Journal
NATURE COMMUNICATIONS
Volume 12, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-021-21095-6
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Funding
- Natural Environment Research Council [NE/L002485/1, NE/M012913/2]
- Biotechnology and Biological Sciences Research Council [BB/R003882/1, BB/S003681/1]
- BBSRC [BB/R003882/1, BB/S003681/1] Funding Source: UKRI
- NERC [NE/M012913/2] Funding Source: UKRI
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The study uses machine learning to analyze gene expression profiles of Polistes dominula paper wasps following the loss of a queen, revealing a strong response of individual gene expression to queen loss, even in individuals that appear phenotypically unaffected.
Phenotypic plasticity, the ability to produce multiple phenotypes from a single genotype, represents an excellent model with which to examine the relationship between gene expression and phenotypes. Analyses of the molecular foundations of phenotypic plasticity are challenging, however, especially in the case of complex social phenotypes. Here we apply a machine learning approach to tackle this challenge by analyzing individual-level gene expression profiles of Polistes dominula paper wasps following the loss of a queen. We find that caste-associated gene expression profiles respond strongly to queen loss, and that this change is partly explained by attributes such as age but occurs even in individuals that appear phenotypically unaffected. These results demonstrate that large changes in gene expression may occur in the absence of outwardly detectable phenotypic changes, resulting here in a socially mediated de-differentiation of individuals at the transcriptomic level but not at the levels of ovarian development or behavior. Connecting genotypes to complex social behaviour is challenging. Taylor et al. use machine learning to show a strong response of caste-associated gene expression to queen loss, wherein individual wasp's expression profiles become intermediate between queen and worker states, even in the absence of behavioural changes.
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