permit genome-wide analysis of virologic and cellular functions. In this review, similarities and differences of these two yeasts are described. Studies of virologic activities such as viral translation, viral replication and genome-wide study of virus-cell interactions in yeasts are highlighted. Impacts of viral proteins on basic cellular functions such as cell cycle regulation and programed cell death are discussed. Potential applications of using yeasts as hosts to carry out functional analysis of small viral genome and to develop high throughput drug screening platform for the discovery of antiviral drugs are presented. PMID:29082230

Prior knowledge guided active modules identification: an integrated multi-objective approach.

PubMed

Chen, Weiqi; Liu, Jing; He, Shan

2017-03-14

Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance.

Molecular Oxygen and Reactive Oxygen Species in Bread-making Processes: Scarce, but Nevertheless Important.

PubMed

Decamps, Karolien; Joye, Iris J; De Vos, Dirk E; Courtin, Christophe M; Delcour, Jan A

2016-01-01

In bread making, O2 is consumed by flour constituents, yeast, and, optionally, some additives optimizing dough processing and/or product quality. It plays a major role especially in the oxidation/reduction phenomena in dough, impacting gluten network structure. The O2 level is about 7.2 mmol/kg dough, of which a significant part stems from wheat flour. We speculate that O2 is quickly lost to the atmosphere during flour hydration. Later, when the gluten network structure develops, some O2 is incorporated in dough through mixing-in of air. O2 is consumed by yeast respiration and in a number of reactions catalyzed by a wide range of enzymes present or added. About 60% of the O2 consumption in yeastless dough is ascribed to oxidation of fatty acids by wheat lipoxygenase activity. In yeasted dough, about 70% of the O2 in dough is consumed by yeast and wheat lipoxygenase. This would leave only about 30% for other reactions. The severe competition between endogenous (and added) O2-consuming systems impacts the gluten network. Moreover, the scarce literature data available suggest that exogenous oxidative enzymes but not those in flour may promote crosslinking of arabinoxylan in yeastless dough. In any case, dough turns anaerobic during the first minutes of fermentation.

The yeast protein kinase Sch9 adjusts V-ATPase assembly/disassembly to control pH homeostasis and longevity in response to glucose availability.

PubMed

Wilms, Tobias; Swinnen, Erwin; Eskes, Elja; Dolz-Edo, Laura; Uwineza, Alice; Van Essche, Ruben; Rosseels, Joëlle; Zabrocki, Piotr; Cameroni, Elisabetta; Franssens, Vanessa; De Virgilio, Claudio; Smits, Gertien J; Winderickx, Joris

2017-06-01

The conserved protein kinase Sch9 is a central player in the nutrient-induced signaling network in yeast, although only few of its direct substrates are known. We now provide evidence that Sch9 controls the vacuolar proton pump (V-ATPase) to maintain cellular pH homeostasis and ageing. A synthetic sick phenotype arises when deletion of SCH9 is combined with a dysfunctional V-ATPase, and the lack of Sch9 has a significant impact on cytosolic pH (pHc) homeostasis. Sch9 physically interacts with, and influences glucose-dependent assembly/disassembly of the V-ATPase, thereby integrating input from TORC1. Moreover, we show that the role of Sch9 in regulating ageing is tightly connected with V-ATPase activity and vacuolar acidity. As both Sch9 and the V-ATPase are highly conserved in higher eukaryotes, it will be interesting to further clarify their cooperative action on the cellular processes that influence growth and ageing.

Characterization of the interaction between protein Snu13p/15.5K and the Rsa1p/NUFIP factor and demonstration of its functional importance for snoRNP assembly

PubMed Central

Rothé, Benjamin; Back, Régis; Quinternet, Marc; Bizarro, Jonathan; Robert, Marie-Cécile; Blaud, Magali; Romier, Christophe; Manival, Xavier; Charpentier, Bruno; Bertrand, Edouard; Branlant, Christiane

2014-01-01

The yeast Snu13p protein and its 15.5K human homolog both bind U4 snRNA and box C/D snoRNAs. They also bind the Rsa1p/NUFIP assembly factor, proposed to scaffold immature snoRNPs and to recruit the Hsp90-R2TP chaperone complex. However, the nature of the Snu13p/15.5K–Rsa1p/NUFIP interaction and its exact role in snoRNP assembly remained to be elucidated. By using biophysical, molecular and imaging approaches, here, we identify residues needed for Snu13p/15.5K–Rsa1p/NUFIP interaction. By NMR structure determination and docking approaches, we built a 3D model of the Snup13p–Rsa1p interface, suggesting that residues R249, R246 and K250 in Rsa1p and E72 and D73 in Snu13p form a network of electrostatic interactions shielded from the solvent by hydrophobic residues from both proteins and that residue W253 of Rsa1p is inserted in a hydrophobic cavity of Snu13p. Individual mutations of residues in yeast demonstrate the functional importance of the predicted interactions for both cell growth and snoRNP formation. Using archaeal box C/D sRNP 3D structures as templates, the association of Snu13p with Rsa1p is predicted to be exclusive of interactions in active snoRNPs. Rsa1p and NUFIP may thus prevent premature activity of pre-snoRNPs, and their removal may be a key step for active snoRNP production. PMID:24234454

The Calcineurin Signaling Network Evolves Via Conserved Kinase–Phosphatase Modules That Transcend Substrate Identity

PubMed Central

Bodenmiller, Bernd; Wanka, Stefanie; Landry, Christian R.; Aebersold, Ruedi; Cyert, Martha S.

2014-01-01

Summary To define the first functional network for calcineurin, the conserved Ca2+/calmodulin-regulated phosphatase, we systematically identified its substrates in S. cerevisiae using phosphoproteomics and bioinformatics, followed by co-purification and dephosphorylation assays. This study establishes new calcineurin functions and reveals mechanisms that shape calcineurin network evolution. Analyses of closely related yeasts show that many proteins were recently recruited to the network by acquiring a calcineurin-recognition motif. Calcineurin substrates in yeast and mammals are distinct due to network rewiring but surprisingly are phosphorylated by similar kinases. We postulate that co-recognition of conserved substrate features, including phosphorylation and docking motifs, preserves calcineurin-kinase opposition during evolution. One example we document is a composite docking site that confers substrate recognition by both calcineurin and MAPK. We propose that conserved kinase-phosphatase pairs define the architecture of signaling networks and allow other connections between kinases and phosphatases to develop and establish common regulatory motifs in signaling networks. PMID:24930733

Stabilizing Motifs in Autonomous Boolean Networks and the Yeast Cell Cycle Oscillator

NASA Astrophysics Data System (ADS)

Sevim, Volkan; Gong, Xinwei; Socolar, Joshua

2009-03-01

Synchronously updated Boolean networks are widely used to model gene regulation. Some properties of these model networks are known to be artifacts of the clocking in the update scheme. Autonomous updating is a less artificial scheme that allows one to introduce small timing perturbations and study stability of the attractors. We argue that the stabilization of a limit cycle in an autonomous Boolean network requires a combination of motifs such as feed-forward loops and auto-repressive links that can correct small fluctuations in the timing of switching events. A recently published model of the transcriptional cell-cycle oscillator in yeast contains the motifs necessary for stability under autonomous updating [1]. [1] D. A. Orlando, et al. Nature (London), 4530 (7197):0 944--947, 2008.

Sphingolipid hydroxylation in mammals, yeast and plants - An integrated view.

PubMed

Marquês, Joaquim Trigo; Susana Marinho, H; de Almeida, Rodrigo Freire Martins

2018-05-07

This review is focused on sphingolipid backbone hydroxylation, a small but widespread structural feature, with profound impact on membrane biophysical properties. We start by summarizing sphingolipid metabolism in mammalian cells, yeast and plants, focusing on how distinct hydroxylation patterns emerge in different eukaryotic kingdoms. Then, a comparison of the biophysical properties in membrane model systems and cellular membranes from diverse organisms is made. From an integrative perspective, these results can be rationalized considering that superficial hydroxyl groups in the backbone of sphingolipids (by intervening in the H-bond network) alter the balance of favorable interactions between membrane lipids. They may strengthen the bonding or compete with other hydroxyl groups, in particular the one of membrane sterols. Different sphingolipid hydroxylation patterns can stabilize/disrupt specific membrane domains or change whole plasma membrane properties, and therefore be important in the control of protein distribution, function and lateral diffusion and in the formation and overtime stability of signaling platforms. The recent examples explored throughout this review unveil a potentially key role for sphingolipid backbone hydroxylation in both physiological and pathological situations, as they can be of extreme importance for the proper organization of cell membranes in mammalian cells, yeast and, most likely, also in plants. Copyright © 2017. Published by Elsevier Ltd.

High-resolution genome-wide scan of genes, gene-networks and cellular systems impacting the yeast ionome

USDA-ARS?s Scientific Manuscript database

To balance the demand for uptake of essential elements with their potential toxicity living cells have complex regulatory mechanisms. Here, we describe a genome-wide screen to identify genes that impact the elemental composition (‘ionome’) of yeast Saccharomyces cerevisiae. Using inductively coupled...

Stoichiometric network constraints on xylose metabolism by recombinant Saccharomyces cerevisiae

Treesearch

Yong-Su Jin; Thomas W. Jeffries

2004-01-01

Metabolic pathway engineering is constrained by the thermodynamic and stoichiometric feasibility of enzymatic activities of introduced genes. Engineering of xylose metabolism in Saccharomyces cerevisiae has focused on introducing genes for the initial xylose assimilation steps from Pichia stipitis, a xylose-fermenting yeast, into S. cerevisiae, a yeast raditionally...

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae

PubMed Central

Jarnuczak, Andrew F.; Eyers, Claire E.; Schwartz, Jean‐Marc; Grant, Christopher M.

2015-01-01

Molecular chaperones play an important role in protein homeostasis and the cellular response to stress. In particular, the HSP70 chaperones in yeast mediate a large volume of protein folding through transient associations with their substrates. This chaperone interaction network can be disturbed by various perturbations, such as environmental stress or a gene deletion. Here, we consider deletions of two major chaperone proteins, SSA1 and SSB1, from the chaperone network in Sacchromyces cerevisiae. We employ a SILAC‐based approach to examine changes in global and local protein abundance and rationalise our results via network analysis and graph theoretical approaches. Although the deletions result in an overall increase in intracellular protein content, correlated with an increase in cell size, this is not matched by substantial changes in individual protein concentrations. Despite the phenotypic robustness to deletion of these major hub proteins, it cannot be simply explained by the presence of paralogues. Instead, network analysis and a theoretical consideration of folding workload suggest that the robustness to perturbation is a product of the overall network structure. This highlights how quantitative proteomics and systems modelling can be used to rationalise emergent network properties, and how the HSP70 system can accommodate the loss of major hubs. PMID:25689132

Spatial signals link exit from mitosis to spindle position.

PubMed

Falk, Jill Elaine; Tsuchiya, Dai; Verdaasdonk, Jolien; Lacefield, Soni; Bloom, Kerry; Amon, Angelika

2016-05-11

In budding yeast, if the spindle becomes mispositioned, cells prevent exit from mitosis by inhibiting the mitotic exit network (MEN). The MEN is a signaling cascade that localizes to spindle pole bodies (SPBs) and activates the phosphatase Cdc14. There are two competing models that explain MEN regulation by spindle position. In the 'zone model', exit from mitosis occurs when a MEN-bearing SPB enters the bud. The 'cMT-bud neck model' posits that cytoplasmic microtubule (cMT)-bud neck interactions prevent MEN activity. Here we find that 1) eliminating cMT- bud neck interactions does not trigger exit from mitosis and 2) loss of these interactions does not precede Cdc14 activation. Furthermore, using binucleate cells, we show that exit from mitosis occurs when one SPB enters the bud despite the presence of a mispositioned spindle. We conclude that exit from mitosis is triggered by a correctly positioned spindle rather than inhibited by improper spindle position.

Phenotypic Diagnosis of Lineage and Differentiation During Sake Yeast Breeding

PubMed Central

Ohnuki, Shinsuke; Okada, Hiroki; Friedrich, Anne; Kanno, Yoichiro; Goshima, Tetsuya; Hasuda, Hirokazu; Inahashi, Masaaki; Okazaki, Naoto; Tamura, Hiroyasu; Nakamura, Ryo; Hirata, Dai; Fukuda, Hisashi; Shimoi, Hitoshi; Kitamoto, Katsuhiko; Watanabe, Daisuke; Schacherer, Joseph; Akao, Takeshi; Ohya, Yoshikazu

2017-01-01

Sake yeast was developed exclusively in Japan. Its diversification during breeding remains largely uncharacterized. To evaluate the breeding processes of the sake lineage, we thoroughly investigated the phenotypes and differentiation of 27 sake yeast strains using high-dimensional, single-cell, morphological phenotyping. Although the genetic diversity of the sake yeast lineage is relatively low, its morphological diversity has expanded substantially compared to that of the Saccharomyces cerevisiae species as a whole. Evaluation of the different types of breeding processes showed that the generation of hybrids (crossbreeding) has more profound effects on cell morphology than the isolation of mutants (mutation breeding). Analysis of phenotypic robustness revealed that some sake yeast strains are more morphologically heterogeneous, possibly due to impairment of cellular network hubs. This study provides a new perspective for studying yeast breeding genetics and micro-organism breeding strategies. PMID:28642365

Transport and cytotoxicity of the anticancer drug 3-bromopyruvate in the yeast Saccharomyces cerevisiae.

PubMed

Lis, Paweł; Zarzycki, Marek; Ko, Young H; Casal, Margarida; Pedersen, Peter L; Goffeau, Andre; Ułaszewski, Stanisław

2012-02-01

We have investigated the cytotoxicity in Saccharomyces cerevisiae of the novel antitumor agent 3-bromopyruvate (3-BP). 3-BP enters the yeast cells through the lactate/pyruvate H(+) symporter Jen1p and inhibits cell growth at minimal inhibitory concentration of 1.8 mM when grown on non-glucose conditions. It is not submitted to the efflux pumps conferring Pleiotropic Drug Resistance in yeast. Yeast growth is more sensitive to 3-BP than Gleevec (Imatinib methanesulfonate) which in contrast to 3-BP is submitted to the PDR network of efflux pumps. The sensitivity of yeast to 3-BP is increased considerably by mutations or chemical treatment by buthionine sulfoximine that decrease the intracellular concentration of glutathione.

Interactions between yeasts and bacteria in the smear surface-ripened cheeses.

PubMed

Corsetti, A; Rossi, J; Gobbetti, M

2001-09-19

In the initial phase of ripening, the microflora of bacterial smear surface-ripened cheeses such as Limburger, Taleggio, Brick, Münster and Saint-Paulin and that of surface mould-ripened cheeses such as Camembert and Brie may be similar, but at the end of the ripening, bacteria such as Brevibacterium spp., Arthrobacter spp., Micrococcus spp., Corynebacterium spp. and moulds such as Penicillium camemberti are, respectively, the dominant microorganisms. Yeasts such as Candida spp., Cryptococcus spp., Debaryomyces spp., Geotrichum candidum, Pichia spp., Rhodotorula spp., Saccharomyces spp. and Yarrowia lipolytica are often and variably isolated from the smear surface-ripened cheeses. Although not dominant within the microorganisms of the smear surface-ripened cheeses, yeasts establish significant interactions with moulds and especially bacteria, including surface bacteria and lactic acid bacteria. Some aspects of the interactions between yeasts and bacteria in such type of cheeses are considered in this paper.

Catalytic Subunit 1 of Protein Phosphatase 2A Is a Subunit of the STRIPAK Complex and Governs Fungal Sexual Development.

PubMed

Beier, Anna; Teichert, Ines; Krisp, Christoph; Wolters, Dirk A; Kück, Ulrich

2016-06-21

The generation of complex three-dimensional structures is a key developmental step for most eukaryotic organisms. The details of the molecular machinery controlling this step remain to be determined. An excellent model system to study this general process is the generation of three-dimensional fruiting bodies in filamentous fungi like Sordaria macrospora Fruiting body development is controlled by subunits of the highly conserved striatin-interacting phosphatase and kinase (STRIPAK) complex, which has been described in organisms ranging from yeasts to humans. The highly conserved heterotrimeric protein phosphatase PP2A is a subunit of STRIPAK. Here, catalytic subunit 1 of PP2A was functionally characterized. The Δpp2Ac1 strain is sterile, unable to undergo hyphal fusion, and devoid of ascogonial septation. Further, PP2Ac1, together with STRIPAK subunit PRO22, governs vegetative and stress-related growth. We revealed in vitro catalytic activity of wild-type PP2Ac1, and our in vivo analysis showed that inactive PP2Ac1 blocks the complementation of the sterile deletion strain. Tandem affinity purification, followed by mass spectrometry and yeast two-hybrid analysis, verified that PP2Ac1 is a subunit of STRIPAK. Further, these data indicate links between the STRIPAK complex and other developmental signaling pathways, implying the presence of a large interconnected signaling network that controls eukaryotic developmental processes. The insights gained in our study can be transferred to higher eukaryotes and will be important for understanding eukaryotic cellular development in general. The striatin-interacting phosphatase and kinase (STRIPAK) complex is highly conserved from yeasts to humans and is an important regulator of numerous eukaryotic developmental processes, such as cellular signaling and cell development. Although functional insights into the STRIPAK complex are accumulating, the detailed molecular mechanisms of single subunits are only partially understood. The first fungal STRIPAK was described in Sordaria macrospora, which is a well-established model organism used to study the formation of fungal fruiting bodies, three-dimensional organ-like structures. We analyzed STRIPAK subunit PP2Ac1, catalytic subunit 1 of protein phosphatase PP2A, to study the importance of the catalytic activity of this protein during sexual development. The results of our yeast two-hybrid analysis and tandem affinity purification, followed by mass spectrometry, indicate that PP2Ac1 activity connects STRIPAK with other signaling pathways and thus forms a large interconnected signaling network. Copyright © 2016 Beier et al.

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.

Nectar yeasts: a natural microcosm for ecology.

PubMed

Chappell, Callie R; Fukami, Tadashi

2018-06-01

The species of yeasts that colonize floral nectar can modify the mutualistic relationships between plants and pollinators by changing the chemical properties of nectar. Recent evidence supporting this possibility has led to increased interest among ecologists in studying these fungi as well as the bacteria that interact with them in nectar. Although not fully explored, nectar yeasts also constitute a promising natural microcosm that can be used to facilitate development of general ecological theory. We discuss the methodological and conceptual advantages of using nectar yeasts from this perspective, including simplicity of communities, tractability of dispersal, replicability of community assembly, and the ease with which the mechanisms of species interactions can be studied in complementary experiments conducted in the field and the laboratory. To illustrate the power of nectar yeasts as a study system, we discuss several topics in community ecology, including environmental filtering, priority effects, and metacommunity dynamics. An exciting new direction is to integrate metagenomics and comparative genomics into nectar yeast research to address these fundamental ecological topics. Copyright © 2018 John Wiley & Sons, Ltd.

The RXL motif of the African cassava mosaic virus Rep protein is necessary for rereplication of yeast DNA and viral infection in plants

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

Hipp, Katharina; Rau, Peter; Schäfer, Benjamin

Geminiviruses, single-stranded DNA plant viruses, encode a replication-initiator protein (Rep) that is indispensable for virus replication. A potential cyclin interaction motif (RXL) in the sequence of African cassava mosaic virus Rep may be an alternative link to cell cycle controls to the known interaction with plant homologs of retinoblastoma protein (pRBR). Mutation of this motif abrogated rereplication in fission yeast induced by expression of wildtype Rep suggesting that Rep interacts via its RXL motif with one or several yeast proteins. The RXL motif is essential for viral infection of Nicotiana benthamiana plants, since mutation of this motif in infectious clonesmore » prevented any symptomatic infection. The cell-cycle link (Clink) protein of a nanovirus (faba bean necrotic yellows virus) was investigated that activates the cell cycle by binding via its LXCXE motif to pRBR. Expression of wildtype Clink and a Clink mutant deficient in pRBR-binding did not trigger rereplication in fission yeast. - Highlights: • A potential cyclin interaction motif is conserved in geminivirus Rep proteins. • In ACMV Rep, this motif (RXL) is essential for rereplication of fission yeast DNA. • Mutating RXL abrogated viral infection completely in Nicotiana benthamiana. • Expression of a nanovirus Clink protein in yeast did not induce rereplication. • Plant viruses may have evolved multiple routes to exploit host DNA synthesis.« less

Comprehensive analysis of mitogen-activated protein kinase cascades in chrysanthemum

PubMed Central

Ding, Lian; Zhang, Xue; Li, Peiling; Liu, Ye

2018-01-01

Background Mitogen-activated protein kinase (MAPK) cascades, an important type of pathway in eukaryotic signaling networks, play a key role in plant defense responses, growth and development. Methods Phylogenetic analysis and conserved motif analysis of the MKK and MPK families in Arabidopsis thaliana, Helianthus annuus and Chrysanthemum morifolium classified MKK genes and MPK genes. qRT-PCR was used for the expression patterns of CmMPK and CmMKK genes, and yeast two-hybrid assay was applied to clear the interaction between CmMPKs and CmMKKs. Results We characterized six MKK genes and 11 MPK genes in chrysanthemum based on transcriptomic sequences and classified these genes into four groups. qRT-PCR analysis demonstrated that CmMKKs and CmMPKs exhibited various expression patterns in different organs of chrysanthemum and in response to abiotic stresses and phytohormone treatments. Furthermore, a yeast two-hybrid assay was applied to analyze the interaction between CmMKKs and CmMPKs and reveal the MAPK cascades in chrysanthemum. Discussion Our data led us to propose that CmMKK4-CmMPK13 and CmMKK2-CmMPK4 may be involved in regulating salt resistance and in the relationship between CmMKK9 and CmMPK6 and temperature stress. PMID:29942696

Fast and reliable prediction of domain-peptide binding affinity using coarse-grained structure models.

PubMed

Tian, Feifei; Tan, Rui; Guo, Tailin; Zhou, Peng; Yang, Li

2013-07-01

Domain-peptide recognition and interaction are fundamentally important for eukaryotic signaling and regulatory networks. It is thus essential to quantitatively infer the binding stability and specificity of such interaction based upon large-scale but low-accurate complex structure models which could be readily obtained from sophisticated molecular modeling procedure. In the present study, a new method is described for the fast and reliable prediction of domain-peptide binding affinity with coarse-grained structure models. This method is designed to tolerate strong random noises involved in domain-peptide complex structures and uses statistical modeling approach to eliminate systematic bias associated with a group of investigated samples. As a paradigm, this method was employed to model and predict the binding behavior of various peptides to four evolutionarily unrelated peptide-recognition domains (PRDs), i.e. human amph SH3, human nherf PDZ, yeast syh GYF and yeast bmh 14-3-3, and moreover, we explored the molecular mechanism and biological implication underlying the binding of cognate and noncognate peptide ligands to their domain receptors. It is expected that the newly proposed method could be further used to perform genome-wide inference of domain-peptide binding at three-dimensional structure level. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Development of viable TAP-tagged dengue virus for investigation of host-virus interactions in viral replication.

PubMed

Poyomtip, Teera; Hodge, Kenneth; Matangkasombut, Ponpan; Sakuntabhai, Anavaj; Pisitkun, Trairak; Jirawatnotai, Siwanon; Chimnaronk, Sarin

2016-03-01

Dengue virus (DENV) is a mosquito-borne flavivirus responsible for life-threatening dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS). The viral replication machinery containing the core non-structural protein 5 (NS5) is implicated in severe dengue symptoms but molecular details remain obscure. To date, studies seeking to catalogue and characterize interaction networks between viral NS5 and host proteins have been limited to the yeast two-hybrid system, computational prediction and co-immunoprecipitation (IP) of ectopically expressed NS5. However, these traditional approaches do not reproduce a natural course of infection in which a number of DENV NS proteins colocalize and tightly associate during the replication process. Here, we demonstrate the development of a recombinant DENV that harbours a TAP tag in NS5 to study host-virus interactions in vivo. We show that our engineered DENV was infective in several human cell lines and that the tags were stable over multiple viral passages, suggesting negligible structural and functional disturbance of NS5. We further provide proof-of-concept for the use of rationally tagged virus by revealing a high confidence NS5 interaction network in human hepatic cells. Our analysis uncovered previously unrecognized hnRNP complexes and several low-abundance fatty acid metabolism genes, which have been implicated in the viral life cycle. This study sets a new standard for investigation of host-flavivirus interactions.

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.

Candida krusei form mycelia along agar surfaces towards each other and other Candida species.

PubMed

Fleischmann, Jacob; Broeckling, Corey D; Lyons, Sarah

2017-03-11

Candida krusei has been known to exhibit communal interactions such as pellicle formation and crawling out of nutritional broth. We noticed another possible interaction on agar surfaces, where C. krusei yeast cells formed mycelia along agar surfaces toward each other. We report here the results of experiments to study this interaction. When C.krusei yeast cells are plated in parallel streaks, they form mycelia along agar surfaces toward other yeasts. They also detect the presence of Candida albicans and Candida glabrata across agar surfaces, while the latter two react neither to their own kind, nor to C. krusei. Secreted molecule(s) are likely involved as C.krusei does not react to heat killed C. krusei. Timing and rate of mycelia formation across distances suggests that mycelia start forming when a secreted molecule(s) on agar surface reaches a certain concentration. We detected farnesol, tyrosol and tryptophol molecules that may be involved with mycelial formation, on the agar surfaces between yeast streaks. Unexpectedly the amounts detected between streaks were significantly higher than would have expected from additive amounts of two streaks. All three Candida species secreted these molecules. When tested on agar surface however, none of these molecules individually or combined induced mycelia formation by C. krusei. Our data confirms another communal interaction by C. krusei, manifested by formation of mycelia by yeast cells toward their own kind and other yeasts on agar surfaces. We detected secretion of farnesol, tyrosol and tryptophol by C. krusei but none of these molecules induced this activity on agar surface making it unlikely that they are the ones utilized by this yeast for this activity.

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

The Robustness of a Signaling Complex to Domain Rearrangements Facilitates Network Evolution

PubMed Central

Sato, Paloma M.; Yoganathan, Kogulan; Jung, Jae H.; Peisajovich, Sergio G.

2014-01-01

The rearrangement of protein domains is known to have key roles in the evolution of signaling networks and, consequently, is a major tool used to synthetically rewire networks. However, natural mutational events leading to the creation of proteins with novel domain combinations, such as in frame fusions followed by domain loss, retrotranspositions, or translocations, to name a few, often simultaneously replace pre-existing genes. Thus, while proteins with new domain combinations may establish novel network connections, it is not clear how the concomitant deletions are tolerated. We investigated the mechanisms that enable signaling networks to tolerate domain rearrangement-mediated gene replacements. Using as a model system the yeast mitogen activated protein kinase (MAPK)-mediated mating pathway, we analyzed 92 domain-rearrangement events affecting 11 genes. Our results indicate that, while domain rearrangement events that result in the loss of catalytic activities within the signaling complex are not tolerated, domain rearrangements can drastically alter protein interactions without impairing function. This suggests that signaling complexes can maintain function even when some components are recruited to alternative sites within the complex. Furthermore, we also found that the ability of the complex to tolerate changes in interaction partners does not depend on long disordered linkers that often connect domains. Taken together, our results suggest that some signaling complexes are dynamic ensembles with loose spatial constraints that could be easily re-shaped by evolution and, therefore, are ideal targets for cellular engineering. PMID:25490747

Construction of regulatory networks using expression time-series data of a genotyped population.

PubMed

Yeung, Ka Yee; Dombek, Kenneth M; Lo, Kenneth; Mittler, John E; Zhu, Jun; Schadt, Eric E; Bumgarner, Roger E; Raftery, Adrian E

2011-11-29

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

Finding undetected protein associations in cell signaling by belief propagation.

PubMed

Bailly-Bechet, M; Borgs, C; Braunstein, A; Chayes, J; Dagkessamanskaia, A; François, J-M; Zecchina, R

2011-01-11

External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High-throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.

Enhanced leavening ability of baker's yeast by overexpression of SNR84 with PGM2 deletion.

PubMed

Lin, Xue; Zhang, Cui-Ying; Bai, Xiao-Wen; Xiao, Dong-Guang

2015-06-01

Dough-leavening ability is one of the main aspects considered when selecting a baker's yeast strain for baking industry. Generally, modification of maltose metabolic pathway and known regulatory networks of maltose metabolism were used to increase maltose metabolism to improve leavening ability in lean dough. In this study, we focus on the effects of PGM2 (encoding for the phosphoglucomutase) and SNR84 (encoding for the H/ACA snoRNA) that are not directly related to both the maltose metabolic pathway and known regulatory networks of maltose metabolism on the leavening ability of baker's yeast in lean dough. The results show that the modifications on PGM2 and/or SNR84 are effective ways in improving leavening ability of baker's yeast in lean dough. Deletion of PGM2 decreased cellular glucose-1-phosphate and overexpression of SNR84 increased the maltose permease activity. These changes resulted in 11, 19 and 21% increases of the leavening ability for PGM2 deletion, SNR84 overexpression and SNR84 overexpression combining deleted PGM2, respectively.

Development of petri net-based dynamic model for improved production of farnesyl pyrophosphate by integrating mevalonate and methylerythritol phosphate pathways in yeast.

PubMed

Baadhe, Rama Raju; Mekala, Naveen Kumar; Palagiri, Satwik Reddy; Parcha, Sreenivasa Rao

2012-07-01

In this case study, we designed a farnesyl pyrophosphate (FPP) biosynthetic network using hybrid functional Petri net with extension (HFPNe) which is derived from traditional Petri net theory and allows easy modeling with graphical approach of various types of entities in the networks together. Our main objective is to improve the production of FPP in yeast, which is further converted to amorphadiene (AD), a precursor of artemisinin (antimalarial drug). Natively, mevalonate (MEV) pathway is present in yeast. Methyl erythritol phosphate pathways (MEP) are present only in higher plant plastids and eubacteria, but not present in yeast. IPP and DAMP are common isomeric intermediate in these two pathways, which immediately yields FPP. By integrating these two pathways in yeast, we augmented the FPP synthesis approximately two folds higher (431.16 U/pt) than in MEV pathway alone (259.91 U/pt) by using HFPNe technique. Further enhanced FPP levels converted to AD by amorphadiene synthase gene yielding 436.5 U/pt of AD which approximately two folds higher compared to the AD (258.5 U/pt) synthesized by MEV pathway exclusively. Simulation and validation processes performed using these models are reliable with identified biological information and data.

An N-terminal fragment of yeast ribosomal protein L3 inhibits the cytotoxicity of pokeweed antiviral protein in Saccharomyces cerevisiae.

PubMed

Di, Rong; Tumer, Nilgun E

2014-04-11

We have previously shown that ribosomal protein L3 is required for pokeweed antiviral protein (PAP), a type I ribosome inactivating protein, to bind to ribosomes and depurinate the α-sarcin/ricin loop (SRL) in yeast. Co-expression of the N-terminal 99 amino acids of yeast L3 (L3Δ99) with PAP in transgenic tobacco plants completely abolished the toxicity of PAP. In this study, we investigated the interaction between PAP and L3Δ99 in Saccharomyces cerevisiae. Yeast cells co-transformed with PAP and L3Δ99 showed markedly reduced growth inhibition and reduced rRNA depurination by PAP, compared to cells transformed with PAP alone. Co-transformation of yeast with PAP and L3Δ21 corresponding to the highly conserved N-terminal 21 amino acids of L3Δ99, reduced the cytotoxicity of PAP. PAP mRNA and protein levels were elevated and L3Δ99 or L3Δ21 mRNA and protein levels were reduced in yeast co-transformed with PAP and L3Δ99 or with PAP and L3Δ21, respectively. PAP interacted with L3Δ21 in yeast cells in vivo and by Biacore analysis in vitro, suggesting that the interaction between L3Δ21 and PAP may inhibit PAP-mediated depurination of the SRL, leading to a reduction in the cytotoxicity of PAP.

The Disordered C-Terminus of Yeast Hsf1 Contains a Cryptic Low-Complexity Amyloidogenic Region.

PubMed

Pujols, Jordi; Santos, Jaime; Pallarès, Irantzu; Ventura, Salvador

2018-05-06

Response mechanisms to external stress rely on networks of proteins able to activate specific signaling pathways to ensure the maintenance of cell proteostasis. Many of the proteins mediating this kind of response contain intrinsically disordered regions, which lack a defined structure, but still are able to interact with a wide range of clients that modulate the protein function. Some of these interactions are mediated by specific short sequences embedded in the longer disordered regions. Because the physicochemical properties that promote functional and abnormal interactions are similar, it has been shown that, in globular proteins, aggregation-prone and binding regions tend to overlap. It could be that the same principle applies for disordered protein regions. In this context, we show here that a predicted low-complexity interacting region in the disordered C-terminus of the stress response master regulator heat shock factor 1 (Hsf1) protein corresponds to a cryptic amyloid region able to self-assemble into fibrillary structures resembling those found in neurodegenerative disorders.

Cell-autonomous mechanisms of chronological aging in the yeast Saccharomyces cerevisiae.

PubMed

Arlia-Ciommo, Anthony; Leonov, Anna; Piano, Amanda; Svistkova, Veronika; Titorenko, Vladimir I

2014-05-27

A body of evidence supports the view that the signaling pathways governing cellular aging - as well as mechanisms of their modulation by longevity-extending genetic, dietary and pharmacological interventions - are conserved across species. The scope of this review is to critically analyze recent advances in our understanding of cell-autonomous mechanisms of chronological aging in the budding yeast Saccharomyces cerevisiae . Based on our analysis, we propose a concept of a biomolecular network underlying the chronology of cellular aging in yeast. The concept posits that such network progresses through a series of lifespan checkpoints. At each of these checkpoints, the intracellular concentrations of some key intermediates and products of certain metabolic pathways - as well as the rates of coordinated flow of such metabolites within an intricate network of intercompartmental communications - are monitored by some checkpoint-specific "master regulator" proteins. The concept envisions that a synergistic action of these master regulator proteins at certain early-life and late-life checkpoints modulates the rates and efficiencies of progression of such processes as cell metabolism, growth, proliferation, stress resistance, macromolecular homeostasis, survival and death. The concept predicts that, by modulating these vital cellular processes throughout lifespan (i.e., prior to an arrest of cell growth and division, and following such arrest), the checkpoint-specific master regulator proteins orchestrate the development and maintenance of a pro- or anti-aging cellular pattern and, thus, define longevity of chronologically aging yeast.

Screening of binding proteins that interact with Chinese sacbrood virus VP3 capsid protein in Apis cerana larvae cDNA library by the yeast two-hybrid method.

PubMed

Fei, Dongliang; Wei, Dong; Yu, Xiaolei; Yue, Jinjin; Li, Ming; Sun, Li; Jiang, Lili; Li, Yijing; Diao, Qingyun; Ma, Mingxiao

2018-03-15

Chinese sacbrood virus (CSBV) causes larval death and apiary collapse of Apis cerana. VP3 is a capsid protein of CSBV but its function is poorly understood. To determine the function of VP3 and screen for novel binding proteins that interact with VP3, we conducted yeast two-hybrid screening, glutathione S-transferase pull-down, and co-immunoprecipitation assays. Galectin (GAL) is a protein involved in immune regulation and host-pathogen interactions. The yeast two-hybrid screen implicated GAL as a major VP3-binding candidate. The assays showed that the VP3 interacted with GAL. Identification of these cellular targets and clarifying their contributions to the host-pathogen interaction may be useful for the development of novel therapeutic and prevention strategies against CSBV infection. Copyright © 2018 Elsevier B.V. All rights reserved.

The contribution of glutathione to the destabilizing effect of yeast on wheat dough.

PubMed

Verheyen, C; Albrecht, A; Herrmann, J; Strobl, M; Jekle, M; Becker, T

2015-04-15

Any factor which impairs the development of the gluten network affects the gas retention capacity and the overall baking performance. This study aimed to examine why rising yeast concentrations (Saccharomyces cerevisiae) decrease the dough elasticity in an asymptotic manner. Since in 27 commercial fresh and dry yeasts up to 81 mg glutathione (GSH) per 1g dry sample were found. Through the addition of reduced GSH in dough without yeast, the extent of dough weakening was analysed. Indeed rheological measurements confirmed that yeast-equivalent levels of GSH had a softening effect and during 3h fermentation the weakening coefficient increased from 0.3% to 20.4% in a Rheofermentometer. The present results indicate that free -SH compounds, as represented by GSH, considerably contribute to the softening of dough through dead yeast cells. Copyright © 2014 Elsevier Ltd. All rights reserved.

Yeast Communities of Diverse Drosophila Species: Comparison of Two Symbiont Groups in the Same Hosts

PubMed Central

Eisen, Jonathan A.; Kopp, Artyom

2012-01-01

The combination of ecological diversity with genetic and experimental tractability makes Drosophila a powerful model for the study of animal-associated microbial communities. Despite the known importance of yeasts in Drosophila physiology, behavior, and fitness, most recent work has focused on Drosophila-bacterial interactions. In order to get a more complete understanding of the Drosophila microbiome, we characterized the yeast communities associated with different Drosophila species collected around the world. We focused on the phylum Ascomycota because it constitutes the vast majority of the Drosophila-associated yeasts. Our sampling strategy allowed us to compare the distribution and structure of the yeast and bacterial communities in the same host populations. We show that yeast communities are dominated by a small number of abundant taxa, that the same yeast lineages are associated with different host species and populations, and that host diet has a greater effect than host species on yeast community composition. These patterns closely parallel those observed in Drosophila bacterial communities. However, we do not detect a significant correlation between the yeast and bacterial communities of the same host populations. Comparative analysis of different symbiont groups provides a more comprehensive picture of host-microbe interactions. Future work on the role of symbiont communities in animal physiology, ecological adaptation, and evolution would benefit from a similarly holistic approach. PMID:22885750

Isolation and characterization of a J domain protein that interacts with ARC1 from ornamental kale (Brassica oleracea var. acephala).

PubMed

Lan, Xingguo; Yang, Jia; Cao, Mingming; Wang, Yanhong; Kawabata, Saneyuki; Li, Yuhua

2015-05-01

A novel J domain protein, JDP1, was isolated from ornamental kale. The C-terminus of JDP1 specifically interacted with ARC1, which has a conserved role in self-incompatibility signaling. Armadillo (ARM)-repeat containing 1 (ARC1) plays a conserved role in self-incompatibility signaling across the Brassicaceae and functions downstream of the S-locus receptor kinase. Here, we identified a J domain protein 1 (JDP1) that interacts with ARC1 using a yeast two-hybrid screen against a stigma cDNA library from ornamental kale (Brassica oleracea var. acephala). JDP1, a 38.4-kDa protein with 344 amino acids, is a member of the Hsp40 family. Fragment JDP1(57-344), originally isolated from a yeast two-hybrid cDNA library, interacted specifically with ARC1 in yeast two-hybrid assays. The N-terminus of JDP1 (JDP1(1-68)) contains a J domain, and the C-terminus of JDP1 (JDP1(69-344)) contains an X domain of unknown function. However, JDP1(69-344) was required and sufficient for interaction with ARC1 in yeast two-hybrid assays and in vitro binding assays. Moreover, JDP1(69-344) regulated the trafficking of ARC1 from the cytoplasm to the plasma membrane by interacting with ARC1 in Arabidopsis mesophyll protoplasts. Finally, Tyr(8) in the JDP1 N-terminal region was identified to be the specific site for regulating the interaction between JDP1 and BoARC1 in yeast two-hybrid assays. Possible roles of JDP1 as an interactor with ARC1 in Brassica are discussed.

Analysis of trehalose-6-phosphate synthase (TPS) gene family suggests the formation of TPS complexes in rice.

PubMed

Zang, Baisheng; Li, Haowen; Li, Wenjun; Deng, Xing Wang; Wang, Xiping

2011-08-01

Trehalose-6-phosphate (T6P), an intermediate in the trehalose biosynthesis pathway, is emerging as an important regulator of plant metabolism and development. T6P levels are potentially modulated by a group of trehalose-6-phosphate synthase (TPS) and trehalose-6-phosphate phosphatase (TPP) homologues. In this study, we have isolated 11 TPS genes encoding proteins with both TPS and TPP domains, from rice. Functional complement assays performed in yeast tps1 and tps2 mutants, revealed that only OsTPS1 encodes an active TPS enzyme and no OsTPS protein possesses TPP activity. By using a yeast two-hybrid analysis, a complicated interaction network occurred among OsTPS proteins, and the TPS domain might be essential for this interaction to occur. The interaction between OsTPS1 and OsTPS8 in vivo was confirmed by bimolecular fluorescence complementation and coimmunoprecipitation assays. Furthermore, our gel filtration assay showed that there may exist two forms of OsTPS1 (OsTPS1a and OsTPS1b) with different elution profiles in rice. OsTPS1b was particularly cofractionated with OsTPS5 and OsTPS8 in the 360 kDa complex, while OsTPS1a was predominantly incorporated into the complexes larger than 360 kDa. Collectively, these results suggest that OsTPS family members may form trehalose-6-phosphate synthase complexes and therefore potentially modify T6P levels to regulate plant development.

The Yeast Gene, MDM20, Is Necessary for Mitochondrial Inheritance and Organization of the Actin Cytoskeleton

PubMed Central

Hermann, Greg J.; King, Edward J.; Shaw, Janet M.

1997-01-01

In Saccharomyces cerevisiae, the growing bud inherits a portion of the mitochondrial network from the mother cell soon after it emerges. Although this polarized transport of mitochondria is thought to require functions of the cytoskeleton, there are conflicting reports concerning the nature of the cytoskeletal element involved. Here we report the isolation of a yeast mutant, mdm20, in which both mitochondrial inheritance and actin cables (bundles of actin filaments) are disrupted. The MDM20 gene encodes a 93-kD polypeptide with no homology to other characterized proteins. Extra copies of TPM1, a gene encoding the actin filament–binding protein tropomyosin, suppress mitochondrial inheritance defects and partially restore actin cables in mdm20Δ cells. Synthetic lethality is also observed between mdm20 and tpm1 mutant strains. Overexpression of a second yeast tropomyosin, Tpm2p, rescues mutant phenotypes in the mdm20 strain to a lesser extent. Together, these results provide compelling evidence that mitochondrial inheritance in yeast is an actin-mediated process. MDM20 and TPM1 also exhibit the same pattern of genetic interactions; mutations in MDM20 are synthetically lethal with mutations in BEM2 and MYO2 but not SAC6. Although MDM20 and TPM1 are both required for the formation and/or stabilization of actin cables, mutations in these genes disrupt mitochondrial inheritance and nuclear segregation to different extents. Thus, Mdm20p and Tpm1p may act in vivo to establish molecular and functional heterogeneity of the actin cytoskeleton. PMID:9105043

Control activity of yeast geranylgeranyl diphosphate synthase from dimer interface through H-bonds and hydrophobic interaction.

PubMed

Chang, Chih-Kang; Teng, Kuo-Hsun; Lin, Sheng-Wei; Chang, Tao-Hsin; Liang, Po-Huang

2013-04-23

Previously we showed that yeast geranylgeranyl diphosphate synthase (GGPPS) becomes an inactive monomer when the first N-terminal helix involved in dimerization is deleted. This raises questions regarding why dimerization is required for GGPPS activity and which amino acids in the dimer interface are essential for dimerization-mediated activity. According to the GGPPS crystal structure, three amino acids (N101, N104, and Y105) located in the helix F of one subunit are near the active site of the other subunit. As presented here, when these residues were replaced individually with Ala caused insignificant activity changes, N101A/Y105A and N101A/N104A but not N104A/Y105A showed remarkably decreased k(cat) values (200-250-fold). The triple mutant N101A/N104A/Y105A displayed no detectable activity, although dimer was retained in these mutants. Because N101 and Y105 form H-bonds with H139 and R140 in the other subunit, respectively, we generated H139A/R140A double mutant and found it was inactive and became monomeric. Therefore, the multiple mutations apparently influence the integrity of the catalytic site due to the missing H-bonding network. Moreover, Met111, also on the highly conserved helix F, was necessary for dimer formation and enzyme activity. When Met111 was replaced with Glu, the negative-charged repulsion converted half of the dimer into a monomer. In conclusion, the H-bonds mainly through N101 for maintaining substrate binding stability and the hydrophobic interaction of M111 in dimer interface are essential for activity of yeast GGPPS.

RRW: repeated random walks on genome-scale protein networks for local cluster discovery

PubMed Central

Macropol, Kathy; Can, Tolga; Singh, Ambuj K

2009-01-01

Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters. PMID:19740439

Control of complex networks requires both structure and dynamics

NASA Astrophysics Data System (ADS)

Gates, Alexander J.; Rocha, Luis M.

2016-04-01

The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies to revert a diseased cell to a healthy state, or a mature cell to a pluripotent state. Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. We demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.

[Expression of the Drosophila melanogaster limk1 gene 3'-UTRs mRNA in Yeast Saccharomyces cerevisiae].

PubMed

Rumyantsev, A M; Zakharov, G A; Zhuravlev, A V; Padkina, M V; Savvateeva-Popova, E V; Sambuk, E V

2014-06-01

The stability of mRNA and its translation efficacy in higher eukaryotes are influenced by the interaction of 3'-untranscribed regions (3'-UTRs) with microRNAs and RNA-binding proteins. Since Saccharomyces cerevisiae lack microRNAs, it is possible to evaluate the contribution of only 3'-UTRs' and RNA-binding proteins' interaction in post-transcriptional regulation. For this, the post-transcriptional regulation of Drosophila limk1 gene encoding for the key enzyme of actin remodeling was studied in yeast. Analysis of limkl mRNA 3'-UTRs revealed the potential sites of yeast transcriptional termination. Computer remodeling demonstrated the possibility of secondary structure formation in limkl mRNA 3'-UTRs. For an evaluation of the functional activity of Drosophila 3'-UTRs in yeast, the reporter gene PHO5 encoding for yeast acid phosphatase (AP) fused to different variants of Drosophila limk1 mRNA 3'-UTRs (513, 1075, 1554 bp) was used. Assessments of AP activity and RT-PCR demonstrated that Drosophila limkl gene 3'-UTRs were functionally active and recognized in yeast. Therefore, yeast might be used as an appropriate model system for studies of 3'-UTR's role in post-transcriptional regulation.

Effect of different aging techniques on the polysaccharide and phenolic composition and sensory characteristics of Syrah red wines fermented using different yeast strains.

PubMed

del Barrio-Galán, Rubén; Medel-Marabolí, Marcela; Peña-Neira, Álvaro

2015-07-15

The effect of high levels of the polysaccharide Saccharomyces cerevisiae yeast strain (HPS) and another conventional yeast strain (FERM) on the polysaccharide and phenolic composition of Syrah red wines during alcoholic fermentation and subsequent aging on lees, with or without oak wood chips, and on inactive dry yeast was investigated. The HPS yeast released higher amounts of polysaccharides during alcoholic fermentation than FERM yeast (485 g L(-1) and 403 g L(-1), respectively) and after the aging period (516 g L(-1) and 500 g L(-1), respectively). The different aging techniques increased the polysaccharide concentration; the concentration was dependent on the aging technique applied. The interaction of the polysaccharides with the phenolic compounds depended on the yeast strain, aging technique, aging period and compound analysed. The HPS wines exhibited better sensory characteristics than the FERM wines after alcoholic fermentation; however, during the aging period, it was difficult to determine which technique produced the best wine due to the interactions of aging technique, aging period and sensory attribute evaluated. Copyright © 2015 Elsevier Ltd. All rights reserved.

Binding properties of SUMO-interacting motifs (SIMs) in yeast.

PubMed

Jardin, Christophe; Horn, Anselm H C; Sticht, Heinrich

2015-03-01

Small ubiquitin-like modifier (SUMO) conjugation and interaction play an essential role in many cellular processes. A large number of yeast proteins is known to interact non-covalently with SUMO via short SUMO-interacting motifs (SIMs), but the structural details of this interaction are yet poorly characterized. In the present work, sequence analysis of a large dataset of 148 yeast SIMs revealed the existence of a hydrophobic core binding motif and a preference for acidic residues either within or adjacent to the core motif. Thus the sequence properties of yeast SIMs are highly similar to those described for human. Molecular dynamics simulations were performed to investigate the binding preferences for four representative SIM peptides differing in the number and distribution of acidic residues. Furthermore, the relative stability of two previously observed alternative binding orientations (parallel, antiparallel) was assessed. For all SIMs investigated, the antiparallel binding mode remained stable in the simulations and the SIMs were tightly bound via their hydrophobic core residues supplemented by polar interactions of the acidic residues. In contrary, the stability of the parallel binding mode is more dependent on the sequence features of the SIM motif like the number and position of acidic residues or the presence of additional adjacent interaction motifs. This information should be helpful to enhance the prediction of SIMs and their binding properties in different organisms to facilitate the reconstruction of the SUMO interactome.

An ensemble framework for identifying essential proteins.

PubMed

Zhang, Xue; Xiao, Wangxin; Acencio, Marcio Luis; Lemke, Ney; Wang, Xujing

2016-08-25

Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small. In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins. This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end.

A Bayesian approach to estimating hidden variables as well as missing and wrong molecular interactions in ordinary differential equation-based mathematical models.

PubMed

Engelhardt, Benjamin; Kschischo, Maik; Fröhlich, Holger

2017-06-01

Ordinary differential equations (ODEs) are a popular approach to quantitatively model molecular networks based on biological knowledge. However, such knowledge is typically restricted. Wrongly modelled biological mechanisms as well as relevant external influence factors that are not included into the model are likely to manifest in major discrepancies between model predictions and experimental data. Finding the exact reasons for such observed discrepancies can be quite challenging in practice. In order to address this issue, we suggest a Bayesian approach to estimate hidden influences in ODE-based models. The method can distinguish between exogenous and endogenous hidden influences. Thus, we can detect wrongly specified as well as missed molecular interactions in the model. We demonstrate the performance of our Bayesian dynamic elastic-net with several ordinary differential equation models from the literature, such as human JAK-STAT signalling, information processing at the erythropoietin receptor, isomerization of liquid α -Pinene, G protein cycling in yeast and UV-B triggered signalling in plants. Moreover, we investigate a set of commonly known network motifs and a gene-regulatory network. Altogether our method supports the modeller in an algorithmic manner to identify possible sources of errors in ODE-based models on the basis of experimental data. © 2017 The Author(s).

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes.

PubMed

Srihari, Sriganesh; Yong, Chern Han; Patil, Ashwini; Wong, Limsoon

2015-09-14

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organisation of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight their limitations and challenges, in particular at detecting sparse and small or sub-complexes and discerning overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area. Copyright © 2015 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Signature pathway expression of xylose utilization in the genetically engineered industrial yeast Saccharomyces cerevisiae.

PubMed

Feng, Quanzhou; Liu, Z Lewis; Weber, Scott A; Li, Shizhong

2018-01-01

Haploid laboratory strains of Saccharomyces cerevisiae are commonly used for genetic engineering to enable their xylose utilization but little is known about the industrial yeast which is often recognized as diploid and as well as haploid and tetraploid. Here we report three unique signature pathway expression patterns and gene interactions in the centre metabolic pathways that signify xylose utilization of genetically engineered industrial yeast S. cerevisiae NRRL Y-50463, a diploid yeast. Quantitative expression analysis revealed outstanding high levels of constitutive expression of YXI, a synthesized yeast codon-optimized xylose isomerase gene integrated into chromosome XV of strain Y-50463. Comparative expression analysis indicated that the YXI was necessary to initiate the xylose metabolic pathway along with a set of heterologous xylose transporter and utilization facilitating genes including XUT4, XUT6, XKS1 and XYL2. The highly activated transketolase and transaldolase genes TKL1, TKL2, TAL1 and NQM1 as well as their complex interactions in the non-oxidative pentose phosphate pathway branch were critical for the serial of sugar transformation to drive the metabolic flow into glycolysis for increased ethanol production. The significantly increased expression of the entire PRS gene family facilitates functions of the life cycle and biosynthesis superpathway for the yeast. The outstanding higher levels of constitutive expression of YXI and the first insight into the signature pathway expression and the gene interactions in the closely related centre metabolic pathways from the industrial yeast aid continued efforts for development of the next-generation biocatalyst. Our results further suggest the industrial yeast is a desirable delivery vehicle for new strain development for efficient lignocellulose-to-advanced biofuels production.

Signature pathway expression of xylose utilization in the genetically engineered industrial yeast Saccharomyces cerevisiae

PubMed Central

Feng, Quanzhou; Weber, Scott A.; Li, Shizhong

2018-01-01

Haploid laboratory strains of Saccharomyces cerevisiae are commonly used for genetic engineering to enable their xylose utilization but little is known about the industrial yeast which is often recognized as diploid and as well as haploid and tetraploid. Here we report three unique signature pathway expression patterns and gene interactions in the centre metabolic pathways that signify xylose utilization of genetically engineered industrial yeast S. cerevisiae NRRL Y-50463, a diploid yeast. Quantitative expression analysis revealed outstanding high levels of constitutive expression of YXI, a synthesized yeast codon-optimized xylose isomerase gene integrated into chromosome XV of strain Y-50463. Comparative expression analysis indicated that the YXI was necessary to initiate the xylose metabolic pathway along with a set of heterologous xylose transporter and utilization facilitating genes including XUT4, XUT6, XKS1 and XYL2. The highly activated transketolase and transaldolase genes TKL1, TKL2, TAL1 and NQM1 as well as their complex interactions in the non-oxidative pentose phosphate pathway branch were critical for the serial of sugar transformation to drive the metabolic flow into glycolysis for increased ethanol production. The significantly increased expression of the entire PRS gene family facilitates functions of the life cycle and biosynthesis superpathway for the yeast. The outstanding higher levels of constitutive expression of YXI and the first insight into the signature pathway expression and the gene interactions in the closely related centre metabolic pathways from the industrial yeast aid continued efforts for development of the next-generation biocatalyst. Our results further suggest the industrial yeast is a desirable delivery vehicle for new strain development for efficient lignocellulose-to-advanced biofuels production. PMID:29621349

XRN1 Is a Species-Specific Virus Restriction Factor in Yeasts

PubMed Central

Rowley, Paul A.; Ho, Brandon; Bushong, Sarah; Johnson, Arlen; Sawyer, Sara L.

2016-01-01

In eukaryotes, the degradation of cellular mRNAs is accomplished by Xrn1 and the cytoplasmic exosome. Because viral RNAs often lack canonical caps or poly-A tails, they can also be vulnerable to degradation by these host exonucleases. Yeast lack sophisticated mechanisms of innate and adaptive immunity, but do use RNA degradation as an antiviral defense mechanism. We find a highly refined, species-specific relationship between Xrn1p and the “L-A” totiviruses of different Saccharomyces yeast species. We show that the gene XRN1 has evolved rapidly under positive natural selection in Saccharomyces yeast, resulting in high levels of Xrn1p protein sequence divergence from one yeast species to the next. We also show that these sequence differences translate to differential interactions with the L-A virus, where Xrn1p from S. cerevisiae is most efficient at controlling the L-A virus that chronically infects S. cerevisiae, and Xrn1p from S. kudriavzevii is most efficient at controlling the L-A-like virus that we have discovered within S. kudriavzevii. All Xrn1p orthologs are equivalent in their interaction with another virus-like parasite, the Ty1 retrotransposon. Thus, Xrn1p appears to co-evolve with totiviruses to maintain its potent antiviral activity and limit viral propagation in Saccharomyces yeasts. We demonstrate that Xrn1p physically interacts with the Gag protein encoded by the L-A virus, suggesting a host-virus interaction that is more complicated than just Xrn1p-mediated nucleolytic digestion of viral RNAs. PMID:27711183

Form and function of topologically associating genomic domains in budding yeast.

PubMed

Eser, Umut; Chandler-Brown, Devon; Ay, Ferhat; Straight, Aaron F; Duan, Zhijun; Noble, William Stafford; Skotheim, Jan M

2017-04-11

The genome of metazoan cells is organized into topologically associating domains (TADs) that have similar histone modifications, transcription level, and DNA replication timing. Although similar structures appear to be conserved in fission yeast, computational modeling and analysis of high-throughput chromosome conformation capture (Hi-C) data have been used to argue that the small, highly constrained budding yeast chromosomes could not have these structures. In contrast, herein we analyze Hi-C data for budding yeast and identify 200-kb scale TADs, whose boundaries are enriched for transcriptional activity. Furthermore, these boundaries separate regions of similarly timed replication origins connecting the long-known effect of genomic context on replication timing to genome architecture. To investigate the molecular basis of TAD formation, we performed Hi-C experiments on cells depleted for the Forkhead transcription factors, Fkh1 and Fkh2, previously associated with replication timing. Forkhead factors do not regulate TAD formation, but do promote longer-range genomic interactions and control interactions between origins near the centromere. Thus, our work defines spatial organization within the budding yeast nucleus, demonstrates the conserved role of genome architecture in regulating DNA replication, and identifies a molecular mechanism specifically regulating interactions between pericentric origins.

Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.

PubMed

An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Yan, Gui-Ying; Hu, Ji-Pu

2016-10-01

Predicting protein-protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high-throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM-BiGP that combines the relevance vector machine (RVM) model and Bi-gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi-gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five-fold cross-validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-BiGP method is significantly better than the SVM-based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM-BiGP-PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/BiGP/. © 2016 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Botrytis and native grape yeasts – not all interactions are created equal

USDA-ARS?s Scientific Manuscript database

Native yeasts are of increasing interest to grape growers and winemakers in Washington State because of their potential to contribute to vineyard health and wine quality. In this pilot project, we used eleven strains of native yeasts and nine isolates of the Botrytis bunch rot pathogen, all obtained...

The growth, properties and interactions of yeasts and bacteria associated with the maturation of Camembert and blue-veined cheeses.

PubMed

Addis, E; Fleet, G H; Cox, J M; Kolak, D; Leung, T

2001-09-19

The growth of yeasts and bacteria were monitored during the maturation of Camembert and blue-veined cheese produced in Australia. Yeasts were prominent throughout maturation, growing to 10(5)-10(9)/g, depending on the manufacturer. Debaryomyces hansenii predominated, but there were lesser, inconsistent contributions from Yarrowia lipolytica. Of the non-lactic acid bacteria, Acinetobacter species were significant during the maturation of Camembert but not blue-veined cheeses, and grew to 10(6)-10(8) cfu/g. Staphylococcus and Micrococcus species were consistently isolated from the cheeses with Staphylococcus xylosus growing to 10(5)-10(9) cfu/g, depending on the product. Lactic acid bacteria (10(7)-10(9) cfu/g) were present throughout maturation but were not identified. Interactions between the various yeasts and bacterial isolates were examined. Several strains of D. hansenii exhibited killer activity but not against Y. lipolytica. None of the yeasts were antagonistic towards the bacteria but some strains of D. hansenii enhanced the growth of Y. lipolytica and S. xylosus. The yeast and bacterial isolates exhibited various degrees of extracellular proteolytic and lipolytic activities.

Yeast prions are useful for studying protein chaperones and protein quality control.

PubMed

Masison, Daniel C; Reidy, Michael

2015-01-01

Protein chaperones help proteins adopt and maintain native conformations and play vital roles in cellular processes where proteins are partially folded. They comprise a major part of the cellular protein quality control system that protects the integrity of the proteome. Many disorders are caused when proteins misfold despite this protection. Yeast prions are fibrous amyloid aggregates of misfolded proteins. The normal action of chaperones on yeast prions breaks the fibers into pieces, which results in prion replication. Because this process is necessary for propagation of yeast prions, even small differences in activity of many chaperones noticeably affect prion phenotypes. Several other factors involved in protein processing also influence formation, propagation or elimination of prions in yeast. Thus, in much the same way that the dependency of viruses on cellular functions has allowed us to learn much about cell biology, the dependency of yeast prions on chaperones presents a unique and sensitive way to monitor the functions and interactions of many components of the cell's protein quality control system. Our recent work illustrates the utility of this system for identifying and defining chaperone machinery interactions.

Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

PubMed Central

Mannakee, Brian K.; Gutenkunst, Ryan N.

2016-01-01

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. PMID:27380265

[Yeast urinary tract infections. Multicentre study in 14 hospitals belonging to the Buenos Aires City Mycology Network].

PubMed

Maldonado, Ivana; Arechavala, Alicia; Guelfand, Liliana; Relloso, Silvia; Garbasz, Claudia

2016-01-01

Urinary tract infections are a frequent ailment in patients in intensive care units. Candida and other yeasts cause 5-12% of these infections. The value of the finding of any yeast is controversial, and there is no consensus about which parameters are adequate for differentiating urinary infections from colonization or contamination. To analyse the epidemiological characteristics of patients with funguria, to determine potential cut-off points in cultures (to distinguish an infection from other conditions), to identify the prevalent yeast species, and to determine the value of a second urine sample. A multicentre study was conducted in intensive care units of 14 hospitals in the Buenos Aires City Mycology Network. The first and second samples of urine from every patient were cultured. The presence of white cells and yeasts in direct examination, colony counts, and the identification of the isolated species, were evaluated. Yeasts grew in 12.2% of the samples. There was no statistical correlation between the number of white cells and the fungal colony-forming units. Eighty five percent of the patients had indwelling catheters. Funguria was not prevalent in women or in patients over the age of 65. Candida albicans, followed by Candida tropicalis, were the most frequently isolated yeasts. Candida parapsilosis and Candida glabrata appeared less frequently. The same species were isolated in 70% of second samples, and in 23% of the cases the second culture was negative. It was not possible to determine a useful cut-off point for colony counts to help in the diagnosis of urinary infections. As in other publications, C. albicans, followed by C. tropicalis, were the most prevalent species. Copyright © 2015 Asociación Española de Micología. Published by Elsevier Espana. All rights reserved.

Yeast killer systems.

PubMed Central

Magliani, W; Conti, S; Gerloni, M; Bertolotti, D; Polonelli, L

1997-01-01

The killer phenomenon in yeasts has been revealed to be a multicentric model for molecular biologists, virologists, phytopathologists, epidemiologists, industrial and medical microbiologists, mycologists, and pharmacologists. The surprisingly widespread occurrence of the killer phenomenon among taxonomically unrelated microorganisms, including prokaryotic and eukaryotic pathogens, has engendered a new interest in its biological significance as well as its theoretical and practical applications. The search for therapeutic opportunities by using yeast killer systems has conceptually opened new avenues for the prevention and control of life-threatening fungal diseases through the idiotypic network that is apparently exploited by the immune system in the course of natural infections. In this review, the biology, ecology, epidemiology, therapeutics, serology, and idiotypy of yeast killer systems are discussed. PMID:9227858

A Synthetic Biology Framework for Programming Eukaryotic Transcription Functions

PubMed Central

Khalil, Ahmad S.; Lu, Timothy K.; Bashor, Caleb J.; Ramirez, Cherie L.; Pyenson, Nora C.; Joung, J. Keith; Collins, James J.

2013-01-01

SUMMARY Eukaryotic transcription factors (TFs) perform complex and combinatorial functions within transcriptional networks. Here, we present a synthetic framework for systematically constructing eukaryotic transcription functions using artificial zinc fingers, modular DNA-binding domains found within many eukaryotic TFs. Utilizing this platform, we construct a library of orthogonal synthetic transcription factors (sTFs) and use these to wire synthetic transcriptional circuits in yeast. We engineer complex functions, such as tunable output strength and transcriptional cooperativity, by rationally adjusting a decomposed set of key component properties, e.g., DNA specificity, affinity, promoter design, protein-protein interactions. We show that subtle perturbations to these properties can transform an individual sTF between distinct roles (activator, cooperative factor, inhibitory factor) within a transcriptional complex, thus drastically altering the signal processing behavior of multi-input systems. This platform provides new genetic components for synthetic biology and enables bottom-up approaches to understanding the design principles of eukaryotic transcriptional complexes and networks. PMID:22863014

PINTA: a web server for network-based gene prioritization from expression data

PubMed Central

Nitsch, Daniela; Tranchevent, Léon-Charles; Gonçalves, Joana P.; Vogt, Josef Korbinian; Madeira, Sara C.; Moreau, Yves

2011-01-01

PINTA (available at http://www.esat.kuleuven.be/pinta/; this web site is free and open to all users and there is no login requirement) is a web resource for the prioritization of candidate genes based on the differential expression of their neighborhood in a genome-wide protein–protein interaction network. Our strategy is meant for biological and medical researchers aiming at identifying novel disease genes using disease specific expression data. PINTA supports both candidate gene prioritization (starting from a user defined set of candidate genes) as well as genome-wide gene prioritization and is available for five species (human, mouse, rat, worm and yeast). As input data, PINTA only requires disease specific expression data, whereas various platforms (e.g. Affymetrix) are supported. As a result, PINTA computes a gene ranking and presents the results as a table that can easily be browsed and downloaded by the user. PMID:21602267

Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks

PubMed Central

Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A.; Stewart, Ron; Gasch, Audrey P.

2013-01-01

Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development. PMID:24146602

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae.

PubMed

Jarnuczak, Andrew F; Eyers, Claire E; Schwartz, Jean-Marc; Grant, Christopher M; Hubbard, Simon J

2015-09-01

Molecular chaperones play an important role in protein homeostasis and the cellular response to stress. In particular, the HSP70 chaperones in yeast mediate a large volume of protein folding through transient associations with their substrates. This chaperone interaction network can be disturbed by various perturbations, such as environmental stress or a gene deletion. Here, we consider deletions of two major chaperone proteins, SSA1 and SSB1, from the chaperone network in Sacchromyces cerevisiae. We employ a SILAC-based approach to examine changes in global and local protein abundance and rationalise our results via network analysis and graph theoretical approaches. Although the deletions result in an overall increase in intracellular protein content, correlated with an increase in cell size, this is not matched by substantial changes in individual protein concentrations. Despite the phenotypic robustness to deletion of these major hub proteins, it cannot be simply explained by the presence of paralogues. Instead, network analysis and a theoretical consideration of folding workload suggest that the robustness to perturbation is a product of the overall network structure. This highlights how quantitative proteomics and systems modelling can be used to rationalise emergent network properties, and how the HSP70 system can accommodate the loss of major hubs. © 2015 The Authors. PROTEOMICS published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Using Genetic Buffering Relationships Identified in Fission Yeast to Elucidate the Molecular Pathology of Tuberous Sclerosis

DTIC Science & Technology

2015-07-01

AWARD NUMBER: W81XWH-14-1-0169 TITLE: Using Genetic Buffering Relationships Identified in Fission Yeast to Elucidate the Molecular Pathology of...DATES COVERED 1 July 2014 - 30 June 2015 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Using Genetic Buffering Relationships Identified in Fission Yeast ...SUPPLEMENTARY NOTES 14. ABSTRACT Using the genetically tractable fission yeast as a model, we sought to exploit recent advances in gene interaction

A novel functional domain of Cdc15 kinase is required for its interaction with Tem1 GTPase in Saccharomyces cerevisiae.

PubMed Central

Asakawa, K; Yoshida, S; Otake, F; Toh-e, A

2001-01-01

Exit from mitosis requires the inactivation of cyclin-dependent kinase (CDK) activity. In the budding yeast Saccharomyces cerevisiae, a number of gene products have been identified as components of the signal transduction network regulating inactivation of CDK (called the MEN, for the mitotic exit network). Cdc15, one of such components of the MEN, is an essential protein kinase. By the two-hybrid screening, we identified Cdc15 as a binding protein of Tem1 GTPase, another essential regulator of the MEN. Coprecipitation experiments revealed that Tem1 binds to Cdc15 in vivo. By deletion analysis, we found that the Tem1-binding domain resides near the conserved kinase domain of Cdc15. The cdc15-LF mutation, which was introduced into the Tem1-binding domain, reduced the interaction with Cdc15 and Tem1 and caused temperature-sensitive growth.The kinase activity of Cdc15 was not so much affected by the cdc15-LF mutation. However, Cdc15-LF failed to localize to the SPB at the restrictive temperature. Our data show that the interaction with Tem1 is important for the function of Cdc15 and that Cdc15 and Tem1 function in a complex to direct the exit from mitosis. PMID:11290702

Characterization of known protein complexes using k-connectivity and other topological measures

PubMed Central

Gallagher, Suzanne R; Goldberg, Debra S

2015-01-01

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs.We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms. PMID:26913183

Identification of a molecular recognition feature in the E1A oncoprotein that binds the SUMO conjugase UBC9 and likely interferes with polySUMOylation.

PubMed

Yousef, A F; Fonseca, G J; Pelka, P; Ablack, J N G; Walsh, C; Dick, F A; Bazett-Jones, D P; Shaw, G S; Mymryk, J S

2010-08-19

Hub proteins have central roles in regulating cellular processes. By targeting a single cellular hub, a viral oncogene may gain control over an entire module in the cellular interaction network that is potentially comprised of hundreds of proteins. The adenovirus E1A oncoprotein is a viral hub that interacts with many cellular hub proteins by short linear motifs/molecular recognition features (MoRFs). These interactions transform the architecture of the cellular protein interaction network and virtually reprogram the cell. To identify additional MoRFs within E1A, we screened portions of E1A for their ability to activate yeast pseudohyphal growth or differentiation. This identified a novel functional region within E1A conserved region 2 comprised of the sequence EVIDLT. This MoRF is necessary and sufficient to bind the N-terminal region of the SUMO conjugase UBC9, which also interacts with SUMO noncovalently and is involved in polySUMOylation. Our results suggest that E1A interferes with polySUMOylation, but not with monoSUMOylation. These data provide the first insight into the consequences of the interaction of E1A with UBC9, which was initially described in 1996. We further demonstrate that polySUMOylation regulates pseudohyphal growth and promyelocytic leukemia body reorganization by E1A. In conclusion, the interaction of the E1A oncogene with UBC9 mimics the normal binding between SUMO and UBC9 and represents a novel mechanism to modulate polySUMOylation.

Strong FANCA/FANCG but weak FANCA/FANCC interaction in the yeast 2-hybrid system.

PubMed

Reuter, T; Herterich, S; Bernhard, O; Hoehn, H; Gross, H J

2000-01-15

Three of at least 8 Fanconi anemia (FA) genes have been cloned (FANCA, FANCC, FANCG), but their functions remain unknown. Using the yeast 2-hybrid system and full-length cDNA, the authors found a strong interaction between FANCA and FANCG proteins. They also obtained evidence for a weak interaction between FANCA and FANCC. Neither FANCA nor FANCC was found to interact with itself. These results support the notion of a functional association between the FA gene products. (Blood. 2000;95:719-720)

Different Metabolic Pathways Are Involved in Response of Saccharomyces cerevisiae to L-A and M Viruses

PubMed Central

Lukša, Juliana; Ravoitytė, Bazilė; Konovalovas, Aleksandras; Aitmanaitė, Lina; Butenko, Anzhelika; Serva, Saulius; Servienė, Elena

2017-01-01

Competitive and naturally occurring yeast killer phenotype is governed by coinfection with dsRNA viruses. Long-term relationship between the host cell and viruses appear to be beneficial and co-adaptive; however, the impact of viral dsRNA on the host gene expression has barely been investigated. Here, we determined the transcriptomic profiles of the host Saccharomyces cerevisiae upon the loss of the M-2 dsRNA alone and the M-2 along with the L-A-lus dsRNAs. We provide a comprehensive study based on the high-throughput RNA-Seq data, Gene Ontology and the analysis of the interaction networks. We identified 486 genes differentially expressed after curing yeast cells of the M-2 dsRNA and 715 genes affected by the elimination of both M-2 and L-A-lus dsRNAs. We report that most of the transcriptional responses induced by viral dsRNAs are moderate. Differently expressed genes are related to ribosome biogenesis, mitochondrial functions, stress response, biosynthesis of lipids and amino acids. Our study also provided insight into the virus–host and virus–virus interplays. PMID:28757599

Different Metabolic Pathways Are Involved in Response of Saccharomyces cerevisiae to L-A and M Viruses.

PubMed

Lukša, Juliana; Ravoitytė, Bazilė; Konovalovas, Aleksandras; Aitmanaitė, Lina; Butenko, Anzhelika; Yurchenko, Vyacheslav; Serva, Saulius; Servienė, Elena

2017-07-25

Competitive and naturally occurring yeast killer phenotype is governed by coinfection with dsRNA viruses. Long-term relationship between the host cell and viruses appear to be beneficial and co-adaptive; however, the impact of viral dsRNA on the host gene expression has barely been investigated. Here, we determined the transcriptomic profiles of the host Saccharomyces cerevisiae upon the loss of the M-2 dsRNA alone and the M-2 along with the L-A-lus dsRNAs. We provide a comprehensive study based on the high-throughput RNA-Seq data, Gene Ontology and the analysis of the interaction networks. We identified 486 genes differentially expressed after curing yeast cells of the M-2 dsRNA and 715 genes affected by the elimination of both M-2 and L-A-lus dsRNAs. We report that most of the transcriptional responses induced by viral dsRNAs are moderate. Differently expressed genes are related to ribosome biogenesis, mitochondrial functions, stress response, biosynthesis of lipids and amino acids. Our study also provided insight into the virus-host and virus-virus interplays.

A chemometric method to identify enzymatic reactions leading to the transition from glycolytic oscillations to waves

NASA Astrophysics Data System (ADS)

Zimányi, László; Khoroshyy, Petro; Mair, Thomas

2010-06-01

In the present work we demonstrate that FTIR-spectroscopy is a powerful tool for the time resolved and noninvasive measurement of multi-substrate/product interactions in complex metabolic networks as exemplified by the oscillating glycolysis in a yeast extract. Based on a spectral library constructed from the pure glycolytic intermediates, chemometric analysis of the complex spectra allowed us the identification of many of these intermediates. Singular value decomposition and multiple level wavelet decomposition were used to separate drifting substances from oscillating ones. This enabled us to identify slow and fast variables of glycolytic oscillations. Most importantly, we can attribute a qualitative change in the positive feedback regulation of the autocatalytic reaction to the transition from homogeneous oscillations to travelling waves. During the oscillatory phase the enzyme phosphofructokinase is mainly activated by its own product ADP, whereas the transition to waves is accompanied with a shift of the positive feedback from ADP to AMP. This indicates that the overall energetic state of the yeast extract determines the transition between spatially homogeneous oscillations and travelling waves.

Caloric restriction delays yeast chronological aging by remodeling carbohydrate and lipid metabolism, altering peroxisomal and mitochondrial functionalities, and postponing the onsets of apoptotic and liponecrotic modes of regulated cell death

PubMed Central

Arlia-Ciommo, Anthony; Leonov, Anna; Beach, Adam; Richard, Vincent R.; Bourque, Simon D.; Burstein, Michelle T.; Kyryakov, Pavlo; Gomez-Perez, Alejandra; Koupaki, Olivia; Feldman, Rachel; Titorenko, Vladimir I.

2018-01-01

A dietary regimen of caloric restriction delays aging in evolutionarily distant eukaryotes, including the budding yeast Saccharomyces cerevisiae. Here, we assessed how caloric restriction influences morphological, biochemical and cell biological properties of chronologically aging yeast advancing through different stages of the aging process. Our findings revealed that this low-calorie diet slows yeast chronological aging by mechanisms that coordinate the spatiotemporal dynamics of various cellular processes before entry into a non-proliferative state and after such entry. Caloric restriction causes a stepwise establishment of an aging-delaying cellular pattern by tuning a network that assimilates the following: 1) pathways of carbohydrate and lipid metabolism; 2) communications between the endoplasmic reticulum, lipid droplets, peroxisomes, mitochondria and the cytosol; and 3) a balance between the processes of mitochondrial fusion and fission. Through different phases of the aging process, the caloric restriction-dependent remodeling of this intricate network 1) postpones the age-related onsets of apoptotic and liponecrotic modes of regulated cell death; and 2) actively increases the chance of cell survival by supporting the maintenance of cellular proteostasis. Because caloric restriction decreases the risk of cell death and actively increases the chance of cell survival throughout chronological lifespan, this dietary intervention extends longevity of chronologically aging yeast. PMID:29662634

MAP4 Mechanism that Stabilizes Mitochondrial Permeability Transition in Hypoxia: Microtubule Enhancement and DYNLT1 Interaction with VDAC1

PubMed Central

Zhang, Yi-ming; Zhang, Jia-ping; Hu, Jiong-yu; Zhang, Qiong; Dai, Xia; Teng, Miao; Zhang, Dong-xia; Huang, Yue-sheng

2011-01-01

Mitochondrial membrane permeability has received considerable attention recently because of its key role in apoptosis and necrosis induced by physiological events such as hypoxia. The manner in which mitochondria interact with other molecules to regulate mitochondrial permeability and cell destiny remains elusive. Previously we verified that hypoxia-induced phosphorylation of microtubule-associated protein 4 (MAP4) could lead to microtubules (MTs) disruption. In this study, we established the hypoxic (1% O2) cell models of rat cardiomyocytes, H9c2 and HeLa cells to further test MAP4 function. We demonstrated that increase in the pool of MAP4 could promote the stabilization of MT networks by increasing the synthesis and polymerization of tubulin in hypoxia. Results showed MAP4 overexpression could enhance cell viability and ATP content under hypoxic conditions. Subsequently we employed a yeast two-hybrid system to tag a protein interacting with mitochondria, dynein light chain Tctex-type 1 (DYNLT1), by hVDAC1 bait. We confirmed that DYNLT1 had protein-protein interactions with voltage-dependent anion channel 1 (VDAC1) using co-immunoprecipitation; and immunofluorescence technique showed that DYNLT1 was closely associated with MTs and VDAC1. Furthermore, DYNLT1 interactions with MAP4 were explored using a knockdown technique. We thus propose two possible mechanisms triggered by MAP4: (1) stabilization of MT networks, (2) DYNLT1 modulation, which is connected with VDAC1, and inhibition of hypoxia-induced mitochondrial permeabilization. PMID:22164227

Mitochondrial-morphology-targeted breeding of industrial yeast strains for alcohol fermentation.

PubMed

Kitagaki, Hiroshi

2009-05-29

Since mitochondrial genes are repressed under high glucose and low O2, and these conditions correspond to the conditions in which yeast cells are exposed during alcohol fermentation, the existence and structure of yeast mitochondria during alcohol fermentation have not been elucidated. Yeast mitochondria can be observed throughout brewing of sake (Japanese rice wine) and fragment during brewing. Furthermore, it has been revealed that Fis1 [fission 1 (mitochondrial outer membrane) homologue (Saccharomyces cerevisiae)], which is a transmembrane protein with its C-terminal anchor embedded in the outer membrane of mitochondria, is required for fragmentation of yeast mitochondria during sake brewing. By utilizing this knowledge, a fis1 disruptant of a sake yeast strain has been generated that has a networked mitochondrial structure throughout sake brewing. It transpired that this strain produces a high content of malate, which imparts a crisp acidic taste, during sake brewing. This strategy is a useful and a completely novel strategy towards developing a new yeast strain which produces a high content of malate in sake, and mitochondrial morphology has now emerged as a promising target for the breeding of practical industrial strains.

Physiological and environmental control of yeast prions

PubMed Central

Chernova, Tatiana A.; Wilkinson, Keith D.; Chernoff, Yury O.

2014-01-01

Prions are self-perpetuating protein isoforms that cause fatal and incurable neurodegenerative disease in mammals. Recent evidence indicates that a majority of human proteins involved in amyloid and neural inclusion disorders possess at least some prion properties. In lower eukaryotes, such as yeast, prions act as epigenetic elements, which increase phenotypic diversity by altering a range of cellular processes. While some yeast prions are clearly pathogenic, it is also postulated that prion formation could be beneficial in variable environmental conditions. Yeast and mammalian prions have similar molecular properties. Crucial cellular factors and conditions influencing prion formation and propagation were uncovered in the yeast models. Stress-related chaperones, protein quality control deposits, degradation pathways and cytoskeletal networks control prion formation and propagation in yeast. Environmental stresses trigger prion formation and loss, supposedly acting via influencing intracellular concentrations of the prion-inducing proteins, and/or by localizing prionogenic proteins to the prion induction sites via heterologous ancillary helpers. Physiological and environmental modulation of yeast prions points to new opportunities for pharmacological intervention and/or prophylactic measures targeting general cellular systems rather than the properties of individual amyloids and prions. PMID:24236638

A Physical Interaction Network of Dengue Virus and Human Proteins*

PubMed Central

Khadka, Sudip; Vangeloff, Abbey D.; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S.; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J.; Perera, Rushika; LaCount, Douglas J.

2011-01-01

Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection. PMID:21911577

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.

From protein interaction profile to functional assignment: the human protein Ki-1/57 is associated with pre-mRNA splicing events.

PubMed

Bressan, Gustavo Costa; Kobarg, Jörg

2010-01-01

The mapping of protein-protein interactions of a determined organism is considered fundamental to assign protein function in the post-genomic era. As part of this effort, screenings for pairwise interactions by yeast two-hybrid system have been used popularly to reveal protein interaction networks in different biological systems. Through the identification of protein interaction partners we have successfully obtained interesting functional clues for Ki-1/57, a human protein with no previous functional annotation, in the context of RNA metabolism. We briefly discuss the way we approached protein-protein interaction data to conduct and interpret further molecular biological and cellular studies as well as structural analyses on this protein. Our data suggest that Ki-1/57 belongs to the family of intrinsically unstructured proteins and that the structural flexibility may be crucial for its capacity to interact with many different proteins. A large fraction of these proteins are involved in pre-mRNA splicing control. Finally, Ki-1/57 is localized to several subnuclear domains, all of which have been described to splicing and other RNA processing events.

A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

PubMed

Kentzoglanakis, Kyriakos; Poole, Matthew

2012-01-01

In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

Information Flow in Interaction Networks II: Channels, Path Lengths, and Potentials

PubMed Central

Stojmirović, Aleksandar

2012-01-01

Abstract In our previous publication, a framework for information flow in interaction networks based on random walks with damping was formulated with two fundamental modes: emitting and absorbing. While many other network analysis methods based on random walks or equivalent notions have been developed before and after our earlier work, one can show that they can all be mapped to one of the two modes. In addition to these two fundamental modes, a major strength of our earlier formalism was its accommodation of context-specific directed information flow that yielded plausible and meaningful biological interpretation of protein functions and pathways. However, the directed flow from origins to destinations was induced via a potential function that was heuristic. Here, with a theoretically sound approach called the channel mode, we extend our earlier work for directed information flow. This is achieved by constructing a potential function facilitating a purely probabilistic interpretation of the channel mode. For each network node, the channel mode combines the solutions of emitting and absorbing modes in the same context, producing what we call a channel tensor. The entries of the channel tensor at each node can be interpreted as the amount of flow passing through that node from an origin to a destination. Similarly to our earlier model, the channel mode encompasses damping as a free parameter that controls the locality of information flow. Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of our new framework. PMID:22409812

A Functional Genomics Approach to Identify Novel Breast Cancer Gene Targets in Yeast

DTIC Science & Technology

2004-05-01

AD Award Number: DAMD17-03-1-0232 TITLE: A Functional Genomics Approach to Identify Novel Breast Cancer Gene Targets in Yeast PRINCIPAL INVESTIGATOR...Approach to Identify Novel Breast DAMD17-03-1-0232 Cancer Gene Targets in Yeast 6. A UTHOR(S) Craig Bennett, Ph.D. 7. PERFORMING ORGANIZA TION NAME(S...Unlimited 13. ABSTRACT (Maximum 200 Words) We are using the yeast Saccharomyces cerevisiae to identify new cancer gene targets that interact with the

Molecular details of the yeast frataxin-Isu1 interaction during mitochondrial Fe-S cluster assembly

PubMed Central

Cook, Jeremy D.; Kondapalli, Kalyan C.; Rawat, Swati; Childs, William C.; Murugesan, Yogapriya; Dancis, Andrew; Stemmler, Timothy L.

2010-01-01

Frataxin, a conserved nuclear encoded mitochondrial protein, plays a direct role in iron-sulfur cluster biosynthesis within the ISC assembly pathway. Humans with frataxin deficiency have Friedreich’s ataxia, a neurodegenerative disorder characterized by mitochondrial iron overload and disruption in Fe-S cluster synthesis. Biochemical and genetic studies have shown frataxin interacts with the iron-sulfur cluster assembly scaffold protein (in yeast, there are two: Isu1 and Isu2), indicating frataxin plays a direct role in cluster assembly, possibly by serving as an iron chaperone n the assembly pathway. Here we provide molecular details of how yeast frataxin (Yfh1) interacts with Isu1 as a structural module to better understand the multiprotein complex assembly that completes Fe-S cluster assembly; this complex also includes the cysteine desulfurase (Nfs1 in yeast) and the accessory protein (Isd11), together in the mitochondria. Thermodynamic binding parameters for protein partner and iron binding were measured for the yeast orthologs using isothermal titration calorimetry (ITC). Nuclear magnetic resonance spectroscopy was used to provide the molecular details to understand how Yfh1 interacts with Isu1. X-ray absorption studies were used to electronically and structurally characterize how iron is transferred to Isu1 and then incorporated into a Fe-S cluster. These results were combined with previously published data to generate a structural model for how the Fe-S cluster protein assembly complex can come together to accomplish Fe-S cluster assembly. PMID:20815377

Molecular Details of the Yeast Frataxin-Isu1 Interaction during Mitochondrial Fe-S Cluster Assembly

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

Cook, J.; Kondapalli, K; Rawat, S

2010-01-01

Frataxin, a conserved nuclear-encoded mitochondrial protein, plays a direct role in iron-sulfur cluster biosynthesis within the ISC assembly pathway. Humans with frataxin deficiency have Friedreich's ataxia, a neurodegenerative disorder characterized by mitochondrial iron overload and disruption in Fe-S cluster synthesis. Biochemical and genetic studies have shown frataxin interacts with the iron-sulfur cluster assembly scaffold protein (in yeast, there are two, Isu1 and Isu2), indicating frataxin plays a direct role in cluster assembly, possibly by serving as an iron chaperone in the assembly pathway. Here we provide molecular details of how yeast frataxin (Yfh1) interacts with Isu1 as a structural modulemore » to improve our understanding of the multiprotein complex assembly that completes Fe-S cluster assembly; this complex also includes the cysteine desulfurase (Nfs1 in yeast) and the accessory protein (Isd11), together in the mitochondria. Thermodynamic binding parameters for protein partner and iron binding were measured for the yeast orthologs using isothermal titration calorimetry. Nuclear magnetic resonance spectroscopy was used to provide the molecular details to understand how Yfh1 interacts with Isu1. X-ray absorption studies were used to electronically and structurally characterize how iron is transferred to Isu1 and then incorporated into an Fe-S cluster. These results were combined with previously published data to generate a structural model for how the Fe-S cluster protein assembly complex can come together to accomplish Fe-S cluster assembly.« less

Molecular details of the yeast frataxin-Isu1 interaction during mitochondrial Fe-S cluster assembly.

PubMed

Cook, Jeremy D; Kondapalli, Kalyan C; Rawat, Swati; Childs, William C; Murugesan, Yogapriya; Dancis, Andrew; Stemmler, Timothy L

2010-10-12

Frataxin, a conserved nuclear-encoded mitochondrial protein, plays a direct role in iron-sulfur cluster biosynthesis within the ISC assembly pathway. Humans with frataxin deficiency have Friedreich's ataxia, a neurodegenerative disorder characterized by mitochondrial iron overload and disruption in Fe-S cluster synthesis. Biochemical and genetic studies have shown frataxin interacts with the iron-sulfur cluster assembly scaffold protein (in yeast, there are two, Isu1 and Isu2), indicating frataxin plays a direct role in cluster assembly, possibly by serving as an iron chaperone in the assembly pathway. Here we provide molecular details of how yeast frataxin (Yfh1) interacts with Isu1 as a structural module to improve our understanding of the multiprotein complex assembly that completes Fe-S cluster assembly; this complex also includes the cysteine desulfurase (Nfs1 in yeast) and the accessory protein (Isd11), together in the mitochondria. Thermodynamic binding parameters for protein partner and iron binding were measured for the yeast orthologs using isothermal titration calorimetry. Nuclear magnetic resonance spectroscopy was used to provide the molecular details to understand how Yfh1 interacts with Isu1. X-ray absorption studies were used to electronically and structurally characterize how iron is transferred to Isu1 and then incorporated into an Fe-S cluster. These results were combined with previously published data to generate a structural model for how the Fe-S cluster protein assembly complex can come together to accomplish Fe-S cluster assembly.

Vitality Stains and Real Time PCR Studies to Delineate the Interactions of Pichia anomala and Aspergillus flavus

USDA-ARS?s Scientific Manuscript database

The objectives of this study were to probe the effect of the yeast, P. anomala against A flavus by using real time RT-PCR technique and vitality fluorescent stains. Yeast and fungi were inoculated into a 250 ml-flask containing 50 ml potato dextrose broth (PDB) at yeast to fungus (Y : F) ratios of ...

Quantitative Analysis of NF-κB Transactivation Specificity Using a Yeast-Based Functional Assay

PubMed Central

Sharma, Vasundhara; Jordan, Jennifer J.; Ciribilli, Yari; Resnick, Michael A.; Bisio, Alessandra; Inga, Alberto

2015-01-01

The NF-κB transcription factor family plays a central role in innate immunity and inflammation processes and is frequently dysregulated in cancer. We developed an NF-κB functional assay in yeast to investigate the following issues: transactivation specificity of NF-κB proteins acting as homodimers or heterodimers; correlation between transactivation capacity and in vitro DNA binding measurements; impact of co-expressed interacting proteins or of small molecule inhibitors on NF-κB-dependent transactivation. Full-length p65 and p50 cDNAs were cloned into centromeric expression vectors under inducible GAL1 promoter in order to vary their expression levels. Since p50 lacks a transactivation domain (TAD), a chimeric construct containing the TAD derived from p65 was also generated (p50TAD) to address its binding and transactivation potential. The p50TAD and p65 had distinct transactivation specificities towards seventeen different κB response elements (κB-REs) where single nucleotide changes could greatly impact transactivation. For four κB-REs, results in yeast were predictive of transactivation potential measured in the human MCF7 cell lines treated with the NF-κB activator TNFα. Transactivation results in yeast correlated only partially with in vitro measured DNA binding affinities, suggesting that features other than strength of interaction with naked DNA affect transactivation, although factors such as chromatin context are kept constant in our isogenic yeast assay. The small molecules BAY11-7082 and ethyl-pyruvate as well as expressed IkBα protein acted as NF-κB inhibitors in yeast, more strongly towards p65. Thus, the yeast-based system can recapitulate NF-κB features found in human cells, thereby providing opportunities to address various NF-κB functions, interactions and chemical modulators. PMID:26147604

Spatial signals link exit from mitosis to spindle position

PubMed Central

Falk, Jill Elaine; Tsuchiya, Dai; Verdaasdonk, Jolien; Lacefield, Soni; Bloom, Kerry; Amon, Angelika

2016-01-01

In budding yeast, if the spindle becomes mispositioned, cells prevent exit from mitosis by inhibiting the mitotic exit network (MEN). The MEN is a signaling cascade that localizes to spindle pole bodies (SPBs) and activates the phosphatase Cdc14. There are two competing models that explain MEN regulation by spindle position. In the 'zone model', exit from mitosis occurs when a MEN-bearing SPB enters the bud. The 'cMT-bud neck model' posits that cytoplasmic microtubule (cMT)-bud neck interactions prevent MEN activity. Here we find that 1) eliminating cMT– bud neck interactions does not trigger exit from mitosis and 2) loss of these interactions does not precede Cdc14 activation. Furthermore, using binucleate cells, we show that exit from mitosis occurs when one SPB enters the bud despite the presence of a mispositioned spindle. We conclude that exit from mitosis is triggered by a correctly positioned spindle rather than inhibited by improper spindle position. DOI: http://dx.doi.org/10.7554/eLife.14036.001 PMID:27166637

Phenotypic states become increasingly sensitive to perturbations near a bifurcation in a synthetic gene network.

PubMed

Axelrod, Kevin; Sanchez, Alvaro; Gore, Jeff

2015-08-24

Microorganisms often exhibit a history-dependent phenotypic response after exposure to a stimulus which can be imperative for proper function. However, cells frequently experience unexpected environmental perturbations that might induce phenotypic switching. How cells maintain phenotypic states in the face of environmental fluctuations remains an open question. Here, we use environmental perturbations to characterize the resilience of phenotypic states in a synthetic gene network near a critical transition. We find that far from the critical transition an environmental perturbation may induce little to no phenotypic switching, whereas close to the critical transition the same perturbation can cause many cells to switch phenotypic states. This loss of resilience was observed for perturbations that interact directly with the gene circuit as well as for a variety of generic perturbations-such as salt, ethanol, or temperature shocks-that alter the state of the cell more broadly. We obtain qualitatively similar findings in natural gene circuits, such as the yeast GAL network. Our findings illustrate how phenotypic memory can become destabilized by environmental variability near a critical transition.

Development of an In Vitro Model for the Multi-Parametric Quantification of the Cellular Interactions between Candida Yeasts and Phagocytes

PubMed Central

Noël, Thierry

2012-01-01

We developed a new in vitro model for a multi-parameter characterization of the time course interaction of Candida fungal cells with J774 murine macrophages and human neutrophils, based on the use of combined microscopy, fluorometry, flow cytometry and viability assays. Using fluorochromes specific to phagocytes and yeasts, we could accurately quantify various parameters simultaneously in a single infection experiment: at the individual cell level, we measured the association of phagocytes to fungal cells and phagocyte survival, and monitored in parallel the overall phagocytosis process by measuring the part of ingested fungal cells among the total fungal biomass that changed over time. Candida albicans, C. glabrata, and C. lusitaniae were used as a proof of concept: they exhibited species-specific differences in their association rate with phagocytes. The fungal biomass uptaken by the phagocytes differed significantly according to the Candida species. The measure of the survival of fungal and immune cells during the interaction showed that C. albicans was the more aggressive yeast in vitro, destroying the vast majority of the phagocytes within five hours. All three species of Candida were able to survive and to escape macrophage phagocytosis either by the intraphagocytic yeast-to-hyphae transition (C. albicans) and the fungal cell multiplication until phagocytes burst (C. glabrata, C. lusitaniae), or by the avoidance of phagocytosis (C. lusitaniae). We demonstrated that our model was sensitive enough to quantify small variations of the parameters of the interaction. The method has been conceived to be amenable to the high-throughput screening of mutants in order to unravel the molecular mechanisms involved in the interaction between yeasts and host phagocytes. PMID:22479332

Interactions of Saprophytic Yeasts with a nor Mutant of Aspergillus flavus

PubMed Central

Hua, Sui-Sheng T.; Baker, James L.; Flores-Espiritu, Melanie

1999-01-01

The nor mutant of Aspergillus flavus has a defective norsolorinic acid reductase, and thus the aflatoxin biosynthetic pathway is blocked, resulting in the accumulation of norsolorinic acid, a bright red-orange pigment. We developed a visual agar plate assay to monitor yeast strains for their ability to inhibit aflatoxin production by visually scoring the accumulation of this pigment of the nor mutant. We identified yeast strains that reduced the red-orange pigment accumulation in the nor mutant. These yeasts also reduced aflatoxin accumulation by a toxigenic strain of A. flavus. These yeasts may be useful for reducing aflatoxin contamination of food commodities. PMID:10347069

Applications of yeast surface display for protein engineering

PubMed Central

Cherf, Gerald M.; Cochran, Jennifer R.

2015-01-01

The method of displaying recombinant proteins on the surface of Saccharomyces cerevisiae via genetic fusion to an abundant cell wall protein, a technology known as yeast surface display, or simply, yeast display, has become a valuable protein engineering tool for a broad spectrum of biotechnology and biomedical applications. This review focuses on the use of yeast display for engineering protein affinity, stability, and enzymatic activity. Strategies and examples for each protein engineering goal are discussed. Additional applications of yeast display are also briefly presented, including protein epitope mapping, identification of protein-protein interactions, and uses of displayed proteins in industry and medicine. PMID:26060074

Diversity and killer activity of yeasts in Malaysian fermented food samples.

PubMed

Lim, S L; Tay, S T

2011-08-01

The biodiversity and the killer activity of yeasts isolated from various types of fermented food in Malaysia were investigated in this study. Of 252 yeasts isolated from 48 fermented food samples in this study, 19 yeast species were identified based on sequence analysis of the ITS1-5.8S-ITS2 partial fragments of the yeasts. A total of 29 (11.5%) of the yeast isolates demonstrated killer activity to at least one Candida species tested in this study; including 22 isolates of Trichosporon asahii, 4 isolates of Pichia anomala, and one isolate each of Pichia norvegensis, Pichia fermentans and Issatchenkia orientalis, respectively. The presence of killer yeasts reflects antagonism that occurs during microbial interaction in the fermented food, whereby certain yeasts produce killer toxins and possibly other toxic substances in competition for limited nutrients and space. The anti-Candida activity demonstrated by killer yeasts in this study should be further explored for development of alternative therapy against candidiasis.

Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.

PubMed

Shen, Xianjun; Yi, Li; Jiang, Xingpeng; He, Tingting; Yang, Jincai; Xie, Wei; Hu, Po; Hu, Xiaohua

2017-01-01

How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI) data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment). It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate.

Competitive inhibition can linearize dose-response and generate a linear rectifier

PubMed Central

Savir, Yonatan; Tu, Benjamin P.; Springer, Michael

2015-01-01

Summary Many biological responses require a dynamic range that is larger than standard bi-molecular interactions allow, yet the also ability to remain off at low input. Here we mathematically show that an enzyme reaction system involving a combination of competitive inhibition, conservation of the total level of substrate and inhibitor, and positive feedback can behave like a linear rectifier—that is, a network motif with an input-output relationship that is linearly sensitive to substrate above a threshold but unresponsive below the threshold. We propose that the evolutionarily conserved yeast SAGA histone acetylation complex may possess the proper physiological response characteristics and molecular interactions needed to perform as a linear rectifier, and we suggest potential experiments to test this hypothesis. One implication of this work is that linear responses and linear rectifiers might be easier to evolve or synthetically construct than is currently appreciated. PMID:26495436

Competitive inhibition can linearize dose-response and generate a linear rectifier.

PubMed

Savir, Yonatan; Tu, Benjamin P; Springer, Michael

2015-09-23

Many biological responses require a dynamic range that is larger than standard bi-molecular interactions allow, yet the also ability to remain off at low input. Here we mathematically show that an enzyme reaction system involving a combination of competitive inhibition, conservation of the total level of substrate and inhibitor, and positive feedback can behave like a linear rectifier-that is, a network motif with an input-output relationship that is linearly sensitive to substrate above a threshold but unresponsive below the threshold. We propose that the evolutionarily conserved yeast SAGA histone acetylation complex may possess the proper physiological response characteristics and molecular interactions needed to perform as a linear rectifier, and we suggest potential experiments to test this hypothesis. One implication of this work is that linear responses and linear rectifiers might be easier to evolve or synthetically construct than is currently appreciated.

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

A postprocessing method in the HMC framework for predicting gene function based on biological instrumental data

NASA Astrophysics Data System (ADS)

Feng, Shou; Fu, Ping; Zheng, Wenbin

2018-03-01

Predicting gene function based on biological instrumental data is a complicated and challenging hierarchical multi-label classification (HMC) problem. When using local approach methods to solve this problem, a preliminary results processing method is usually needed. This paper proposed a novel preliminary results processing method called the nodes interaction method. The nodes interaction method revises the preliminary results and guarantees that the predictions are consistent with the hierarchy constraint. This method exploits the label dependency and considers the hierarchical interaction between nodes when making decisions based on the Bayesian network in its first phase. In the second phase, this method further adjusts the results according to the hierarchy constraint. Implementing the nodes interaction method in the HMC framework also enhances the HMC performance for solving the gene function prediction problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph that is more difficult to tackle. The experimental results validate the promising performance of the proposed method compared to state-of-the-art methods on eight benchmark yeast data sets annotated by the GO.

GraphCrunch 2: Software tool for network modeling, alignment and clustering.

PubMed

Kuchaiev, Oleksii; Stevanović, Aleksandar; Hayes, Wayne; Pržulj, Nataša

2011-01-19

Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL") for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an algorithm for clustering nodes within a network based solely on their topological similarities. Using GraphCrunch 2, we demonstrate that eukaryotic and viral PPI networks may belong to different graph model families and show that topology-based clustering can reveal important functional similarities between proteins within yeast and human PPI networks. GraphCrunch 2 is a software tool that implements the latest research on biological network analysis. It parallelizes computationally intensive tasks to fully utilize the potential of modern multi-core CPUs. It is open-source and freely available for research use. It runs under the Windows and Linux platforms.

Ongoing resolution of duplicate gene functions shapes the diversification of a metabolic network

PubMed Central

Kuang, Meihua Christina; Hutchins, Paul D; Russell, Jason D; Coon, Joshua J; Hittinger, Chris Todd

2016-01-01

The evolutionary mechanisms leading to duplicate gene retention are well understood, but the long-term impacts of paralog differentiation on the regulation of metabolism remain underappreciated. Here we experimentally dissect the functions of two pairs of ancient paralogs of the GALactose sugar utilization network in two yeast species. We show that the Saccharomyces uvarum network is more active, even as over-induction is prevented by a second co-repressor that the model yeast Saccharomyces cerevisiae lacks. Surprisingly, removal of this repression system leads to a strong growth arrest, likely due to overly rapid galactose catabolism and metabolic overload. Alternative sugars, such as fructose, circumvent metabolic control systems and exacerbate this phenotype. We further show that S. cerevisiae experiences homologous metabolic constraints that are subtler due to how the paralogs have diversified. These results show how the functional differentiation of paralogs continues to shape regulatory network architectures and metabolic strategies long after initial preservation. DOI: http://dx.doi.org/10.7554/eLife.19027.001 PMID:27690225

Gene regulatory network identification from the yeast cell cycle based on a neuro-fuzzy system.

PubMed

Wang, B H; Lim, J W; Lim, J S

2016-08-30

Many studies exist for reconstructing gene regulatory networks (GRNs). In this paper, we propose a method based on an advanced neuro-fuzzy system, for gene regulatory network reconstruction from microarray time-series data. This approach uses a neural network with a weighted fuzzy function to model the relationships between genes. Fuzzy rules, which determine the regulators of genes, are very simplified through this method. Additionally, a regulator selection procedure is proposed, which extracts the exact dynamic relationship between genes, using the information obtained from the weighted fuzzy function. Time-series related features are extracted from the original data to employ the characteristics of temporal data that are useful for accurate GRN reconstruction. The microarray dataset of the yeast cell cycle was used for our study. We measured the mean squared prediction error for the efficiency of the proposed approach and evaluated the accuracy in terms of precision, sensitivity, and F-score. The proposed method outperformed the other existing approaches.

New scaling relation for information transfer in biological networks

PubMed Central

Kim, Hyunju; Davies, Paul; Walker, Sara Imari

2015-01-01

We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781–4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös–Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties. PMID:26701883

Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells.

PubMed

Recouvreux, Pierre; Sokolowski, Thomas R; Grammoustianou, Aristea; ten Wolde, Pieter Rein; Dogterom, Marileen

2016-02-16

Cell polarity refers to a functional spatial organization of proteins that is crucial for the control of essential cellular processes such as growth and division. To establish polarity, cells rely on elaborate regulation networks that control the distribution of proteins at the cell membrane. In fission yeast cells, a microtubule-dependent network has been identified that polarizes the distribution of signaling proteins that restricts growth to cell ends and targets the cytokinetic machinery to the middle of the cell. Although many molecular components have been shown to play a role in this network, it remains unknown which molecular functionalities are minimally required to establish a polarized protein distribution in this system. Here we show that a membrane-binding protein fragment, which distributes homogeneously in wild-type fission yeast cells, can be made to concentrate at cell ends by attaching it to a cytoplasmic microtubule end-binding protein. This concentration results in a polarized pattern of chimera proteins with a spatial extension that is very reminiscent of natural polarity patterns in fission yeast. However, chimera levels fluctuate in response to microtubule dynamics, and disruption of microtubules leads to disappearance of the pattern. Numerical simulations confirm that the combined functionality of membrane anchoring and microtubule tip affinity is in principle sufficient to create polarized patterns. Our chimera protein may thus represent a simple molecular functionality that is able to polarize the membrane, onto which additional layers of molecular complexity may be built to provide the temporal robustness that is typical of natural polarity patterns.

Outreach and online training services at the Saccharomyces Genome Database.

PubMed

MacPherson, Kevin A; Starr, Barry; Wong, Edith D; Dalusag, Kyla S; Hellerstedt, Sage T; Lang, Olivia W; Nash, Robert S; Skrzypek, Marek S; Engel, Stacia R; Cherry, J Michael

2017-01-01

The Saccharomyces Genome Database (SGD; www.yeastgenome.org ), the primary genetics and genomics resource for the budding yeast S. cerevisiae , provides free public access to expertly curated information about the yeast genome and its gene products. As the central hub for the yeast research community, SGD engages in a variety of social outreach efforts to inform our users about new developments, promote collaboration, increase public awareness of the importance of yeast to biomedical research, and facilitate scientific discovery. Here we describe these various outreach methods, from networking at scientific conferences to the use of online media such as blog posts and webinars, and include our perspectives on the benefits provided by outreach activities for model organism databases. http://www.yeastgenome.org. © The Author(s) 2017. Published by Oxford University Press.

Armadillo Repeat Containing 8α Binds to HRS and Promotes HRS Interaction with Ubiquitinated Proteins

PubMed Central

Tomaru, Koji; Ueda, Atsuhisa; Suzuki, Takeyuki; Kobayashi, Nobuaki; Yang, Jun; Yamamoto, Masaki; Takeno, Mitsuhiro; Kaneko, Takeshi; Ishigatsubo, Yoshiaki

2010-01-01

Recently, we reported that a complex with an essential role in the degradation of Fructose-1,6-bisphosphatase in yeast is well conserved in mammalian cells; we named this mammalian complex C-terminal to the Lissencephaly type-1-like homology (CTLH) complex. Although the function of the CTLH complex remains unclear, here we used yeast two-hybrid screening to isolate Hepatocyte growth factor-regulated tyrosine kinase substrate (HRS) as a protein binding to a key component of CTLH complex, Armadillo repeat containing 8 (ARMc8) α. The association was confirmed by a yeast two-hybrid assay and a co-immunoprecipitation assay. The proline-rich domain of HRS was essential for the association. As demonstrated through immunofluorescence microscopy, ARMc8α co-localized with HRS. ARMc8α promoted the interaction of HRS with various ubiquitinated proteins through the ubiquitin-interacting motif. These findings suggest that HRS mediates protein endosomal trafficking partly through its interaction with ARMc8α. PMID:20224683

Lager Yeast Comes of Age

PubMed Central

2014-01-01

Alcoholic fermentations have accompanied human civilizations throughout our history. Lager yeasts have a several-century-long tradition of providing fresh beer with clean taste. The yeast strains used for lager beer fermentation have long been recognized as hybrids between two Saccharomyces species. We summarize the initial findings on this hybrid nature, the genomics/transcriptomics of lager yeasts, and established targets of strain improvements. Next-generation sequencing has provided fast access to yeast genomes. Its use in population genomics has uncovered many more hybridization events within Saccharomyces species, so that lager yeast hybrids are no longer the exception from the rule. These findings have led us to propose network evolution within Saccharomyces species. This “web of life” recognizes the ability of closely related species to exchange DNA and thus drain from a combined gene pool rather than be limited to a gene pool restricted by speciation. Within the domesticated lager yeasts, two groups, the Saaz and Frohberg groups, can be distinguished based on fermentation characteristics. Recent evidence suggests that these groups share an evolutionary history. We thus propose to refer to the Saaz group as Saccharomyces carlsbergensis and to the Frohberg group as Saccharomyces pastorianus based on their distinct genomes. New insight into the hybrid nature of lager yeast will provide novel directions for future strain improvement. PMID:25084862

Functional adaptation between yeast actin and its cognate myosin motors.

PubMed

Stark, Benjamin C; Wen, Kuo-Kuang; Allingham, John S; Rubenstein, Peter A; Lord, Matthew

2011-09-02

We employed budding yeast and skeletal muscle actin to examine the contribution of the actin isoform to myosin motor function. While yeast and muscle actin are highly homologous, they exhibit different charge density at their N termini (a proposed myosin-binding interface). Muscle myosin-II actin-activated ATPase activity is significantly higher with muscle versus yeast actin. Whether this reflects inefficiency in the ability of yeast actin to activate myosin is not known. Here we optimized the isolation of two yeast myosins to assess actin function in a homogenous system. Yeast myosin-II (Myo1p) and myosin-V (Myo2p) accommodate the reduced N-terminal charge density of yeast actin, showing greater activity with yeast over muscle actin. Increasing the number of negative charges at the N terminus of yeast actin from two to four (as in muscle) had little effect on yeast myosin activity, while other substitutions of charged residues at the myosin interface of yeast actin reduced activity. Thus, yeast actin functions most effectively with its native myosins, which in part relies on associations mediated by its outer domain. Compared with yeast myosin-II and myosin-V, muscle myosin-II activity was very sensitive to salt. Collectively, our findings suggest differing degrees of reliance on electrostatic interactions during weak actomyosin binding in yeast versus muscle. Our study also highlights the importance of native actin isoforms when considering the function of myosins.

The yeast actin cytoskeleton.

PubMed

Mishra, Mithilesh; Huang, Junqi; Balasubramanian, Mohan K

2014-03-01

The actin cytoskeleton is a complex network of dynamic polymers, which plays an important role in various fundamental cellular processes, including maintenance of cell shape, polarity, cell division, cell migration, endocytosis, vesicular trafficking, and mechanosensation. Precise spatiotemporal assembly and disassembly of actin structures is regulated by the coordinated activity of about 100 highly conserved accessory proteins, which nucleate, elongate, cross-link, and sever actin filaments. Both in vivo studies in a wide range of organisms from yeast to metazoans and in vitro studies of purified proteins have helped shape the current understanding of actin dynamics and function. Molecular genetics, genome-wide functional analysis, sophisticated real-time imaging, and ultrastructural studies in concert with biochemical analysis have made yeast an attractive model to understand the actin cytoskeleton, its molecular dynamics, and physiological function. Studies of the yeast actin cytoskeleton have contributed substantially in defining the universal mechanism regulating actin assembly and disassembly in eukaryotes. Here, we review some of the important insights generated by the study of actin cytoskeleton in two important yeast models the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Conserved composition of mammalian box H/ACA and box C/D small nucleolar ribonucleoprotein particles and their interaction with the common factor Nopp140.

PubMed

Yang, Y; Isaac, C; Wang, C; Dragon, F; Pogacic, V; Meier, U T

2000-02-01

Small nucleolar ribonucleoprotein particles (snoRNPs) mainly catalyze the modification of rRNA. The two major classes of snoRNPs, box H/ACA and box C/D, function in the pseudouridylation and 2'-O-methylation, respectively, of specific nucleotides. The emerging view based on studies in yeast is that each class of snoRNPs is composed of a unique set of proteins. Here we present a characterization of mammalian snoRNPs. We show that the previously characterized NAP57 is specific for box H/ACA snoRNPs, whereas the newly identified NAP65, the rat homologue of yeast Nop5/58p, is a component of the box C/D class. Using coimmunoprecipitation experiments, we show that the nucleolar and coiled-body protein Nopp140 interacts with both classes of snoRNPs. This interaction is corroborated in vivo by the exclusive depletion of snoRNP proteins from nucleoli in cells transfected with a dominant negative Nopp140 construct. Interestingly, RNA polymerase I transcription is arrested in nucleoli depleted of snoRNPs, raising the possibility of a feedback mechanism between rRNA modification and transcription. Moreover, the Nopp140-snoRNP interaction appears to be conserved in yeast, because depletion of Srp40p, the yeast Nopp140 homologue, in a conditional lethal strain induces the loss of box H/ACA small nucleolar RNAs. We propose that Nopp140 functions as a chaperone of snoRNPs in yeast and vertebrate cells.

Conserved Composition of Mammalian Box H/ACA and Box C/D Small Nucleolar Ribonucleoprotein Particles and Their Interaction with the Common Factor Nopp140

PubMed Central

Yang, Yunfeng; Isaac, Cynthia; Wang, Chen; Dragon, François; Pogac̆ić, Vanda; Meier, U. Thomas

2000-01-01

Small nucleolar ribonucleoprotein particles (snoRNPs) mainly catalyze the modification of rRNA. The two major classes of snoRNPs, box H/ACA and box C/D, function in the pseudouridylation and 2′-O-methylation, respectively, of specific nucleotides. The emerging view based on studies in yeast is that each class of snoRNPs is composed of a unique set of proteins. Here we present a characterization of mammalian snoRNPs. We show that the previously characterized NAP57 is specific for box H/ACA snoRNPs, whereas the newly identified NAP65, the rat homologue of yeast Nop5/58p, is a component of the box C/D class. Using coimmunoprecipitation experiments, we show that the nucleolar and coiled-body protein Nopp140 interacts with both classes of snoRNPs. This interaction is corroborated in vivo by the exclusive depletion of snoRNP proteins from nucleoli in cells transfected with a dominant negative Nopp140 construct. Interestingly, RNA polymerase I transcription is arrested in nucleoli depleted of snoRNPs, raising the possibility of a feedback mechanism between rRNA modification and transcription. Moreover, the Nopp140-snoRNP interaction appears to be conserved in yeast, because depletion of Srp40p, the yeast Nopp140 homologue, in a conditional lethal strain induces the loss of box H/ACA small nucleolar RNAs. We propose that Nopp140 functions as a chaperone of snoRNPs in yeast and vertebrate cells. PMID:10679015

Yeast‐insect associations: It takes guts

PubMed Central

2018-01-01

Abstract Insects interact with microorganisms in several situations, ranging from the accidental interaction to locate attractive food or the acquisition of essential nutrients missing in the main food source. Despite a wealth of studies recently focused on bacteria, the interactions between insects and yeasts have relevant implications for both of the parties involved. The insect intestine shows several structural and physiological differences among species, but it is generally a hostile environment for many microorganisms, selecting against the most sensitive and at the same time guaranteeing a less competitive environment to resistant ones. An intensive characterization of the interactions between yeasts and insects has highlighted their relevance not only for attraction to food but also for the insect's development and behaviour. Conversely, some yeasts have been shown to benefit from interactions with insects, in some cases by being carried among different environments. In addition, the insect intestine may provide a place to reside for prolonged periods and possibly mate or generate sexual forms able to mate once back in the external environments. YEA‐May‐17‐0084.R3 PMID:29363168

Simultaneous and successive inoculations of yeasts and lactic acid bacteria on the fermentation of an unsulfited Tannat grape must

PubMed Central

Muñoz, Viviana; Beccaria, Bruno; Abreo, Eduardo

2014-01-01

Interactions between yeasts and lactic acid bacteria are strain specific, and their outcome is expected to change in simultaneous alcoholic - malolactic fermentations from the pattern observed in successive fermentations. One Oenococcus oeni strain Lalvin VP41™ was inoculated with two Saccharomyces cerevisiae strains either simultaneously, three days after the yeast inoculation, or when alcoholic fermentation was close to finish. Early bacterial inoculations with each yeast strain allowed for the growth of the bacterial populations, and the length of malolactic fermentation was reduced to six days. Alcoholic fermentation by Lalvin ICV D80® yeast strain left the highest residual sugar, suggesting a negative effect of the bacterial growth and malolactic activity on its performance. In sequential inoculations the bacterial populations did not show actual growth with either yeast strain. In this strategy, both yeast strains finished the alcoholic fermentations, and malolactic fermentations took longer to finish. Lalvin ICV D80® allowed for higher viability and activity of the bacterial strain than Fermicru UY4® under the three inoculation strategies. This was beneficial for the sequential completion of both fermentations, but negatively affected the completion of alcoholic fermentation by Lalvin ICV D80® in the early bacteria additions. Conversely, Fermicru UY4®, which was rather inhibitory towards the bacteria, favored the timely completion of both fermentations simultaneously. As bacteria in early inoculations with low or no SO2 addition can be expected to multiply and interact with fermenting yeasts, not only are the yeast-bacterium strains combination and time point of the inoculation to be considered, but also the amount of bacteria inoculated. PMID:24948914

Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms.

PubMed

Lin, Xiaotong; Liu, Mei; Chen, Xue-wen

2009-04-29

Protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data. In this paper, a novel Bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. We develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. Our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. Furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. Applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. Finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. We observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach. This study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. The proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. We expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application.

Tombusviruses upregulate phospholipid biosynthesis via interaction between p33 replication protein and yeast lipid sensor proteins during virus replication in yeast

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

Barajas, Daniel; Xu, Kai; Sharma, Monika

Positive-stranded RNA viruses induce new membranous structures and promote membrane proliferation in infected cells to facilitate viral replication. In this paper, the authors show that a plant-infecting tombusvirus upregulates transcription of phospholipid biosynthesis genes, such as INO1, OPI3 and CHO1, and increases phospholipid levels in yeast model host. This is accomplished by the viral p33 replication protein, which interacts with Opi1p FFAT domain protein and Scs2p VAP protein. Opi1p and Scs2p are phospholipid sensor proteins and they repress the expression of phospholipid genes. Accordingly, deletion of OPI1 transcription repressor in yeast has a stimulatory effect on TBSV RNA accumulation andmore » enhanced tombusvirus replicase activity in an in vitro assay. Altogether, the presented data convincingly demonstrate that de novo lipid biosynthesis is required for optimal TBSV replication. Overall, this work reveals that a (+)RNA virus reprograms the phospholipid biosynthesis pathway in a unique way to facilitate its replication in yeast cells. - Highlights: • Tombusvirus p33 replication protein interacts with FFAT-domain host protein. • Tombusvirus replication leads to upregulation of phospholipids. • Tombusvirus replication depends on de novo lipid synthesis. • Deletion of FFAT-domain host protein enhances TBSV replication. • TBSV rewires host phospholipid synthesis.« less

Subcellular targeting and interactions among the Potato virus X TGB proteins.

PubMed

Samuels, Timmy D; Ju, Ho-Jong; Ye, Chang-Ming; Motes, Christy M; Blancaflor, Elison B; Verchot-Lubicz, Jeanmarie

2007-10-25

Potato virus X (PVX) encodes three proteins named TGBp1, TGBp2, and TGBp3 which are required for virus cell-to-cell movement. To determine whether PVX TGB proteins interact during virus cell-cell movement, GFP was fused to each TGB coding sequence within the viral genome. Confocal microscopy was used to study subcellular accumulation of each protein in virus-infected plants and protoplasts. GFP:TGBp2 and TGBp3:GFP were both seen in the ER, ER-associated granular vesicles, and perinuclear X-bodies suggesting that these proteins interact in the same subdomains of the endomembrane network. When plasmids expressing CFP:TGBp2 and TGBp3:GFP were co-delivered to tobacco leaf epidermal cells, the fluorescent signals overlapped in ER-associated granular vesicles indicating that these proteins colocalize in this subcellular compartment. GFP:TGBp1 was seen in the nucleus, cytoplasm, rod-like inclusion bodies, and in punctate sites embedded in the cell wall. The puncta were reminiscent of previous reports showing viral proteins in plasmodesmata. Experiments using CFP:TGBp1 and YFP:TGBp2 or TGBp3:GFP showed CFP:TGBp1 remained in the cytoplasm surrounding the endomembrane network. There was no evidence that the granular vesicles contained TGBp1. Yeast two hybrid experiments showed TGBp1 self associates but failed to detect interactions between TGBp1 and TGBp2 or TGBp3. These experiments indicate that the PVX TGB proteins have complex subcellular accumulation patterns and likely cooperate across subcellular compartments to promote virus infection.

Direct interaction of the Usher syndrome 1G protein SANS and myomegalin in the retina.

PubMed

Overlack, Nora; Kilic, Dilek; Bauss, Katharina; Märker, Tina; Kremer, Hannie; van Wijk, Erwin; Wolfrum, Uwe

2011-10-01

The human Usher syndrome (USH) is the most frequent cause of combined hereditary deaf-blindness. USH is genetically heterogeneous with at least 11 chromosomal loci assigned to 3 clinical types, USH1-3. We have previously demonstrated that all USH1 and 2 proteins in the eye and the inner ear are organized into protein networks by scaffold proteins. This has contributed essentially to our current understanding of the function of USH proteins and explains why defects in proteins of different families cause very similar phenotypes. We have previously shown that the USH1G protein SANS (scaffold protein containing ankyrin repeats and SAM domain) contributes to the periciliary protein network in retinal photoreceptor cells. This study aimed to further elucidate the role of SANS by identifying novel interaction partners. In yeast two-hybrid screens of retinal cDNA libraries we identified 30 novel putative interacting proteins binding to the central domain of SANS (CENT). We confirmed the direct binding of the phosphodiesterase 4D interacting protein (PDE4DIP), a Golgi associated protein synonymously named myomegalin, to the CENT domain of SANS by independent assays. Correlative immunohistochemical and electron microscopic analyses showed a co-localization of SANS and myomegalin in mammalian photoreceptor cells in close association with microtubules. Based on the present results we propose a role of the SANS-myomegalin complex in microtubule-dependent inner segment cargo transport towards the ciliary base of photoreceptor cells. Copyright © 2011 Elsevier B.V. All rights reserved.

Effect of Temperature on Synthetic Positive and Negative Feedback Gene Networks

NASA Astrophysics Data System (ADS)

Charlebois, Daniel A.; Marshall, Sylvia; Balazsi, Gabor

Synthetic biological systems are built and tested under well controlled laboratory conditions. How altering the environment, such as the ambient temperature affects their function is not well understood. To address this question for synthetic gene networks with positive and negative feedback, we used mathematical modeling coupled with experiments in the budding yeast Saccharomyces cerevisiae. We found that cellular growth rates and gene expression dose responses change significantly at temperatures above and below the physiological optimum for yeast. Gene expression distributions for the negative feedback-based circuit changed from unimodal to bimodal at high temperature, while the bifurcation point of the positive feedback circuit shifted up with temperature. These results demonstrate that synthetic gene network function is context-dependent. Temperature effects should thus be tested and incorporated into their design and validation for real-world applications. NSERC Postdoctoral Fellowship (Grant No. PDF-453977-2014).

Effect of Yeast Cell Morphology, Cell Wall Physical Structure and Chemical Composition on Patulin Adsorption

PubMed Central

Luo, Ying; Wang, Jianguo; Liu, Bin; Wang, Zhouli; Yuan, Yahong; Yue, Tianli

2015-01-01

The capability of yeast to adsorb patulin in fruit juice can aid in substantially reducing the patulin toxic effect on human health. This study aimed to investigate the capability of yeast cell morphology and cell wall internal structure and composition to adsorb patulin. To compare different yeast cell morphologies, cell wall internal structure and composition, scanning electron microscope, transmission electron microscope and ion chromatography were used. The results indicated that patulin adsorption capability of yeast was influenced by cell surface areas, volume, and cell wall thickness, as well as 1,3-β-glucan content. Among these factors, cell wall thickness and 1,3-β-glucan content serve significant functions. The investigation revealed that patulin adsorption capability was mainly affected by the three-dimensional network structure of the cell wall composed of 1,3-β-glucan. Finally, patulin adsorption in commercial kiwi fruit juice was investigated, and the results indicated that yeast cells could adsorb patulin from commercial kiwi fruit juice efficiently. This study can potentially simulate in vitro cell walls to enhance patulin adsorption capability and successfully apply to fruit juice industry. PMID:26295574

Effect of Yeast Cell Morphology, Cell Wall Physical Structure and Chemical Composition on Patulin Adsorption.

PubMed

Luo, Ying; Wang, Jianguo; Liu, Bin; Wang, Zhouli; Yuan, Yahong; Yue, Tianli

2015-01-01

The capability of yeast to adsorb patulin in fruit juice can aid in substantially reducing the patulin toxic effect on human health. This study aimed to investigate the capability of yeast cell morphology and cell wall internal structure and composition to adsorb patulin. To compare different yeast cell morphologies, cell wall internal structure and composition, scanning electron microscope, transmission electron microscope and ion chromatography were used. The results indicated that patulin adsorption capability of yeast was influenced by cell surface areas, volume, and cell wall thickness, as well as 1,3-β-glucan content. Among these factors, cell wall thickness and 1,3-β-glucan content serve significant functions. The investigation revealed that patulin adsorption capability was mainly affected by the three-dimensional network structure of the cell wall composed of 1,3-β-glucan. Finally, patulin adsorption in commercial kiwi fruit juice was investigated, and the results indicated that yeast cells could adsorb patulin from commercial kiwi fruit juice efficiently. This study can potentially simulate in vitro cell walls to enhance patulin adsorption capability and successfully apply to fruit juice industry.

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.

The interactions between CdSe quantum dots and yeast Saccharomyces cerevisiae: adhesion of quantum dots to the cell surface and the protection effect of ZnS shell.

PubMed

Mei, Jie; Yang, Li-Yun; Lai, Lu; Xu, Zi-Qiang; Wang, Can; Zhao, Jie; Jin, Jian-Cheng; Jiang, Feng-Lei; Liu, Yi

2014-10-01

The interactions between quantum dots (QDs) and biological systems have attracted increasing attention due to concerns on possible toxicity of the nanoscale materials. The biological effects of CdSe QDs and CdSe/ZnS QDs with nearly identical hydrodynamic size on Saccharomyces cerevisiae were investigated via microcalorimetric, spectroscopic and microscopic methods, demonstrating a toxic order CdSe>CdSe/ZnS QDs. CdSe QDs damaged yeast cell wall and reduced the mitochondrial membrane potential. Noteworthy, adhesion of QDs to the yeast cell surface renders this work a good example of interaction site at cell surface, and the epitaxial coating of ZnS could greatly reduce the toxicity of Cd-containing QDs. These results will contribute to the safety evaluation of quantum dots, and provide valuable information for design of nanomaterials. Copyright © 2014 Elsevier Ltd. All rights reserved.

Three-dimensional tertiary structure of yeast phenylalanine transfer RNA

NASA Technical Reports Server (NTRS)

Kim, S. H.; Sussman, J. L.; Suddath, F. L.; Quigley, G. J.; Mcpherson, A.; Wang, A. H. J.; Seeman, N. C.; Rich, A.

1974-01-01

Results of an analysis and interpretation of a 3-A electron density map of yeast phenylalanine transfer RNA. Some earlier detailed assignments of nucleotide residues to electron density peaks are found to be in error, even though the overall tracing of the backbone conformation of yeast phenylalanine transfer RNA was generally correct. A new, more comprehensive interpretation is made which makes it possible to define the tertiary interactions in the molecule. The new interpretation makes it possible to visualize a number of tertiary interactions which not only explain the structural role of most of the bases which are constant in transfer RNAs, but also makes it possible to understand in a direct and simple fashion the chemical modification data on transfer RNA. In addition, this pattern of tertiary interactions provides a basis for understanding the general three-dimensional folding of all transfer RNA molecules.

Markov Logic Networks in the Analysis of Genetic Data

PubMed Central

Sakhanenko, Nikita A.

2010-01-01

Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

Study of budding yeast colony formation and its characterizations by using circular granular cell

NASA Astrophysics Data System (ADS)

Aprianti, D.; Haryanto, F.; Purqon, A.; Khotimah, S. N.; Viridi, S.

2016-03-01

Budding yeast can exhibit colony formation in solid substrate. The colony of pathogenic budding yeast can colonize various surfaces of the human body and medical devices. Furthermore, it can form biofilm that resists drug effective therapy. The formation of the colony is affected by the interaction between cells and with its growth media. The cell budding pattern holds an important role in colony expansion. To study this colony growth, the molecular dynamic method was chosen to simulate the interaction between budding yeast cells. Every cell was modelled by circular granular cells, which can grow and produce buds. Cohesion force, contact force, and Stokes force govern this model to mimic the interaction between cells and with the growth substrate. Characterization was determined by the maximum (L max) and minimum (L min) distances between two cells within the colony and whether two lines that connect the two cells in the maximum and minimum distances intersect each other. Therefore, it can be recognized the colony shape in circular, oval, and irregular shapes. Simulation resulted that colony formation are mostly in oval shape with little branch. It also shows that greater cohesion strength obtains more compact colony formation.

Expression profiling reveals an unexpected growth-stimulating effect of surplus iron on the yeast Saccharomyces cerevisiae.

PubMed

Du, Yang; Cheng, Wang; Li, Wei-Fang

2012-08-01

Iron homeostasis plays a crucial role in growth and division of cells in all kingdoms of life. Although yeast iron metabolism has been extensively studied, little is known about the molecular mechanism of response to surplus iron. In this study, expression profiling of Saccharomyces cerevisiae in the presence of surplus iron revealed a dual effect at 1 and 4 h. A cluster of stress-responsive genes was upregulated via activation of the stress-resistance transcription factor Msn4, which indicated the stress effect of surplus iron on yeast metabolism. Genes involved in aerobic metabolism and several anabolic pathways are also upregulated in iron-surplus conditions, which could significantly accelerate yeast growth. This dual effect suggested that surplus iron might participate in a more complex metabolic network, in addition to serving as a stress inducer. These findings contribute to our understanding of the global response of yeast to the fluctuating availability of iron in the environment.

Global alterations of the transcriptional landscape during yeast growth and development in the absence of Ume6-dependent chromatin modification

PubMed Central

Lardenois, Aurélie; Becker, Emmanuelle; Walther, Thomas; Law, Michael J.; Xie, Bingning; Demougin, Philippe; Strich, Randy

2017-01-01

Chromatin modification enzymes are important regulators of gene expression and some are evolutionarily conserved from yeast to human. Saccharomyces cerevisiae is a major model organism for genome-wide studies that aim at the identification of target genes under the control of conserved epigenetic regulators. Ume6 interacts with the upstream repressor site 1 (URS1) and represses transcription by recruiting both the conserved histone deacetylase Rpd3 (through the co-repressor Sin3) and the chromatin-remodeling factor Isw2. Cells lacking Ume6 are defective in growth, stress response, and meiotic development. RNA profiling studies and in vivo protein-DNA binding assays identified mRNAs or transcript isoforms that are directly repressed by Ume6 in mitosis. However, a comprehensive understanding of the transcriptional alterations, which underlie the complex ume6Δ mutant phenotype during fermentation, respiration, or sporulation, is lacking. We report the protein-coding transcriptome of a diploid MATa/α wild-type and ume6/ume6 mutant strains cultured in rich media with glucose or acetate as a carbon source, or sporulation-inducing medium. We distinguished direct from indirect effects on mRNA levels by combining GeneChip data with URS1 motif predictions and published high-throughput in vivo Ume6-DNA binding data. To gain insight into the molecular interactions between successive waves of Ume6-dependent meiotic genes, we integrated expression data with information on protein networks. Our work identifies novel Ume6 repressed genes during growth and development and reveals a strong effect of the carbon source on the derepression pattern of transcripts in growing and developmentally arrested ume6/ume6 mutant cells. Since yeast is a useful model organism for chromatin-mediated effects on gene expression, our results provide a rich source for further genetic and molecular biological work on the regulation of cell growth and cell differentiation in eukaryotes. PMID:25957495

Global alterations of the transcriptional landscape during yeast growth and development in the absence of Ume6-dependent chromatin modification.

PubMed

Lardenois, Aurélie; Becker, Emmanuelle; Walther, Thomas; Law, Michael J; Xie, Bingning; Demougin, Philippe; Strich, Randy; Primig, Michael

2015-10-01

Chromatin modification enzymes are important regulators of gene expression and some are evolutionarily conserved from yeast to human. Saccharomyces cerevisiae is a major model organism for genome-wide studies that aim at the identification of target genes under the control of conserved epigenetic regulators. Ume6 interacts with the upstream repressor site 1 (URS1) and represses transcription by recruiting both the conserved histone deacetylase Rpd3 (through the co-repressor Sin3) and the chromatin-remodeling factor Isw2. Cells lacking Ume6 are defective in growth, stress response, and meiotic development. RNA profiling studies and in vivo protein-DNA binding assays identified mRNAs or transcript isoforms that are directly repressed by Ume6 in mitosis. However, a comprehensive understanding of the transcriptional alterations, which underlie the complex ume6Δ mutant phenotype during fermentation, respiration, or sporulation, is lacking. We report the protein-coding transcriptome of a diploid MAT a/α wild-type and ume6/ume6 mutant strains cultured in rich media with glucose or acetate as a carbon source, or sporulation-inducing medium. We distinguished direct from indirect effects on mRNA levels by combining GeneChip data with URS1 motif predictions and published high-throughput in vivo Ume6-DNA binding data. To gain insight into the molecular interactions between successive waves of Ume6-dependent meiotic genes, we integrated expression data with information on protein networks. Our work identifies novel Ume6 repressed genes during growth and development and reveals a strong effect of the carbon source on the derepression pattern of transcripts in growing and developmentally arrested ume6/ume6 mutant cells. Since yeast is a useful model organism for chromatin-mediated effects on gene expression, our results provide a rich source for further genetic and molecular biological work on the regulation of cell growth and cell differentiation in eukaryotes.

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

Conjugated Oligoelectrolytes Increase Current Response and Organic Containment Removal in Wastewater Microbial Fuel Cells

DTIC Science & Technology

2012-01-01

certain COEs have been demonstrated to sponta- neously insert into liposomes and within the membranes of yeast .14 The driving force for this...ofDSSN+ and a cartoon illustration of its incorporation into a cell membrane. Introduction of yeast modified withDSSN+ into microbial fuel cells (MFCs...leads to an increase of generated current, compared to unmodified yeast , which suggests improved interaction with the charge-collecting electrode.14

Discovering protein complexes in protein interaction networks via exploring the weak ties effect

PubMed Central

2012-01-01

Background Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges. Results To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. Conclusions We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes. PMID:23046740

Spores of the mycorrhizal fungus Glomus mosseae host yeasts that solubilize phosphate and accumulate polyphosphates.

PubMed

Mirabal Alonso, Loreli; Kleiner, Diethelm; Ortega, Eduardo

2008-04-01

The present paper reports the presence of bacteria and yeasts tightly associated with spores of an isolate of Glomus mosseae. Healthy spores were surface disinfected by combining chloramine-T 5%, Tween-40, and cephalexin 2.5 g L(-1) (CTCf). Macerates of these spores were incubated on agar media, microorganisms were isolated, and two yeasts were characterized (EndoGm1, EndoGm11). Both yeasts were able to solubilize low-soluble P sources (Ca and Fe phosphates) and accumulate polyphosphates (polyPs). Sequence analysis of 18S ribosomal deoxyribonucleic acid showed that the yeasts belong to the genera Rhodotorula or Rhodosporidium (EndoGm1) and Cryptococcus (EndoGm11). Results from inoculation experiments showed an effect of the spore-associated yeasts on the root growth of rice, suggesting potential tripartite interactions with mycorrhizal fungi and plants.

Impact of sodium chloride on wheat flour dough for yeast-leavened products. I. Rheological attributes.

PubMed

Beck, Margit; Jekle, Mario; Becker, Thomas

2012-02-01

The rheological properties of wheat dough for yeast-leavened products were tested at different levels of sodium chloride (NaCl) addition ranging from 0 to 40 g NaCl kg⁻¹ wheat flour. Rheological tests carried out to make this evaluation included (1) empirical rheological methods of the Farinograph, load extension and a dough stickiness test and (2) fundamental rheological methods of creep recovery and dynamic rheometry. Modifications to the gluten matrix microstructure by NaCl were examined by confocal laser-scanning microscopy. Highly significant (P⩽0.001) differences due to NaCl addition could be determined in particular by the stickiness test as well as by examination of the creep test with the Burger model. Rheological changes measured in the creep test probably depend on protein charge shielding due to NaCl interaction, resulting in an improvement in gluten network formation. An increase in dough stickiness was measured when using NaCl. The present result for stickiness is contrary to the common subjective results. Therefore the theory proposed here for increased stickiness suggests that it is based on more non-protein-bound water in the dough system due to NaCl interaction and thus more viscous dough behaviour, which leads to higher stickiness as measured with the stickiness test. This may also suggest that the objectively measured 'stickiness' in this case does not properly indicate the subjectively measured stickiness it was designed to represent. Copyright © 2011 Society of Chemical Industry.

Nup93, a Vertebrate Homologue of Yeast Nic96p, Forms a Complex with a Novel 205-kDa Protein and Is Required for Correct Nuclear Pore Assembly

PubMed Central

Grandi, Paola; Dang, Tam; Pané, Nelly; Shevchenko, Andrej; Mann, Matthias; Forbes, Douglass; Hurt, Ed

1997-01-01

Yeast and vertebrate nuclear pores display significant morphological similarity by electron microscopy, but sequence similarity between the respective proteins has been more difficult to observe. Herein we have identified a vertebrate nucleoporin, Nup93, in both human and Xenopus that has proved to be an evolutionarily related homologue of the yeast nucleoporin Nic96p. Polyclonal antiserum to human Nup93 detects corresponding proteins in human, rat, and Xenopus cells. Immunofluorescence and immunoelectron microscopy localize vertebrate Nup93 at the nuclear basket and at or near the nuclear entry to the gated channel of the pore. Immunoprecipitation from both mammalian and Xenopus cell extracts indicates that a small fraction of Nup93 physically interacts with the nucleoporin p62, just as yeast Nic96p interacts with the yeast p62 homologue. However, a large fraction of vertebrate Nup93 is extracted from pores and is also present in Xenopus egg extracts in complex with a newly discovered 205-kDa protein. Mass spectrometric sequencing of the human 205-kDa protein reveals that this protein is encoded by an open reading frame, KIAAO225, present in the human database. The putative human nucleoporin of 205 kDa has related sequence homologues in Caenorhabditis elegans and Saccharomyces cerevisiae. To analyze the role of the Nup93 complex in the pore, nuclei were assembled that lack the Nup93 complex after immunodepletion of a Xenopus nuclear reconstitution extract. The Nup93-complex–depleted nuclei are clearly defective for correct nuclear pore assembly. From these experiments, we conclude that the vertebrate and yeast pore have significant homology in their functionally important cores and that, with the identification of Nup93 and the 205-kDa protein, we have extended the knowledge of the nearest-neighbor interactions of this core in both yeast and vertebrates. PMID:9348540

A Conserved Deubiquitinating Enzyme Uses Intrinsically Disordered Regions to Scaffold Multiple Protein Interaction Sites*

PubMed Central

Reed, Benjamin J.; Locke, Melissa N.; Gardner, Richard G.

2015-01-01

In the canonical view of protein function, it is generally accepted that the three-dimensional structure of a protein determines its function. However, the past decade has seen a dramatic growth in the identification of proteins with extensive intrinsically disordered regions (IDRs), which are conformationally plastic and do not appear to adopt single three-dimensional structures. One current paradigm for IDR function is that disorder enables IDRs to adopt multiple conformations, expanding the ability of a protein to interact with a wide variety of disparate proteins. The capacity for many interactions is an important feature of proteins that occupy the hubs of protein networks, in particular protein-modifying enzymes that usually have a broad spectrum of substrates. One such protein modification is ubiquitination, where ubiquitin is attached to proteins through ubiquitin ligases (E3s) and removed through deubiquitinating enzymes. Numerous proteomic studies have found that thousands of proteins are dynamically regulated by cycles of ubiquitination and deubiquitination. Thus, how these enzymes target their wide array of substrates is of considerable importance for understanding the function of the cell's diverse ubiquitination networks. Here, we characterize a yeast deubiquitinating enzyme, Ubp10, that possesses IDRs flanking its catalytic protease domain. We show that Ubp10 possesses multiple, distinct binding modules within its IDRs that are necessary and sufficient for directing protein interactions important for Ubp10's known roles in gene silencing and ribosome biogenesis. The human homolog of Ubp10, USP36, also has IDRs flanking its catalytic domain, and these IDRs similarly contain binding modules important for protein interactions. This work highlights the significant protein interaction scaffolding abilities of IDRs in the regulation of dynamic protein ubiquitination. PMID:26149687

Role of Candida albicans polymorphism in interactions with oral epithelial cells.

PubMed

Villar, C C; Kashleva, H; Dongari-Bagtzoglou, A

2004-08-01

Candida albicans is a polymorphic organism which undergoes morphologic transition between yeast, pseudohyphal and hyphal forms. The ability of C. albicans to change from yeast to filamentous types is a major virulence determinant of this organism. However, the exact role of hyphal transformation in establishing oral mucosal infection is still poorly understood. In this study we used mutants with defects in filamentation, as well as oral strains, which differ in their capacity to form true hyphae, to examine the role of hyphal transformation in the interactions of C. albicans with oral epithelial cells in vitro. These interactions included the ability of these strains to adhere to and injure epithelial cells, as well as their ability to trigger a proinflammatory cytokine response. We found that strains SC5314 and ATCC28366 formed true hyphae on epithelial cells, whereas strain ATCC32077 and the tup1/tup1 mutant formed only pseudohyphae. Double mutant efg1/efg1cph1/cph1 grew exclusively as blastospores. We also found that yeast and pseudohyphal strains showed reduced adherence capacity to oral keratinocytes and caused minimal cell damage. Moreover, we showed that both yeast and pseudohyphal forms have a strongly attenuated proinflammatory phenotype, since they failed to induce significant interleukin (IL)-1alpha and IL-8 responses by oral epithelial cells. Germination of C. albicans into true hyphae is particularly important in the interactions with oral epithelial cells in vitro.

The Vineyard Yeast Microbiome, a Mixed Model Microbial Map

PubMed Central

Setati, Mathabatha Evodia; Jacobson, Daniel; Andong, Ursula-Claire; Bauer, Florian

2012-01-01

Vineyards harbour a wide variety of microorganisms that play a pivotal role in pre- and post-harvest grape quality and will contribute significantly to the final aromatic properties of wine. The aim of the current study was to investigate the spatial distribution of microbial communities within and between individual vineyard management units. For the first time in such a study, we applied the Theory of Sampling (TOS) to sample gapes from adjacent and well established commercial vineyards within the same terroir unit and from several sampling points within each individual vineyard. Cultivation-based and molecular data sets were generated to capture the spatial heterogeneity in microbial populations within and between vineyards and analysed with novel mixed-model networks, which combine sample correlations and microbial community distribution probabilities. The data demonstrate that farming systems have a significant impact on fungal diversity but more importantly that there is significant species heterogeneity between samples in the same vineyard. Cultivation-based methods confirmed that while the same oxidative yeast species dominated in all vineyards, the least treated vineyard displayed significantly higher species richness, including many yeasts with biocontrol potential. The cultivatable yeast population was not fully representative of the more complex populations seen with molecular methods, and only the molecular data allowed discrimination amongst farming practices with multivariate and network analysis methods. Importantly, yeast species distribution is subject to significant intra-vineyard spatial fluctuations and the frequently reported heterogeneity of tank samples of grapes harvested from single vineyards at the same stage of ripeness might therefore, at least in part, be due to the differing microbiota in different sections of the vineyard. PMID:23300721

Reconstructing the regulatory circuit of cell fate determination in yeast mating response.

PubMed

Shao, Bin; Yuan, Haiyu; Zhang, Rongfei; Wang, Xuan; Zhang, Shuwen; Ouyang, Qi; Hao, Nan; Luo, Chunxiong

2017-07-01

Massive technological advances enabled high-throughput measurements of proteomic changes in biological processes. However, retrieving biological insights from large-scale protein dynamics data remains a challenging task. Here we used the mating differentiation in yeast Saccharomyces cerevisiae as a model and developed integrated experimental and computational approaches to analyze the proteomic dynamics during the process of cell fate determination. When exposed to a high dose of mating pheromone, the yeast cell undergoes growth arrest and forms a shmoo-like morphology; however, at intermediate doses, chemotropic elongated growth is initialized. To understand the gene regulatory networks that control this differentiation switch, we employed a high-throughput microfluidic imaging system that allows real-time and simultaneous measurements of cell growth and protein expression. Using kinetic modeling of protein dynamics, we classified the stimulus-dependent changes in protein abundance into two sources: global changes due to physiological alterations and gene-specific changes. A quantitative framework was proposed to decouple gene-specific regulatory modes from the growth-dependent global modulation of protein abundance. Based on the temporal patterns of gene-specific regulation, we established the network architectures underlying distinct cell fates using a reverse engineering method and uncovered the dose-dependent rewiring of gene regulatory network during mating differentiation. Furthermore, our results suggested a potential crosstalk between the pheromone response pathway and the target of rapamycin (TOR)-regulated ribosomal biogenesis pathway, which might underlie a cell differentiation switch in yeast mating response. In summary, our modeling approach addresses the distinct impacts of the global and gene-specific regulation on the control of protein dynamics and provides new insights into the mechanisms of cell fate determination. We anticipate that our integrated experimental and modeling strategies could be widely applicable to other biological systems.

Altered Fermentation Performances, Growth, and Metabolic Footprints Reveal Competition for Nutrients between Yeast Species Inoculated in Synthetic Grape Juice-Like Medium.

PubMed

Rollero, Stephanie; Bloem, Audrey; Ortiz-Julien, Anne; Camarasa, Carole; Divol, Benoit

2018-01-01

The sequential inoculation of non- Saccharomyces yeasts and Saccharomyces cerevisiae in grape juice is becoming an increasingly popular practice to diversify wine styles and/or to obtain more complex wines with a peculiar microbial footprint. One of the main interactions is competition for nutrients, especially nitrogen sources, that directly impacts not only fermentation performance but also the production of aroma compounds. In order to better understand the interactions taking place between non-Saccharomyces yeasts and S. cerevisiae during alcoholic fermentation, sequential inoculations of three yeast species ( Pichia burtonii, Kluyveromyces marxianus, Zygoascus meyerae ) with S. cerevisiae were performed individually in a synthetic medium. Different species-dependent interactions were evidenced. Indeed, the three sequential inoculations resulted in three different behaviors in terms of growth. P. burtonii and Z. meyerae declined after the inoculation of S. cerevisiae which promptly outcompeted the other two species. However, while the presence of P. burtonii did not impact the fermentation kinetics of S. cerevisiae , that of Z. meyerae rendered the overall kinetics very slow and with no clear exponential phase. K. marxianus and S. cerevisiae both declined and became undetectable before fermentation completion. The results also demonstrated that yeasts differed in their preference for nitrogen sources. Unlike Z. meyerae and P. burtonii, K. marxianus appeared to be a competitor for S. cerevisiae (as evidenced by the uptake of ammonium and amino acids), thereby explaining the resulting stuck fermentation. Nevertheless, the results suggested that competition for other nutrients (probably vitamins) occurred during the sequential inoculation of Z. meyerae with S. cerevisiae . The metabolic footprint of the non- Saccharomyces yeasts determined after 48 h of fermentation remained until the end of fermentation and combined with that of S. cerevisiae . For instance, fermentations performed with K. marxianus were characterized by the formation of phenylethanol and phenylethyl acetate, while those performed with P. burtonii or Z. meyerae displayed higher production of isoamyl alcohol and ethyl esters. When considering sequential inoculation of yeasts, the nutritional requirements of the yeasts used should be carefully considered and adjusted accordingly. Finally, our chemical data suggests that the organoleptic properties of the wine are altered in a species specific manner.

Role of Glucosyltransferase B in Interactions of Candida albicans with Streptococcus mutans and with an Experimental Pellicle on Hydroxyapatite Surfaces ▿ †

PubMed Central

Gregoire, S.; Xiao, J.; Silva, B. B.; Gonzalez, I.; Agidi, P. S.; Klein, M. I.; Ambatipudi, K. S.; Rosalen, P. L.; Bauserman, R.; Waugh, R. E.; Koo, H.

2011-01-01

Candida albicans and mutans streptococci are frequently detected in dental plaque biofilms from toddlers afflicted with early childhood caries. Glucosyltransferases (Gtfs) secreted by Streptococcus mutans bind to saliva-coated apatite (sHA) and to bacterial surfaces, synthesizing exopolymers in situ, which promote cell clustering and adherence to tooth enamel. We investigated the potential role Gtfs may play in mediating the interactions between C. albicans SC5314 and S. mutans UA159, both with each other and with the sHA surface. GtfB adhered effectively to the C. albicans yeast cell surface in an enzymatically active form, as determined by scintillation spectroscopy and fluorescence imaging. The glucans formed on the yeast cell surface were more susceptible to dextranase than those synthesized in solution or on sHA and bacterial cell surfaces (P < 0.05), indicating an elevated α-1,6-linked glucose content. Fluorescence imaging revealed that larger numbers of S. mutans cells bound to C. albicans cells with glucans present on their surface than to yeast cells without surface glucans (uncoated). The glucans formed in situ also enhanced C. albicans interactions with sHA, as determined by a novel single-cell micromechanical method. Furthermore, the presence of glucan-coated yeast cells significantly increased the accumulation of S. mutans on the sHA surface (versus S. mutans incubated alone or mixed with uncoated C. albicans; P < 0.05). These data reveal a novel cross-kingdom interaction that is mediated by bacterial GtfB, which readily attaches to the yeast cell surface. Surface-bound GtfB promotes the formation of a glucan-rich matrix in situ and may enhance the accumulation of S. mutans on the tooth enamel surface, thereby modulating the development of virulent biofilms. PMID:21803906

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function prediction in the context of PPI networks, the explicit consideration of network autocorrelation increases the predictive performance of the learned models. Overall, we found that this holds for different gene features/ descriptions, functional annotation schemes, and PPI networks: Best results are achieved when the PPI network is dense and contains a large proportion of function-relevant interactions.

Proteomic analysis of interaction between P7-1 of Southern rice black-streaked dwarf virus and the insect vector reveals diverse insect proteins involved in successful transmission.

PubMed

Mar, ThiThi; Liu, Wenwen; Wang, Xifeng

2014-05-06

Southern rice black-streaked dwarf virus (SRBSDV), transmitted by the white-backed planthopper (Sogatella furcifera) in a persistent-propagative manner, has caused serious yield losses in Asia. Here in a yeast two-hybrid system, protein interactions between SRBSDV P7-1 as a bait protein and a cDNA library of S. furcifera as prey protein were assessed. Of 153 proteins identified as putative interactors, 24 were selected for further analysis. Of the 24 proteins, 18 were further confirmed in a chemiluminescent coimmunoprecipitation (Co-IP) assay as true positive interactors with different strengths of interactions. Six potential candidate proteins (neuroglian, myosin light chain 2 [MLC2], polyubiquitin, E3 ubiquitin ligase, ribophorin ii, and profilin) were analyzed for gene expression in five organs by qRT-PCR; mRNA levels were highest in the gut for neuroglian, MLC2, polyubiquitin and profilin, in the salivary glands for ribophorin ii, and in the haemolymph for E3 ubiquitin ligase. A virus-host protein interaction network was constructed using SRBSDV P7-1 and 18 prey positive protein homologs of Drosophila melanogaster. Our findings suggest that these proteins are involved in the complex host reaction to infection by SRBSDV and provide new insights into the molecular basis of transmission. Southern rice black-streaked dwarf virus (SRBSDV), transmitted by S. furcifera in a persistent-propagative manner, is a new found virus and a tentative member of the genus Fijivirus in the family Reoviridae. It was widely noted by plant virologist, government officials and the farmers in Asia in recent years because of its epidemic outbreak and causing serious yield losses after 2009. However, the molecular mechanism by which SRBSDV successfully infects and replicates in both plant and insect hosts remains unclear, and much less is known about how the virus spreads from initially infected cells to adjacent cells in the insect vector. In the present study, we examined protein interactions between SRBSDV P7-1 as the bait and cDNA library of WBPH as the prey by using yeast two-hybrid system, 153 proteins were identified as putative interactors and 24 putative proteins were selected for chemiluminescent coimmunoprecipitation (Co-IP) assay, and then constructed a viral protein-host protein interaction network with homologs of D. melanogaster. Six WBPH proteins were confirmed as potential P7-1 partners that take part in a pivotal role for viral movement in insect vector. These findings will greatly facilitate the understanding of the transmission mechanisms of SRBSDV by its insect vector. This is the first to study the molecular interaction between SRBSDV and its insect vector. Copyright © 2014 Elsevier B.V. All rights reserved.

Effect of chromosome tethering on nuclear organization in yeast.

PubMed

Avşaroğlu, Barış; Bronk, Gabriel; Gordon-Messer, Susannah; Ham, Jungoh; Bressan, Debra A; Haber, James E; Kondev, Jane

2014-01-01

Interphase chromosomes in Saccharomyces cerevisiae are tethered to the nuclear envelope at their telomeres and to the spindle pole body (SPB) at their centromeres. Using a polymer model of yeast chromosomes that includes these interactions, we show theoretically that telomere attachment to the nuclear envelope is a major determinant of gene positioning within the nucleus only for genes within 10 kb of the telomeres. We test this prediction by measuring the distance between the SPB and the silent mating locus (HML) on chromosome III in wild-type and mutant yeast strains that contain altered chromosome-tethering interactions. In wild-type yeast cells we find that disruption of the telomere tether does not dramatically change the position of HML with respect to the SPB, in agreement with theoretical predictions. Alternatively, using a mutant strain with a synthetic tether that localizes an HML-proximal site to the nuclear envelope, we find a significant change in the SPB-HML distance, again as predicted by theory. Our study quantifies the importance of tethering at telomeres on the organization of interphase chromosomes in yeast, which has been shown to play a significant role in determining chromosome function such as gene expression and recombination.

Effect of Chromosome Tethering on Nuclear Organization in Yeast

PubMed Central

Avşaroğlu, Barış; Bronk, Gabriel; Gordon-Messer, Susannah; Ham, Jungoh; Bressan, Debra A.; Haber, James E.; Kondev, Jane

2014-01-01

Interphase chromosomes in Saccharomyces cerevisiae are tethered to the nuclear envelope at their telomeres and to the spindle pole body (SPB) at their centromeres. Using a polymer model of yeast chromosomes that includes these interactions, we show theoretically that telomere attachment to the nuclear envelope is a major determinant of gene positioning within the nucleus only for genes within 10 kb of the telomeres. We test this prediction by measuring the distance between the SPB and the silent mating locus (HML) on chromosome III in wild–type and mutant yeast strains that contain altered chromosome-tethering interactions. In wild-type yeast cells we find that disruption of the telomere tether does not dramatically change the position of HML with respect to the SPB, in agreement with theoretical predictions. Alternatively, using a mutant strain with a synthetic tether that localizes an HML-proximal site to the nuclear envelope, we find a significant change in the SPB-HML distance, again as predicted by theory. Our study quantifies the importance of tethering at telomeres on the organization of interphase chromosomes in yeast, which has been shown to play a significant role in determining chromosome function such as gene expression and recombination. PMID:25020108

Identification of Chemical-Genetic Interactions via Parallel Analysis of Barcoded Yeast Strains.

PubMed

Suresh, Sundari; Schlecht, Ulrich; Xu, Weihong; Miranda, Molly; Davis, Ronald W; Nislow, Corey; Giaever, Guri; St Onge, Robert P

2016-09-01

The Yeast Knockout Collection is a complete set of gene deletion strains for the budding yeast, Saccharomyces cerevisiae In each strain, one of approximately 6000 open-reading frames is replaced with a dominant selectable marker flanked by two DNA barcodes. These barcodes, which are unique to each gene, allow the growth of thousands of strains to be individually measured from a single pooled culture. The collection, and other resources that followed, has ushered in a new era in chemical biology, enabling unbiased and systematic identification of chemical-genetic interactions (CGIs) with remarkable ease. CGIs link bioactive compounds to biological processes, and hence can reveal the mechanism of action of growth-inhibitory compounds in vivo, including those of antifungal, antibiotic, and anticancer drugs. The chemogenomic profiling method described here measures the sensitivity induced in yeast heterozygous and homozygous deletion strains in the presence of a chemical inhibitor of growth (termed haploinsufficiency profiling and homozygous profiling, respectively, or HIPHOP). The protocol is both scalable and amenable to automation. After competitive growth of yeast knockout collection cultures, with and without chemical inhibitors, CGIs can be identified and quantified using either array- or sequencing-based approaches as described here. © 2016 Cold Spring Harbor Laboratory Press.

Skin diseases associated with Malassezia yeasts: facts and controversies.

PubMed

Gaitanis, Georgios; Velegraki, Aristea; Mayser, Peter; Bassukas, Ioannis D

2013-01-01

The implication of the yeast genus Malassezia in skin diseases has been characterized by controversy, since the first description of the fungal nature of pityriasis versicolor in 1846 by Eichstedt. This is underscored by the existence of Malassezia yeasts as commensal but also by their implication in diseases with distinct absence of inflammation despite the heavy fungal load (pityriasis versicolor) or with characteristic inflammation (eg, seborrheic dermatitis, atopic dermatitis, folliculitis, or psoriasis). The description of 14 Malassezia species and subsequent worldwide epidemiologic studies did not reveal pathogenic species but rather disease-associated subtypes within species. Emerging evidence demonstrates that the interaction of Malassezia yeasts with the skin is multifaceted and entails constituents of the fungal wall (melanin, lipid cover), enzymes (lipases, phospholipases), and metabolic products (indoles), as well as the cellular components of the epidermis (keratinocytes, dendritic cells, and melanocytes). Understanding the complexity of their interactions will highlight the controversies on the clinical presentation of Malassezia-associated diseases and unravel the complexity of skin homeostatic mechanisms. Copyright © 2013 Elsevier Inc. All rights reserved.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network.

PubMed

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C; Mohanty, Bidyut K; Gao, Nan; Tang, Jijun; Lawson, Andrew B; Hannun, Yusuf A; Zheng, W Jim

2014-10-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms' corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network

PubMed Central

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C.; Mohanty, Bidyut K.; Gao, Nan; Tang, Jijun; Lawson, Andrew B.; Hannun, Yusuf A.; Zheng, W. Jim

2014-01-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes’ Ontology Fingerprints—a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms’ corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. PMID:25063300

A Novel Hybrid Iron Regulation Network Combines Features from Pathogenic and Nonpathogenic Yeasts

PubMed Central

Gerwien, Franziska; Safyan, Abu; Wisgott, Stephanie; Hille, Fabrice; Kaemmer, Philipp; Linde, Jörg; Brunke, Sascha; Kasper, Lydia

2016-01-01

ABSTRACT Iron is an essential micronutrient for both pathogens and their hosts, which restrict iron availability during infections in an effort to prevent microbial growth. Successful human pathogens like the yeast Candida glabrata have thus developed effective iron acquisition strategies. Their regulation has been investigated well for some pathogenic fungi and in the model organism Saccharomyces cerevisiae, which employs an evolutionarily derived system. Here, we show that C. glabrata uses a regulation network largely consisting of components of the S. cerevisiae regulon but also of elements of other pathogenic fungi. Specifically, similarly to baker’s yeast, Aft1 is the main positive regulator under iron starvation conditions, while Cth2 degrades mRNAs encoding iron-requiring enzymes. However, unlike the case with S. cerevisiae, a Sef1 ortholog is required for full growth under iron limitation conditions, making C. glabrata an evolutionary intermediate to SEF1-dependent fungal pathogens. Therefore, C. glabrata has evolved an iron homeostasis system which seems to be unique within the pathogenic fungi. PMID:27795405

Visualization and quantification of three-dimensional distribution of yeast in bread dough.

PubMed

Maeda, Tatsuro; DO, Gab-Soo; Sugiyama, Junichi; Araki, Tetsuya; Tsuta, Mizuki; Shiraga, Seizaburo; Ueda, Mitsuyoshi; Yamada, Masaharu; Takeya, Koji; Sagara, Yasuyuki

2009-07-01

A three-dimensional (3-D) bio-imaging technique was developed for visualizing and quantifying the 3-D distribution of yeast in frozen bread dough samples in accordance with the progress of the mixing process of the samples, applying cell-surface engineering to the surfaces of the yeast cells. The fluorescent yeast was recognized as bright spots at the wavelength of 520 nm. Frozen dough samples were sliced at intervals of 1 microm by an micro-slicer image processing system (MSIPS) equipped with a fluorescence microscope for acquiring cross-sectional images of the samples. A set of successive two-dimensional images was reconstructed to analyze the 3-D distribution of the yeast. The average shortest distance between centroids of enhanced green fluorescent protein (EGFP) yeasts was 10.7 microm at the pick-up stage, 9.7 microm at the clean-up stage, 9.0 microm at the final stage, and 10.2 microm at the over-mixing stage. The results indicated that the distribution of the yeast cells was the most uniform in the dough of white bread at the final stage, while the heterogeneous distribution at the over-mixing stage was possibly due to the destruction of the gluten network structure within the samples.

Studying p53 family proteins in yeast: Induction of autophagic cell death and modulation by interactors and small molecules

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

Leão, Mariana; Gomes, Sara; Bessa, Cláudia

In this work, the yeast Saccharomyces cerevisiae was used to individually study human p53, p63 (full length and truncated forms) and p73. Using this cell system, the effect of these proteins on cell proliferation and death, and the influence of MDM2 and MDMX on their activities were analyzed. When expressed in yeast, wild-type p53, TAp63, ΔNp63 and TAp73 induced growth inhibition associated with S-phase cell cycle arrest. This growth inhibition was accompanied by reactive oxygen species production and autophagic cell death. Furthermore, they stimulated rapamycin-induced autophagy. On the contrary, none of the tested p53 family members induced apoptosis either permore » se or after apoptotic stimuli. As previously reported for p53, also TAp63, ΔNp63 and TAp73 increased actin expression levels and its depolarization, suggesting that ACT1 is also a p63 and p73 putative yeast target gene. Additionally, MDM2 and MDMX inhibited the activity of all tested p53 family members in yeast, although the effect was weaker on TAp63. Moreover, Nutlin-3a and SJ-172550 were identified as potential inhibitors of the p73 interaction with MDM2 and MDMX, respectively. Altogether, the yeast-based assays herein developed can be envisaged as a simplified cell system to study the involvement of p53 family members in autophagy, the modulation of their activities by specific interactors (MDM2 and MDMX), and the potential of new small molecules to modulate these interactions. - Highlights: • p53, p63 and p73 are individually studied in the yeast S. cerevisiae. • p53 family members induce ROS production, cell cycle arrest and autophagy in yeast. • p53 family members increase actin depolarization and expression levels in yeast. • MDM2 and MDMX inhibit the activity of p53 family members in yeast. • Yeast can be a useful tool to study the biology and drugability of p53, p63 and p73.« less

Interaction between lactic acid bacteria and yeasts in airag, an alcoholic fermented milk.

PubMed

Sudun; Wulijideligen; Arakawa, Kensuke; Miyamoto, Mari; Miyamoto, Taku

2013-01-01

The interaction between nine lactic acid bacteria (LAB) and five yeast strains isolated from airag of Inner Mongolia Autonomic Region, China was investigated. Three representative LAB and two yeasts showed symbioses were selected and incubated in 10% (w/v) reconstituted skim milk as single and mixed cultures to measure viable count, titratable acidity, ethanol and sugar content every 24 h for 1 week. LAB and yeasts showed high viable counts in the mixed cultures compared to the single cultures. Titratable acidity of the mixed cultures was obviously enhanced compared with that of the single cultures, except for the combinations of Lactobacillus reuteri 940B3 with Saccharomyces cerevisiae 4C and Lactobacillus helveticus 130B4 with Candida kefyr 2Y305. C. kefyr 2Y305 produced large amounts of ethanol (maximum 1.35 g/L), whereas non-lactose-fermenting S. cerevisiae 4C produced large amounts of ethanol only in the mixed cultures. Total glucose and galactose content increased while lactose content decreased in the single cultures of Leuconostoc mesenteroides 6B2081 and Lb. helveticus 130B4. However, both glucose and galactose were completely consumed and lactose was markedly reduced in the mixed cultures with yeasts. The result suggests that yeasts utilize glucose and galactose produced by LAB lactase to promote cell growth. © 2012 The Authors. Animal Science Journal © 2012 Japanese Society of Animal Science.

Identification and functional analysis of a core gene module associated with hepatitis C virus-induced human hepatocellular carcinoma progression.

PubMed

Bai, Gaobo; Zheng, Wenling; Ma, Wenli

2018-05-01

Hepatitis C virus (HCV)-induced human hepatocellular carcinoma (HCC) progression may be due to a complex multi-step processes. The developmental mechanism of these processes is worth investigating for the prevention, diagnosis and therapy of HCC. The aim of the present study was to investigate the molecular mechanism underlying the progression of HCV-induced hepatocarcinogenesis. First, the dynamic gene module, consisting of key genes associated with progression between the normal stage and HCC, was identified using the Weighted Gene Co-expression Network Analysis tool from R language. By defining those genes in the module as seeds, the change of co-expression in differentially expressed gene sets in two consecutive stages of pathological progression was examined. Finally, interaction pairs of HCV viral proteins and their directly targeted proteins in the identified module were extracted from the literature and a comprehensive interaction dataset from yeast two-hybrid experiments. By combining the interactions between HCV and their targets, and protein-protein interactions in the Search Tool for the Retrieval of Interacting Genes database (STRING), the HCV-key genes interaction network was constructed and visualized using Cytoscape software 3.2. As a result, a module containing 44 key genes was identified to be associated with HCC progression, due to the dynamic features and functions of those genes in the module. Several important differentially co-expressed gene pairs were identified between non-HCC and HCC stages. In the key genes, cyclin dependent kinase 1 (CDK1), NDC80, cyclin A2 (CCNA2) and rac GTPase activating protein 1 (RACGAP1) were shown to be targeted by the HCV nonstructural proteins NS5A, NS3 and NS5B, respectively. The four genes perform an intermediary role between the HCV viral proteins and the dysfunctional module in the HCV key genes interaction network. These findings provided valuable information for understanding the mechanism of HCV-induced HCC progression and for seeking drug targets for the therapy and prevention of HCC.

Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.

PubMed

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.

Cytoskeletal dynamics in fission yeast: a review of models for polarization and division

PubMed Central

Drake, Tyler; Vavylonis, Dimitrios

2010-01-01

We review modeling studies concerning cytoskeletal activity of fission yeast. Recent models vary in length and time scales, describing a range of phenomena from cellular morphogenesis to polymer assembly. The components of cytoskeleton act in concert to mediate cell-scale events and interactions such as polarization. The mathematical models reduce these events and interactions to their essential ingredients, describing the cytoskeleton by its bulk properties. On a smaller scale, models describe cytoskeletal subcomponents and how bulk properties emerge. PMID:21119765

Yeast nitrogen utilization in the phyllosphere during plant lifespan under regulation of autophagy

PubMed Central

Shiraishi, Kosuke; Oku, Masahide; Kawaguchi, Kosuke; Uchida, Daichi; Yurimoto, Hiroya; Sakai, Yasuyoshi

2015-01-01

Recently, microbe-plant interactions at the above-ground parts have attracted great attention. Here we describe nitrogen metabolism and regulation of autophagy in the methylotrophic yeast Candida boidinii, proliferating and surviving on the leaves of Arabidopsis thaliana. After quantitative analyses of yeast growth on the leaves of A. thaliana with the wild-type and several mutant yeast strains, we showed that on young leaves, nitrate reductase (Ynr1) was necessary for yeast proliferation, and the yeast utilized nitrate as nitrogen source. On the other hand, a newly developed methylamine sensor revealed appearance of methylamine on older leaves, and methylamine metabolism was induced in C. boidinii, and Ynr1 was subjected to degradation. Biochemical and microscopic analysis of Ynr1 in vitro during a shift of nitrogen source from nitrate to methylamine revealed that Ynr1 was transported to the vacuole being the cargo for biosynthetic cytoplasm-to-vacuole targeting (Cvt) pathway, and degraded. Our results reveal changes in the nitrogen source composition for phyllospheric yeasts during plant aging, and subsequent adaptation of the yeasts to this environmental change mediated by regulation of autophagy. PMID:25900611

Interaction Between Yeasts and Zinc

NASA Astrophysics Data System (ADS)

Nicola, Raffaele De; Walker, Graeme

Zinc is an essential trace element in biological systems. For example, it acts as a cellular membrane stabiliser, plays a critical role in gene expression and genome modification and activates nearly 300 enzymes, including alcohol dehydrogenase. The present chapter will be focused on the influence of zinc on cell physiology of industrial yeast strains of Saccharomyces cerevisiae, with special regard to the uptake and subsequent utilisation of this metal. Zinc uptake by yeast is metabolism-dependent, with most of the available zinc translocated very quickly into the vacuole. At cell division, zinc is distributed from mother to daughter cells and this effectively lowers the individual cellular zinc concentration, which may become zinc depleted at the onset of the fermentation. Zinc influences yeast fermentative performance and examples will be provided relating to brewing and wine fermentations. Industrial yeasts are subjected to several stresses that may impair fermentation performance. Such stresses may also impact on yeast cell zinc homeostasis. This chapter will discuss the practical implications for the correct management of zinc bioavailability for yeast-based biotechnologies aimed at improving yeast growth, viability, fermentation performance and resistance to environmental stresses

Downsides and benefits of unicellularity in budding yeast

NASA Astrophysics Data System (ADS)

Balazsi, Gabor; Chen, Lin; Kuzdzal-Fick, Jennie

Yeast cells that do not separate after cell division form clumps. Clumping was shown to aid utilization of certain sugars, but its effects in stressful conditions are unknown. Generally speaking, what are the costs and benefits of unicellularity versus clumping multicellularity in normal and stressful conditions? To address this question, we evolved clumping yeast towards unicellularity by continuously propagating only those cells that remain suspended in liquid culture after settling. Whole-genome sequencing indicated that mutations in the AMN1 (antagonist of mitotic exit network) gene underlie the changes from clumping to unicellular phenotypes in these evolved yeast cells. Simple models predict that clumping should hinder growth in normal conditions while being protective in stress. Accordingly, we find experimentally that yeast clumps are more resistant to freeze/thaw, hydrogen peroxide, and ethanol stressors than their unicellular counterparts. On the other hand, unicellularity seems to be advantageous in normal conditions. Overall, these results reveal the downsides and benefits of unicellularity in different environmental conditions and uncover its genetic bases in yeast. This research was supported by the NIH Director's New Innovator Award Program (1DP2 OD006481-01), by NSF/IOS 1021675 and the Laufer Center for Physical & Quantitative Biology.

Global investigation of protein-protein interactions in yeast Saccharomyces cerevisiae using re-occurring short polypeptide sequences.

PubMed

Pitre, S; North, C; Alamgir, M; Jessulat, M; Chan, A; Luo, X; Green, J R; Dumontier, M; Dehne, F; Golshani, A

2008-08-01

Protein-protein interaction (PPI) maps provide insight into cellular biology and have received considerable attention in the post-genomic era. While large-scale experimental approaches have generated large collections of experimentally determined PPIs, technical limitations preclude certain PPIs from detection. Recently, we demonstrated that yeast PPIs can be computationally predicted using re-occurring short polypeptide sequences between known interacting protein pairs. However, the computational requirements and low specificity made this method unsuitable for large-scale investigations. Here, we report an improved approach, which exhibits a specificity of approximately 99.95% and executes 16,000 times faster. Importantly, we report the first all-to-all sequence-based computational screen of PPIs in yeast, Saccharomyces cerevisiae in which we identify 29,589 high confidence interactions of approximately 2 x 10(7) possible pairs. Of these, 14,438 PPIs have not been previously reported and may represent novel interactions. In particular, these results reveal a richer set of membrane protein interactions, not readily amenable to experimental investigations. From the novel PPIs, a novel putative protein complex comprised largely of membrane proteins was revealed. In addition, two novel gene functions were predicted and experimentally confirmed to affect the efficiency of non-homologous end-joining, providing further support for the usefulness of the identified PPIs in biological investigations.

Tools and Models for Integrating Multiple Cellular Networks

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

Gerstein, Mark

2015-11-06

In this grant, we have systematically investigated the integrated networks, which are responsible for the coordination of activity between metabolic pathways in prokaryotes. We have developed several computational tools to analyze the topology of the integrated networks consisting of metabolic, regulatory, and physical interaction networks. The tools are all open-source, and they are available to download from Github, and can be incorporated in the Knowledgebase. Here, we summarize our work as follow. Understanding the topology of the integrated networks is the first step toward understanding its dynamics and evolution. For Aim 1 of this grant, we have developed a novelmore » algorithm to determine and measure the hierarchical structure of transcriptional regulatory networks [1]. The hierarchy captures the direction of information flow in the network. The algorithm is generally applicable to regulatory networks in prokaryotes, yeast and higher organisms. Integrated datasets are extremely beneficial in understanding the biology of a system in a compact manner due to the conflation of multiple layers of information. Therefore for Aim 2 of this grant, we have developed several tools and carried out analysis for integrating system-wide genomic information. To make use of the structural data, we have developed DynaSIN for protein-protein interactions networks with various dynamical interfaces [2]. We then examined the association between network topology with phenotypic effects such as gene essentiality. In particular, we have organized E. coli and S. cerevisiae transcriptional regulatory networks into hierarchies. We then correlated gene phenotypic effects by tinkering with different layers to elucidate which layers were more tolerant to perturbations [3]. In the context of evolution, we also developed a workflow to guide the comparison between different types of biological networks across various species using the concept of rewiring [4], and Furthermore, we have developed CRIT for correlation analysis in systems biology [5]. For Aim 3, we have further investigated the scaling relationship that the number of Transcription Factors (TFs) in a genome is proportional to the square of the total number of genes. We have extended the analysis from transcription factors to various classes of functional categories, and from individual categories to joint distribution [6]. By introducing a new analytical framework, we have generalized the original toolbox model to take into account of metabolic network with arbitrary network topology [7].« less

Transcriptome-Based Characterization of Interactions between Saccharomyces cerevisiae and Lactobacillus delbrueckii subsp. bulgaricus in Lactose-Grown Chemostat Cocultures

PubMed Central

Mendes, Filipa; Sieuwerts, Sander; de Hulster, Erik; Almering, Marinka J. H.; Luttik, Marijke A. H.; Pronk, Jack T.; Smid, Eddy J.; Bron, Peter A.

2013-01-01

Mixed populations of Saccharomyces cerevisiae yeasts and lactic acid bacteria occur in many dairy, food, and beverage fermentations, but knowledge about their interactions is incomplete. In the present study, interactions between Saccharomyces cerevisiae and Lactobacillus delbrueckii subsp. bulgaricus, two microorganisms that co-occur in kefir fermentations, were studied during anaerobic growth on lactose. By combining physiological and transcriptome analysis of the two strains in the cocultures, five mechanisms of interaction were identified. (i) Lb. delbrueckii subsp. bulgaricus hydrolyzes lactose, which cannot be metabolized by S. cerevisiae, to galactose and glucose. Subsequently, galactose, which cannot be metabolized by Lb. delbrueckii subsp. bulgaricus, is excreted and provides a carbon source for yeast. (ii) In pure cultures, Lb. delbrueckii subsp. bulgaricus grows only in the presence of increased CO2 concentrations. In anaerobic mixed cultures, the yeast provides this CO2 via alcoholic fermentation. (iii) Analysis of amino acid consumption from the defined medium indicated that S. cerevisiae supplied alanine to the bacterium. (iv) A mild but significant low-iron response in the yeast transcriptome, identified by DNA microarray analysis, was consistent with the chelation of iron by the lactate produced by Lb. delbrueckii subsp. bulgaricus. (v) Transcriptome analysis of Lb. delbrueckii subsp. bulgaricus in mixed cultures showed an overrepresentation of transcripts involved in lipid metabolism, suggesting either a competition of the two microorganisms for fatty acids or a response to the ethanol produced by S. cerevisiae. This study demonstrates that chemostat-based transcriptome analysis is a powerful tool to investigate microbial interactions in mixed populations. PMID:23872557

Transcriptome-based characterization of interactions between Saccharomyces cerevisiae and Lactobacillus delbrueckii subsp. bulgaricus in lactose-grown chemostat cocultures.

PubMed

Mendes, Filipa; Sieuwerts, Sander; de Hulster, Erik; Almering, Marinka J H; Luttik, Marijke A H; Pronk, Jack T; Smid, Eddy J; Bron, Peter A; Daran-Lapujade, Pascale

2013-10-01

Mixed populations of Saccharomyces cerevisiae yeasts and lactic acid bacteria occur in many dairy, food, and beverage fermentations, but knowledge about their interactions is incomplete. In the present study, interactions between Saccharomyces cerevisiae and Lactobacillus delbrueckii subsp. bulgaricus, two microorganisms that co-occur in kefir fermentations, were studied during anaerobic growth on lactose. By combining physiological and transcriptome analysis of the two strains in the cocultures, five mechanisms of interaction were identified. (i) Lb. delbrueckii subsp. bulgaricus hydrolyzes lactose, which cannot be metabolized by S. cerevisiae, to galactose and glucose. Subsequently, galactose, which cannot be metabolized by Lb. delbrueckii subsp. bulgaricus, is excreted and provides a carbon source for yeast. (ii) In pure cultures, Lb. delbrueckii subsp. bulgaricus grows only in the presence of increased CO2 concentrations. In anaerobic mixed cultures, the yeast provides this CO2 via alcoholic fermentation. (iii) Analysis of amino acid consumption from the defined medium indicated that S. cerevisiae supplied alanine to the bacterium. (iv) A mild but significant low-iron response in the yeast transcriptome, identified by DNA microarray analysis, was consistent with the chelation of iron by the lactate produced by Lb. delbrueckii subsp. bulgaricus. (v) Transcriptome analysis of Lb. delbrueckii subsp. bulgaricus in mixed cultures showed an overrepresentation of transcripts involved in lipid metabolism, suggesting either a competition of the two microorganisms for fatty acids or a response to the ethanol produced by S. cerevisiae. This study demonstrates that chemostat-based transcriptome analysis is a powerful tool to investigate microbial interactions in mixed populations.

Differential Adsorption of Ochratoxin A and Anthocyanins by Inactivated Yeasts and Yeast Cell Walls during Simulation of Wine Aging

PubMed Central

Petruzzi, Leonardo; Baiano, Antonietta; De Gianni, Antonio; Sinigaglia, Milena; Corbo, Maria Rosaria; Bevilacqua, Antonio

2015-01-01

The adsorption of ochratoxin A (OTA) by yeasts is a promising approach for the decontamination of musts and wines, but some potential competitive or interactive phenomena between mycotoxin, yeast cells, and anthocyanins might modify the intensity of the phenomenon. The aim of this study was to examine OTA adsorption by two strains of Saccharomyces cerevisiae (the wild strain W13, and the commercial isolate BM45), previously inactivated by heat, and a yeast cell wall preparation. Experiments were conducted using Nero di Troia red wine contaminated with 2 μg/L OTA and supplemented with yeast biomass (20 g/L). The samples were analyzed periodically to assess mycotoxin concentration, chromatic characteristics, and total anthocyanins over 84 days of aging. Yeast cell walls revealed the highest OTA-adsorption in comparison to thermally-inactivated cells (50% vs. 43% toxin reduction), whilst no significant differences were found for the amount of adsorbed anthocyanins in OTA-contaminated and control wines. OTA and anthocyanins adsorption were not competitive phenomena. Unfortunately, the addition of yeast cells to wine could cause color loss; therefore, yeast selection should also focus on this trait to select the best strain. PMID:26516913

On the contributions of topological features to transcriptional regulatory network robustness

PubMed Central

2012-01-01

Background Because biological networks exhibit a high-degree of robustness, a systemic understanding of their architecture and function requires an appraisal of the network design principles that confer robustness. In this project, we conduct a computational study of the contribution of three degree-based topological properties (transcription factor-target ratio, degree distribution, cross-talk suppression) and their combinations on the robustness of transcriptional regulatory networks. We seek to quantify the relative degree of robustness conferred by each property (and combination) and also to determine the extent to which these properties alone can explain the robustness observed in transcriptional networks. Results To study individual properties and their combinations, we generated synthetic, random networks that retained one or more of the three properties with values derived from either the yeast or E. coli gene regulatory networks. Robustness of these networks were estimated through simulation. Our results indicate that the combination of the three properties we considered explains the majority of the structural robustness observed in the real transcriptional networks. Surprisingly, scale-free degree distribution is, overall, a minor contributor to robustness. Instead, most robustness is gained through topological features that limit the complexity of the overall network and increase the transcription factor subnetwork sparsity. Conclusions Our work demonstrates that (i) different types of robustness are implemented by different topological aspects of the network and (ii) size and sparsity of the transcription factor subnetwork play an important role for robustness induction. Our results are conserved across yeast and E Coli, which suggests that the design principles examined are present within an array of living systems. PMID:23194062

Growth of non-Saccharomyces yeasts affects nutrient availability for Saccharomyces cerevisiae during wine fermentation.

PubMed

Medina, Karina; Boido, Eduardo; Dellacassa, Eduardo; Carrau, Francisco

2012-07-02

Yeast produces numerous secondary metabolites during fermentation that impact final wine quality. Although it is widely recognized that growth of diverse non-Saccharomyces (NS) yeast can positively affect flavor complexity during Saccharomyces cerevisiae wine fermentation, the inability to control spontaneous or co-fermentation processes by NS yeast has restricted their use in winemaking. We selected two NS yeasts from our Uruguayan native collection to study NS-S. cerevisiae interactions during wine fermentation. The selected strains of Hanseniaspora vineae and Metschnikowia pulcherrima had different yeast assimilable nitrogen consumption profiles and had different effects on S. cerevisiae fermentation and growth kinetics. Studies in which we varied inoculum size and using either simultaneous or sequential inoculation of NS yeast and S. cerevisiae suggested that competition for nutrients had a significant effect on fermentation kinetics. Sluggish fermentations were more pronounced when S. cerevisiae was inoculated 24h after the initial stage of fermentation with a NS strain compared to co-inoculation. Monitoring strain populations using differential WL nutrient agar medium and fermentation kinetics of mixed cultures allowed for a better understanding of strain interactions and nutrient addition effects. Limitation of nutrient availability for S. cerevisiae was shown to result in stuck fermentations as well as to reduce sensory desirability of the resulting wine. Addition of diammonium phosphate (DAP) and a vitamin mix to a defined medium allowed for a comparison of nutrient competition between strains. Addition of DAP and the vitamin mix was most effective in preventing stuck fermentations. Copyright © 2012 Elsevier B.V. All rights reserved.

Expression level, cellular compartment and metabolic network position all influence the average selective constraint on mammalian enzymes

PubMed Central

2011-01-01

Background A gene's position in regulatory, protein interaction or metabolic networks can be predictive of the strength of purifying selection acting on it, but these relationships are neither universal nor invariably strong. Following work in bacteria, fungi and invertebrate animals, we explore the relationship between selective constraint and metabolic function in mammals. Results We measure the association between selective constraint, estimated by the ratio of nonsynonymous (Ka) to synonymous (Ks) substitutions, and several, primarily metabolic, measures of gene function. We find significant differences between the selective constraints acting on enzyme-coding genes from different cellular compartments, with the nucleus showing higher constraint than genes from either the cytoplasm or the mitochondria. Among metabolic genes, the centrality of an enzyme in the metabolic network is significantly correlated with Ka/Ks. In contrast to yeasts, gene expression magnitude does not appear to be the primary predictor of selective constraint in these organisms. Conclusions Our results imply that the relationship between selective constraint and enzyme centrality is complex: the strength of selective constraint acting on mammalian genes is quite variable and does not appear to exclusively follow patterns seen in other organisms. PMID:21470417

Phenotypic states become increasingly sensitive to perturbations near a bifurcation in a synthetic gene network

PubMed Central

Axelrod, Kevin; Sanchez, Alvaro; Gore, Jeff

2015-01-01

Microorganisms often exhibit a history-dependent phenotypic response after exposure to a stimulus which can be imperative for proper function. However, cells frequently experience unexpected environmental perturbations that might induce phenotypic switching. How cells maintain phenotypic states in the face of environmental fluctuations remains an open question. Here, we use environmental perturbations to characterize the resilience of phenotypic states in a synthetic gene network near a critical transition. We find that far from the critical transition an environmental perturbation may induce little to no phenotypic switching, whereas close to the critical transition the same perturbation can cause many cells to switch phenotypic states. This loss of resilience was observed for perturbations that interact directly with the gene circuit as well as for a variety of generic perturbations-such as salt, ethanol, or temperature shocks-that alter the state of the cell more broadly. We obtain qualitatively similar findings in natural gene circuits, such as the yeast GAL network. Our findings illustrate how phenotypic memory can become destabilized by environmental variability near a critical transition. DOI: http://dx.doi.org/10.7554/eLife.07935.001 PMID:26302311

Yeast: An Overlooked Component of Bactrocera tryoni (Diptera: Tephritidae) Larval Gut Microbiota.

PubMed

Deutscher, Ania T; Reynolds, Olivia L; Chapman, Toni A

2017-02-01

Yeasts, often in hydrolyzed form, are key ingredients in the larval and adult diets of tephritid fruit fly colonies. However, very little is known about the presence or role of yeasts in the diets of tephritid fruit flies in nature. Previous studies have identified bacteria but not detected yeasts in the gut of Queensland fruit fly, Bactrocera tryoni (Froggatt), one of Australia's most economically damaging insect pests of horticultural crops and of significant biosecurity concern domestically and internationally. Here we demonstrate that cultivable yeasts are commonly found in the gut of B. tryoni larvae from fruit hosts. Analysis of the ITS1, 5.8S rRNA gene, and ITS2 sequences of randomly selected isolates identified yeasts and yeast-like fungi of the genera Aureobasidium, Candida, Cryptococcus, Hanseniaspora, Pichia, and Starmerella. The prevalence of these yeasts in fruits suggests that larvae consume the yeasts as part of their diet. This work highlights that yeasts should be considered in future tephritid larval gut microbiota studies. Understanding tephritid-microbial symbiont interactions will lead to improvements in artificial diets and the quality of mass-reared tephritids for the sterile insect technique. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An unconventional interaction between Dis1/TOG and Mal3/EB1 in fission yeast promotes the fidelity of chromosome segregation.

PubMed

Matsuo, Yuzy; Maurer, Sebastian P; Yukawa, Masashi; Zakian, Silva; Singleton, Martin R; Surrey, Thomas; Toda, Takashi

2016-12-15

Dynamic microtubule plus-ends interact with various intracellular target regions such as the cell cortex and the kinetochore. Two conserved families of microtubule plus-end-tracking proteins, the XMAP215, ch-TOG or CKAP5 family and the end-binding 1 (EB1, also known as MAPRE1) family, play pivotal roles in regulating microtubule dynamics. Here, we study the functional interplay between fission yeast Dis1, a member of the XMAP215/TOG family, and Mal3, an EB1 protein. Using an in vitro microscopy assay, we find that purified Dis1 autonomously tracks growing microtubule ends and is a bona fide microtubule polymerase. Mal3 recruits additional Dis1 to microtubule ends, explaining the synergistic enhancement of microtubule dynamicity by these proteins. A non-canonical binding motif in Dis1 mediates the interaction with Mal3. X-ray crystallography shows that this new motif interacts in an unconventional configuration with the conserved hydrophobic cavity formed within the Mal3 C-terminal region that typically interacts with the canonical SXIP motif. Selectively perturbing the Mal3-Dis1 interaction in living cells demonstrates that it is important for accurate chromosome segregation. Whereas, in some metazoans, the interaction between EB1 and the XMAP215/TOG family members requires an additional binding partner, fission yeast relies on a direct interaction, indicating evolutionary plasticity of this critical interaction module. © 2016. Published by The Company of Biologists Ltd.

A novel, easy and rapid method for constructing yeast two-hybrid vectors using In-Fusion technology.

PubMed

Yu, Deshui; Liao, Libing; Zhang, Ju; Zhang, Yi; Xu, Kedong; Liu, Kun; Li, Xiaoli; Tan, Guangxuan; Chen, Ran; Wang, Yulu; Liu, Xia; Zhang, Xuan; Han, Xiaomeng; Wei, Zhangkun; Li, Chengwei

2018-05-01

Yeast two-hybrid systems are powerful tools for analyzing interactions between proteins. Vector construction is an essential step in yeast two-hybrid experiments, which require bait and prey plasmids. In this study, we modified the multiple cloning site sequence of the yeast plasmid pGADT7 by site-directed mutagenesis PCR to generate the pGADT7-In vector, which resulted in an easy and rapid method for constructing yeast two-hybrid vectors using the In-Fusion cloning technique. This method has three key advantages: only one pair of primers and one round of PCR are needed to generate bait and prey plasmids for each gene, it is restriction endonuclease- and ligase-independent, and it is fast and easily performed.

Fluorescence Lifetime and UV-Vis Spectroscopy to Evaluate the Interactions Between Quercetin and Its Yeast Microcapsule.

PubMed

Pham-Hoang, Bao-Ngoc; Winckler, Pascale; Waché, Yves

2018-01-01

Quercetin is a fragile bioactive compound. Several works have tried to preserve it by encapsulation but the form of encapsulation (mono- or supra-molecular structure, tautomeric form), though important for stability and bioavailability, remains unknown. The present work aims at developing a fluorescence lifetime technique to evaluate the structure of quercetin during encapsulation in a vector capsule that has already proven efficiency, yeast cells. Molecular stabilization was observed during a 4-month storage period. The time-correlated single-photon counting (TCSPC) technique was used to evaluate the interaction between quercetin molecules and the yeast capsule. The various tautomeric forms, as identified by UV-Vis spectroscopy, result in various lifetimes in TCSPC, although they varied also with the buffer environment. Quercetin in buffer exhibited a three-to-four longer long-time after 24 h (changing from 6-7 to 18-23 ns), suggesting an aggregation of molecules. In yeast microcapsules, the long-time population exhibited a longer lifetime (around 27 ns) from the beginning and concerned about 20% of molecules compared to dispersed quercetin. This shows that lifetime analysis can show the monomolecular instability of quercetin in buffer and the presence of interactions between quercetin molecules and their microcapsules. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Interactions of grape tannins and wine polyphenols with a yeast protein extract, mannoproteins and β-glucan.

PubMed

Mekoue Nguela, J; Poncet-Legrand, C; Sieczkowski, N; Vernhet, A

2016-11-01

At present, there is a great interest in enology for yeast derived products to replace aging on lees in winemaking or as an alternative for wine fining. These are yeast protein extracts (YPE), cell walls and mannoproteins. Our aim was to further understand the mechanisms that drive interactions between these components and red wine polyphenols. To this end, interactions between grape skin tannins or wine polyphenols or tannins and a YPE, a mannoprotein fraction and a β-glucan were monitored by binding experiments, ITC and DLS. Depending on the tannin structure, a different affinity between the polyphenols and the YPE was observed, as well as differences in the stability of the aggregates. This was attributed to the mean degree of polymerization of tannins in the polyphenol fractions and to chemical changes that occur during winemaking. Much lower affinities were found between polyphenols and polysaccharides, with different behaviors between mannoproteins and β-glucans. Copyright © 2016 Elsevier Ltd. All rights reserved.

Identifying cooperative transcriptional regulations using protein–protein interactions

PubMed Central

Nagamine, Nobuyoshi; Kawada, Yuji; Sakakibara, Yasubumi

2005-01-01

Cooperative transcriptional activations among multiple transcription factors (TFs) are important to understand the mechanisms of complex transcriptional regulations in eukaryotes. Previous studies have attempted to find cooperative TFs based on gene expression data with gene expression profiles as a measure of similarity of gene regulations. In this paper, we use protein–protein interaction data to infer synergistic binding of cooperative TFs. Our fundamental idea is based on the assumption that genes contributing to a similar biological process are regulated under the same control mechanism. First, the protein–protein interaction networks are used to calculate the similarity of biological processes among genes. Second, we integrate this similarity and the chromatin immuno-precipitation data to identify cooperative TFs. Our computational experiments in yeast show that predictions made by our method have successfully identified eight pairs of cooperative TFs that have literature evidences but could not be identified by the previous method. Further, 12 new possible pairs have been inferred and we have examined the biological relevances for them. However, since a typical problem using protein–protein interaction data is that many false-positive data are contained, we propose a method combining various biological data to increase the prediction accuracy. PMID:16126847

Cooperation of TOM and TIM23 complexes during translocation of proteins into mitochondria.

PubMed

Waegemann, Karin; Popov-Čeleketić, Dušan; Neupert, Walter; Azem, Abdussalam; Mokranjac, Dejana

2015-03-13

Translocation of the majority of mitochondrial proteins from the cytosol into mitochondria requires the cooperation of TOM and TIM23 complexes in the outer and inner mitochondrial membranes. The molecular mechanisms underlying this cooperation remain largely unknown. Here, we present biochemical and genetic evidence that at least two contacts from the side of the TIM23 complex play an important role in TOM-TIM23 cooperation in vivo. Tim50, likely through its very C-terminal segment, interacts with Tom22. This interaction is stimulated by translocating proteins and is independent of any other TOM-TIM23 contact known so far. Furthermore, the exposure of Tim23 on the mitochondrial surface depends not only on its interaction with Tim50 but also on the dynamics of the TOM complex. Destabilization of the individual contacts reduces the efficiency of import of proteins into mitochondria and destabilization of both contacts simultaneously is not tolerated by yeast cells. We conclude that an intricate and coordinated network of protein-protein interactions involving primarily Tim50 and also Tim23 is required for efficient translocation of proteins across both mitochondrial membranes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Characterization of pollen-expressed bZIP protein interactions and the role of ATbZIP18 in the male gametophyte.

PubMed

Gibalová, Antónia; Steinbachová, Lenka; Hafidh, Said; Bláhová, Veronika; Gadiou, Zuzana; Michailidis, Christos; Műller, Karel; Pleskot, Roman; Dupľáková, Nikoleta; Honys, David

2017-03-01

KEY MESSAGE : bZIP TF network in pollen. Transcriptional control of gene expression represents an important mechanism guiding organisms through developmental processes and providing plasticity towards environmental stimuli. Because of their sessile nature, plants require effective gene regulation for rapid response to variation in environmental and developmental conditions. Transcription factors (TFs) provide such control ensuring correct gene expression in spatial and temporal manner. Our work reports the interaction network of six bZIP TFs expressed in Arabidopsis thaliana pollen and highlights the potential functional role for AtbZIP18 in pollen. AtbZIP18 was shown to interact with three other pollen-expressed bZIP TFs-AtbZIP34, AtbZIP52, and AtbZIP61 in yeast two-hybrid assays. AtbZIP18 transcripts are highly expressed in pollen, and at the subcellular level, an AtbZIP18-GFP fusion protein was located in the nucleus and cytoplasm/ER. To address the role of AtbZIP18 in the male gametophyte, we performed phenotypic analysis of a T-DNA knockout allele, which showed slightly reduced transmission through the male gametophyte. Some of the phenotype defects in atbzip18 pollen, although observed at low penetrance, were similar to those seen at higher frequency in the T-DNA knockout of the interacting partner, AtbZIP34. To gain deeper insight into the regulatory role of AtbZIP18, we analysed atbzip18/- pollen microarray data. Our results point towards a potential repressive role for AtbZIP18 and its functional redundancy with AtbZIP34 in pollen.

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

Mimosa: Mixture Model of Co-expression to Detect Modulators of Regulatory Interaction

NASA Astrophysics Data System (ADS)

Hansen, Matthew; Everett, Logan; Singh, Larry; Hannenhalli, Sridhar

Functionally related genes tend to be correlated in their expression patterns across multiple conditions and/or tissue-types. Thus co-expression networks are often used to investigate functional groups of genes. In particular, when one of the genes is a transcription factor (TF), the co-expression-based interaction is interpreted, with caution, as a direct regulatory interaction. However, any particular TF, and more importantly, any particular regulatory interaction, is likely to be active only in a subset of experimental conditions. Moreover, the subset of expression samples where the regulatory interaction holds may be marked by presence or absence of a modifier gene, such as an enzyme that post-translationally modifies the TF. Such subtlety of regulatory interactions is overlooked when one computes an overall expression correlation. Here we present a novel mixture modeling approach where a TF-Gene pair is presumed to be significantly correlated (with unknown coefficient) in a (unknown) subset of expression samples. The parameters of the model are estimated using a Maximum Likelihood approach. The estimated mixture of expression samples is then mined to identify genes potentially modulating the TF-Gene interaction. We have validated our approach using synthetic data and on three biological cases in cow and in yeast. While limited in some ways, as discussed, the work represents a novel approach to mine expression data and detect potential modulators of regulatory interactions.

Made for Each Other: Ascomycete Yeasts and Insects.

PubMed

Blackwell, Meredith

2017-06-01

Fungi and insects live together in the same habitats, and many species of both groups rely on each other for success. Insects, the most successful animals on Earth, cannot produce sterols, essential vitamins, and many enzymes; fungi, often yeast-like in growth form, make up for these deficits. Fungi, however, require constantly replenished substrates because they consume the previous ones, and insects, sometimes lured by volatile fungal compounds, carry fungi directly to a similar, but fresh, habitat. Yeasts associated with insects include Ascomycota (Saccharomycotina, Pezizomycotina) and a few Basidiomycota. Beetles, homopterans, and flies are important associates of fungi, and in turn the insects carry yeasts in pits, specialized external pouches, and modified gut pockets. Some yeasts undergo sexual reproduction within the insect gut, where the genetic diversity of the population is increased, while others, well suited to their stable environment, may never mate. The range of interactions extends from dispersal of yeasts on the surface of insects (e.g., cactus- Drosophila -yeast and ephemeral flower communities, ambrosia beetles, yeasts with holdfasts) to extremely specialized associations of organisms that can no longer exist independently, as in the case of yeast-like symbionts of planthoppers. In a few cases yeast-like fungus-insect associations threaten butterflies and other species with extinction. Technical advances improve discovery and identification of the fungi but also inform our understanding of the evolution of yeast-insect symbioses, although there is much more to learn.

Yeast succession in the Amazon fruit Parahancornia amapa as resource partitioning among Drosophila spp.

PubMed Central

Morais, P B; Martins, M B; Klaczko, L B; Mendonça-Hagler, L C; Hagler, A N

1995-01-01

The succession of yeasts colonizing the fallen ripe amapa fruit, from Parahancornia amapa, was examined. The occupation of the substrate depended on both the competitive interactions of yeast species, such as the production of killer toxins, and the selective dispersion by the drosophilid guild of the amapa fruit. The yeast community associated with this Amazon fruit differed from those isolated from other fruits in the same forest. The physiological profile of these yeasts was mostly restricted to the assimilation of a few simple carbon sources, mainly L-sorbose, D-glycerol, DL-lactate, cellobiose, and salicin. Common fruit-associated yeasts of the genera Kloeckera and Hanseniaspora, Candida guilliermondii, and Candida krusei colonized fruits during the first three days after the fruit fell. These yeasts were dispersed and served as food for the invader Drosophila malerkotliana. The resident flies of the Drosophila willistoni group fed selectively on patches of yeasts colonizing fruits 3 to 10 days after the fruit fell. The killer toxin-producing yeasts Pichia kluyveri var. kluyveri and Candida fructus were probably involved in the exclusion of some species during the intermediate stages of fruit deterioration. An increase in pH, inhibiting toxin activity and the depletion of simple sugars, may have promoted an increase in yeast diversity in the later stages of decomposition. The yeast succession provided a patchy environment for the drosophilids sharing this ephemeral substrate. PMID:8534092

Ongoing resolution of duplicate gene functions shapes the diversification of a metabolic network

DOE PAGES

Kuang, Meihua Christina; Hutchins, Paul D.; Russell, Jason D.; ...

2016-09-30

The evolutionary mechanisms leading to duplicate gene retention are well understood, but the long-term impacts of paralog differentiation on the regulation of metabolism remain underappreciated. Here we experimentally dissect the functions of two pairs of ancient paralogs of theGALactose sugar utilization network in two yeast species. Here, we show that theSaccharomyces uvarumnetwork is more active, even as over-induction is prevented by a second co-repressor that the model yeastSaccharomyces cerevisiaelacks. Surprisingly, removal of this repression system leads to a strong growth arrest, likely due to overly rapid galactose catabolism and metabolic overload. Alternative sugars, such as fructose, circumvent metabolic control systemsmore » and exacerbate this phenotype. Furthermore, we show thatS. cerevisiaeexperiences homologous metabolic constraints that are subtler due to how the paralogs have diversified. Our results show how the functional differentiation of paralogs continues to shape regulatory network architectures and metabolic strategies long after initial preservation.« less

From Saccharomyces cerevisiae to human: The important gene co-expression modules.

PubMed

Liu, Wei; Li, Li; Ye, Hua; Chen, Haiwei; Shen, Weibiao; Zhong, Yuexian; Tian, Tian; He, Huaqin

2017-08-01

Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Yeast has long been a popular model organism for biomedical research. In the current study, a weighted gene co-expression network analysis algorithm was applied to construct a gene co-expression network in Saccharomyces cerevisiae . Seventeen stable gene co-expression modules were detected from 2,814 S. cerevisiae microarray data. Further characterization of these modules with the Database for Annotation, Visualization and Integrated Discovery tool indicated that these modules were associated with certain biological processes, such as heat response, cell cycle, translational regulation, mitochondrion oxidative phosphorylation, amino acid metabolism and autophagy. Hub genes were also screened by intra-modular connectivity. Finally, the module conservation was evaluated in a human disease microarray dataset. Functional modules were identified in budding yeast, some of which are associated with patient survival. The current study provided a paradigm for single cell microorganisms and potentially other organisms.

Polo boxes and Cut23 (Apc8) mediate an interaction between polo kinase and the anaphase-promoting complex for fission yeast mitosis

PubMed Central

May, Karen M.; Reynolds, Nicola; Cullen, C. Fiona; Yanagida, Mitsuhiro; Ohkura, Hiroyuki

2002-01-01

The fission yeast plo1 + gene encodes a polo-like kinase, a member of a conserved family of kinases which play multiple roles during the cell cycle. We show that Plo1 kinase physically interacts with the anaphase-promoting complex (APC)/cyclosome through the noncatalytic domain of Plo1 and the tetratricopeptide repeat domain of the subunit, Cut23. A new cut23 mutation, which specifically disrupts the interaction with Plo1, results in a metaphase arrest. This arrest can be rescued by high expression of Plo1 kinase. We suggest that this physical interaction is crucial for mitotic progression by targeting polo kinase activity toward the APC. PMID:11777938

Genetic interactions between the chromosome axis-associated protein Hop1 and homologous recombination determinants in Schizosaccharomyces pombe.

PubMed

Brown, Simon David; Jarosinska, Olga Dorota; Lorenz, Alexander

2018-03-17

Hop1 is a component of the meiosis-specific chromosome axis and belongs to the evolutionarily conserved family of HORMA domain proteins. Hop1 and its orthologs in higher eukaryotes are a major factor in promoting double-strand DNA break formation and inter-homolog recombination. In budding yeast and mammals, they are also involved in a meiotic checkpoint kinase cascade monitoring the completion of double-strand DNA break repair. We used the fission yeast, Schizosaccharomyces pombe, which lacks a canonical synaptonemal complex to test whether Hop1 has a role beyond supporting the generation of double-strand DNA breaks and facilitating inter-homolog recombination events. We determined how mutants of homologous recombination factors genetically interact with hop1, studied the role(s) of the HORMA domain of Hop1, and characterized a bio-informatically predicted interactor of Hop1, Aho1 (SPAC688.03c). Our observations indicate that in fission yeast, Hop1 does require its HORMA domain to support wild-type levels of meiotic recombination and localization to meiotic chromatin. Furthermore, we show that hop1∆ only weakly interacts genetically with mutants of homologous recombination factors, and in fission yeast likely has no major role beyond break formation and promoting inter-homolog events. We speculate that after the evolutionary loss of the synaptonemal complex, Hop1 likely has become less important for modulating recombination outcome during meiosis in fission yeast, and that this led to a concurrent rewiring of genetic pathways controlling meiotic recombination.

Gut microbiota dictates the metabolic response of Drosophila to diet

PubMed Central

Wong, Adam C.-N.; Dobson, Adam J.; Douglas, Angela E.

2014-01-01

Animal nutrition is profoundly influenced by the gut microbiota, but knowledge of the scope and core mechanisms of the underlying animal–microbiota interactions is fragmentary. To investigate the nutritional traits shaped by the gut microbiota of Drosophila, we determined the microbiota-dependent response of multiple metabolic and performance indices to systematically varied diet composition. Diet-dependent differences between Drosophila bearing its unmanipulated microbiota (conventional flies) and experimentally deprived of its microbiota (axenic flies) revealed evidence for: microbial sparing of dietary B vitamins, especially riboflavin, on low-yeast diets; microbial promotion of protein nutrition, particularly in females; and microbiota-mediated suppression of lipid/carbohydrate storage, especially on high sugar diets. The microbiota also sets the relationship between energy storage and body mass, indicative of microbial modulation of the host signaling networks that coordinate metabolism with body size. This analysis identifies the multiple impacts of the microbiota on the metabolism of Drosophila, and demonstrates that the significance of these different interactions varies with diet composition and host sex. PMID:24577449

Constraints and consequences of the emergence of amino acid repeats in eukaryotic proteins.

PubMed

Chavali, Sreenivas; Chavali, Pavithra L; Chalancon, Guilhem; de Groot, Natalia Sanchez; Gemayel, Rita; Latysheva, Natasha S; Ing-Simmons, Elizabeth; Verstrepen, Kevin J; Balaji, Santhanam; Babu, M Madan

2017-09-01

Proteins with amino acid homorepeats have the potential to be detrimental to cells and are often associated with human diseases. Why, then, are homorepeats prevalent in eukaryotic proteomes? In yeast, homorepeats are enriched in proteins that are essential and pleiotropic and that buffer environmental insults. The presence of homorepeats increases the functional versatility of proteins by mediating protein interactions and facilitating spatial organization in a repeat-dependent manner. During evolution, homorepeats are preferentially retained in proteins with stringent proteostasis, which might minimize repeat-associated detrimental effects such as unregulated phase separation and protein aggregation. Their presence facilitates rapid protein divergence through accumulation of amino acid substitutions, which often affect linear motifs and post-translational-modification sites. These substitutions may result in rewiring protein interaction and signaling networks. Thus, homorepeats are distinct modules that are often retained in stringently regulated proteins. Their presence facilitates rapid exploration of the genotype-phenotype landscape of a population, thereby contributing to adaptation and fitness.

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

InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk.

PubMed

Cheng, Liang; Jiang, Yue; Ju, Hong; Sun, Jie; Peng, Jiajie; Zhou, Meng; Hu, Yang

2018-01-19

Since the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and Human Phenotype Ontology (HPO). Because of the advantage of identifying novel relationships between terms, calculating similarity between ontology terms is one of the major tasks in this research area. Though similarities between terms within each ontology have been studied with in silico methods, term similarities across different ontologies were not investigated as deeply. The latest method took advantage of gene functional interaction network (GFIN) to explore such inter-ontology similarities of terms. However, it only used gene interactions and failed to make full use of the connectivity among gene nodes of the network. In addition, all existent methods are particularly designed for GO and their performances on the extended ontology community remain unknown. We proposed a method InfAcrOnt to infer similarities between terms across ontologies utilizing the entire GFIN. InfAcrOnt builds a term-gene-gene network which comprised ontology annotations and GFIN, and acquires similarities between terms across ontologies through modeling the information flow within the network by random walk. In our benchmark experiments on sub-ontologies of GO, InfAcrOnt achieves a high average area under the receiver operating characteristic curve (AUC) (0.9322 and 0.9309) and low standard deviations (1.8746e-6 and 3.0977e-6) in both human and yeast benchmark datasets exhibiting superior performance. Meanwhile, comparisons of InfAcrOnt results and prior knowledge on pair-wise DO-HPO terms and pair-wise DO-GO terms show high correlations. The experiment results show that InfAcrOnt significantly improves the performance of inferring similarities between terms across ontologies in benchmark set.

Nectar bacteria, but not yeast, weaken a plant-pollinator mutualism.

PubMed

Vannette, Rachel L; Gauthier, Marie-Pierre L; Fukami, Tadashi

2013-02-07

Mutualistic interactions are often subject to exploitation by species that are not directly involved in the mutualism. Understanding which organisms act as such 'third-party' species and how they do so is a major challenge in the current study of mutualistic interactions. Here, we show that even species that appear ecologically similar can have contrasting effects as third-party species. We experimentally compared the effects of nectar-inhabiting bacteria and yeasts on the strength of a mutualism between a hummingbird-pollinated shrub, Mimulus aurantiacus, and its pollinators. We found that the common bacterium Gluconobacter sp., but not the common yeast Metschnikowia reukaufii, reduced pollination success, seed set and nectar consumption by pollinators, thereby weakening the plant-pollinator mutualism. We also found that the bacteria reduced nectar pH and total sugar concentration more greatly than the yeasts did and that the bacteria decreased glucose concentration and increased fructose concentration whereas the yeasts affected neither. These distinct changes to nectar chemistry may underlie the microbes' contrasting effects on the mutualism. Our results suggest that it is necessary to understand the determinants of microbial species composition in nectar and their differential modification of floral rewards to explain the mutual benefits that plants and pollinators gain from each other.

Nectar bacteria, but not yeast, weaken a plant–pollinator mutualism

PubMed Central

Vannette, Rachel L.; Gauthier, Marie-Pierre L.; Fukami, Tadashi

2013-01-01

Mutualistic interactions are often subject to exploitation by species that are not directly involved in the mutualism. Understanding which organisms act as such ‘third-party’ species and how they do so is a major challenge in the current study of mutualistic interactions. Here, we show that even species that appear ecologically similar can have contrasting effects as third-party species. We experimentally compared the effects of nectar-inhabiting bacteria and yeasts on the strength of a mutualism between a hummingbird-pollinated shrub, Mimulus aurantiacus, and its pollinators. We found that the common bacterium Gluconobacter sp., but not the common yeast Metschnikowia reukaufii, reduced pollination success, seed set and nectar consumption by pollinators, thereby weakening the plant–pollinator mutualism. We also found that the bacteria reduced nectar pH and total sugar concentration more greatly than the yeasts did and that the bacteria decreased glucose concentration and increased fructose concentration whereas the yeasts affected neither. These distinct changes to nectar chemistry may underlie the microbes' contrasting effects on the mutualism. Our results suggest that it is necessary to understand the determinants of microbial species composition in nectar and their differential modification of floral rewards to explain the mutual benefits that plants and pollinators gain from each other. PMID:23222453

Target of rapamycin complex 2 signals to downstream effector yeast protein kinase 2 (Ypk2) through adheres-voraciously-to-target-of-rapamycin-2 protein 1 (Avo1) in Saccharomyces cerevisiae.

PubMed

Liao, Hsien-Ching; Chen, Mei-Yu

2012-02-24

The conserved Ser/Thr kinase target of rapamycin (TOR) serves as a central regulator in controlling cell growth-related functions. There exist two distinct TOR complexes, TORC1 and TORC2, each coupling to specific downstream effectors and signaling pathways. In Saccharomyces cerevisiae, TORC2 is involved in regulating actin organization and maintaining cell wall integrity. Ypk2 (yeast protein kinase 2), a member of the cAMP-dependent, cGMP-dependent, and PKC (AGC) kinase family, is a TORC2 substrate known to participate in actin and cell wall regulation. Employing avo3(ts) mutants with defects in TORC2 functions that are suppressible by active Ypk2, we investigated the molecular interactions involved in mediating TORC2 signaling to Ypk2. GST pulldown assays in yeast lysates demonstrated physical interactions between Ypk2 and components of TORC2. In vitro binding assays revealed that Avo1 directly binds to Ypk2. In avo3(ts) mutants, the TORC2-Ypk2 interaction was reduced and could be restored by AVO1 overexpression, highlighting the important role of Avo1 in coupling TORC2 to Ypk2. The interaction was mapped to an internal region (amino acids 600-840) of Avo1 and a C-terminal region of Ypk2. Ypk2(334-677), a truncated form of Ypk2 containing the Avo1-interacting region, was able to interfere with Avo1-Ypk2 interaction in vitro. Overexpressing Ypk2(334-677) in yeast cells resulted in a perturbation of TORC2 functions, causing defective cell wall integrity, aberrant actin organization, and diminished TORC2-dependent Ypk2 phosphorylation evidenced by the loss of an electrophoretic mobility shift. Together, our data support the conclusion that the direct Avo1-Ypk2 interaction is crucial for TORC2 signaling to the downstream Ypk2 pathway.

Glucose repression in Saccharomyces cerevisiae.

PubMed

Kayikci, Ömur; Nielsen, Jens

2015-09-01

Glucose is the primary source of energy for the budding yeast Saccharomyces cerevisiae. Although yeast cells can utilize a wide range of carbon sources, presence of glucose suppresses molecular activities involved in the use of alternate carbon sources as well as it represses respiration and gluconeogenesis. This dominant effect of glucose on yeast carbon metabolism is coordinated by several signaling and metabolic interactions that mainly regulate transcriptional activity but are also effective at post-transcriptional and post-translational levels. This review describes effects of glucose repression on yeast carbon metabolism with a focus on roles of the Snf3/Rgt2 glucose-sensing pathway and Snf1 signal transduction in establishment and relief of glucose repression. © FEMS 2015.

Altered Fermentation Performances, Growth, and Metabolic Footprints Reveal Competition for Nutrients between Yeast Species Inoculated in Synthetic Grape Juice-Like Medium

PubMed Central

Rollero, Stephanie; Bloem, Audrey; Ortiz-Julien, Anne; Camarasa, Carole; Divol, Benoit

2018-01-01

The sequential inoculation of non-Saccharomyces yeasts and Saccharomyces cerevisiae in grape juice is becoming an increasingly popular practice to diversify wine styles and/or to obtain more complex wines with a peculiar microbial footprint. One of the main interactions is competition for nutrients, especially nitrogen sources, that directly impacts not only fermentation performance but also the production of aroma compounds. In order to better understand the interactions taking place between non-Saccharomyces yeasts and S. cerevisiae during alcoholic fermentation, sequential inoculations of three yeast species (Pichia burtonii, Kluyveromyces marxianus, Zygoascus meyerae) with S. cerevisiae were performed individually in a synthetic medium. Different species-dependent interactions were evidenced. Indeed, the three sequential inoculations resulted in three different behaviors in terms of growth. P. burtonii and Z. meyerae declined after the inoculation of S. cerevisiae which promptly outcompeted the other two species. However, while the presence of P. burtonii did not impact the fermentation kinetics of S. cerevisiae, that of Z. meyerae rendered the overall kinetics very slow and with no clear exponential phase. K. marxianus and S. cerevisiae both declined and became undetectable before fermentation completion. The results also demonstrated that yeasts differed in their preference for nitrogen sources. Unlike Z. meyerae and P. burtonii, K. marxianus appeared to be a competitor for S. cerevisiae (as evidenced by the uptake of ammonium and amino acids), thereby explaining the resulting stuck fermentation. Nevertheless, the results suggested that competition for other nutrients (probably vitamins) occurred during the sequential inoculation of Z. meyerae with S. cerevisiae. The metabolic footprint of the non-Saccharomyces yeasts determined after 48 h of fermentation remained until the end of fermentation and combined with that of S. cerevisiae. For instance, fermentations performed with K. marxianus were characterized by the formation of phenylethanol and phenylethyl acetate, while those performed with P. burtonii or Z. meyerae displayed higher production of isoamyl alcohol and ethyl esters. When considering sequential inoculation of yeasts, the nutritional requirements of the yeasts used should be carefully considered and adjusted accordingly. Finally, our chemical data suggests that the organoleptic properties of the wine are altered in a species specific manner. PMID:29487584

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...

An overview of bioinformatics methods for modeling biological pathways in yeast

PubMed Central

Hou, Jie; Acharya, Lipi; Zhu, Dongxiao

2016-01-01

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein–protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae. In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways in S. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed. PMID:26476430

Through its F-BAR and RhoGAP domains, Rgd1p acts in different polarized growth processes in budding yeast

PubMed Central

Lefebvre, Fabien; Prouzet-Mauléon, Valérie; Vieillemard, Aurélie; Thoraval, Didier; Crouzet, Marc

2009-01-01

Protein domain architecture can be used to construct supramolecular structures, to carry out specific functions and to mediate signaling in prokaryotic and eukaryotic cells. The Rgd1p protein of budding yeast contains two domains with different functions in the cell: the F-BAR and RhoGAP domains. The F-BAR domain has been shown to interact with membrane phospholipids and is thought to induce or sense membrane curvature. The RhoGAP domain activates the GTP hydrolysis of two Rho GTPases, thereby regulating different cellular pathways. Specific molecular interactions with the F-BAR and RhoGAP domains, cell signaling and interplay between these domains may allow the Rgd1p protein to act in several different biological processes, all of which are required for polarized growth in yeast. PMID:19704907

Antagonistic interactions between garden yeasts and microfungal garden pathogens of leaf-cutting ants.

PubMed

Rodrigues, Andre; Cable, Rachel N; Mueller, Ulrich G; Bacci, Maurício; Pagnocca, Fernando C

2009-10-01

We investigate the diversity of yeasts isolated in gardens of the leafcutter ant Atta texana. Repeated sampling of gardens from four nests over a 1-year time period showed that gardens contain a diverse assemblage of yeasts. The yeast community in gardens consisted mostly of yeasts associated with plants or soil, but community composition changed between sampling periods. In order to understand the potential disease-suppressing roles of the garden yeasts, we screened isolates for antagonistic effects against known microfungal garden contaminants. In vitro assays revealed that yeasts inhibited the mycelial growth of two strains of Escovopsis (a specialized attine garden parasite), Syncephalastrum racemosum (a fungus often growing in gardens of leafcutter lab nests), and the insect pathogen Beauveria bassiana. These garden yeasts add to the growing list of disease-suppressing microbes in attine nests that may contribute synergistically, together with actinomycetes and Burkholderia bacteria, to protect the gardens and the ants against diseases. Additionally, we suggest that garden immunity against problem fungi may therefore derive not only from the presence of disease-suppressing Pseudonocardia actinomycetes, but from an enrichment of multiple disease-suppressing microorganisms in the garden matrix.

Dietary live yeast alters metabolic profiles, protein biosynthesis and thermal stress tolerance of Drosophila melanogaster.

PubMed

Colinet, Hervé; Renault, David

2014-04-01

The impact of nutritional factors on insect's life-history traits such as reproduction and lifespan has been excessively examined; however, nutritional determinant of insect's thermal tolerance has not received a lot of attention. Dietary live yeast represents a prominent source of proteins and amino acids for laboratory-reared drosophilids. In this study, Drosophila melanogaster adults were fed on diets supplemented or not with live yeast. We hypothesized that manipulating nutritional conditions through live yeast supplementation would translate into altered physiology and stress tolerance. We verified how live yeast supplementation affected body mass characteristics, total lipids and proteins, metabolic profiles and cold tolerance (acute and chronic stress). Females fed with live yeast had increased body mass and contained more lipids and proteins. Using GC/MS profiling, we found distinct metabolic fingerprints according to nutritional conditions. Metabolite pathway enrichment analysis corroborated that live yeast supplementation was associated with amino acid and protein biosyntheses. The cold assays revealed that the presence of dietary live yeast greatly promoted cold tolerance. Hence, this study conclusively demonstrates a significant interaction between nutritional conditions and thermal tolerance. Copyright © 2014 Elsevier Inc. All rights reserved.

Genetic interactions contribute less than additive effects to quantitative trait variation in yeast

PubMed Central

Bloom, Joshua S.; Kotenko, Iulia; Sadhu, Meru J.; Treusch, Sebastian; Albert, Frank W.; Kruglyak, Leonid

2015-01-01

Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation. Genetic interactions are often proposed as a contributing factor to trait variation, but the relative contribution of interactions to trait variation is a subject of debate. Here we use a very large cross between two yeast strains to accurately estimate the fraction of phenotypic variance due to pairwise QTL–QTL interactions for 20 quantitative traits. We find that this fraction is 9% on average, substantially less than the contribution of additive QTL (43%). Statistically significant QTL–QTL pairs typically have small individual effect sizes, but collectively explain 40% of the pairwise interaction variance. We show that pairwise interaction variance is largely explained by pairs of loci at least one of which has a significant additive effect. These results refine our understanding of the genetic architecture of quantitative traits and help guide future mapping studies. PMID:26537231

Oxygen Consumption by Postfermentation Wine Yeast Lees: Factors Affecting Its Rate and Extent under Oenological Conditions

PubMed Central

Müller, Jonas; Schmidt, Dominik

2016-01-01

Summary Postfermentation wine yeast lees show antioxidant properties based on their ability to consume dissolved oxygen. The oxygen consumption capacity of suspended yeast lees obtained after fermentations with six commercial active dry yeast strains was investigated in model, white and red wines using fluorescence-based oxygen sensors operating in a nondestructive way. In model solution, the oxygen consumption rate of yeast lees was shown to depend on their amount, yeast strain, sulfur dioxide and temperature. It is slightly lower in red than in white wines. It is strongly decreased by current levels of free sulfur dioxide, thus excluding the complementary use of both as antioxidants in wine. However, in 25 randomly sampled white wines produced under commercial conditions, the rate and extent of oxygen consumption during the first six months of postfermentation had no significant correlation with any of these interacting factors, making it difficult to predict the actual antioxidant effect of yeast lees. In these wines, yeast lees consumed 0 to 47% of the dissolved oxygen. Although total oxygen consumption capacity of yeast lees is not a limiting factor under commercial winemaking conditions, their oxygen consumption proceeds at a limited rate that reduces but cannot totally prevent concomitant chemical oxidation of the wine. PMID:28115896

Oxygen Consumption by Postfermentation Wine Yeast Lees: Factors Affecting Its Rate and Extent under Oenological Conditions.

PubMed

Schneider, Volker; Müller, Jonas; Schmidt, Dominik

2016-12-01

Postfermentation wine yeast lees show antioxidant properties based on their ability to consume dissolved oxygen. The oxygen consumption capacity of suspended yeast lees obtained after fermentations with six commercial active dry yeast strains was investigated in model, white and red wines using fluorescence-based oxygen sensors operating in a nondestructive way. In model solution, the oxygen consumption rate of yeast lees was shown to depend on their amount, yeast strain, sulfur dioxide and temperature. It is slightly lower in red than in white wines. It is strongly decreased by current levels of free sulfur dioxide, thus excluding the complementary use of both as antioxidants in wine. However, in 25 randomly sampled white wines produced under commercial conditions, the rate and extent of oxygen consumption during the first six months of postfermentation had no significant correlation with any of these interacting factors, making it difficult to predict the actual antioxidant effect of yeast lees. In these wines, yeast lees consumed 0 to 47% of the dissolved oxygen. Although total oxygen consumption capacity of yeast lees is not a limiting factor under commercial winemaking conditions, their oxygen consumption proceeds at a limited rate that reduces but cannot totally prevent concomitant chemical oxidation of the wine.

Missense variants in hMLH1 identified in patients from the German HNPCC consortium and functional studies.

PubMed

Hardt, Karin; Heick, Sven Boris; Betz, Beate; Goecke, Timm; Yazdanparast, Haniyeh; Küppers, Robin; Servan, Kati; Steinke, Verena; Rahner, Nils; Morak, Monika; Holinski-Feder, Elke; Engel, Christoph; Möslein, Gabriela; Schackert, Hans-Konrad; von Knebel Doeberitz, Magnus; Pox, Christian; Hegemann, Johannes H; Royer-Pokora, Brigitte

2011-06-01

Missense mutations of the DNA mismatch repair gene MLH1 are found in a significant fraction of patients with Lynch syndrome (hereditary nonpolyposis colorectal cancer, HNPCC) and their pathogenicity often remains unclear. We report here all 88 MLH1 missense variants identified in families from the German HNPCC consortium with clinical details of these patients/families. We investigated 23 MLH1 missense variants by two functional in vivo assays in yeast; seven map to the ATPase and 16 to the protein interaction domain. In the yeast-2-hybrid (Y2H) assay three variants in the ATPase and twelve variants in the interaction domain showed no or a reduced interaction with PMS2; seven showed a normal and one a significantly higher interaction. Using the Lys2A (14) reporter system to study the dominant negative mutator effect (DNE), 16 variants showed no or a low mutator effect, suggesting that these are nonfunctional, three were intermediate and four wild type in this assay. The DNE and Y2H results were concordant for all variants in the interaction domain, whereas slightly divergent results were obtained for variants in the ATPase domain. Analysis of the stability of the missense proteins in yeast and human embryonic kidney cells (293T) revealed a very low expression for seven of the variants in yeast and for nine in human cells. In total 15 variants were classified as deleterious, five were classified as variants of unclassified significance (VUS) and three were basically normal in the functional assays, P603R, K618R, Q689R, suggesting that these are neutral.

Yeast identification in floral nectar of Mimulus aurantiacus (Invited)

NASA Astrophysics Data System (ADS)

Kyauk, C.; Belisle, M.; Fukami, T.

2009-12-01

Nectar is such a sugar-rich resource that serves as a natural habitat in which microbes thrive. As a result, yeasts arrive to nectar on the bodies of pollinators such as hummingbirds and bees. Yeasts use the sugar in nectar for their own needs when introduced. This research focuses on the identification of different types of yeast that are found in the nectar of Mimulus aurantiacus (commonly known as sticky monkey-flower). Unopened Mimulus aurantiacus flower buds were tagged at Jasper Ridge and bagged three days later. Floral nectar was then extracted and plated on potato dextrose agar. Colonies on the plates were isolated and DNA was extracted from each sample using QIAGEN DNeasy Plant Mini Kit. The DNA was amplified through PCR and ran through gel electrophoresis. The PCR product was used to clone the nectar samples into an E.coli vector. Finally, a phylogenetic tree was created by BLAST searching sequences in GenBank using the Internal Transcribed Space (ITS) locus. It was found that 18 of the 50 identified species were Candida magnifica, 14 was Candida rancensis, 6 were Crytococcus albidus and there were 3 or less of the following: Starmella bombicola, Candida floricola, Aureobasidium pullulans, Pichia kluyvera, Metschnikowa cibodaserisis, Rhodotorua colostri, and Malassezia globosa. The low diversity of the yeast could have been due to several factors: time of collection, demographics of Jasper Ridge, low variety of pollinators, and sugar concentration of the nectar. The results of this study serve as a necessary first step for a recently started research project on ecological interactions between plants, pollinators, and nectar-living yeast. More generally, this research studies the use of the nectar-living yeast community as a natural microcosm for addressing basic questions about the role of dispersal and competitive and facilitative interactions in ecological succession.

Interrogation of ethnomedicinal plants for synthetic lethality effects in combination with deficiency in the DNA repair endonuclease RAD1 using a yeast cell-based assay.

PubMed

Aung, Hsu Mon; Huangteerakul, Chananya; Panvongsa, Wittaya; Jensen, Amornrat N; Chairoungdua, Arthit; Sukrong, Suchada; Jensen, Laran T

2018-09-15

Plant materials used in this study were selected based on the ethnobotanical literature. Plants have either been utilized by Thai practitioners as alternative treatments for cancer or identified to exhibit anti-cancer properties. To screen ethnomedicinal plants using a yeast cell-based assay for synthetic lethal interactions with cells deleted for RAD1, the yeast homologue of human ERCC4 (XPF) MATERIALS AND METHODS: Ethanolic extracts from thirty-two species of medicinal plants utilized in Thai traditional medicine were screened for synthetic lethal/sick interactions using a yeast cell-based assay. Cell growth was compared between the parental strain and rad1∆ yeast following exposure to select for specific toxicity of plant extracts. Candidate extracts were further examined for the mode of action using genetic and biochemical approaches. Screening a library of ethanolic extracts from medicinal plants identified Bacopa monnieri and Colubrina asiatica as having synthetic lethal effects in the rad1∆ cells but not the parental strain. Synthetic lethal effects for B. monneiri extracts were more apparent and this plant was examined further. Genetic analysis indicates that pro-oxidant activities and defective excision repair pathways do not significantly contribute to enhanced sensitivity to B. monneiri extracts. Exposure to B. monneiri extracts resulted in nuclear fragmentation and elevated levels of ethidium bromide staining in rad1∆ yeast suggesting promotion of an apoptosis-like event. Growth inhibition also observed in the human Caco-2 cell line suggesting the effects of B. monnieri extracts on both yeast and human cells may be similar. B. monneiri extracts may have utility in treatment of colorectal cancers that exhibit deficiency in ERCC4 (XPF). Copyright © 2018 Elsevier B.V. All rights reserved.

The ecology and evolution of non-domesticated Saccharomyces species.

PubMed

Boynton, Primrose J; Greig, Duncan

2014-12-01

Yeast researchers need model systems for ecology and evolution, but the model yeast Saccharomyces cerevisiae is not ideal because its evolution has been affected by domestication. Instead, ecologists and evolutionary biologists are focusing on close relatives of S. cerevisiae, the seven species in the genus Saccharomyces. The best-studied Saccharomyces yeast, after S. cerevisiae, is S. paradoxus, an oak tree resident throughout the northern hemisphere. In addition, several more members of the genus Saccharomyces have recently been discovered. Some Saccharomyces species are only found in nature, while others include both wild and domesticated strains. Comparisons between domesticated and wild yeasts have pinpointed hybridization, introgression and high phenotypic diversity as signatures of domestication. But studies of wild Saccharomyces natural history, biogeography and ecology are only beginning. Much remains to be understood about wild yeasts' ecological interactions and life cycles in nature. We encourage researchers to continue to investigate Saccharomyces yeasts in nature, both to place S. cerevisiae biology into its ecological context and to develop the genus Saccharomyces as a model clade for ecology and evolution. © 2014 The Authors. Yeast published by John Wiley & Sons, Ltd.

The ecology and evolution of non-domesticated Saccharomyces species

PubMed Central

Boynton, Primrose J; Greig, Duncan

2014-01-01

Yeast researchers need model systems for ecology and evolution, but the model yeast Saccharomyces cerevisiae is not ideal because its evolution has been affected by domestication. Instead, ecologists and evolutionary biologists are focusing on close relatives of S. cerevisiae, the seven species in the genus Saccharomyces. The best-studied Saccharomyces yeast, after S. cerevisiae, is S. paradoxus, an oak tree resident throughout the northern hemisphere. In addition, several more members of the genus Saccharomyces have recently been discovered. Some Saccharomyces species are only found in nature, while others include both wild and domesticated strains. Comparisons between domesticated and wild yeasts have pinpointed hybridization, introgression and high phenotypic diversity as signatures of domestication. But studies of wild Saccharomyces natural history, biogeography and ecology are only beginning. Much remains to be understood about wild yeasts' ecological interactions and life cycles in nature. We encourage researchers to continue to investigate Saccharomyces yeasts in nature, both to place S. cerevisiae biology into its ecological context and to develop the genus Saccharomyces as a model clade for ecology and evolution. © 2014 The Authors. Yeast published by John Wiley & Sons Ltd. PMID:25242436

Interaction of proliferating cell nuclear antigen with PMS2 is required for MutLα activation and function in mismatch repair

PubMed Central

Genschel, Jochen; Kadyrova, Lyudmila Y.; Iyer, Ravi R.; Dahal, Basanta K.; Kadyrov, Farid A.; Modrich, Paul

2017-01-01

Eukaryotic MutLα (mammalian MLH1–PMS2 heterodimer; MLH1–PMS1 in yeast) functions in early steps of mismatch repair as a latent endonuclease that requires a mismatch, MutSα/β, and DNA-loaded proliferating cell nuclear antigen (PCNA) for activation. We show here that human PCNA and MutLα interact specifically but weakly in solution to form a complex of approximately 1:1 stoichiometry that depends on PCNA interaction with the C-terminal endonuclease domain of the MutLα PMS2 subunit. Amino acid substitution mutations within a PMS2 C-terminal 721QRLIAP motif attenuate or abolish human MutLα interaction with PCNA, as well as PCNA-dependent activation of MutLα endonuclease, PCNA- and DNA-dependent activation of MutLα ATPase, and MutLα function in in vitro mismatch repair. Amino acid substitution mutations within the corresponding yeast PMS1 motif (723QKLIIP) reduce or abolish mismatch repair in vivo. Coupling of a weak allele within this motif (723AKLIIP) with an exo1Δ null mutation, which individually confer only weak mutator phenotypes, inactivates mismatch repair in the yeast cell. PMID:28439008

Interaction of proliferating cell nuclear antigen with PMS2 is required for MutLα activation and function in mismatch repair.

PubMed

Genschel, Jochen; Kadyrova, Lyudmila Y; Iyer, Ravi R; Dahal, Basanta K; Kadyrov, Farid A; Modrich, Paul

2017-05-09

Eukaryotic MutLα (mammalian MLH1-PMS2 heterodimer; MLH1-PMS1 in yeast) functions in early steps of mismatch repair as a latent endonuclease that requires a mismatch, MutSα/β, and DNA-loaded proliferating cell nuclear antigen (PCNA) for activation. We show here that human PCNA and MutLα interact specifically but weakly in solution to form a complex of approximately 1:1 stoichiometry that depends on PCNA interaction with the C-terminal endonuclease domain of the MutLα PMS2 subunit. Amino acid substitution mutations within a PMS2 C-terminal 721 QRLIAP motif attenuate or abolish human MutLα interaction with PCNA, as well as PCNA-dependent activation of MutLα endonuclease, PCNA- and DNA-dependent activation of MutLα ATPase, and MutLα function in in vitro mismatch repair. Amino acid substitution mutations within the corresponding yeast PMS1 motif ( 723 QKLIIP) reduce or abolish mismatch repair in vivo. Coupling of a weak allele within this motif ( 723 AKLIIP) with an exo1 Δ null mutation, which individually confer only weak mutator phenotypes, inactivates mismatch repair in the yeast cell.

Chemical signaling and insect attraction is a conserved trait in yeasts.

PubMed

Becher, Paul G; Hagman, Arne; Verschut, Vasiliki; Chakraborty, Amrita; Rozpędowska, Elżbieta; Lebreton, Sébastien; Bengtsson, Marie; Flick, Gerhard; Witzgall, Peter; Piškur, Jure

2018-03-01

Yeast volatiles attract insects, which apparently is of mutual benefit, for both yeasts and insects. However, it is unknown whether biosynthesis of metabolites that attract insects is a basic and general trait, or if it is specific for yeasts that live in close association with insects. Our goal was to study chemical insect attractants produced by yeasts that span more than 250 million years of evolutionary history and vastly differ in their metabolism and lifestyle. We bioassayed attraction of the vinegar fly Drosophila melanogaster to odors of phylogenetically and ecologically distinct yeasts grown under controlled conditions. Baker's yeast Saccharomyces cerevisiae , the insect-associated species Candida californica , Pichia kluyveri and Metschnikowia andauensis , wine yeast Dekkera bruxellensis , milk yeast Kluyveromyces lactis , the vertebrate pathogens Candida albicans and Candida glabrata , and oleophilic Yarrowia lipolytica were screened for fly attraction in a wind tunnel. Yeast headspace was chemically analyzed, and co-occurrence of insect attractants in yeasts and flowering plants was investigated through a database search. In yeasts with known genomes, we investigated the occurrence of genes involved in the synthesis of key aroma compounds. Flies were attracted to all nine yeasts studied. The behavioral response to baker's yeast was independent of its growth stage. In addition to Drosophila , we tested the basal hexapod Folsomia candida (Collembola) in a Y-tube assay to the most ancient yeast, Y. lipolytica, which proved that early yeast signals also function on clades older than neopteran insects. Behavioral and chemical data and a search for selected genes of volatile metabolites underline that biosynthesis of chemical signals is found throughout the yeast clade and has been conserved during the evolution of yeast lifestyles. Literature and database reviews corroborate that yeast signals mediate mutualistic interactions between insects and yeasts. Moreover, volatiles emitted by yeasts are commonly found also in flowers and attract many insect species. The collective evidence suggests that the release of volatile signals by yeasts is a widespread and phylogenetically ancient trait, and that insect-yeast communication evolved prior to the emergence of flowering plants. Co-occurrence of the same attractant signals in yeast and flowers suggests that yeast-insect communication may have contributed to the evolution of insect-mediated pollination in flowers.

De-novo assembly and characterization of the transcriptome of Metschnikowia fructicola reveals differences in gene expression following interaction with Penicillium digitatum and grapefruit peel

PubMed Central

2013-01-01

Background The yeast Metschnikowia fructicola is an antagonist with biological control activity against postharvest diseases of several fruits. We performed a transcriptome analysis, using RNA-Seq technology, to examine the response of M. fructicola with citrus fruit and with the postharvest pathogen, Penicillium digitatum. Results More than 26 million sequencing reads were assembled into 9,674 unigenes. Approximately 50% of the unigenes could be annotated based on homology matches in the NCBI database. Based on homology, sequences were annotated with a gene description, gene ontology (GO term), and clustered into functional groups. An analysis of differential expression when the yeast was interacting with the fruit vs. the pathogen revealed more than 250 genes with specific expression responses. In the antagonist-pathogen interaction, genes related to transmembrane, multidrug transport and to amino acid metabolism were induced. In the antagonist-fruit interaction, expression of genes involved in oxidative stress, iron homeostasis, zinc homeostasis, and lipid metabolism were induced. Patterns of gene expression in the two interactions were examined at the individual transcript level by quantitative real-time PCR analysis (RT-qPCR). Conclusion This study provides new insight into the biology of the tritrophic interactions that occur in a biocontrol system such as the use of the yeast, M. fructicola for the control of green mold on citrus caused by P. digitatum. PMID:23496978

Classifying transcription factor targets and discovering relevant biological features

PubMed Central

Holloway, Dustin T; Kon, Mark; DeLisi, Charles

2008-01-01

Background An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties. Principal Findings (1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4). (2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression. (3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties. (4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter. Conclusion Postprocessing of regulatory-classifier results can provide high quality predictions, and feature ranking strategies can deliver insight into the regulatory functions of TFs. Predictions are available at an online web-server, including the full transcriptional network, which can be analyzed using VisAnt network analysis suite. Reviewers This article was reviewed by Igor Jouline, Todd Mockler(nominated by Valerian Dolja), and Sandor Pongor. PMID:18513408

Identification of a DNA-binding site for the transcription factor Haa1, required for Saccharomyces cerevisiae response to acetic acid stress

PubMed Central

Mira, Nuno P.; Henriques, Sílvia F.; Keller, Greg; Teixeira, Miguel C.; Matos, Rute G.; Arraiano, Cecília M.; Winge, Dennis R.; Sá-Correia, Isabel

2011-01-01

The transcription factor Haa1 is the main player in reprogramming yeast genomic expression in response to acetic acid stress. Mapping of the promoter region of one of the Haa1-activated genes, TPO3, allowed the identification of an acetic acid responsive element (ACRE) to which Haa1 binds in vivo. The in silico analysis of the promoter regions of the genes of the Haa1-regulon led to the identification of an Haa1-responsive element (HRE) 5′-GNN(G/C)(A/C)(A/G)G(A/G/C)G-3′. Using surface plasmon resonance experiments and electrophoretic mobility shift assays it is demonstrated that Haa1 interacts with high affinity (KD of 2 nM) with the HRE motif present in the ACRE region of TPO3 promoter. No significant interaction was found between Haa1 and HRE motifs having adenine nucleotides at positions 6 and 8 (KD of 396 and 6780 nM, respectively) suggesting that Haa1p does not recognize these motifs in vivo. A lower affinity of Haa1 toward HRE motifs having mutations in the guanine nucleotides at position 7 and 9 (KD of 21 and 119 nM, respectively) was also observed. Altogether, the results obtained indicate that the minimal functional binding site of Haa1 is 5′-(G/C)(A/C)GG(G/C)G-3′. The Haa1-dependent transcriptional regulatory network active in yeast response to acetic acid stress is proposed. PMID:21586585

tRNA anticodon loop modifications ensure protein homeostasis and cell morphogenesis in yeast.

PubMed

Klassen, Roland; Ciftci, Akif; Funk, Johanna; Bruch, Alexander; Butter, Falk; Schaffrath, Raffael

2016-12-15

Using budding yeast, we investigated a negative interaction network among genes for tRNA modifications previously implicated in anticodon-codon interaction: 5-methoxy-carbonyl-methyl-2-thio-uridine (mcm 5 s 2 U34: ELP3, URM1), pseudouridine (Ψ38/39: DEG1) and cyclic N6-threonyl-carbamoyl-adenosine (ct 6 A37: TCD1). In line with functional cross talk between these modifications, we find that combined removal of either ct 6 A37 or Ψ38/39 and mcm 5 U34 or s 2 U34 results in morphologically altered cells with synthetic growth defects. Phenotypic suppression by tRNA overexpression suggests that these defects are caused by malfunction of tRNA Lys UUU or tRNA Gln UUG , respectively. Indeed, mRNA translation and synthesis of the Gln-rich prion Rnq1 are severely impaired in the absence of Ψ38/39 and mcm 5 U34 or s 2 U34, and this defect can be rescued by overexpression of tRNA Gln UUG Surprisingly, we find that combined modification defects in the anticodon loops of different tRNAs induce similar cell polarity- and nuclear segregation defects that are accompanied by increased aggregation of cellular proteins. Since conditional expression of an artificial aggregation-prone protein triggered similar cytological aberrancies, protein aggregation is likely responsible for loss of morphogenesis and cytokinesis control in mutants with inappropriate tRNA anticodon loop modifications. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Promiscuous activities of heterologous enzymes lead to unintended metabolic rerouting in Saccharomyces cerevisiae engineered to assimilate various sugars from renewable biomass.

PubMed

Yun, Eun Ju; Oh, Eun Joong; Liu, Jing-Jing; Yu, Sora; Kim, Dong Hyun; Kwak, Suryang; Kim, Kyoung Heon; Jin, Yong-Su

2018-01-01

Understanding the global metabolic network, significantly perturbed upon promiscuous activities of foreign enzymes and different carbon sources, is crucial for systematic optimization of metabolic engineering of yeast Saccharomyces cerevisiae . Here, we studied the effects of promiscuous activities of overexpressed enzymes encoded by foreign genes on rerouting of metabolic fluxes of an engineered yeast capable of assimilating sugars from renewable biomass by profiling intracellular and extracellular metabolites. Unbiased metabolite profiling of the engineered S. cerevisiae strain EJ4 revealed promiscuous enzymatic activities of xylose reductase and xylitol dehydrogenase on galactose and galactitol, respectively, resulting in accumulation of galactitol and tagatose during galactose fermentation. Moreover, during glucose fermentation, a trisaccharide consisting of glucose accumulated outside of the cells probably owing to the promiscuous and transglycosylation activity of β-glucosidase expressed for hydrolyzing cellobiose. Meanwhile, higher accumulation of fatty acids and secondary metabolites was observed during xylose and cellobiose fermentations, respectively. The heterologous enzymes functionally expressed in S. cerevisiae showed promiscuous activities that led to unintended metabolic rerouting in strain EJ4. Such metabolic rerouting could result in a low yield and productivity of a final product due to the formation of unexpected metabolites. Furthermore, the global metabolic network can be significantly regulated by carbon sources, thus yielding different patterns of metabolite production. This metabolomic study can provide useful information for yeast strain improvement and systematic optimization of yeast metabolism to manufacture bio-based products.

Beyond bread and beer: whole cell protein extracts from baker's yeast as a bulk source for 3D cell culture matrices.

PubMed

Bodenberger, Nicholas; Kubiczek, Dennis; Paul, Patrick; Preising, Nico; Weber, Lukas; Bosch, Ramona; Hausmann, Rudolf; Gottschalk, Kay-Eberhard; Rosenau, Frank

2017-03-01

Here, we present a novel approach to form hydrogels from yeast whole cell protein. Countless hydrogels are available for sophisticated research, but their fabrication is often difficult to reproduce, with the gels being complicated to handle or simply too expensive. The yeast hydrogels presented here are polymerized using a four-armed, amine reactive crosslinker and show a high chemical and thermal resistance. The free water content was determined by measuring swelling ratios for different protein concentrations, and in a freeze-drying approach, pore sizes of up to 100 μm in the gel could be created without destabilizing the 3D network. Elasticity was proofed to be adjustable with the help of atomic force microscopy by merely changing the amount of used protein. Furthermore, the material was tested for possible cell culture applications; diffusion rates in the network are high enough for sufficient supply of human breast cancer cells and adenocarcinomic human alveolar basal epithelial cells with nutrition, and cells showed high viabilities when tested for compatibility with the material. Furthermore, hydrogels could be functionalized with RGD peptide and the optimal concentration for sufficient cell adhesion was determined to be 150 μM. Given that yeast protein is one of the cheapest and easiest available protein sources and that hydrogels are extremely easy to handle, the developed material has highly promising potential for both sophisticated cell culture techniques as well as for larger scale industrial applications.

Yeast Genetics and Biotechnological Applications

NASA Astrophysics Data System (ADS)

Mishra, Saroj; Baranwal, Richa

Yeast can be recognized as one of the very important groups of microorganisms on account of its extensive use in the fermentation industry and as a basic eukaryotic model cellular system. The yeast Saccharomyces cerevisiae has been extensively used to elucidate the genetics and regulation of several key functions in the cell such as cell mating, electron transport chain, protein trafficking, cell cycle events and others. Even before the genome sequence of the yeast was out, the structural organization and function of several of its genes was known. With the availability of the origin of replication from the 2 μm plasmid and the development of transformation system, it became the host of choice for expression of a number of important proteins. A large number of episomal and integrative shuttle vectors are available for expression of mammalian proteins. The latest developments in genomics and micro-array technology have allowed investigations of individual gene function by site-specific deletion method. The application of metabolic profiling has also assisted in understanding the cellular network operating in this yeast. This chapter is aimed at reviewing the use of this system as an experimental tool for conducting classical genetics. Various vector systems available, foreign genes expressed and the limitations as a host will be discussed. Finally, the use of various yeast enzymes in biotechnology sector will be reviewed.

Macromitophagy is a longevity assurance process that in chronologically aging yeast limited in calorie supply sustains functional mitochondria and maintains cellular lipid homeostasis

PubMed Central

Burstein, Michelle T.; Koupaki, Olivia; Gomez-Perez, Alejandra; Levy, Sean; Pluska, Lukas; Mattie, Sevan; Rafeh, Rami; Iouk, Tatiana; Sheibani, Sara; Greenwood, Michael; Vali, Hojatollah; Titorenko, Vladimir I.

2013-01-01

Macromitophagy controls mitochondrial quality and quantity. It involves the sequestration of dysfunctional or excessive mitochondria within double-membrane autophagosomes, which then fuse with the vacuole/lysosome to deliver these mitochondria for degradation. To investigate a physiological role of macromitophagy in yeast, we examined how the atg32Δ-dependent mutational block of this process influences the chronological lifespan of cells grown in a nutrient-rich medium containing low (0.2%) concentration of glucose. Under these longevity-extending conditions of caloric restriction (CR) yeast cells are not starving. We also assessed a role of macromitophagy in lifespan extension by lithocholic acid (LCA), a bile acid that prolongs yeast longevity under CR conditions. Our findings imply that macromitophagy is a longevity assurance process underlying the synergistic beneficial effects of CR and LCA on yeast lifespan. Our analysis of how the atg32Δ mutation influences mitochondrial morphology, composition and function revealed that macromitophagy is required to maintain a network of healthy mitochondria. Our comparative analysis of the membrane lipidomes of organelles purified from wild-type and atg32Δ cells revealed that macromitophagy is required for maintaining cellular lipid homeostasis. We concluded that macromitophagy defines yeast longevity by modulating vital cellular processes inside and outside of mitochondria. PMID:23553280

A Stochastic Model of the Yeast Cell Cycle Reveals Roles for Feedback Regulation in Limiting Cellular Variability.

PubMed

Barik, Debashis; Ball, David A; Peccoud, Jean; Tyson, John J

2016-12-01

The cell division cycle of eukaryotes is governed by a complex network of cyclin-dependent protein kinases (CDKs) and auxiliary proteins that govern CDK activities. The control system must function reliably in the context of molecular noise that is inevitable in tiny yeast cells, because mistakes in sequencing cell cycle events are detrimental or fatal to the cell or its progeny. To assess the effects of noise on cell cycle progression requires not only extensive, quantitative, experimental measurements of cellular heterogeneity but also comprehensive, accurate, mathematical models of stochastic fluctuations in the CDK control system. In this paper we provide a stochastic model of the budding yeast cell cycle that accurately accounts for the variable phenotypes of wild-type cells and more than 20 mutant yeast strains simulated in different growth conditions. We specifically tested the role of feedback regulations mediated by G1- and SG2M-phase cyclins to minimize the noise in cell cycle progression. Details of the model are informed and tested by quantitative measurements (by fluorescence in situ hybridization) of the joint distributions of mRNA populations in yeast cells. We use the model to predict the phenotypes of ~30 mutant yeast strains that have not yet been characterized experimentally.

A Stochastic Model of the Yeast Cell Cycle Reveals Roles for Feedback Regulation in Limiting Cellular Variability

PubMed Central

Ball, David A.

2016-01-01

The cell division cycle of eukaryotes is governed by a complex network of cyclin-dependent protein kinases (CDKs) and auxiliary proteins that govern CDK activities. The control system must function reliably in the context of molecular noise that is inevitable in tiny yeast cells, because mistakes in sequencing cell cycle events are detrimental or fatal to the cell or its progeny. To assess the effects of noise on cell cycle progression requires not only extensive, quantitative, experimental measurements of cellular heterogeneity but also comprehensive, accurate, mathematical models of stochastic fluctuations in the CDK control system. In this paper we provide a stochastic model of the budding yeast cell cycle that accurately accounts for the variable phenotypes of wild-type cells and more than 20 mutant yeast strains simulated in different growth conditions. We specifically tested the role of feedback regulations mediated by G1- and SG2M-phase cyclins to minimize the noise in cell cycle progression. Details of the model are informed and tested by quantitative measurements (by fluorescence in situ hybridization) of the joint distributions of mRNA populations in yeast cells. We use the model to predict the phenotypes of ~30 mutant yeast strains that have not yet been characterized experimentally. PMID:27935947

Ingestion of genetically modified yeast symbiont reduces fitness of an insect pest via RNA interference

PubMed Central

Murphy, Katherine A.; Tabuloc, Christine A.; Cervantes, Kevin R.; Chiu, Joanna C.

2016-01-01

RNA interference has had major advances as a developing tool for pest management. In laboratory experiments, double-stranded RNA (dsRNA) is often administered to the insect by genetic modification of the crop, or synthesized in vitro and topically applied to the crop. Here, we engineered genetically modified yeast that express dsRNA targeting y-Tubulin in Drosophila suzukii. Our design takes advantage of the symbiotic interactions between Drosophila, yeast, and fruit crops. Yeast is naturally found growing on the surface of fruit crops, constitutes a major component of the Drosophila microbiome, and is highly attractive to Drosophila. Thus, this naturally attractive yeast biopesticide can deliver dsRNA to an insect pest without the need for genetic crop modification. We demonstrate that this biopesticide decreases larval survivorship, and reduces locomotor activity and reproductive fitness in adults, which are indicative of general health decline. To our knowledge, this is the first study to show that yeast can be used to deliver dsRNA to an insect pest. PMID:26931800

The Proliferating Cell Nuclear Antigen (PCNA)-interacting Protein (PIP) Motif of DNA Polymerase η Mediates Its Interaction with the C-terminal Domain of Rev1*

PubMed Central

Boehm, Elizabeth M.; Powers, Kyle T.; Kondratick, Christine M.; Spies, Maria; Houtman, Jon C. D.; Washington, M. Todd

2016-01-01

Y-family DNA polymerases, such as polymerase η, polymerase ι, and polymerase κ, catalyze the bypass of DNA damage during translesion synthesis. These enzymes are recruited to sites of DNA damage by interacting with the essential replication accessory protein proliferating cell nuclear antigen (PCNA) and the scaffold protein Rev1. In most Y-family polymerases, these interactions are mediated by one or more conserved PCNA-interacting protein (PIP) motifs that bind in a hydrophobic pocket on the front side of PCNA as well as by conserved Rev1-interacting region (RIR) motifs that bind in a hydrophobic pocket on the C-terminal domain of Rev1. Yeast polymerase η, a prototypical translesion synthesis polymerase, binds both PCNA and Rev1. It possesses a single PIP motif but not an RIR motif. Here we show that the PIP motif of yeast polymerase η mediates its interactions both with PCNA and with Rev1. Moreover, the PIP motif of polymerase η binds in the hydrophobic pocket on the Rev1 C-terminal domain. We also show that the RIR motif of human polymerase κ and the PIP motif of yeast Msh6 bind both PCNA and Rev1. Overall, these findings demonstrate that PIP motifs and RIR motifs have overlapping specificities and can interact with both PCNA and Rev1 in structurally similar ways. These findings also suggest that PIP motifs are a more versatile protein interaction motif than previously believed. PMID:26903512

Potyvirus helper component-proteinase self-interaction in the yeast two-hybrid system and delineation of the interaction domain involved.

PubMed

Urcuqui-Inchima, S; Walter, J; Drugeon, G; German-Retana, S; Haenni, A L; Candresse, T; Bernardi, F; Le Gall, O

1999-05-25

Using the yeast two-hybrid system, a screen was performed for possible interactions between the proteins encoded by the 5' region of potyviral genomes [P1, helper component-proteinase (HC-Pro), and P3]. A positive self-interaction involving HC-Pro was detected with lettuce mosaic virus (LMV) and potato virus Y (PVY). The possibility of heterologous interaction between the HC-Pro of LMV and of PVY was also demonstrated. No interaction involving either the P1 or the P3 proteins was detected. A series of ordered deletions from either the N- or C-terminal end of the LMV HC-Pro was used to map the domain involved in interaction to the 72 N-terminal amino acids of the protein, a region known to be dispensable for virus viability but necessary for aphid transmission. A similar but less detailed analysis mapped the interacting domain to the N-terminal half of the PVY HC-Pro. Copyright 1999 Academic Press.

A Dual-Color Reporter Assay of Cohesin-Mediated Gene Regulation in Budding Yeast Meiosis.

PubMed

Fan, Jinbo; Jin, Hui; Yu, Hong-Guo

2017-01-01

In this chapter, we describe a quantitative fluorescence-based assay of gene expression using the ratio of the reporter green fluorescence protein (GFP) to the internal red fluorescence protein (RFP) control. With this dual-color heterologous reporter assay, we have revealed cohesin-regulated genes and discovered a cis-acting DNA element, the Ty1-LTR, which interacts with cohesin and regulates gene expression during yeast meiosis. The method described here provides an effective cytological approach for quantitative analysis of global gene expression in budding yeast meiosis.

Yeast two-hybrid and pull-down assays propose an interaction between P50 of apple chlorotic leaf spot virus and PR-10 of Malus sylvestris cv. R12740-7A.

PubMed

Wang, Y; Li, N; Zhao, X; Hu, J; He, Y; Hu, T; Wang, S; Wang, Y; Cao, K

Apple chlorotic leaf spot virus (ACLSV) movement protein (P50) is involved in cell-to-cell transport and influences the long-distance spread of silencing activity. Previously, we obtained 69 P50-interacting proteins from Malus sylvestris cv. R12740-7A and using bioinformatics analyzed their biological functions. In this study, we used the GAL4-based two-hybrid yeast system and His pull-down assays to confirm an interaction between PR-10 of M. sylvestris cv. R12740-7A and ACLSV P50. Our results provide a theoretical basis for further research on the biological function of PR-10 in ACLSV infection and the interacting mechanism between host and virus.

Quantifying variation in the ability of yeasts to attract Drosophila melanogaster.

PubMed

Palanca, Loida; Gaskett, Anne C; Günther, Catrin S; Newcomb, Richard D; Goddard, Matthew R

2013-01-01

Yeasts that invade and colonise fruit significantly enhance the volatile chemical diversity of this ecosystem. These modified bouquets are thought to be more attractive to Drosophila flies than the fruit alone, but the variance of attraction in natural yeast populations is uncharacterised. Here we investigate how a range of yeast isolates affect the attraction of female D. melanogaster to fruit in a simple two choice assay comparing yeast to sterile fruit. Of the 43 yeast isolates examined, 33 were attractive and seven repellent to the flies. The results of isolate-versus-isolate comparisons provided the same relative rankings. Attractiveness varied significantly by yeast, with the strongly fermenting Saccharomyces species generally being more attractive than the mostly respiring non-Saccharomyces species (P = 0.0035). Overall the habitat (fruit or other) from which the isolates were directly sampled did not explain attraction (P = 0.2352). However, yeasts isolated from fruit associated niches were more attractive than those from non-fruit associated niches (P = 0.0188) regardless of taxonomic positioning. These data suggest that while attractiveness is primarily correlated with phylogenetic status, the ability to attract Drosophila is a labile trait among yeasts that is potentially associated with those inhabiting fruit ecosystems. Preliminary analysis of the volatiles emitted by four yeast isolates in grape juice show the presence/absence of ethanol and acetic acid were not likely explanations for the observed variation in attraction. These data demonstrate variation among yeasts for their ability to attract Drosophila in a pattern that is consistent with the hypothesis that certain yeasts are manipulating fruit odours to mediate interactions with their Drosophila dispersal agent.

Gγ recruitment system incorporating a novel signal amplification circuit to screen transient protein-protein interactions.

PubMed

Fukuda, Nobuo; Ishii, Jun; Kondo, Akihiko

2011-09-01

Weak and transient protein-protein interactions are associated with biological processes, but many are still undefined because of the difficulties in their identification. Here, we describe a redesigned method to screen transient protein-protein interactions by using a novel signal amplification circuit, which is incorporated into yeast to artificially magnify the signal responding to the interactions. This refined method is based on the previously established Gγ recruitment system, which utilizes yeast G-protein signaling and mating growth selection to screen interacting protein pairs. In the current study, to test the capability of our method, we chose mutants of the Z-domain derived from Staphylococcus aureus protein A as candidate proteins, and the Fc region of human IgG as the counterpart. By introduction of an artificial signal amplifier into the previous Gγ recruitment system, the signal transduction responding to transient interactions between Z-domain mutants and the Fc region with significantly low affinity (8.0 × 10(3) M(-1)) was successfully amplified in recombinant haploid yeast cells. As a result of zygosis with the opposite mating type of wild-type haploid cells, diploid colonies were vigorously and selectively generated on the screening plates, whereas our previous system rarely produced positive colonies. This new approach will be useful for exploring the numerous transient interactions that remain undefined because of the lack of powerful screening tools for their identification. © 2011 The Authors Journal compilation © 2011 FEBS.

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.

Maintaining protein homeostasis: early and late endosomal dual recycling for the maintenance of intracellular pools of the plasma membrane protein Chs3

PubMed Central

Arcones, Irene; Sacristán, Carlos; Roncero, Cesar

2016-01-01

The major chitin synthase activity in yeast cells, Chs3, has become a paradigm in the study of the intracellular traffic of transmembrane proteins due to its tightly regulated trafficking. This includes an efficient mechanism for the maintenance of an extensive reservoir of Chs3 at the trans-Golgi network/EE, which allows for the timely delivery of the protein to the plasma membrane. Here we show that this intracellular reservoir of Chs3 is maintained not only by its efficient AP-1–mediated recycling, but also by recycling through the retromer complex, which interacts with Chs3 at a defined region in its N-terminal cytosolic domain. Moreover, the N-terminal ubiquitination of Chs3 at the plasma membrane by Rsp5/Art4 distinctly labels the protein and regulates its retromer-mediated recycling by enabling Chs3 to be recognized by the ESCRT machinery and degraded in the vacuole. Therefore the combined action of two independent but redundant endocytic recycling mechanisms, together with distinct labels for vacuolar degradation, determines the final fate of the intracellular traffic of the Chs3 protein, allowing yeast cells to regulate morphogenesis, depending on environmental constraints. PMID:27798229

Population growth of the floricolous yeast Metschnikowia reukaufii: effects of nectar host, yeast genotype, and host × genotype interaction.

PubMed

Herrera, Carlos M

2014-05-01

Genetic diversity and genotypic diversity of wild populations of the floricolous yeast Metschnikowia reukaufii exhibit a strong host-mediated component, with genotypes being nonrandomly distributed among flowers of different plant species. To unravel the causal mechanism of this pattern of host-mediated genetic diversity, this paper examines experimentally whether floral nectars of different host plants differ in their quality as a growing substrate for M. reukaufii and also whether genetically distinct yeast strains differ in their relative ability to thrive in nectars of different species (host × genotype interaction). Genetically distinct M. reukaufii strains were grown in natural nectar of different hosts under controlled conditions. Population growth varied widely among nectar hosts, revealing that different host plants provided microhabitats of different quality for M. reukaufii. Different M. reukaufii strains responded in different ways to interspecific nectar variation, and variable growth responses were significantly associated with genetic differences between strains, thus leading to a significant host × genotype interaction. Results of this study provide support for the diversifying selection hypothesis as the underlying mechanism preserving high genetic diversity in wild M. reukaufii populations and also suggest that consequences of functional plant-pollinator diversity may surpass the domain of the mutualistic organisms to implicate associated microorganisms. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Metabolic reconstruction and flux analysis of industrial Pichia yeasts.

PubMed

Chung, Bevan Kai-Sheng; Lakshmanan, Meiyappan; Klement, Maximilian; Ching, Chi Bun; Lee, Dong-Yup

2013-03-01

Pichia yeasts have been recognized as important microbial cell factories in the biotechnological industry. Notably, the Pichia pastoris and Pichia stipitis species have attracted much research interest due to their unique cellular physiology and metabolic capability: P. pastoris has the ability to utilize methanol for cell growth and recombinant protein production, while P. stipitis is capable of assimilating xylose to produce ethanol under oxygen-limited conditions. To harness these characteristics for biotechnological applications, it is highly required to characterize their metabolic behavior. Recently, following the genome sequencing of these two Pichia species, genome-scale metabolic networks have been reconstructed to model the yeasts' metabolism from a systems perspective. To date, there are three genome-scale models available for each of P. pastoris and P. stipitis. In this mini-review, we provide an overview of the models, discuss certain limitations of previous studies, and propose potential future works that can be conducted to better understand and engineer Pichia yeasts for industrial applications.

Strand invasion structures in the inverted repeat of Candida albicans mitochondrial DNA reveal a role for homologous recombination in replication.

PubMed

Gerhold, Joachim M; Aun, Anu; Sedman, Tiina; Jõers, Priit; Sedman, Juhan

2010-09-24

Molecular recombination and transcription are proposed mechanisms to initiate mitochondrial DNA (mtDNA) replication in yeast. We conducted a comprehensive analysis of mtDNA from the yeast Candida albicans. Two-dimensional agarose gel electrophoresis of mtDNA intermediates reveals no bubble structures diagnostic of specific replication origins, but rather supports recombination-driven replication initiation of mtDNA in yeast. Specific species of Y structures together with DNA copy number analyses of a C. albicans mutant strain provide evidence that a region in a mainly noncoding inverted repeat is predominantly involved in replication initiation via homologous recombination. Our further findings show that the C. albicans mtDNA forms a complex branched network that does not contain detectable amounts of circular molecules. We provide topological evidence for recombination-driven mtDNA replication initiation and introduce C. albicans as a suitable model organism to study wild-type mtDNA maintenance in yeast. Copyright © 2010 Elsevier Inc. All rights reserved.

Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources

PubMed Central

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available. PMID:23826291

Linking gene regulation and the exo-metabolome: A comparative transcriptomics approach to identify genes that impact on the production of volatile aroma compounds in yeast

PubMed Central

Rossouw, Debra; Næs, Tormod; Bauer, Florian F

2008-01-01

Background 'Omics' tools provide novel opportunities for system-wide analysis of complex cellular functions. Secondary metabolism is an example of a complex network of biochemical pathways, which, although well mapped from a biochemical point of view, is not well understood with regards to its physiological roles and genetic and biochemical regulation. Many of the metabolites produced by this network such as higher alcohols and esters are significant aroma impact compounds in fermentation products, and different yeast strains are known to produce highly divergent aroma profiles. Here, we investigated whether we can predict the impact of specific genes of known or unknown function on this metabolic network by combining whole transcriptome and partial exo-metabolome analysis. Results For this purpose, the gene expression levels of five different industrial wine yeast strains that produce divergent aroma profiles were established at three different time points of alcoholic fermentation in synthetic wine must. A matrix of gene expression data was generated and integrated with the concentrations of volatile aroma compounds measured at the same time points. This relatively unbiased approach to the study of volatile aroma compounds enabled us to identify candidate genes for aroma profile modification. Five of these genes, namely YMR210W, BAT1, AAD10, AAD14 and ACS1 were selected for overexpression in commercial wine yeast, VIN13. Analysis of the data show a statistically significant correlation between the changes in the exo-metabome of the overexpressing strains and the changes that were predicted based on the unbiased alignment of transcriptomic and exo-metabolomic data. Conclusion The data suggest that a comparative transcriptomics and metabolomics approach can be used to identify the metabolic impacts of the expression of individual genes in complex systems, and the amenability of transcriptomic data to direct applications of biotechnological relevance. PMID:18990252

Polyhexamethyl biguanide can eliminate contaminant yeasts from fuel-ethanol fermentation process.

PubMed

Elsztein, Carolina; de Menezes, João Assis Scavuzzi; de Morais, Marcos Antonio

2008-09-01

Industrial ethanol fermentation is a non-sterile process and contaminant microorganisms can lead to a decrease in industrial productivity and significant economic loss. Nowadays, some distilleries in Northeastern Brazil deal with bacterial contamination by decreasing must pH and adding bactericides. Alternatively, contamination can be challenged by adding a pure batch of Saccharomyces cerevisiae-a time-consuming and costly process. A better strategy might involve the development of a fungicide that kills contaminant yeasts while preserving S. cerevisiae cells. Here, we show that polyhexamethyl biguanide (PHMB) inhibits and kills the most important contaminant yeasts detected in the distilleries of Northeastern Brazil without affecting the cell viability and fermentation capacity of S. cerevisiae. Moreover, some physiological data suggest that PHMB acts through interaction with the yeast membrane. These results support the development of a new strategy for controlling contaminant yeast population whilst keeping industrial yields high.

The Yeast Eukaryotic Translation Initiation Factor 2B Translation Initiation Complex Interacts with the Fatty Acid Synthesis Enzyme YBR159W and Endoplasmic Reticulum Membranes

PubMed Central

Browne, Christopher M.; Samir, Parimal; Fites, J. Scott; Villarreal, Seth A.

2013-01-01

Using affinity purifications coupled with mass spectrometry and yeast two-hybrid assays, we show the Saccharomyces cerevisiae translation initiation factor complex eukaryotic translation initiation factor 2B (eIF2B) and the very-long-chain fatty acid (VLCFA) synthesis keto-reductase enzyme YBR159W physically interact. The data show that the interaction is specifically between YBR159W and eIF2B and not between other members of the translation initiation or VLCFA pathways. A ybr159wΔ null strain has a slow-growth phenotype and a reduced translation rate but a normal GCN4 response to amino acid starvation. Although YBR159W localizes to the endoplasmic reticulum membrane, subcellular fractionation experiments show that a fraction of eIF2B cofractionates with lipid membranes in a YBR159W-independent manner. We show that a ybr159wΔ yeast strain and other strains with null mutations in the VLCFA pathway cause eIF2B to appear as numerous foci throughout the cytoplasm. PMID:23263984

Topology-function conservation in protein-protein interaction networks.

PubMed

Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

2015-05-15

Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

Control of the mitotic exit network during meiosis

PubMed Central

Attner, Michelle A.; Amon, Angelika

2012-01-01

The mitotic exit network (MEN) is an essential GTPase signaling pathway that triggers exit from mitosis in budding yeast. We show here that during meiosis, the MEN is dispensable for exit from meiosis I but contributes to the timely exit from meiosis II. Consistent with a role for the MEN during meiosis II, we find that the signaling pathway is active only during meiosis II. Our analysis further shows that MEN signaling is modulated during meiosis in several key ways. Whereas binding of MEN components to spindle pole bodies (SPBs) is necessary for MEN signaling during mitosis, during meiosis MEN signaling occurs off SPBs and does not require the SPB recruitment factor Nud1. Furthermore, unlike during mitosis, MEN signaling is controlled through the regulated interaction between the MEN kinase Dbf20 and its activating subunit Mob1. Our data lead to the conclusion that a pathway essential for vegetative growth is largely dispensable for the specialized meiotic divisions and provide insights into how cell cycle regulatory pathways are modulated to accommodate different modes of cell division. PMID:22718910

Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

PubMed Central

Todd, Robert G.; van der Zee, Lucas

2016-01-01

Abstract The eukaryotic cell cycle is robustly designed, with interacting molecules organized within a definite topology that ensures temporal precision of its phase transitions. Its underlying dynamics are regulated by molecular switches, for which remarkable insights have been provided by genetic and molecular biology efforts. In a number of cases, this information has been made predictive, through computational models. These models have allowed for the identification of novel molecular mechanisms, later validated experimentally. Logical modeling represents one of the youngest approaches to address cell cycle regulation. We summarize the advances that this type of modeling has achieved to reproduce and predict cell cycle dynamics. Furthermore, we present the challenge that this type of modeling is now ready to tackle: its integration with intracellular networks, and its formalisms, to understand crosstalks underlying systems level properties, ultimate aim of multi-scale models. Specifically, we discuss and illustrate how such an integration may be realized, by integrating a minimal logical model of the cell cycle with a metabolic network. PMID:27993914

A stele-enriched gene regulatory network in the Arabidopsis root

PubMed Central

Brady, Siobhan M; Zhang, Lifang; Megraw, Molly; Martinez, Natalia J; Jiang, Eric; Yi, Charles S; Liu, Weilin; Zeng, Anna; Taylor-Teeples, Mallorie; Kim, Dahae; Ahnert, Sebastian; Ohler, Uwe; Ware, Doreen; Walhout, Albertha J M; Benfey, Philip N

2011-01-01

Tightly controlled gene expression is a hallmark of multicellular development and is accomplished by transcription factors (TFs) and microRNAs (miRNAs). Although many studies have focused on identifying downstream targets of these molecules, less is known about the factors that regulate their differential expression. We used data from high spatial resolution gene expression experiments and yeast one-hybrid (Y1H) and two-hybrid (Y2H) assays to delineate a subset of interactions occurring within a gene regulatory network (GRN) that determines tissue-specific TF and miRNA expression in plants. We find that upstream TFs are expressed in more diverse cell types than their targets and that promoters that are bound by a relatively large number of TFs correspond to key developmental regulators. The regulatory consequence of many TFs for their target was experimentally determined using genetic analysis. Remarkably, molecular phenotypes were identified for 65% of the TFs, but morphological phenotypes were associated with only 16%. This indicates that the GRN is robust, and that gene expression changes may be canalized or buffered. PMID:21245844

Novel Interactome of Saccharomyces cerevisiae Myosin Type II Identified by a Modified Integrated Membrane Yeast Two-Hybrid (iMYTH) Screen.

PubMed

Santiago, Ednalise; Akamine, Pearl; Snider, Jamie; Wong, Victoria; Jessulat, Matthew; Deineko, Viktor; Gagarinova, Alla; Aoki, Hiroyuki; Minic, Zoran; Phanse, Sadhna; San Antonio, Andrea; Cubano, Luis A; Rymond, Brian C; Babu, Mohan; Stagljar, Igor; Rodriguez-Medina, Jose R

2016-05-03

Nonmuscle myosin type II (Myo1p) is required for cytokinesis in the budding yeast Saccharomyces cerevisiae Loss of Myo1p activity has been associated with growth abnormalities and enhanced sensitivity to osmotic stress, making it an appealing antifungal therapeutic target. The Myo1p tail-only domain was previously reported to have functional activity equivalent to the full-length Myo1p whereas the head-only domain did not. Since Myo1p tail-only constructs are biologically active, the tail domain must have additional functions beyond its previously described role in myosin dimerization or trimerization. The identification of new Myo1p-interacting proteins may shed light on the other functions of the Myo1p tail domain. To identify novel Myo1p-interacting proteins, and determine if Myo1p can serve as a scaffold to recruit proteins to the bud neck during cytokinesis, we used the integrated split-ubiquitin membrane yeast two-hybrid (iMYTH) system. Myo1p was iMYTH-tagged at its C-terminus, and screened against both cDNA and genomic prey libraries to identify interacting proteins. Control experiments showed that the Myo1p-bait construct was appropriately expressed, and that the protein colocalized to the yeast bud neck. Thirty novel Myo1p-interacting proteins were identified by iMYTH. Eight proteins were confirmed by coprecipitation (Ape2, Bzz1, Fba1, Pdi1, Rpl5, Tah11, and Trx2) or mass spectrometry (AP-MS) (Abp1). The novel Myo1p-interacting proteins identified come from a range of different processes, including cellular organization and protein synthesis. Actin assembly/disassembly factors such as the SH3 domain protein Bzz1 and the actin-binding protein Abp1 represent likely Myo1p interactions during cytokinesis. Copyright © 2016 Santiago et al.

Cyclin C influences the timing of mitosis in fission yeast

PubMed Central

Banyai, Gabor; Szilagyi, Zsolt; Baraznenok, Vera; Khorosjutina, Olga; Gustafsson, Claes M.

2017-01-01

The multiprotein Mediator complex is required for the regulated transcription of nearly all RNA polymerase II–dependent genes. Mediator contains the Cdk8 regulatory subcomplex, which directs periodic transcription and influences cell cycle progression in fission yeast. Here we investigate the role of CycC, the cognate cyclin partner of Cdk8, in cell cycle control. Previous reports suggested that CycC interacts with other cellular Cdks, but a fusion of CycC to Cdk8 reported here did not cause any obvious cell cycle phenotypes. We find that Cdk8 and CycC interactions are stabilized within the Mediator complex and the activity of Cdk8-CycC is regulated by other Mediator components. Analysis of a mutant yeast strain reveals that CycC, together with Cdk8, primarily affects M-phase progression but mutations that release Cdk8 from CycC control also affect timing of entry into S phase. PMID:28515143

Inferring genetic interactions via a nonlinear model and an optimization algorithm.

PubMed

Chen, Chung-Ming; Lee, Chih; Chuang, Cheng-Long; Wang, Chia-Chang; Shieh, Grace S

2010-02-26

Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target. An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT. GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

Improving analytical methods for protein-protein interaction through implementation of chemically inducible dimerization

PubMed Central

Andersen, Tonni Grube; Nintemann, Sebastian J.; Marek, Magdalena; Halkier, Barbara A.; Schulz, Alexander; Burow, Meike

2016-01-01

When investigating interactions between two proteins with complementary reporter tags in yeast two-hybrid or split GFP assays, it remains troublesome to discriminate true- from false-negative results and challenging to compare the level of interaction across experiments. This leads to decreased sensitivity and renders analysis of weak or transient interactions difficult to perform. In this work, we describe the development of reporters that can be chemically induced to dimerize independently of the investigated interactions and thus alleviate these issues. We incorporated our reporters into the widely used split ubiquitin-, bimolecular fluorescence complementation (BiFC)- and Förster resonance energy transfer (FRET)- based methods and investigated different protein-protein interactions in yeast and plants. We demonstrate the functionality of this concept by the analysis of weakly interacting proteins from specialized metabolism in the model plant Arabidopsis thaliana. Our results illustrate that chemically induced dimerization can function as a built-in control for split-based systems that is easily implemented and allows for direct evaluation of functionality. PMID:27282591

Kluyveromyces marxianus and Saccharomyces boulardii Induce Distinct Levels of Dendritic Cell Cytokine Secretion and Significantly Different T Cell Responses In Vitro

PubMed Central

Smith, Ida M.; Baker, Adam; Christensen, Jeffrey E.; Boekhout, Teun; Frøkiær, Hanne; Arneborg, Nils; Jespersen, Lene

2016-01-01

Interactions between members of the intestinal microbiota and the mucosal immune system can significantly impact human health, and in this context, fungi and food-related yeasts are known to influence intestinal inflammation through direct interactions with specialized immune cells in vivo. The aim of the present study was to characterize the immune modulating properties of the food-related yeast Kluyveromyces marxianus in terms of adaptive immune responses indicating inflammation versus tolerance and to explore the mechanisms behind the observed responses. Benchmarking against a Saccharomyces boulardii strain with probiotic effects documented in clinical trials, we evaluated the ability of K. marxianus to modulate human dendritic cell (DC) function in vitro. Further, we assessed yeast induced DC modulation of naive T cells toward effector responses dominated by secretion of IFNγ and IL-17 versus induction of a Treg response characterized by robust IL-10 secretion. In addition, we blocked relevant DC surface receptors and investigated the stimulating properties of β-glucan containing yeast cell wall extracts. K. marxianus and S. boulardii induced distinct levels of DC cytokine secretion, primarily driven by Dectin-1 recognition of β-glucan components in their cell walls. Upon co-incubation of yeast exposed DCs and naive T cells, S. boulardii induced a potent IFNγ response indicating TH1 mobilization. In contrast, K. marxianus induced a response dominated by Foxp3+ Treg cells, a characteristic that may benefit human health in conditions characterized by excessive inflammation and positions K. marxianus as a strong candidate for further development as a novel yeast probiotic. PMID:27898740

Kluyveromyces marxianus and Saccharomyces boulardii Induce Distinct Levels of Dendritic Cell Cytokine Secretion and Significantly Different T Cell Responses In Vitro.

PubMed

Smith, Ida M; Baker, Adam; Christensen, Jeffrey E; Boekhout, Teun; Frøkiær, Hanne; Arneborg, Nils; Jespersen, Lene

2016-01-01

Interactions between members of the intestinal microbiota and the mucosal immune system can significantly impact human health, and in this context, fungi and food-related yeasts are known to influence intestinal inflammation through direct interactions with specialized immune cells in vivo. The aim of the present study was to characterize the immune modulating properties of the food-related yeast Kluyveromyces marxianus in terms of adaptive immune responses indicating inflammation versus tolerance and to explore the mechanisms behind the observed responses. Benchmarking against a Saccharomyces boulardii strain with probiotic effects documented in clinical trials, we evaluated the ability of K. marxianus to modulate human dendritic cell (DC) function in vitro. Further, we assessed yeast induced DC modulation of naive T cells toward effector responses dominated by secretion of IFNγ and IL-17 versus induction of a Treg response characterized by robust IL-10 secretion. In addition, we blocked relevant DC surface receptors and investigated the stimulating properties of β-glucan containing yeast cell wall extracts. K. marxianus and S. boulardii induced distinct levels of DC cytokine secretion, primarily driven by Dectin-1 recognition of β-glucan components in their cell walls. Upon co-incubation of yeast exposed DCs and naive T cells, S. boulardii induced a potent IFNγ response indicating TH1 mobilization. In contrast, K. marxianus induced a response dominated by Foxp3+ Treg cells, a characteristic that may benefit human health in conditions characterized by excessive inflammation and positions K. marxianus as a strong candidate for further development as a novel yeast probiotic.

The Sla1 adaptor-clathrin interaction regulates coat formation and progression of endocytosis.

PubMed

Tolsma, Thomas O; Cuevas, Lena M; Di Pietro, Santiago M

2018-06-01

Clathrin-mediated endocytosis is a fundamental transport pathway that depends on numerous protein-protein interactions. Testing the importance of the adaptor protein-clathrin interaction for coat formation and progression of endocytosis in vivo has been difficult due to experimental constrains. Here, we addressed this question using the yeast clathrin adaptor Sla1, which is unique in showing a cargo endocytosis defect upon substitution of 3 amino acids in its clathrin-binding motif (sla1 AAA ) that disrupt clathrin binding. Live-cell imaging showed an impaired Sla1-clathrin interaction causes reduced clathrin levels but increased Sla1 levels at endocytic sites. Moreover, the rate of Sla1 recruitment was reduced indicating proper dynamics of both clathrin and Sla1 depend on their interaction. sla1 AAA cells showed a delay in progression through the various stages of endocytosis. The Arp2/3-dependent actin polymerization machinery was present for significantly longer time before actin polymerization ensued, revealing a link between coat formation and activation of actin polymerization. Ultimately, in sla1 AAA cells a larger than normal actin network was formed, dramatically higher levels of various machinery proteins other than clathrin were recruited, and the membrane profile of endocytic invaginations was longer. Thus, the Sla1-clathrin interaction is important for coat formation, regulation of endocytic progression and membrane bending. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Scaling laws and universality for the strength of genetic interactions in yeast

NASA Astrophysics Data System (ADS)

Velenich, Andrea; Dai, Mingjie; Gore, Jeff

2012-02-01

Genetic interactions provide a window to the organization of the thousands of biochemical reactions in living cells. If two mutations affect unrelated cellular functions, the fitness effects of their combination can be easily predicted from the two separate fitness effects. However, because of interactions, for some pairs of mutations their combined fitness effect deviates from the naive prediction. We study genetic interactions in yeast cells by analyzing a publicly available database containing experimental growth rates of 5 million double mutants. We show that the characteristic strength of genetic interactions has a simple power law dependence on the fitness effects of the two interacting mutations and that the probability distribution of genetic interactions is a universal function. We further argue that the strength of genetic interactions depends only on the fitness effects of the interacting mutations and not on their biological origin in terms of single point mutations, entire gene knockouts or even more complicated physiological perturbations. Finally, we discuss the implications of the power law scaling of genetic interactions on the ruggedness of fitness landscapes and the consequent evolutionary dynamics.

Proteomic and Genomic Analyses of the Rvb1 and Rvb2 Interaction Network upon Deletion of R2TP Complex Components*

PubMed Central

Lakshminarasimhan, Mahadevan; Boanca, Gina; Banks, Charles A. S.; Hattem, Gaye L.; Gabriel, Ana E.; Groppe, Brad D.; Smoyer, Christine; Malanowski, Kate E.; Peak, Allison; Florens, Laurence; Washburn, Michael P.

2016-01-01

The highly conserved yeast R2TP complex, consisting of Rvb1, Rvb2, Pih1, and Tah1, participates in diverse cellular processes ranging from assembly of protein complexes to apoptosis. Rvb1 and Rvb2 are closely related proteins belonging to the AAA+ superfamily and are essential for cell survival. Although Rvbs have been shown to be associated with various protein complexes including the Ino80 and Swr1chromatin remodeling complexes, we performed a systematic quantitative proteomic analysis of their associated proteins and identified two additional complexes that associate with Rvb1 and Rvb2: the chaperonin-containing T-complex and the 19S regulatory particle of the proteasome complex. We also analyzed Rvb1 and Rvb2 purified from yeast strains devoid of PIH1 and TAH1. These analyses revealed that both Rvb1 and Rvb2 still associated with Hsp90 and were highly enriched with RNA polymerase II complex components. Our analyses also revealed that both Rvb1 and Rvb2 were recruited to the Ino80 and Swr1 chromatin remodeling complexes even in the absence of Pih1 and Tah1 proteins. Using further biochemical analysis, we showed that Rvb1 and Rvb2 directly interacted with Hsp90 as well as with the RNA polymerase II complex. RNA-Seq analysis of the deletion strains compared with the wild-type strains revealed an up-regulation of ribosome biogenesis and ribonucleoprotein complex biogenesis genes, down-regulation of response to abiotic stimulus genes, and down-regulation of response to temperature stimulus genes. A Gene Ontology analysis of the 80 proteins whose protein associations were altered in the PIH1 or TAH1 deletion strains found ribonucleoprotein complex proteins to be the most enriched category. This suggests an important function of the R2TP complex in ribonucleoprotein complex biogenesis at both the proteomic and genomic levels. Finally, these results demonstrate that deletion network analyses can provide novel insights into cellular systems. PMID:26831523

Water-Transfer Slows Aging in Saccharomyces cerevisiae

PubMed Central

Cohen, Aviv; Weindling, Esther; Rabinovich, Efrat; Nachman, Iftach; Fuchs, Shai; Chuartzman, Silvia; Gal, Lihi; Schuldiner, Maya; Bar-Nun, Shoshana

2016-01-01

Transferring Saccharomyces cerevisiae cells to water is known to extend their lifespan. However, it is unclear whether this lifespan extension is due to slowing the aging process or merely keeping old yeast alive. Here we show that in water-transferred yeast, the toxicity of polyQ proteins is decreased and the aging biomarker 47Q aggregates at a reduced rate and to a lesser extent. These beneficial effects of water-transfer could not be reproduced by diluting the growth medium and depended on de novo protein synthesis and proteasomes levels. Interestingly, we found that upon water-transfer 27 proteins are downregulated, 4 proteins are upregulated and 81 proteins change their intracellular localization, hinting at an active genetic program enabling the lifespan extension. Furthermore, the aging-related deterioration of the heat shock response (HSR), the unfolded protein response (UPR) and the endoplasmic reticulum-associated protein degradation (ERAD), was largely prevented in water-transferred yeast, as the activities of these proteostatic network pathways remained nearly as robust as in young yeast. The characteristics of young yeast that are actively maintained upon water-transfer indicate that the extended lifespan is the outcome of slowing the rate of the aging process. PMID:26862897

The ecology of the Drosophila-yeast mutualism in wineries

PubMed Central

2018-01-01

The fruit fly, Drosophila melanogaster, is preferentially found on fermenting fruits. The yeasts that dominate the microbial communities of these substrates are the primary food source for developing D. melanogaster larvae, and adult flies manifest a strong olfactory system-mediated attraction for the volatile compounds produced by these yeasts during fermentation. Although most work on this interaction has focused on the standard laboratory yeast Saccharomyces cerevisiae, a wide variety of other yeasts naturally ferment fallen fruit. Here we address the open question of whether D. melanogaster preferentially associates with distinct yeasts in different, closely-related environments. We characterized the spatial and temporal dynamics of Drosophila-associated fungi in Northern California wineries that use organic grapes and natural fermentation using high-throughput, short-amplicon sequencing. We found that there is nonrandom structure in the fungal communities that are vectored by flies both between and within vineyards. Within wineries, the fungal communities associated with flies in cellars, fermentation tanks, and pomace piles are distinguished by varying abundances of a small number of yeast species. To investigate the origins of this structure, we assayed Drosophila attraction to, oviposition on, larval development in, and longevity when consuming the yeasts that distinguish vineyard microhabitats from each other. We found that wild fly lines did not respond differentially to the yeast species that distinguish winery habitats in habitat specific manner. Instead, this subset of yeast shares traits that make them attractive to and ensure their close association with Drosophila. PMID:29768432

The ecology of the Drosophila-yeast mutualism in wineries.

PubMed

Quan, Allison S; Eisen, Michael B

2018-01-01

The fruit fly, Drosophila melanogaster, is preferentially found on fermenting fruits. The yeasts that dominate the microbial communities of these substrates are the primary food source for developing D. melanogaster larvae, and adult flies manifest a strong olfactory system-mediated attraction for the volatile compounds produced by these yeasts during fermentation. Although most work on this interaction has focused on the standard laboratory yeast Saccharomyces cerevisiae, a wide variety of other yeasts naturally ferment fallen fruit. Here we address the open question of whether D. melanogaster preferentially associates with distinct yeasts in different, closely-related environments. We characterized the spatial and temporal dynamics of Drosophila-associated fungi in Northern California wineries that use organic grapes and natural fermentation using high-throughput, short-amplicon sequencing. We found that there is nonrandom structure in the fungal communities that are vectored by flies both between and within vineyards. Within wineries, the fungal communities associated with flies in cellars, fermentation tanks, and pomace piles are distinguished by varying abundances of a small number of yeast species. To investigate the origins of this structure, we assayed Drosophila attraction to, oviposition on, larval development in, and longevity when consuming the yeasts that distinguish vineyard microhabitats from each other. We found that wild fly lines did not respond differentially to the yeast species that distinguish winery habitats in habitat specific manner. Instead, this subset of yeast shares traits that make them attractive to and ensure their close association with Drosophila.

Experimental Study of Membrane Fouling during Crossflow Microfiltration of Yeast and Bacteria Suspensions: Towards an Analysis at the Microscopic Level

PubMed Central

Ben Hassan, Ines; Ennouri, Monia; Lafforgue, Christine; Schmitz, Philippe; Ayadi, Abdelmoneim

2013-01-01

Microfiltration of model cell suspensions combining macroscopic and microscopic approaches was studied in order to better understand microbial membrane fouling mechanisms. The respective impact of Saccharomyces cerevisiae yeast and Escherichia coli bacteria on crossflow microfiltration performances was investigated using a multichannel ceramic 0.2 µm membrane. Pure yeast suspensions (5 µm ovoid cells) and mixtures of yeast and bacteria (1 to 2.5 µm rod shape cells) were considered in order to analyse the effect of interaction between these two microorganisms on fouling reversibility. The resistances varied significantly with the concentration and characteristics of the microorganisms. Membrane fouling with pure yeast suspension was mainly reversible. For yeast and bacteria mixed suspensions (6 g L−1 yeast concentration) the increase in bacteria from 0.15 to 0.30 g L−1 increased the percentage of normalized reversible resistance. At 10 g L−1 yeast concentration, the addition of bacteria tends to increase the percentage of normalized irreversible resistance. For the objective of performing local analysis of fouling, an original filtration chamber allowing direct in situ observation of the cake by confocal laser scanning microscopy (CLSM) was designed, developed and validated. This device will be used in future studies to characterize cake structure at the microscopic scale. PMID:24958619

Functional analysis of the isoforms of an ABI3-like factor of Pisum sativum generated by alternative splicing.

PubMed

Gagete, Andrés P; Riera, Marta; Franco, Luis; Rodrigo, M Isabel

2009-01-01

At least seven isoforms (PsABI3-1 to PsABI3-7) of a putative, pea ABI3-like factor, originated by alternative splicing, have been identified after cDNA cloning. A similar variability had previously only been described for monocot genes. The full-length isoform, PsABI3-1, contains the typical N-terminal acidic domains and C-terminal basic subdomains, B1 to B3. Reverse transcriptase-PCR analysis revealed that the gene is expressed just in seeds, starting at middle embryogenesis; no gene products are observed in embryo axes after 18 h post-imbibition although they are more persistent in cotyledons. The activity of the isoforms was studied by yeast one-hybrid assays. When yeast was transformed with the isoforms fused to the DNA binding domain of Gal4p, only the polypeptides PsABI3-2 and PsABI3-7 failed to complement the activity of Gal4p. Acidic domains A1 and A2 exhibit transactivating activity, but the former requires a small C-terminal extension to be active. Yeast two-hybrid analysis showed that PsABI3 is able to heterodimerize with Arabidopsis thaliana ABI5, thus proving that PsABI3 is functionally active. The minimum requirement for the interaction PsABI3-AtABI5 is the presence of the subdomain B1 with an extension, 81 amino acids long, at their C-terminal side. Finally, a transient onion transformation assay showed that both the active PsABI3-1 and the inactive PsABI3-2 isoforms are localized to nuclei. Considering that the major isoforms remain approximately constant in developing seeds although their relative proportion varied, the possible role of splicing in the regulatory network of ABA signalling is discussed.

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

Genome-Wide Protein Interaction Screens Reveal Functional Networks Involving Sm-Like Proteins

PubMed Central

Fromont-Racine, Micheline; Mayes, Andrew E.; Brunet-Simon, Adeline; Rain, Jean-Christophe; Colley, Alan; Dix, Ian; Decourty, Laurence; Joly, Nicolas; Ricard, Florence; Beggs, Jean D.

2000-01-01

A set of seven structurally related Sm proteins forms the core of the snRNP particles containing the spliceosomal U1, U2, U4 and U5 snRNAs. A search of the genomic sequence of Saccharomyces cerevisiae has identified a number of open reading frames that potentially encode structurally similar proteins termed Lsm (Like Sm) proteins. With the aim of analysing all possible interactions between the Lsm proteins and any protein encoded in the yeast genome, we performed exhaustive and iterative genomic two-hybrid screens, starting with the Lsm proteins as baits. Indeed, extensive interactions amongst eight Lsm proteins were found that suggest the existence of a Lsm complex or complexes. These Lsm interactions apparently involve the conserved Sm domain that also mediates interactions between the Sm proteins. The screens also reveal functionally significant interactions with splicing factors, in particular with Prp4 and Prp24, compatible with genetic studies and with the reported association of Lsm proteins with spliceosomal U6 and U4/U6 particles. In addition, interactions with proteins involved in mRNA turnover, such as Mrt1, Dcp1, Dcp2 and Xrn1, point to roles for Lsm complexes in distinct RNA metabolic processes, that are confirmed in independent functional studies. These results provide compelling evidence that two-hybrid screens yield functionally meaningful information about protein–protein interactions and can suggest functions for uncharacterized proteins, especially when they are performed on a genome-wide scale. PMID:10900456

Extracellular nanovesicles released from the commensal yeast Malassezia sympodialis are enriched in allergens and interact with cells in human skin.

PubMed

Johansson, Henrik J; Vallhov, Helen; Holm, Tina; Gehrmann, Ulf; Andersson, Anna; Johansson, Catharina; Blom, Hans; Carroni, Marta; Lehtiö, Janne; Scheynius, Annika

2018-06-15

Malassezia sympodialis is a dominant commensal fungi in the human skin mycobiome but is also associated with common skin disorders including atopic eczema (AE). M. sympodialis releases extracellular vesicles, designated MalaEx, which are carriers of small RNAs and allergens, and they can induce inflammatory cytokine responses. Here we explored how MalaEx are involved in host-microbe interactions by comparing protein content of MalaEx with that of the parental yeast cells, and by investigating interactions of MalaEx with cells in the skin. Cryo-electron tomography revealed a heterogeneous population of MalaEx. iTRAQ based quantitative proteomics identified in total 2439 proteins in all replicates of which 110 were enriched in MalaEx compared to the yeast cells. Among the MalaEx enriched proteins were two of the M. sympodialis allergens, Mala s 1 and s 7. Functional experiments indicated an active binding and internalization of MalaEx into human keratinocytes and monocytes, and MalaEx were found in close proximity of the nuclei using super-resolution fluorescence 3D-SIM imaging. Our results provides new insights into host-microbe interactions, supporting that MalaEx may have a role in the sensitization and maintenance of inflammation in AE by containing enriched amounts of allergens and with their ability to interact with skin cells.

Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle.

PubMed

Gibbs, David L; Shmulevich, Ilya

2017-06-01

The Influence Maximization Problem (IMP) aims to discover the set of nodes with the greatest influence on network dynamics. The problem has previously been applied in epidemiology and social network analysis. Here, we demonstrate the application to cell cycle regulatory network analysis for Saccharomyces cerevisiae. Fundamentally, gene regulation is linked to the flow of information. Therefore, our implementation of the IMP was framed as an information theoretic problem using network diffusion. Utilizing more than 26,000 regulatory edges from YeastMine, gene expression dynamics were encoded as edge weights using time lagged transfer entropy, a method for quantifying information transfer between variables. By picking a set of source nodes, a diffusion process covers a portion of the network. The size of the network cover relates to the influence of the source nodes. The set of nodes that maximizes influence is the solution to the IMP. By solving the IMP over different numbers of source nodes, an influence ranking on genes was produced. The influence ranking was compared to other metrics of network centrality. Although the top genes from each centrality ranking contained well-known cell cycle regulators, there was little agreement and no clear winner. However, it was found that influential genes tend to directly regulate or sit upstream of genes ranked by other centrality measures. The influential nodes act as critical sources of information flow, potentially having a large impact on the state of the network. Biological events that affect influential nodes and thereby affect information flow could have a strong effect on network dynamics, potentially leading to disease. Code and data can be found at: https://github.com/gibbsdavidl/miergolf.

Evidence for the interaction of the regulatory protein Ki-1/57 with p53 and its interacting proteins

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

Nery, Flavia C.; Departamento de Genetica e Evolucao, Universidade Estadual de Campinas, Campinas, SP; Rui, Edmilson

Ki-1/57 is a cytoplasmic and nuclear phospho-protein of 57 kDa and interacts with the adaptor protein RACK1, the transcription factor MEF2C, and the chromatin remodeling factor CHD3, suggesting that it might be involved in the regulation of transcription. Here, we describe yeast two-hybrid studies that identified a total of 11 proteins interacting with Ki-1/57, all of which interact or are functionally associated with p53 or other members of the p53 family of proteins. We further found that Ki-1/57 is able to interact with p53 itself in the yeast two-hybrid system when the interaction was tested directly. This interaction could bemore » confirmed by pull down assays with purified proteins in vitro and by reciprocal co-immunoprecipitation assays from the human Hodgkin analogous lymphoma cell line L540. Furthermore, we found that the phosphorylation of p53 by PKC abolishes its interaction with Ki-1/57 in vitro.« less

Reconstituting protein interaction networks using parameter-dependent domain-domain interactions

PubMed Central

2013-01-01

Background We can describe protein-protein interactions (PPIs) as sets of distinct domain-domain interactions (DDIs) that mediate the physical interactions between proteins. Experimental data confirm that DDIs are more consistent than their corresponding PPIs, lending support to the notion that analyses of DDIs may improve our understanding of PPIs and lead to further insights into cellular function, disease, and evolution. However, currently available experimental DDI data cover only a small fraction of all existing PPIs and, in the absence of structural data, determining which particular DDI mediates any given PPI is a challenge. Results We present two contributions to the field of domain interaction analysis. First, we introduce a novel computational strategy to merge domain annotation data from multiple databases. We show that when we merged yeast domain annotations from six annotation databases we increased the average number of domains per protein from 1.05 to 2.44, bringing it closer to the estimated average value of 3. Second, we introduce a novel computational method, parameter-dependent DDI selection (PADDS), which, given a set of PPIs, extracts a small set of domain pairs that can reconstruct the original set of protein interactions, while attempting to minimize false positives. Based on a set of PPIs from multiple organisms, our method extracted 27% more experimentally detected DDIs than existing computational approaches. Conclusions We have provided a method to merge domain annotation data from multiple sources, ensuring large and consistent domain annotation for any given organism. Moreover, we provided a method to extract a small set of DDIs from the underlying set of PPIs and we showed that, in contrast to existing approaches, our method was not biased towards DDIs with low or high occurrence counts. Finally, we used these two methods to highlight the influence of the underlying annotation density on the characteristics of extracted DDIs. Although increased annotations greatly expanded the possible DDIs, the lack of knowledge of the true biological false positive interactions still prevents an unambiguous assignment of domain interactions responsible for all protein network interactions. Executable files and examples are given at: http://www.bhsai.org/downloads/padds/ PMID:23651452

Rapid Methods for the Laboratory Identification of Pathogenic Microorganisms.

DTIC Science & Technology

1981-09-01

Preliminary results provide strong evidence to show that the fungi, Candida and Cryptococcus , can be raoidly differentiated by a lectin test. SFor Oro...SUMMATION LECTIN-YEAST INTERACTIONS Objective: To find a lectin that selectively agglutinates Cryptococcus neoformans (the etiologic agent of...peanut), Conavalia ensiformis (Con A) and mango extract may potentially be utilized to differentiate Cryptococcus from the other yeasts most commonly

Direct cytotoxicity evaluation of 63S bioactive glass and bone-derived hydroxyapatite particles using yeast model and human chondrocyte cells by microcalorimetry.

PubMed

Doostmohammadi, A; Monshi, A; Fathi, M H; Karbasi, S; Braissant, O; Daniels, A U

2011-10-01

In this study, the cytotoxicity evaluation of prepared 63S bioactive glass and bone-derived hydroxyapatite particles with yeast and human chondrocyte cells was carried out using isothermal micro-nano calorimetry (IMNC), which is a new method for studying cell/biomaterial interactions. Bioactive glass particles were made via sol-gel method and hydroxyapatite was obtained from bovine bone. Elemental analysis was carried out by XRF and EDXRF. Amorphous structure of the glass and completely crystalline structure of HA were detected by XRD analysis. Finally, the cytotoxicity of bioactive glass and bone-derived HA particles with yeast and cultured human chondrocyte cells was evaluated using IMNC. The results confirmed the viability, growth and proliferation of human chondrocyte cells in contact with 63S bioactive glass, and bone-derived HA particles. Also the results indicated that yeast model which is much easier to handle, can be considered as a good proxy and can provide a rapid primary estimate of the ranges to be used in assays involving human cells. All of these results confirmed that IMNC is a convenient method which caters to measuring the cell-biomaterial interactions alongside the current methods.