Co-expression networks uncover regulation of splicing and transcription markers of disease

Abstract

Gene co-expression networks based on gene expression data are usually used to capture biologically significant patterns, enabling the discovery of biomarkers and interpretation of regulatory relationships. However, the coordination of numerous splicing changes within and across genes can exert a substantial impact on the function of these genes. This is particularly impactful in studies of the properties of the nervous system, which can be masked in the networks that only assess the correlation between gene expression levels. A bioinformatics approach was developed to uncover the role of alternative splicing and associated transcriptional networks using RNA-seq profiles. Data from 40 samples, including control and two treatments associated with sensitivity to stimuli across two central nervous system regions that can present differential splicing, were explored. The gene expression and relative isoform levels were integrated into a transcriptome-wide matrix, and then Graphical Lasso was applied to capture the interactions between genes and isoforms. Next, functional enrichment analysis enabled the discovery of pathways dysregulated at the isoform or gene levels and the interpretation of these interactions within a central nervous region. In addition, a Bayesian biclustering strategy was used to reconstruct treatment-specific networks from gene expression profile, allowing the identification of hub molecules and visualization of highly connected modules of isoforms and genes in specific conditions. Our bioinformatics approach can offer comparable insights into the discovery of biomarkers and therapeutic targets for a wide range of diseases and conditions.

Alternative Splicing Mechanisms Underlying Opioid-Induced Hyperalgesia

“…Differential abundance of splice variants annotated to receptors, ion channels and growth factors associated with nociception and pain have been reported [17][18][19]. Our targeted study of circadian rhythm and toll-like receptor networks demonstrated the transcript isoform profiling can offer additional insights on OIH [12]. Moreover, alternative splicing impacts the function of genes in many pathways involved in OIH such as glutamatergic and myelination processes.…”

“…The isoform-level output from STAR was subsequently processed in RSEM (v. 1.3.1) to quantify the transcript isoform expression in raw reads counts [26]. This pipeline differed from our previous analysis [10,12,23] and optimized the quantification of expression at the transcript isoform level.…”

“…Rodent models of OIH have advanced the understanding of the central nervous system (CNS) regions and molecular mechanisms underlying OIH [6][7][8][9][10][11]. Rodents repeatedly administered morphine presented hypersensitivity to noxious stimuli evidenced by the higher mechanical or thermal sensitivity to von Frey filaments and hot plate tests [7,9], and alterations of molecular pathways [6,[10][11][12]. For example, our studies of chronic morphine exposure in mice detected changes the abundance of numerous neuropeptides across multiple CNS regions [6].…”

“…The PSI is a frequently used metric to characterize alternative splicing [30,31] and is computed based on the number of RNA-seq reads that support a particular exon or group of exons in a transcript isoform. The change in PSI (∆PSI) was used to quantify the relative differential expression of each transcript isoform between the OIH and control mice and the statistical test for differential splicing combines the ∆PSI across all transcript isoforms within a cluster to provide one P-value per gene intron cluster [11,12]. Multiple isoform clusters may be tested at different locations within a gene and genome locations annotated to multiple gene will share test results.…”

Acorde: unraveling functionally-interpretable networks of isoform co-usage from single cell data

“…Alternative Splicing (AS) is known to be a tightly-regulated process in which splicing factors interact to create cell type-specific isoform expression patterns 78 . The transcriptome-level consequences of AS regulation have been studied in different ways, including, but not limited to, the detection of within-isoform coordination of alternative sites 40,41 , the generation of gene-isoform networks to uncover novel regulatory relationships [79][80][81][82] and the application of single-cell data to unravel cell type-specific expression patterns for same-gene isoforms 27,83 . However, the extent to which AS regulation creates co-expression patterns among alternative isoforms from different genes has not yet been fully addressed.…”