Literature DB >> 35596083

Relationship between gene expression networks and muscle contractile physiology differences in Anolis lizards.

Luke B Smith1,2, Christopher V Anderson1, Miyuraj H Hikkaduwa Withangage1,3, Andrew Koch1, Thomas J Roberts4, Andrea L Liebl5.   

Abstract

Muscles facilitate most animal behavior, from eating to fleeing. However, to generate the variation in behavior necessary for survival, different muscles must perform differently; for instance, sprinting requires multiple rapid muscle contractions, whereas biting may require fewer contractions but greater force. Here, we use a transcriptomic approach to identify genes associated with variation in muscle contractile physiology among different muscles from the same individual. We measured differential gene expression between a leg and jaw muscle of Anolis lizards known to differ in muscle contractile physiology and performance. For each individual, one muscle was used to measure muscle contractile physiology, including contractile velocity (Vmax and V40), specific tension, power ratio, and twitch time, whereas the contralateral muscle was used to extract RNA for transcriptomic sequencing. Using the transcriptomic data, we found clear clustering of muscle type. Expression of genes clustered in gene ontology (GO) terms related to muscle contraction and extracellular matrix was, on average, negatively correlated with Vmax and slower twitch times but positively correlated to power ratio and V40. Conversely, genes related to the GO terms related to aerobic respiration were downregulated in muscles with higher power ratio and V40, and over-expressed as twitch time decreased. Determining the molecular mechanisms that underlie variation in muscle contractile physiology can begin to explain how organisms are able to optimize behavior under variable conditions. Future studies pursuing the effects of differential gene expression across muscle types in different environments might inform researchers about how differences develop across species, populations, and individuals varying in ecological history.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Keywords:  Functional transcriptomics; Gene expression; RNA-seq

Mesh:

Year:  2022        PMID: 35596083     DOI: 10.1007/s00360-022-01441-w

Source DB:  PubMed          Journal:  J Comp Physiol B        ISSN: 0174-1578            Impact factor:   2.230


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