Showing papers on "Gene interaction published in 2016"

DGIdb 2.0: mining clinically relevant drug-gene interactions.

Abstract: The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that consolidates disparate data sources describing drug-gene interactions and gene druggability. It provides an intuitive graphical user interface and a documented application programming interface (API) for querying these data. DGIdb was assembled through an extensive manual curation effort, reflecting the combined information of twenty-seven sources. For DGIdb 2.0, substantial updates have been made to increase content and improve its usefulness as a resource for mining clinically actionable drug targets. Specifically, nine new sources of drug-gene interactions have been added, including seven resources specifically focused on interactions linked to clinical trials. These additions have more than doubled the overall count of drug-gene interactions. The total number of druggable gene claims has also increased by 30%. Importantly, a majority of the unrestricted, publicly-accessible sources used in DGIdb are now automatically updated on a weekly basis, providing the most current information for these sources. Finally, a new web view and API have been developed to allow searching for interactions by drug identifiers to complement existing gene-based search functionality. With these updates, DGIdb represents a comprehensive and user friendly tool for mining the druggable genome for precision medicine hypothesis generation.

SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets.

Abstract: Synthetic lethality (SL) is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. SL reflects the biologically endogenous difference between cancer cells and normal cells, and thus the inhibition of SL partners of genes with cancer-specific mutations could selectively kill cancer cells but spare normal cells. Therefore, SL is emerging as a promising anticancer strategy that could potentially overcome the drawbacks of traditional chemotherapies by reducing severe side effects. Researchers have developed experimental technologies and computational prediction methods to identify SL gene pairs on human and a few model species. However, there has not been a comprehensive database dedicated to collecting SL pairs and related knowledge. In this paper, we propose a comprehensive database, SynLethDB (http://histone.sce.ntu.edu.sg/SynLethDB/), which contains SL pairs collected from biochemical assays, other related databases, computational predictions and text mining results on human and four model species, i.e. mouse, fruit fly, worm and yeast. For each SL pair, a confidence score was calculated by integrating individual scores derived from different evidence sources. We also developed a statistical analysis module to estimate the druggability and sensitivity of cancer cells upon drug treatments targeting human SL partners, based on large-scale genomic data, gene expression profiles and drug sensitivity profiles on more than 1000 cancer cell lines. To help users access and mine the wealth of the data, we developed other practical functionalities, such as search and filtering, orthology search, gene set enrichment analysis. Furthermore, a user-friendly web interface has been implemented to facilitate data analysis and interpretation. With the integrated data sets and analytics functionalities, SynLethDB would be a useful resource for biomedical research community and pharmaceutical industry.

Long non-coding RNA ROR promotes proliferation, migration and chemoresistance of nasopharyngeal carcinoma.

Abstract: Nasopharyngeal carcinoma (NPC) is one of the most common malignancies of the head and neck. It arises from the nasopharynx epithelium and is associated with high morbidity and mortality. Long non-coding RNA (lncRNA) have been reported to regulate gene interaction and play critical roles in carcinogenesis and progression. LncRNA-ROR, a recently identified lncRNA, has been shown to be involved in initiation, progression and metastasis of several tumors, including hepatocellular carcinoma, breast cancer and glioma. However, whether lncRNA-ROR is associated with the progression of NPC remains unknown. Resistance to radiotherapy and chemotherapy is the primary cause of NPC patients' death. In this study, we found that lncRNA-ROR was significantly upregulated in NPC tissues compared with normal tissues. Next, our study proved that lncRNA-ROR was highly associated with the proliferation, metastasis and apoptosis of NPC. The enrichment of lncRNA-ROR played a critucal functional role in chemoresistance. The mechanism by which NPC resists chemotherapy might be that lncRNA-ROR suppress p53 signal pathway. Taken together, these data suggested that lncRNA-ROR played an important role in the progression of NPC; thereby it might become a therapeutic target and reduce chemoresistance for NPC.

Whole-genome transcription and DNA methylation analysis of peripheral blood mononuclear cells identified aberrant gene regulation pathways in systemic lupus erythematosus

Abstract: Recent achievement in genetics and epigenetics has led to the exploration of the pathogenesis of systemic lupus erythematosus (SLE). Identification of differentially expressed genes and their regulatory mechanism(s) at whole-genome level will provide a comprehensive understanding of the development of SLE and its devastating complications, lupus nephritis (LN). We performed whole-genome transcription and DNA methylation analysis in PBMC of 30 SLE patients, including 15 with LN (SLE LN+) and 15 without LN (SLE LN−), and 25 normal controls (NC) using HumanHT-12 Beadchips and Illumina Human Methy450 chips. The serum proinflammatory cytokines were quantified using Bio-plex Human Cytokine 27-plex assay. Differentially expressed genes and differentially methylated CpG were analyzed with GenomeStudio, R, and SAM software. The association between DNA methylation and gene expression were tested. Gene interaction pathways of the differentially expressed genes were analyzed by IPA software. We identified 552 upregulated genes and 550 downregulated genes in PBMC of SLE. Integration of DNA methylation and gene expression profiling showed that 334 upregulated genes were hypomethylated, and 479 downregulated genes were hypermethylated. Pathway analysis on the differential genes in SLE revealed significant enrichment in interferon (IFN) signaling and toll-like receptor (TLR) signaling pathways. Nine IFN- and seven TLR-related genes were identified and displayed step-wise increase in SLE LN− and SLE LN+. Hypomethylated CpG sites were detected on these genes. The gene expressions for MX1, GPR84, and E2F2 were increased in SLE LN+ as compared to SLE LN− patients. The serum levels of inflammatory cytokines, including IL17A, IP-10, bFGF, TNF-α, IL-6, IL-15, GM-CSF, IL-1RA, IL-5, and IL-12p70, were significantly elevated in SLE compared with NC. The levels of IL-15 and IL1RA correlated with their mRNA expression. The upregulation of IL-15 may be regulated by hypomethylated CpG sites in the promotor region of the gene. Our study has demonstrated that significant number of differential genes in SLE were involved in IFN, TLR signaling pathways, and inflammatory cytokines. The enrichment of differential genes has been associated with aberrant DNA methylation, which may be relevant to the pathogenesis of SLE. Our observations have laid the groundwork for further diagnostic and mechanistic studies of SLE and LN.

Bovine Genome Database: new tools for gleaning function from the Bos taurus genome

Abstract: We report an update of the Bovine Genome Database (BGD) (http://BovineGenome.org). The goal of BGD is to support bovine genomics research by providing genome annotation and data mining tools. We have developed new genome and annotation browsers using JBrowse and WebApollo for two Bos taurus genome assemblies, the reference genome assembly (UMD3.1.1) and the alternate genome assembly (Btau_4.6.1). Annotation tools have been customized to highlight priority genes for annotation, and to aid annotators in selecting gene evidence tracks from 91 tissue specific RNAseq datasets. We have also developed BovineMine, based on the InterMine data warehousing system, to integrate the bovine genome, annotation, QTL, SNP and expression data with external sources of orthology, gene ontology, gene interaction and pathway information. BovineMine provides powerful query building tools, as well as customized query templates, and allows users to analyze and download genome-wide datasets. With BovineMine, bovine researchers can use orthology to leverage the curated gene pathways of model organisms, such as human, mouse and rat. BovineMine will be especially useful for gene ontology and pathway analyses in conjunction with GWAS and QTL studies.

Insights from the Cold Transcriptome and Metabolome of Dendrobium officinale: Global Reprogramming of Metabolic and Gene Regulation Networks during Cold Acclimation

Abstract: Plant cold acclimation (CA) is a genetically complex phenomenon involving gene regulation and expression. Little is known about the cascading pattern of gene regulatroy network and the link between genes and metabolites during CA. Dendrobium officinale (DOKM) is an important medicinal and ornamental plant and hypersensitive to low temperature. Here, we used the large scale metabolomic and transcriptomic technologies to reveal the response to CA in DOKM seedlings based on the physiological profile analyses. Lowering temperature from 4 to -2°C resulted in significant increase (P < 0.01) in antioxidant activities and electrolyte leakage (EL) during 24 h. The fitness CA piont of 0°C and control (20°C) during 20 h were firstly obtained according to physiological analyses. Subsequently, massive transcriptome and metabolome reprogramming occurred during CA. The gene to metabolite network demonstrated that the CA associated processes are highly energy demanding through activating hydrolysis of sugars, amino acids catabolism and citrate cycle. The expression levels of 2,767 genes were significantly affected by CA, including 153-fold upregulation of CBF transcription factor, 56-fold upregulation of MAPKKK16 protein kinase. Moreover, the gene interaction and regulation network analysis revealed that the CA as an active process, was regulated at the transcriptional, post-transcriptional, translational and post-translational levels. Our findings highligted a comprehensive regulatory mechanism including cold signal transduction, transcriptional regulation, and gene expression, which contributes a deeper understanding of the highly complex regulatory program during CA in DOKM. Some marker genes identified in DOKM seedlings will allow us to understand the role of each individual during CA by further functional analyses.

Epistasis and covariance: how gene interaction translates into genomic relationship.

Abstract: Models based on additive marker effects and on epistatic interactions can be translated into genomic relationship models. This equivalence allows to perform predictions based on complex gene interaction models and reduces computational effort significantly. In the theory of genome-assisted prediction, the equivalence of a linear model based on independent and identically normally distributed marker effects and a model based on multivariate Gaussian distributed breeding values with genomic relationship as covariance matrix is well known. In this work, we demonstrate equivalences of marker effect models incorporating epistatic interactions and corresponding mixed models based on relationship matrices and show how to exploit these equivalences computationally for genome-assisted prediction. In particular, we show how models with epistatic interactions of higher order (e.g., three-factor interactions) translate into linear models with certain covariance matrices and demonstrate how to construct epistatic relationship matrices for the linear mixed model, if we restrict the model to interactions defined a priori. We illustrate the practical relevance of our results with a publicly available data set on grain yield of wheat lines growing in four different environments. For this purpose, we select important interactions in one environment and use this knowledge on the network of interactions to increase predictive ability of grain yield under other environmental conditions. Our results provide a guide for building relationship matrices based on knowledge on the structure of trait-related gene networks.

lnCaNet: pan-cancer co-expression network for human lncRNA and cancer genes

Abstract: UNLABELLED Thousands of human long non-coding RNAs (lncRNAs) have been identified in cancers and played important roles in a wide range of tumorigenesis. However, the functions of vast majority of human lncRNAs are still elusive. Emerging studies revealed that the expression level of majority lncRNAs shows discordant expression pattern with their protein-coding gene neighbors in various model organisms. Therefore, it may be useful to infer lncRNAs' potential biological function in cancer development by more comprehensive functional views of co-expressed cancer genes beyond mere physical proximity of genes. To this aim, we performed thorough searches and analyses of the interactions between lncRNA and non-neighboring cancer genes and provide a comprehensive co-expression data resource, LnCaNet. In current version, LnCaNet contains the pre-computed 8 494 907 significant co-expression pairs of 9641 lncRNAs and 2544 well-classified cancer genes in 2922 matched TCGA samples. In detail, we integrated 10 cancer gene lists from public database and calculate the co-expression with all the lncRNAs in 11 TCGA cancer types separately. Based on the resulted 110 co-expression networks, we identified 17 common regulatory pairs related to extracellular space shared in 11 cancers. We expect LnCaNet will enable researcher to explore lncRNA expression pattern, their affected cancer genes and pathways, biological significance in the context of specific cancer types and other useful annotation related to particular kind of lncRNA-cancer gene interaction. AVAILABILITY AND IMPLEMENTATION http://lncanet.bioinfo-minzhao.org/ CONTACT : m.zhao@uq.edu.au SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Differential effector gene expression underpins epistasis in a plant fungal disease

Abstract: Fungal effector-host sensitivity gene interactions play a key role in determining the outcome of septoria nodorum blotch disease (SNB) caused by Parastagonospora nodorum on wheat. The pathosystem is complex and mediated by interaction of multiple fungal necrotrophic effector-host sensitivity gene systems. Three effector sensitivity gene systems are well characterized in this pathosystem; SnToxA-Tsn1, SnTox1-Snn1 and SnTox3-Snn3. We tested a wheat mapping population that segregated for Snn1 and Snn3 with SN15, an aggressive P. nodorum isolate that produces SnToxA, SnTox1 and SnTox3, to study the inheritance of sensitivity to SnTox1 and SnTox3 and disease susceptibility. Interval quantitative trait locus (QTL) mapping showed that the SnTox1-Snn1 interaction was paramount in SNB development on both seedlings and adult plants. No effect of the SnTox3-Snn3 interaction was observed under SN15 infection. The SnTox3-Snn3 interaction was however, detected in a strain of SN15 in which SnTox1 had been deleted (tox1-6). Gene expression analysis indicates increased SnTox3 expression in tox1-6 compared with SN15. This indicates that the failure to detect the SnTox3-Snn3 interaction in SN15 is due - at least in part - to suppressed expression of SnTox3 mediated by SnTox1. Furthermore, infection of the mapping population with a strain deleted in SnToxA, SnTox1 and SnTox3 (toxa13) unmasked a significant SNB QTL on 2DS where the SnTox2 effector sensitivity gene, Snn2, is located. This QTL was not observed in SN15 and tox1-6 infections and thus suggesting that SnToxA and/or SnTox3 were epistatic. Additional QTLs responding to SNB and effectors sensitivity were detected on 2AS1 and 3AL.

Dwarfing genes Rht-B1b and Rht-D1b are associated with both type I FHB susceptibility and low anther extrusion in two bread wheat populations

Abstract: It has been well documented that dwarfing genes Rht-B1b and Rht-D1b are associated with Type I susceptibility to Fusarium head blight (FHB) in wheat; but the underlying mechanism has not been well delineated. Anther extrusion (AE) has also been related to Type I resistance for initial FHB infection, where high AE renders FHB resistance. In this study, two doubled haploid populations were used to investigate the impact of the two dwarfing genes on FHB resistance and AE, and to elucidate the role of AE in Rht-mediated FHB susceptibility. Both populations were derived by crossing the FHB susceptible cultivar ‘Ocoroni F86’ (Rht-B1a/Rht-D1b) with an FHB resistant variety (Rht-B1b/Rht-D1a), which was ‘TRAP#1/BOW//Taigu derivative’ in one population (the TO population) and ‘Ivan/Soru#2’ in the other (the IO population). Field experiments were carried out from 2010 to 2012 in El Batan, Mexico, where spray inoculation was adopted and FHB index, plant height (PH), and AE were evaluated, with the latter two traits showing always significantly negative correlations with FHB severity. The populations were genotyped with the DArTseq GBS platform, the two dwarfing genes and a few SSRs for QTL analysis, and the results indicated that Rht-B1b and Rht-D1b collectively accounted for 0–41% of FHB susceptibility and 13–23% of reduced AE. It was also observed that three out of the four AE QTL in the TO population and four out of the five AE QTL in the IO population were associated with FHB resistance. Collectively, our results demonstrated the effects of Rht-B1b and Rht-D1b on Type I FHB susceptibility and reducing AE, and proposed that their impacts on Type I FHB susceptibility may partly be explained by their effects on reducing AE. The implication of the relationship between the two dwarfing genes and AE for hybrid wheat breeding was also discussed.

Comprehensive analysis of the long noncoding RNA HOXA11-AS gene interaction regulatory network in NSCLC cells

Abstract: Long noncoding RNAs (lncRNAs) are related to different biological processes in non-small cell lung cancer (NSCLC). However, the possible molecular mechanisms underlying the effects of the long noncoding RNA HOXA11-AS (HOXA11 antisense RNA) in NSCLC are unknown. HOXA11-AS was knocked down in the NSCLC A549 cell line and a high throughput microarray assay was applied to detect changes in the gene profiles of the A549 cells. Bioinformatics analyses (gene ontology (GO), pathway, Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses) were performed to investigate the potential pathways and networks of the differentially expressed genes. The molecular signatures database (MSigDB) was used to display the expression profiles of these differentially expressed genes. Furthermore, the relationships between the HOXA11-AS, de-regulated genes and clinical NSCLC parameters were verified by using NSCLC patient information from The Cancer Genome Atlas (TCGA) database. In addition, the relationship between HOXA11-AS expression and clinical diagnostic value was analyzed by receiver operating characteristic (ROC) curve. Among the differentially expressed genes, 277 and 80 genes were upregulated and downregulated in NSCLC, respectively (fold change ≥2.0, P < 0.05 and false discovery rate (FDR) < 0.05). According to the degree of the fold change, six upregulated and three downregulated genes were selected for further investigation. Only four genes (RSPO3, ADAMTS8, DMBT1, and DOCK8) were reported to be related with the development or progression of NSCLC based on a PubMed search. Among all possible pathways, three pathways (the PI3K-Akt, TGF-beta and Hippo signaling pathways) were the most likely to be involved in NSCLC development and progression. Furthermore, we found that HOXA11-AS was highly expressed in both lung adenocarcinoma and squamous cell carcinoma based on TCGA database. The ROC curve showed that the area under curve (AUC) of HOXA11-AS was 0.727 (95% CI 0.663–0.790) for lung adenocarcinoma and 0.933 (95% CI 0.906–0.960) for squamous cell carcinoma patients. Additionally, the original data from TCGA verified that ADAMTS8, DMBT1 and DOCK8 were downregulated in both lung adenocarcinoma and squamous cell carcinoma, whereas RSPO3 expression was upregulated in lung adenocarcinoma and downregulated in lung squamous cell carcinoma. For the other five genes (STMN2, SPINK6, TUSC3, LOC100128054, and C8orf22), we found that STMN2, TUSC3 and C8orf22 were upregulated in squamous cell carcinoma and that STMN2 and USC3 were upregulated in lung adenocarcinoma. Furthermore, we compared the correlation between HOXA11-AS and de-regulated genes in NSCLC based on TCGA. The results showed that the HOXA11-AS expression was negatively correlated with DOCK8 in squamous cell carcinoma (r = −0.124, P = 0.048) and lung adenocarcinoma (r = −0.176, P = 0.005). In addition, RSPO3, ADAMTS8 and DOCK8 were related to overall survival and disease-free survival (all P < 0.05) of lung adenocarcinoma patients in TCGA. Our results showed that the gene profiles were significantly changed after HOXA11-AS knock-down in NSCLC cells. We speculated that HOXA11-AS may play an important role in NSCLC development and progression by regulating the expression of various pathways and genes, especially DOCK8 and TGF-beta pathway. However, the exact mechanism should be verified by functional experiments.

Optimal design of gene knockout experiments for gene regulatory network inference

Abstract: Motivation: We addressed the problem of inferring gene regulatory network (GRN) from gene expression data of knockout (KO) experiments. This inference is known to be underdetermined and the GRN is not identifiable from data. Past studies have shown that suboptimal design of experiments (DOE) contributes significantly to the identifiability issue of biological networks, including GRNs. However, optimizing DOE has received much less attention than developing methods for GRN inference. Results: We developed REDuction of UnCertain Edges (REDUCE) algorithm for finding the optimal gene KO experiment for inferring directed graphs (digraphs) of GRNs. REDUCE employed ensemble inference to define uncertain gene interactions that could not be verified by prior data. The optimal experiment corresponds to the maximum number of uncertain interactions that could be verified by the resulting data. For this purpose, we introduced the concept of edge separatoid which gave a list of nodes (genes) that upon their removal would allow the verification of a particular gene interaction. Finally, we proposed a procedure that iterates over performing KO experiments, ensemble update and optimal DOE. The case studies including the inference of Escherichia coli GRN and DREAM 4 100-gene GRNs, demonstrated the efficacy of the iterative GRN inference. In comparison to systematic KOs, REDUCE could provide much higher information return per gene KO experiment and consequently more accurate GRN estimates. Conclusions: REDUCE represents an enabling tool for tackling the underdetermined GRN inference. Along with advances in gene deletion and automation technology, the iterative procedure brings an efficient and fully automated GRN inference closer to reality. Availability and implementation: MATLAB and Python scripts of REDUCE are available on www.cabsel.ethz.ch/tools/REDUCE. Contact: hc.zhte.mehc@nawanug.idur Supplementary information: Supplementary data are available at Bioinformatics online.

BicNET: Flexible module discovery in large-scale biological networks using biclustering

Abstract: Despite the recognized importance of module discovery in biological networks to enhance our understanding of complex biological systems, existing methods generally suffer from two major drawbacks. First, there is a focus on modules where biological entities are strongly connected, leading to the discovery of trivial/well-known modules and to the inaccurate exclusion of biological entities with subtler yet relevant roles. Second, there is a generalized intolerance towards different forms of noise, including uncertainty associated with less-studied biological entities (in the context of literature-driven networks) and experimental noise (in the context of data-driven networks). Although state-of-the-art biclustering algorithms are able to discover modules with varying coherency and robustness to noise, their application for the discovery of non-dense modules in biological networks has been poorly explored and it is further challenged by efficiency bottlenecks. This work proposes Biclustering NETworks (BicNET), a biclustering algorithm to discover non-trivial yet coherent modules in weighted biological networks with heightened efficiency. Three major contributions are provided. First, we motivate the relevance of discovering network modules given by constant, symmetric, plaid and order-preserving biclustering models. Second, we propose an algorithm to discover these modules and to robustly handle noisy and missing interactions. Finally, we provide new searches to tackle time and memory bottlenecks by effectively exploring the inherent structural sparsity of network data. Results in synthetic network data confirm the soundness, efficiency and superiority of BicNET. The application of BicNET on protein interaction and gene interaction networks from yeast, E. coli and Human reveals new modules with heightened biological significance. BicNET is, to our knowledge, the first method enabling the efficient unsupervised analysis of large-scale network data for the discovery of coherent modules with parameterizable homogeneity.

Microarray-based identification of genes associated with cancer progression and prognosis in hepatocellular carcinoma

Abstract: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths. The average survival and 5-year survival rates of HCC patients still remains poor. Thus, there is an urgent need to better understand the mechanisms of cancer progression in HCC and to identify useful biomarkers to predict prognosis. Public data portals including Oncomine, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) profiles were used to retrieve the HCC-related microarrays and to identify potential genes contributed to cancer progression. Bioinformatics analyses including pathway enrichment, protein/gene interaction and text mining were used to explain the potential roles of the identified genes in HCC. Quantitative real-time polymerase chain reaction analysis and Western blotting were used to measure the expression of the targets. The data were analysed by SPSS 20.0 software. We identified 80 genes that were significantly dysregulated in HCC according to four independent microarrays covering 386 cases of HCC and 327 normal liver tissues. Twenty genes were consistently and stably dysregulated in the four microarrays by at least 2-fold and detection of gene expression by RT-qPCR and western blotting showed consistent expression profiles in 11 HCC tissues compared with corresponding paracancerous tissues. Eleven of these 20 genes were associated with disease-free survival (DFS) or overall survival (OS) in a cohort of 157 HCC patients, and eight genes were associated with tumour pathologic PT, tumour stage or vital status. Potential roles of those 20 genes in regulation of HCC progression were predicted, primarily in association with metastasis. INTS8 was specifically correlated with most clinical characteristics including DFS, OS, stage, metastasis, invasiveness, diagnosis, and age. The significantly dysregulated genes identified in this study were associated with cancer progression and prognosis in HCC, and might be potential therapeutic targets for HCC treatment or potential biomarkers for diagnosis and prognosis.

m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks.

Abstract: As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m6A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Specifically, m6A-Driver integrates the PPI network and the predicted differential m6A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m6A-driven network of m6A-driven genes. To evaluate the performance, we applied m6A-Driver to build the context-specific m6A-driven networks for 4 known m6A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.

Systematic analysis to identify a key role of CDK1 in mediating gene interaction networks in cervical cancer development.

Abstract: This study aims to identify corresponding differentially expressed genes in cervical cancer by comparing gene expression profiles between normal and cervical cancer samples. To identify differentially expressed genes in cervical cancer, two groups of Affymetrix microarray data available online were analyzed. One group consisted of 43 carcinomatous cervical epithelial cell samples, and the other was composed of 17 healthy cervical epithelial cell samples, both from the Amerindian. R packages—GO.db, KEGG.db and KEGGREST were used to detect GO categories and KEGG pathways with significant overrepresentation in differentially expressed genes comparing with the whole genome. Cytoscape was utilized to construct biological networks. By comparing gene expression profile of normal and cervical cancer samples, 122 differentially expressed genes were identified including 46 up-regulated genes and 76 down-regulated genes. Using the identified differentially expressed genes, a large and a small biological network was constructed. In addition, 402 GO biological processes and 9 KEGG pathways were over-represented. Top significant biological processes included cell cycle and cell proliferation. Moreover, top significant KEGG pathways were oocyte meiosis, cell cycle and progesterone-mediated oocyte maturation. Most importantly, CDK1 frequently appeared in these processes and pathways, which indicated its significant role in the progression of cervical cancer. CDK1 plays a comprehensive role in mediating genetic networks implicated in the progression of cervical cancer. Novel therapeutics targeting CDK1 or its related pathways might help improve prognosis of advanced stage cervical cancer.

A Dopamine Pathway Gene Risk Score for Cognitive Recovery Following Traumatic Brain Injury: Methodological Considerations, Preliminary Findings, and Interactions With Sex.

Abstract: OBJECTIVES: With evidence of sexual dimorphism involving the dopamine (DA)-pathway, and the importance of DA pathways in traumatic brain injury (TBI) recovery, we hypothesized that sex × DA-gene interactions may influence cognition post-TBI. PARTICIPANTS: Adult survivors of severe TBI (n = 193) consecutively recruited from a level 1 trauma center. DESIGN: Risk allele assignments were made for multiple DA pathway genes using a sex-specific stratified approach. Genetic risk alleles, and their impacts on cognition, were assessed at 6 and 12 months postinjury using unweighted, semiweighted, and weighted gene risk score (GRS) approaches. MAIN MEASURES: A cognitive composite score generated from 8 standardized neuropsychological tests targeting multiple cognitive domains. RESULTS: A significant sex × gene interaction was observed at 6 and 12 months for ANKK1 rs1800497 (6M: P =.002, 12M: P =.001) and COMT rs4680 (6M: P =.048; 12M: P =.004); DRD2 rs6279 (P =.001) and VMAT rs363226 (P =.043) genotypes were independently associated with cognition at 6 months, with trends for a sex × gene interaction at 12 months. All GRS methods were significant predictors of cognitive performance in multivariable models. Weighted GRS multivariate models captured the greatest variance in cognition: R = 0.344 (6 months); R = 0.441 (12 months), significantly increasing the variance captured from the base prediction models. CONCLUSIONS: A sex-specific DA-pathway GRS may be a valuable tool when predicting cognitive recovery post-TBI. Future work should validate these findings and explore how DA-pathway genetics may guide therapeutic intervention. Language: en

Pathophysiology of Nonalcoholic Fatty Liver Disease: Lifestyle-Gut-Gene Interaction.

Abstract: Background: The accumulation of fat droplets in the hepatic parenchyma is driven by several factors, synergistically acting to increase triglyceride flow to the liver (diet and metabolic factors, endotoxemia from gut microbiota, genetic factors). Key Messages: In the presence of unhealthy lifestyles and behavioral factors, leading to enlarged adipose tissue and insulin resistance (IR), both lipolysis and de novo lipogenesis are expected to increase the risk of hepatic lipid depots, in association with high calorie (either high-fat or high-carbohydrate) diets. The gut microbiota may also be involved via obesity, IR and hepatic inflammation generated by gut-derived toxic factors. Finally, several data also support a primary role of genetic factors. A few gene polymorphisms have also been associated with the risk of nonalcoholic fatty liver disease development and nonalcoholic steatohepatitis progression to more fibrosis and advanced liver disease. In a few cases (e.g., patatin-like phospholipase domain-containing 3/adiponutrin), steatosis carries a high risk of both liver disease and cardiovascular morbidity/mortality; in other cases (e.g., transmembrane 6 superfamily 2 human gene), dissociation has been observed between the increased risk of liver disease versus cardiovascular disease. Conclusions: A variable interplay between the genetic background and the metabolic milieu is the likely physiopathologic mechanism involved in individual cases, which must be considered for implementing effective treatment strategies.

Nutrient-Gene Interaction in Colon Cancer, from the Membrane to Cellular Physiology

Abstract: The International Agency for Research on Cancer recently released an assessment classifying red and processed meat as "carcinogenic to humans" on the basis of the positive association between increased consumption and risk for colorectal cancer. Diet, however, can also decrease the risk for colorectal cancer and be used as a chemopreventive strategy. Bioactive dietary molecules, such as n-3 polyunsaturated fatty acids, curcumin, and fermentable fiber, have been proposed to exert chemoprotective effects, and their molecular mechanisms have been the focus of research in the dietary/chemoprevention field. Using these bioactives as examples, this review surveys the proposed mechanisms by which they exert their effects, from the nucleus to the cellular membrane. In addition, we discuss emerging technologies involving the culturing of colonic organoids to study the physiological effects of dietary bioactives. Finally, we address future challenges to the field regarding the identification of additional molecular mechanisms and other bioactive dietary molecules that can be utilized in our fight to reduce the incidence of colorectal cancer.

Fine mapping and identification of candidate genes for a QTL affecting Meloidogyne incognita reproduction in Upland cotton

Abstract: The southern root-knot nematode (Meloidogyne incognita; RKN) is one of the most important economic pests of Upland cotton (Gossypium hirsutum L.). Host plant resistance, the ability of a plant to suppress nematode reproduction, is the most economical, practical, and environmentally sound method to provide protection against this subterranean pest. The resistant line Auburn 623RNR and a number of elite breeding lines derived from it remain the most important source of root-knot nematode (RKN) resistance. Prior genetic analysis has identified two epistatically interacting RKN resistance QTLs, qMi-C11 and qMi-C14, affecting gall formation and RKN reproduction, respectively. We developed a genetic population segregating only for the qMi-C14 locus and evaluated the genetic effects of this QTL on RKN resistance in the absence of the qMi-C11 locus. The qMi-C14 locus had a LOD score of 12 and accounted for 24.5 % of total phenotypic variation for egg production. In addition to not being significantly associated with gall formation, this locus had a lower main effect on RKN reproduction than found in our previous study, which lends further support to evidence of epistasis with qMi-C11 in imparting RKN resistance in the Auburn 623RNR source. The locus qMi-C14 was fine-mapped with the addition of 16 newly developed markers. By using the reference genome sequence of G. raimondii, we identified 20 candidate genes encoding disease resistance protein homologs in the newly defined 2.3 Mb region flanked by two SSR markers. Resequencing of an RKN resistant and susceptible G. hirsutum germplasm revealed non-synonymous mutations in only four of the coding regions of candidate genes, and these four genes are consequently of high interest. Our mapping results validated the effects of the qMi-C14 resistance locus, delimiting the QTL to a smaller region, and identified tightly linked SSR markers to improve the efficiency of marker-assisted selection. The candidate genes identified warrant functional studies that will help in identifying and characterizing the actual qMi-C14 defense gene(s) against root-knot nematodes.

Genic and Intergenic SSR Database Generation, SNPs Determination and Pathway Annotations, in Date Palm (Phoenix dactylifera L.).

Abstract: The present investigation was carried out aiming to use the bioinformatics tools in order to identify and characterize, simple sequence repeats within the third Version of the date palm genome and develop a new SSR primers database. In addition single nucleotide polymorphisms (SNPs) that are located within the SSR flanking regions were recognized. Moreover, the pathways for the sequences assigned by SSR primers, the biological functions and gene interaction were determined. A total of 172,075 SSR motifs was identified on date palm genome sequence with a frequency of 450.97 SSRs per Mb. Out of these, 130,014 SSRs (75.6%) were located within the intergenic regions with a frequency of 499 SSRs per Mb. While, only 42,061 SSRs (24.4%) were located within the genic regions with a frequency of 347.5 SSRs per Mb. A total of 111,403 of SSR primer pairs were designed, that represents 291.9 SSR primers per Mb. Out of the 111,403, only 31,380 SSR primers were in the genic regions, while 80,023 primers were in the intergenic regions. A number of 250,507 SNPs were recognized in 84,172 SSR flanking regions, which represents 75.55% of the total SSR flanking regions. Out of 12,274 genes only 463 genes comprising 896 SSR primers were mapped onto 111 pathways using KEGG data base. The most abundant enzymes were identified in the pathway related to the biosynthesis of antibiotics. We tested 1031 SSR primers using both publicly available date palm genome sequences as templates in the in silico PCR reactions. Concerning in vitro validation, 31 SSR primers among those used in the in silico PCR were synthesized and tested for their ability to detect polymorphism among six Egyptian date palm cultivars. All tested primers have successfully amplified products, but only 18 primers detected polymorphic amplicons among the studied date palm cultivars.

Environmental and genetic interactions reveal FLOWERING LOCUS C as a modulator of the natural variation for the plasticity of flowering in Arabidopsis.

Abstract: The timing of flowering initiation depends strongly on the environment, a property termed as the plasticity of flowering. Such plasticity determines the adaptive potential of plants because it provides phenotypic buffer against environmental changes, and its natural variation contributes to evolutionary adaptation. We addressed the genetic mechanisms of the natural variation for this plasticity in Arabidopsis thaliana by analysing a population of recombinant inbred lines derived from Don-0 and Ler accessions collected from distinct climates. Quantitative trait locus (QTL) mapping in four environmental conditions differing in photoperiod, vernalization treatment and ambient temperature detected the folllowing: (i) FLOWERING LOCUS C (FLC) as a large effect QTL affecting flowering time differentially in all environments; (ii) numerous QTL displaying smaller effects specifically in some conditions; and (iii) significant genetic interactions between FLC and other loci. Hence, the variation for the plasticity of flowering is determined by a combination of environmentally sensitive and specific QTL, and epistasis. Analysis of FLC from Don identified a new and more active allele likely caused by a cis-regulatory deletion covering the non-coding RNA COLDAIR. Further characterization of four FLC natural alleles showed different environmental and genetic interactions. Thus, FLC appears as a major modulator of the natural variation for the plasticity of flowering to multiple environmental factors.

Gene–gene interaction of ATG5, ATG7, BLK and BANK1 in systemic lupus erythematosus

Abstract: Aim Autophagy-related gene 5 (ATG5), ATG7, B-lymphoid tyrosine kinase (BLK) and B-cell scaffold protein with ankyrin repeats 1 (BANK1) are involved in B-cell signaling; several genome-wide association studies detected these genes as candidates involved in systemic lupus erythematosus (SLE). We aimed to replicate the association of these genes with SLE in Chinese Han and to search for possible gene–gene interactions. Methods TaqMan single-nucleotide polymorphism (SNP) genotyping was used to detect rs548234, rs665791 in ATG5, rs11706903 in ATG7, rs2736340 in BLK and rs10516487 in BANK1 in 382 SLE patients and 660 healthy controls. The epistasis effect was analyzed by logistic regression, multifactor dimensionality reduction (MDR) and linear regression analysis. Results SLE was associated with frequency of rs548234 (P = 0.010; odds ratio [OR] = 1.298), rs2736340 (P = 2.47 × 10−5; OR = 1.574) and rs10516487 (P = 0.002; OR = 0.642). Although no epistasis effects were found among three autophagy-related gene loci or with rs2736340 and rs10516487, BLK and BANK1 had the closest interaction effect on logistic regression analysis (P = 0.013; OR = 1.205), MDR (P < 0.0001), and linear regression analysis (P = 0.0017; R2 = 0.1806). The risk genotype TT of rs2736340 was associated with decreased messenger RNA level of BLK; BLK transcript level was lower in SLE patients than healthy controls. Conclusion We confirmed the association of rs548234, rs2736340 and rs10516487 with SLE in Chinese Han and reinforced our hypothesis of their epistasis effect in regulating B-cell signaling in SLE.

Distinct roles of the histone chaperones NAP1 and NRP and the chromatin-remodeling factor INO80 in somatic homologous recombination in Arabidopsis thaliana.

Abstract: Homologous recombination (HR) of nuclear DNA occurs within the context of a highly complex chromatin structure. Despite extensive studies of HR in diverse organisms, mechanisms regulating HR within the chromatin context remain poorly elucidated. Here we investigate the role and interplay of the histone chaperones NUCLEOSOME ASSEMBLY PROTEIN1 (NAP1) and NAP1-RELATED PROTEIN (NRP) and the ATP-dependent chromatin-remodeling factor INOSITOL AUXOTROPHY80 (INO80) in regulating somatic HR in Arabidopsis thaliana. We show that simultaneous knockout of the four AtNAP1 genes and the two NRP genes in the sextuple mutant m123456-1 barely affects normal plant growth and development. Interestingly, compared with the respective AtNAP1 (m123-1 and m1234-1) or NRP (m56-1) loss-of-function mutants, the sextuple mutant m123456-1 displays an enhanced plant hypersensitivity to UV or bleomycin treatments. Using HR reporter constructs, we show that AtNAP1 and NRP act in parallel to synergistically promote somatic HR. Distinctively, the AtINO80 loss-of-function mutation (atino80-5) is epistatic to m56-1 in plant phenotype and telomere length but hypostatic to m56-1 in HR determinacy. Further analyses show that expression of HR machinery genes and phosphorylation of H2A.X (γ-H2A.X) are not impaired in the mutants. Collectively, our study indicates that NRP and AtNAP1 synergistically promote HR upstream of AtINO80-mediated chromatin remodeling after the formation of γ-H2A.X foci during DNA damage repair.

Actionable pathways: interactive discovery of therapeutic targets using signaling pathway models

Abstract: The discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org.

Genetic interaction of hnRNPA2B1 and DNAJB6 in a Drosophila model of multisystem proteinopathy

Abstract: Adult-onset inherited myopathies with similar pathological features, including hereditary inclusion body myopathy (hIBM) and limb-girdle muscular dystrophy (LGMD), are a genetically heterogeneous group of muscle diseases. It is unclear whether these inherited myopathies initiated by mutations in distinct classes of genes are etiologically related. Here, we exploit a genetic model system to establish a mechanistic link between diseases caused by mutations in two distinct genes, hnRNPA2B1 and DNAJB6. Hrb98DE and mrj are the Drosophila melanogaster homologs of human hnRNPA2B1 and DNAJB6, respectively. We introduced disease-homologous mutations to Hrb98DE, thus capturing mutation-dependent phenotypes in a genetically tractable model system. Ectopic expression of the disease-associated mutant form of hnRNPA2B1 or Hrb98DE in fly muscle resulted in progressive, age-dependent cytoplasmic inclusion pathology, as observed in humans with hnRNPA2B1-related myopathy. Cytoplasmic inclusions consisted of hnRNPA2B1 or Hrb98DE protein in association with the stress granule marker ROX8 and additional endogenous RNA-binding proteins (RBPs), suggesting that these pathological inclusions are related to stress granules. Notably, TDP-43 was also recruited to these cytoplasmic inclusions. Remarkably, overexpression of MRJ rescued this phenotype and suppressed the formation of cytoplasmic inclusions, whereas reduction of endogenous MRJ by a classical loss of function allele enhanced it. Moreover, wild-type, but not disease-associated, mutant forms of MRJ interacted with RBPs after heat shock and prevented their accumulation in aggregates. These results indicate both genetic and physical interactions between disease-linked RBPs and DNAJB6/mrj, suggesting etiologic overlap between the pathogenesis of hIBM and LGMD initiated by mutations in hnRNPA2B1 and DNAJB6.

How interacting pathways are regulated by miRNAs in breast cancer subtypes.

Abstract: An important challenge in cancer biology is to understand the complex aspects of the disease. It is increasingly evident that genes are not isolated from each other and the comprehension of how different genes are related to each other could explain biological mechanisms causing diseases. Biological pathways are important tools to reveal gene interaction and reduce the large number of genes to be studied by partitioning it into smaller paths. Furthermore, recent scientific evidence has proven that a combination of pathways, instead than a single element of the pathway or a single pathway, could be responsible for pathological changes in a cell. In this paper we develop a new method that can reveal miRNAs able to regulate, in a coordinated way, networks of gene pathways. We applied the method to subtypes of breast cancer. The basic idea is the identification of pathways significantly enriched with differentially expressed genes among the different breast cancer subtypes and normal tissue. Looking at the pairs of pathways that were found to be functionally related, we created a network of dependent pathways and we focused on identifying miRNAs that could act as miRNA drivers in a coordinated regulation process. Our approach enables miRNAs identification that could have an important role in the development of breast cancer.