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

View ORCID ProfileSteffen Möller, View ORCID ProfileNadine Saul, View ORCID ProfileIsrael Barrantes, View ORCID ProfileAndrás Gézsi, View ORCID ProfileMichael Walter, View ORCID ProfilePéter Antal, View ORCID ProfileGeorg Fuellen
doi: https://doi.org/10.1101/2021.04.08.439030
Steffen Möller
1Rostock University Medical Center, Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock, Germany
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  • For correspondence: steffen.moeller@uni-rostock.de
Nadine Saul
2Humboldt-University of Berlin, Institute of Biology, Berlin, Germany
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Israel Barrantes
1Rostock University Medical Center, Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock, Germany
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András Gézsi
3Budapest University of Technology and Economics, Department of Measurement and Information Systems, Budapest, Hungary
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Michael Walter
4Rostock University Medical Center, Institute for Clinical Chemistry and Laboratory Medicine, Rostock, Germany
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Péter Antal
3Budapest University of Technology and Economics, Department of Measurement and Information Systems, Budapest, Hungary
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Georg Fuellen
1Rostock University Medical Center, Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock, Germany
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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-analysis of the resulting modules related to health(span), yielding a small set of health(span) candidate genes, backed by the literature.

For each experiment, WGCNA (weighted gene correlation network analysis) was thus used to infer modules of genes which correlate in their expression with a “health phenotype score” and to determine the most-connected (hub) genes for each such module, and their interactions. 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 finds 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).

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://bitbucket.org/ibima/healthspantranscriptomicsnetworks

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 10, 2021.
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Gene co-expression analyses of health(span) across multiple species
Steffen Möller, Nadine Saul, Israel Barrantes, András Gézsi, Michael Walter, Péter Antal, Georg Fuellen
bioRxiv 2021.04.08.439030; doi: https://doi.org/10.1101/2021.04.08.439030
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Gene co-expression analyses of health(span) across multiple species
Steffen Möller, Nadine Saul, Israel Barrantes, András Gézsi, Michael Walter, Péter Antal, Georg Fuellen
bioRxiv 2021.04.08.439030; doi: https://doi.org/10.1101/2021.04.08.439030

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