Meta-analysis of heat-stressed transcriptomes using the public gene expression database from human and mouse samples

Abstract

BackgroundClimate change has significantly increased the frequency of exposure to heat, adversely affecting human health and various industrial sectors. Heat stress is an environmental stress defined as the exposure of organisms and cells to abnormally high temperatures. Heat-stress research has predominantly focused on response systems involving heat shock factors acting as transcription factors and heat shock proteins functioning as molecular chaperones. However, to comprehensively elucidate the mechanisms underlying an organisms response to heat stress, it is essential to investigate and analyze genes that have been underrepresented, less well-known, or overlooked in previous studies. In this study, we analyzed heat stress-responsive genes using a meta-analysis of numerous gene expression datasets. ResultsFirst, we collected paired heat exposure and control data from public databases. Gene expression data were obtained for 322 human and 242 mouse pairs. The expression ratios (HN-ratios) of the collected pairs were calculated, and the identification of upregulated and downregulated expression profiles was determined according to defined thresholds. The number of upregulated and downregulated genes was calculated as the heat stress - non-treatment score (HN-score), which is the value of: [number of upregulated genes] - [number of downregulated genes] for each gene and was used as the index of analysis. The HN-score comprehensively evaluated gene expression variation, and 76 upregulated and 37 downregulated genes common to human and mouse were identified. We performed enrichment, protein-protein interaction network, and transcription factor target gene analyses. Furthermore, we evaluated the extracted genes through integrated analysis using publicly available ChIP-seq data for HSF1, HSF2, and PPARGC1A (PGC1-), and gene2pubmed data, which were sourced from previous literature. The results identified previously overlooked genes, such as ABHD3, ZFAND2A, and USPL1, as commonly upregulated genes. ConclusionsBased on the findings of this study, further functional analysis of the extracted genes using genome editing and other technologies has the potential to contribute to coping with climate change and potentially lead to new knowledge and technological advances.