Predictive features of gene expression variation reveal mechanistic link with differential expression. Issue 8 (7th August 2020)

• Record Type: Journal Article
• Authors: Sigalova, Olga M; Shaeiri, Amirreza; Forneris, Mattia; Furlong, Eileen EM; Zaugg, Judith B
• Is Part Of: Molecular systems biology. Volume 16:Issue 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-07
• Subjects: embryogenesis -- expression variation -- gene expression -- promoters -- transcriptional regulation; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.20209539
• Languages: English
• ISSNs: 1744-4292

Abstract

Abstract: For most biological processes, organisms must respond to extrinsic cues, while maintaining essential gene expression programmes. Although studied extensively in single cells, it is still unclear how variation is controlled in multicellular organisms. Here, we used a machine‐learning approach to identify genomic features that are predictive of genes with high versus low variation in their expression across individuals, using bulk data to remove stochastic cell‐to‐cell variation. Using embryonic gene expression across 75 Drosophila isogenic lines, we identify features predictive of expression variation (controlling for expression level), many of which are promoter‐related. Genes with low variation fall into two classes reflecting different mechanisms to maintain robust expression, while genes with high variation seem to lack both types of stabilizing mechanisms. Applying this framework to humans revealed similar predictive features, indicating that promoter architecture is an ancient mechanism to control expression variation. Remarkably, expression variation features could also partially predict differential expression after diverse perturbations in both Drosophila and humans. Differential gene expression signatures may therefore be partially explained by genetically encoded gene‐specific features, unrelated to the studied treatment. Synopsis: Conserved genomic features predictive of gene expression variation across individuals are identified in Drosophila and human using a machine‐learning approach. The same features predict differential expression upon perturbation revealing a link between variation and differential expression. Expression variation across individuals is a robust gene‐specific property. Expression variation reflects regulatory properties inherent to genes and discriminates functional gene groups. Promoter architecture and overall gene regulatory complexity are predictive of gene expression variation. Expression variation features could also predict differential expression upon diverse perturbations. Abstract : Conserved genomic features predictive of gene expression variation across individuals are identified in Drosophila and human using a machine‐learning approach. The same features predict differential expression upon perturbation revealing a link between variation and differential expression.