Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights

Abstract

Motivation Co-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behaviour for the genes involved. They can be applied to bulk-tissue gene expression profiling and assign function, and usually cell type specificity, to a high percentage of the gene pool used to construct the network. One of the limitations of this method is that each gene is predicted to play a role in a specific set of coherent functions in a single cell type (i.e. at most we get a single <gene, function, cell type> for each gene). We present here GMSCA (Gene Multifunctionality Secondary Co-expression Analysis), a software tool that exploits the co-expression paradigm to increase the number of functions and cell types ascribed to a gene in bulk-tissue co-expression networks. Results We applied GMSCA to 27 co-expression networks derived from bulk-tissue gene expression profiling of a variety of brain tissues. Neurons and glial cells (microglia, astrocytes and oligodendrocytes) were considered the main cell types. Applying this approach, we increase the overall number of predicted triplets <gene, function, cell type> by 46.73%. Moreover, GMSCA predicts that the SNCA gene, traditionally associated to work mainly in neurons, also plays a relevant function in oligodendrocytes. Availability The tool is available at GitHub, https://github.com/drlaguna/GMSCA as open-source software. Supplementary information Supplementary data are available at Bioinformatics online.

The chromatin modulating NSL complex regulates genes and pathways genetically linked to Parkinson’s disease

“…We used the GMSCA tool to test this possibility, and generated secondary GCNs, from which the contribution of four major cell types (neurons, astrocytes, microglia and oligodendrocytes) was removed in turn prior to network construction. 40 As might be expected, this correction altered the clustering of NSL genes and their co-expression patterns with Parkinson’s disease-associated genes across secondary GCN modules (Fig. 2a,c).…”

“…Secondary GCNs allowed us to examine the activity of the genes encoding the NSL complex within different cell type contexts. 40 Cell type enrichments annotated to modules containing NSL genes when present were almost always neuronal. These findings support in vitro experimental results demonstrating changes in multiple markers of mitochondrial dysfunction following NSL KD in neuronal models.…”

“…39 Four secondary GCNs were also examined, for which the gene multifunctionality in secondary co-expression network analysis (GMSCA) R package was used to remove the contribution of neuronal, microglial, astrocytic and oligodendrocytic cell types from the expression matrix before GCN reconstruction. 40…”

“…For gene list enrichment analyses, we obtained p-values using Fisher’s Exact tests comparing the overlap in input genes and genes contained within each module, with false discovery rate (FDR) corrections for multiple testing. 39,40 GCNs were annotated with gene ontology (GO) terms, categorized into biological process (BP), molecular function (MF) or cellular component (CC), within the CoExp R packages. GO terms were reduced to parent terms using the Rutils github package to aid visualization (doi: 10.5281/zenodo.6127446).…”