GeneticsGeneticsPeer-reviewed journal article
mir-71 mediates age-dependent opposing contributions of the stress activated kinase KGB-1 in Caenorhabditis elegans
Cyrus T. RuedigeriD, Siavash Karimzadegan, Sonya Lin, Michael ShapiraiD
Abstract
Studying the evolutionary processes that shaped aging offers a path for understanding the causes of aging. The Antagonistic Pleiotropy theory for the evolution of aging proposes that the inverse correlation between natural selection strength and aging allows positive selection of gene variants with early-life beneficial contributions to fitness in spite of detrimental late-life consequences. However, mechanistic understanding of how this principle manifests in aging is still lacking. We previously identified antagonistic pleiotropy in the function of the Caenorhabditis elegans JNK homolog KGB-1, which provided stress protection in developing larvae, but sensitized adults to stress and shortened their lifespan. To a large extent, KGB-1s contributions depended on age-dependent and opposing regulation of the stress transcription factor DAF-16, but the underlying mechanisms remained unknown. Here we describe a role for the microRNA mir-71 in mediating effects of KGB-1 on DAF-16 and on downstream phenotypes. Fluorescent imaging along with genetic and survival analyses revealed age-dependent regulation of mir-71 expression by KGB-1 - upregulation in larvae, but downregulation in adults, and showed that mir-71 was required both for late-life effects of KGB-1 (infection sensitivity and shortened lifespan), as well as for early life resistance to cadmium. While mir-71 disruption did not compromise development under protein folding stress (known to depend on KGB-1), disruption of the argonaute gene alg-1, a central component of the microRNA machinery, did. These results suggest that microRNAs play a role in mediating age-dependent antagonistic contributions of KGB-1 to survival, with mir-71 playing a central role and additional microRNAs contributing redundantly.
- organism: C. elegans
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 7
- FWCI
- 0.63
- Field percentile
- top 43%
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