New support vector machine-based method for microRNA target prediction.

PubMed

Li, L; Gao, Q; Mao, X; Cao, Y

2014-06-09

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model. The model supplies information of two binding sites (primary and secondary) for a radial basis function kernel as a similarity measure for SVM features. The information is categorized based on structural, thermodynamic, and sequence conservation. Using high-confidence datasets selected from public miRNA target databases, we obtained a human miRNA target SVM classifier model with high performance and provided an efficient tool for human miRNA target gene identification. Experiments have shown that our method is a reliable tool for miRNA target-gene prediction, and a successful application of an SVM classifier. Compared with other methods, the method proposed here improves the sensitivity and accuracy of miRNA prediction. Its performance can be further improved by providing more training examples.

Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

PubMed Central

Emig, Dorothea; Ivliev, Alexander; Pustovalova, Olga; Lancashire, Lee; Bureeva, Svetlana; Nikolsky, Yuri; Bessarabova, Marina

2013-01-01

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example. PMID:23593264

The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

PubMed

Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

2014-06-01

With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

Gene Therapy and Targeted Toxins for Glioma

PubMed Central

Castro, Maria G.; Candolfi, Marianela; Kroeger, Kurt; King, Gwendalyn D.; Curtin, James F.; Yagiz, Kader; Mineharu, Yohei; Assi, Hikmat; Wibowo, Mia; Muhammad, AKM Ghulam; Foulad, David; Puntel, Mariana; Lowenstein, Pedro R.

2011-01-01

The most common primary brain tumor in adults is glioblastoma. These tumors are highly invasive and aggressive with a mean survival time of nine to twelve months from diagnosis to death. Current treatment modalities are unable to significantly prolong survival in patients diagnosed with glioblastoma. As such, glioma is an attractive target for developing novel therapeutic approaches utilizing gene therapy. This review will examine the available preclinical models for glioma including xenographs, syngeneic and genetic models. Several promising therapeutic targets are currently being pursued in pre-clinical investigations. These targets will be reviewed by mechanism of action, i.e., conditional cytotoxic, targeted toxins, oncolytic viruses, tumor suppressors/oncogenes, and immune stimulatory approaches. Preclinical gene therapy paradigms aim to determine which strategies will provide rapid tumor regression and long-term protection from recurrence. While a wide range of potential targets are being investigated preclinically, only the most efficacious are further transitioned into clinical trial paradigms. Clinical trials reported to date are summarized including results from conditionally cytotoxic, targeted toxins, oncolytic viruses and oncogene targeting approaches. Clinical trial results have not been as robust as preclinical models predicted; this could be due to the limitations of the GBM models employed. Once this is addressed, and we develop effective gene therapies in models that better replicate the clinical scenario, gene therapy will provide a powerful approach to treat and manage brain tumors. PMID:21453286

Human microRNA target analysis and gene ontology clustering by GOmir, a novel stand-alone application

PubMed Central

Roubelakis, Maria G; Zotos, Pantelis; Papachristoudis, Georgios; Michalopoulos, Ioannis; Pappa, Kalliopi I; Anagnou, Nicholas P; Kossida, Sophia

2009-01-01

Background microRNAs (miRNAs) are single-stranded RNA molecules of about 20–23 nucleotides length found in a wide variety of organisms. miRNAs regulate gene expression, by interacting with target mRNAs at specific sites in order to induce cleavage of the message or inhibit translation. Predicting or verifying mRNA targets of specific miRNAs is a difficult process of great importance. Results GOmir is a novel stand-alone application consisting of two separate tools: JTarget and TAGGO. JTarget integrates miRNA target prediction and functional analysis by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar computational tools as well as the experimentally supported targets from TarBase and also providing a full gene description and functional analysis for each target gene. On the other hand, TAGGO application is designed to automatically group gene ontology annotations, taking advantage of the Gene Ontology (GO), in order to extract the main attributes of sets of proteins. GOmir represents a new tool incorporating two separate Java applications integrated into one stand-alone Java application. Conclusion GOmir (by using up to five different databases) introduces miRNA predicted targets accompanied by (a) full gene description, (b) functional analysis and (c) detailed gene ontology clustering. Additionally, a reverse search initiated by a potential target can also be conducted. GOmir can freely be downloaded BRFAA. PMID:19534746

Human microRNA target analysis and gene ontology clustering by GOmir, a novel stand-alone application.

PubMed

Roubelakis, Maria G; Zotos, Pantelis; Papachristoudis, Georgios; Michalopoulos, Ioannis; Pappa, Kalliopi I; Anagnou, Nicholas P; Kossida, Sophia

2009-06-16

microRNAs (miRNAs) are single-stranded RNA molecules of about 20-23 nucleotides length found in a wide variety of organisms. miRNAs regulate gene expression, by interacting with target mRNAs at specific sites in order to induce cleavage of the message or inhibit translation. Predicting or verifying mRNA targets of specific miRNAs is a difficult process of great importance. GOmir is a novel stand-alone application consisting of two separate tools: JTarget and TAGGO. JTarget integrates miRNA target prediction and functional analysis by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar computational tools as well as the experimentally supported targets from TarBase and also providing a full gene description and functional analysis for each target gene. On the other hand, TAGGO application is designed to automatically group gene ontology annotations, taking advantage of the Gene Ontology (GO), in order to extract the main attributes of sets of proteins. GOmir represents a new tool incorporating two separate Java applications integrated into one stand-alone Java application. GOmir (by using up to five different databases) introduces miRNA predicted targets accompanied by (a) full gene description, (b) functional analysis and (c) detailed gene ontology clustering. Additionally, a reverse search initiated by a potential target can also be conducted. GOmir can freely be downloaded BRFAA.

In Silico Prediction and Validation of Gfap as an miR-3099 Target in Mouse Brain.

PubMed

Abidin, Shahidee Zainal; Leong, Jia-Wen; Mahmoudi, Marzieh; Nordin, Norshariza; Abdullah, Syahril; Cheah, Pike-See; Ling, King-Hwa

2017-08-01

MicroRNAs are small non-coding RNAs that play crucial roles in the regulation of gene expression and protein synthesis during brain development. MiR-3099 is highly expressed throughout embryogenesis, especially in the developing central nervous system. Moreover, miR-3099 is also expressed at a higher level in differentiating neurons in vitro, suggesting that it is a potential regulator during neuronal cell development. This study aimed to predict the target genes of miR-3099 via in-silico analysis using four independent prediction algorithms (miRDB, miRanda, TargetScan, and DIANA-micro-T-CDS) with emphasis on target genes related to brain development and function. Based on the analysis, a total of 3,174 miR-3099 target genes were predicted. Those predicted by at least three algorithms (324 genes) were subjected to DAVID bioinformatics analysis to understand their overall functional themes and representation. The analysis revealed that nearly 70% of the target genes were expressed in the nervous system and a significant proportion were associated with transcriptional regulation and protein ubiquitination mechanisms. Comparison of in situ hybridization (ISH) expression patterns of miR-3099 in both published and in-house-generated ISH sections with the ISH sections of target genes from the Allen Brain Atlas identified 7 target genes (Dnmt3a, Gabpa, Gfap, Itga4, Lxn, Smad7, and Tbx18) having expression patterns complementary to miR-3099 in the developing and adult mouse brain samples. Of these, we validated Gfap as a direct downstream target of miR-3099 using the luciferase reporter gene system. In conclusion, we report the successful prediction and validation of Gfap as an miR-3099 target gene using a combination of bioinformatics resources with enrichment of annotations based on functional ontologies and a spatio-temporal expression dataset.

Experimental validation of predicted cancer genes using FRET

NASA Astrophysics Data System (ADS)

Guala, Dimitri; Bernhem, Kristoffer; Ait Blal, Hammou; Jans, Daniel; Lundberg, Emma; Brismar, Hjalmar; Sonnhammer, Erik L. L.

2018-07-01

Huge amounts of data are generated in genome wide experiments, designed to investigate diseases with complex genetic causes. Follow up of all potential leads produced by such experiments is currently cost prohibitive and time consuming. Gene prioritization tools alleviate these constraints by directing further experimental efforts towards the most promising candidate targets. Recently a gene prioritization tool called MaxLink was shown to outperform other widely used state-of-the-art prioritization tools in a large scale in silico benchmark. An experimental validation of predictions made by MaxLink has however been lacking. In this study we used Fluorescence Resonance Energy Transfer, an established experimental technique for detection of protein-protein interactions, to validate potential cancer genes predicted by MaxLink. Our results provide confidence in the use of MaxLink for selection of new targets in the battle with polygenic diseases.

Methylation of WNT target genes AXIN2 and DKK1 as robust biomarkers for recurrence prediction in stage II colon cancer

PubMed Central

Kandimalla, R; Linnekamp, J F; van Hooff, S; Castells, A; Llor, X; Andreu, M; Jover, R; Goel, A; Medema, J P

2017-01-01

Stage II colon cancer (CC) still remains a clinical challenge with patient stratification for adjuvant therapy (AT) largely relying on clinical parameters. Prognostic biomarkers are urgently needed for better stratification. Previously, we have shown that WNT target genes AXIN2, DKK1, APCDD1, ASCL2 and LGR5 are silenced by DNA methylation and could serve as prognostic markers in stage II CC patients using methylation-specific PCR. Here, we have extended our discovery cohort AMC90-AJCC-II (N=65) and methylation was analyzed by quantitative pyrosequencing. Subsequently, we validated the results in an independent EPICOLON1 CC cohort (N=79). Methylation of WNT target genes is negatively correlated to mRNA expression. A combination of AXIN2 and DKK1 methylation significantly predicted recurrences in univariate (area under the curve (AUC)=0.83, confidence interval (CI): 0.72–0.94, P<0.0001) analysis in stage II microsatellite stable (MSS) CC patients. This two marker combination showed an AUC of 0.80 (CI: 0.68–0.91, P<0.0001) in the EPICOLON1 validation cohort. Multivariate analysis in the Academic Medical Center (AMC) cohort revealed that both WNT target gene methylation and consensus molecular subtype 4 (CMS4) are significantly associated with poor recurrence-free survival (hazard ratio (HR)methylation: 3.84, 95% CI: 1.14–12.43; HRCMS4: 3.73, 95% CI: 1.22–11.48). CMS4 subtype tumors with WNT target methylation showed worse prognosis. Combining WNT target gene methylation and CMS4 subtype lead to an AUC of 0.89 (0.791–0.982, P<0.0001) for recurrence prediction. Notably, we observed that methylation of DKK1 is high in BRAF mutant and CIMP (CpG island methylator phenotype)-positive cancers, whereas AXIN2 methylation appears to be associated with CMS4. Methylation of AXIN2 and DKK1 were found to be robust markers for recurrence prediction in stage II MSS CC patients. Further validation of these findings in a randomized and prospective manner could pave a way to

Identification of regulatory targets of tissue-specific transcription factors: application to retina-specific gene regulation

PubMed Central

Qian, Jiang; Esumi, Noriko; Chen, Yangjian; Wang, Qingliang; Chowers, Itay; Zack, Donald J.

2005-01-01

Identification of tissue-specific gene regulatory networks can yield insights into the molecular basis of a tissue's development, function and pathology. Here, we present a computational approach designed to identify potential regulatory target genes of photoreceptor cell-specific transcription factors (TFs). The approach is based on the hypothesis that genes related to the retina in terms of expression, disease and/or function are more likely to be the targets of retina-specific TFs than other genes. A list of genes that are preferentially expressed in retina was obtained by integrating expressed sequence tag, SAGE and microarray datasets. The regulatory targets of retina-specific TFs are enriched in this set of retina-related genes. A Bayesian approach was employed to integrate information about binding site location relative to a gene's transcription start site. Our method was applied to three retina-specific TFs, CRX, NRL and NR2E3, and a number of potential targets were predicted. To experimentally assess the validity of the bioinformatic predictions, mobility shift, transient transfection and chromatin immunoprecipitation assays were performed with five predicted CRX targets, and the results were suggestive of CRX regulation in 5/5, 3/5 and 4/5 cases, respectively. Together, these experiments strongly suggest that RP1, GUCY2D, ABCA4 are novel targets of CRX. PMID:15967807

PreCisIon: PREdiction of CIS-regulatory elements improved by gene's positION.

PubMed

Elati, Mohamed; Nicolle, Rémy; Junier, Ivan; Fernández, David; Fekih, Rim; Font, Julio; Képès, François

2013-02-01

Conventional approaches to predict transcriptional regulatory interactions usually rely on the definition of a shared motif sequence on the target genes of a transcription factor (TF). These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices, which may match large numbers of sites and produce an unreliable list of target genes. To improve the prediction of binding sites, we propose to additionally use the unrelated knowledge of the genome layout. Indeed, it has been shown that co-regulated genes tend to be either neighbors or periodically spaced along the whole chromosome. This study demonstrates that respective gene positioning carries significant information. This novel type of information is combined with traditional sequence information by a machine learning algorithm called PreCisIon. To optimize this combination, PreCisIon builds a strong gene target classifier by adaptively combining weak classifiers based on either local binding sequence or global gene position. This strategy generically paves the way to the optimized incorporation of any future advances in gene target prediction based on local sequence, genome layout or on novel criteria. With the current state of the art, PreCisIon consistently improves methods based on sequence information only. This is shown by implementing a cross-validation analysis of the 20 major TFs from two phylogenetically remote model organisms. For Bacillus subtilis and Escherichia coli, respectively, PreCisIon achieves on average an area under the receiver operating characteristic curve of 70 and 60%, a sensitivity of 80 and 70% and a specificity of 60 and 56%. The newly predicted gene targets are demonstrated to be functionally consistent with previously known targets, as assessed by analysis of Gene Ontology enrichment or of the relevant literature and databases.

Cis-regulatory element based targeted gene finding: genome-wide identification of abscisic acid- and abiotic stress-responsive genes in Arabidopsis thaliana.

PubMed

Zhang, Weixiong; Ruan, Jianhua; Ho, Tuan-Hua David; You, Youngsook; Yu, Taotao; Quatrano, Ralph S

2005-07-15

A fundamental problem of computational genomics is identifying the genes that respond to certain endogenous cues and environmental stimuli. This problem can be referred to as targeted gene finding. Since gene regulation is mainly determined by the binding of transcription factors and cis-regulatory DNA sequences, most existing gene annotation methods, which exploit the conservation of open reading frames, are not effective in finding target genes. A viable approach to targeted gene finding is to exploit the cis-regulatory elements that are known to be responsible for the transcription of target genes. Given such cis-elements, putative target genes whose promoters contain the elements can be identified. As a case study, we apply the above approach to predict the genes in model plant Arabidopsis thaliana which are inducible by a phytohormone, abscisic acid (ABA), and abiotic stress, such as drought, cold and salinity. We first construct and analyze two ABA specific cis-elements, ABA-responsive element (ABRE) and its coupling element (CE), in A.thaliana, based on their conservation in rice and other cereal plants. We then use the ABRE-CE module to identify putative ABA-responsive genes in A.thaliana. Based on RT-PCR verification and the results from literature, this method has an accuracy rate of 67.5% for the top 40 predictions. The cis-element based targeted gene finding approach is expected to be widely applicable since a large number of cis-elements in many species are available.

Predicting miRNA targets for head and neck squamous cell carcinoma using an ensemble method.

PubMed

Gao, Hong; Jin, Hui; Li, Guijun

2018-01-01

This study aimed to uncover potential microRNA (miRNA) targets in head and neck squamous cell carcinoma (HNSCC) using an ensemble method which combined 3 different methods: Pearson's correlation coefficient (PCC), Lasso and a causal inference method (i.e., intervention calculus when the directed acyclic graph (DAG) is absent [IDA]), based on Borda count election. The Borda count election method was used to integrate the top 100 predicted targets of each miRNA generated by individual methods. Afterwards, to validate the performance ability of our method, we checked the TarBase v6.0, miRecords v2013, miRWalk v2.0 and miRTarBase v4.5 databases to validate predictions for miRNAs. Pathway enrichment analysis of target genes in the top 1,000 miRNA-messenger RNA (mRNA) interactions was conducted to focus on significant KEGG pathways. Finally, we extracted target genes based on occurrence frequency ≥3. Based on an absolute value of PCC >0.7, we found 33 miRNAs and 288 mRNAs for further analysis. We extracted 10 target genes with predicted frequencies not less than 3. The target gene MYO5C possessed the highest frequency, which was predicted by 7 different miRNAs. Significantly, a total of 8 pathways were identified; the pathways of cytokine-cytokine receptor interaction and chemokine signaling pathway were the most significant. We successfully predicted target genes and pathways for HNSCC relying on miRNA expression data, mRNA expression profile, an ensemble method and pathway information. Our results may offer new information for the diagnosis and estimation of the prognosis of HNSCC.

Computational Predictions Provide Insights into the Biology of TAL Effector Target Sites

PubMed Central

Grau, Jan; Wolf, Annett; Reschke, Maik; Bonas, Ulla; Posch, Stefan; Boch, Jens

2013-01-01

Transcription activator-like (TAL) effectors are injected into host plant cells by Xanthomonas bacteria to function as transcriptional activators for the benefit of the pathogen. The DNA binding domain of TAL effectors is composed of conserved amino acid repeat structures containing repeat-variable diresidues (RVDs) that determine DNA binding specificity. In this paper, we present TALgetter, a new approach for predicting TAL effector target sites based on a statistical model. In contrast to previous approaches, the parameters of TALgetter are estimated from training data computationally. We demonstrate that TALgetter successfully predicts known TAL effector target sites and often yields a greater number of predictions that are consistent with up-regulation in gene expression microarrays than an existing approach, Target Finder of the TALE-NT suite. We study the binding specificities estimated by TALgetter and approve that different RVDs are differently important for transcriptional activation. In subsequent studies, the predictions of TALgetter indicate a previously unreported positional preference of TAL effector target sites relative to the transcription start site. In addition, several TAL effectors are predicted to bind to the TATA-box, which might constitute one general mode of transcriptional activation by TAL effectors. Scrutinizing the predicted target sites of TALgetter, we propose several novel TAL effector virulence targets in rice and sweet orange. TAL-mediated induction of the candidates is supported by gene expression microarrays. Validity of these targets is also supported by functional analogy to known TAL effector targets, by an over-representation of TAL effector targets with similar function, or by a biological function related to pathogen infection. Hence, these predicted TAL effector virulence targets are promising candidates for studying the virulence function of TAL effectors. TALgetter is implemented as part of the open-source Java library

Prediction of effective RNA interference targets and pathway-related genes in lepidopteran insects by RNA sequencing analysis.

PubMed

Guan, Ruo-Bing; Li, Hai-Chao; Miao, Xue-Xia

2018-06-01

When using RNA interference (RNAi) to study gene functions in Lepidoptera insects, we discovered that some genes could not be suppressed; instead, their expression levels could be up-regulated by double-stranded RNA (dsRNA). To predict which genes could be easily silenced, we treated the Asian corn borer (Ostrinia furnacalis) with dsGFP (green fluorescent protein) and dsMLP (muscle lim protein). A transcriptome sequence analysis was conducted using the cDNAs 6 h after treatment with dsRNA. The results indicated that 160 genes were up-regulated and 44 genes were down-regulated by the two dsRNAs. Then, 50 co-up-regulated, 25 co-down-regulated and 43 unaffected genes were selected to determine their RNAi responses. All the 25 down-regulated genes were knocked down by their corresponding dsRNA. However, several of the up-regulated and unaffected genes were up-regulated when treated with their corresponding dsRNAs instead of being knocked down. The genes up-regulated by the dsGFP treatment may be involved in insect immune responses or the RNAi pathway. When the immune-related genes were excluded, only seven genes were induced by dsGFP, including ago-2 and dicer-2. These results not only provide a reference for efficient RNAi target predications, but also provide some potential RNAi pathway-related genes for further study. © 2017 Institute of Zoology, Chinese Academy of Sciences.

Gene Expression Profiling Predicts the Development of Oral Cancer

PubMed Central

Saintigny, Pierre; Zhang, Li; Fan, You-Hong; El-Naggar, Adel K.; Papadimitrakopoulou, Vali; Feng, Lei; Lee, J. Jack; Kim, Edward S.; Hong, Waun Ki; Mao, Li

2011-01-01

Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. PMID:21292635

Identification of Homeotic Target Genes in Drosophila Melanogaster Including Nervy, a Proto-Oncogene Homologue

PubMed Central

Feinstein, P. G.; Kornfeld, K.; Hogness, D. S.; Mann, R. S.

1995-01-01

In Drosophila, the specific morphological characteristics of each segment are determined by the homeotic genes that regulate the expression of downstream target genes. We used a subtractive hybridization procedure to isolate activated target genes of the homeotic gene Ultrabithorax (Ubx). In addition, we constructed a set of mutant genotypes that measures the regulatory contribution of individual homeotic genes to a complex target gene expression pattern. Using these mutants, we demonstrate that homeotic genes can regulate target gene expression at the start of gastrulation, suggesting a previously unknown role for the homeotic genes at this early stage. We also show that, in abdominal segments, the levels of expression for two target genes increase in response to high levels of Ubx, demonstrating that the normal down-regulation of Ubx in these segments is functional. Finally, the DNA sequence of cDNAs for one of these genes predicts a protein that is similar to a human proto-oncogene involved in acute myeloid leukemias. These results illustrate potentially general rules about the homeotic control of target gene expression and suggest that subtractive hybridization can be used to isolate interesting homeotic target genes. PMID:7498738

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites.

PubMed

Betel, Doron; Koppal, Anjali; Agius, Phaedra; Sander, Chris; Leslie, Christina

2010-01-01

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

Identification and consequences of miRNA-target interactions--beyond repression of gene expression.

PubMed

Hausser, Jean; Zavolan, Mihaela

2014-09-01

Comparative genomics analyses and high-throughput experimental studies indicate that a microRNA (miRNA) binds to hundreds of sites across the transcriptome. Although the knockout of components of the miRNA biogenesis pathway has profound phenotypic consequences, most predicted miRNA targets undergo small changes at the mRNA and protein levels when the expression of the miRNA is perturbed. Alternatively, miRNAs can establish thresholds in and increase the coherence of the expression of their target genes, as well as reduce the cell-to-cell variability in target gene expression. Here, we review the recent progress in identifying miRNA targets and the emerging paradigms of how miRNAs shape the dynamics of target gene expression.

Seqping: gene prediction pipeline for plant genomes using self-training gene models and transcriptomic data.

PubMed

Chan, Kuang-Lim; Rosli, Rozana; Tatarinova, Tatiana V; Hogan, Michael; Firdaus-Raih, Mohd; Low, Eng-Ti Leslie

2017-01-27

Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion. We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure). Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.

A new computational strategy for predicting essential genes.

PubMed

Cheng, Jian; Wu, Wenwu; Zhang, Yinwen; Li, Xiangchen; Jiang, Xiaoqian; Wei, Gehong; Tao, Shiheng

2013-12-21

Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms. We first collected features that were widely used by previous predictive models and assessed the relationships between gene features and gene essentiality using a stepwise regression model. We found two issues that could significantly reduce model accuracy: (i) the effect of multicollinearity among gene features and (ii) the diverse and even contrasting correlations between gene features and gene essentiality existing within and among different species. To address these issues, we developed a novel model called feature-based weighted Naïve Bayes model (FWM), which is based on Naïve Bayes classifiers, logistic regression, and genetic algorithm. The proposed model assesses features and filters out the effects of multicollinearity and diversity. The performance of FWM was compared with other popular models, such as support vector machine, Naïve Bayes model, and logistic regression model, by applying FWM to reciprocally predict essential genes among and within 21 species. Our results showed that FWM significantly improves the accuracy and robustness of essential gene prediction. FWM can remarkably improve the accuracy of essential gene prediction and may be used as an alternative method for other classification work. This method can contribute substantially to the knowledge of the minimum gene sets required for living organisms and the discovery of new drug targets.

MicroRNA expression, target genes, and signaling pathways in infants with a ventricular septal defect.

PubMed

Chai, Hui; Yan, Zhaoyuan; Huang, Ke; Jiang, Yuanqing; Zhang, Lin

2018-02-01

This study aimed to systematically investigate the relationship between miRNA expression and the occurrence of ventricular septal defect (VSD), and characterize the miRNA target genes and pathways that can lead to VSD. The miRNAs that were differentially expressed in blood samples from VSD and normal infants were screened and validated by implementing miRNA microarrays and qRT-PCR. The target genes regulated by differentially expressed miRNAs were predicted using three target gene databases. The functions and signaling pathways of the target genes were enriched using the GO database and KEGG database, respectively. The transcription and protein expression of specific target genes in critical pathways were compared in the VSD and normal control groups using qRT-PCR and western blotting, respectively. Compared with the normal control group, the VSD group had 22 differentially expressed miRNAs; 19 were downregulated and three were upregulated. The 10,677 predicted target genes participated in many biological functions related to cardiac development and morphogenesis. Four target genes (mGLUR, Gq, PLC, and PKC) were involved in the PKC pathway and four (ECM, FAK, PI3 K, and PDK1) were involved in the PI3 K-Akt pathway. The transcription and protein expression of these eight target genes were significantly upregulated in the VSD group. The 22 miRNAs that were dysregulated in the VSD group were mainly downregulated, which may result in the dysregulation of several key genes and biological functions related to cardiac development. These effects could also be exerted via the upregulation of eight specific target genes, the subsequent over-activation of the PKC and PI3 K-Akt pathways, and the eventual abnormal cardiac development and VSD.

Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data

PubMed Central

2013-01-01

Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs) and Support Vector Machines (SVMs) were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression. PMID:23369200

Training set selection for the prediction of essential genes.

PubMed

Cheng, Jian; Xu, Zhao; Wu, Wenwu; Zhao, Li; Li, Xiangchen; Liu, Yanlin; Tao, Shiheng

2014-01-01

Various computational models have been developed to transfer annotations of gene essentiality between organisms. However, despite the increasing number of microorganisms with well-characterized sets of essential genes, selection of appropriate training sets for predicting the essential genes of poorly-studied or newly sequenced organisms remains challenging. In this study, a machine learning approach was applied reciprocally to predict the essential genes in 21 microorganisms. Results showed that training set selection greatly influenced predictive accuracy. We determined four criteria for training set selection: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. We then analyzed the performance of an incomplete training set and an integrated training set with multiple organisms. We found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Additionally, the integrated training sets exhibited remarkable increase in stability and accuracy compared with single sets. Finally, we compared the performance of the integrated training sets with the four criteria and with random selection. The results revealed that a rational selection of training sets based on our criteria yields better performance than random selection. Thus, our results provide empirical guidance on training set selection for the identification of essential genes on a genome-wide scale.

Transcriptome-wide identification of Rauvolfia serpentina microRNAs and prediction of their potential targets.

PubMed

Prakash, Pravin; Rajakani, Raja; Gupta, Vikrant

2016-04-01

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 19-24 nucleotides (nt) in length and considered as potent regulators of gene expression at transcriptional and post-transcriptional levels. Here we report the identification and characterization of 15 conserved miRNAs belonging to 13 families from Rauvolfia serpentina through in silico analysis of available nucleotide dataset. The identified mature R. serpentina miRNAs (rse-miRNAs) ranged between 20 and 22nt in length, and the average minimal folding free energy index (MFEI) value of rse-miRNA precursor sequences was found to be -0.815 kcal/mol. Using the identified rse-miRNAs as query, their potential targets were predicted in R. serpentina and other plant species. Gene Ontology (GO) annotation showed that predicted targets of rse-miRNAs include transcription factors as well as genes involved in diverse biological processes such as primary and secondary metabolism, stress response, disease resistance, growth, and development. Few rse-miRNAs were predicted to target genes of pharmaceutically important secondary metabolic pathways such as alkaloids and anthocyanin biosynthesis. Phylogenetic analysis showed the evolutionary relationship of rse-miRNAs and their precursor sequences to homologous pre-miRNA sequences from other plant species. The findings under present study besides giving first hand information about R. serpentina miRNAs and their targets, also contributes towards the better understanding of miRNA-mediated gene regulatory processes in plants. Copyright © 2015 Elsevier Ltd. All rights reserved.

A targeted resequencing gene panel for focal epilepsy.

PubMed

Hildebrand, Michael S; Myers, Candace T; Carvill, Gemma L; Regan, Brigid M; Damiano, John A; Mullen, Saul A; Newton, Mark R; Nair, Umesh; Gazina, Elena V; Milligan, Carol J; Reid, Christopher A; Petrou, Steven; Scheffer, Ingrid E; Berkovic, Samuel F; Mefford, Heather C

2016-04-26

We report development of a targeted resequencing gene panel for focal epilepsy, the most prevalent phenotypic group of the epilepsies. The targeted resequencing gene panel was designed using molecular inversion probe (MIP) capture technology and sequenced using massively parallel Illumina sequencing. We demonstrated proof of principle that mutations can be detected in 4 previously genotyped focal epilepsy cases. We searched for both germline and somatic mutations in 251 patients with unsolved sporadic or familial focal epilepsy and identified 11 novel or very rare missense variants in 5 different genes: CHRNA4, GRIN2B, KCNT1, PCDH19, and SCN1A. Of these, 2 were predicted to be pathogenic or likely pathogenic, explaining ∼0.8% of the cohort, and 8 were of uncertain significance based on available data. We have developed and validated a targeted resequencing panel for focal epilepsies, the most important clinical class of epilepsies, accounting for about 60% of all cases. Our application of MIP technology is an innovative approach that will be advantageous in the clinical setting because it is highly sensitive, efficient, and cost-effective for screening large patient cohorts. Our findings indicate that mutations in known genes likely explain only a small proportion of focal epilepsy cases. This is not surprising given the established clinical and genetic heterogeneity of these disorders and underscores the importance of further gene discovery studies in this complex syndrome. © 2016 American Academy of Neurology.

Enhancing emotional-based target prediction

NASA Astrophysics Data System (ADS)

Gosnell, Michael; Woodley, Robert

2008-04-01

This work extends existing agent-based target movement prediction to include key ideas of behavioral inertia, steady states, and catastrophic change from existing psychological, sociological, and mathematical work. Existing target prediction work inherently assumes a single steady state for target behavior, and attempts to classify behavior based on a single emotional state set. The enhanced, emotional-based target prediction maintains up to three distinct steady states, or typical behaviors, based on a target's operating conditions and observed behaviors. Each steady state has an associated behavioral inertia, similar to the standard deviation of behaviors within that state. The enhanced prediction framework also allows steady state transitions through catastrophic change and individual steady states could be used in an offline analysis with additional modeling efforts to better predict anticipated target reactions.

A quick reality check for microRNA target prediction.

PubMed

Kast, Juergen

2011-04-01

The regulation of protein abundance by microRNA (miRNA)-mediated repression of mRNA translation is a rapidly growing area of interest in biochemical research. In animal cells, the miRNA seed sequence does not perfectly match that of the mRNA it targets, resulting in a large number of possible miRNA targets and varied extents of repression. Several software tools are available for the prediction of miRNA targets, yet the overlap between them is limited. Jovanovic et al. have developed and applied a targeted, quantitative approach to validate predicted miRNA target proteins. Using a proteome database, they have set up and tested selected reaction monitoring assays for approximately 20% of more than 800 predicted let-7 targets, as well as control genes in Caenorhabditis elegans. Their results demonstrate that such assays can be developed quickly and with relative ease, and applied in a high-throughput setup to verify known and identify novel miRNA targets. They also show, however, that the choice of the biological system and material has a noticeable influence on the frequency, extent and direction of the observed changes. Nonetheless, selected reaction monitoring assays, such as those developed by Jovanovic et al., represent an attractive new tool in the study of miRNA function at the organism level.

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

PubMed Central

2015-01-01

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

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

PubMed

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

2015-01-01

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

An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

PubMed

Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting; Guo, Feng-Biao

Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus , which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA-target interaction data.

PubMed

Ahadi, Alireza; Sablok, Gaurav; Hutvagner, Gyorgy

2017-04-07

MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of cell-type specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Murine Hyperglycemic Vasculopathy and Cardiomyopathy: Whole-Genome Gene Expression Analysis Predicts Cellular Targets and Regulatory Networks Influenced by Mannose Binding Lectin

PubMed Central

Zou, Chenhui; La Bonte, Laura R.; Pavlov, Vasile I.; Stahl, Gregory L.

2012-01-01

Hyperglycemia, in the absence of type 1 or 2 diabetes, is an independent risk factor for cardiovascular disease. We have previously demonstrated a central role for mannose binding lectin (MBL)-mediated cardiac dysfunction in acute hyperglycemic mice. In this study, we applied whole-genome microarray data analysis to investigate MBL’s role in systematic gene expression changes. The data predict possible intracellular events taking place in multiple cellular compartments such as enhanced insulin signaling pathway sensitivity, promoted mitochondrial respiratory function, improved cellular energy expenditure and protein quality control, improved cytoskeleton structure, and facilitated intracellular trafficking, all of which may contribute to the organismal health of MBL null mice against acute hyperglycemia. Our data show a tight association between gene expression profile and tissue function which might be a very useful tool in predicting cellular targets and regulatory networks connected with in vivo observations, providing clues for further mechanistic studies. PMID:22375142

Prediction of miRNA targets.

PubMed

Oulas, Anastasis; Karathanasis, Nestoras; Louloupi, Annita; Pavlopoulos, Georgios A; Poirazi, Panayiota; Kalantidis, Kriton; Iliopoulos, Ioannis

2015-01-01

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.

EBF factors drive expression of multiple classes of target genes governing neuronal development.

PubMed

Green, Yangsook S; Vetter, Monica L

2011-04-30

Early B cell factor (EBF) family members are transcription factors known to have important roles in several aspects of vertebrate neurogenesis, including commitment, migration and differentiation. Knowledge of how EBF family members contribute to neurogenesis is limited by a lack of detailed understanding of genes that are transcriptionally regulated by these factors. We performed a microarray screen in Xenopus animal caps to search for targets of EBF transcriptional activity, and identified candidate targets with multiple roles, including transcription factors of several classes. We determined that, among the most upregulated candidate genes with expected neuronal functions, most require EBF activity for some or all of their expression, and most have overlapping expression with ebf genes. We also found that the candidate target genes that had the most strongly overlapping expression patterns with ebf genes were predicted to be direct transcriptional targets of EBF transcriptional activity. The identification of candidate targets that are transcription factor genes, including nscl-1, emx1 and aml1, improves our understanding of how EBF proteins participate in the hierarchy of transcription control during neuronal development, and suggests novel mechanisms by which EBF activity promotes migration and differentiation. Other candidate targets, including pcdh8 and kcnk5, expand our knowledge of the types of terminal differentiated neuronal functions that EBF proteins regulate.

Target mimics: an embedded layer of microRNA-involved gene regulatory networks in plants.

PubMed

Meng, Yijun; Shao, Chaogang; Wang, Huizhong; Jin, Yongfeng

2012-05-21

MicroRNAs (miRNAs) play an essential role in gene regulation in plants. At the same time, the expression of miRNA genes is also tightly controlled. Recently, a novel mechanism called "target mimicry" was discovered, providing another layer for modulating miRNA activities. However, except for the artificial target mimics manipulated for functional studies on certain miRNA genes, only one example, IPS1 (Induced by Phosphate Starvation 1)-miR399 was experimentally confirmed in planta. To date, few analyses for comprehensive identification of natural target mimics have been performed in plants. Thus, limited evidences are available to provide detailed information for interrogating the questionable issue whether target mimicry was widespread in planta, and implicated in certain biological processes. In this study, genome-wide computational prediction of endogenous miRNA mimics was performed in Arabidopsis and rice, and dozens of target mimics were identified. In contrast to a recent report, the densities of target mimic sites were found to be much higher within the untranslated regions (UTRs) when compared to those within the coding sequences (CDSs) in both plants. Some novel sequence characteristics were observed for the miRNAs that were potentially regulated by the target mimics. GO (Gene Ontology) term enrichment analysis revealed some functional insights into the predicted mimics. After degradome sequencing data-based identification of miRNA targets, the regulatory networks constituted by target mimics, miRNAs and their downstream targets were constructed, and some intriguing subnetworks were further exploited. These results together suggest that target mimicry may be widely implicated in regulating miRNA activities in planta, and we hope this study could expand the current understanding of miRNA-involved regulatory networks.

TarPmiR: a new approach for microRNA target site prediction.

PubMed

Ding, Jun; Li, Xiaoman; Hu, Haiyan

2016-09-15

The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites. The recently published CLASH (crosslinking ligation and sequencing of hybrids) data provide an unprecedented opportunity to study the characteristics of miRNA target sites and improve miRNA target site prediction methods. Applying four different machine learning approaches to the CLASH data, we identified seven new features of miRNA target sites. Combining these new features with those commonly used by existing miRNA target prediction algorithms, we developed an approach called TarPmiR for miRNA target site prediction. Testing on two human and one mouse non-CLASH datasets, we showed that TarPmiR predicted more than 74.2% of true miRNA target sites in each dataset. Compared with three existing approaches, we demonstrated that TarPmiR is superior to these existing approaches in terms of better recall and better precision. The TarPmiR software is freely available at http://hulab.ucf.edu/research/projects/miRNA/TarPmiR/ CONTACTS: haihu@cs.ucf.edu or xiaoman@mail.ucf.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Targeted gene insertion for molecular medicine.

PubMed

Voigt, Katrin; Izsvák, Zsuzsanna; Ivics, Zoltán

2008-11-01

Genomic insertion of a functional gene together with suitable transcriptional regulatory elements is often required for long-term therapeutical benefit in gene therapy for several genetic diseases. A variety of integrating vectors for gene delivery exist. Some of them exhibit random genomic integration, whereas others have integration preferences based on attributes of the targeted site, such as primary DNA sequence and physical structure of the DNA, or through tethering to certain DNA sequences by host-encoded cellular factors. Uncontrolled genomic insertion bears the risk of the transgene being silenced due to chromosomal position effects, and can lead to genotoxic effects due to mutagenesis of cellular genes. None of the vector systems currently used in either preclinical experiments or clinical trials displays sufficient preferences for target DNA sequences that would ensure appropriate and reliable expression of the transgene and simultaneously prevent hazardous side effects. We review in this paper the advantages and disadvantages of both viral and non-viral gene delivery technologies, discuss mechanisms of target site selection of integrating genetic elements (viruses and transposons), and suggest distinct molecular strategies for targeted gene delivery.

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

PubMed

Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

2018-06-01

MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.

EGFR Gene Amplification and KRAS Mutation Predict Response to Combination Targeted Therapy in Metastatic Colorectal Cancer.

PubMed

Khan, Sajid A; Zeng, Zhaoshi; Shia, Jinru; Paty, Philip B

2017-07-01

Genetic variability in KRAS and EGFR predicts response to cetuximab in irinotecan refractory colorectal cancer. Whether these markers or others remain predictive in combination biologic therapies including bevacizumab is unknown. We identified predictive biomarkers from patients with irinotecan refractory metastatic colorectal cancer treated with cetuximab plus bevacizumab. Patients who received cetuximab plus bevacizumab for irinotecan refractory colorectal cancer in either of two Phase II trials conducted were identified. Tumor tissue was available for 33 patients. Genomic DNA was extracted and used for mutational analysis of KRAS, BRAF, and p53 genes. Fluorescence in situ hybridization was performed to assess EGFR copy number. The status of single genes and various combinations were tested for association with response. Seven of 33 patients responded to treatment. KRAS mutations were found in 14/33 cases, and 0 responded to treatment (p = 0.01). EGFR gene amplification was seen in 3/33 of tumors and in every case was associated with response to treatment (p < 0.001). TP53 and BRAF mutations were found in 18/33 and 0/33 tumors, respectively, and there were no associations with response to either gene. EGFR gene amplification and KRAS mutations are predictive markers for patients receiving combination biologic therapy of cetuximab plus bevacizumab for metastatic colorectal cancer. One marker or the other is present in the tumor of half of all patients allowing treatment response to be predicted with a high degree of certainty. The role for molecular markers in combination biologic therapy seems promising.

Chemopreventive agents alters global gene expression pattern: predicting their mode of action and targets.

PubMed

Narayanan, Bhagavathi A

2006-12-01

Chemoprevention has the potential to be a major component of colon, breast, prostate and lung cancer control. Epidemiological, experimental, and clinical studies provide evidence that antioxidants, anti-inflammatory agents, n-3 polyunsaturated fatty acids and several other phytochemicals possess unique modes of action against cancer growth. However, the mode of action of several of these agents at the gene transcription level is not completely understood. Completion of the human genome sequence and the advent of DNA microarrays using cDNAs enhanced the detection and identification of hundreds of differentially expressed genes in response to anticancer drugs or chemopreventive agents. In this review, we are presenting an extensive analysis of the key findings from studies using potential chemopreventive agents on global gene expression patterns, which lead to the identification of cancer drug targets. The summary of the study reports discussed in this review explains the extent of gene alterations mediated by more than 20 compounds including antioxidants, fatty acids, NSAIDs, phytochemicals, retinoids, selenium, vitamins, aromatase inhibitor, lovastatin, oltipraz, salvicine, and zinc. The findings from these studies further reveal the utility of DNA microarray in characterizing and quantifying the differentially expressed genes that are possibly reprogrammed by the above agents against colon, breast, prostate, lung, liver, pancreatic and other cancer types. Phenolic antioxidant resveratrol found in berries and grapes inhibits the formation of prostate tumors by acting on the regulatory genes such as p53 while activating a cascade of genes involved in cell cycle and apoptosis including p300, Apaf-1, cdk inhibitor p21, p57 (KIP2), p53 induced Pig 7, Pig 8, Pig 10, cyclin D, DNA fragmentation factor 45. The group of genes significantly altered by selenium includes cyclin D1, cdk5, cdk4, cdk2, cdc25A and GADD 153. Vitamine D shows impact on p21(Waf1/Cip1) p27 cyclin B

A gene expression biomarker accurately predicts estrogen ...

EPA Pesticide Factsheets

The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1 screening tests. The ToxCast program currently includes 18 HTS in vitro assays that evaluate the ability of chemicals to modulate estrogen receptor α (ERα), an important endocrine target. We propose microarray-based gene expression profiling as a complementary approach to predict ERα modulation and have developed computational methods to identify ERα modulators in an existing database of whole-genome microarray data. The ERα biomarker consisted of 46 ERα-regulated genes with consistent expression patterns across 7 known ER agonists and 3 known ER antagonists. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression data sets from experiments in MCF-7 cells. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% or 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) OECD ER reference chemicals including “very weak” agonists and replicated predictions based on 18 in vitro ER-associated HTS assays. For 114 chemicals present in both the HTS data and the MCF-7 c

GeneBuilder: interactive in silico prediction of gene structure.

PubMed

Milanesi, L; D'Angelo, D; Rogozin, I B

1999-01-01

Prediction of gene structure in newly sequenced DNA becomes very important in large genome sequencing projects. This problem is complicated due to the exon-intron structure of eukaryotic genes and because gene expression is regulated by many different short nucleotide domains. In order to be able to analyse the full gene structure in different organisms, it is necessary to combine information about potential functional signals (promoter region, splice sites, start and stop codons, 3' untranslated region) together with the statistical properties of coding sequences (coding potential), information about homologous proteins, ESTs and repeated elements. We have developed the GeneBuilder system which is based on prediction of functional signals and coding regions by different approaches in combination with similarity searches in proteins and EST databases. The potential gene structure models are obtained by using a dynamic programming method. The program permits the use of several parameters for gene structure prediction and refinement. During gene model construction, selecting different exon homology levels with a protein sequence selected from a list of homologous proteins can improve the accuracy of the gene structure prediction. In the case of low homology, GeneBuilder is still able to predict the gene structure. The GeneBuilder system has been tested by using the standard set (Burset and Guigo, Genomics, 34, 353-367, 1996) and the performances are: 0.89 sensitivity and 0.91 specificity at the nucleotide level. The total correlation coefficient is 0.88. The GeneBuilder system is implemented as a part of the WebGene a the URL: http://www.itba.mi. cnr.it/webgene and TRADAT (TRAncription Database and Analysis Tools) launcher URL: http://www.itba.mi.cnr.it/tradat.

Evolving phage vectors for cell targeted gene delivery.

PubMed

Larocca, David; Burg, Michael A; Jensen-Pergakes, Kristen; Ravey, Edward Prenn; Gonzalez, Ana Maria; Baird, Andrew

2002-03-01

We adapted filamentous phage vectors for targeted gene delivery to mammalian cells by inserting a mammalian reporter gene expression cassette (GFP) into the vector backbone and fusing the pIII coat protein to a cell targeting ligand (i.e. FGF2, EGF). Like transfection with animal viral vectors, targeted phage gene delivery is concentration, time, and ligand dependent. Importantly, targeted phage particles are specific for the appropriate target cell surface receptor. Phage have distinct advantages over existing gene therapy vectors because they are simple, economical to produce at high titer, have no intrinsic tropism for mammalian cells, and are relatively simple to genetically modify and evolve. Initially transduction by targeted phage particles was low resulting in foreign gene expression in 1-2% of transfected cells. We increased transduction efficiency by modifying both the transfection protocol and vector design. For example, we stabilized the display of the targeting ligand to create multivalent phagemid-based vectors with transduction efficiencies of up to 45% in certain cell lines when combined with genotoxic treatment. Taken together, these studies establish that the efficiency of phage-mediated gene transfer can be significantly improved through genetic modification. We are currently evolving phage vectors with enhanced cell targeting, increased stability, reduced immunogenicity and other properties suitable for gene therapy.

A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis

PubMed Central

Ni, Ying; Aghamirzaie, Delasa; Elmarakeby, Haitham; Collakova, Eva; Li, Song; Grene, Ruth; Heath, Lenwood S.

2016-01-01

Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date. Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developing Arabidopsis thaliana embryos. We developed the Beacon GRN inference tool to predict GRNs occurring during seed development in Arabidopsis based on a support vector machine (SVM) model. We developed both global and local inference models and compared their performance, demonstrating that local models are generally superior for our application. Using both the expression levels of the genes expressed in developing embryos and prior known regulatory relationships, GRNs were predicted for specific embryonic developmental stages. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. Potential direct targets were identified based on a match between the promoter regions of these inferred targets and the cis elements recognized by specific regulators. Our analysis also provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression. The Beacon GRN inference tool provides a valuable model system for context-specific GRN inference and is freely available at https://github.com/BeaconProjectAtVirginiaTech/beacon_network_inference.git. PMID:28066488

Targeting gene therapy to cancer: a review.

PubMed

Dachs, G U; Dougherty, G J; Stratford, I J; Chaplin, D J

1997-01-01

In recent years the idea of using gene therapy as a modality in the treatment of diseases other than genetically inherited, monogenic disorders has taken root. This is particularly obvious in the field of oncology where currently more than 100 clinical trials have been approved worldwide. This report will summarize some of the exciting progress that has recently been made with respect to both targeting the delivery of potentially therapeutic genes to tumor sites and regulating their expression within the tumor microenvironment. In order to specifically target malignant cells while at the same time sparing normal tissue, cancer gene therapy will need to combine highly selective gene delivery with highly specific gene expression, specific gene product activity, and, possibly, specific drug activation. Although the efficient delivery of DNA to tumor sites remains a formidable task, progress has been made in recent years using both viral (retrovirus, adenovirus, adeno-associated virus) and nonviral (liposomes, gene gun, injection) methods. In this report emphasis will be placed on targeted rather than high-efficiency delivery, although those would need to be combined in the future for effective therapy. To date delivery has been targeted to tumor-specific and tissue-specific antigens, such as epithelial growth factor receptor, c-kit receptor, and folate receptor, and these will be described in some detail. To increase specificity and safety of gene therapy further, the expression of the therapeutic gene needs to be tightly controlled within the target tissue. Targeted gene expression has been analyzed using tissue-specific promoters (breast-, prostate-, and melanoma-specific promoters) and disease-specific promoters (carcinoembryonic antigen, HER-2/neu, Myc-Max response elements, DF3/MUC). Alternatively, expression could be regulated externally with the use of radiation-induced promoters or tetracycline-responsive elements. Another novel possibility that will be

Zinc-finger protein-targeted gene regulation: Genomewide single-gene specificity

PubMed Central

Tan, Siyuan; Guschin, Dmitry; Davalos, Albert; Lee, Ya-Li; Snowden, Andrew W.; Jouvenot, Yann; Zhang, H. Steven; Howes, Katherine; McNamara, Andrew R.; Lai, Albert; Ullman, Chris; Reynolds, Lindsey; Moore, Michael; Isalan, Mark; Berg, Lutz-Peter; Campos, Bradley; Qi, Hong; Spratt, S. Kaye; Case, Casey C.; Pabo, Carl O.; Campisi, Judith; Gregory, Philip D.

2003-01-01

Zinc-finger protein transcription factors (ZFP TFs) can be designed to control the expression of any desired target gene, and thus provide potential therapeutic tools for the study and treatment of disease. Here we report that a ZFP TF can repress target gene expression with single-gene specificity within the human genome. A ZFP TF repressor that binds an 18-bp recognition sequence within the promoter of the endogenous CHK2 gene gives a >10-fold reduction in CHK2 mRNA and protein. This level of repression was sufficient to generate a functional phenotype, as demonstrated by the loss of DNA damage-induced CHK2-dependent p53 phosphorylation. We determined the specificity of repression by using DNA microarrays and found that the ZFP TF repressed a single gene (CHK2) within the monitored genome in two different cell types. These data demonstrate the utility of ZFP TFs as precise tools for target validation, and highlight their potential as clinical therapeutics. PMID:14514889

A signature inferred from Drosophila mitotic genes predicts survival of breast cancer patients.

PubMed

Damasco, Christian; Lembo, Antonio; Somma, Maria Patrizia; Gatti, Maurizio; Di Cunto, Ferdinando; Provero, Paolo

2011-02-28

The classification of breast cancer patients into risk groups provides a powerful tool for the identification of patients who will benefit from aggressive systemic therapy. The analysis of microarray data has generated several gene expression signatures that improve diagnosis and allow risk assessment. There is also evidence that cell proliferation-related genes have a high predictive power within these signatures. We thus constructed a gene expression signature (the DM signature) using the human orthologues of 108 Drosophila melanogaster genes required for either the maintenance of chromosome integrity (36 genes) or mitotic division (72 genes). The DM signature has minimal overlap with the extant signatures and is highly predictive of survival in 5 large breast cancer datasets. In addition, we show that the DM signature outperforms many widely used breast cancer signatures in predictive power, and performs comparably to other proliferation-based signatures. For most genes of the DM signature, an increased expression is negatively correlated with patient survival. The genes that provide the highest contribution to the predictive power of the DM signature are those involved in cytokinesis. This finding highlights cytokinesis as an important marker in breast cancer prognosis and as a possible target for antimitotic therapies.

Comparative analysis of grapevine whole-genome gene predictions, functional annotation, categorization and integration of the predicted gene sequences

PubMed Central

2012-01-01

Background The first draft assembly and gene prediction of the grapevine genome (8X base coverage) was made available to the scientific community in 2007, and functional annotation was developed on this gene prediction. Since then additional Sanger sequences were added to the 8X sequences pool and a new version of the genomic sequence with superior base coverage (12X) was produced. Results In order to more efficiently annotate the function of the genes predicted in the new assembly, it is important to build on as much of the previous work as possible, by transferring 8X annotation of the genome to the 12X version. The 8X and 12X assemblies and gene predictions of the grapevine genome were compared to answer the question, “Can we uniquely map 8X predicted genes to 12X predicted genes?” The results show that while the assemblies and gene structure predictions are too different to make a complete mapping between them, most genes (18,725) showed a one-to-one relationship between 8X predicted genes and the last version of 12X predicted genes. In addition, reshuffled genomic sequence structures appeared. These highlight regions of the genome where the gene predictions need to be taken with caution. Based on the new grapevine gene functional annotation and in-depth functional categorization, twenty eight new molecular networks have been created for VitisNet while the existing networks were updated. Conclusions The outcomes of this study provide a functional annotation of the 12X genes, an update of VitisNet, the system of the grapevine molecular networks, and a new functional categorization of genes. Data are available at the VitisNet website (http://www.sdstate.edu/ps/research/vitis/pathways.cfm). PMID:22554261

Targeted polymeric nanoparticles for cancer gene therapy

PubMed Central

Kim, Jayoung; Wilson, David R.; Zamboni, Camila G.; Green, Jordan J.

2015-01-01

In this article, advances in designing polymeric nanoparticles for targeted cancer gene therapy are reviewed. Characterization and evaluation of biomaterials, targeting ligands, and transcriptional elements are each discussed. Advances in biomaterials have driven improvements to nanoparticle stability and tissue targeting, conjugation of ligands to the surface of polymeric nanoparticles enable binding to specific cancer cells, and the design of transcriptional elements has enabled selective DNA expression specific to the cancer cells. Together, these features have improved the performance of polymeric nanoparticles as targeted non-viral gene delivery vectors to treat cancer. As polymeric nanoparticles can be designed to be biodegradable, non-toxic, and to have reduced immunogenicity and tumorigenicity compared to viral platforms, they have significant potential for clinical use. Results of polymeric gene therapy in clinical trials and future directions for the engineering of nanoparticle systems for targeted cancer gene therapy are also presented. PMID:26061296

Prediction of Host-Derived miRNAs with the Potential to Target PVY in Potato Plants

PubMed Central

Iqbal, Muhammad S.; Hafeez, Muhammad N.; Wattoo, Javed I.; Ali, Arfan; Sharif, Muhammad N.; Rashid, Bushra; Tabassum, Bushra; Nasir, Idrees A.

2016-01-01

Potato virus Y has emerged as a threatening problem in all potato growing areas around the globe. PVY reduces the yield and quality of potato cultivars. During the last 30 years, significant genetic changes in PVY strains have been observed with an increased incidence associated with crop damage. In the current study, computational approaches were applied to predict Potato derived miRNA targets in the PVY genome. The PVY genome is approximately 9 thousand nucleotides, which transcribes the following 6 genes:CI, NIa, NIb-Pro, HC-Pro, CP, and VPg. A total of 343 mature miRNAs were retrieved from the miRBase database and were examined for their target sequences in PVY genes using the minimum free energy (mfe), minimum folding energy, sequence complementarity and mRNA-miRNA hybridization approaches. The identified potato miRNAs against viral mRNA targets have antiviral activities, leading to translational inhibition by mRNA cleavage and/or mRNA blockage. We found 86 miRNAs targeting the PVY genome at 151 different sites. Moreover, only 36 miRNAs potentially targeted the PVY genome at 101 loci. The CI gene of the PVY genome was targeted by 32 miRNAs followed by the complementarity of 26, 19, 18, 16, and 13 miRNAs. Most importantly, we found 5 miRNAs (miR160a-5p, miR7997b, miR166c-3p, miR399h, and miR5303d) that could target the CI, NIa, NIb-Pro, HC-Pro, CP, and VPg genes of PVY. The predicted miRNAs can be used for the development of PVY-resistant potato crops in the future. PMID:27683585

Gene targeting in mosquito cells: a demonstration of 'knockout' technology in extrachromosomal gene arrays

PubMed Central

Eggleston, Paul; Zhao, Yuguang

2001-01-01

Background Gene targeting would offer a number of advantages over current transposon-based strategies for insect transformation. These include freedom from both position effects associated with quasi-random integration and concerns over transgene instability mediated by endogenous transposases, independence from phylogenetic restrictions on transposon mobility and the ability to generate gene knockouts. Results We describe here our initial investigations of gene targeting in the mosquito. The target site was a hygromycin resistance gene, stably maintained as part of an extrachromosomal array. Using a promoter-trap strategy to enrich for targeted events, a neomycin resistance gene was integrated into the target site. This resulted in knockout of hygromycin resistance concurrent with the expression of high levels of neomycin resistance from the resident promoter. PCR amplification of the targeted site generated a product that was specific to the targeted cell line and consistent with precise integration of the neomycin resistance gene into the 5' end of the hygromycin resistance gene. Sequencing of the PCR product and Southern analysis of cellular DNA subsequently confirmed this molecular structure. Conclusions These experiments provide the first demonstration of gene targeting in mosquito tissue and show that mosquito cells possess the necessary machinery to bring about precise integration of exogenous sequences through homologous recombination. Further development of these procedures and their extension to chromosomally located targets hold much promise for the exploitation of gene targeting in a wide range of medically and economically important insect species. PMID:11513755

miRWalk--database: prediction of possible miRNA binding sites by "walking" the genes of three genomes.

PubMed

Dweep, Harsh; Sticht, Carsten; Pandey, Priyanka; Gretz, Norbert

2011-10-01

MicroRNAs are small, non-coding RNA molecules that can complementarily bind to the mRNA 3'-UTR region to regulate the gene expression by transcriptional repression or induction of mRNA degradation. Increasing evidence suggests a new mechanism by which miRNAs may regulate target gene expression by binding in promoter and amino acid coding regions. Most of the existing databases on miRNAs are restricted to mRNA 3'-UTR region. To address this issue, we present miRWalk, a comprehensive database on miRNAs, which hosts predicted as well as validated miRNA binding sites, information on all known genes of human, mouse and rat. All mRNAs, mitochondrial genes and 10 kb upstream flanking regions of all known genes of human, mouse and rat were analyzed by using a newly developed algorithm named 'miRWalk' as well as with eight already established programs for putative miRNA binding sites. An automated and extensive text-mining search was performed on PubMed database to extract validated information on miRNAs. Combined information was put into a MySQL database. miRWalk presents predicted and validated information on miRNA-target interaction. Such a resource enables researchers to validate new targets of miRNA not only on 3'-UTR, but also on the other regions of all known genes. The 'Validated Target module' is updated every month and the 'Predicted Target module' is updated every 6 months. miRWalk is freely available at http://mirwalk.uni-hd.de/. Copyright © 2011 Elsevier Inc. All rights reserved.

Dual CRISPR-Cas9 Cleavage Mediated Gene Excision and Targeted Integration in Yarrowia lipolytica.

PubMed

Gao, Difeng; Smith, Spencer; Spagnuolo, Michael; Rodriguez, Gabriel; Blenner, Mark

2018-05-29

CRISPR-Cas9 technology has been successfully applied in Yarrowia lipolytica for targeted genomic editing including gene disruption and integration; however, disruptions by existing methods typically result from small frameshift mutations caused by indels within the coding region, which usually resulted in unnatural protein. In this study, a dual cleavage strategy directed by paired sgRNAs is developed for gene knockout. This method allows fast and robust gene excision, demonstrated on six genes of interest. The targeted regions for excision vary in length from 0.3 kb up to 3.5 kb and contain both non-coding and coding regions. The majority of the gene excisions are repaired by perfect nonhomologous end-joining without indel. Based on this dual cleavage system, two targeted markerless integration methods are developed by providing repair templates. While both strategies are effective, homology mediated end joining (HMEJ) based method are twice as efficient as homology recombination (HR) based method. In both cases, dual cleavage leads to similar or improved gene integration efficiencies compared to gene excision without integration. This dual cleavage strategy will be useful for not only generating more predictable and robust gene knockout, but also for efficient targeted markerless integration, and simultaneous knockout and integration in Y. lipolytica. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

PubMed

Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

2018-02-23

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

The Mechanism of Gene Targeting in Human Somatic Cells

PubMed Central

Kan, Yinan; Ruis, Brian; Lin, Sherry; Hendrickson, Eric A.

2014-01-01

Gene targeting in human somatic cells is of importance because it can be used to either delineate the loss-of-function phenotype of a gene or correct a mutated gene back to wild-type. Both of these outcomes require a form of DNA double-strand break (DSB) repair known as homologous recombination (HR). The mechanism of HR leading to gene targeting, however, is not well understood in human cells. Here, we demonstrate that a two-end, ends-out HR intermediate is valid for human gene targeting. Furthermore, the resolution step of this intermediate occurs via the classic DSB repair model of HR while synthesis-dependent strand annealing and Holliday Junction dissolution are, at best, minor pathways. Moreover, and in contrast to other systems, the positions of Holliday Junction resolution are evenly distributed along the homology arms of the targeting vector. Most unexpectedly, we demonstrate that when a meganuclease is used to introduce a chromosomal DSB to augment gene targeting, the mechanism of gene targeting is inverted to an ends-in process. Finally, we demonstrate that the anti-recombination activity of mismatch repair is a significant impediment to gene targeting. These observations significantly advance our understanding of HR and gene targeting in human cells. PMID:24699519

Simple Monitoring of Gene Targeting Efficiency in Human Somatic Cell Lines Using the PIGA Gene

PubMed Central

Karnan, Sivasundaram; Konishi, Yuko; Ota, Akinobu; Takahashi, Miyuki; Damdindorj, Lkhagvasuren; Hosokawa, Yoshitaka; Konishi, Hiroyuki

2012-01-01

Gene targeting in most of human somatic cell lines has been labor-intensive because of low homologous recombination efficiency. The development of an experimental system that permits a facile evaluation of gene targeting efficiency in human somatic cell lines is the first step towards the improvement of this technology and its application to a broad range of cell lines. In this study, we utilized phosphatidylinositol glycan anchor biosynthesis class A (PIGA), a gene essential for the synthesis of glycosylphosphatidyl inositol (GPI) anchors, as a reporter of gene targeting events in human somatic cell lines. Targeted disruption of PIGA was quantitatively detected with FLAER, a reagent that specifically binds to GPI anchors. Using this PIGA-based reporter system, we successfully detected adeno-associated virus (AAV)-mediated gene targeting events both with and without promoter-trap enrichment of gene-targeted cell population. The PIGA-based reporter system was also capable of reproducing previous findings that an AAV-mediated gene targeting achieves a remarkably higher ratio of homologous versus random integration (H/R ratio) of targeting vectors than a plasmid-mediated gene targeting. The PIGA-based system also detected an approximately 2-fold increase in the H/R ratio achieved by a small negative selection cassette introduced at the end of the AAV-based targeting vector with a promoter-trap system. Thus, our PIGA-based system is useful for monitoring AAV-mediated gene targeting and will assist in improving gene targeting technology in human somatic cell lines. PMID:23056640

Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning.

PubMed

He, Zhili; Zhang, Ping; Wu, Linwei; Rocha, Andrea M; Tu, Qichao; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D; Wu, Liyou; Yang, Yunfeng; Elias, Dwayne A; Watson, David B; Adams, Michael W W; Fields, Matthew W; Alm, Eric J; Hazen, Terry C; Adams, Paul D; Arkin, Adam P; Zhou, Jizhong

2018-02-20

Contamination from anthropogenic activities has significantly impacted Earth's biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly ( P < 0.05) as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning. IMPORTANCE Disentangling the relationships between biodiversity and ecosystem functioning is an important but poorly understood topic in ecology. Predicting ecosystem functioning on the basis of biodiversity is even more difficult, particularly with microbial biomarkers. As an exploratory effort, this study used key microbial functional genes as biomarkers to provide predictive understanding of environmental contamination and ecosystem functioning. The results indicated that the overall functional gene richness

Gene function prediction based on the Gene Ontology hierarchical structure.

PubMed

Cheng, Liangxi; Lin, Hongfei; Hu, Yuncui; Wang, Jian; Yang, Zhihao

2014-01-01

The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship.

Identification of downstream metastasis-associated target genes regulated by LSD1 in colon cancer cells.

PubMed

Chen, Jiang; Ding, Jie; Wang, Ziwei; Zhu, Jian; Wang, Xuejian; Du, Jiyi

2017-03-21

This study aims to identify downstream target genes regulated by lysine-specific demethylase 1 (LSD1) in colon cancer cells and investigate the molecular mechanisms of LSD1 influencing invasion and metastasis of colon cancer. We obtained the expression changes of downstream target genes regulated by small-interfering RNA-LSD1 and LSD1-overexpression via gene expression profiling in two human colon cancer cell lines. An Affymetrix Human Transcriptome Array 2.0 was used to identify differentially expressed genes (DEGs). We screened out LSD1-target gene associated with proliferation, metastasis, and invasion from DEGs via Gene Ontology and Pathway Studio. Subsequently, four key genes (CABYR, FOXF2, TLE4, and CDH1) were computationally predicted as metastasis-related LSD1-target genes. ChIp-PCR was applied after RT-PCR and Western blot validations to detect the occupancy of LSD1-target gene promoter-bound LSD1. A total of 3633 DEGs were significantly upregulated, and 4642 DEGs were downregulated in LSD1-silenced SW620 cells. A total of 4047 DEGs and 4240 DEGs were upregulated and downregulated in LSD1-overexpressed HT-29 cells, respectively. RT-PCR and Western blot validated the microarray analysis results. ChIP assay results demonstrated that LSD1 might be negative regulators for target genes CABYR and CDH1. The expression level of LSD1 is negatively correlated with mono- and dimethylation of histone H3 lysine4(H3K4) at LSD1- target gene promoter region. No significant mono-methylation and dimethylation of H3 lysine9 methylation was detected at the promoter region of CABYR and CDH1. LSD1- depletion contributed to the upregulation of CABYR and CDH1 through enhancing the dimethylation of H3K4 at the LSD1-target genes promoter. LSD1- overexpression mediated the downregulation of CABYR and CDH1expression through decreasing the mono- and dimethylation of H3K4 at LSD1-target gene promoter in colon cancer cells. CABYR and CDH1 might be potential LSD1-target genes in colon

Deep-Learning-Based Drug-Target Interaction Prediction.

PubMed

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

Targeted gene flow for conservation.

PubMed

Kelly, Ella; Phillips, Ben L

2016-04-01

Anthropogenic threats often impose strong selection on affected populations, causing rapid evolutionary responses. Unfortunately, these adaptive responses are rarely harnessed for conservation. We suggest that conservation managers pay close attention to adaptive processes and geographic variation, with an eye to using them for conservation goals. Translocating pre-adapted individuals into recipient populations is currently considered a potentially important management tool in the face of climate change. Targeted gene flow, which involves moving individuals with favorable traits to areas where these traits would have a conservation benefit, could have a much broader application in conservation. Across a species' range there may be long-standing geographic variation in traits or variation may have rapidly developed in response to a threatening process. Targeted gene flow could be used to promote natural resistance to threats to increase species resilience. We suggest that targeted gene flow is a currently underappreciated strategy in conservation that has applications ranging from the management of invasive species and their impacts to controlling the impact and virulence of pathogens. © 2015 Society for Conservation Biology.

TargetSpy: a supervised machine learning approach for microRNA target prediction.

PubMed

Sturm, Martin; Hackenberg, Michael; Langenberger, David; Frishman, Dmitrij

2010-05-28

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences.In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila

TargetSpy: a supervised machine learning approach for microRNA target prediction

PubMed Central

2010-01-01

Background Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. Results We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences. In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Conclusion Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well

Bacteriophage-Derived Vectors for Targeted Cancer Gene Therapy

PubMed Central

Pranjol, Md Zahidul Islam; Hajitou, Amin

2015-01-01

Cancer gene therapy expanded and reached its pinnacle in research in the last decade. Both viral and non-viral vectors have entered clinical trials, and significant successes have been achieved. However, a systemic administration of a vector, illustrating safe, efficient, and targeted gene delivery to solid tumors has proven to be a major challenge. In this review, we summarize the current progress and challenges in the targeted gene therapy of cancer. Moreover, we highlight the recent developments of bacteriophage-derived vectors and their contributions in targeting cancer with therapeutic genes following systemic administration. PMID:25606974

Bacteriophage-derived vectors for targeted cancer gene therapy.

PubMed

Pranjol, Md Zahidul Islam; Hajitou, Amin

2015-01-19

Cancer gene therapy expanded and reached its pinnacle in research in the last decade. Both viral and non-viral vectors have entered clinical trials, and significant successes have been achieved. However, a systemic administration of a vector, illustrating safe, efficient, and targeted gene delivery to solid tumors has proven to be a major challenge. In this review, we summarize the current progress and challenges in the targeted gene therapy of cancer. Moreover, we highlight the recent developments of bacteriophage-derived vectors and their contributions in targeting cancer with therapeutic genes following systemic administration.

Integrating alternative splicing detection into gene prediction.

PubMed

Foissac, Sylvain; Schiex, Thomas

2005-02-10

Alternative splicing (AS) is now considered as a major actor in transcriptome/proteome diversity and it cannot be neglected in the annotation process of a new genome. Despite considerable progresses in term of accuracy in computational gene prediction, the ability to reliably predict AS variants when there is local experimental evidence of it remains an open challenge for gene finders. We have used a new integrative approach that allows to incorporate AS detection into ab initio gene prediction. This method relies on the analysis of genomically aligned transcript sequences (ESTs and/or cDNAs), and has been implemented in the dynamic programming algorithm of the graph-based gene finder EuGENE. Given a genomic sequence and a set of aligned transcripts, this new version identifies the set of transcripts carrying evidence of alternative splicing events, and provides, in addition to the classical optimal gene prediction, alternative optimal predictions (among those which are consistent with the AS events detected). This allows for multiple annotations of a single gene in a way such that each predicted variant is supported by a transcript evidence (but not necessarily with a full-length coverage). This automatic combination of experimental data analysis and ab initio gene finding offers an ideal integration of alternatively spliced gene prediction inside a single annotation pipeline.

Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

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

He, Zhili; Zhang, Ping; Wu, Linwei

Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminantsmore » would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P < 0.05) as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. Here, this study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning.« less

Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

PubMed Central

Zhang, Ping; Wu, Linwei; Rocha, Andrea M.; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D.; Wu, Liyou; Watson, David B.; Adams, Michael W. W.; Alm, Eric J.; Adams, Paul D.; Arkin, Adam P.

2018-01-01

ABSTRACT Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P < 0.05) as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning. PMID:29463661

Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

DOE PAGES

He, Zhili; Zhang, Ping; Wu, Linwei; ...

2018-02-20

Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminantsmore » would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P < 0.05) as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. Here, this study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning.« less

FrameD: A flexible program for quality check and gene prediction in prokaryotic genomes and noisy matured eukaryotic sequences.

PubMed

Schiex, Thomas; Gouzy, Jérôme; Moisan, Annick; de Oliveira, Yannick

2003-07-01

We describe FrameD, a program that predicts coding regions in prokaryotic and matured eukaryotic sequences. Initially targeted at gene prediction in bacterial GC rich genomes, the gene model used in FrameD also allows to predict genes in the presence of frameshifts and partially undetermined sequences which makes it also very suitable for gene prediction and frameshift correction in unfinished sequences such as EST and EST cluster sequences. Like recent eukaryotic gene prediction programs, FrameD also includes the ability to take into account protein similarity information both in its prediction and its graphical output. Its performances are evaluated on different bacterial genomes. The web site (http://genopole.toulouse.inra.fr/bioinfo/FrameD/FD) allows direct prediction, sequence correction and translation and the ability to learn new models for new organisms.

Generation of novel resistance genes using mutation and targeted gene editing.

PubMed

Gal-On, Amit; Fuchs, Marc; Gray, Stewart

2017-10-01

Classical breeding for virus resistance is a lengthy process and is restricted by the availability of resistance genes. Precise genome editing is a 'dream technology' to improve plants for virus resistance and these tools have opened new and very promising ways to generate virus resistant plants by disrupting host susceptibility genes, or by increasing the expression of viral resistance genes. However, precise targets must be identified and their roles understood to minimize potential negative effects on the plant. Nonetheless, the opportunities for genome editing are expanding, as are the technologies to generate effective and broad-spectrum resistance against plant viruses. Here we provide insights into recent progress related to gene targets and gene editing technologies. Published by Elsevier B.V.

Differences in DNA Binding Specificity of Floral Homeotic Protein Complexes Predict Organ-Specific Target Genes.

PubMed

Smaczniak, Cezary; Muiño, Jose M; Chen, Dijun; Angenent, Gerco C; Kaufmann, Kerstin

2017-08-01

Floral organ identities in plants are specified by the combinatorial action of homeotic master regulatory transcription factors. However, how these factors achieve their regulatory specificities is still largely unclear. Genome-wide in vivo DNA binding data show that homeotic MADS domain proteins recognize partly distinct genomic regions, suggesting that DNA binding specificity contributes to functional differences of homeotic protein complexes. We used in vitro systematic evolution of ligands by exponential enrichment followed by high-throughput DNA sequencing (SELEX-seq) on several floral MADS domain protein homo- and heterodimers to measure their DNA binding specificities. We show that specification of reproductive organs is associated with distinct binding preferences of a complex formed by SEPALLATA3 and AGAMOUS. Binding specificity is further modulated by different binding site spacing preferences. Combination of SELEX-seq and genome-wide DNA binding data allows differentiation between targets in specification of reproductive versus perianth organs in the flower. We validate the importance of DNA binding specificity for organ-specific gene regulation by modulating promoter activity through targeted mutagenesis. Our study shows that intrafamily protein interactions affect DNA binding specificity of floral MADS domain proteins. Differential DNA binding of MADS domain protein complexes plays a role in the specificity of target gene regulation. © 2017 American Society of Plant Biologists. All rights reserved.

Core Promoter Functions in the Regulation of Gene Expression of Drosophila Dorsal Target Genes*

PubMed Central

Zehavi, Yonathan; Kuznetsov, Olga; Ovadia-Shochat, Avital; Juven-Gershon, Tamar

2014-01-01

Developmental processes are highly dependent on transcriptional regulation by RNA polymerase II. The RNA polymerase II core promoter is the ultimate target of a multitude of transcription factors that control transcription initiation. Core promoters consist of core promoter motifs, e.g. the initiator, TATA box, and the downstream core promoter element (DPE), which confer specific properties to the core promoter. Here, we explored the importance of core promoter functions in the dorsal-ventral developmental gene regulatory network. This network includes multiple genes that are activated by different nuclear concentrations of Dorsal, an NFκB homolog transcription factor, along the dorsal-ventral axis. We show that over two-thirds of Dorsal target genes contain DPE sequence motifs, which is significantly higher than the proportion of DPE-containing promoters in Drosophila genes. We demonstrate that multiple Dorsal target genes are evolutionarily conserved and functionally dependent on the DPE. Furthermore, we have analyzed the activation of key Dorsal target genes by Dorsal, as well as by another Rel family transcription factor, Relish, and the dependence of their activation on the DPE motif. Using hybrid enhancer-promoter constructs in Drosophila cells and embryo extracts, we have demonstrated that the core promoter composition is an important determinant of transcriptional activity of Dorsal target genes. Taken together, our results provide evidence for the importance of core promoter composition in the regulation of Dorsal target genes. PMID:24634215

In Situ Gene Therapy via AAV-CRISPR-Cas9-Mediated Targeted Gene Regulation.

PubMed

Moreno, Ana M; Fu, Xin; Zhu, Jie; Katrekar, Dhruva; Shih, Yu-Ru V; Marlett, John; Cabotaje, Jessica; Tat, Jasmine; Naughton, John; Lisowski, Leszek; Varghese, Shyni; Zhang, Kang; Mali, Prashant

2018-04-25

Development of efficacious in vivo delivery platforms for CRISPR-Cas9-based epigenome engineering will be critical to enable the ability to target human diseases without permanent modification of the genome. Toward this, we utilized split-Cas9 systems to develop a modular adeno-associated viral (AAV) vector platform for CRISPR-Cas9 delivery to enable the full spectrum of targeted in situ gene regulation functionalities, demonstrating robust transcriptional repression (up to 80%) and activation (up to 6-fold) of target genes in cell culture and mice. We also applied our platform for targeted in vivo gene-repression-mediated gene therapy for retinitis pigmentosa. Specifically, we engineered targeted repression of Nrl, a master regulator of rod photoreceptor determination, and demonstrated Nrl knockdown mediates in situ reprogramming of rod cells into cone-like cells that are resistant to retinitis pigmentosa-specific mutations, with concomitant prevention of secondary cone loss. Furthermore, we benchmarked our results from Nrl knockdown with those from in vivo Nrl knockout via gene editing. Taken together, our AAV-CRISPR-Cas9 platform for in vivo epigenome engineering enables a robust approach to target disease in a genomically scarless and potentially reversible manner. Copyright © 2018 The American Society of Gene and Cell Therapy. Published by Elsevier Inc. All rights reserved.

Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer

PubMed Central

Makondi, Precious Takondwa; Lee, Chia-Hwa; Huang, Chien-Yu; Chu, Chi-Ming; Chang, Yu-Jia

2018-01-01

Bevacizumab combined with cytotoxic chemotherapy is the backbone of metastatic colorectal cancer (mCRC) therapy; however, its treatment efficacy is hampered by therapeutic resistance. Therefore, understanding the mechanisms underlying bevacizumab resistance is crucial to increasing the therapeutic efficacy of bevacizumab. The Gene Expression Omnibus (GEO) database (dataset, GSE86525) was used to identify the key genes and pathways involved in bevacizumab-resistant mCRC. The GEO2R web tool was used to identify differentially expressed genes (DEGs). Functional and pathway enrichment analyses of the DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery(DAVID). Protein–protein interaction (PPI) networks were established using the Search Tool for the Retrieval of Interacting Genes/Proteins database(STRING) and visualized using Cytoscape software. A total of 124 DEGs were obtained, 57 of which upregulated and 67 were downregulated. PPI network analysis showed that seven upregulated genes and nine downregulated genes exhibited high PPI degrees. In the functional enrichment, the DEGs were mainly enriched in negative regulation of phosphate metabolic process and positive regulation of cell cycle process gene ontologies (GOs); the enriched pathways were the phosphoinositide 3-kinase-serine/threonine kinase signaling pathway, bladder cancer, and microRNAs in cancer. Cyclin-dependent kinase inhibitor 1A(CDKN1A), toll-like receptor 4 (TLR4), CD19 molecule (CD19), breast cancer 1, early onset (BRCA1), platelet-derived growth factor subunit A (PDGFA), and matrix metallopeptidase 1 (MMP1) were the DEGs involved in the pathways and the PPIs. The clinical validation of the DEGs in mCRC (TNM clinical stages 3 and 4) revealed that high PDGFA expression levels were associated with poor overall survival, whereas high BRCA1 and MMP1 expression levels were associated with favorable progress free survival(PFS). The identified genes and pathways

Cell-specific prediction and application of drug-induced gene expression profiles.

PubMed

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

2018-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Cell-specific prediction and application of drug-induced gene expression profiles

PubMed Central

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R.; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David

2017-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes. PMID:29218867

Extensive complementarity between gene function prediction methods.

PubMed

Vidulin, Vedrana; Šmuc, Tomislav; Supek, Fran

2016-12-01

The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesized that AFP approaches that draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions. Our pipeline amalgamates 5 133 543 genes from 2071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1227 Gene Ontology (GO) terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse genomic AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to rely on a single most-confident prediction per gene/function, rather than enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known Escherichia coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the Critical Assessment of Function Annotation 2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them. The individual and integrated GO predictions for the complete set of genes are available from http://gorbi.irb.hr/ CONTACT: fran.supek@irb.hrSupplementary information: Supplementary materials are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Problem-Solving Test: Targeted Gene Disruption

ERIC Educational Resources Information Center

Szeberenyi, Jozsef

2008-01-01

Mutational inactivation of a specific gene is the most powerful technique to analyze the biological function of the gene. This approach has been used for a long time in viruses, bacteria, yeast, and fruit fly, but looked quite hopeless in more complex organisms. Targeted inactivation of specific genes (also known as knock-out mutation) in mice is…

Motion prediction of a non-cooperative space target

NASA Astrophysics Data System (ADS)

Zhou, Bang-Zhao; Cai, Guo-Ping; Liu, Yun-Meng; Liu, Pan

2018-01-01

Capturing a non-cooperative space target is a tremendously challenging research topic. Effective acquisition of motion information of the space target is the premise to realize target capture. In this paper, motion prediction of a free-floating non-cooperative target in space is studied and a motion prediction algorithm is proposed. In order to predict the motion of the free-floating non-cooperative target, dynamic parameters of the target must be firstly identified (estimated), such as inertia, angular momentum and kinetic energy and so on; then the predicted motion of the target can be acquired by substituting these identified parameters into the Euler's equations of the target. Accurate prediction needs precise identification. This paper presents an effective method to identify these dynamic parameters of a free-floating non-cooperative target. This method is based on two steps, (1) the rough estimation of the parameters is computed using the motion observation data to the target, and (2) the best estimation of the parameters is found by an optimization method. In the optimization problem, the objective function is based on the difference between the observed and the predicted motion, and the interior-point method (IPM) is chosen as the optimization algorithm, which starts at the rough estimate obtained in the first step and finds a global minimum to the objective function with the guidance of objective function's gradient. So the speed of IPM searching for the global minimum is fast, and an accurate identification can be obtained in time. The numerical results show that the proposed motion prediction algorithm is able to predict the motion of the target.

Specific c-Jun target genes in malignant melanoma.

PubMed

Schummer, Patrick; Kuphal, Silke; Vardimon, Lily; Bosserhoff, Anja K; Kappelmann, Melanie

2016-05-03

A fundamental event in the development and progression of malignant melanoma is the de-regulation of cancer-relevant transcription factors. We recently showed that c-Jun is a main regulator of melanoma progression and, thus, is the most important member of the AP-1 transcription factor family in this disease. Surprisingly, no cancer-related specific c-Jun target genes in melanoma were described in the literature, so far. Therefore, we focused on pre-existing ChIP-Seq data (Encyclopedia of DNA Elements) of 3 different non-melanoma cell lines to screen direct c-Jun target genes. Here, a specific c-Jun antibody to immunoprecipitate the associated promoter DNA was used. Consequently, we identified 44 direct c-Jun targets and a detailed analysis of 6 selected genes confirmed their deregulation in malignant melanoma. The identified genes were differentially regulated comparing 4 melanoma cell lines and normal human melanocytes and we confirmed their c-Jun dependency. Direct interaction between c-Jun and the promoter/enhancer regions of the identified genes was confirmed by us via ChIP experiments. Interestingly, we revealed that the direct regulation of target gene expression via c-Jun can be independent of the existence of the classical AP-1 (5´-TGA(C/G)TCA-3´) consensus sequence allowing for the subsequent down- or up-regulation of the expression of these cancer-relevant genes. In summary, the results of this study indicate that c-Jun plays a crucial role in the development and progression of malignant melanoma via direct regulation of cancer-relevant target genes and that inhibition of direct c-Jun targets through inhibition of c-Jun is a potential novel therapeutic option for treatment of malignant melanoma.

In silico study of breast cancer associated gene 3 using LION Target Engine and other tools.

PubMed

León, Darryl A; Cànaves, Jaume M

2003-12-01

Sequence analysis of individual targets is an important step in annotation and validation. As a test case, we investigated human breast cancer associated gene 3 (BCA3) with LION Target Engine and with other bioinformatics tools. LION Target Engine confirmed that the BCA3 gene is located on 11p15.4 and that the two most likely splice variants (lacking exon 3 and exons 3 and 5, respectively) exist. Based on our manual curation of sequence data, it is proposed that an additional variant (missing only exon 5) published in a public sequence repository, is a prediction artifact. A significant number of new orthologs were also identified, and these were the basis for a high-quality protein secondary structure prediction. Moreover, our research confirmed several distinct functional domains as described in earlier reports. Sequence conservation from multiple sequence alignments, splice variant identification, secondary structure predictions, and predicted phosphorylation sites suggest that the removal of interaction sites through alternative splicing might play a modulatory role in BCA3. This in silico approach shows the depth and relevance of an analysis that can be accomplished by including a variety of publicly available tools with an integrated and customizable life science informatics platform.

Identification and characterization of microRNAs and their target genes from Nile tilapia (Oreochromis niloticus).

PubMed

Huang, Yong; Ma, Xiu Ying; Yang, You Bing; Ren, Hong Tao; Sun, Xi Hong; Wang, Li Rui

MicroRNAs (miRNAs) are a class of small single-stranded, endogenous 21-22 nt non-coding RNAs that regulate their target mRNA levels by causing either inactivation or degradation of the mRNAs. In recent years, miRNA genes have been identified from mammals, insects, worms, plants, and viruses. In this research, bioinformatics approaches were used to predict potential miRNAs and their targets in Nile tilapia from the expressed sequence tag (EST) and genomic survey sequence (GSS) database, respectively, based on the conservation of miRNAs in many animal species. A total of 19 potential miRNAs were detected following a range of strict filtering criteria. To test the validity of the bioinformatics method, seven predicted Nile tilapia miRNA genes were selected for further biological validation, and their mature miRNA transcripts were successfully detected by stem-loop RT-PCR experiments. Using these potential miRNAs, we found 56 potential targets in this species. Most of the target mRNAs appear to be involved in development, metabolism, signal transduction, transcription regulation and stress responses. Overall, our findings will provide an important foundation for further research on miRNAs function in the Nile tilapia.

Challenging the state of the art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10.

PubMed

Kryshtafovych, Andriy; Moult, John; Bales, Patrick; Bazan, J Fernando; Biasini, Marco; Burgin, Alex; Chen, Chen; Cochran, Frank V; Craig, Timothy K; Das, Rhiju; Fass, Deborah; Garcia-Doval, Carmela; Herzberg, Osnat; Lorimer, Donald; Luecke, Hartmut; Ma, Xiaolei; Nelson, Daniel C; van Raaij, Mark J; Rohwer, Forest; Segall, Anca; Seguritan, Victor; Zeth, Kornelius; Schwede, Torsten

2014-02-01

For the last two decades, CASP has assessed the state of the art in techniques for protein structure prediction and identified areas which required further development. CASP would not have been possible without the prediction targets provided by the experimental structural biology community. In the latest experiment, CASP10, more than 100 structures were suggested as prediction targets, some of which appeared to be extraordinarily difficult for modeling. In this article, authors of some of the most challenging targets discuss which specific scientific question motivated the experimental structure determination of the target protein, which structural features were especially interesting from a structural or functional perspective, and to what extent these features were correctly reproduced in the predictions submitted to CASP10. Specifically, the following targets will be presented: the acid-gated urea channel, a difficult to predict transmembrane protein from the important human pathogen Helicobacter pylori; the structure of human interleukin (IL)-34, a recently discovered helical cytokine; the structure of a functionally uncharacterized enzyme OrfY from Thermoproteus tenax formed by a gene duplication and a novel fold; an ORFan domain of mimivirus sulfhydryl oxidase R596; the fiber protein gene product 17 from bacteriophage T7; the bacteriophage CBA-120 tailspike protein; a virus coat protein from metagenomic samples of the marine environment; and finally, an unprecedented class of structure prediction targets based on engineered disulfide-rich small proteins. Copyright © 2013 The Authors. Wiley Periodicals, Inc.

Incorrectly predicted genes in rice?

PubMed

Cruveiller, Stéphane; Jabbari, Kamel; Clay, Oliver; Bernardi, Giorgio

2004-05-26

Between one third and one half of the proposed rice genes appear to have no homologs in other species, including Arabidopsis. Compositional considerations, and a comparison of curated rice sequences with ex novo predictions, suggest that many or most of the putative genes without homologs may be false positive predictions, i.e., sequences that are never translated into functional proteins in vivo.

Systematic mapping of two component response regulators to gene targets in a model sulfate reducing bacterium.

PubMed

Rajeev, Lara; Luning, Eric G; Dehal, Paramvir S; Price, Morgan N; Arkin, Adam P; Mukhopadhyay, Aindrila

2011-10-12

Two component regulatory systems are the primary form of signal transduction in bacteria. Although genomic binding sites have been determined for several eukaryotic and bacterial transcription factors, comprehensive identification of gene targets of two component response regulators remains challenging due to the lack of knowledge of the signals required for their activation. We focused our study on Desulfovibrio vulgaris Hildenborough, a sulfate reducing bacterium that encodes unusually diverse and largely uncharacterized two component signal transduction systems. We report the first systematic mapping of the genes regulated by all transcriptionally acting response regulators in a single bacterium. Our results enabled functional predictions for several response regulators and include key processes of carbon, nitrogen and energy metabolism, cell motility and biofilm formation, and responses to stresses such as nitrite, low potassium and phosphate starvation. Our study also led to the prediction of new genes and regulatory networks, which found corroboration in a compendium of transcriptome data available for D. vulgaris. For several regulators we predicted and experimentally verified the binding site motifs, most of which were discovered as part of this study. The gene targets identified for the response regulators allowed strong functional predictions to be made for the corresponding two component systems. By tracking the D. vulgaris regulators and their motifs outside the Desulfovibrio spp. we provide testable hypotheses regarding the functions of orthologous regulators in other organisms. The in vitro array based method optimized here is generally applicable for the study of such systems in all organisms.

E2F1 somatic mutation within miRNA target site impairs gene regulation in colorectal cancer.

PubMed

Lopes-Ramos, Camila M; Barros, Bruna P; Koyama, Fernanda C; Carpinetti, Paola A; Pezuk, Julia; Doimo, Nayara T S; Habr-Gama, Angelita; Perez, Rodrigo O; Parmigiani, Raphael B

2017-01-01

Genetic studies have largely concentrated on the impact of somatic mutations found in coding regions, and have neglected mutations outside of these. However, 3' untranslated regions (3' UTR) mutations can also disrupt or create miRNA target sites, and trigger oncogene activation or tumor suppressor inactivation. We used next-generation sequencing to widely screen for genetic alterations within predicted miRNA target sites of oncogenes associated with colorectal cancer, and evaluated the functional impact of a new somatic mutation. Target sequencing of 47 genes was performed for 29 primary colorectal tumor samples. For 71 independent samples, Sanger methodology was used to screen for E2F1 mutations in miRNA predicted target sites, and the functional impact of these mutations was evaluated by luciferase reporter assays. We identified germline and somatic alterations in E2F1. Of the 100 samples evaluated, 3 had germline alterations at the MIR205-5p target site, while one had a somatic mutation at MIR136-5p target site. E2F1 gene expression was similar between normal and tumor tissues bearing the germline alteration; however, expression was increased 4-fold in tumor tissue that harbored a somatic mutation compared to that in normal tissue. Luciferase reporter assays revealed both germline and somatic alterations increased E2F1 activity relative to wild-type E2F1. We demonstrated that somatic mutation within E2F1:MIR136-5p target site impairs miRNA-mediated regulation and leads to increased gene activity. We conclude that somatic mutations that disrupt miRNA target sites have the potential to impact gene regulation, highlighting an important mechanism of oncogene activation.

Drosophila CLOCK target gene characterization: implications for circadian tissue-specific gene expression

PubMed Central

Abruzzi, Katharine Compton; Rodriguez, Joseph; Menet, Jerome S.; Desrochers, Jennifer; Zadina, Abigail; Luo, Weifei; Tkachev, Sasha; Rosbash, Michael

2011-01-01

CLOCK (CLK) is a master transcriptional regulator of the circadian clock in Drosophila. To identify CLK direct target genes and address circadian transcriptional regulation in Drosophila, we performed chromatin immunoprecipitation (ChIP) tiling array assays (ChIP–chip) with a number of circadian proteins. CLK binding cycles on at least 800 sites with maximal binding in the early night. The CLK partner protein CYCLE (CYC) is on most of these sites. The CLK/CYC heterodimer is joined 4–6 h later by the transcriptional repressor PERIOD (PER), indicating that the majority of CLK targets are regulated similarly to core circadian genes. About 30% of target genes also show cycling RNA polymerase II (Pol II) binding. Many of these generate cycling RNAs despite not being documented in prior RNA cycling studies. This is due in part to different RNA isoforms and to fly head tissue heterogeneity. CLK has specific targets in different tissues, implying that important CLK partner proteins and/or mechanisms contribute to gene-specific and tissue-specific regulation. PMID:22085964

Predicting oligonucleotide affinity to nucleic acid targets.

PubMed Central

Mathews, D H; Burkard, M E; Freier, S M; Wyatt, J R; Turner, D H

1999-01-01

A computer program, OligoWalk, is reported that predicts the equilibrium affinity of complementary DNA or RNA oligonucleotides to an RNA target. This program considers the predicted stability of the oligonucleotide-target helix and the competition with predicted secondary structure of both the target and the oligonucleotide. Both unimolecular and bimolecular oligonucleotide self structure are considered with a user-defined concentration. The application of OligoWalk is illustrated with three comparisons to experimental results drawn from the literature. PMID:10580474

Genome-wide identification and characterization of microRNA genes and their targets in flax (Linum usitatissimum): Characterization of flax miRNA genes.

PubMed

Barvkar, Vitthal T; Pardeshi, Varsha C; Kale, Sandip M; Qiu, Shuqing; Rollins, Meaghen; Datla, Raju; Gupta, Vidya S; Kadoo, Narendra Y

2013-04-01

MicroRNAs (miRNAs) are small (20-24 nucleotide long) endogenous regulatory RNAs that play important roles in plant growth and development. They regulate gene expression at the post-transcriptional level by translational repression or target degradation and gene silencing. In this study, we identified 116 conserved miRNAs belonging to 23 families from the flax (Linum usitatissimum L.) genome using a computational approach. The precursor miRNAs varied in length; while most of the mature miRNAs were 21 nucleotide long, intergenic and showed conserved signatures of RNA polymerase II transcripts in their upstream regions. Promoter region analysis of the flax miRNA genes indicated prevalence of MYB transcription factor binding sites. Four miRNA gene clusters containing members of three phylogenetic groups were identified. Further, 142 target genes were predicted for these miRNAs and most of these represent transcriptional regulators. The miRNA encoding genes were expressed in diverse tissues as determined by digital expression analysis as well as real-time PCR. The expression of fourteen miRNAs and nine target genes was independently validated using the quantitative reverse transcription PCR (qRT-PCR). This study suggests that a large number of conserved plant miRNAs are also found in flax and these may play important roles in growth and development of flax.

GeneSilico protein structure prediction meta-server.

PubMed

Kurowski, Michal A; Bujnicki, Janusz M

2003-07-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.

STAT3 or USF2 Contributes to HIF Target Gene Specificity

PubMed Central

Pawlus, Matthew R.; Wang, Liyi; Murakami, Aya; Dai, Guanhai; Hu, Cheng-Jun

2013-01-01

The HIF1- and HIF2-mediated transcriptional responses play critical roles in solid tumor progression. Despite significant similarities, including their binding to promoters of both HIF1 and HIF2 target genes, HIF1 and HIF2 proteins activate unique subsets of target genes under hypoxia. The mechanism for HIF target gene specificity has remained unclear. Using siRNA or inhibitor, we previously reported that STAT3 or USF2 is specifically required for activation of endogenous HIF1 or HIF2 target genes. In this study, using reporter gene assays and chromatin immuno-precipitation, we find that STAT3 or USF2 exhibits specific binding to the promoters of HIF1 or HIF2 target genes respectively even when over-expressed. Functionally, HIF1α interacts with STAT3 to activate HIF1 target gene promoters in a HIF1α HLH/PAS and N-TAD dependent manner while HIF2α interacts with USF2 to activate HIF2 target gene promoters in a HIF2α N-TAD dependent manner. Physically, HIF1α HLH and PAS domains are required for its interaction with STAT3 while both N- and C-TADs of HIF2α are involved in physical interaction with USF2. Importantly, addition of functional USF2 binding sites into a HIF1 target gene promoter increases the basal activity of the promoter as well as its response to HIF2+USF2 activation while replacing HIF binding site with HBS from a HIF2 target gene does not change the specificity of the reporter gene. Importantly, RNA Pol II on HIF1 or HIF2 target genes is primarily associated with HIF1α or HIF2α in a STAT3 or USF2 dependent manner. Thus, we demonstrate here for the first time that HIF target gene specificity is achieved by HIF transcription partners that are required for HIF target gene activation, exhibit specific binding to the promoters of HIF1 or HIF2 target genes and selectively interact with HIF1α or HIF2α protein. PMID:23991099

In vitro perturbations of targets in cancer hallmark processes predict rodent chemical carcinogenesis.

PubMed

Kleinstreuer, Nicole C; Dix, David J; Houck, Keith A; Kavlock, Robert J; Knudsen, Thomas B; Martin, Matthew T; Paul, Katie B; Reif, David M; Crofton, Kevin M; Hamilton, Kerry; Hunter, Ronald; Shah, Imran; Judson, Richard S

2013-01-01

Thousands of untested chemicals in the environment require efficient characterization of carcinogenic potential in humans. A proposed solution is rapid testing of chemicals using in vitro high-throughput screening (HTS) assays for targets in pathways linked to disease processes to build models for priority setting and further testing. We describe a model for predicting rodent carcinogenicity based on HTS data from 292 chemicals tested in 672 assays mapping to 455 genes. All data come from the EPA ToxCast project. The model was trained on a subset of 232 chemicals with in vivo rodent carcinogenicity data in the Toxicity Reference Database (ToxRefDB). Individual HTS assays strongly associated with rodent cancers in ToxRefDB were linked to genes, pathways, and hallmark processes documented to be involved in tumor biology and cancer progression. Rodent liver cancer endpoints were linked to well-documented pathways such as peroxisome proliferator-activated receptor signaling and TP53 and novel targets such as PDE5A and PLAUR. Cancer hallmark genes associated with rodent thyroid tumors were found to be linked to human thyroid tumors and autoimmune thyroid disease. A model was developed in which these genes/pathways function as hypothetical enhancers or promoters of rat thyroid tumors, acting secondary to the key initiating event of thyroid hormone disruption. A simple scoring function was generated to identify chemicals with significant in vitro evidence that was predictive of in vivo carcinogenicity in different rat tissues and organs. This scoring function was applied to an external test set of 33 compounds with carcinogenicity classifications from the EPA's Office of Pesticide Programs and successfully (p = 0.024) differentiated between chemicals classified as "possible"/"probable"/"likely" carcinogens and those designated as "not likely" or with "evidence of noncarcinogenicity." This model represents a chemical carcinogenicity prioritization tool supporting targeted

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

PubMed

Akram, Pakeeza; Liao, Li

2017-12-06

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

Multi-targeted priming for genome-wide gene expression assays.

PubMed

Adomas, Aleksandra B; Lopez-Giraldez, Francesc; Clark, Travis A; Wang, Zheng; Townsend, Jeffrey P

2010-08-17

Complementary approaches to assaying global gene expression are needed to assess gene expression in regions that are poorly assayed by current methodologies. A key component of nearly all gene expression assays is the reverse transcription of transcribed sequences that has traditionally been performed by priming the poly-A tails on many of the transcribed genes in eukaryotes with oligo-dT, or by priming RNA indiscriminately with random hexamers. We designed an algorithm to find common sequence motifs that were present within most protein-coding genes of Saccharomyces cerevisiae and of Neurospora crassa, but that were not present within their ribosomal RNA or transfer RNA genes. We then experimentally tested whether degenerately priming these motifs with multi-targeted primers improved the accuracy and completeness of transcriptomic assays. We discovered two multi-targeted primers that would prime a preponderance of genes in the genomes of Saccharomyces cerevisiae and Neurospora crassa while avoiding priming ribosomal RNA or transfer RNA. Examining the response of Saccharomyces cerevisiae to nitrogen deficiency and profiling Neurospora crassa early sexual development, we demonstrated that using multi-targeted primers in reverse transcription led to superior performance of microarray profiling and next-generation RNA tag sequencing. Priming with multi-targeted primers in addition to oligo-dT resulted in higher sensitivity, a larger number of well-measured genes and greater power to detect differences in gene expression. Our results provide the most complete and detailed expression profiles of the yeast nitrogen starvation response and N. crassa early sexual development to date. Furthermore, our multi-targeting priming methodology for genome-wide gene expression assays provides selective targeting of multiple sequences and counter-selection against undesirable sequences, facilitating a more complete and precise assay of the transcribed sequences within the genome.

Fe₃O₄ Nanoparticles in Targeted Drug/Gene Delivery Systems.

PubMed

Shen, Lazhen; Li, Bei; Qiao, Yongsheng

2018-02-23

Fe₃O₄ nanoparticles (NPs), the most traditional magnetic nanoparticles, have received a great deal of attention in the biomedical field, especially for targeted drug/gene delivery systems, due to their outstanding magnetism, biocompatibility, lower toxicity, biodegradability, and other features. Naked Fe₃O₄ NPs are easy to aggregate and oxidize, and thus are often made with various coatings to realize superior properties for targeted drug/gene delivery. In this review, we first list the three commonly utilized synthesis methods of Fe₃O₄ NPs, and their advantages and disadvantages. In the second part, we describe coating materials that exhibit noticeable features that allow functionalization of Fe₃O₄ NPs and summarize their methods of drug targeting/gene delivery. Then our efforts will be devoted to the research status and progress of several different functionalized Fe₃O₄ NP delivery systems loaded with chemotherapeutic agents, and we present targeted gene transitive carriers in detail. In the following section, we illuminate the most effective treatment systems of the combined drug and gene therapy. Finally, we propose opportunities and challenges of the clinical transformation of Fe₃O₄ NPs targeting drug/gene delivery systems.

Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy.

PubMed

Jung, Jinmyung; Kwon, Mijin; Bae, Sunghwa; Yim, Soorin; Lee, Doheon

2018-03-05

Muscle atrophy, an involuntary loss of muscle mass, is involved in various diseases and sometimes leads to mortality. However, therapeutics for muscle atrophy thus far have had limited effects. Here, we present a new approach for therapeutic target prediction using Petri net simulation of the status of phosphorylation, with a reasonable assumption that the recovery of abnormally phosphorylated proteins can be a treatment for muscle atrophy. The Petri net model was employed to simulate phosphorylation status in three states, i.e. reference, atrophic and each gene-inhibited state based on the myocyte-specific phosphorylation network. Here, we newly devised a phosphorylation specific Petri net that involves two types of transitions (phosphorylation or de-phosphorylation) and two types of places (activation with or without phosphorylation). Before predicting therapeutic targets, the simulation results in reference and atrophic states were validated by Western blotting experiments detecting five marker proteins, i.e. RELA, SMAD2, SMAD3, FOXO1 and FOXO3. Finally, we determined 37 potential therapeutic targets whose inhibition recovers the phosphorylation status from an atrophic state as indicated by the five validated marker proteins. In the evaluation, we confirmed that the 37 potential targets were enriched for muscle atrophy-related terms such as actin and muscle contraction processes, and they were also significantly overlapping with the genes associated with muscle atrophy reported in the Comparative Toxicogenomics Database (p-value < 0.05). Furthermore, we noticed that they included several proteins that could not be characterized by the shortest path analysis. The three potential targets, i.e. BMPR1B, ROCK, and LEPR, were manually validated with the literature. In this study, we suggest a new approach to predict potential therapeutic targets of muscle atrophy with an analysis of phosphorylation status simulated by Petri net. We generated a list of the potential

BLISTER Regulates Polycomb-Target Genes, Represses Stress-Regulated Genes and Promotes Stress Responses in Arabidopsis thaliana.

PubMed

Kleinmanns, Julia A; Schatlowski, Nicole; Heckmann, David; Schubert, Daniel

2017-01-01

HIGHLIGHTS The PRC2 interacting protein BLISTER likely acts downstream of PRC2 to silence Polycomb target genes and is a key regulator of specific stress responses in Arabidopsis . Polycomb group (PcG) proteins are key epigenetic regulators of development. The highly conserved Polycomb repressive complex 2 (PRC2) represses thousands of target genes by trimethylating H3K27 (H3K27me3). Plant specific PcG components and functions are largely unknown, however, we previously identified the plant-specific protein BLISTER (BLI) as a PRC2 interactor. BLI regulates PcG target genes and promotes cold stress resistance. To further understand the function of BLI , we analyzed the transcriptional profile of bli-1 mutants. Approximately 40% of the up-regulated genes in bli are PcG target genes, however, bli-1 mutants did not show changes in H3K27me3 levels at all tested genes, indicating that BLI regulates PcG target genes downstream of or in parallel to PRC2. Interestingly, a significant number of BLI regulated H3K27me3 target genes is regulated by the stress hormone absciscic acid (ABA). We further reveal an overrepresentation of genes responding to abiotic stresses such as drought, high salinity, or heat stress among the up-regulated genes in bli mutants. Consistently, bli mutants showed reduced desiccation stress tolerance. We conclude that the PRC2 associated protein BLI is a key regulator of stress-responsive genes in Arabidopsis : it represses ABA-responsive PcG target genes, likely downstream of PRC2, and promotes resistance to several stresses such as cold and drought.

Computational prediction of the Crc regulon identifies genus-wide and species-specific targets of catabolite repression control in Pseudomonas bacteria.

PubMed

Browne, Patrick; Barret, Matthieu; O'Gara, Fergal; Morrissey, John P

2010-11-25

Catabolite repression control (CRC) is an important global control system in Pseudomonas that fine tunes metabolism in order optimise growth and metabolism in a range of different environments. The mechanism of CRC in Pseudomonas spp. centres on the binding of a protein, Crc, to an A-rich motif on the 5' end of an mRNA resulting in translational down-regulation of target genes. Despite the identification of several Crc targets in Pseudomonas spp. the Crc regulon has remained largely unexplored. In order to predict direct targets of Crc, we used a bioinformatics approach based on detection of A-rich motifs near the initiation of translation of all protein-encoding genes in twelve fully sequenced Pseudomonas genomes. As expected, our data predict that genes related to the utilisation of less preferred nutrients, such as some carbohydrates, nitrogen sources and aromatic carbon compounds are targets of Crc. A general trend in this analysis is that the regulation of transporters is conserved across species whereas regulation of specific enzymatic steps or transcriptional activators are often conserved only within a species. Interestingly, some nucleoid associated proteins (NAPs) such as HU and IHF are predicted to be regulated by Crc. This finding indicates a possible role of Crc in indirect control over a subset of genes that depend on the DNA bending properties of NAPs for expression or repression. Finally, some virulence traits such as alginate and rhamnolipid production also appear to be regulated by Crc, which links nutritional status cues with the regulation of virulence traits. Catabolite repression control regulates a broad spectrum of genes in Pseudomonas. Some targets are genus-wide and are typically related to central metabolism, whereas other targets are species-specific, or even unique to particular strains. Further study of these novel targets will enhance our understanding of how Pseudomonas bacteria integrate nutritional status cues with the regulation

Progress in gene targeting and gene therapy for retinitis pigmentosa

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

Farrar, G.J.; Humphries, M.M.; Erven, A.

1994-09-01

Previously, we localized disease genes involved in retinitis pigmentosa (RP), an inherited retinal degeneration, close to the rhodopsin and peripherin genes on 3q and 6p. Subsequently, we and others identified mutations in these genes in RP patients. Currently animal models for human retinopathies are being generated using gene targeting by homologous recombination in embryonic stem (ES) cells. Genomic clones for retinal genes including rhodopsin and peripherin have been obtained from a phage library carrying mouse DNA isogenic with the ES cell line (CC1.2). The peripherin clone has been sequenced to establish the genomic structure of the mouse gene. Targeting vectorsmore » for rhodopsin and peripherin including a neomycin cassette for positive selection and thymidine kinase genes enabling selection against random intergrants are under construction. Progress in vector construction will be presented. Simultaneously we are developing systems for delivery of gene therapies to retinal tissues utilizing replication-deficient adenovirus (Ad5). Efficacy of infection subsequent to various methods of intraocular injection and with varying viral titers is being assayed using an adenovirus construct containing a CMV promoter LacZ fusion as reporter and the range of tissues infected and the level of duration of LacZ expression monitored. Viral constructs with the LacZ reporter gene under the control of retinal specific promoters such as rhodopsin and IRBP cloned into pXCJL.1 are under construction. An update on developments in photoreceptor cell-directed expression of virally delivered genes will be presented.« less

Genotoxic chemical carcinogens target inducible genes in vivo

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

Hamilton, J.W.; McCaffrey, J.; Caron, R.M.

1994-12-31

Our laboratory is interested in whether carcinogen-induced DNA damage is distributed nonrandomly in the genome - that is, {open_quotes}targeted{close_quotes} to specific genes or gene regions in vivo. As an indirect measure of whether targeting occurs at the gene level, we have examined whether carcinogens differentially alter the expression of individual genes. We have compared the effects of model genotoxic carcinogens that principally induce either strand breaks, simple alkylations, bulky lesions, or DNA cross-links on the expression of several constitutive and inducible genes in a simple in vivo system, the chick embryo. Each agent was examined for its effects on genemore » expression over a 24 hour period corresponding to the period of maximal DNA damage and repair induced by each compound. The doses used in these studies represented the maximum doses that caused no overt toxicity over a 96 hour period but that induced significant levels of DNA damage. Our results demonstrate that inducible genes are targeted by chemical carcinogens. We hypothesize that such effects may be a result of DNA damage specifically altering DNA-protein interactions within the promoters of inducible genes.« less

Systematic mapping of two component response regulators to gene targets in a model sulfate reducing bacterium

PubMed Central

2011-01-01

Background Two component regulatory systems are the primary form of signal transduction in bacteria. Although genomic binding sites have been determined for several eukaryotic and bacterial transcription factors, comprehensive identification of gene targets of two component response regulators remains challenging due to the lack of knowledge of the signals required for their activation. We focused our study on Desulfovibrio vulgaris Hildenborough, a sulfate reducing bacterium that encodes unusually diverse and largely uncharacterized two component signal transduction systems. Results We report the first systematic mapping of the genes regulated by all transcriptionally acting response regulators in a single bacterium. Our results enabled functional predictions for several response regulators and include key processes of carbon, nitrogen and energy metabolism, cell motility and biofilm formation, and responses to stresses such as nitrite, low potassium and phosphate starvation. Our study also led to the prediction of new genes and regulatory networks, which found corroboration in a compendium of transcriptome data available for D. vulgaris. For several regulators we predicted and experimentally verified the binding site motifs, most of which were discovered as part of this study. Conclusions The gene targets identified for the response regulators allowed strong functional predictions to be made for the corresponding two component systems. By tracking the D. vulgaris regulators and their motifs outside the Desulfovibrio spp. we provide testable hypotheses regarding the functions of orthologous regulators in other organisms. The in vitro array based method optimized here is generally applicable for the study of such systems in all organisms. PMID:21992415

Efficient CRISPR/Cas9-Mediated Versatile, Predictable, and Donor-Free Gene Knockout in Human Pluripotent Stem Cells.

PubMed

Liu, Zhongliang; Hui, Yi; Shi, Lei; Chen, Zhenyu; Xu, Xiangjie; Chi, Liankai; Fan, Beibei; Fang, Yujiang; Liu, Yang; Ma, Lin; Wang, Yiran; Xiao, Lei; Zhang, Quanbin; Jin, Guohua; Liu, Ling; Zhang, Xiaoqing

2016-09-13

Loss-of-function studies in human pluripotent stem cells (hPSCs) require efficient methodologies for lesion of genes of interest. Here, we introduce a donor-free paired gRNA-guided CRISPR/Cas9 knockout strategy (paired-KO) for efficient and rapid gene ablation in hPSCs. Through paired-KO, we succeeded in targeting all genes of interest with high biallelic targeting efficiencies. More importantly, during paired-KO, the cleaved DNA was repaired mostly through direct end joining without insertions/deletions (precise ligation), and thus makes the lesion product predictable. The paired-KO remained highly efficient for one-step targeting of multiple genes and was also efficient for targeting of microRNA, while for long non-coding RNA over 8 kb, cleavage of a short fragment of the core promoter region was sufficient to eradicate downstream gene transcription. This work suggests that the paired-KO strategy is a simple and robust system for loss-of-function studies for both coding and non-coding genes in hPSCs. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Identification of novel microRNAs in Hevea brasiliensis and computational prediction of their targets

PubMed Central

2012-01-01

Background Plants respond to external stimuli through fine regulation of gene expression partially ensured by small RNAs. Of these, microRNAs (miRNAs) play a crucial role. They negatively regulate gene expression by targeting the cleavage or translational inhibition of target messenger RNAs (mRNAs). In Hevea brasiliensis, environmental and harvesting stresses are known to affect natural rubber production. This study set out to identify abiotic stress-related miRNAs in Hevea using next-generation sequencing and bioinformatic analysis. Results Deep sequencing of small RNAs was carried out on plantlets subjected to severe abiotic stress using the Solexa technique. By combining the LeARN pipeline, data from the Plant microRNA database (PMRD) and Hevea EST sequences, we identified 48 conserved miRNA families already characterized in other plant species, and 10 putatively novel miRNA families. The results showed the most abundant size for miRNAs to be 24 nucleotides, except for seven families. Several MIR genes produced both 20-22 nucleotides and 23-27 nucleotides. The two miRNA class sizes were detected for both conserved and putative novel miRNA families, suggesting their functional duality. The EST databases were scanned with conserved and novel miRNA sequences. MiRNA targets were computationally predicted and analysed. The predicted targets involved in "responses to stimuli" and to "antioxidant" and "transcription activities" are presented. Conclusions Deep sequencing of small RNAs combined with transcriptomic data is a powerful tool for identifying conserved and novel miRNAs when the complete genome is not yet available. Our study provided additional information for evolutionary studies and revealed potentially specific regulation of the control of redox status in Hevea. PMID:22330773

Genome-wide Expression Profiling, In Vivo DNA Binding Analysis, and Probabilistic Motif Prediction Reveal Novel Abf1 Target Genes during Fermentation, Respiration, and Sporulation in Yeast

PubMed Central

Schlecht, Ulrich; Erb, Ionas; Demougin, Philippe; Robine, Nicolas; Borde, Valérie; van Nimwegen, Erik; Nicolas, Alain

2008-01-01

The autonomously replicating sequence binding factor 1 (Abf1) was initially identified as an essential DNA replication factor and later shown to be a component of the regulatory network controlling mitotic and meiotic cell cycle progression in budding yeast. The protein is thought to exert its functions via specific interaction with its target site as part of distinct protein complexes, but its roles during mitotic growth and meiotic development are only partially understood. Here, we report a comprehensive approach aiming at the identification of direct Abf1-target genes expressed during fermentation, respiration, and sporulation. Computational prediction of the protein's target sites was integrated with a genome-wide DNA binding assay in growing and sporulating cells. The resulting data were combined with the output of expression profiling studies using wild-type versus temperature-sensitive alleles. This work identified 434 protein-coding loci as being transcriptionally dependent on Abf1. More than 60% of their putative promoter regions contained a computationally predicted Abf1 binding site and/or were bound by Abf1 in vivo, identifying them as direct targets. The present study revealed numerous loci previously unknown to be under Abf1 control, and it yielded evidence for the protein's variable DNA binding pattern during mitotic growth and meiotic development. PMID:18305101

The Application of Gene Expression Profiling in Predictions of Occult Lymph Node Metastasis in Colorectal Cancer Patients

PubMed Central

Peyravian, Noshad; Larki, Pegah; Gharib, Ehsan; Nazemalhosseini-Mojarad, Ehsan; Anaraki, Fakhrosadate; Young, Chris; McClellan, James; Ashrafian Bonab, Maziar; Asadzadeh-Aghdaei, Hamid; Zali, Mohammad Reza

2018-01-01

A key factor in determining the likely outcome for a patient with colorectal cancer is whether or not the tumour has metastasised to the lymph nodes—information which is also important in assessing any possibilities of lymph node resection so as to improve survival. In this review we perform a wide-range assessment of literature relating to recent developments in gene expression profiling (GEP) of the primary tumour, to determine their utility in assessing node status. A set of characteristic genes seems to be involved in the prediction of lymph node metastasis (LNM) in colorectal patients. Hence, GEP is applicable in personalised/individualised/tailored therapies and provides insights into developing novel therapeutic targets. Not only is GEP useful in prediction of LNM, but it also allows classification based on differences such as sample size, target gene expression, and examination method. PMID:29498671

Targeted resequencing of candidate genes reveals novel variants associated with severe Behçet's uveitis.

PubMed

Kim, Sang Jin; Lee, Seungbok; Park, Changho; Seo, Jeong-Sun; Kim, Jong-Il; Yu, Hyeong Gon

2013-10-18

Behçet's disease (BD) is a chronic systemic inflammatory disorder characterized by four major manifestations: recurrent uveitis, oral and genital ulcers and skin lesions. To identify some pathogenic variants associated with severe Behçet's uveitis, we used targeted and massively parallel sequencing methods to explore the genetic diversity of target regions. A solution-based target enrichment kit was designed to capture whole-exonic regions of 132 candidate genes. Using a multiplexing strategy, 32 samples from patients with a severe type of Behçet's uveitis were sequenced with a Genome Analyzer IIx. We compared the frequency of each variant with that of 59 normal Korean controls, and selected five rare and eight common single-nucleotide variants as the candidates for a replication study. The selected variants were genotyped in 61 cases and 320 controls and, as a result, two rare and seven common variants showed significant associations with severe Behçet's uveitis (P<0.05). Some of these, including rs199955684 in KIR3DL3, rs1801133 in MTHFR, rs1051790 in MICA and rs1051456 in KIR2DL4, were predicted to be damaging by either the PolyPhen-2 or SIFT prediction program. Variants on FCGR3A (rs396991) and ICAM1 (rs5498) have been previously reported as susceptibility loci of this disease, and those on IFNAR1, MTFHR and MICA also replicated the previous reports at the gene level. The KIR3DL3 and KIR2DL4 genes are novel susceptibility genes that have not been reported in association with BD. In conclusion, this study showed that target enrichment and next-generation sequencing technologies can provide valuable information on the genetic predisposition for Behçet's uveitis.

GeneSilico protein structure prediction meta-server

PubMed Central

Kurowski, Michal A.; Bujnicki, Janusz M.

2003-01-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta. PMID:12824313

Screening for microsatellite instability target genes in colorectal cancers

PubMed Central

Vilkki, S; Launonen, V; Karhu, A; Sistonen, P; Vastrik, I; Aaltonen, L

2002-01-01

Background: Defects in the DNA repair system lead to genetic instability because replication errors are not corrected. This type of genetic instability is a key event in the malignant progression of HNPCC and a subset of sporadic colon cancers and mutation rates are particularly high at short repetitive sequences. Somatic deletions of coding mononucleotide repeats have been detected, for example, in the TGFßRII and BAX genes, and recently many novel target genes for microsatellite instability (MSI) have been proposed. Novel target genes are likely to be discovered in the future. More data should be created on background mutation rates in MSI tumours to evaluate mutation rates observed in the candidate target genes. Methods: Mutation rates in 14 neutral intronic repeats were evaluated in MSI tumours. Bioinformatic searches combined with keywords related to cancer and tumour suppressor or CRC related gene homology were used to find new candidate MSI target genes. By comparison of mutation frequencies observed in intronic mononucleotide repeats versus exonic coding repeats of potential MSI target genes, the significance of the exonic mutations was estimated. Results: As expected, the length of an intronic mononucleotide repeat correlated positively with the number of slippages for both G/C and A/T repeats (p=0.0020 and p=0.0012, respectively). BRCA1, CtBP1, and Rb1 associated CtIP and other candidates were found in a bioinformatic search combined with keywords related to cancer. Sequencing showed a significantly increased mutation rate in the exonic A9 repeat of CtIP (25/109=22.9%) as compared with similar intronic repeats (p≤0.001). Conclusions: We propose a new candidate MSI target gene CtIP to be evaluated in further studies. PMID:12414815

CRISPR/Cas9-mediated gene targeting in Arabidopsis using sequential transformation.

PubMed

Miki, Daisuke; Zhang, Wenxin; Zeng, Wenjie; Feng, Zhengyan; Zhu, Jian-Kang

2018-05-17

Homologous recombination-based gene targeting is a powerful tool for precise genome modification and has been widely used in organisms ranging from yeast to higher organisms such as Drosophila and mouse. However, gene targeting in higher plants, including the most widely used model plant Arabidopsis thaliana, remains challenging. Here we report a sequential transformation method for gene targeting in Arabidopsis. We find that parental lines expressing the bacterial endonuclease Cas9 from the egg cell- and early embryo-specific DD45 gene promoter can improve the frequency of single-guide RNA-targeted gene knock-ins and sequence replacements via homologous recombination at several endogenous sites in the Arabidopsis genome. These heritable gene targeting can be identified by regular PCR. Our approach enables routine and fine manipulation of the Arabidopsis genome.

Identification of neuronal target genes for CCAAT/Enhancer Binding Proteins

PubMed Central

Kfoury, N.; Kapatos, G.

2009-01-01

CCAAT/Enhancer Binding Proteins (C/EBPs) play pivotal roles in development and plasticity of the nervous system. Identification of the physiological targets of C/EBPs (C/EBP target genes) should therefore provide insight into the underlying biology of these processes. We used unbiased genome-wide mapping to identify 115 C/EBPβ target genes in PC12 cells that include transcription factors, neurotransmitter receptors, ion channels, protein kinases and synaptic vesicle proteins. C/EBPβ binding sites were located primarily within introns, suggesting novel regulatory functions, and were associated with binding sites for other developmentally important transcription factors. Experiments using dominant negatives showed C/EBPβ to repress transcription of a subset of target genes. Target genes in rat brain were subsequently found to preferentially bind C/EBPα, β and δ. Analysis of the hippocampal transcriptome of C/EBPβ knockout mice revealed dysregulation of a high percentage of transcripts identified as C/EBP target genes. These results support the hypothesis that C/EBPs play non-redundant roles in the brain. PMID:19103292

Targeted gene therapy and cell reprogramming in Fanconi anemia

PubMed Central

Rio, Paula; Baños, Rocio; Lombardo, Angelo; Quintana-Bustamante, Oscar; Alvarez, Lara; Garate, Zita; Genovese, Pietro; Almarza, Elena; Valeri, Antonio; Díez, Begoña; Navarro, Susana; Torres, Yaima; Trujillo, Juan P; Murillas, Rodolfo; Segovia, Jose C; Samper, Enrique; Surralles, Jordi; Gregory, Philip D; Holmes, Michael C; Naldini, Luigi; Bueren, Juan A

2014-01-01

Gene targeting is progressively becoming a realistic therapeutic alternative in clinics. It is unknown, however, whether this technology will be suitable for the treatment of DNA repair deficiency syndromes such as Fanconi anemia (FA), with defects in homology-directed DNA repair. In this study, we used zinc finger nucleases and integrase-defective lentiviral vectors to demonstrate for the first time that FANCA can be efficiently and specifically targeted into the AAVS1 safe harbor locus in fibroblasts from FA-A patients. Strikingly, up to 40% of FA fibroblasts showed gene targeting 42 days after gene editing. Given the low number of hematopoietic precursors in the bone marrow of FA patients, gene-edited FA fibroblasts were then reprogrammed and re-differentiated toward the hematopoietic lineage. Analyses of gene-edited FA-iPSCs confirmed the specific integration of FANCA in the AAVS1 locus in all tested clones. Moreover, the hematopoietic differentiation of these iPSCs efficiently generated disease-free hematopoietic progenitors. Taken together, our results demonstrate for the first time the feasibility of correcting the phenotype of a DNA repair deficiency syndrome using gene-targeting and cell reprogramming strategies. PMID:24859981

Drug-Target Interactions: Prediction Methods and Applications.

PubMed

Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

2018-01-01

Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana.

PubMed

Hansen, Bjoern Oest; Meyer, Etienne H; Ferrari, Camilla; Vaid, Neha; Movahedi, Sara; Vandepoele, Klaas; Nikoloski, Zoran; Mutwil, Marek

2018-03-01

Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co-function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user-friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting-edge community tool to guide experimentalists. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

Systematic Characterization and Prediction of Human Hypertension Genes.

PubMed

Li, Yan-Hui; Zhang, Gai-Gai; Wang, Nanping

2017-02-01

Hypertension is a major cardiovascular risk factor and accounts for a large part of cardiovascular mortality. In this work, we analyzed the properties of hypertension genes and found that when compared with genes not yet known to be involved in hypertension regulation, known hypertension genes display distinguishing features: (1) hypertension genes tend to be located at network center; (2) hypertension genes tend to interact with each other; and (3) hypertension genes tend to enrich in certain biological processes and show certain phenotypes. Based on these features, we developed a machine-learning algorithm to predict new hypertension genes. One hundred and seventy-seven candidates were predicted with a posterior probability >0.9. Evidence supporting 17 of the predictions has been found. © 2016 American Heart Association, Inc.

Ecological transition predictably associated with gene degeneration.

PubMed

Wessinger, Carolyn A; Rausher, Mark D

2015-02-01

Gene degeneration or loss can significantly contribute to phenotypic diversification, but may generate genetic constraints on future evolutionary trajectories, potentially restricting phenotypic reversal. Such constraints may manifest as directional evolutionary trends when parallel phenotypic shifts consistently involve gene degeneration or loss. Here, we demonstrate that widespread parallel evolution in Penstemon from blue to red flowers predictably involves the functional inactivation and degeneration of the enzyme flavonoid 3',5'-hydroxylase (F3'5'H), an anthocyanin pathway enzyme required for the production of blue floral pigments. Other types of genetic mutations do not consistently accompany this phenotypic shift. This pattern may be driven by the relatively large mutational target size of degenerative mutations to this locus and the apparent lack of associated pleiotropic effects. The consistent degeneration of F3'5'H may provide a mechanistic explanation for the observed asymmetry in the direction of flower color evolution in Penstemon: Blue to red transitions are common, but reverse transitions have not been observed. Although phenotypic shifts in this system are likely driven by natural selection, internal constraints may generate predictable genetic outcomes and may restrict future evolutionary trajectories. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

[A mini-review of targeting gene-virotherapy of cancer].

PubMed

Liu, Xin-Yuan; Gu, Jin-Fa

2006-10-01

New progress has been made on the project "targeting gene-virotherapy of cancer" proposed by us, which is "targeting dual gene-virotherapy of cancer". By the use of two genes, all the xenograft tumors in nude mice could be completely eliminated. The researches have been published in international journals, such as Hepatology and Cancer Research (a highlight paper). In this study, a further superior strategy--"double targeting virus-dual gene therapy" was introduced. This strategy was specialized by the use of tumor specific promoter to control the tumor specific suppressor gene, such as alpha-fetoprotein (AFP), which controls hepatoma specific suppressor gene LFIRE or HCCS1. In addition, a second tumor specific promoter, such as hTERT or survivin was used to control E1A or E1B in the construct, as hTERT-E1A-AFP-E1B-HCCS1 or LFIRE, a double tumor specific promoter controlling hepatoma specific LFIRE or HCCS1 gene. By the combined use of this construct with a very strong antitumor construct, such as hTERT-E1A-AFP-E1B-IL-24, a strategy with both excellent tumor killing effect and excellent safety with very little damage to normal cells was obtained. Therefore, double targeting virus-dual gene therapy might be one of the most potential strategies for cancer treatment. Furthermore, a new type of interferon was also introduced, which might be an ideal antitumor drug.

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

DOE PAGES

Wang, Pin; Wang, Yunshan; Hang, Bo; ...

2016-07-11

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

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

Wang, Pin; Wang, Yunshan; Hang, Bo

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

Motor cortex guides selection of predictable movement targets

PubMed Central

Woodgate, Philip J.W.; Strauss, Soeren; Sami, Saber A.; Heinke, Dietmar

2016-01-01

The present paper asks whether the motor cortex contributes to prediction-based guidance of target selection. This question was inspired by recent evidence that suggests (i) recurrent connections from the motor system into the attentional system may extract movement-relevant perceptual information and (ii) that the motor cortex cannot only generate predictions of the sensory consequences of movements but may also operate as predictor of perceptual events in general. To test this idea we employed a choice reaching task requiring participants to rapidly reach and touch a predictable or unpredictable colour target. Motor cortex activity was modulated via transcranial direct current stimulation (tDCS). In Experiment 1 target colour repetitions were predictable. Under such conditions anodal tDCS facilitated selection versus sham and cathodal tDCS. This improvement was apparent for trajectory curvature but not movement initiation. Conversely, where no predictability of colour was embedded reach performance was unaffected by tDCS. Finally, the results of a key-press experiment suggested that motor cortex involvement is restricted to tasks where the predictable target colour is movement-relevant. The outcomes are interpreted as evidence that the motor system contributes to the top-down guidance of selective attention to movement targets. PMID:25835319

Towards β-globin gene-targeting with integrase-defective lentiviral vectors.

PubMed

Inanlou, Davoud Nouri; Yakhchali, Bagher; Khanahmad, Hossein; Gardaneh, Mossa; Movassagh, Hesam; Cohan, Reza Ahangari; Ardestani, Mehdi Shafiee; Mahdian, Reza; Zeinali, Sirous

2010-11-01

We have developed an integrase-defective lentiviral (LV) vector in combination with a gene-targeting approach for gene therapy of β-thalassemia. The β-globin gene-targeting construct has two homologous stems including sequence upstream and downstream of the β-globin gene, a β-globin gene positioned between hygromycin and neomycin resistant genes and a herpes simplex virus type 1 thymidine kinase (HSVtk) suicide gene. Utilization of integrase-defective LV as a vector for the β-globin gene increased the number of selected clones relative to non-viral methods. This method represents an important step toward the ultimate goal of a clinical gene therapy for β-thalassemia.

Targeted gene therapy and cell reprogramming in Fanconi anemia.

PubMed

Rio, Paula; Baños, Rocio; Lombardo, Angelo; Quintana-Bustamante, Oscar; Alvarez, Lara; Garate, Zita; Genovese, Pietro; Almarza, Elena; Valeri, Antonio; Díez, Begoña; Navarro, Susana; Torres, Yaima; Trujillo, Juan P; Murillas, Rodolfo; Segovia, Jose C; Samper, Enrique; Surralles, Jordi; Gregory, Philip D; Holmes, Michael C; Naldini, Luigi; Bueren, Juan A

2014-06-01

Gene targeting is progressively becoming a realistic therapeutic alternative in clinics. It is unknown, however, whether this technology will be suitable for the treatment of DNA repair deficiency syndromes such as Fanconi anemia (FA), with defects in homology-directed DNA repair. In this study, we used zinc finger nucleases and integrase-defective lentiviral vectors to demonstrate for the first time that FANCA can be efficiently and specifically targeted into the AAVS1 safe harbor locus in fibroblasts from FA-A patients. Strikingly, up to 40% of FA fibroblasts showed gene targeting 42 days after gene editing. Given the low number of hematopoietic precursors in the bone marrow of FA patients, gene-edited FA fibroblasts were then reprogrammed and re-differentiated toward the hematopoietic lineage. Analyses of gene-edited FA-iPSCs confirmed the specific integration of FANCA in the AAVS1 locus in all tested clones. Moreover, the hematopoietic differentiation of these iPSCs efficiently generated disease-free hematopoietic progenitors. Taken together, our results demonstrate for the first time the feasibility of correcting the phenotype of a DNA repair deficiency syndrome using gene-targeting and cell reprogramming strategies. © 2014 The Authors. Published under the terms of the CC BY 4.0 license.

Novel β-catenin target genes identified in thalamic neurons encode modulators of neuronal excitability

PubMed Central

2012-01-01

Background LEF1/TCF transcription factors and their activator β-catenin are effectors of the canonical Wnt pathway. Although Wnt/β-catenin signaling has been implicated in neurodegenerative and psychiatric disorders, its possible role in the adult brain remains enigmatic. To address this issue, we sought to identify the genetic program activated by β-catenin in neurons. We recently showed that β-catenin accumulates specifically in thalamic neurons where it activates Cacna1g gene expression. In the present study, we combined bioinformatics and experimental approaches to find new β-catenin targets in the adult thalamus. Results We first selected the genes with at least two conserved LEF/TCF motifs within the regulatory elements. The resulting list of 428 putative LEF1/TCF targets was significantly enriched in known Wnt targets, validating our approach. Functional annotation of the presumed targets also revealed a group of 41 genes, heretofore not associated with Wnt pathway activity, that encode proteins involved in neuronal signal transmission. Using custom polymerase chain reaction arrays, we profiled the expression of these genes in the rat forebrain. We found that nine of the analyzed genes were highly expressed in the thalamus compared with the cortex and hippocampus. Removal of nuclear β-catenin from thalamic neurons in vitro by introducing its negative regulator Axin2 reduced the expression of six of the nine genes. Immunoprecipitation of chromatin from the brain tissues confirmed the interaction between β-catenin and some of the predicted LEF1/TCF motifs. The results of these experiments validated four genes as authentic and direct targets of β-catenin: Gabra3 for the receptor of GABA neurotransmitter, Calb2 for the Ca2+-binding protein calretinin, and the Cacna1g and Kcna6 genes for voltage-gated ion channels. Two other genes from the latter cluster, Cacna2d2 and Kcnh8, appeared to be regulated by β-catenin, although the binding of β-catenin to the

Gene-carried hepatoma targeting complex induced high gene transfection efficiency with low toxicity and significant antitumor activity.

PubMed

Zhao, Qing-Qing; Hu, Yu-Lan; Zhou, Yang; Li, Ni; Han, Min; Tang, Gu-Ping; Qiu, Feng; Tabata, Yasuhiko; Gao, Jian-Qing

2012-01-01

The success of gene transfection is largely dependent on the development of a vehicle or vector that can efficiently deliver a gene to cells with minimal toxicity. A liver cancer-targeted specific peptide (FQHPSF sequence) was successfully synthesized and linked with chitosan-linked polyethylenimine (CP) to form a new targeted gene delivery vector called CPT (CP/peptide). The structure of CPT was confirmed by (1)H nuclear magnetic resonance spectroscopy and ultraviolet spectrophotometry. The particle size of CPT/ DNA complexes was measured using laser diffraction spectrometry and the cytotoxicity of the copolymer was evaluated by methylthiazol tetrazolium method. The transfection efficiency evaluation of the CP copolymer was performed using luciferase activity assay. Cellular internalization of the CP/DNA complex was observed under confocal laser scanning microscopy. The targeting specificity of the polymer coupled to peptide was measured by competitive inhibition transfection study. The liver targeting specificity of the CPT copolymer in vivo was demonstrated by combining the copolymer with a therapeutic gene, interleukin-12, and assessed by its abilities in suppressing the growth of ascites tumor in mouse model. The results showed that the liver cancer-targeted specific peptide was successfully synthesized and linked with CP to form a new targeted gene delivery vector called CPT. The composition of CPT was confirmed and the vector showed low cytotoxicity and strong targeting specificity to liver tumors in vitro. The in vivo study results showed that interleukin-12 delivered by the new gene vector CPT/DNA significantly enhanced the antitumor effect on ascites tumor-bearing imprinting control region mice as compared with polyethylenimine (25 kDa), CP, and other controls, which further demonstrate the targeting specificity of the new synthesized polymer. The synthesized CPT copolymer was proven to be an effective liver cancer-targeted vector for therapeutic gene

Specific genetic modifications of domestic animals by gene targeting and animal cloning

PubMed Central

Wang, Bin; Zhou, Jiangfeng

2003-01-01

The technology of gene targeting through homologous recombination has been extremely useful for elucidating gene functions in mice. The application of this technology was thought impossible in the large livestock species until the successful creation of the first mammalian clone "Dolly" the sheep. The combination of the technologies for gene targeting of somatic cells with those of animal cloning made it possible to introduce specific genetic mutations into domestic animals. In this review, the principles of gene targeting in somatic cells and the challenges of nuclear transfer using gene-targeted cells are discussed. The relevance of gene targeting in domestic animals for applications in bio-medicine and agriculture are also examined. PMID:14614774

Predicting drug-target interactions using restricted Boltzmann machines.

PubMed

Wang, Yuhao; Zeng, Jianyang

2013-07-01

In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are

A new approach to human microRNA target prediction using ensemble pruning and rotation forest.

PubMed

Mousavi, Reza; Eftekhari, Mahdi; Haghighi, Mehdi Ghezelbash

2015-12-01

MicroRNAs (miRNAs) are small non-coding RNAs that have important functions in gene regulation. Since finding miRNA target experimentally is costly and needs spending much time, the use of machine learning methods is a growing research area for miRNA target prediction. In this paper, a new approach is proposed by using two popular ensemble strategies, i.e. Ensemble Pruning and Rotation Forest (EP-RTF), to predict human miRNA target. For EP, the approach utilizes Genetic Algorithm (GA). In other words, a subset of classifiers from the heterogeneous ensemble is first selected by GA. Next, the selected classifiers are trained based on the RTF method and then are combined using weighted majority voting. In addition to seeking a better subset of classifiers, the parameter of RTF is also optimized by GA. Findings of the present study confirm that the newly developed EP-RTF outperforms (in terms of classification accuracy, sensitivity, and specificity) the previously applied methods over four datasets in the field of human miRNA target. Diversity-error diagrams reveal that the proposed ensemble approach constructs individual classifiers which are more accurate and usually diverse than the other ensemble approaches. Given these experimental results, we highly recommend EP-RTF for improving the performance of miRNA target prediction.

A comparison of Agrobacterium-mediated transformation and protoplast-mediated transformation with CRISPR-Cas9 and bipartite gene targeting substrates, as effective gene targeting tools for Aspergillus carbonarius.

PubMed

Weyda, István; Yang, Lei; Vang, Jesper; Ahring, Birgitte K; Lübeck, Mette; Lübeck, Peter S

2017-04-01

In recent years, versatile genetic tools have been developed and applied to a number of filamentous fungi of industrial importance. However, the existing techniques have limitations when it comes to achieve the desired genetic modifications, especially for efficient gene targeting. In this study, we used Aspergillus carbonarius as a host strain due to its potential as a cell factory, and compared three gene targeting techniques by disrupting the ayg1 gene involved in the biosynthesis of conidial pigment in A. carbonarius. The absence of the ayg1 gene leads to phenotypic change in conidia color, which facilitated the analysis on the gene targeting frequency. The examined transformation techniques included Agrobacterium-mediated transformation (AMT) and protoplast-mediated transformation (PMT). Furthermore, the PMT for the disruption of the ayg1 gene was carried out with bipartite gene targeting fragments and the recently adapted CRISPR-Cas9 system. All three techniques were successful in generating Δayg1 mutants, but showed different efficiencies. The most efficient method for gene targeting was AMT, but further it was shown to be dependent on the choice of Agrobacterium strain. However, there are different advantages and disadvantages of all three gene targeting methods which are discussed, in order to facilitate future approaches for fungal strain improvements. Copyright © 2017 Elsevier B.V. All rights reserved.

New target genes of MITF-induced microRNA-211 contribute to melanoma cell invasion.

PubMed

Margue, Christiane; Philippidou, Demetra; Reinsbach, Susanne E; Schmitt, Martina; Behrmann, Iris; Kreis, Stephanie

2013-01-01

The non-coding microRNAs (miRNA) have tissue- and disease-specific expression patterns. They down-regulate target mRNAs, which likely impacts on most fundamental cellular processes. Differential expression patterns of miRNAs are currently being exploited for identification of biomarkers for early disease diagnosis, prediction of progression for melanoma and other cancers and as promising drug targets, since they can easily be inhibited or replaced in a given cellular context. Before successfully manipulating miRNAs in clinical settings, their precise expression levels, endogenous functions and thus their target genes have to be determined. MiR-211, a melanocyte lineage-specific small non-coding miRNA, is located in an intron of TRPM1, a target gene of the microphtalmia-associated transcription factor (MITF). By transcriptionally up-regulating TRPM1, MITF, which is critical for both melanocyte differentiation and survival and for melanoma progression, indirectly drives the expression of miR-211. Expression of this miRNA is often reduced in melanoma samples. Here, we investigated functional roles of miR-211 by identifying and studying new target genes. We show that MITF-correlated miR-211 expression levels are mostly but not always reduced in a panel of 11 melanoma cell lines and in primary and metastatic melanoma compared to normal melanocytes and nevi, respectively. MiR-211 itself only marginally impacted on cell invasion and migration, while perturbation of some new miR-211 target genes, such as AP1S2, SOX11, IGFBP5, and SERINC3 significantly increased invasion. These results and the variable expression levels of miR-211 raise serious doubts on the value of miR-211 as a melanoma tumor-suppressing miRNA and/or as a biomarker for melanoma.

Identification, Expression Analysis, and Target Prediction of Flax Genotroph MicroRNAs Under Normal and Nutrient Stress Conditions

PubMed Central

Melnikova, Nataliya V.; Dmitriev, Alexey A.; Belenikin, Maxim S.; Koroban, Nadezhda V.; Speranskaya, Anna S.; Krinitsina, Anastasia A.; Krasnov, George S.; Lakunina, Valentina A.; Snezhkina, Anastasiya V.; Sadritdinova, Asiya F.; Kishlyan, Natalya V.; Rozhmina, Tatiana A.; Klimina, Kseniya M.; Amosova, Alexandra V.; Zelenin, Alexander V.; Muravenko, Olga V.; Bolsheva, Nadezhda L.; Kudryavtseva, Anna V.

2016-01-01

Cultivated flax (Linum usitatissimum L.) is an important plant valuable for industry. Some flax lines can undergo heritable phenotypic and genotypic changes (LIS-1 insertion being the most common) in response to nutrient stress and are called plastic lines. Offspring of plastic lines, which stably inherit the changes, are called genotrophs. MicroRNAs (miRNAs) are involved in a crucial regulatory mechanism of gene expression. They have previously been assumed to take part in nutrient stress response and can, therefore, participate in genotroph formation. In the present study, we performed high-throughput sequencing of small RNAs (sRNAs) extracted from flax plants grown under normal, phosphate deficient and nutrient excess conditions to identify miRNAs and evaluate their expression. Our analysis revealed expression of 96 conserved miRNAs from 21 families in flax. Moreover, 475 novel potential miRNAs were identified for the first time, and their targets were predicted. However, none of the identified miRNAs were transcribed from LIS-1. Expression of seven miRNAs (miR168, miR169, miR395, miR398, miR399, miR408, and lus-miR-N1) with up- or down-regulation under nutrient stress (on the basis of high-throughput sequencing data) was evaluated on extended sampling using qPCR. Reference gene search identified ETIF3H and ETIF3E genes as most suitable for this purpose. Down-regulation of novel potential lus-miR-N1 and up-regulation of conserved miR399 were revealed under the phosphate deficient conditions. In addition, the negative correlation of expression of lus-miR-N1 and its predicted target, ubiquitin-activating enzyme E1 gene, as well as, miR399 and its predicted target, ubiquitin-conjugating enzyme E2 gene, was observed. Thus, in our study, miRNAs expressed in flax plastic lines and genotrophs were identified and their expression and expression of their targets was evaluated using high-throughput sequencing and qPCR for the first time. These data provide new insights

A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.

PubMed

Seok, Junhee; Davis, Ronald W; Xiao, Wenzhong

2015-01-01

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.

A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data

PubMed Central

Seok, Junhee; Davis, Ronald W.; Xiao, Wenzhong

2015-01-01

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge. PMID:25933378

Cancer gene therapy with targeted adenoviruses.

PubMed

Bachtarzi, Houria; Stevenson, Mark; Fisher, Kerry

2008-11-01

Clinical experience with adenovirus vectors has highlighted the need for improved delivery and targeting. This manuscript aims to provide an overview of the techniques currently under development for improving adenovirus delivery to malignant cells in vivo. Primary research articles reporting improvements in adenoviral gene delivery are described. Strategies include genetic modification of viral coat proteins, non-genetic modifications including polymer encapsulation approaches and pharmacological interventions. Reprogramming adenovirus tropism in vitro has been convincingly demonstrated using a range of genetic and physical strategies. These studies have provided new insights into our understanding of virology and the field is progressing. However, there are still some limitations that need special consideration before adenovirus-targeted cancer gene therapy emerges as a routine treatment in the clinical setting.

Differential Sensitivity of Target Genes to Translational Repression by miR-17~92

PubMed Central

Jin, Hyun Yong; Oda, Hiroyo; Chen, Pengda; Kang, Seung Goo; Valentine, Elizabeth; Liao, Lujian; Zhang, Yaoyang; Gonzalez-Martin, Alicia; Shepherd, Jovan; Head, Steven R.; Kim, Pyeung-Hyeun; Fu, Guo; Liu, Wen-Hsien; Han, Jiahuai

2017-01-01

MicroRNAs (miRNAs) are thought to exert their functions by modulating the expression of hundreds of target genes and each to a small degree, but it remains unclear how small changes in hundreds of target genes are translated into the specific function of a miRNA. Here, we conducted an integrated analysis of transcriptome and translatome of primary B cells from mutant mice expressing miR-17~92 at three different levels to address this issue. We found that target genes exhibit differential sensitivity to miRNA suppression and that only a small fraction of target genes are actually suppressed by a given concentration of miRNA under physiological conditions. Transgenic expression and deletion of the same miRNA gene regulate largely distinct sets of target genes. miR-17~92 controls target gene expression mainly through translational repression and 5’UTR plays an important role in regulating target gene sensitivity to miRNA suppression. These findings provide molecular insights into a model in which miRNAs exert their specific functions through a small number of key target genes. PMID:28241004

Identification of Biomarker Genes To Predict Biodegradation of 1,4-Dioxane

PubMed Central

Gedalanga, Phillip B.; Pornwongthong, Peerapong; Mora, Rebecca; Chiang, Sheau-Yun Dora; Baldwin, Brett; Ogles, Dora

2014-01-01

Bacterial multicomponent monooxygenase gene targets in Pseudonocardia dioxanivorans CB1190 were evaluated for their use as biomarkers to identify the potential for 1,4-dioxane biodegradation in pure cultures and environmental samples. Our studies using laboratory pure cultures and industrial activated sludge samples suggest that the presence of genes associated with dioxane monooxygenase, propane monooxygenase, alcohol dehydrogenase, and aldehyde dehydrogenase are promising indicators of 1,4-dioxane biotransformation; however, gene abundance was insufficient to predict actual biodegradation. A time course gene expression analysis of dioxane and propane monooxygenases in Pseudonocardia dioxanivorans CB1190 and mixed communities in wastewater samples revealed important associations with the rates of 1,4-dioxane removal. In addition, transcripts of alcohol dehydrogenase and aldehyde dehydrogenase genes were upregulated during biodegradation, although only the aldehyde dehydrogenase was significantly correlated with 1,4-dioxane concentrations. Expression of the propane monooxygenase demonstrated a time-dependent relationship with 1,4-dioxane biodegradation in P. dioxanivorans CB1190, with increased expression occurring after over 50% of the 1,4-dioxane had been removed. While the fraction of P. dioxanivorans CB1190-like bacteria among the total bacterial population significantly increased with decrease in 1,4-dioxane concentrations in wastewater treatment samples undergoing active biodegradation, the abundance and expression of monooxygenase-based biomarkers were better predictors of 1,4-dioxane degradation than taxonomic 16S rRNA genes. This study illustrates that specific bacterial monooxygenase and dehydrogenase gene targets together can serve as effective biomarkers for 1,4-dioxane biodegradation in the environment. PMID:24632253

MOST: most-similar ligand based approach to target prediction.

PubMed

Huang, Tao; Mi, Hong; Lin, Cheng-Yuan; Zhao, Ling; Zhong, Linda L D; Liu, Feng-Bin; Zhang, Ge; Lu, Ai-Ping; Bian, Zhao-Xiang

2017-03-11

Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power. Here we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

PubMed

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

NASA Astrophysics Data System (ADS)

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

Whole-Genome Thermodynamic Analysis Reduces siRNA Off-Target Effects

PubMed Central

Chen, Xi; Liu, Peng; Chou, Hui-Hsien

2013-01-01

Small interfering RNAs (siRNAs) are important tools for knocking down targeted genes, and have been widely applied to biological and biomedical research. To design siRNAs, two important aspects must be considered: the potency in knocking down target genes and the off-target effect on any nontarget genes. Although many studies have produced useful tools to design potent siRNAs, off-target prevention has mostly been delegated to sequence-level alignment tools such as BLAST. We hypothesize that whole-genome thermodynamic analysis can identify potential off-targets with higher precision and help us avoid siRNAs that may have strong off-target effects. To validate this hypothesis, two siRNA sets were designed to target three human genes IDH1, ITPR2 and TRIM28. They were selected from the output of two popular siRNA design tools, siDirect and siDesign. Both siRNA design tools have incorporated sequence-level screening to avoid off-targets, thus their output is believed to be optimal. However, one of the sets we tested has off-target genes predicted by Picky, a whole-genome thermodynamic analysis tool. Picky can identify off-target genes that may hybridize to a siRNA within a user-specified melting temperature range. Our experiments validated that some off-target genes predicted by Picky can indeed be inhibited by siRNAs. Similar experiments were performed using commercially available siRNAs and a few off-target genes were also found to be inhibited as predicted by Picky. In summary, we demonstrate that whole-genome thermodynamic analysis can identify off-target genes that are missed in sequence-level screening. Because Picky prediction is deterministic according to thermodynamics, if a siRNA candidate has no Picky predicted off-targets, it is unlikely to cause off-target effects. Therefore, we recommend including Picky as an additional screening step in siRNA design. PMID:23484018

Quantitative self-assembly prediction yields targeted nanomedicines

NASA Astrophysics Data System (ADS)

Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

2018-02-01

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements.

PubMed

Lan, Hui; Carson, Rachel; Provart, Nicholas J; Bonner, Anthony J

2007-09-21

Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl. Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount

A comparative analysis of soft computing techniques for gene prediction.

PubMed

Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

2013-07-01

The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

Inductive matrix completion for predicting gene-disease associations.

PubMed

Natarajan, Nagarajan; Dhillon, Inderjit S

2014-06-15

Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive. Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better-it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has <15% chance. We demonstrate that the inductive method is particularly effective for a query disease with no previously known gene associations, and for predicting novel genes, i.e. genes that are previously not linked to diseases. Thus the method is capable of predicting novel genes even for well-characterized diseases. We also validate the novelty of predictions by evaluating the method on recently reported OMIM associations and on associations recently reported in the literature

Targeted gene deletion of miRNAs in mice by TALEN system.

PubMed

Takada, Shuji; Sato, Tempei; Ito, Yoshiaki; Yamashita, Satoshi; Kato, Tomoko; Kawasumi, Miyuri; Kanai-Azuma, Masami; Igarashi, Arisa; Kato, Tomomi; Tamano, Moe; Asahara, Hiroshi

2013-01-01

Mice are among the most valuable model animal species with an enormous amount of heritage in genetic modification studies. However, targeting genes in mice is sometimes difficult, especially for small genes, such as microRNAs (miRNAs) and targeting genes in repeat sequences. Here we optimized the application of TALEN system for mice and successfully obtained gene targeting technique in mice for intergenic region and series of microRNAs. Microinjection of synthesized RNA of TALEN targeting each gene in one cell stage of embryo was carried out and injected oocytes were transferred into pseudopregnant ICR female mice, producing a high success rate of the targeted deletion of miRNA genes. In our condition, TALEN RNA without poly(A) tail worked better than that of with poly(A) tail. This mutated allele in miRNA was transmitted to the next generation, suggesting the successful germ line transmission of this targeting method. Consistent with our notion of miRNAs maturation mechanism, in homozygous mutant mice of miR-10a, the non- mutated strand of miRNAs expression was completely diminished. This method will lead us to expand and accelerate our genetic research using mice in a high throughput way.

Bioinformatic prediction of leader genes in human periodontitis.

PubMed

Covani, Ugo; Marconcini, Simone; Giacomelli, Luca; Sivozhelevov, Victor; Barone, Antonio; Nicolini, Claudio

2008-10-01

Genes involved in different biologic processes form complex interaction networks. However, only a few have a high number of interactions with the other genes in the network. In previous bioinformatics and experimental studies concerning the T lymphocyte cell cycle, these genes were identified and termed "leader genes." In this work, genes involved in human periodontitis were tentatively identified and ranked according to their number of interactions to obtain a preliminary, broader view of molecular mechanisms of periodontitis and plan targeted experimentation. Genes were identified with interrelated queries of several databases. The interactions among these genes were mapped and given a significance score. The weighted number of links (weighted sum of scores for every interaction in which the given gene is involved) was calculated for each gene. Genes were clustered according to this parameter. The genes in the highest cluster were termed leader genes. Sixty-one genes involved or potentially involved in periodontitis were identified. Only five were identified as leader genes, whereas 12 others were ranked in an immediately lower cluster. For 10 of 17 genes there is evidence of involvement in periodontitis; seven new genes that are potentially involved in this disease were identified. The involvement in periodontitis has been completely established for only two leader genes. We applied a validated bioinformatics algorithm to increase our knowledge of molecular mechanisms of periodontitis. Even with the limitations of this ab initio analysis, this theoretical study can suggest ad hoc experimentation targeted on significant genes and, therefore, simpler than mass-scale molecular genomics. Moreover, the identification of leader genes might suggest new potential risk factors and therapeutic targets.

RNA-guided genome editing for target gene mutations in wheat.

PubMed

Upadhyay, Santosh Kumar; Kumar, Jitesh; Alok, Anshu; Tuli, Rakesh

2013-12-09

The clustered, regularly interspaced, short palindromic repeats (CRISPR) and CRISPR-associated protein (Cas) system has been used as an efficient tool for genome editing. We report the application of CRISPR-Cas-mediated genome editing to wheat (Triticum aestivum), the most important food crop plant with a very large and complex genome. The mutations were targeted in the inositol oxygenase (inox) and phytoene desaturase (pds) genes using cell suspension culture of wheat and in the pds gene in leaves of Nicotiana benthamiana. The expression of chimeric guide RNAs (cgRNA) targeting single and multiple sites resulted in indel mutations in all the tested samples. The expression of Cas9 or sgRNA alone did not cause any mutation. The expression of duplex cgRNA with Cas9 targeting two sites in the same gene resulted in deletion of DNA fragment between the targeted sequences. Multiplexing the cgRNA could target two genes at one time. Target specificity analysis of cgRNA showed that mismatches at the 3' end of the target site abolished the cleavage activity completely. The mismatches at the 5' end reduced cleavage, suggesting that the off target effects can be abolished in vivo by selecting target sites with unique sequences at 3' end. This approach provides a powerful method for genome engineering in plants.

Isolation and expression analysis of four HD-ZIP III family genes targeted by microRNA166 in peach.

PubMed

Zhang, C H; Zhang, B B; Ma, R J; Yu, M L; Guo, S L; Guo, L

2015-10-30

MicroRNA166 (miR166) is known to have highly conserved targets that encode proteins of the class III homeodomain-leucine zipper (HD-ZIP III) family, in a broad range of plant species. To further understand the relationship between HD-ZIP III genes and miR166, four HD-ZIP III family genes (PpHB14, PpHB15, PpHB8, and PpREV) were isolated from peach (Prunus persica) tissue and characterized. Spatio-temporal expression profiles of the genes were analyzed. Genes of the peach HD-ZIP III family were predicted to encode five conserved domains. Deduced amino acid sequences and tertiary structures of the four peach HD-ZIP III genes were highly conserved, with corresponding genes in Arabidopsis thaliana. The expression level of four targets displayed the opposite trend to that of miR166 throughout fruit development, with the exception of PpHB14 from 35 to 55 days after full bloom (DAFB). This finding indicates that miR166 may negatively regulate its four targets throughout fruit development. As for leaf and phloem, the same trend in expression level was observed between four targets and miR166 from 75 to 105 DAFB. However, the opposite trend was observed for the transcript level between four targets and miR166 from 35 to 55 DAFB. miRNA166 may negatively regulate four targets in some but not all developmental stages for a given tissue. The four genes studied were observed to have, exactly or generally, the same change tendency as individual tissue development, a finding that suggests genes of the HD-ZIP III family in peach may have complementary or cooperative functions in various tissues.

Applicability of a gene expression based prediction method to SD and Wistar rats: an example of CARCINOscreen®.

PubMed

Matsumoto, Hiroshi; Saito, Fumiyo; Takeyoshi, Masahiro

2015-12-01

Recently, the development of several gene expression-based prediction methods has been attempted in the fields of toxicology. CARCINOscreen® is a gene expression-based screening method to predict carcinogenicity of chemicals which target the liver with high accuracy. In this study, we investigated the applicability of the gene expression-based screening method to SD and Wistar rats by using CARCINOscreen®, originally developed with F344 rats, with two carcinogens, 2,4-diaminotoluen and thioacetamide, and two non-carcinogens, 2,6-diaminotoluen and sodium benzoate. After the 28-day repeated dose test was conducted with each chemical in SD and Wistar rats, microarray analysis was performed using total RNA extracted from each liver. Obtained gene expression data were applied to CARCINOscreen®. Predictive scores obtained by the CARCINOscreen® for known carcinogens were > 2 in all strains of rats, while non-carcinogens gave prediction scores below 0.5. These results suggested that the gene expression based screening method, CARCINOscreen®, can be applied to SD and Wistar rats, widely used strains in toxicological studies, by setting of an appropriate boundary line of prediction score to classify the chemicals into carcinogens and non-carcinogens.

Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network

PubMed Central

Hwang, Sohyun; Rhee, Seung Y; Marcotte, Edward M; Lee, Insuk

2012-01-01

AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests. PMID:21886106

Synthesis of galactosyl compounds for targeted gene delivery.

PubMed

Ren, T; Zhang, G; Liu, D

2001-11-01

Cell-specific DNA delivery offers a great potential for targeted gene therapy. Toward this end, we have synthesized a series of compounds carrying galactose residues as a targeting ligand for asialoglycoprotein receptors of hepatocytes and primary amine groups as a functional domain for DNA binding. Biological activity of these galactosyl compounds in DNA delivery was evaluated in HepG2 and BL-6 cells and compared with respect to the number of galactose residues as well as primary amine groups in each molecule. Transfection experiments using a firefly luciferase gene as a reporter revealed that compounds with multivalent binding properties were more active in DNA delivery. An optimal transfection activity in HepG2 cells requires seven primary amine groups and a minimum of two galactose residues in each molecule. The transfection activity of compounds carrying multi-galactose residues can be inhibited by asialofetuin, a natural substrate for asialoglycoprotein receptors of hepatocytes, suggesting that gene transfer by these galactosyl compounds is asialoglycoprotein receptor-mediated. These results provide direct evidence in support of our new strategy for the use of small and synthetic compounds for cell specific and targeted gene delivery.

Genomic identification of direct target genes of LEAFY

PubMed Central

William, Dilusha A.; Su, Yanhui; Smith, Michael R.; Lu, Meina; Baldwin, Don A.; Wagner, Doris

2004-01-01

The switch from vegetative to reproductive development in plants necessitates a switch in the developmental program of the descendents of the stem cells in the shoot apical meristem. Genetic and molecular investigations have demonstrated that the plant-specific transcription factor and meristem identity regulator LEAFY (LFY) controls this developmental transition by inducing expression of a second transcription factor, APETALA1, and by regulating the expression of additional, as yet unknown, genes. Here we show that the additional LFY targets include the APETALA1-related factor, CAULI-FLOWER, as well as three transcription factors and two putative signal transduction pathway components. These genes are up-regulated by LFY even when protein synthesis is inhibited and, hence, appear to be direct targets of LFY. Supporting this conclusion, cis-regulatory regions upstream of these genes are bound by LFY in vivo. The newly identified LFY targets likely initiate the transcriptional changes that are required for the switch from vegetative to reproductive development in Arabidopsis. PMID:14736918

Macromolecular target prediction by self-organizing feature maps.

PubMed

Schneider, Gisbert; Schneider, Petra

2017-03-01

Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.

Applications of CRISPR/Cas9 technology for targeted mutagenesis, gene replacement and stacking of genes in higher plants.

PubMed

Luo, Ming; Gilbert, Brian; Ayliffe, Michael

2016-07-01

Mutagenesis continues to play an essential role for understanding plant gene function and, in some instances, provides an opportunity for plant improvement. The development of gene editing technologies such as TALENs and zinc fingers has revolutionised the targeted mutation specificity that can now be achieved. The CRISPR/Cas9 system is the most recent addition to gene editing technologies and arguably the simplest requiring only two components; a small guide RNA molecule (sgRNA) and Cas9 endonuclease protein which complex to recognise and cleave a specific 20 bp target site present in a genome. Target specificity is determined by complementary base pairing between the sgRNA and target site sequence enabling highly specific, targeted mutation to be readily engineered. Upon target site cleavage, error-prone endogenous repair mechanisms produce small insertion/deletions at the target site usually resulting in loss of gene function. CRISPR/Cas9 gene editing has been rapidly adopted in plants and successfully undertaken in numerous species including major crop species. Its applications are not restricted to mutagenesis and target site cleavage can be exploited to promote sequence insertion or replacement by recombination. The multiple applications of this technology in plants are described.

Massive expression of germ cell-specific genes is a hallmark of cancer and a potential target for novel treatment development.

PubMed

Bruggeman, Jan Willem; Koster, Jan; Lodder, Paul; Repping, Sjoerd; Hamer, Geert

2018-06-15

Cancer cells have been found to frequently express genes that are normally restricted to the testis, often referred to as cancer/testis (CT) antigens or genes. Because germ cell-specific antigens are not recognized as "self" by the innate immune system, CT-genes have previously been suggested as ideal candidate targets for cancer therapy. The use of CT-genes in cancer therapy has thus far been unsuccessful, most likely because their identification has relied on gene expression in whole testis, including the testicular somatic cells, precluding the detection of true germ cell-specific genes. By comparing the transcriptomes of micro-dissected germ cell subtypes, representing the main developmental stages of human spermatogenesis, with the publicly accessible transcriptomes of 2617 samples from 49 different healthy somatic tissues and 9232 samples from 33 tumor types, we here discover hundreds of true germ cell-specific cancer expressed genes. Strikingly, we found these germ cell cancer genes (GC-genes) to be widely expressed in all analyzed tumors. Many GC-genes appeared to be involved in processes that are likely to actively promote tumor viability, proliferation and metastasis. Targeting these true GC-genes thus has the potential to inhibit tumor growth with infertility being the only possible side effect. Moreover, we identified a subset of GC-genes that are not expressed in spermatogonial stem cells. Targeting of this GC-gene subset is predicted to only lead to temporary infertility, as untargeted spermatogonial stem cells can recover spermatogenesis after treatment. Our GC-gene dataset enables improved understanding of tumor biology and provides multiple novel targets for cancer treatment.

Predicting Drug-Target Interactions With Multi-Information Fusion.

PubMed

Peng, Lihong; Liao, Bo; Zhu, Wen; Li, Zejun; Li, Keqin

2017-03-01

Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.

Hypoxia regulates alternative splicing of HIF and non-HIF target genes.

PubMed

Sena, Johnny A; Wang, Liyi; Heasley, Lynn E; Hu, Cheng-Jun

2014-09-01

Hypoxia is a common characteristic of many solid tumors. The hypoxic microenvironment stabilizes hypoxia-inducible transcription factor 1α (HIF1α) and 2α (HIF2α/EPAS1) to activate gene transcription, which promotes tumor cell survival. The majority of human genes are alternatively spliced, producing RNA isoforms that code for functionally distinct proteins. Thus, an effective hypoxia response requires increased HIF target gene expression as well as proper RNA splicing of these HIF-dependent transcripts. However, it is unclear if and how hypoxia regulates RNA splicing of HIF targets. This study determined the effects of hypoxia on alternative splicing (AS) of HIF and non-HIF target genes in hepatocellular carcinoma cells and characterized the role of HIF in regulating AS of HIF-induced genes. The results indicate that hypoxia generally promotes exon inclusion for hypoxia-induced, but reduces exon inclusion for hypoxia-reduced genes. Mechanistically, HIF activity, but not hypoxia per se is found to be necessary and sufficient to increase exon inclusion of several HIF targets, including pyruvate dehydrogenase kinase 1 (PDK1). PDK1 splicing reporters confirm that transcriptional activation by HIF is sufficient to increase exon inclusion of PDK1 splicing reporter. In contrast, transcriptional activation of a PDK1 minigene by other transcription factors in the absence of endogenous HIF target gene activation fails to alter PDK1 RNA splicing. This study demonstrates a novel function of HIF in regulating RNA splicing of HIF target genes. ©2014 American Association for Cancer Research.

Multifunctional Nucleus-targeting Nanoparticles with Ultra-high Gene Transfection Efficiency for In Vivo Gene Therapy

PubMed Central

Li, Ling; Li, Xia; Wu, Yuzhe; Song, Linjiang; Yang, Xi; He, Tao; Wang, Ning; Yang, Suleixin; Zeng, Yan; Wu, Qinjie; Qian, Zhiyong; Wei, Yuquan; Gong, Changyang

2017-01-01

Cancer stem cell-like cells (CSCL) are responsible for tumor recurrence associated with conventional therapy (e.g. surgery, radiation, and chemotherapy). Here, we developed a novel multifunctional nucleus-targeting nanoparticle-based gene delivery system which is capable of targeting and eradicating CSCL. These nanoparticles can facilitate efficient endosomal escape and spontaneously penetrate into nucleus without additional nuclear localization signal. They also induced extremely high gene transfection efficiency (>95%) even in culture medium containing 30% serum, which significantly surpassed that of some commercial transfection reagents, such as Lipofectamine 2000 and Lipofectamine 3000 etc. Especially, when loaded with the TRAIL gene, this system mediated remarkable depletion of CSCL. Upon systemic administration, the nanoparticles accumulated in tumor sites while sparing the non-cancer tissues and significantly inhibited the growth of tumors with no evident systemic toxicity. Taken together, our results suggest that these novel multifunctional, nucleus-targeting nanoparticles are a very promising in vivo gene delivery system capable of targeting CSCL and represent a new treatment candidate for improving the survival of cancer patients. PMID:28529641

STOPGAP: a database for systematic target opportunity assessment by genetic association predictions.

PubMed

Shen, Judong; Song, Kijoung; Slater, Andrew J; Ferrero, Enrico; Nelson, Matthew R

2017-09-01

We developed the STOPGAP (Systematic Target OPportunity assessment by Genetic Association Predictions) database, an extensive catalog of human genetic associations mapped to effector gene candidates. STOPGAP draws on a variety of publicly available GWAS associations, linkage disequilibrium (LD) measures, functional genomic and variant annotation sources. Algorithms were developed to merge the association data, partition associations into non-overlapping LD clusters, map variants to genes and produce a variant-to-gene score used to rank the relative confidence among potential effector genes. This database can be used for a multitude of investigations into the genes and genetic mechanisms underlying inter-individual variation in human traits, as well as supporting drug discovery applications. Shell, R, Perl and Python scripts and STOPGAP R data files (version 2.5.1 at publication) are available at https://github.com/StatGenPRD/STOPGAP . Some of the most useful STOPGAP fields can be queried through an R Shiny web application at http://stopgapwebapp.com . matthew.r.nelson@gsk.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Targeted gene flow and rapid adaptation in an endangered marsupial.

PubMed

Kelly, Ella; Phillips, Ben L

2018-06-13

Targeted gene flow is an emerging conservation strategy. It involves translocating individuals with favorable genes to areas where they will have a conservation benefit. The applications for targeted gene flow are wide-ranging, but include pre-adapting natives to the arrival of invasive species. The endangered carnivorous marsupial, the northern quoll, has declined rapidly since the introduction of the cane toad, which fatally poisons quolls that attack them. There are, however, a few remaining toad-invaded quoll populations in which the quolls survive because they know not to eat cane toads. It is this "toad-smart" behavior that we hope to promote through targeted gene flow. For targeted gene flow to be feasible, however, toad-smarts must have a genetic basis. To assess this, we used a common garden experiment and found offspring from toad-exposed populations were substantially less likely to eat toads than those with toad-naïve parents. Hybrid offspring showed similar responses to quolls with two toad-exposed parents, indicating the trait may be dominant. Together, these results suggest a heritable trait and rapid adaptive response in small number of toad-impacted populations. Although questions remain about outbreeding depression, our results are encouraging for targeted gene flow: suggesting it should be possible to introduce toad-smart behavior into soon to be impacted quoll populations. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2016-12-22

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was

Drug-target interaction prediction: A Bayesian ranking approach.

PubMed

Peska, Ladislav; Buza, Krisztian; Koller, Júlia

2017-12-01

In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

Combined serial analysis of gene expression and transcription factor binding site prediction identifies novel-candidate-target genes of Nr2e1 in neocortex development.

PubMed

Schmouth, Jean-François; Arenillas, David; Corso-Díaz, Ximena; Xie, Yuan-Yun; Bohacec, Slavita; Banks, Kathleen G; Bonaguro, Russell J; Wong, Siaw H; Jones, Steven J M; Marra, Marco A; Simpson, Elizabeth M; Wasserman, Wyeth W

2015-07-24

Nr2e1 (nuclear receptor subfamily 2, group e, member 1) encodes a transcription factor important in neocortex development. Previous work has shown that nuclear receptors can have hundreds of target genes, and bind more than 300 co-interacting proteins. However, recognition of the critical role of Nr2e1 in neural stem cells and neocortex development is relatively recent, thus the molecular mechanisms involved for this nuclear receptor are only beginning to be understood. Serial analysis of gene expression (SAGE), has given researchers both qualitative and quantitative information pertaining to biological processes. Thus, in this work, six LongSAGE mouse libraries were generated from laser microdissected tissue samples of dorsal VZ/SVZ (ventricular zone and subventricular zone) from the telencephalon of wild-type (Wt) and Nr2e1-null embryos at the critical development ages E13.5, E15.5, and E17.5. We then used a novel approach, implementing multiple computational methods followed by biological validation to further our understanding of Nr2e1 in neocortex development. In this work, we have generated a list of 1279 genes that are differentially expressed in response to altered Nr2e1 expression during in vivo neocortex development. We have refined this list to 64 candidate direct-targets of NR2E1. Our data suggested distinct roles for Nr2e1 during different neocortex developmental stages. Most importantly, our results suggest a possible novel pathway by which Nr2e1 regulates neurogenesis, which includes Lhx2 as one of the candidate direct-target genes, and SOX9 as a co-interactor. In conclusion, we have provided new candidate interacting partners and numerous well-developed testable hypotheses for understanding the pathways by which Nr2e1 functions to regulate neocortex development.

A VEGF-dependent gene signature enriched in mesenchymal ovarian cancer predicts patient prognosis.

PubMed

Yin, Xia; Wang, Xiaojie; Shen, Boqiang; Jing, Ying; Li, Qing; Cai, Mei-Chun; Gu, Zhuowei; Yang, Qi; Zhang, Zhenfeng; Liu, Jin; Li, Hongxia; Di, Wen; Zhuang, Guanglei

2016-08-08

We have previously reported surrogate biomarkers of VEGF pathway activities with the potential to provide predictive information for anti-VEGF therapies. The aim of this study was to systematically evaluate a new VEGF-dependent gene signature (VDGs) in relation to molecular subtypes of ovarian cancer and patient prognosis. Using microarray profiling and cross-species analysis, we identified 140-gene mouse VDGs and corresponding 139-gene human VDGs, which displayed enrichment of vasculature and basement membrane genes. In patients who received bevacizumab therapy and showed partial response, the expressions of VDGs (summarized to yield VDGs scores) were markedly decreased in post-treatment biopsies compared with pre-treatment baselines. In contrast, VDGs scores were not significantly altered following bevacizumab treatment in patients with stable or progressive disease. Analysis of VDGs in ovarian cancer showed that VDGs as a prognostic signature was able to predict patient outcome. Correlation estimation of VDGs scores and molecular features revealed that VDGs was overrepresented in mesenchymal subtype and BRCA mutation carriers. These findings highlighted the prognostic role of VEGF-mediated angiogenesis in ovarian cancer, and proposed a VEGF-dependent gene signature as a molecular basis for developing novel diagnostic strategies to aid patient selection for VEGF-targeted agents.

Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

PubMed

Vlot, Anna H C; de Witte, Wilhelmus E A; Danhof, Meindert; van der Graaf, Piet H; van Westen, Gerard J P; de Lange, Elizabeth C M

2017-12-04

Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D ) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ 8 -tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

Fe3O4 Nanoparticles in Targeted Drug/Gene Delivery Systems

PubMed Central

Shen, Lazhen; Li, Bei; Qiao, Yongsheng

2018-01-01

Fe3O4 nanoparticles (NPs), the most traditional magnetic nanoparticles, have received a great deal of attention in the biomedical field, especially for targeted drug/gene delivery systems, due to their outstanding magnetism, biocompatibility, lower toxicity, biodegradability, and other features. Naked Fe3O4 NPs are easy to aggregate and oxidize, and thus are often made with various coatings to realize superior properties for targeted drug/gene delivery. In this review, we first list the three commonly utilized synthesis methods of Fe3O4 NPs, and their advantages and disadvantages. In the second part, we describe coating materials that exhibit noticeable features that allow functionalization of Fe3O4 NPs and summarize their methods of drug targeting/gene delivery. Then our efforts will be devoted to the research status and progress of several different functionalized Fe3O4 NP delivery systems loaded with chemotherapeutic agents, and we present targeted gene transitive carriers in detail. In the following section, we illuminate the most effective treatment systems of the combined drug and gene therapy. Finally, we propose opportunities and challenges of the clinical transformation of Fe3O4 NPs targeting drug/gene delivery systems. PMID:29473914

Bacteriophages and medical oncology: targeted gene therapy of cancer.

PubMed

Bakhshinejad, Babak; Karimi, Marzieh; Sadeghizadeh, Majid

2014-08-01

Targeted gene therapy of cancer is of paramount importance in medical oncology. Bacteriophages, viruses that specifically infect bacterial cells, offer a variety of potential applications in biomedicine. Their genetic flexibility to go under a variety of surface modifications serves as a basis for phage display methodology. These surface manipulations allow bacteriophages to be exploited for targeted delivery of therapeutic genes. Moreover, the excellent safety profile of these viruses paves the way for their potential use as cancer gene therapy platforms. The merge of phage display and combinatorial technology has led to the emergence of phage libraries turning phage display into a high throughput technology. Random peptide libraries, as one of the most frequently used phage libraries, provide a rich source of clinically useful peptide ligands. Peptides are known as a promising category of pharmaceutical agents in medical oncology that present advantages such as inexpensive synthesis, efficient tissue penetration and the lack of immunogenicity. Phage peptide libraries can be screened, through biopanning, against various targets including cancer cells and tissues that results in obtaining cancer-homing ligands. Cancer-specific peptides isolated from phage libraries show huge promise to be utilized for targeting of various gene therapy vectors towards malignant cells. Beyond doubt, bacteriophages will play a more impressive role in the future of medical oncology.

A system for the measurement of gene targeting efficiency in human cell lines using an antibiotic resistance-GFP fusion gene.

PubMed

Konishi, Yuko; Karnan, Sivasundaram; Takahashi, Miyuki; Ota, Akinobu; Damdindorj, Lkhagvasuren; Hosokawa, Yoshitaka; Konishi, Hiroyuki

2012-09-01

Gene targeting in a broad range of human somatic cell lines has been hampered by inefficient homologous recombination. To improve this technology and facilitate its widespread application, it is critical to first have a robust and efficient research system for measuring gene targeting efficiency. Here, using a fusion gene consisting of hygromycin B phosphotransferase and 3'-truncated enhanced GFP (HygR-5' EGFP) as a reporter gene, we created a molecular system monitoring the ratio of homologous to random integration (H/R ratio) of targeting vectors into the genome. Cell clones transduced with a reporter vector containing HygR-5' EGFP were efficiently established from two human somatic cell lines. Established HygR-5' EGFP reporter clones retained their capacity to monitor gene targeting efficiency for a longer duration than a conventional reporter system using an unfused 5' EGFP gene. With the HygR-5' EGFP reporter system, we reproduced previous findings of gene targeting frequency being up-regulated by the use of an adeno-associated viral (AAV) backbone, a promoter-trap system, or a longer homology arm in a targeting vector, suggesting that this system accurately monitors H/R ratio. Thus, our HygR-5' EGFP reporter system will assist in the development of an efficient AAV-based gene targeting technology.

Combining Gene Signatures Improves Prediction of Breast Cancer Survival

PubMed Central

Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

2011-01-01

Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

Ewing's Sarcoma: An Analysis of miRNA Expression Profiles and Target Genes in Paraffin-Embedded Primary Tumor Tissue.

PubMed

Parafioriti, Antonina; Bason, Caterina; Armiraglio, Elisabetta; Calciano, Lucia; Daolio, Primo Andrea; Berardocco, Martina; Di Bernardo, Andrea; Colosimo, Alessia; Luksch, Roberto; Berardi, Anna C

2016-04-30

The molecular mechanism responsible for Ewing's Sarcoma (ES) remains largely unknown. MicroRNAs (miRNAs), a class of small non-coding RNAs able to regulate gene expression, are deregulated in tumors and may serve as a tool for diagnosis and prediction. However, the status of miRNAs in ES has not yet been thoroughly investigated. This study compared global miRNAs expression in paraffin-embedded tumor tissue samples from 20 ES patients, affected by primary untreated tumors, with miRNAs expressed in normal human mesenchymal stromal cells (MSCs) by microarray analysis. A miRTarBase database was used to identify the predicted target genes for differentially expressed miRNAs. The miRNAs microarray analysis revealed distinct patterns of miRNAs expression between ES samples and normal MSCs. 58 of the 954 analyzed miRNAs were significantly differentially expressed in ES samples compared to MSCs. Moreover, the qRT-PCR analysis carried out on three selected miRNAs showed that miR-181b, miR-1915 and miR-1275 were significantly aberrantly regulated, confirming the microarray results. Bio-database analysis identified BCL-2 as a bona fide target gene of the miR-21, miR-181a, miR-181b, miR-29a, miR-29b, miR-497, miR-195, miR-let-7a, miR-34a and miR-1915. Using paraffin-embedded tissues from ES patients, this study has identified several potential target miRNAs and one gene that might be considered a novel critical biomarker for ES pathogenesis.

TargetCompare: A web interface to compare simultaneous miRNAs targets.

PubMed

Moreira, Fabiano Cordeiro; Dustan, Bruno; Hamoy, Igor G; Ribeiro-Dos-Santos, André M; Dos Santos, Andrea Ribeiro

2014-01-01

MicroRNAs (miRNAs) are small non-coding nucleotide sequences between 17 and 25 nucleotides in length that primarily function in the regulation of gene expression. A since miRNA has thousand of predict targets in a complex, regulatory cell signaling network. Therefore, it is of interest to study multiple target genes simultaneously. Hence, we describe a web tool (developed using Java programming language and MySQL database server) to analyse multiple targets of pre-selected miRNAs. We cross validated the tool in eight most highly expressed miRNAs in the antrum region of stomach. This helped to identify 43 potential genes that are target of at least six of the referred miRNAs. The developed tool aims to reduce the randomness and increase the chance of selecting strong candidate target genes and miRNAs responsible for playing important roles in the studied tissue. http://lghm.ufpa.br/targetcompare.

Immuno-Oncology-The Translational Runway for Gene Therapy: Gene Therapeutics to Address Multiple Immune Targets.

PubMed

Weß, Ludger; Schnieders, Frank

2017-12-01

Cancer therapy is once again experiencing a paradigm shift. This shift is based on extensive clinical experience demonstrating that cancer cannot be successfully fought by addressing only single targets or pathways. Even the combination of several neo-antigens in cancer vaccines is not sufficient for successful, lasting tumor eradication. The focus has therefore shifted to the immune system's role in cancer and the striking abilities of cancer cells to manipulate and/or deactivate the immune system. Researchers and pharma companies have started to target the processes and cells known to support immune surveillance and the elimination of tumor cells. Immune processes, however, require novel concepts beyond the traditional "single-target-single drug" paradigm and need parallel targeting of diverse cells and mechanisms. This review gives a perspective on the role of gene therapy technologies in the evolving immuno-oncology space and identifies gene therapy as a major driver in the development and regulation of effective cancer immunotherapy. Present challenges and breakthroughs ranging from chimeric antigen receptor T-cell therapy, gene-modified oncolytic viruses, combination cancer vaccines, to RNA therapeutics are spotlighted. Gene therapy is recognized as the most prominent technology enabling effective immuno-oncology strategies.

A systems approach to identifying correlated gene targets for the loss of colour pigmentation in plants

PubMed Central

2011-01-01

Background The numerous diverse metabolic pathways by which plant compounds can be produced make it difficult to predict how colour pigmentation is lost for different tissues and plants. This study employs mathematical and in silico methods to identify correlated gene targets for the loss of colour pigmentation in plants from a whole cell perspective based on the full metabolic network of Arabidopsis. This involves extracting a self-contained flavonoid subnetwork from the AraCyc database and calculating feasible metabolic routes or elementary modes (EMs) for it. Those EMs leading to anthocyanin compounds are taken to constitute the anthocyanin biosynthetic pathway (ABP) and their interplay with the rest of the EMs is used to study the minimal cut sets (MCSs), which are different combinations of reactions to block for eliminating colour pigmentation. By relating the reactions to their corresponding genes, the MCSs are used to explore the phenotypic roles of the ABP genes, their relevance to the ABP and the impact their eliminations would have on other processes in the cell. Results Simulation and prediction results of the effect of different MCSs for eliminating colour pigmentation correspond with existing experimental observations. Two examples are: i) two MCSs which require the simultaneous suppression of genes DFR and ANS to eliminate colour pigmentation, correspond to observational results of the same genes being co-regulated for eliminating floral pigmentation in Aquilegia and; ii) the impact of another MCS requiring CHS suppression, corresponds to findings where the suppression of the early gene CHS eliminated nearly all flavonoids but did not affect the production of volatile benzenoids responsible for floral scent. Conclusions From the various MCSs identified for eliminating colour pigmentation, several correlate to existing experimental observations, indicating that different MCSs are suitable for different plants, different cells, and different conditions

Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

PubMed

Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

2014-08-01

Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

GenePRIMP: A Gene Prediction Improvement Pipeline For Prokaryotic Genomes

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

Kyrpides, Nikos C.; Ivanova, Natalia N.; Pati, Amrita

2010-07-08

GenePRIMP (Gene Prediction Improvement Pipeline, Http://geneprimp.jgi-psf.org), a computational process that performs evidence-based evaluation of gene models in prokaryotic genomes and reports anomalies including inconsistent start sites, missing genes, and split genes. We show that manual curation of gene models using the anomaly reports generated by GenePRIMP improves their quality and demonstrate the applicability of GenePRIMP in improving finishing quality and comparing different genome sequencing and annotation technologies. Keywords in context: Gene model, Quality Control, Translation start sites, Automatic correction. Hardware requirements; PC, MAC; Operating System: UNIX/LINUX; Compiler/Version: Perl 5.8.5 or higher; Special requirements: NCBI Blast and nr installation; File Types:more » Source Code, Executable module(s), Sample problem input data; installation instructions other; programmer documentation. Location/transmission: http://geneprimp.jgi-psf.org/gp.tar.gz« less

Drug-target interaction prediction from PSSM based evolutionary information.

PubMed

Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

2016-01-01

The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

Seamless Genome Editing in Rice via Gene Targeting and Precise Marker Elimination.

PubMed

Nishizawa-Yokoi, Ayako; Saika, Hiroaki; Toki, Seiichi

2016-01-01

Positive-negative selection using hygromycin phosphotransferase (hpt) and diphtheria toxin A-fragment (DT-A) as positive and negative selection markers, respectively, allows enrichment of cells harboring target genes modified via gene targeting (GT). We have developed a successful GT system employing positive-negative selection and subsequent precise marker excision via the piggyBac transposon derived from the cabbage looper moth to introduce desired modifications into target genes in the rice genome. This approach could be applied to the precision genome editing of almost all endogenous genes throughout the genome, at least in rice.

shRNA target prediction informed by comprehensive enquiry (SPICE): a supporting system for high-throughput screening of shRNA library.

PubMed

Kamatuka, Kenta; Hattori, Masahiro; Sugiyama, Tomoyasu

2016-12-01

RNA interference (RNAi) screening is extensively used in the field of reverse genetics. RNAi libraries constructed using random oligonucleotides have made this technology affordable. However, the new methodology requires exploration of the RNAi target gene information after screening because the RNAi library includes non-natural sequences that are not found in genes. Here, we developed a web-based tool to support RNAi screening. The system performs short hairpin RNA (shRNA) target prediction that is informed by comprehensive enquiry (SPICE). SPICE automates several tasks that are laborious but indispensable to evaluate the shRNAs obtained by RNAi screening. SPICE has four main functions: (i) sequence identification of shRNA in the input sequence (the sequence might be obtained by sequencing clones in the RNAi library), (ii) searching the target genes in the database, (iii) demonstrating biological information obtained from the database, and (iv) preparation of search result files that can be utilized in a local personal computer (PC). Using this system, we demonstrated that genes targeted by random oligonucleotide-derived shRNAs were not different from those targeted by organism-specific shRNA. The system facilitates RNAi screening, which requires sequence analysis after screening. The SPICE web application is available at http://www.spice.sugysun.org/.

Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

PubMed

Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

2015-07-15

Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

Reranking candidate gene models with cross-species comparison for improved gene prediction

PubMed Central

Liu, Qian; Crammer, Koby; Pereira, Fernando CN; Roos, David S

2008-01-01

Background Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc). Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models. PMID:18854050

Targeted systemic gene therapy and molecular imaging of cancer contribution of the vascular-targeted AAVP vector.

PubMed

Hajitou, Amin

2010-01-01

Gene therapy and molecular-genetic imaging have faced a major problem: the lack of an efficient systemic gene delivery vector. Unquestionably, eukaryotic viruses have been the vectors of choice for gene delivery to mammalian cells; however, they have had limited success in systemic gene therapy. This is mainly due to undesired uptake by the liver and reticuloendothelial system, broad tropism for mammalian cells causing toxicity, and their immunogenicity. On the other hand, prokaryotic viruses such as bacteriophage (phage) have no tropism for mammalian cells, but can be engineered to deliver genes to these cells. However, phage-based vectors have inherently been considered poor vectors for mammalian cells. We have reported a new generation of vascular-targeted systemic hybrid prokaryotic-eukaryotic vectors as chimeras between an adeno-associated virus (AAV) and targeted bacteriophage (termed AAV/phage; AAVP). In this hybrid vector, the targeted bacteriophage serves as a shuttle to deliver the AAV transgene cassette inserted in an intergenomic region of the phage DNA genome. As a proof of concept, we assessed the in vivo efficacy of vector in animal models of cancer by displaying on the phage capsid the cyclic Arg-Gly-Asp (RGD-4C) ligand that binds to alphav integrin receptors specifically expressed on the angiogenic blood vessels of tumors. The ligand-directed vector was able to specifically deliver imaging and therapeutic transgenes to tumors in mice, rats, and dogs while sparing the normal organs. This chapter reviews some gene transfer strategies and the potential of the vascular-targeted AAVP vector for enhancing the effectiveness of existing systemic gene delivery and genetic-imaging technologies. Copyright (c) 2010 Elsevier Inc. All rights reserved.

STAT3 Target Genes Relevant to Human Cancers

PubMed Central

Carpenter, Richard L.; Lo, Hui-Wen

2014-01-01

Since its discovery, the STAT3 transcription factor has been extensively studied for its function as a transcriptional regulator and its role as a mediator of development, normal physiology, and pathology of many diseases, including cancers. These efforts have uncovered an array of genes that can be positively and negatively regulated by STAT3, alone and in cooperation with other transcription factors. Through regulating gene expression, STAT3 has been demonstrated to play a pivotal role in many cellular processes including oncogenesis, tumor growth and progression, and stemness. Interestingly, recent studies suggest that STAT3 may behave as a tumor suppressor by activating expression of genes known to inhibit tumorigenesis. Additional evidence suggested that STAT3 may elicit opposing effects depending on cellular context and tumor types. These mixed results signify the need for a deeper understanding of STAT3, including its upstream regulators, parallel transcription co-regulators, and downstream target genes. To help facilitate fulfilling this unmet need, this review will be primarily focused on STAT3 downstream target genes that have been validated to associate with tumorigenesis and/or malignant biology of human cancers. PMID:24743777

Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes.

PubMed

Zhao, Lue Ping; Carlsson, Annelie; Larsson, Helena Elding; Forsander, Gun; Ivarsson, Sten A; Kockum, Ingrid; Ludvigsson, Johnny; Marcus, Claude; Persson, Martina; Samuelsson, Ulf; Örtqvist, Eva; Pyo, Chul-Woo; Bolouri, Hamid; Zhao, Michael; Nelson, Wyatt C; Geraghty, Daniel E; Lernmark, Åke

2017-11-01

It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies. Utilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D. In the training set, estimated risk scores were significantly different between patients and controls (P = 8.12 × 10 -92 ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a "biological validation" by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score = 3.628, P < 0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime. Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations. Copyright © 2017 John Wiley & Sons, Ltd.

Targeted Sequencing of Venom Genes from Cone Snail Genomes Improves Understanding of Conotoxin Molecular Evolution

PubMed Central

Mahardika, Gusti N

2018-01-01

Abstract To expand our capacity to discover venom sequences from the genomes of venomous organisms, we applied targeted sequencing techniques to selectively recover venom gene superfamilies and nontoxin loci from the genomes of 32 cone snail species (family, Conidae), a diverse group of marine gastropods that capture their prey using a cocktail of neurotoxic peptides (conotoxins). We were able to successfully recover conotoxin gene superfamilies across all species with high confidence (> 100× coverage) and used these data to provide new insights into conotoxin evolution. First, we found that conotoxin gene superfamilies are composed of one to six exons and are typically short in length (mean = ∼85 bp). Second, we expanded our understanding of the following genetic features of conotoxin evolution: 1) positive selection, where exons coding the mature toxin region were often three times more divergent than their adjacent noncoding regions, 2) expression regulation, with comparisons to transcriptome data showing that cone snails only express a fraction of the genes available in their genome (24–63%), and 3) extensive gene turnover, where Conidae species varied from 120 to 859 conotoxin gene copies. Finally, using comparative phylogenetic methods, we found that while diet specificity did not predict patterns of conotoxin evolution, dietary breadth was positively correlated with total conotoxin gene diversity. Overall, the targeted sequencing technique demonstrated here has the potential to radically increase the pace at which venom gene families are sequenced and studied, reshaping our ability to understand the impact of genetic changes on ecologically relevant phenotypes and subsequent diversification. PMID:29514313

Magnetic nanoparticles for targeted therapeutic gene delivery and magnetic-inducing heating on hepatoma

NASA Astrophysics Data System (ADS)

Yuan, Chenyan; An, Yanli; Zhang, Jia; Li, Hongbo; Zhang, Hao; Wang, Ling; Zhang, Dongsheng

2014-08-01

Gene therapy holds great promise for treating cancers, but their clinical applications are being hampered due to uncontrolled gene delivery and expression. To develop a targeted, safe and efficient tumor therapy system, we constructed a tissue-specific suicide gene delivery system by using magnetic nanoparticles (MNPs) as carriers for the combination of gene therapy and hyperthermia on hepatoma. The suicide gene was hepatoma-targeted and hypoxia-enhanced, and the MNPs possessed the ability to elevate temperature to the effective range for tumor hyperthermia as imposed on an alternating magnetic field (AMF). The tumoricidal effects of targeted gene therapy associated with hyperthermia were evaluated in vitro and in vivo. The experiment demonstrated that hyperthermia combined with a targeted gene therapy system proffer an effective tool for tumor therapy with high selectivity and the synergistic effect of hepatoma suppression.

Inflammatory macrophage-associated 3-gene signature predicts subclinical allograft injury and graft survival.

PubMed

Azad, Tej D; Donato, Michele; Heylen, Line; Liu, Andrew B; Shen-Orr, Shai S; Sweeney, Timothy E; Maltzman, Jonathan Scott; Naesens, Maarten; Khatri, Purvesh

2018-01-25

Late allograft failure is characterized by cumulative subclinical insults manifesting over many years. Although immunomodulatory therapies targeting host T cells have improved short-term survival rates, rates of chronic allograft loss remain high. We hypothesized that other immune cell types may drive subclinical injury, ultimately leading to graft failure. We collected whole-genome transcriptome profiles from 15 independent cohorts composed of 1,697 biopsy samples to assess the association of an inflammatory macrophage polarization-specific gene signature with subclinical injury. We applied penalized regression to a subset of the data sets and identified a 3-gene inflammatory macrophage-derived signature. We validated discriminatory power of the 3-gene signature in 3 independent renal transplant data sets with mean AUC of 0.91. In a longitudinal cohort, the 3-gene signature strongly correlated with extent of injury and accurately predicted progression of subclinical injury 18 months before clinical manifestation. The 3-gene signature also stratified patients at high risk of graft failure as soon as 15 days after biopsy. We found that the 3-gene signature also distinguished acute rejection (AR) accurately in 3 heart transplant data sets but not in lung transplant. Overall, we identified a parsimonious signature capable of diagnosing AR, recognizing subclinical injury, and risk-stratifying renal transplant patients. Our results strongly suggest that inflammatory macrophages may be a viable therapeutic target to improve long-term outcomes for organ transplantation patients.

TargetCompare: A web interface to compare simultaneous miRNAs targets

PubMed Central

Moreira, Fabiano Cordeiro; Dustan, Bruno; Hamoy, Igor G; Ribeiro-dos-Santos, André M; dos Santos, Ândrea Ribeiro

2014-01-01

MicroRNAs (miRNAs) are small non-coding nucleotide sequences between 17 and 25 nucleotides in length that primarily function in the regulation of gene expression. A since miRNA has thousand of predict targets in a complex, regulatory cell signaling network. Therefore, it is of interest to study multiple target genes simultaneously. Hence, we describe a web tool (developed using Java programming language and MySQL database server) to analyse multiple targets of pre-selected miRNAs. We cross validated the tool in eight most highly expressed miRNAs in the antrum region of stomach. This helped to identify 43 potential genes that are target of at least six of the referred miRNAs. The developed tool aims to reduce the randomness and increase the chance of selecting strong candidate target genes and miRNAs responsible for playing important roles in the studied tissue. Availability http://lghm.ufpa.br/targetcompare PMID:25352731

Challenging the state-of-the-art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10

PubMed Central

Kryshtafovych, Andriy; Moult, John; Bales, Patrick; Bazan, J. Fernando; Biasini, Marco; Burgin, Alex; Chen, Chen; Cochran, Frank V.; Craig, Timothy K.; Das, Rhiju; Fass, Deborah; Garcia-Doval, Carmela; Herzberg, Osnat; Lorimer, Donald; Luecke, Hartmut; Ma, Xiaolei; Nelson, Daniel C.; van Raaij, Mark J.; Rohwer, Forest; Segall, Anca; Seguritan, Victor; Zeth, Kornelius; Schwede, Torsten

2014-01-01

For the last two decades, CASP has assessed the state of the art in techniques for protein structure prediction and identified areas which required further development. CASP would not have been possible without the prediction targets provided by the experimental structural biology community. In the latest experiment, CASP10, over 100 structures were suggested as prediction targets, some of which appeared to be extraordinarily difficult for modeling. In this paper, authors of some of the most challenging targets discuss which specific scientific question motivated the experimental structure determination of the target protein, which structural features were especially interesting from a structural or functional perspective, and to what extent these features were correctly reproduced in the predictions submitted to CASP10. Specifically, the following targets will be presented: the acid-gated urea channel, a difficult to predict trans-membrane protein from the important human pathogen Helicobacter pylori; the structure of human interleukin IL-34, a recently discovered helical cytokine; the structure of a functionally uncharacterized enzyme OrfY from Thermoproteus tenax formed by a gene duplication and a novel fold; an ORFan domain of mimivirus sulfhydryl oxidase R596; the fibre protein gp17 from bacteriophage T7; the Bacteriophage CBA-120 tailspike protein; a virus coat protein from metagenomic samples of the marine environment; and finally an unprecedented class of structure prediction targets based on engineered disulfide-rich small proteins. PMID:24318984

TAPIR, a web server for the prediction of plant microRNA targets, including target mimics.

PubMed

Bonnet, Eric; He, Ying; Billiau, Kenny; Van de Peer, Yves

2010-06-15

We present a new web server called TAPIR, designed for the prediction of plant microRNA targets. The server offers the possibility to search for plant miRNA targets using a fast and a precise algorithm. The precise option is much slower but guarantees to find less perfectly paired miRNA-target duplexes. Furthermore, the precise option allows the prediction of target mimics, which are characterized by a miRNA-target duplex having a large loop, making them undetectable by traditional tools. The TAPIR web server can be accessed at: http://bioinformatics.psb.ugent.be/webtools/tapir. Supplementary data are available at Bioinformatics online.

Histidine-rich stabilized polyplexes for cMet-directed tumor-targeted gene transfer

NASA Astrophysics Data System (ADS)

Kos, Petra; Lächelt, Ulrich; Herrmann, Annika; Mickler, Frauke Martina; Döblinger, Markus; He, Dongsheng; Krhač Levačić, Ana; Morys, Stephan; Bräuchle, Christoph; Wagner, Ernst

2015-03-01

Overexpression of the hepatocyte growth factor receptor/c-Met proto oncogene on the surface of a variety of tumor cells gives an opportunity to specifically target cancerous tissues. Herein, we report the first use of c-Met as receptor for non-viral tumor-targeted gene delivery. Sequence-defined oligomers comprising the c-Met binding peptide ligand cMBP2 for targeting, a monodisperse polyethylene glycol (PEG) for polyplex surface shielding, and various cationic (oligoethanamino) amide cores containing terminal cysteines for redox-sensitive polyplex stabilization, were assembled by solid-phase supported syntheses. The resulting oligomers exhibited a greatly enhanced cellular uptake and gene transfer over non-targeted control sequences, confirming the efficacy and target-specificity of the formed polyplexes. Implementation of endosomal escape-promoting histidines in the cationic core was required for gene expression without additional endosomolytic agent. The histidine-enriched polyplexes demonstrated stability in serum as well as receptor-specific gene transfer in vivo upon intratumoral injection. The co-formulation with an analogous PEG-free cationic oligomer led to a further compaction of pDNA polyplexes with an obvious change of shape as demonstrated by transmission electron microscopy. Such compaction was critically required for efficient intravenous gene delivery which resulted in greatly enhanced, cMBP2 ligand-dependent gene expression in the distant tumor.Overexpression of the hepatocyte growth factor receptor/c-Met proto oncogene on the surface of a variety of tumor cells gives an opportunity to specifically target cancerous tissues. Herein, we report the first use of c-Met as receptor for non-viral tumor-targeted gene delivery. Sequence-defined oligomers comprising the c-Met binding peptide ligand cMBP2 for targeting, a monodisperse polyethylene glycol (PEG) for polyplex surface shielding, and various cationic (oligoethanamino) amide cores containing

Neurobiology of autism gene products: towards pathogenesis and drug targets.

PubMed

Kleijer, Kristel T E; Schmeisser, Michael J; Krueger, Dilja D; Boeckers, Tobias M; Scheiffele, Peter; Bourgeron, Thomas; Brose, Nils; Burbach, J Peter H

2014-03-01

The genetic heterogeneity of autism spectrum disorders (ASDs) is enormous, and the neurobiology of proteins encoded by genes associated with ASD is very diverse. Revealing the mechanisms on which different neurobiological pathways in ASD pathogenesis converge may lead to the identification of drug targets. The main objective is firstly to outline the main molecular networks and neuronal mechanisms in which ASD gene products participate and secondly to answer the question how these converge. Finally, we aim to pinpoint drug targets within these mechanisms. Literature review of the neurobiological properties of ASD gene products with a special focus on the developmental consequences of genetic defects and the possibility to reverse these by genetic or pharmacological interventions. The regulation of activity-dependent protein synthesis appears central in the pathogenesis of ASD. Through sequential consequences for axodendritic function, neuronal disabilities arise expressed as behavioral abnormalities and autistic symptoms in ASD patients. Several known ASD gene products have their effect on this central process by affecting protein synthesis intrinsically, e.g., through enhancing the mammalian target of rapamycin (mTOR) signal transduction pathway or through impairing synaptic function in general. These are interrelated processes and can be targeted by compounds from various directions: inhibition of protein synthesis through Lovastatin, mTOR inhibition using rapamycin, or mGluR-related modulation of synaptic activity. ASD gene products may all feed into a central process of translational control that is important for adequate glutamatergic regulation of dendritic properties. This process can be modulated by available compounds but may also be targeted by yet unexplored routes.

Single molecule targeted sequencing for cancer gene mutation detection.

PubMed

Gao, Yan; Deng, Liwei; Yan, Qin; Gao, Yongqian; Wu, Zengding; Cai, Jinsen; Ji, Daorui; Li, Gailing; Wu, Ping; Jin, Huan; Zhao, Luyang; Liu, Song; Ge, Liangjin; Deem, Michael W; He, Jiankui

2016-05-19

With the rapid decline in cost of sequencing, it is now affordable to examine multiple genes in a single disease-targeted clinical test using next generation sequencing. Current targeted sequencing methods require a separate step of targeted capture enrichment during sample preparation before sequencing. Although there are fast sample preparation methods available in market, the library preparation process is still relatively complicated for physicians to use routinely. Here, we introduced an amplification-free Single Molecule Targeted Sequencing (SMTS) technology, which combined targeted capture and sequencing in one step. We demonstrated that this technology can detect low-frequency mutations using artificially synthesized DNA sample. SMTS has several potential advantages, including simple sample preparation thus no biases and errors are introduced by PCR reaction. SMTS has the potential to be an easy and quick sequencing technology for clinical diagnosis such as cancer gene mutation detection, infectious disease detection, inherited condition screening and noninvasive prenatal diagnosis.

Gene replacements and insertions in rice by intron targeting using CRISPR-Cas9.

PubMed

Li, Jun; Meng, Xiangbing; Zong, Yuan; Chen, Kunling; Zhang, Huawei; Liu, Jinxing; Li, Jiayang; Gao, Caixia

2016-09-12

Sequence-specific nucleases have been exploited to create targeted gene knockouts in various plants(1), but replacing a fragment and even obtaining gene insertions at specific loci in plant genomes remain a serious challenge. Here, we report efficient intron-mediated site-specific gene replacement and insertion approaches that generate mutations using the non-homologous end joining (NHEJ) pathway using the clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) system. Using a pair of single guide RNAs (sgRNAs) targeting adjacent introns and a donor DNA template including the same pair of sgRNA sites, we achieved gene replacements in the rice endogenous gene 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) at a frequency of 2.0%. We also obtained targeted gene insertions at a frequency of 2.2% using a sgRNA targeting one intron and a donor DNA template including the same sgRNA site. Rice plants harbouring the OsEPSPS gene with the intended substitutions were glyphosate-resistant. Furthermore, the site-specific gene replacements and insertions were faithfully transmitted to the next generation. These newly developed approaches can be generally used to replace targeted gene fragments and to insert exogenous DNA sequences into specific genomic sites in rice and other plants.

Production of Prnp-/- goats by gene targeting in adult fibroblasts.

PubMed

Zhu, Caihong; Li, Bei; Yu, Guohua; Chen, Jianquan; Yu, Huiqing; Chen, Juan; Xu, Xujun; Wu, Youbing; Zhang, Aimin; Cheng, Guoxiang

2009-04-01

Homozygous mice devoid of functional Prnp are resistant to scrapie and prion propagation, but heterozygous mice for Prnp disruption still suffer from prion disease and prion deposition. We have previously generated heterozygous cloned goats with one allele of Prnp functional disruption. To obtain goats with both alleles of Prnp be disrupted which would be resistant to scrapie completely, a second-round gene targeting was applied to disrupt the wild type allele of Prnp in the heterozygous goats. By second-round gene targeting, we successfully disrupted the wild type allele of Prnp in primary Prnp (+/-) goat skin fibroblasts and obtained a Prnp (-/-) cell line without Prnp expression. This is the first report on successful targeting modification in primary adult somatic cells of animals. These cells were used as nuclear donors for somatic cell cloning to produce Prnp (-/-) goats. A total of 57 morulae or blastocytes developed from the reconstructed embryos were transferred to 31 recipients, which produced 7 pregnancies at day 35. At 73 days of gestation, we obtained one cloned fetus with Prnp (-/-) genotype. Our research not only indicated that multiple genetic modifications could be accomplished by multi-round gene targeting in primary somatic cells, but also provided strong evidence that gene targeting in adult cells other than fetal cells could be applied to introduce precise genetic modifications in animals without destroying the embryos.

Predicting features of breast cancer with gene expression patterns.

PubMed

Lu, Xuesong; Lu, Xin; Wang, Zhigang C; Iglehart, J Dirk; Zhang, Xuegong; Richardson, Andrea L

2008-03-01

Data from gene expression arrays hold an enormous amount of biological information. We sought to determine if global gene expression in primary breast cancers contained information about biologic, histologic, and anatomic features of the disease in individual patients. Microarray data from the tumors of 129 patients were analyzed for the ability to predict biomarkers [estrogen receptor (ER) and HER2], histologic features [grade and lymphatic-vascular invasion (LVI)], and stage parameters (tumor size and lymph node metastasis). Multiple statistical predictors were used and the prediction accuracy was determined by cross-validation error rate; multidimensional scaling (MDS) allowed visualization of the predicted states under study. Models built from gene expression data accurately predict ER and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data is inaccurate at predicting tumor size, lymph node status or LVI. The best model for prediction of nodal status included tumor size, LVI status and pathologically defined tumor subtype (based on combinations of ER, HER2, and grade); the addition of microarray-based prediction to this model failed to improve the prediction accuracy. Global gene expression supports a binary division of ER, HER2, and grade, clearly separating tumors into two categories; intermediate values for these bio-indicators do not define intermediate tumor subsets. Results are consistent with a model of regional metastasis that depends on inherent biologic differences in metastatic propensity between breast cancer subtypes, upon which time and chance then operate.

Gene and translation initiation site prediction in metagenomic sequences

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

Hyatt, Philip Douglas; LoCascio, Philip F; Hauser, Loren John

2012-01-01

Gene prediction in metagenomic sequences remains a difficult problem. Current sequencing technologies do not achieve sufficient coverage to assemble the individual genomes in a typical sample; consequently, sequencing runs produce a large number of short sequences whose exact origin is unknown. Since these sequences are usually smaller than the average length of a gene, algorithms must make predictions based on very little data. We present MetaProdigal, a metagenomic version of the gene prediction program Prodigal, that can identify genes in short, anonymous coding sequences with a high degree of accuracy. The novel value of the method consists of enhanced translationmore » initiation site identification, ability to identify sequences that use alternate genetic codes and confidence values for each gene call. We compare the results of MetaProdigal with other methods and conclude with a discussion of future improvements.« less

Inference of Expanded Lrp-Like Feast/Famine Transcription Factor Targets in a Non-Model Organism Using Protein Structure-Based Prediction

PubMed Central

Ashworth, Justin; Plaisier, Christopher L.; Lo, Fang Yin; Reiss, David J.; Baliga, Nitin S.

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer. PMID:25255272

Inference of expanded Lrp-like feast/famine transcription factor targets in a non-model organism using protein structure-based prediction.

PubMed

Ashworth, Justin; Plaisier, Christopher L; Lo, Fang Yin; Reiss, David J; Baliga, Nitin S

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer.

Identification of STAT target genes in adipocytes

PubMed Central

Zhao, Peng; Stephens, Jacqueline M.

2013-01-01

Adipocytes play important roles in lipid storage, energy homeostasis and whole body insulin sensitivity. Studies in the last two decades have identified the hormones and cytokines that activate specific STATs in adipocytes in vitro and in vivo. Five of the seven STAT family members are expressed in adipocyte (STATs 1, 3, 5A, 5B and 6). Many transcription factors, including STATs, have been shown to play an important role in adipose tissue development and function. This review will summarize the importance of adipocytes, indicate the cytokines and hormones that utilize the JAK-STAT signaling pathway in fat cells and focus on the identification of STAT target genes in mature adipocytes. To date, specific target genes have been identified for STATs, 1, 5A and 5B, but not for STATs 3 and 6. PMID:24058802

A Novel Method to Predict Highly Expressed Genes Based on Radius Clustering and Relative Synonymous Codon Usage.

PubMed

Tran, Tuan-Anh; Vo, Nam Tri; Nguyen, Hoang Duc; Pham, Bao The

2015-12-01

Recombinant proteins play an important role in many aspects of life and have generated a huge income, notably in the industrial enzyme business. A gene is introduced into a vector and expressed in a host organism-for example, E. coli-to obtain a high productivity of target protein. However, transferred genes from particular organisms are not usually compatible with the host's expression system because of various reasons, for example, codon usage bias, GC content, repetitive sequences, and secondary structure. The solution is developing programs to optimize for designing a nucleotide sequence whose origin is from peptide sequences using properties of highly expressed genes (HEGs) of the host organism. Existing data of HEGs determined by practical and computer-based methods do not satisfy for qualifying and quantifying. Therefore, the demand for developing a new HEG prediction method is critical. We proposed a new method for predicting HEGs and criteria to evaluate gene optimization. Codon usage bias was weighted by amplifying the difference between HEGs and non-highly expressed genes (non-HEGs). The number of predicted HEGs is 5% of the genome. In comparison with Puigbò's method, the result is twice as good as Puigbò's one, in kernel ratio and kernel sensitivity. Concerning transcription/translation factor proteins (TF), the proposed method gives low TF sensitivity, while Puigbò's method gives moderate one. In summary, the results indicated that the proposed method can be a good optional applying method to predict optimized genes for particular organisms, and we generated an HEG database for further researches in gene design.

Xander: employing a novel method for efficient gene-targeted metagenomic assembly

DOE PAGES

Wang, Qiong; Fish, Jordan A.; Gilman, Mariah; ...

2015-08-05

Here, metagenomics can provide important insight into microbial communities. However, assembling metagenomic datasets has proven to be computationally challenging. Current methods often assemble only fragmented partial genes. We present a novel method for targeting assembly of specific protein-coding genes. This method combines a de Bruijn graph, as used in standard assembly approaches, and a protein profile hidden Markov model (HMM) for the gene of interest, as used in standard annotation approaches. These are used to create a novel combined weighted assembly graph. Xander performs both assembly and annotation concomitantly using information incorporated in this graph. We demonstrate the utility ofmore » this approach by assembling contigs for one phylogenetic marker gene and for two functional marker genes, first on Human Microbiome Project (HMP)-defined community Illumina data and then on 21 rhizosphere soil metagenomic datasets from three different crops totaling over 800 Gbp of unassembled data. We compared our method to a recently published bulk metagenome assembly method and a recently published gene-targeted assembler and found our method produced more, longer, and higher quality gene sequences. In conclusion, xander combines gene assignment with the rapid assembly of full-length or near full-length functional genes from metagenomic data without requiring bulk assembly or post-processing to find genes of interest. HMMs used for assembly can be tailored to the targeted genes, allowing flexibility to improve annotation over generic annotation pipelines.« less

Xander: employing a novel method for efficient gene-targeted metagenomic assembly.

PubMed

Wang, Qiong; Fish, Jordan A; Gilman, Mariah; Sun, Yanni; Brown, C Titus; Tiedje, James M; Cole, James R

2015-01-01

Metagenomics can provide important insight into microbial communities. However, assembling metagenomic datasets has proven to be computationally challenging. Current methods often assemble only fragmented partial genes. We present a novel method for targeting assembly of specific protein-coding genes. This method combines a de Bruijn graph, as used in standard assembly approaches, and a protein profile hidden Markov model (HMM) for the gene of interest, as used in standard annotation approaches. These are used to create a novel combined weighted assembly graph. Xander performs both assembly and annotation concomitantly using information incorporated in this graph. We demonstrate the utility of this approach by assembling contigs for one phylogenetic marker gene and for two functional marker genes, first on Human Microbiome Project (HMP)-defined community Illumina data and then on 21 rhizosphere soil metagenomic datasets from three different crops totaling over 800 Gbp of unassembled data. We compared our method to a recently published bulk metagenome assembly method and a recently published gene-targeted assembler and found our method produced more, longer, and higher quality gene sequences. Xander combines gene assignment with the rapid assembly of full-length or near full-length functional genes from metagenomic data without requiring bulk assembly or post-processing to find genes of interest. HMMs used for assembly can be tailored to the targeted genes, allowing flexibility to improve annotation over generic annotation pipelines. This method is implemented as open source software and is available at https://github.com/rdpstaff/Xander_assembler.

A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation.

PubMed

Artemov, Artem; Aliper, Alexander; Korzinkin, Michael; Lezhnina, Ksenia; Jellen, Leslie; Zhukov, Nikolay; Roumiantsev, Sergey; Gaifullin, Nurshat; Zhavoronkov, Alex; Borisov, Nicolas; Buzdin, Anton

2015-10-06

A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.

Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium

PubMed Central

Ryan, Natalia; Chorley, Brian; Tice, Raymond R.; Judson, Richard; Corton, J. Christopher

2016-01-01

Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including “very weak” agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. PMID:26865669

Targeted adenoviral vectors

NASA Astrophysics Data System (ADS)

Douglas, Joanne T.

The practical implementation of gene therapy in the clinical setting mandates gene delivery vehicles, or vectors, capable of efficient gene delivery selectively to the target disease cells. The utility of adenoviral vectors for gene therapy is restricted by their dependence on the native adenoviral primary cellular receptor for cell entry. Therefore, a number of strategies have been developed to allow CAR-independent infection of specific cell types, including the use of bispecific conjugates and genetic modifications to the adenoviral capsid proteins, in particular the fibre protein. These targeted adenoviral vectors have demonstrated efficient gene transfer in vitro , correlating with a therapeutic benefit in preclinical animal models. Such vectors are predicted to possess enhanced efficacy in human clinical studies, although anatomical barriers to their use must be circumvented.

A deep auto-encoder model for gene expression prediction.

PubMed

Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

2017-11-17

Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

PubMed

Zhang, Wen; Chen, Yanlin; Li, Dingfang

2017-11-25

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

Gene silencing in Tribolium castaneum as a tool for the targeted identification of candidate RNAi targets in crop pests.

PubMed

Knorr, Eileen; Fishilevich, Elane; Tenbusch, Linda; Frey, Meghan L F; Rangasamy, Murugesan; Billion, Andre; Worden, Sarah E; Gandra, Premchand; Arora, Kanika; Lo, Wendy; Schulenberg, Greg; Valverde-Garcia, Pablo; Vilcinskas, Andreas; Narva, Kenneth E

2018-02-01

RNAi shows potential as an agricultural technology for insect control, yet, a relatively low number of robust lethal RNAi targets have been demonstrated to control insects of agricultural interest. In the current study, a selection of lethal RNAi target genes from the iBeetle (Tribolium castaneum) screen were used to demonstrate efficacy of orthologous targets in the economically important coleopteran pests Diabrotica virgifera virgifera and Meligethes aeneus. Transcript orthologs of 50 selected genes were analyzed in D. v. virgifera diet-based RNAi bioassays; 21 of these RNAi targets showed mortality and 36 showed growth inhibition. Low dose injection- and diet-based dsRNA assays in T. castaneum and D. v. virgifera, respectively, enabled the identification of the four highly potent RNAi target genes: Rop, dre4, ncm, and RpII140. Maize was genetically engineered to express dsRNA directed against these prioritized candidate target genes. T 0 plants expressing Rop, dre4, or RpII140 RNA hairpins showed protection from D. v. virgifera larval feeding damage. dsRNA targeting Rop, dre4, ncm, and RpII140 in M. aeneus also caused high levels of mortality both by injection and feeding. In summary, high throughput systems for model organisms can be successfully used to identify potent RNA targets for difficult-to-work with agricultural insect pests.

In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions.

PubMed

Ivanov, Sergey; Semin, Maxim; Lagunin, Alexey; Filimonov, Dmitry; Poroikov, Vladimir

2017-07-01

Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2017-10-01

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive. This motivates us to derive a reduced feature set for prediction. In addition, since ensemble learning techniques are widely used to improve the classification performance, it is also worthwhile to design an ensemble learning framework to enhance the performance for drug-target interaction prediction. In this paper, we propose a framework for drug-target interaction prediction leveraging both feature dimensionality reduction and ensemble learning. First, we conducted feature subspacing to inject diversity into the classifier ensemble. Second, we applied three different dimensionality reduction methods to the subspaced features. Third, we trained homogeneous base learners with the reduced features and then aggregated their scores to derive the final predictions. For base learners, we selected two classifiers, namely Decision Tree and Kernel Ridge Regression, resulting in two variants of ensemble models, EnsemDT and EnsemKRR, respectively. In our experiments, we utilized AUC (Area under ROC Curve) as an evaluation metric. We compared our proposed methods with various state-of-the-art methods under 5-fold cross validation. Experimental results showed EnsemKRR achieving the highest AUC (94.3%) for predicting drug-target interactions. In addition, dimensionality reduction helped improve the performance of EnsemDT. In conclusion, our proposed methods produced significant improvements for drug-target interaction prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

Generation of TALE nickase-mediated gene-targeted cows expressing human serum albumin in mammary glands.

PubMed

Luo, Yan; Wang, Yongsheng; Liu, Jun; Cui, Chenchen; Wu, Yongyan; Lan, Hui; Chen, Qi; Liu, Xu; Quan, Fusheng; Guo, Zekun; Zhang, Yong

2016-02-08

Targeting exogenous genes at milk protein loci via gene-targeting technology is an ideal strategy for producing large quantities of pharmaceutical proteins. Transcription-activator-like effector (TALE) nucleases (TALENs) are an efficient genome-editing tool. However, the off-target effects may lead to unintended gene mutations. In this study, we constructed TALENs and TALE nickases directed against exon 2 of the bovine β-lactoglobulin (BLG) locus. The nickases can induce a site-specific DNA single-strand break, without inducing double-strand break and nonhomologous end joining mediated gene mutation, and lower cell apoptosis rate than TALENs. After co-transfecting the bovine fetal fibroblasts with human serum albumin (HSA) gene-targeting vector and TALE nickase expression vectors, approximately 4.8% (40/835) of the cell clones contained HSA at BLG locus. Unexpectedly, one homozygous gene-targeted cell clone (1/835, 0.1%) was obtained by targeting both alleles of BLG in a single round of transfection. The recombinant protein mimicking the endogenous BLG was highly expressed and correctly folded in the mammary glands of the targeted cows, and the expression level of HSA was significantly increased in the homozygous targeted cows. Results suggested that the combination of TALE nickase-mediated gene targeting and somatic cell nuclear transfer is a feasible and safe approach in producing gene-targeted livestock.

Prediction of gene expression with cis-SNPs using mixed models and regularization methods.

PubMed

Zeng, Ping; Zhou, Xiang; Huang, Shuiping

2017-05-11

It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases. We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data. The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R 2 of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R 2  ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R 2  ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks. Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in

GeneMachine: gene prediction and sequence annotation.

PubMed

Makalowska, I; Ryan, J F; Baxevanis, A D

2001-09-01

A number of free-standing programs have been developed in order to help researchers find potential coding regions and deduce gene structure for long stretches of what is essentially 'anonymous DNA'. As these programs apply inherently different criteria to the question of what is and is not a coding region, multiple algorithms should be used in the course of positional cloning and positional candidate projects to assure that all potential coding regions within a previously-identified critical region are identified. We have developed a gene identification tool called GeneMachine which allows users to query multiple exon and gene prediction programs in an automated fashion. BLAST searches are also performed in order to see whether a previously-characterized coding region corresponds to a region in the query sequence. A suite of Perl programs and modules are used to run MZEF, GENSCAN, GRAIL 2, FGENES, RepeatMasker, Sputnik, and BLAST. The results of these runs are then parsed and written into ASN.1 format. Output files can be opened using NCBI Sequin, in essence using Sequin as both a workbench and as a graphical viewer. The main feature of GeneMachine is that the process is fully automated; the user is only required to launch GeneMachine and then open the resulting file with Sequin. Annotations can then be made to these results prior to submission to GenBank, thereby increasing the intrinsic value of these data. GeneMachine is freely-available for download at http://genome.nhgri.nih.gov/genemachine. A public Web interface to the GeneMachine server for academic and not-for-profit users is available at http://genemachine.nhgri.nih.gov. The Web supplement to this paper may be found at http://genome.nhgri.nih.gov/genemachine/supplement/.

Self-focusing therapeutic gene delivery with intelligent gene vector swarms: intra-swarm signalling through receptor transgene expression in targeted cells.

PubMed

Tolmachov, Oleg E

2015-01-01

Gene delivery in vivo that is tightly focused on the intended target cells is essential to maximize the benefits of gene therapy and to reduce unwanted side-effects. Cell surface markers are immediately available for probing by therapeutic gene vectors and are often used to direct gene transfer with these vectors to specific target cell populations. However, it is not unusual for the choice of available extra-cellular markers to be too scarce to provide a reliable definition of the desired therapeutically relevant set of target cells. Therefore, interrogation of intra-cellular determinants of cell-specificity, such as tissue-specific transcription factors, can be vital in order to provide detailed cell-guiding information to gene vector particles. An important improvement in cell-specific gene delivery can be achieved through auto-buildup in vector homing efficiency using intelligent 'self-focusing' of swarms of vector particles on target cells. Vector self-focusing was previously suggested to rely on the release of diffusible chemo-attractants after a successful target-specific hit by 'scout' vector particles. I hypothesize that intelligent self-focusing behaviour of swarms of cell-targeted therapeutic gene vectors can be accomplished without the employment of difficult-to-use diffusible chemo-attractants, instead relying on the intra-swarm signalling through cells expressing a non-diffusible extra-cellular receptor for the gene vectors. In the proposed model, cell-guiding information is gathered by the 'scout' gene vector particles, which: (1) attach to a variety of cells via a weakly binding (low affinity) receptor; (2) successfully facilitate gene transfer into these cells; (3) query intra-cellular determinants of cell-specificity with their transgene expression control elements and (4) direct the cell-specific biosynthesis of a vector-encoded strongly binding (high affinity) cell-surface receptor. Free members of the vector swarm loaded with therapeutic cargo

Predicting Gene Structures from Multiple RT-PCR Tests

NASA Astrophysics Data System (ADS)

Kováč, Jakub; Vinař, Tomáš; Brejová, Broňa

It has been demonstrated that the use of additional information such as ESTs and protein homology can significantly improve accuracy of gene prediction. However, many sources of external information are still being omitted from consideration. Here, we investigate the use of product lengths from RT-PCR experiments in gene finding. We present hardness results and practical algorithms for several variants of the problem and apply our methods to a real RT-PCR data set in the Drosophila genome. We conclude that the use of RT-PCR data can improve the sensitivity of gene prediction and locate novel splicing variants.

Contextual remapping in visual search after predictable target-location changes.

PubMed

Conci, Markus; Sun, Luning; Müller, Hermann J

2011-07-01

Invariant spatial context can facilitate visual search. For instance, detection of a target is faster if it is presented within a repeatedly encountered, as compared to a novel, layout of nontargets, demonstrating a role of contextual learning for attentional guidance ('contextual cueing'). Here, we investigated how context-based learning adapts to target location (and identity) changes. Three experiments were performed in which, in an initial learning phase, observers learned to associate a given context with a given target location. A subsequent test phase then introduced identity and/or location changes to the target. The results showed that contextual cueing could not compensate for target changes that were not 'predictable' (i.e. learnable). However, for predictable changes, contextual cueing remained effective even immediately after the change. These findings demonstrate that contextual cueing is adaptive to predictable target location changes. Under these conditions, learned contextual associations can be effectively 'remapped' to accommodate new task requirements.

MicroTrout: A comprehensive, genome-wide miRNA target prediction framework for rainbow trout, Oncorhynchus mykiss.

PubMed

Mennigen, Jan A; Zhang, Dapeng

2016-12-01

Rainbow trout represent an important teleost research model and aquaculture species. As such, rainbow trout are employed in diverse areas of biological research, including basic biological disciplines such as comparative physiology, toxicology, and, since rainbow trout have undergone both teleost- and salmonid-specific rounds of genome duplication, molecular evolution. In recent years, microRNAs (miRNAs, small non-protein coding RNAs) have emerged as important posttranscriptional regulators of gene expression in animals. Given the increasingly recognized importance of miRNAs as an additional layer in the regulation of gene expression and hence biological function, recent efforts using RNA- and genome sequencing approaches have resulted in the creation of several resources for the construction of a comprehensive repertoire of rainbow trout miRNAs and isomiRs (variant miRNA sequences that all appear to derive from the same gene but vary in sequence due to post-transcriptional processing). Importantly, through the recent publication of the rainbow trout genome (Berthelot et al., 2014), mRNA 3'UTR information has become available, allowing for the first time the genome-wide prediction of miRNA-target RNA relationships in this species. We here report the creation of the microtrout database, a comprehensive resource for rainbow trout miRNA and annotated 3'UTRs. The comprehensive database was used to implement an algorithm to predict genome-wide rainbow trout-specific miRNA-mRNA target relationships, generating an improved predictive framework over previously published approaches. This work will serve as a useful framework and sequence resource to experimentally address the role of miRNAs in several research areas using the rainbow trout model, examples of which are discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

Multi-kilobase homozygous targeted gene replacement in human induced pluripotent stem cells.

PubMed

Byrne, Susan M; Ortiz, Luis; Mali, Prashant; Aach, John; Church, George M

2015-02-18

Sequence-specific nucleases such as TALEN and the CRISPR/Cas9 system have so far been used to disrupt, correct or insert transgenes at precise locations in mammalian genomes. We demonstrate efficient 'knock-in' targeted replacement of multi-kilobase genes in human induced pluripotent stem cells (iPSC). Using a model system replacing endogenous human genes with their mouse counterpart, we performed a comprehensive study of targeting vector design parameters for homologous recombination. A 2.7 kilobase (kb) homozygous gene replacement was achieved in up to 11% of iPSC without selection. The optimal homology arm length was around 2 kb, with homology length being especially critical on the arm not adjacent to the cut site. Homologous sequence inside the cut sites was detrimental to targeting efficiency, consistent with a synthesis-dependent strand annealing (SDSA) mechanism. Using two nuclease sites, we observed a high degree of gene excisions and inversions, which sometimes occurred more frequently than indel mutations. While homozygous deletions of 86 kb were achieved with up to 8% frequency, deletion frequencies were not solely a function of nuclease activity and deletion size. Our results analyzing the optimal parameters for targeting vector design will inform future gene targeting efforts involving multi-kilobase gene segments, particularly in human iPSC. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Modularly assembled designer TAL effector nucleases for targeted gene knockout and gene replacement in eukaryotes

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

Li, T; Huang, S; Zhao, XF

Recent studies indicate that the DNA recognition domain of transcription activator-like (TAL) effectors can be combined with the nuclease domain of FokI restriction enzyme to produce TAL effector nucleases (TALENs) that, in pairs, bind adjacent DNA target sites and produce double-strand breaks between the target sequences, stimulating non-homologous end-joining and homologous recombination. Here, we exploit the four prevalent TAL repeats and their DNA recognition cipher to develop a 'modular assembly' method for rapid production of designer TALENs (dTALENs) that recognize unique DNA sequence up to 23 bases in any gene. We have used this approach to engineer 10 dTALENs tomore » target specific loci in native yeast chromosomal genes. All dTALENs produced high rates of site-specific gene disruptions and created strains with expected mutant phenotypes. Moreover, dTALENs stimulated high rates (up to 34%) of gene replacement by homologous recombination. Finally, dTALENs caused no detectable cytotoxicity and minimal levels of undesired genetic mutations in the treated yeast strains. These studies expand the realm of verified TALEN activity from cultured human cells to an intact eukaryotic organism and suggest that low-cost, highly dependable dTALENs can assume a significant role for gene modifications of value in human and animal health, agriculture and industry.« less

Benchmark data sets for structure-based computational target prediction.

PubMed

Schomburg, Karen T; Rarey, Matthias

2014-08-25

Structure-based computational target prediction methods identify potential targets for a bioactive compound. Methods based on protein-ligand docking so far face many challenges, where the greatest probably is the ranking of true targets in a large data set of protein structures. Currently, no standard data sets for evaluation exist, rendering comparison and demonstration of improvements of methods cumbersome. Therefore, we propose two data sets and evaluation strategies for a meaningful evaluation of new target prediction methods, i.e., a small data set consisting of three target classes for detailed proof-of-concept and selectivity studies and a large data set consisting of 7992 protein structures and 72 drug-like ligands allowing statistical evaluation with performance metrics on a drug-like chemical space. Both data sets are built from openly available resources, and any information needed to perform the described experiments is reported. We describe the composition of the data sets, the setup of screening experiments, and the evaluation strategy. Performance metrics capable to measure the early recognition of enrichments like AUC, BEDROC, and NSLR are proposed. We apply a sequence-based target prediction method to the large data set to analyze its content of nontrivial evaluation cases. The proposed data sets are used for method evaluation of our new inverse screening method iRAISE. The small data set reveals the method's capability and limitations to selectively distinguish between rather similar protein structures. The large data set simulates real target identification scenarios. iRAISE achieves in 55% excellent or good enrichment a median AUC of 0.67 and RMSDs below 2.0 Å for 74% and was able to predict the first true target in 59 out of 72 cases in the top 2% of the protein data set of about 8000 structures.

Inheritable Silencing of Endogenous Genes by Hit-and-Run Targeted Epigenetic Editing.

PubMed

Amabile, Angelo; Migliara, Alessandro; Capasso, Paola; Biffi, Mauro; Cittaro, Davide; Naldini, Luigi; Lombardo, Angelo

2016-09-22

Gene silencing is instrumental to interrogate gene function and holds promise for therapeutic applications. Here, we repurpose the endogenous retroviruses' silencing machinery of embryonic stem cells to stably silence three highly expressed genes in somatic cells by epigenetics. This was achieved by transiently expressing combinations of engineered transcriptional repressors that bind to and synergize at the target locus to instruct repressive histone marks and de novo DNA methylation, thus ensuring long-term memory of the repressive epigenetic state. Silencing was highly specific, as shown by genome-wide analyses, sharply confined to the targeted locus without spreading to nearby genes, resistant to activation induced by cytokine stimulation, and relieved only by targeted DNA demethylation. We demonstrate the portability of this technology by multiplex gene silencing, adopting different DNA binding platforms and interrogating thousands of genomic loci in different cell types, including primary T lymphocytes. Targeted epigenome editing might have broad application in research and medicine. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Network pharmacology-based prediction of active compounds and molecular targets in Yijin-Tang acting on hyperlipidaemia and atherosclerosis.

PubMed

Lee, A Yeong; Park, Won; Kang, Tae-Wook; Cha, Min Ho; Chun, Jin Mi

2018-07-15

Yijin-Tang (YJT) is a traditional prescription for the treatment of hyperlipidaemia, atherosclerosis and other ailments related to dampness phlegm, a typical pathological symptom of abnormal body fluid metabolism in Traditional Korean Medicine. However, a holistic network pharmacology approach to understanding the therapeutic mechanisms underlying hyperlipidaemia and atherosclerosis has not been pursued. To examine the network pharmacological potential effects of YJT on hyperlipidaemia and atherosclerosis, we analysed components, performed target prediction and network analysis, and investigated interacting pathways using a network pharmacology approach. Information on compounds in herbal medicines was obtained from public databases, and oral bioavailability and drug-likeness was screened using absorption, distribution, metabolism, and excretion (ADME) criteria. Correlations between compounds and genes were linked using the STITCH database, and genes related to hyperlipidaemia and atherosclerosis were gathered using the GeneCards database. Human genes were identified and subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Network analysis identified 447 compounds in five herbal medicines that were subjected to ADME screening, and 21 compounds and 57 genes formed the main pathways linked to hyperlipidaemia and atherosclerosis. Among them, 10 compounds (naringenin, nobiletin, hesperidin, galangin, glycyrrhizin, homogentisic acid, stigmasterol, 6-gingerol, quercetin and glabridin) were linked to more than four genes, and are bioactive compounds and key chemicals. Core genes in this network were CASP3, CYP1A1, CYP1A2, MMP2 and MMP9. The compound-target gene network revealed close interactions between multiple components and multiple targets, and facilitates a better understanding of the potential therapeutic effects of YJT. Pharmacological network analysis can help to explain the potential effects of YJT for treating dampness phlegm

Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements

PubMed Central

Mumbach, Maxwell R; Satpathy, Ansuman T; Boyle, Evan A; Dai, Chao; Gowen, Benjamin G; Cho, Seung Woo; Nguyen, Michelle L; Rubin, Adam J; Granja, Jeffrey M; Kazane, Katelynn R; Wei, Yuning; Nguyen, Trieu; Greenside, Peyton G; Corces, M Ryan; Tycko, Josh; Simeonov, Dimitre R; Suliman, Nabeela; Li, Rui; Xu, Jin; Flynn, Ryan A; Kundaje, Anshul; Khavari, Paul A; Marson, Alexander; Corn, Jacob E; Quertermous, Thomas; Greenleaf, William J; Chang, Howard Y

2018-01-01

The challenge of linking intergenic mutations to target genes has limited molecular understanding of human diseases. Here we show that H3K27ac HiChIP generates high-resolution contact maps of active enhancers and target genes in rare primary human T cell subtypes and coronary artery smooth muscle cells. Differentiation of naive T cells into T helper 17 cells or regulatory T cells creates subtype-specific enhancer–promoter interactions, specifically at regions of shared DNA accessibility. These data provide a principled means of assigning molecular functions to autoimmune and cardiovascular disease risk variants, linking hundreds of noncoding variants to putative gene targets. Target genes identified with HiChIP are further supported by CRISPR interference and activation at linked enhancers, by the presence of expression quantitative trait loci, and by allele-specific enhancer loops in patient-derived primary cells. The majority of disease-associated enhancers contact genes beyond the nearest gene in the linear genome, leading to a fourfold increase in the number of potential target genes for autoimmune and cardiovascular diseases. PMID:28945252

Mining, identification and function analysis of microRNAs and target genes in peanut (Arachis hypogaea L.).

PubMed

Zhang, Tingting; Hu, Shuhao; Yan, Caixia; Li, Chunjuan; Zhao, Xiaobo; Wan, Shubo; Shan, Shihua

2017-02-01

In the present investigation, a total of 60 conserved peanut (Arachis hypogaea L.) microRNA (miRNA) sequences, belonging to 16 families, were identified using bioinformatics methods. There were 392 target gene sequences, identified from 58 miRNAs with Target-align software and BLASTx analyses. Gene Ontology (GO) functional analysis suggested that these target genes were involved in mediating peanut growth and development, signal transduction and stress resistance. There were 55 miRNA sequences, verified employing a poly (A) tailing test, with a success rate of up to 91.67%. Twenty peanut target gene sequences were randomly selected, and the 5' rapid amplification of the cDNA ends (5'-RACE) method were used to validate the cleavage sites of these target genes. Of these, 14 (70%) peanut miRNA targets were verified by means of gel electrophoresis, cloning and sequencing. Furthermore, functional analysis and homologous sequence retrieval were conducted for target gene sequences, and 26 target genes were chosen as the objects for stress resistance experimental study. Real-time fluorescence quantitative PCR (qRT-PCR) technology was applied to measure the expression level of resistance-associated miRNAs and their target genes in peanut exposed to Aspergillus flavus (A. flavus) infection and drought stress, respectively. In consequence, 5 groups of miRNAs & targets were found accorded with the mode of miRNA negatively controlling the expression of target genes. This study, preliminarily determined the biological functions of some resistance-associated miRNAs and their target genes in peanut. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

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

MicroRNA profiling reveals dysregulated microRNAs and their target gene regulatory networks in cemento-ossifying fibroma.

PubMed

Pereira, Thaís Dos Santos Fontes; Brito, João Artur Ricieri; Guimarães, André Luiz Sena; Gomes, Carolina Cavaliéri; de Lacerda, Júlio Cesar Tanos; de Castro, Wagner Henriques; Coimbra, Roney Santos; Diniz, Marina Gonçalves; Gomez, Ricardo Santiago

2018-01-01

Cemento-ossifying fibroma (COF) is a benign fibro-osseous neoplasm of uncertain pathogenesis, and its treatment results in morbidity. MicroRNAs (miRNA) are small non-coding RNAs that regulate gene expression and may represent therapeutic targets. The purpose of the study was to generate a comprehensive miRNA profile of COF compared to normal bone. Additionally, the most relevant pathways and target genes of differentially expressed miRNA were investigated by in silico analysis. Nine COF and ten normal bone samples were included in the study. miRNA profiling was carried out by using TaqMan® OpenArray® Human microRNA panel containing 754 validated human miRNAs. We identified the most relevant miRNAs target genes through the leader gene approach, using STRING and Cytoscape software. Pathways enrichment analysis was performed using DIANA-miRPath. Eleven miRNAs were downregulated (hsa-miR-95-3p, hsa-miR-141-3p, hsa-miR-205-5p, hsa-miR-223-3p, hsa-miR-31-5p, hsa-miR-944, hsa-miR-200b-3p, hsa-miR-135b-5p, hsa-miR-31-3p, hsa-miR-223-5p and hsa-miR-200c-3p), and five were upregulated (hsa-miR-181a-5p, hsa-miR-181c-5p, hsa-miR-149-5p, hsa-miR-138-5p and hsa-miR-199a-3p) in COF compared to normal bone. Eighteen common target genes were predicted, and the leader genes approach identified the following genes involved in human COF: EZH2, XIAP, MET and TGFBR1. According to the biology of bone and COF, the most relevant KEGG pathways revealed by enrichment analysis were proteoglycans in cancer, miRNAs in cancer, pathways in cancer, p53-, PI3K-Akt-, FoxO- and TGF-beta signalling pathways, which were previously found to be differentially regulated in bone neoplasms, odontogenic tumours and osteogenesis. miRNA dysregulation occurs in COF, and EZH2, XIAP, MET and TGFBR1 are potential targets for functional analysis validation. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Tuning Gene Activity by Inducible and Targeted Regulation of Gene Expression in Minimal Bacterial Cells.

PubMed

Mariscal, Ana M; Kakizawa, Shigeyuki; Hsu, Jonathan Y; Tanaka, Kazuki; González-González, Luis; Broto, Alicia; Querol, Enrique; Lluch-Senar, Maria; Piñero-Lambea, Carlos; Sun, Lijie; Weyman, Philip D; Wise, Kim S; Merryman, Chuck; Tse, Gavin; Moore, Adam J; Hutchison, Clyde A; Smith, Hamilton O; Tomita, Masaru; Venter, J Craig; Glass, John I; Piñol, Jaume; Suzuki, Yo

2018-05-22

Functional genomics studies in minimal mycoplasma cells enable unobstructed access to some of the most fundamental processes in biology. Conventional transposon bombardment and gene knockout approaches often fail to reveal functions of genes that are essential for viability, where lethality precludes phenotypic characterization. Conditional inactivation of genes is effective for characterizing functions central to cell growth and division, but tools are limited for this purpose in mycoplasmas. Here we demonstrate systems for inducible repression of gene expression based on clustered regularly interspaced short palindromic repeats-mediated interference (CRISPRi) in Mycoplasma pneumoniae and synthetic Mycoplasma mycoides, two organisms with reduced genomes actively used in systems biology studies. In the synthetic cell, we also demonstrate inducible gene expression for the first time. Time-course data suggest rapid kinetics and reversible engagement of CRISPRi. Targeting of six selected endogenous genes with this system results in lowered transcript levels or reduced growth rates that agree with lack or shortage of data in previous transposon bombardment studies, and now produces actual cells to analyze. The ksgA gene encodes a methylase that modifies 16S rRNA, rendering it vulnerable to inhibition by the antibiotic kasugamycin. Targeting the ksgA gene with CRISPRi removes the lethal effect of kasugamycin and enables cell growth, thereby establishing specific and effective gene modulation with our system. The facile methods for conditional gene activation and inactivation in mycoplasmas open the door to systematic dissection of genetic programs at the core of cellular life.

Gene Therapy Targeting Glaucoma: Where Are We?

PubMed Central

Liu, Xuyang; Rasmussen, Carol A.; Gabelt, B’Ann T.; Brandt, Curtis R.; Kaufman, Paul L.

2010-01-01

In a chronic disease such as glaucoma, a therapy that provides a long lasting local effect, with minimal systemic side effects, while circumventing the issue of patient compliance, is very attractive. The field of gene therapy is growing rapidly and ocular applications are expanding. Our understanding of the molecular pathogenesis of glaucoma is leading to greater specificity in ocular tissue targeting. Improvements in gene delivery techniques, refinement of vector construction methods, and development of better animal models combine to bring this potential therapy closer to reality. PMID:19539835

Integrated microarray and ChIP analysis identifies multiple Foxa2 dependent target genes in the notochord.

PubMed

Tamplin, Owen J; Cox, Brian J; Rossant, Janet

2011-12-15

The node and notochord are key tissues required for patterning of the vertebrate body plan. Understanding the gene regulatory network that drives their formation and function is therefore important. Foxa2 is a key transcription factor at the top of this genetic hierarchy and finding its targets will help us to better understand node and notochord development. We performed an extensive microarray-based gene expression screen using sorted embryonic notochord cells to identify early notochord-enriched genes. We validated their specificity to the node and notochord by whole mount in situ hybridization. This provides the largest available resource of notochord-expressed genes, and therefore candidate Foxa2 target genes in the notochord. Using existing Foxa2 ChIP-seq data from adult liver, we were able to identify a set of genes expressed in the notochord that had associated regions of Foxa2-bound chromatin. Given that Foxa2 is a pioneer transcription factor, we reasoned that these sites might represent notochord-specific enhancers. Candidate Foxa2-bound regions were tested for notochord specific enhancer function in a zebrafish reporter assay and 7 novel notochord enhancers were identified. Importantly, sequence conservation or predictive models could not have readily identified these regions. Mutation of putative Foxa2 binding elements in two of these novel enhancers abrogated reporter expression and confirmed their Foxa2 dependence. The combination of highly specific gene expression profiling and genome-wide ChIP analysis is a powerful means of understanding developmental pathways, even for small cell populations such as the notochord. Copyright © 2011 Elsevier Inc. All rights reserved.

HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease.

PubMed

Ward, Lucas D; Kellis, Manolis

2016-01-04

More than 90% of common variants associated with complex traits do not affect proteins directly, but instead the circuits that control gene expression. This has increased the urgency of understanding the regulatory genome as a key component for translating genetic results into mechanistic insights and ultimately therapeutics. To address this challenge, we developed HaploReg (http://compbio.mit.edu/HaploReg) to aid the functional dissection of genome-wide association study (GWAS) results, the prediction of putative causal variants in haplotype blocks, the prediction of likely cell types of action, and the prediction of candidate target genes by systematic mining of comparative, epigenomic and regulatory annotations. Since first launching the website in 2011, we have greatly expanded HaploReg, increasing the number of chromatin state maps to 127 reference epigenomes from ENCODE 2012 and Roadmap Epigenomics, incorporating regulator binding data, expanding regulatory motif disruption annotations, and integrating expression quantitative trait locus (eQTL) variants and their tissue-specific target genes from GTEx, Geuvadis, and other recent studies. We present these updates as HaploReg v4, and illustrate a use case of HaploReg for attention deficit hyperactivity disorder (ADHD)-associated SNPs with putative brain regulatory mechanisms. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Gene function prediction with gene interaction networks: a context graph kernel approach.

PubMed

Li, Xin; Chen, Hsinchun; Li, Jiexun; Zhang, Zhu

2010-01-01

Predicting gene functions is a challenge for biologists in the postgenomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.

TARGET Research Goals

Cancer.gov

TARGET researchers use various sequencing and array-based methods to examine the genomes, transcriptomes, and for some diseases epigenomes of select childhood cancers. This “multi-omic” approach generates a comprehensive profile of molecular alterations for each cancer type. Alterations are changes in DNA or RNA, such as rearrangements in chromosome structure or variations in gene expression, respectively. Through computational analyses and assays to validate biological function, TARGET researchers predict which alterations disrupt the function of a gene or pathway and promote cancer growth, progression, and/or survival. Researchers identify candidate therapeutic targets and/or prognostic markers from the cancer-associated alterations.

Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways.

PubMed

Chen, Lei; Zhang, Yu-Hang; Wang, ShaoPeng; Zhang, YunHua; Huang, Tao; Cai, Yu-Dong

2017-01-01

Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems.

Targeted Gene Deletion in Cordyceps militaris Using the Split-Marker Approach.

PubMed

Lou, HaiWei; Ye, ZhiWei; Yun, Fan; Lin, JunFang; Guo, LiQiong; Chen, BaiXiong; Mu, ZhiXian

2018-05-01

The macrofungus Cordyceps militaris contains many kinds of bioactive ingredients that are regulated by functional genes, but the functions of many genes in C. militaris are still unknown. In this study, to improve the frequency of homologous integration, a genetic transformation system based on a split-marker approach was developed for the first time in C. militaris to knock out a gene encoding a terpenoid synthase (Tns). The linear and split-marker deletion cassettes were constructed and introduced into C. militaris protoplasts by PEG-mediated transformation. The transformation of split-marker fragments resulted in a higher efficiency of targeted gene disruption than the transformation of linear deletion cassettes did. The color phenotype of the Tns gene deletion mutants was different from that of wild-type C. militaris. Moreover, a PEG-mediated protoplast transformation system was established, and stable genetic transformants were obtained. This method of targeted gene deletion represents an important tool for investigating the role of C. militaris genes.

Highly specific targeting of the TMPRSS2/ERG fusion gene using liposomal nanovectors

PubMed Central

Shao, Longjiang; Tekedereli, Ibrahim; Wang, Jianghua; Yuca, Erkan; Tsang, Susan; Sood, Anil; Lopez-Berestein, Gabriel; Ozpolat, Bulent; Ittmann, Michael

2012-01-01

Purpose The TMPRSS2/ERG (T/E) fusion gene is present in half of all prostate cancer (PCa) tumors. Fusion of the oncogenic ERG gene with the androgen-regulated TMPRSS2 gene promoter results in expression of fusion mRNAs in PCa cells. The junction of theTMPRSS2 and ERG derived portions of the fusion mRNA constitutes a cancer specific target in cells containing the T/E fusion gene. Targeting the most common alternatively spliced fusion gene mRNA junctional isoforms in vivo using siRNAs in liposomal nanovectors may potentially be a novel, low toxicity treatment for PCa. Experimental Design We designed and optimized siRNAs targeting the two most common T/E fusion gene mRNA junctional isoforms (Type III or Type VI). Specificity of siRNAs was assessed by transient co-transfection in vitro. To test their ability to inhibit growth of PCa cells expressing these fusion gene isoforms in vivo, specific siRNAs in liposomal nanovectors were used to treat mice bearing orthotopic or subcutaneous xenograft tumors expressing the targeted fusion isoforms. Results The targeting siRNAs were both potent and highly specific in vitro. In vivo they significantly inhibited tumor growth. The degree of growth inhibition was variable and was correlated with the extent of fusion gene knockdown. The growth inhibition was associated with marked inhibition of angiogenesis and, to a lesser degree, proliferation and a marked increase in apoptosis of tumor cells. No toxicity was observed. Conclusions Targeting the T/E fusion junction in vivo with specific siRNAs delivered via liposomal nanovectors is a promising therapy for men with PCa. PMID:23052253

Highly specific targeting of the TMPRSS2/ERG fusion gene using liposomal nanovectors.

PubMed

Shao, Longjiang; Tekedereli, Ibrahim; Wang, Jianghua; Yuca, Erkan; Tsang, Susan; Sood, Anil; Lopez-Berestein, Gabriel; Ozpolat, Bulent; Ittmann, Michael

2012-12-15

The TMPRSS2/ERG (T/E) fusion gene is present in half of all prostate cancer tumors. Fusion of the oncogenic ERG gene with the androgen-regulated TMPRSS2 gene promoter results in expression of fusion mRNAs in prostate cancer cells. The junction of theTMPRSS2- and ERG-derived portions of the fusion mRNA constitutes a cancer-specific target in cells containing the T/E fusion gene. Targeting the most common alternatively spliced fusion gene mRNA junctional isoforms in vivo using siRNAs in liposomal nanovectors may potentially be a novel, low-toxicity treatment for prostate cancer. We designed and optimized siRNAs targeting the two most common T/E fusion gene mRNA junctional isoforms (type III or type VI). Specificity of siRNAs was assessed by transient co-transfection in vitro. To test their ability to inhibit growth of prostate cancer cells expressing these fusion gene isoforms in vivo, specific siRNAs in liposomal nanovectors were used to treat mice bearing orthotopic or subcutaneous xenograft tumors expressing the targeted fusion isoforms. The targeting siRNAs were both potent and highly specific in vitro. In vivo they significantly inhibited tumor growth. The degree of growth inhibition was variable and was correlated with the extent of fusion gene knockdown. The growth inhibition was associated with marked inhibition of angiogenesis and, to a lesser degree, proliferation and a marked increase in apoptosis of tumor cells. No toxicity was observed. Targeting the T/E fusion junction in vivo with specific siRNAs delivered via liposomal nanovectors is a promising therapy for men with prostate cancer. ©2012 AACR.

Blood Gene Expression Predicts Bronchiolitis Obliterans Syndrome

PubMed Central

Danger, Richard; Royer, Pierre-Joseph; Reboulleau, Damien; Durand, Eugénie; Loy, Jennifer; Tissot, Adrien; Lacoste, Philippe; Roux, Antoine; Reynaud-Gaubert, Martine; Gomez, Carine; Kessler, Romain; Mussot, Sacha; Dromer, Claire; Brugière, Olivier; Mornex, Jean-François; Guillemain, Romain; Dahan, Marcel; Knoop, Christiane; Botturi, Karine; Foureau, Aurore; Pison, Christophe; Koutsokera, Angela; Nicod, Laurent P.; Brouard, Sophie; Magnan, Antoine; Jougon, J.

2018-01-01

Bronchiolitis obliterans syndrome (BOS), the main manifestation of chronic lung allograft dysfunction, leads to poor long-term survival after lung transplantation. Identifying predictors of BOS is essential to prevent the progression of dysfunction before irreversible damage occurs. By using a large set of 107 samples from lung recipients, we performed microarray gene expression profiling of whole blood to identify early biomarkers of BOS, including samples from 49 patients with stable function for at least 3 years, 32 samples collected at least 6 months before BOS diagnosis (prediction group), and 26 samples at or after BOS diagnosis (diagnosis group). An independent set from 25 lung recipients was used for validation by quantitative PCR (13 stables, 11 in the prediction group, and 8 in the diagnosis group). We identified 50 transcripts differentially expressed between stable and BOS recipients. Three genes, namely POU class 2 associating factor 1 (POU2AF1), T-cell leukemia/lymphoma protein 1A (TCL1A), and B cell lymphocyte kinase, were validated as predictive biomarkers of BOS more than 6 months before diagnosis, with areas under the curve of 0.83, 0.77, and 0.78 respectively. These genes allow stratification based on BOS risk (log-rank test p < 0.01) and are not associated with time posttransplantation. This is the first published large-scale gene expression analysis of blood after lung transplantation. The three-gene blood signature could provide clinicians with new tools to improve follow-up and adapt treatment of patients likely to develop BOS. PMID:29375549

Microarray‑based bioinformatics analysis of the prospective target gene network of key miRNAs influenced by long non‑coding RNA PVT1 in HCC.

PubMed

Zhang, Yu; Mo, Wei-Jia; Wang, Xiao; Zhang, Tong-Tong; Qin, Yuan; Wang, Han-Lin; Chen, Gang; Wei, Dan-Ming; Dang, Yi-Wu

2018-05-02

The long non‑coding RNA (lncRNA) PVT1 plays vital roles in the tumorigenesis and development of various types of cancer. However, the potential expression profiling, functions and pathways of PVT1 in HCC remain unknown. PVT1 was knocked down in SMMC‑7721 cells, and a miRNA microarray analysis was performed to detect the differentially expressed miRNAs. Twelve target prediction algorithms were used to predict the underlying targets of these differentially expressed miRNAs. Bioinformatics analysis was performed to explore the underlying functions, pathways and networks of the targeted genes. Furthermore, the relationship between PVT1 and the clinical parameters in HCC was confirmed based on the original data in the TCGA database. Among the differentially expressed miRNAs, the top two upregulated and downregulated miRNAs were selected for further analysis based on the false discovery rate (FDR), fold‑change (FC) and P‑values. Based on the TCGA database, PVT1 was obviously highly expressed in HCC, and a statistically higher PVT1 expression was found for sex (male), ethnicity (Asian) and pathological grade (G3+G4) compared to the control groups (P<0.05). Furthermore, Gene Ontology (GO) analysis revealed that the target genes were involved in complex cellular pathways, such as the macromolecule biosynthetic process, compound metabolic process, and transcription. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the MAPK and Wnt signaling pathways may be correlated with the regulation of the four candidate miRNAs. The results therefore provide significant information on the differentially expressed miRNAs associated with PVT1 in HCC, and we hypothesized that PVT1 may play vital roles in HCC by regulating different miRNAs or target gene expression (particularly MAPK8) via the MAPK or Wnt signaling pathways. Thus, further investigation of the molecular mechanism of PVT1 in HCC is needed.

Over-expression of the miRNA cluster at chromosome 14q32 in the alcoholic brain correlates with suppression of predicted target mRNA required for oligodendrocyte proliferation.

PubMed

Manzardo, A M; Gunewardena, S; Butler, M G

2013-09-10

We examined miRNA expression from RNA isolated from the frontal cortex (Broadman area 9) of 9 alcoholics (6 males, 3 females, mean age 48 years) and 9 matched controls using both the Affymetrix GeneChip miRNA 2.0 and Human Exon 1.0 ST Arrays to further characterize genetic influences in alcoholism and the effects of alcohol consumption on predicted target mRNA expression. A total of 12 human miRNAs were significantly up-regulated in alcohol dependent subjects (fold change≥1.5, false discovery rate (FDR)≤0.3; p<0.05) compared with controls including a cluster of 4 miRNAs (e.g., miR-377, miR-379) from the maternally expressed 14q32 chromosome region. The status of the up-regulated miRNAs was supported using the high-throughput method of exon microarrays showing decreased predicted mRNA gene target expression as anticipated from the same RNA aliquot. Predicted mRNA targets were involved in cellular adhesion (e.g., THBS2), tissue differentiation (e.g., CHN2), neuronal migration (e.g., NDE1), myelination (e.g., UGT8, CNP) and oligodendrocyte proliferation (e.g., ENPP2, SEMA4D1). Our data support an association of alcoholism with up-regulation of a cluster of miRNAs located in the genomic imprinted domain on chromosome 14q32 with their predicted gene targets involved with oligodendrocyte growth, differentiation and signaling. Copyright © 2013 Elsevier B.V. All rights reserved.

Sensitivity analysis of gene ranking methods in phenotype prediction.

PubMed

deAndrés-Galiana, Enrique J; Fernández-Martínez, Juan L; Sonis, Stephen T

2016-12-01

It has become clear that noise generated during the assay and analytical processes has the ability to disrupt accurate interpretation of genomic studies. Not only does such noise impact the scientific validity and costs of studies, but when assessed in the context of clinically translatable indications such as phenotype prediction, it can lead to inaccurate conclusions that could ultimately impact patients. We applied a sequence of ranking methods to damp noise associated with microarray outputs, and then tested the utility of the approach in three disease indications using publically available datasets. This study was performed in three phases. We first theoretically analyzed the effect of noise in phenotype prediction problems showing that it can be expressed as a modeling error that partially falsifies the pathways. Secondly, via synthetic modeling, we performed the sensitivity analysis for the main gene ranking methods to different types of noise. Finally, we studied the predictive accuracy of the gene lists provided by these ranking methods in synthetic data and in three different datasets related to cancer, rare and neurodegenerative diseases to better understand the translational aspects of our findings. In the case of synthetic modeling, we showed that Fisher's Ratio (FR) was the most robust gene ranking method in terms of precision for all the types of noise at different levels. Significance Analysis of Microarrays (SAM) provided slightly lower performance and the rest of the methods (fold change, entropy and maximum percentile distance) were much less precise and accurate. The predictive accuracy of the smallest set of high discriminatory probes was similar for all the methods in the case of Gaussian and Log-Gaussian noise. In the case of class assignment noise, the predictive accuracy of SAM and FR is higher. Finally, for real datasets (Chronic Lymphocytic Leukemia, Inclusion Body Myositis and Amyotrophic Lateral Sclerosis) we found that FR and SAM

A gene expression profile indicative of early stage HER2 targeted therapy response.

PubMed

O'Neill, Fiona; Madden, Stephen F; Clynes, Martin; Crown, John; Doolan, Padraig; Aherne, Sinéad T; O'Connor, Robert

2013-07-01

Efficacious application of HER2-targetting agents requires the identification of novel predictive biomarkers. Lapatinib, afatinib and neratinib are tyrosine kinase inhibitors (TKIs) of HER2 and EGFR growth factor receptors. A panel of breast cancer cell lines was treated with these agents, trastuzumab, gefitinib and cytotoxic therapies and the expression pattern of a specific panel of genes using RT-PCR was investigated as a potential marker of early drug response to HER2-targeting therapies. Treatment of HER2 TKI-sensitive SKBR3 and BT474 cell lines with lapatinib, afatinib and neratinib induced an increase in the expression of RB1CC1, ERBB3, FOXO3a and NR3C1. The response directly correlated with the degree of sensitivity. This expression pattern switched from up-regulated to down-regulated in the HER2 expressing, HER2-TKI insensitive cell line MDAMB453. Expression of the CCND1 gene demonstrated an inversely proportional response to drug exposure. A similar expression pattern was observed following the treatment with both neratinib and afatinib. These patterns were retained following exposure to traztuzumab and lapatinib plus capecitabine. In contrast, gefitinib, dasatinib and epirubicin treatment resulted in a completely different expression pattern change. In these HER2-expressing cell line models, lapatinib, neratinib, afatinib and trastuzumab treatment generated a characteristic and specific gene expression response, proportionate to the sensitivity of the cell lines to the HER2 inhibitor.Characterisation of the induced changes in expression levels of these genes may therefore give a valuable, very early predictor of the likely extent and specificity of tumour HER2 inhibitor response in patients, potentially guiding more specific use of these agents.

Probability-based collaborative filtering model for predicting gene-disease associations.

PubMed

Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

2017-12-28

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

Identification of apoptosis-related PLZF target genes

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

Bernardo, Maria Victoria; Yelo, Estefania; Gimeno, Lourdes

2007-07-27

The PLZF gene encodes a BTB/POZ-zinc finger-type transcription factor, involved in physiological development, proliferation, differentiation, and apoptosis. In this paper, we investigate proliferation, survival, and gene expression regulation in stable clones from the human haematopoietic K562, DG75, and Jurkat cell lines with inducible expression of PLZF. In Jurkat cells, but not in K562 and DG75 cells, PLZF induced growth suppression and apoptosis in a cell density-dependent manner. Deletion of the BTB/POZ domain of PLZF abrogated growth suppression and apoptosis. PLZF was expressed with a nuclear speckled pattern distinctively in the full-length PLZF-expressing Jurkat clones, suggesting that the nuclear speckled localizationmore » is required for PLZF-induced apoptosis. By microarray analysis, we identified that the apoptosis-inducer TP53INP1, ID1, and ID3 genes were upregulated, and the apoptosis-inhibitor TERT gene was downregulated. The identification of apoptosis-related PLZF target genes may have biological and clinical relevance in cancer typified by altered PLZF expression.« less

Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder.

PubMed

Bartsch, Georg; Mitra, Anirban P; Mitra, Sheetal A; Almal, Arpit A; Steven, Kenneth E; Skinner, Donald G; Fry, David W; Lenehan, Peter F; Worzel, William P; Cote, Richard J

2016-02-01

Due to the high recurrence risk of nonmuscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with nonmuscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumor. Whole genome profiling was performed on 112 frozen nonmuscle invasive urothelial carcinoma specimens obtained at first presentation on Human WG-6 BeadChips (Illumina®). A genetic programming algorithm was applied to evolve classifier mathematical models for outcome prediction. Cross-validation based resampling and gene use frequencies were used to identify the most prognostic genes, which were combined into rules used in a voting algorithm to predict the sample target class. Key genes were validated by quantitative polymerase chain reaction. The classifier set included 21 genes that predicted recurrence. Quantitative polymerase chain reaction was done for these genes in a subset of 100 patients. A 5-gene combined rule incorporating a voting algorithm yielded 77% sensitivity and 85% specificity to predict recurrence in the training set, and 69% and 62%, respectively, in the test set. A singular 3-gene rule was constructed that predicted recurrence with 80% sensitivity and 90% specificity in the training set, and 71% and 67%, respectively, in the test set. Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Predicting Gene Structure Changes Resulting from Genetic Variants via Exon Definition Features.

PubMed

Majoros, William H; Holt, Carson; Campbell, Michael S; Ware, Doreen; Yandell, Mark; Reddy, Timothy E

2018-04-25

Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed, and produce functional proteins. We describe a probabilistic approach for predicting recent changes to gene structure that may or may not conserve function. The model is applicable to both coding and noncoding genes, and can be trained on existing gene annotations without requiring curated examples of aberrant splicing. We apply this model to the problem of predicting altered splicing patterns in the genomes of individual humans, and we demonstrate that performing gene-structure prediction without relying on conserved coding features is feasible. The model predicts an unexpected abundance of variants that create de novo splice sites, an observation supported by both simulations and empirical data from RNA-seq experiments. While these de novo splice variants are commonly misinterpreted by other tools as coding or noncoding variants of little or no effect, we find that in some cases they can have large effects on splicing activity and protein products, and we propose that they may commonly act as cryptic factors in disease. The software is available from geneprediction.org/SGRF. bmajoros@duke.edu. Supplementary information is available at Bioinformatics online.

Conservation of σ28-Dependent Non-Coding RNA Paralogs and Predicted σ54-Dependent Targets in Thermophilic Campylobacter Species

PubMed Central

Le, My Thanh; van Veldhuizen, Mart; Porcelli, Ida; Bongaerts, Roy J.; Gaskin, Duncan J. H.; Pearson, Bruce M.; van Vliet, Arnoud H. M.

2015-01-01

Assembly of flagella requires strict hierarchical and temporal control via flagellar sigma and anti-sigma factors, regulatory proteins and the assembly complex itself, but to date non-coding RNAs (ncRNAs) have not been described to regulate genes directly involved in flagellar assembly. In this study we have investigated the possible role of two ncRNA paralogs (CjNC1, CjNC4) in flagellar assembly and gene regulation of the diarrhoeal pathogen Campylobacter jejuni. CjNC1 and CjNC4 are 37/44 nt identical and predicted to target the 5' untranslated region (5' UTR) of genes transcribed from the flagellar sigma factor σ54. Orthologs of the σ54-dependent 5' UTRs and ncRNAs are present in the genomes of other thermophilic Campylobacter species, and transcription of CjNC1 and CNC4 is dependent on the flagellar sigma factor σ28. Surprisingly, inactivation and overexpression of CjNC1 and CjNC4 did not affect growth, motility or flagella-associated phenotypes such as autoagglutination. However, CjNC1 and CjNC4 were able to mediate sequence-dependent, but Hfq-independent, partial repression of fluorescence of predicted target 5' UTRs in an Escherichia coli-based GFP reporter gene system. This hints towards a subtle role for the CjNC1 and CjNC4 ncRNAs in post-transcriptional gene regulation in thermophilic Campylobacter species, and suggests that the currently used phenotypic methodologies are insufficiently sensitive to detect such subtle phenotypes. The lack of a role of Hfq in the E. coli GFP-based system indicates that the CjNC1 and CjNC4 ncRNAs may mediate post-transcriptional gene regulation in ways that do not conform to the paradigms obtained from the Enterobacteriaceae. PMID:26512728

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.

PubMed

Fang, Jiansong; Wu, Zengrui; Cai, Chuipu; Wang, Qi; Tang, Yun; Cheng, Feixiong

2017-11-27

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

PubMed

Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

2014-01-01

Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

Texture metric that predicts target detection performance

NASA Astrophysics Data System (ADS)

Culpepper, Joanne B.

2015-12-01

Two texture metrics based on gray level co-occurrence error (GLCE) are used to predict probability of detection and mean search time. The two texture metrics are local clutter metrics and are based on the statistics of GLCE probability distributions. The degree of correlation between various clutter metrics and the target detection performance of the nine military vehicles in complex natural scenes found in the Search_2 dataset are presented. Comparison is also made between four other common clutter metrics found in the literature: root sum of squares, Doyle, statistical variance, and target structure similarity. The experimental results show that the GLCE energy metric is a better predictor of target detection performance when searching for targets in natural scenes than the other clutter metrics studied.

Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium.

PubMed

Ryan, Natalia; Chorley, Brian; Tice, Raymond R; Judson, Richard; Corton, J Christopher

2016-05-01

Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. Published by Oxford University Press on behalf of the Society of Toxicology 2016. This work is written by US Government employees and is in the public domain in the US.

Targeted Mutagenesis of Duplicated Genes in Soybean with Zinc-Finger Nucleases1[W][OA

PubMed Central

Curtin, Shaun J.; Zhang, Feng; Sander, Jeffry D.; Haun, William J.; Starker, Colby; Baltes, Nicholas J.; Reyon, Deepak; Dahlborg, Elizabeth J.; Goodwin, Mathew J.; Coffman, Andrew P.; Dobbs, Drena; Joung, J. Keith; Voytas, Daniel F.; Stupar, Robert M.

2011-01-01

We performed targeted mutagenesis of a transgene and nine endogenous soybean (Glycine max) genes using zinc-finger nucleases (ZFNs). A suite of ZFNs were engineered by the recently described context-dependent assembly platform—a rapid, open-source method for generating zinc-finger arrays. Specific ZFNs targeting DICER-LIKE (DCL) genes and other genes involved in RNA silencing were cloned into a vector under an estrogen-inducible promoter. A hairy-root transformation system was employed to investigate the efficiency of ZFN mutagenesis at each target locus. Transgenic roots exhibited somatic mutations localized at the ZFN target sites for seven out of nine targeted genes. We next introduced a ZFN into soybean via whole-plant transformation and generated independent mutations in the paralogous genes DCL4a and DCL4b. The dcl4b mutation showed efficient heritable transmission of the ZFN-induced mutation in the subsequent generation. These findings indicate that ZFN-based mutagenesis provides an efficient method for making mutations in duplicate genes that are otherwise difficult to study due to redundancy. We also developed a publicly accessible Web-based tool to identify sites suitable for engineering context-dependent assembly ZFNs in the soybean genome. PMID:21464476

Normal Collagen and Bone Production by Gene-targeted Human Osteogenesis Imperfecta iPSCs

PubMed Central

Deyle, David R; Khan, Iram F; Ren, Gaoying; Wang, Pei-Rong; Kho, Jordan; Schwarze, Ulrike; Russell, David W

2012-01-01

Osteogenesis imperfecta (OI) is caused by dominant mutations in the type I collagen genes. In principle, the skeletal abnormalities of OI could be treated by transplantation of patient-specific, bone-forming cells that no longer express the mutant gene. Here, we develop this approach by isolating mesenchymal cells from OI patients, inactivating their mutant collagen genes by adeno-associated virus (AAV)-mediated gene targeting, and deriving induced pluripotent stem cells (iPSCs) that were expanded and differentiated into mesenchymal stem cells (iMSCs). Gene-targeted iMSCs produced normal collagen and formed bone in vivo, but were less senescent and proliferated more than bone-derived MSCs. To generate iPSCs that would be more appropriate for clinical use, the reprogramming and selectable marker transgenes were removed by Cre recombinase. These results demonstrate that the combination of gene targeting and iPSC derivation can be used to produce potentially therapeutic cells from patients with genetic disease. PMID:22031238

The past and presence of gene targeting: from chemicals and DNA via proteins to RNA.

PubMed

Geel, T M; Ruiters, M H J; Cool, R H; Halby, L; Voshart, D C; Andrade Ruiz, L; Niezen-Koning, K E; Arimondo, P B; Rots, M G

2018-06-05

The ability to target DNA specifically at any given position within the genome allows many intriguing possibilities and has inspired scientists for decades. Early gene-targeting efforts exploited chemicals or DNA oligonucleotides to interfere with the DNA at a given location in order to inactivate a gene or to correct mutations. We here describe an example towards correcting a genetic mutation underlying Pompe's disease using a nucleotide-fused nuclease (TFO-MunI). In addition to the promise of gene correction, scientists soon realized that genes could be inactivated or even re-activated without inducing potentially harmful DNA damage by targeting transcriptional modulators to a particular gene. However, it proved difficult to fuse protein effector domains to the first generation of programmable DNA-binding agents. The engineering of gene-targeting proteins (zinc finger proteins (ZFPs), transcription activator-like effectors (TALEs)) circumvented this problem. The disadvantage of protein-based gene targeting is that a fusion protein needs to be engineered for every locus. The recent introduction of CRISPR/Cas offers a flexible approach to target a (fusion) protein to the locus of interest using cheap designer RNA molecules. Many research groups now exploit this platform and the first human clinical trials have been initiated: CRISPR/Cas has kicked off a new era of gene targeting and is revolutionizing biomedical sciences.This article is part of a discussion meeting issue 'Frontiers in epigenetic chemical biology'. © 2018 The Author(s).

Identification of potential target genes of ROR-alpha in THP1 and HUVEC cell lines.

PubMed

Gulec, Cagri; Coban, Neslihan; Ozsait-Selcuk, Bilge; Sirma-Ekmekci, Sema; Yildirim, Ozlem; Erginel-Unaltuna, Nihan

2017-04-01

ROR-alpha is a nuclear receptor, activity of which can be modulated by natural or synthetic ligands. Due to its possible involvement in, and potential therapeutic target for atherosclerosis, we aimed to identify ROR-alpha target genes in monocytic and endothelial cell lines. We performed chromatin immunoprecipitation (ChIP) followed by tiling array (ChIP-on-chip) for ROR-alpha in monocytic cell line THP1 and endothelial cell line HUVEC. Following bioinformatic analysis of the array data, we tested four candidate genes in terms of dependence of their expression level on ligand-mediated ROR-alpha activity, and two of them in terms of promoter occupancy by ROR-alpha. Bioinformatic analyses of ChIP-on-chip data suggested that ROR-alpha binds to genomic regions near the transcription start site (TSS) of more than 3000 genes in THP1 and HUVEC. Potential ROR-alpha target genes in both cell types seem to be involved mainly in membrane receptor activity, signal transduction and ion transport. While SPP1 and IKBKA were shown to be direct target genes of ROR-alpha in THP1 monocytes, inflammation related gene HMOX1 and heat shock protein gene HSPA8 were shown to be potential target genes of ROR-alpha. Our results suggest that ROR-alpha may regulate signaling receptor activity, and transmembrane transport activity through its potential target genes. ROR-alpha seems also to play role in cellular sensitivity to environmental substances like arsenite and chloroprene. Although, the expression analyses have shown that synthetic ROR-alpha ligands can modulate some of potential ROR-alpha target genes, functional significance of ligand-dependent modulation of gene expression needs to be confirmed with further analyses. Copyright © 2017 Elsevier Inc. All rights reserved.

Human L-DOPA decarboxylase mRNA is a target of miR-145: A prediction to validation workflow.

PubMed

Papadopoulos, Emmanuel I; Fragoulis, Emmanuel G; Scorilas, Andreas

2015-01-10

l-DOPA decarboxylase (DDC) is a multiply-regulated gene which encodes the enzyme that catalyzes the biosynthesis of dopamine in humans. MicroRNAs comprise a novel class of endogenously transcribed small RNAs that can post-transcriptionally regulate the expression of various genes. Given that the mechanism of microRNA target recognition remains elusive, several genes, including DDC, have not yet been identified as microRNA targets. Nevertheless, a number of specifically designed bioinformatic algorithms provide candidate miRNAs for almost every gene, but still their results exhibit moderate accuracy and should be experimentally validated. Motivated by the above, we herein sought to discover a microRNA that regulates DDC expression. By using the current algorithms according to bibliographic recommendations we found that miR-145 could be predicted with high specificity as a candidate regulatory microRNA for DDC expression. Thus, a validation experiment followed by firstly transfecting an appropriate cell culture system with a synthetic miR-145 sequence and sequentially assessing the mRNA and protein levels of DDC via real-time PCR and Western blotting, respectively. Our analysis revealed that miR-145 had no significant impact on protein levels of DDC but managed to dramatically downregulate its mRNA expression. Overall, the experimental and bioinformatic analysis conducted herein indicate that miR-145 has the ability to regulate DDC mRNA expression and potentially this occurs by recognizing its mRNA as a target. Copyright © 2014. Published by Elsevier B.V.

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

On Why Targets Evoke P3 Components in Prediction Tasks: Drawing an Analogy between Prediction and Matching Tasks

PubMed Central

Verleger, Rolf; Cäsar, Stephanie; Siller, Bastian; Śmigasiewicz, Kamila

2017-01-01

P3 is the most conspicuous component in recordings of stimulus-evoked EEG potentials from the human scalp, occurring whenever some task has to be performed with the stimuli. The process underlying P3 has been assumed to be the updating of expectancies. More recently, P3 has been related to decision processing and to activation of established stimulus-response associations (S/R-link hypothesis). However, so far this latter approach has not provided a conception about how to explain the occurrence of P3 with predicted stimuli, although P3 was originally discovered in a prediction task. The present article proposes such a conception. We assume that the internal responses right or wrong both become associatively linked to each predicted target and that one of these two response alternatives gets activated as a function of match or mismatch of the target to the preceding prediction. This seems similar to comparison tasks where responses depend on the matching of the target stimulus with a preceding first stimulus (S1). Based on this idea, this study compared the effects of frequencies of first events (predictions or S1) on target-evoked P3s in prediction and comparison tasks. Indeed, frequencies not only of targets but also of first events had similar effects across tasks on target-evoked P3s. These results support the notion that P3 evoked by predicted stimuli reflects activation of appropriate internal “match” or “mismatch” responses, which is compatible with S/R-link hypothesis. PMID:29066965

Targeted Antiangiogenesis Gene Therapy Using Targeted Cationic Microbubbles Conjugated with CD105 Antibody Compared with Untargeted Cationic and Neutral Microbubbles

PubMed Central

Zhou, Yu; Gu, Haitao; Xu, Yan; Li, Fan; Kuang, Shaojing; Wang, Zhigang; Zhou, Xiyuan; Ma, Huafeng; Li, Pan; Zheng, Yuanyi; Ran, Haitao; Jian, Jia; Zhao, Yajing; Song, Weixiang; Wang, Qiushi; Wang, Dong

2015-01-01

Objective This study aimed to develop targeted cationic microbubbles conjugated with a CD105 antibody (CMB105) for use in targeted vascular endothelial cell gene therapy and ultrasound imaging. We compared the results with untargeted cationic microbubbles (CMB) and neutral microbubbles (NMB). Methods CMB105 were prepared and compared with untargeted CMB and NMB. First, the microbubbles were characterized in terms of size, zeta-potential, antibody binding ability and plasmid DNA loading capacity. A tumor model of subcutaneous breast cancer in nude mice was used for our experiments. The ability of different types of microbubbles to target HUVECs in vitro and tumor neovascularization in vivo was measured. The endostatin gene was selected for its outstanding antiangiogenesis effect. For in vitro experiments, the transfection efficiency and cell cycle were analyzed using flow cytometry, and the transcription and expression of endostatin were measured by qPCR and Western blotting, respectively. Vascular tube cavity formation and tumor cell invasion were used to evaluate the antiangiogenesis gene therapy efficiency in vitro. Tumors were exposed to ultrasound irradiation with different types of microbubbles, and the gene therapy effects were investigated by detecting apoptosis induction and changes in tumor volume. Results CMB105 and CMB differed significantly from NMB in terms of zeta-potential, and the DNA loading capacities were 16.76±1.75 μg, 18.21±1.22 μg, and 0.48±0.04 μg per 5×108 microbubbles, respectively. The charge coupling of plasmid DNA to CMB105 was not affected by the presence of the CD105 antibody. Both CMB105 and CMB could target to HUVECs in vitro, whereas only CMB105 could target to tumor neovascularization in vivo. In in vitro experiments, the transfection efficiency of CMB105 was 24.7-fold higher than the transfection efficiency of NMB and 1.47-fold higher than the transfection efficiency of CMB (P<0.05). With ultrasound-targeted microbubble

Targeted antiangiogenesis gene therapy using targeted cationic microbubbles conjugated with CD105 antibody compared with untargeted cationic and neutral microbubbles.

PubMed

Zhou, Yu; Gu, Haitao; Xu, Yan; Li, Fan; Kuang, Shaojing; Wang, Zhigang; Zhou, Xiyuan; Ma, Huafeng; Li, Pan; Zheng, Yuanyi; Ran, Haitao; Jian, Jia; Zhao, Yajing; Song, Weixiang; Wang, Qiushi; Wang, Dong

2015-01-01

This study aimed to develop targeted cationic microbubbles conjugated with a CD105 antibody (CMB105) for use in targeted vascular endothelial cell gene therapy and ultrasound imaging. We compared the results with untargeted cationic microbubbles (CMB) and neutral microbubbles (NMB). CMB105 were prepared and compared with untargeted CMB and NMB. First, the microbubbles were characterized in terms of size, zeta-potential, antibody binding ability and plasmid DNA loading capacity. A tumor model of subcutaneous breast cancer in nude mice was used for our experiments. The ability of different types of microbubbles to target HUVECs in vitro and tumor neovascularization in vivo was measured. The endostatin gene was selected for its outstanding antiangiogenesis effect. For in vitro experiments, the transfection efficiency and cell cycle were analyzed using flow cytometry, and the transcription and expression of endostatin were measured by qPCR and Western blotting, respectively. Vascular tube cavity formation and tumor cell invasion were used to evaluate the antiangiogenesis gene therapy efficiency in vitro. Tumors were exposed to ultrasound irradiation with different types of microbubbles, and the gene therapy effects were investigated by detecting apoptosis induction and changes in tumor volume. CMB105 and CMB differed significantly from NMB in terms of zeta-potential, and the DNA loading capacities were 16.76±1.75 μg, 18.21±1.22 μg, and 0.48±0.04 μg per 5×10(8) microbubbles, respectively. The charge coupling of plasmid DNA to CMB105 was not affected by the presence of the CD105 antibody. Both CMB105 and CMB could target to HUVECs in vitro, whereas only CMB105 could target to tumor neovascularization in vivo. In in vitro experiments, the transfection efficiency of CMB105 was 24.7-fold higher than the transfection efficiency of NMB and 1.47-fold higher than the transfection efficiency of CMB (P<0.05). With ultrasound-targeted microbubble destruction (UTMD

Identification of target genes of synovial sarcoma-associated fusion oncoprotein using human pluripotent stem cells

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

Hayakawa, Kazuo; Department of Cell Growth and Differentiation, Center for iPS Cell Research and Application, Kyoto University, Kyoto; Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Nagoya City University, Nagoya

2013-03-22

Highlights: ► We tried to identify targets of synovial sarcoma (SS)-associated SYT–SSX fusion gene. ► We established pluripotent stem cell (PSC) lines with inducible SYT–SSX gene. ► SYT–SSX responsive genes were identified by the induction of SYT–SSX in PSC. ► SS-related genes were selected from database by in silico analyses. ► 51 genes were finally identified among SS-related genes as targets of SYT–SSX in PSC. -- Abstract: Synovial sarcoma (SS) is a malignant soft tissue tumor harboring chromosomal translocation t(X; 18)(p11.2; q11.2), which produces SS-specific fusion gene, SYT–SSX. Although precise function of SYT–SSX remains to be investigated, accumulating evidences suggestmore » its role in gene regulation via epigenetic mechanisms, and the product of SYT–SSX target genes may serve as biomarkers of SS. Lack of knowledge about the cell-of-origin of SS, however, has placed obstacle in the way of target identification. Here we report a novel approach to identify SYT–SSX2 target genes using human pluripotent stem cells (hPSCs) containing a doxycycline-inducible SYT–SSX2 gene. SYT–SSX2 was efficiently induced both at mRNA and protein levels within three hours after doxycycline administration, while no morphological change of hPSCs was observed until 24 h. Serial microarray analyses identified genes of which the expression level changed more than twofold within 24 h. Surprisingly, the majority (297/312, 95.2%) were up-regulated genes and a result inconsistent with the current concept of SYT–SSX as a transcriptional repressor. Comparing these genes with SS-related genes which were selected by a series of in silico analyses, 49 and 2 genes were finally identified as candidates of up- and down-regulated target of SYT–SSX, respectively. Association of these genes with SYT–SSX in SS cells was confirmed by knockdown experiments. Expression profiles of SS-related genes in hPSCs and human mesenchymal stem cells (hMSCs) were

Construction and applications of exon-trapping gene-targeting vectors with a novel strategy for negative selection.

PubMed

Saito, Shinta; Ura, Kiyoe; Kodama, Miho; Adachi, Noritaka

2015-06-30

Targeted gene modification by homologous recombination provides a powerful tool for studying gene function in cells and animals. In higher eukaryotes, non-homologous integration of targeting vectors occurs several orders of magnitude more frequently than does targeted integration, making the gene-targeting technology highly inefficient. For this reason, negative-selection strategies have been employed to reduce the number of drug-resistant clones associated with non-homologous vector integration, particularly when artificial nucleases to introduce a DNA break at the target site are unavailable or undesirable. As such, an exon-trap strategy using a promoterless drug-resistance marker gene provides an effective way to counterselect non-homologous integrants. However, constructing exon-trapping targeting vectors has been a time-consuming and complicated process. By virtue of highly efficient att-mediated recombination, we successfully developed a simple and rapid method to construct plasmid-based vectors that allow for exon-trapping gene targeting. These exon-trap vectors were useful in obtaining correctly targeted clones in mouse embryonic stem cells and human HT1080 cells. Most importantly, with the use of a conditionally cytotoxic gene, we further developed a novel strategy for negative selection, thereby enhancing the efficiency of counterselection for non-homologous integration of exon-trap vectors. Our methods will greatly facilitate exon-trapping gene-targeting technologies in mammalian cells, particularly when combined with the novel negative selection strategy.

Protein targeting in the analysis of learning and memory: a potential alternative to gene targeting.

PubMed

Gerlai, R; Williams, S P; Cairns, B; Van Bruggen, N; Moran, P; Shih, A; Caras, I; Sauer, H; Phillips, H S; Winslow, J W

1998-11-01

Gene targeting using homologous recombination in embryonic stem (ES) cells offers unprecedented precision with which one may manipulate single genes and investigate the in vivo effects of defined mutations in the mouse. Geneticists argue that this technique abrogates the lack of highly specific pharmacological tools in the study of brain function and behavior. However, by now it has become clear that gene targeting has some limitations too. One problem is spatial and temporal specificity of the generated mutation, which may appear in multiple brain regions or even in other organs and may also be present throughout development, giving rise to complex, secondary phenotypical alterations. This may be a disadvantage in the functional analysis of a number of genes associated with learning and memory processes. For example, several proteins, including neurotrophins--cell-adhesion molecules--and protein kinases, that play a significant developmental role have recently been suggested to be also involved in neural and behavioral plasticity. Knocking out genes of such proteins may lead to developmental alterations or even embryonic lethality in the mouse, making it difficult to study their function in neural plasticity, learning, and memory. Therefore, alternative strategies to gene targeting may be needed. Here, we suggest a potentially useful in vivo strategy based on systemic application of immunoadhesins, genetically engineered fusion proteins possessing the Fc portion of the human IgG molecule and, for example, a binding domain of a receptor of interest. These proteins are stable in vivo and exhibit high binding specificity and affinity for the endogenous ligand of the receptor, but lack the ability to signal. Thus, if delivered to the brain, immunoadhesins may specifically block signalling of the receptor of interest. Using osmotic minipumps, the protein can be infused in a localized region of the brain for a specified period of time (days or weeks). Thus, the location

Building predictive gene signatures through simultaneous assessment of transcription factor activation and gene expression.

EPA Science Inventory

Building predictive gene signatures through simultaneous assessment of transcription factor activation and gene expression Exposure to many drugs and environmentally-relevant chemicals can cause adverse outcomes. These adverse outcomes, such as cancer, have been linked to mol...

Modeling leaderless transcription and atypical genes results in more accurate gene prediction in prokaryotes.

PubMed

Lomsadze, Alexandre; Gemayel, Karl; Tang, Shiyuyun; Borodovsky, Mark

2018-05-17

In a conventional view of the prokaryotic genome organization, promoters precede operons and ribosome binding sites (RBSs) with Shine-Dalgarno consensus precede genes. However, recent experimental research suggesting a more diverse view motivated us to develop an algorithm with improved gene-finding accuracy. We describe GeneMarkS-2, an ab initio algorithm that uses a model derived by self-training for finding species-specific (native) genes, along with an array of precomputed "heuristic" models designed to identify harder-to-detect genes (likely horizontally transferred). Importantly, we designed GeneMarkS-2 to identify several types of distinct sequence patterns (signals) involved in gene expression control, among them the patterns characteristic for leaderless transcription as well as noncanonical RBS patterns. To assess the accuracy of GeneMarkS-2, we used genes validated by COG (Clusters of Orthologous Groups) annotation, proteomics experiments, and N-terminal protein sequencing. We observed that GeneMarkS-2 performed better on average in all accuracy measures when compared with the current state-of-the-art gene prediction tools. Furthermore, the screening of ∼5000 representative prokaryotic genomes made by GeneMarkS-2 predicted frequent leaderless transcription in both archaea and bacteria. We also observed that the RBS sites in some species with leadered transcription did not necessarily exhibit the Shine-Dalgarno consensus. The modeling of different types of sequence motifs regulating gene expression prompted a division of prokaryotic genomes into five categories with distinct sequence patterns around the gene starts. © 2018 Lomsadze et al.; Published by Cold Spring Harbor Laboratory Press.

Genomic Features That Predict Allelic Imbalance in Humans Suggest Patterns of Constraint on Gene Expression Variation

PubMed Central

Fédrigo, Olivier; Haygood, Ralph; Mukherjee, Sayan; Wray, Gregory A.

2009-01-01

Variation in gene expression is an important contributor to phenotypic diversity within and between species. Although this variation often has a genetic component, identification of the genetic variants driving this relationship remains challenging. In particular, measurements of gene expression usually do not reveal whether the genetic basis for any observed variation lies in cis or in trans to the gene, a distinction that has direct relevance to the physical location of the underlying genetic variant, and which may also impact its evolutionary trajectory. Allelic imbalance measurements identify cis-acting genetic effects by assaying the relative contribution of the two alleles of a cis-regulatory region to gene expression within individuals. Identification of patterns that predict commonly imbalanced genes could therefore serve as a useful tool and also shed light on the evolution of cis-regulatory variation itself. Here, we show that sequence motifs, polymorphism levels, and divergence levels around a gene can be used to predict commonly imbalanced genes in a human data set. Reduction of this feature set to four factors revealed that only one factor significantly differentiated between commonly imbalanced and nonimbalanced genes. We demonstrate that these results are consistent between the original data set and a second published data set in humans obtained using different technical and statistical methods. Finally, we show that variation in the single allelic imbalance-associated factor is partially explained by the density of genes in the region of a target gene (allelic imbalance is less probable for genes in gene-dense regions), and, to a lesser extent, the evenness of expression of the gene across tissues and the magnitude of negative selection on putative regulatory regions of the gene. These results suggest that the genomic distribution of functional cis-regulatory variants in the human genome is nonrandom, perhaps due to local differences in evolutionary

A gene expression profile indicative of early stage HER2 targeted therapy response

PubMed Central

2013-01-01

Background Efficacious application of HER2-targetting agents requires the identification of novel predictive biomarkers. Lapatinib, afatinib and neratinib are tyrosine kinase inhibitors (TKIs) of HER2 and EGFR growth factor receptors. A panel of breast cancer cell lines was treated with these agents, trastuzumab, gefitinib and cytotoxic therapies and the expression pattern of a specific panel of genes using RT-PCR was investigated as a potential marker of early drug response to HER2-targeting therapies. Results Treatment of HER2 TKI-sensitive SKBR3 and BT474 cell lines with lapatinib, afatinib and neratinib induced an increase in the expression of RB1CC1, ERBB3, FOXO3a and NR3C1. The response directly correlated with the degree of sensitivity. This expression pattern switched from up-regulated to down-regulated in the HER2 expressing, HER2-TKI insensitive cell line MDAMB453. Expression of the CCND1 gene demonstrated an inversely proportional response to drug exposure. A similar expression pattern was observed following the treatment with both neratinib and afatinib. These patterns were retained following exposure to traztuzumab and lapatinib plus capecitabine. In contrast, gefitinib, dasatinib and epirubicin treatment resulted in a completely different expression pattern change. Conclusions In these HER2-expressing cell line models, lapatinib, neratinib, afatinib and trastuzumab treatment generated a characteristic and specific gene expression response, proportionate to the sensitivity of the cell lines to the HER2 inhibitor. Characterisation of the induced changes in expression levels of these genes may therefore give a valuable, very early predictor of the likely extent and specificity of tumour HER2 inhibitor response in patients, potentially guiding more specific use of these agents. PMID:23816254

Targeted gene disruption in Koji mold Aspergillus oryzae.

PubMed

Maruyama, Jun-Ichi; Kitamoto, Katsuhiko

2011-01-01

Filamentous fungi have received attentions as hosts for heterologous protein production because of their high secretion capability and eukaryotic post-translational modifications. One of the safest hosts for heterologous protein production is Koji mold Aspergillus oryzae since it has been used in the production of Japanese fermented foods for over 1,000 years. The production levels of proteins from higher eukaryotes are much lower than those of homologous (fungal) proteins. Bottlenecks in the heterologous protein production are suggested to be proteolytic degradation of the produced protein in the medium and the secretory pathway. For construction of excellent host strains, many genes causing the bottlenecks should be disrupted rapidly and efficiently. We developed a marker recycling system with the highly efficient gene-targeting background in A. oryzae. By employing this technique, we performed multiple gene disruption of the ten protease genes. The decuple protease gene disruptant showed fourfold production level of a heterologous protein compared with the wild-type strain.

Retinoschisislike alterations in the mouse eye caused by gene targeting of the Norrie disease gene.

PubMed

Ruether, K; van de Pol, D; Jaissle, G; Berger, W; Tornow, R P; Zrenner, E

1997-03-01

To investigate the retinal function and morphology of mice carrying a replacement mutation in exon 2 of the Norrie disease gene. Recently, Norrie disease mutant mice have been generated using gene targeting technology. The mutation removes the 56 N-terminal amino acids of the Norrie gene product. Ganzfeld electroretinograms (ERGs) were obtained in five animals hemizygous or homozygous for the mutant gene and in three female animals heterozygous for the mutant gene. As controls, three males carrying the wild-type gene were examined. Electroretinogram testing included rod a- and b-wave V-log I functions, oscillatory potentials, and cone responses. The fundus morphology has been visualized by scanning laser ophthalmoscopy. Rod and cone ERG responses and fundus morphology were not significantly different among female heterozygotes and wild-type mice. In contrast, the hemizygous mice displayed a severe loss of ERG b-wave, leading to a negatively shaped scotopic ERG and a marked reduction of oscillatory potentials. The a-wave was normal at low intensities, and only with brighter flashes was there a moderate amplitude loss. Cone amplitudes were barely recordable in the gene-targeted males. Ophthalmoscopy revealed snowflakelike vitreal changes, retinoschisis, and pigment epithelium irregularities in hemizygotes and homozygotes, but no changes in female heterozygotes. The negatively shaped scotopic ERG in male mice with a Norrie disease gene mutation probably was caused by retinoschisis. Pigment epithelial changes and degenerations of the outer retina are relatively mild. These findings may be a clue to the embryonal retinoschisislike pathogenesis of Norrie disease in humans or it may indicate a different expression of the Norrie disease gene defect in mice compared to that in humans.

Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and primary mediastinal B-cell lymphoma allows prediction of therapeutic targets.

PubMed

Raia, Valentina; Schilling, Marcel; Böhm, Martin; Hahn, Bettina; Kowarsch, Andreas; Raue, Andreas; Sticht, Carsten; Bohl, Sebastian; Saile, Maria; Möller, Peter; Gretz, Norbert; Timmer, Jens; Theis, Fabian; Lehmann, Wolf-Dieter; Lichter, Peter; Klingmüller, Ursula

2011-02-01

Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.

TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples.

PubMed

Bandyopadhyay, Sanghamitra; Mitra, Ramkrishna

2009-10-15

Prediction of microRNA (miRNA) target mRNAs using machine learning approaches is an important area of research. However, most of the methods suffer from either high false positive or false negative rates. One reason for this is the marked deficiency of negative examples or miRNA non-target pairs. Systematic identification of non-target mRNAs is still not addressed properly, and therefore, current machine learning approaches are compelled to rely on artificially generated negative examples for training. In this article, we have identified approximately 300 tissue-specific negative examples using a novel approach that involves expression profiling of both miRNAs and mRNAs, miRNA-mRNA structural interactions and seed-site conservation. The newly generated negative examples are validated with pSILAC dataset, which elucidate the fact that the identified non-targets are indeed non-targets.These high-throughput tissue-specific negative examples and a set of experimentally verified positive examples are then used to build a system called TargetMiner, a support vector machine (SVM)-based classifier. In addition to assessing the prediction accuracy on cross-validation experiments, TargetMiner has been validated with a completely independent experimental test dataset. Our method outperforms 10 existing target prediction algorithms and provides a good balance between sensitivity and specificity that is not reflected in the existing methods. We achieve a significantly higher sensitivity and specificity of 69% and 67.8% based on a pool of 90 feature set and 76.5% and 66.1% using a set of 30 selected feature set on the completely independent test dataset. In order to establish the effectiveness of the systematically generated negative examples, the SVM is trained using a different set of negative data generated using the method in Yousef et al. A significantly higher false positive rate (70.6%) is observed when tested on the independent set, while all other factors are kept the

In silico prediction of escherichia coli proteins targeting the host cell nucleus, with special reference to their role in colon cancer etiology.

PubMed

Khan, Abdul Arif

2014-06-01

The potential role of Escherichia coli in the development of colorectal carcinoma (CRC) has been investigated in many studies. Although the exact mechanism is not clear, chronic inflammation caused by E. coli and other related events are suggested as possible causes behind E. coli-induced colon cancer. It has been found that CRC cells, but not normal cells, are colonized by an intracellular form of E. coli. We predicted nuclear targeting of bacterial proteins in the host cell through computational tools nuclear localization signal (NLS) mapper and balanced subcellular localization predictor (BaCeILo). During intracellular E. coli residence, such targeting is highly likely and may have a possible role in colon cancer etiology. We observed that several gene expression-associated proteins of E. coli can migrate to the host nucleus during intracellular infections. This situation provides an opportunity for competitive interaction of host and pathogen proteins with similar cellular substrates, thereby increasing the chances of development of colon cancer. Moreover, the results indicated that proteins localized in the membrane of E. coli mostly act as secretary proteins in host cells. No exact correlation was observed between NLS prediction and nuclear localization prediction by BaCeILo. This is partly because of a number of reasons, including that only 30% of nuclear proteins carry NLS and that proteins <40 kDa molecular weight can passively target the host nucleus. This study concludes that detection of gene expression-specific E. coli proteins and their targeting of the nucleus may have a profound impact on CRC etiology.

The role of gene-gene interaction in the prediction of criminal behavior.

PubMed

Boutwell, Brian B; Menard, Scott; Barnes, J C; Beaver, Kevin M; Armstrong, Todd A; Boisvert, Danielle

2014-04-01

A host of research has examined the possibility that environmental risk factors might condition the influence of genes on various outcomes. Less research, however, has been aimed at exploring the possibility that genetic factors might interact to impact the emergence of human traits. Even fewer studies exist examining the interaction of genes in the prediction of behavioral outcomes. The current study expands this body of research by testing the interaction between genes involved in neural transmission. Our findings suggest that certain dopamine genes interact to increase the odds of criminogenic outcomes in a national sample of Americans. Copyright © 2014 Elsevier Inc. All rights reserved.

Reconstructing directed gene regulatory network by only gene expression data.

PubMed

Zhang, Lu; Feng, Xi Kang; Ng, Yen Kaow; Li, Shuai Cheng

2016-08-18

Accurately identifying gene regulatory network is an important task in understanding in vivo biological activities. The inference of such networks is often accomplished through the use of gene expression data. Many methods have been developed to evaluate gene expression dependencies between transcription factor and its target genes, and some methods also eliminate transitive interactions. The regulatory (or edge) direction is undetermined if the target gene is also a transcription factor. Some methods predict the regulatory directions in the gene regulatory networks by locating the eQTL single nucleotide polymorphism, or by observing the gene expression changes when knocking out/down the candidate transcript factors; regrettably, these additional data are usually unavailable, especially for the samples deriving from human tissues. In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are

Crispr-mediated Gene Targeting of Human Induced Pluripotent Stem Cells.

PubMed

Byrne, Susan M; Church, George M

2015-01-01

CRISPR/Cas9 nuclease systems can create double-stranded DNA breaks at specific sequences to efficiently and precisely disrupt, excise, mutate, insert, or replace genes. However, human embryonic stem or induced pluripotent stem cells (iPSCs) are more difficult to transfect and less resilient to DNA damage than immortalized tumor cell lines. Here, we describe an optimized protocol for genome engineering of human iPSCs using a simple transient transfection of plasmids and/or single-stranded oligonucleotides. With this protocol, we achieve transfection efficiencies greater than 60%, with gene disruption efficiencies from 1-25% and gene insertion/replacement efficiencies from 0.5-10% without any further selection or enrichment steps. We also describe how to design and assess optimal sgRNA target sites and donor targeting vectors; cloning individual iPSC by single cell FACS sorting, and genotyping successfully edited cells.

Hypoxia-inducible tumour-specific promoters as a dual-targeting transcriptional regulation system for cancer gene therapy

PubMed Central

Javan, Bita; Shahbazi, Majid

2017-01-01

Transcriptional targeting is the best approach for specific gene therapy. Hypoxia is a common feature of the tumour microenvironment. Therefore, targeting gene expression in hypoxic cells by placing transgene under the control of a hypoxia-responsive promoter can be a good strategy for cancer-specific gene therapy. The hypoxia-inducible gene expression system has been investigated more in suicide gene therapy and it can also be of great help in knocking down cancer gene therapy with siRNAs. However, this system needs to be optimised to have maximum efficacy with minimum side effects in normal tissues. The combination of tissue-/tumour-specific promoters with HRE core sequences has been found to enhance the specificity and efficacy of this system. In this review, hypoxia-inducible gene expression system as well as gene therapy strategies targeting tumour hypoxia will be discussed. This review will also focus on hypoxia-inducible tumour-specific promoters as a dual-targeting transcriptional regulation systems developed for cancer-specific gene therapy. PMID:28798809

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

PubMed Central

Peña-Castillo, Lourdes; Tasan, Murat; Myers, Chad L; Lee, Hyunju; Joshi, Trupti; Zhang, Chao; Guan, Yuanfang; Leone, Michele; Pagnani, Andrea; Kim, Wan Kyu; Krumpelman, Chase; Tian, Weidong; Obozinski, Guillaume; Qi, Yanjun; Mostafavi, Sara; Lin, Guan Ning; Berriz, Gabriel F; Gibbons, Francis D; Lanckriet, Gert; Qiu, Jian; Grant, Charles; Barutcuoglu, Zafer; Hill, David P; Warde-Farley, David; Grouios, Chris; Ray, Debajyoti; Blake, Judith A; Deng, Minghua; Jordan, Michael I; Noble, William S; Morris, Quaid; Klein-Seetharaman, Judith; Bar-Joseph, Ziv; Chen, Ting; Sun, Fengzhu; Troyanskaya, Olga G; Marcotte, Edward M; Xu, Dong; Hughes, Timothy R; Roth, Frederick P

2008-01-01

Background: Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated. Results: In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%. Conclusion: We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized. PMID:18613946

Prediction of gene expression in embryonic structures of Drosophila melanogaster.

PubMed

Samsonova, Anastasia A; Niranjan, Mahesan; Russell, Steven; Brazma, Alvis

2007-07-01

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms.

Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster

PubMed Central

Samsonova, Anastasia A; Niranjan, Mahesan; Russell, Steven; Brazma, Alvis

2007-01-01

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms. PMID:17658945

Predicting new molecular targets for known drugs

PubMed Central

Keiser, Michael J.; Setola, Vincent; Irwin, John J.; Laggner, Christian; Abbas, Atheir; Hufeisen, Sandra J.; Jensen, Niels H.; Kuijer, Michael B.; Matos, Roberto C.; Tran, Thuy B.; Whaley, Ryan; Glennon, Richard A.; Hert, Jérôme; Thomas, Kelan L.H.; Edwards, Douglas D.; Shoichet, Brian K.; Roth, Bryan L.

2009-01-01

Whereas drugs are intended to be selective, at least some bind to several physiologic targets, explaining both side effects and efficacy. As many drug-target combinations exist, it would be useful to explore possible interactions computationally. Here, we compared 3,665 FDA-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-HT transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug-target associations were confirmed, five of which were potent (< 100 nM). The physiological relevance of one such, the drug DMT on serotonergic receptors, was confirmed in a knock-out mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs. PMID:19881490

Ultrasound-Mediated Vascular Gene Transfection by Cavitation of Endothelial-Targeted Cationic Microbubbles

PubMed Central

Xie, Aris; Belcik, Todd; Qi, Yue; Morgan, Terry K.; Champaneri, Shivam A.; Taylor, Sarah; Davidson, Brian P.; Zhao, Yan; Klibanov, Alexander L.; Kuliszewski, Michael A.; Leong-Poi, Howard; Ammi, Azzdine; Lindner, Jonathan R.

2013-01-01

OBJECTIVES Ultrasound-mediated gene delivery can be amplified by acoustic disruption of microbubble carriers that undergo cavitation. We hypothesized that endothelial targeting of microbubbles bearing cDNA is feasible and, through optimizing proximity to the vessel wall, increases the efficacy of gene transfection. BACKGROUND Contrast ultrasound-mediated gene delivery is a promising approach for site-specific gene therapy, although there are concerns with the reproducibility of this technique and the safety when using high-power ultrasound. METHODS Cationic lipid-shelled decafluorobutane microbubbles bearing a targeting moiety were prepared and compared with nontargeted microbubbles. Microbubble targeting efficiency to endothelial adhesion molecules (P-selectin or intercellular adhesion molecule [ICAM]-1) was tested using in vitro flow chamber studies, intravital microscopy of tumor necrosis factor-alpha (TNF-α)–stimulated murine cremaster muscle, and targeted contrast ultrasound imaging of P-selectin in a model of murine limb ischemia. Ultrasound-mediated transfection of luciferase reporter plasmid charge coupled to microbubbles in the post-ischemic hindlimb muscle was assessed by in vivo optical imaging. RESULTS Charge coupling of cDNA to the microbubble surface was not influenced by the presence of targeting ligand, and did not alter the cavitation properties of cationic microbubbles. In flow chamber studies, surface conjugation of cDNA did not affect attachment of targeted microbubbles at microvascular shear stresses (0.6 and 1.5 dyne/cm2). Attachment in vivo was also not affected by cDNA according to intravital microscopy observations of venular adhesion of ICAM-1–targeted microbubbles and by ultrasound molecular imaging of P-selectin–targeted microbubbles in the post-ischemic hindlimb in mice. Transfection at the site of high acoustic pressures (1.0 and 1.8 MPa) was similar for control and P-selectin–targeted microbubbles but was associated with

Folic-Acid-Targeted Self-Assembling Supramolecular Carrier for Gene Delivery.

PubMed

Liao, Rongqiang; Yi, Shouhui; Liu, Manshuo; Jin, Wenling; Yang, Bo

2015-07-27

A targeting gene carrier for cancer-specific delivery was successfully developed through a "multilayer bricks-mortar" strategy. The gene carrier was composed of adamantane-functionalized folic acid (FA-AD), an adamantane-functionalized poly(ethylene glycol) derivative (PEG-AD), and β-cyclodextrin-grafted low-molecular-weight branched polyethylenimine (PEI-CD). Carriers produced by two different self-assembly schemes, involving either precomplexation of the PEI-CD with the FA-AD and PEG-AD before pDNA condensation (Method A) or pDNA condensation with the PEI-CD prior to addition of the FA-AD and PEG-AD to engage host-guest complexation (Method B) were investigated for their ability to compact pDNA into nanoparticles. Cell viability studies show that the material produced by the Method A assembly scheme has lower cytotoxicity than branched PEI 25 kDa (PEI-25KD) and that the transfection efficiency is maintained. These findings suggest that the gene carrier, based on multivalent host-guest interactions, could be an effective, targeted, and low-toxicity carrier for delivering nucleic acid to target cells. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Prediction of Drug-Disease Correlation Based on Gene Expression Data.

PubMed

Cui, Hui; Zhang, Menghuan; Yang, Qingmin; Li, Xiangyi; Liebman, Michael; Yu, Ying; Xie, Lu

2018-01-01

The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for

Engineering liposomal nanoparticles for targeted gene therapy.

PubMed

Zylberberg, C; Gaskill, K; Pasley, S; Matosevic, S

2017-08-01

Recent mechanistic studies have attempted to deepen our understanding of the process by which liposome-mediated delivery of genetic material occurs. Understanding the interactions between lipid nanoparticles and cells is still largely elusive. Liposome-mediated delivery of genetic material faces systemic obstacles alongside entry into the cell, endosomal escape, lysosomal degradation and nuclear uptake. Rational design approaches for targeted delivery have been developed to reduce off-target effects and enhance transfection. These strategies, which have included the modification of lipid nanoparticles with target-specific ligands to enhance intracellular uptake, have shown significant promise at the proof-of-concept stage. Control of physical and chemical specifications of liposome composition, which includes lipid-to-DNA charge, size, presence of ester bonds, chain length and nature of ligand complexation, is integral to the performance of targeted liposomes as genetic delivery agents. Clinical advances are expected to rely on such systems in the therapeutic application of liposome nanoparticle-based gene therapy. Here, we discuss the latest breakthroughs in the development of targeted liposome-based agents for the delivery of genetic material, paying particular attention to new ligand and cationic lipid design as well as recent in vivo advances.

CRISPR/Cas9-mediated gene knockout screens and target identification via whole-genome sequencing uncover host genes required for picornavirus infection.

PubMed

Kim, Heon Seok; Lee, Kyungjin; Bae, Sangsu; Park, Jeongbin; Lee, Chong-Kyo; Kim, Meehyein; Kim, Eunji; Kim, Minju; Kim, Seokjoong; Kim, Chonsaeng; Kim, Jin-Soo

2017-06-23

Several groups have used genome-wide libraries of lentiviruses encoding small guide RNAs (sgRNAs) for genetic screens. In most cases, sgRNA expression cassettes are integrated into cells by using lentiviruses, and target genes are statistically estimated by the readout of sgRNA sequences after targeted sequencing. We present a new virus-free method for human gene knockout screens using a genome-wide library of CRISPR/Cas9 sgRNAs based on plasmids and target gene identification via whole-genome sequencing (WGS) confirmation of authentic mutations rather than statistical estimation through targeted amplicon sequencing. We used 30,840 pairs of individually synthesized oligonucleotides to construct the genome-scale sgRNA library, collectively targeting 10,280 human genes ( i.e. three sgRNAs per gene). These plasmid libraries were co-transfected with a Cas9-expression plasmid into human cells, which were then treated with cytotoxic drugs or viruses. Only cells lacking key factors essential for cytotoxic drug metabolism or viral infection were able to survive. Genomic DNA isolated from cells that survived these challenges was subjected to WGS to directly identify CRISPR/Cas9-mediated causal mutations essential for cell survival. With this approach, we were able to identify known and novel genes essential for viral infection in human cells. We propose that genome-wide sgRNA screens based on plasmids coupled with WGS are powerful tools for forward genetics studies and drug target discovery. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Bioinformatic prediction of gene functions regulated by quorum sensing in the bioleaching bacterium Acidithiobacillus ferrooxidans.

PubMed

Banderas, Alvaro; Guiliani, Nicolas

2013-08-16

The biomining bacterium Acidithiobacillus ferrooxidans oxidizes sulfide ores and promotes metal solubilization. The efficiency of this process depends on the attachment of cells to surfaces, a process regulated by quorum sensing (QS) cell-to-cell signalling in many Gram-negative bacteria. At. ferrooxidans has a functional QS system and the presence of AHLs enhances its attachment to pyrite. However, direct targets of the QS transcription factor AfeR remain unknown. In this study, a bioinformatic approach was used to infer possible AfeR direct targets based on the particular palindromic features of the AfeR binding site. A set of Hidden Markov Models designed to maintain palindromic regions and vary non-palindromic regions was used to screen for putative binding sites. By annotating the context of each predicted binding site (PBS), we classified them according to their positional coherence relative to other putative genomic structures such as start codons, RNA polymerase promoter elements and intergenic regions. We further used the Multiple EM for Motif Elicitation algorithm (MEME) to further filter out low homology PBSs. In summary, 75 target-genes were identified, 34 of which have a higher confidence level. Among the identified genes, we found afeR itself, zwf, genes encoding glycosyltransferase activities, metallo-beta lactamases, and active transport-related proteins. Glycosyltransferases and Zwf (Glucose 6-phosphate-1-dehydrogenase) might be directly involved in polysaccharide biosynthesis and attachment to minerals by At. ferrooxidans cells during the bioleaching process.

Bioinformatic Prediction of Gene Functions Regulated by Quorum Sensing in the Bioleaching Bacterium Acidithiobacillus ferrooxidans

PubMed Central

Banderas, Alvaro; Guiliani, Nicolas

2013-01-01

The biomining bacterium Acidithiobacillus ferrooxidans oxidizes sulfide ores and promotes metal solubilization. The efficiency of this process depends on the attachment of cells to surfaces, a process regulated by quorum sensing (QS) cell-to-cell signalling in many Gram-negative bacteria. At. ferrooxidans has a functional QS system and the presence of AHLs enhances its attachment to pyrite. However, direct targets of the QS transcription factor AfeR remain unknown. In this study, a bioinformatic approach was used to infer possible AfeR direct targets based on the particular palindromic features of the AfeR binding site. A set of Hidden Markov Models designed to maintain palindromic regions and vary non-palindromic regions was used to screen for putative binding sites. By annotating the context of each predicted binding site (PBS), we classified them according to their positional coherence relative to other putative genomic structures such as start codons, RNA polymerase promoter elements and intergenic regions. We further used the Multiple EM for Motif Elicitation algorithm (MEME) to further filter out low homology PBSs. In summary, 75 target-genes were identified, 34 of which have a higher confidence level. Among the identified genes, we found afeR itself, zwf, genes encoding glycosyltransferase activities, metallo-beta lactamases, and active transport-related proteins. Glycosyltransferases and Zwf (Glucose 6-phosphate-1-dehydrogenase) might be directly involved in polysaccharide biosynthesis and attachment to minerals by At. ferrooxidans cells during the bioleaching process. PMID:23959118

Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network.

PubMed

Roider, Helge G; Pavlova, Nadia; Kirov, Ivaylo; Slavov, Stoyan; Slavov, Todor; Uzunov, Zlatyo; Weiss, Bertram

2014-03-11

Information about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small. As searching through these sources manually is cumbersome, time-consuming and error-prone, integrating all the data is highly desirable. Despite a few attempts, integration has been hampered by the diversity of descriptions of compounds, and by the fact that the reported activity values, coming from different data sets, are not always directly comparable due to usage of different metrics or data formats. We have built Drug2Gene, a knowledge base, which combines the compound/drug-gene/protein information from 19 publicly available databases. A key feature is our rigorous unification and standardization process which makes the data truly comparable on a large scale, allowing for the first time effective data mining in such a large knowledge corpus. As of version 3.2, Drug2Gene contains 4,372,290 unified relations between compounds and their targets most of which include reported bioactivity data. We extend this set with putative (i.e. homology-inferred) relations where sufficient sequence homology between proteins suggests they may bind to similar compounds. Drug2Gene provides powerful search functionalities, very flexible export procedures, and a user-friendly web interface. Drug2Gene v3.2 has become a mature and comprehensive knowledge base providing unified, standardized drug-target related information gathered from publicly available data sources. It can be used to integrate proprietary data sets with publicly available data sets. Its main goal is to be a 'one-stop shop' to identify tool compounds targeting a given gene product or for finding all known targets of a drug. Drug2Gene with its integrated data set of public compound-target relations is freely accessible without

Prognostic and predictive value of VHL gene alteration in renal cell carcinoma: a meta-analysis and review.

PubMed

Kim, Bum Jun; Kim, Jung Han; Kim, Hyeong Su; Zang, Dae Young

2017-02-21

The von Hippel-Lindau (VHL) gene is often inactivated in sporadic renal cell carcinoma (RCC) by mutation or promoter hypermethylation. The prognostic or predictive value of VHL gene alteration is not well established. We conducted this meta-analysis to evaluate the association between the VHL alteration and clinical outcomes in patients with RCC. We searched PUBMED, MEDLINE and EMBASE for articles including following terms in their titles, abstracts, or keywords: 'kidney or renal', 'carcinoma or cancer or neoplasm or malignancy', 'von Hippel-Lindau or VHL', 'alteration or mutation or methylation', and 'prognostic or predictive'. There were six studies fulfilling inclusion criteria and a total of 633 patients with clear cell RCC were included in the study: 244 patients who received anti-vascular endothelial growth factor (VEGF) therapy in the predictive value analysis and 419 in the prognostic value analysis. Out of 663 patients, 410 (61.8%) had VHL alteration. The meta-analysis showed no association between the VHL gene alteration and overall response rate (relative risk = 1.47 [95% CI, 0.81-2.67], P = 0.20) or progression free survival (hazard ratio = 1.02 [95% CI, 0.72-1.44], P = 0.91) in patients with RCC who received VEGF-targeted therapy. There was also no correlation between the VHL alteration and overall survival (HR = 0.80 [95% CI, 0.56-1.14], P = 0.21). In conclusion, this meta-analysis indicates that VHL gene alteration has no prognostic or predictive value in patients with clear cell RCC.

Antibiotic Combinations That Enable One-Step, Targeted Mutagenesis of Chromosomal Genes.

PubMed

Lee, Wonsik; Do, Truc; Zhang, Ge; Kahne, Daniel; Meredith, Timothy C; Walker, Suzanne

2018-06-08

Targeted modification of bacterial chromosomes is necessary to understand new drug targets, investigate virulence factors, elucidate cell physiology, and validate results of -omics-based approaches. For some bacteria, reverse genetics remains a major bottleneck to progress in research. Here, we describe a compound-centric strategy that combines new negative selection markers with known positive selection markers to achieve simple, efficient one-step genome engineering of bacterial chromosomes. The method was inspired by the observation that certain nonessential metabolic pathways contain essential late steps, suggesting that antibiotics targeting a late step can be used to select for the absence of genes that control flux into the pathway. Guided by this hypothesis, we have identified antibiotic/counterselectable markers to accelerate reverse engineering of two increasingly antibiotic-resistant pathogens, Staphylococcus aureus and Acinetobacter baumannii. For S. aureus, we used wall teichoic acid biosynthesis inhibitors to select for the absence of tarO and for A. baumannii, we used colistin to select for the absence of lpxC. We have obtained desired gene deletions, gene fusions, and promoter swaps in a single plating step with perfect efficiency. Our method can also be adapted to generate markerless deletions of genes using FLP recombinase. The tools described here will accelerate research on two important pathogens, and the concept we outline can be readily adapted to any organism for which a suitable target pathway can be identified.

Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites

PubMed Central

Novotny, Peter; Tang, Xiaojia; Kalari, Krishna R.; Gorodkin, Jan

2014-01-01

Traditional mutation assessment methods generally focus on predicting disruptive changes in protein-coding regions rather than non-coding regulatory regions like untranslated regions (UTRs) of mRNAs. The UTRs, however, are known to have many sequence and structural motifs that can regulate translational and transcriptional efficiency and stability of mRNAs through interaction with RNA-binding proteins and other non-coding RNAs like microRNAs (miRNAs). In a recent study, transcriptomes of tumor cells harboring mutant and wild-type KRAS (V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) genes in patients with non-small cell lung cancer (NSCLC) have been sequenced to identify single nucleotide variations (SNVs). About 40% of the total SNVs (73,717) identified were mapped to UTRs, but omitted in the previous analysis. To meet this obvious demand for analysis of the UTRs, we designed a comprehensive pipeline to predict the effect of SNVs on two major regulatory elements, secondary structure and miRNA target sites. Out of 29,290 SNVs in 6462 genes, we predict 472 SNVs (in 408 genes) affecting local RNA secondary structure, 490 SNVs (in 447 genes) affecting miRNA target sites and 48 that do both. Together these disruptive SNVs were present in 803 different genes, out of which 188 (23.4%) were previously known to be cancer-associated. Notably, this ratio is significantly higher (one-sided Fisher's exact test p-value = 0.032) than the ratio (20.8%) of known cancer-associated genes (n = 1347) in our initial data set (n = 6462). Network analysis shows that the genes harboring disruptive SNVs were involved in molecular mechanisms of cancer, and the signaling pathways of LPS-stimulated MAPK, IL-6, iNOS, EIF2 and mTOR. In conclusion, we have found hundreds of SNVs which are highly disruptive with respect to changes in the secondary structure and miRNA target sites within UTRs. These changes hold the potential to alter the expression of known cancer genes or genes

Transcriptome-wide analysis of UTRs in non-small cell lung cancer reveals cancer-related genes with SNV-induced changes on RNA secondary structure and miRNA target sites.

PubMed

Sabarinathan, Radhakrishnan; Wenzel, Anne; Novotny, Peter; Tang, Xiaojia; Kalari, Krishna R; Gorodkin, Jan

2014-01-01

Traditional mutation assessment methods generally focus on predicting disruptive changes in protein-coding regions rather than non-coding regulatory regions like untranslated regions (UTRs) of mRNAs. The UTRs, however, are known to have many sequence and structural motifs that can regulate translational and transcriptional efficiency and stability of mRNAs through interaction with RNA-binding proteins and other non-coding RNAs like microRNAs (miRNAs). In a recent study, transcriptomes of tumor cells harboring mutant and wild-type KRAS (V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) genes in patients with non-small cell lung cancer (NSCLC) have been sequenced to identify single nucleotide variations (SNVs). About 40% of the total SNVs (73,717) identified were mapped to UTRs, but omitted in the previous analysis. To meet this obvious demand for analysis of the UTRs, we designed a comprehensive pipeline to predict the effect of SNVs on two major regulatory elements, secondary structure and miRNA target sites. Out of 29,290 SNVs in 6462 genes, we predict 472 SNVs (in 408 genes) affecting local RNA secondary structure, 490 SNVs (in 447 genes) affecting miRNA target sites and 48 that do both. Together these disruptive SNVs were present in 803 different genes, out of which 188 (23.4%) were previously known to be cancer-associated. Notably, this ratio is significantly higher (one-sided Fisher's exact test p-value = 0.032) than the ratio (20.8%) of known cancer-associated genes (n = 1347) in our initial data set (n = 6462). Network analysis shows that the genes harboring disruptive SNVs were involved in molecular mechanisms of cancer, and the signaling pathways of LPS-stimulated MAPK, IL-6, iNOS, EIF2 and mTOR. In conclusion, we have found hundreds of SNVs which are highly disruptive with respect to changes in the secondary structure and miRNA target sites within UTRs. These changes hold the potential to alter the expression of known cancer genes or genes

Cooperative gene regulation by microRNA pairs and their identification using a computational workflow

PubMed Central

Schmitz, Ulf; Lai, Xin; Winter, Felix; Wolkenhauer, Olaf; Vera, Julio; Gupta, Shailendra K.

2014-01-01

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. Our results suggest that miRNA cooperativity is a frequent mechanism for an enhanced target repression by pairs of miRNAs facilitating distinctive and fine-tuned target gene expression patterns. Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/. PMID:24875477