Tissue-specific Co-expression Networks Research Articles

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

BackgroundThe genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes.ResultsWe studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin.ConclusionWe show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Multi-tissue RNA-Seq Analysis and Long-read-based Genome Assembly Reveal Complex Sex-specific Gene Regulation and Molecular Evolution in the Manila Clam.

The molecular factors and gene regulation involved in sex determination and gonad differentiation in bivalve molluscs are unknown. It has been suggested that doubly uniparental inheritance (DUI) of mitochondria may be involved in these processes in species such as the ubiquitous and commercially relevant Manila clam, Ruditapes philippinarum. We present the first long-read-based de novo genome assembly of a Manila clam, and a RNA-Seq multi-tissue analysis of 15 females and 15 males. The highly contiguous genome assembly was used as reference to investigate gene expression, alternative splicing, sequence evolution, tissue-specific co-expression networks, and sexual contrasting SNPs. Differential expression (DE) and differential splicing (DS) analyses revealed sex-specific transcriptional regulation in gonads, but not in somatic tissues. Co-expression networks revealed complex gene regulation in gonads, and genes in gonad-associated modules showed high tissue specificity. However, male gonad-associated modules showed contrasting patterns of sequence evolution and tissue specificity. One gene set was related to the structural organization of male gametes and presented slow sequence evolution but high pleiotropy, whereas another gene set was enriched in reproduction-related processes and characterized by fast sequence evolution and tissue specificity. Sexual contrasting SNPs were found in genes overrepresented in mitochondrial-related functions, providing new candidates for investigating the relationship between mitochondria and sex in DUI species. Together, these results increase our understanding of the role of DE, DS, and sequence evolution of sex-specific genes in an understudied taxon. We also provide resourceful genomic data for studies regarding sex diagnosis and breeding in bivalves.

Proposed minimal essential co-expression and physical interaction networks involved in the development of cognition impairment in human mid and late life.

The aim of this study was to identify the minimal essential co-expression and physical interaction networks involved in the development of cognition impairment in human mid and late life. We searched the Online Mendelian Inheritance in Man (OMIM) database to extract the validated human genes annotated (until March 2020) for five major disorders of pathophysiological overlap and sequential chronological occurrence in human, including multiple sclerosis, type 2 diabetes mellitus, Alzheimer's disease, vascular dementia, and Lewy body dementia. Gene co-expression and physical interaction networks were subsequently constructed for the overlapping genes across the selected disorders. Remarkably, each of the gene co-expression and physical interaction networks consisted of single clusters (P = 0.0005 and P = 1 × 10-16, respectively). APP was the major hub in the integrated and tissue-specific co-expression networks, whereas insulin was the major hub in the physical interaction network. Several other hubs were identified across the identified networks, including TNF, VEGFA, GAPDH, and NOTCH1. We propose the minimal co-expression and physical interaction networks and their single clustering in the development of cognition impairment in human mid and late life. This is a pilot study, warranting identification of more risk genes, using additional validated databases in the future.

Portrait of Tissue-Specific Coexpression Networks of Noncoding RNAs (miRNA and lncRNA) and mRNAs in Normal Tissues

Genes that encode proteins playing a role in more than one biological process are frequently dependent on their tissue context, and human diseases result from the altered interplay of tissue- and cell-specific processes. In this work, we performed a computational approach that identifies tissue-specific co-expression networks by integrating miRNAs, long-non-coding RNAs, and mRNAs in more than eight thousands of human samples from thirty normal tissue types. Our analysis (1) shows that long-non coding RNAs and miRNAs have a high specificity, (2) confirms several known tissue-specific RNAs, and (3) identifies new tissue-specific co-expressed RNAs that are currently still not described in the literature. Some of these RNAs interact with known tissue-specific RNAs or are crucial in key cancer functions, suggesting that they are implicated in tissue specification or cell differentiation.

MIsoMine: a genome-scale high-resolution data portal of expression, function and networks at the splice isoform level in the mouse

Products of multiexon genes, especially in higher organisms, are a mixture of isoforms with different or even opposing functions, and therefore need to be treated separately. However, most studies and available resources such as Gene Ontology provide only gene-level function annotations, and therefore lose the differential information at the isoform level. Here we report MIsoMine, a high-resolution portal to multiple levels of functional information of alternatively spliced isoforms in the mouse. This data portal provides tissue-specific expression patterns and co-expression networks, along with such previously published functional genomic data as protein domains, predicted isoform-level functions and functional relationships. The core utility of MIsoMine is allowing users to explore a preprocessed, quality-controlled set of RNA-seq data encompassing diverse tissues and cell lineages. Tissue-specific co-expression networks were established, allowing a 2D ranking of isoforms and tissues by co-expression patterns. The results of the multiple isoforms of the same gene are presented in parallel to facilitate direct comparison, with cross-talking to prioritized functions at the isoform level. MIsoMine provides the first isoform-level resolution effort at genome-scale. We envision that this data portal will be a valuable resource for exploring functional genomic data, and will complement the existing functionalities of the mouse genome informatics database and the gene expression database for the laboratory mouse.Database URL: http://guanlab.ccmb.med.umich.edu/misomine/

CressExpress: A Tool For Large-Scale Mining of Expression Data from Arabidopsis

CressExpress is a user-friendly, online, coexpression analysis tool for Arabidopsis (Arabidopsis thaliana) microarray expression data that computes patterns of correlated expression between user-entered query genes and the rest of the genes in the genome. Unlike other coexpression tools, CressExpress allows characterization of tissue-specific coexpression networks through user-driven filtering of input data based on sample tissue type. CressExpress also performs pathway-level coexpression analysis on each set of query genes, identifying and ranking genes based on their common connections with two or more query genes. This allows identification of novel candidates for involvement in common processes and functions represented by the query group. Users launch experiments using an easy-to-use Web-based interface and then receive the full complement of results, along with a record of tool settings and parameters, via an e-mail link to the CressExpress Web site. Data sets featured in CressExpress are strictly versioned and include expression data from MAS5, GCRMA, and RMA array processing algorithms. To demonstrate applications for CressExpress, we present coexpression analyses of cellulose synthase genes, indolic glucosinolate biosynthesis, and flowering. We show that subselecting sample types produces a richer network for genes involved in flowering in Arabidopsis. CressExpress provides direct access to expression values via an easy-to-use URL-based Web service, allowing users to determine quickly if their query genes are coexpressed with each other and likely to yield informative pathway-level coexpression results. The tool is available at http://www.cressexpress.org.