bioRxivPhysiologyPreprint
Insulin signaling and extended longevity in ants
H. Yan, C. Opachaloemphan, F. Carmona-Aldana, G. Mancini, J. Mlejnek, N. Descostes, B. Sieriebriennikov, A. Leibholz, X. Zhou, L. Ding, M. Traficante, C. Desplan, D. Reinberg
Abstract
In most organisms, the cost of reproduction is a shorter lifespan. However, the reproductive caste in eusocial insect species (queen) exhibits an extraordinarily longer lifespan than non-reproductive castes (workers) despite having a similar genome, thus contradicting the aging dogma. In the absence of the queen, Harpegnathos saltator ants can undergo a caste switch from workers to reproductive pseudo-queens (gamergates). Gamergates exhibit a dramatically prolonged lifespan. When placed in the presence of a reproductive, they revert to worker status and their lifespan is then shortened accordingly. To understand this unique relationship between reproduction and longevity, we analyzed tissue-specific gene expression between castes. Insulin is upregulated in the gamergate brain that leads to oogenesis, but surprisingly correlates with extended longevity. This correlates with increased lipid synthesis and elevated production of vitellogenin in the fat body, which are both transported to the egg. We show that the production of vitellogenin in the fat body is due to the systemic activation of the MAPK branch of the insulin/IGF signaling (IIS)-pathway. In contrast, reduced expression of insulin receptors in the fat body of gamergates and the production in their developing ovary of an anti-insulin (Imp-L2) lead to the downregulation of the AKT/FOXO branch of the IIS-signaling pathway in the fat body, and to the dramatically extended longevity. This reveals a dual-pathway mechanism that reconciles increased longevity in the context of active reproduction in eusocial insects. One Sentence SummaryInsulin-dependent reproduction in ants correlates with extended longevity through insulin inhibition by anti-insulin Imp-L2.
- organism: multiple species
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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