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Proteomic Characterization of Inbreeding-Related Cold Sensitivity in Drosophila melanogaster

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  • Cornelis J Vermeulen
  • Kamilla S Pedersen
  • Hans C Beck
  • Jørgen Petersen
  • Kristina Kirilova Gagalova
  • Volker Loeschcke

Abstract

Inbreeding depression is a widespread phenomenon of central importance to agriculture, medicine, conservation biology and evolutionary biology. Although the population genetic principles of inbreeding depression are well understood, we know little about its functional genomic causes. To provide insight into the molecular interplay between intrinsic stress responses, inbreeding depression and temperature tolerance, we performed a proteomic characterization of a well-defined conditional inbreeding effect in a single line of Drosophila melanogaster, which suffers from extreme cold sensitivity and lethality. We identified 48 differentially expressed proteins in a conditional lethal line as compared to two control lines. These proteins were enriched for proteins involved in hexose metabolism, in particular pyruvate metabolism, and many were found to be associated with lipid particles. These processes can be linked to known cold tolerance mechanisms, such as the production of cryoprotectants, membrane remodeling and the build-up of energy reserves. We checked mRNA-expression of seven genes with large differential protein expression. Although protein expression poorly correlated with gene expression, we found a single gene (CG18067) that, after cold shock, was upregulated in the conditional lethal line both at the mRNA and protein level. Expression of CG18067 also increased in control flies after cold shock, and has previously been linked to cold exposure and chill coma recovery time. Many differentially expressed proteins in our study appear to be involved in cold tolerance in non-inbred individuals. This suggest the conditional inbreeding effect to be caused by misregulation of physiological cold tolerance mechanisms.

Suggested Citation

  • Cornelis J Vermeulen & Kamilla S Pedersen & Hans C Beck & Jørgen Petersen & Kristina Kirilova Gagalova & Volker Loeschcke, 2013. "Proteomic Characterization of Inbreeding-Related Cold Sensitivity in Drosophila melanogaster," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
  • Handle: RePEc:plo:pone00:0062680
    DOI: 10.1371/journal.pone.0062680
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    References listed on IDEAS

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    1. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
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