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The transcriptomic signature of responses to larval crowding in Drosophila melanogaster
Authors:Juliano Morimoto  Marius Wenzel  Davina Derous  Youn Henry  Herve Colinet
Affiliation:1. School of Biological Sciences, University of Aberdeen, Aberdeen, United Kingdom;2. CNRS, ECOBIO (Ecosystèmes, biodiversité, évolution)—UMR 6553, University of Rennes, Rennes, France

Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland;3. CNRS, ECOBIO (Ecosystèmes, biodiversité, évolution)—UMR 6553, University of Rennes, Rennes, France

Abstract:Intraspecific competition at the larval stage is an important ecological factor affecting life-history, adaptation and evolutionary trajectory in holometabolous insects. However, the molecular pathways underpinning these ecological processes are poorly characterized. We reared Drosophila melanogaster at three egg densities (5, 60, and 300 eggs/mL) and sequenced the transcriptomes of pooled third-instar larvae. We also examined emergence time, egg-to-adult viability, adult mass, and adult sex-ratio at each density. Medium crowding had minor detrimental effects on adult phenotypes compared to low density and yielded 24 differentially expressed genes (DEGs), including several chitinase enzymes. In contrast, high crowding had substantial detrimental effects on adult phenotypes and yielded 2107 DEGs. Among these, upregulated gene sets were enriched in sugar, steroid and amino acid metabolism as well as DNA replication pathways, whereas downregulated gene sets were enriched in ABC transporters, taurine, Toll/Imd signaling, and P450 xenobiotics metabolism pathways. Overall, our findings show that larval crowding has a large consistent effect on several molecular pathways (i.e., core responses) with few pathways displaying density-specific regulation (i.e., idiosyncratic responses). This provides important insights into how holometabolous insects respond to intraspecific competition during development.
Keywords:intraspecific competition  larval crowding  life-history  trade-offs  transcriptomics
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