4.7 Article

Transcriptomic analysis provides insights into molecular mechanisms of thermal physiology

期刊

BMC GENOMICS
卷 23, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s12864-022-08653-y

关键词

Metabolism; Cardiac metabolism; Critical thermal maximum; Evolutionary adaptation; Co-expression network analysis

资金

  1. National Science Foundation [IOS 1556396, IOS 1754437]

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Physiological trait variation plays a crucial role in health, responses to global climate change, and ecological performance. However, we still have limited knowledge about the genes and genomic architectures that define this variation. By studying the relationship between physiological traits and mRNA expression, we can gain insights into the genetic architecture of physiological processes and understand how mRNA expression is related to heritable fitness-related traits.
Physiological trait variation underlies health, responses to global climate change, and ecological performance. Yet, most physiological traits are complex, and we have little understanding of the genes and genomic architectures that define their variation. To provide insight into the genetic architecture of physiological processes, we related physiological traits to heart and brain mRNA expression using a weighted gene co-expression network analysis. mRNA expression was used to explain variation in six physiological traits (whole animal metabolism (WAM), critical thermal maximum (CTmax), and four substrate specific cardiac metabolic rates (CaM)) under 12 degrees C and 28 degrees C acclimation conditions. Notably, the physiological trait variations among the three geographically close (within 15 km) and genetically similar F. heteroclitus populations are similar to those found among 77 aquatic species spanning 15-20 degrees of latitude (similar to 2,000 km). These large physiological trait variations among genetically similar individuals provide a powerful approach to determine the relationship between mRNA expression and heritable fitness related traits unconfounded by interspecific differences. Expression patterns explained up to 82% of metabolic trait variation and were enriched for multiple signaling pathways known to impact metabolic and thermal tolerance (e.g., AMPK, PPAR, mTOR, FoxO, and MAPK) but also contained several unexpected pathways (e.g., apoptosis, cellular senescence), suggesting that physiological trait variation is affected by many diverse genes.

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