The gene regulatory network in different brain regions of neuropathic pain mouse models

Neuropathic pain is the direct result caused by lesions or somatosensory nervous system diseases that are associated with emotional regulation. The incidence of neuropathic pain in the general population is 7-10% and the mechanisms of neuropathic pain are largely unknown. It is often related to structural and functional abnormalities in multiple brain regions. The forebrain, including nucleus accumbens (NAc), medial prefrontal cortex (mPFC) and periaqueductal gray (PAG) have been shown to correspond with the regulation of neuropathic pain. To investigate the molecular mechanism of neuropathic pain across different brain regions, we identified the differentially expressed genes between the spared nerve injury model (SNI) mice of neuropathic pain and the control Sham mice in NAc, mPFC and PAG and mapped these genes onto comprehensive functional association network. With Random Walk with Restart (RWR) analysis, we identified more novel neuropathic pain genes in NAc, mPFC and PAG, such as Asic3, Cd200r1 and MT2, beside well known Capn11 and CYP2E1. What’s more, we discovered their interactions or cross talks. Our results provided novel insights of neuropathic pain and provided therapeutic targets for treating neuropathic pain.

Gene Set linkage analysis: a tool for interpreting the overall functional impacts of observed transcriptomic changes

Background To date, the majority of software tools developed for high-level interpretation of transcriptomics data were based on annotation enrichment. These tools use existing biological concepts to describe the observed omics changes. However, if an observation cannot be accurately described by an existing concept, these tools can not report any term or will report very general terms (such as “biological process”, GO:0008150), which provides limited assistance for researchers to understand the data and design further investigation. Results We present the gene set linkage analysis (GSLA) tool for interpretation of the collective functional impacts of a set of changed genes. The GSLA algorithm relies on a functional association network to evaluate whether the observed omics changes collectively interfere with functions of known biological processes. Although an omics change may not be accurately described by an existing concept, its functional impact may still be described by well-established concepts. GSLA has been shown useful in several previous studies. It derived novel insights into high-level coordination of physiological processes, where conventional annotation enrichment-based tools did not provide similar insights. This standalone version of GSLA tool integrates interaction networks for four species (i.e., A. thaliana, D. melanogaster, H. sapiens and S. cerevisiae) and using four kinds of annotation gene sets (i.e., Gene Ontology, Reactome pathway, Panther pathway and Wikipathways) for each species. Conclusions The GSLA tool is designed to interpret the collective functional impacts of a set of changed genes. Its usefulness has been demonstrated in a series of previous researches analyzing transcriptomic changes. GSLA is freely available at https://github.com/synergy-zju/gsla.

Predicting Diagnostic Gene Biomarkers for Non-Small-Cell Lung Cancer

Lung cancer is the primary reason for death due to cancer worldwide, and non-small-cell lung cancer (NSCLC) is the most common subtype of lung cancer. Most patients die from complications of NSCLC due to poor diagnosis. In this paper, we aimed to predict gene biomarkers that may be of use for diagnosis of NSCLC by integrating differential gene expression analysis with functional association network analysis. We first constructed an NSCLC-specific functional association network by combining gene expression correlation with functional association. Then, we applied a network partition algorithm to divide the network into gene modules and identify the most NSCLC-specific gene modules based on their differential expression pattern in between normal and NSCLC samples. Finally, from these modules, we identified genes that exhibited the most impact on the expression of their functionally associated genes in between normal and NSCLC samples and predicted them as NSCLC biomarkers. Literature review of the top predicted gene biomarkers suggested that most of them were already considered critical for development of NSCLC.

A Comparative Genomics, Network-Based Approach to Understanding Virulence in Vibrio cholerae

ABSTRACT Our views of the genes that drive phenotypes have generally been built up one locus or operon at a time. However, a given phenotype, such as virulence, is a multilocus phenomenon. To gain a more comprehensive view of the genes and interactions underlying a phenotype, we propose an approach that incorporates information from comparative genomics and network biology and illustrate it by examining the virulence phenotype of Vibrio cholerae O1 El Tor N16961. We assessed the associations among the virulence-associated proteins from Vibrio cholerae and all the other proteins from this bacterium using a functional-association network map. In the context of this map, we were able to identify 262 proteins that are functionally linked to the virulence-associated genes more closely than is typical of the proteins in this strain and 240 proteins that are functionally linked to the virulence-associated proteins with a confidence score greater than 0.9. The roles of these genes were investigated using functional information from online data sources, comparative genomics, and the relationships shown by the protein association map. We also incorporated core proteome data from the family Vibrionaceae; 35% of the virulence-associated proteins have orthologs among the 1,822 orthologous groups of proteins in the core proteome, indicating that they may be dual-role virulence genes or encode functions that have value outside the human host. This approach is a valuable tool in searching for novel functional associations and in investigating the relationship between genotype and phenotype.