Gene co-expression analyses of health(span) across multiple species

Steffen Möller; Nadine Saul; Elias Projahn; Israel Barrantes; András Gézsi; Michael Walter; Péter Antal; Georg Fuellen

doi:10.1093/nargab/lqac083

Gene co-expression analyses of health(span) across multiple species

NAR Genom Bioinform. 2022 Nov 29;4(4):lqac083. doi: 10.1093/nargab/lqac083. eCollection 2022 Dec.

Authors

Steffen Möller¹, Nadine Saul², Elias Projahn¹, Israel Barrantes¹, András Gézsi³, Michael Walter⁴, Péter Antal³, Georg Fuellen¹

Affiliations

¹ Rostock University Medical Center, Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock, Germany.
² Humboldt-University of Berlin, Institute of Biology, Berlin, Germany.
³ Budapest University of Technology and Economics, Department of Measurement and Information Systems, Budapest, Hungary.
⁴ Rostock University Medical Center, Institute for Clinical Chemistry and Laboratory Medicine, Rostock, Germany.

Abstract

Health(span)-related gene clusters/modules were recently identified based on knowledge about the cross-species genetic basis of health, to interpret transcriptomic datasets describing health-related interventions. However, the cross-species comparison of health-related observations reveals a lot of heterogeneity, not least due to widely varying health(span) definitions and study designs, posing a challenge for the exploration of conserved healthspan modules and, specifically, their transfer across species. To improve the identification and exploration of conserved/transferable healthspan modules, here we apply an established workflow based on gene co-expression network analyses employing GEO/ArrayExpress data for human and animal models, and perform a comprehensive meta-study of the resulting modules related to health(span), yielding a small set of literature backed health(span) candidate genes. For each experiment, WGCNA (weighted gene correlation network analysis) was used to infer modules of genes which correlate in their expression with a 'health phenotype score' and to determine the most-connected (hub) genes (and their interactions) for each such module. After mapping these hub genes to their human orthologs, 12 health(span) genes were identified in at least two species (ACTN3, ANK1, MRPL18, MYL1, PAXIP1, PPP1CA, SCN3B, SDCBP, SKIV2L, TUBG1, TYROBP, WIPF1), for which enrichment analysis by g:profiler found an association with actin filament-based movement and associated organelles, as well as muscular structures. We conclude that a meta-study of hub genes from co-expression network analyses for the complex phenotype health(span), across multiple species, can yield molecular-mechanistic insights and can direct experimentalists to further investigate the contribution of individual genes and their interactions to health(span).