Project Title: Exploiting sociogenomics datasets for understanding phenotypic plasticity
Organisation: University of Bristol
Applicants: Dr Seirian Sumner, Dr Gary Baker & Professor Julian Gough
Project Duration: 01 April 2013 – 01 October 2013
NERC Reference: NE/K011316/1
Summary of proposed research: Determining how biological diversity is generated from the most basic building blocks of life (i.e. the genes) is fundamental to our understanding and sustainable stewardship of the environment and its biodiversity. lt is also fundamental for revealing how biological complexity arises and is maintained during the evolution of life.
ln a rapidly changing global environment it is not just genetic variation between individuals in a population that will prove essential to their long-term survival; behavioural flexibility is also likely to be crucial to survival, as it permits an organism to respond rapidly to environmental change. Such behavioural plasticity is mediated at the level of the genes; genes respond to environmental cues, changing their expression, resulting in changes in the types and quantities of proteins produced. This affects the behaviour of cells, and ultimately, of the organism as a whole. Our understanding of the fundamental molecular processes which govern interactions between genome and environment is currently limited, especially for wild organisms in ecologically meaningful contexts.
Biology is entering the ‘omics era, where data on the broad-scale molecular processes underlying phenotype flexibility, adaptation and resilience can be easily produced. Until recently, these datasets were limited to a few lab-based organisms. Advances in molecular technologies means we can now ask these questions of any organism in natural populations. The potential is of immeasurable benefit to the environmental sciences, where organisms need to be studied in meaningful ecological contexts. However currently our ability to generate such ‘omics data far out-strips our capacity to analyse and interpret it. This proposal requests modest resources to exploit existing genomic datasets on ecologically important organisms, to address long-standing questions in biology and simultaneously develop informatics skills and resources for ecologists.
Social insects are a powerful model system for testing cooperative ecological and evolutionary questions and understanding societies function may give insights into our own. Social insects also constitute a vast proportion of the insect pollinators on whom we depend for assuring global food security but who are currently facing global population declines. Within an insect society, all individuals share the same genome which is translated into many different physical and / or behavioural forms (namely queen and worker castes). Which caste an individual becomes depends on its environment (e.g. nutritional, social and ecological). In species with small colonies and simple social organisation (e.g. Polistes paper wasps) individuals show incredible ability to switch between castes in rapid response to changes in their environment. They are therefore excellent organisms for understanding how phenotypic diversity and adaptability are generated at the level of the genes. Yet, these simple insect societies remain relatively understudied from a molecular perspective. We propose to exploit existing datasets to probe the genomic basis of behavioural phenotypes in a range of social insect species. We explore to what extent similar phenotypes are underlain by genes shared across all species (‘conserved genes’), or genes that are specific to a particular species (‘novel genes’).We will provide a first analysis of the relationship between gene expression and protein production in social insects, allowing us to identify the genes involved in regulating phenotypic change. We will share our learning and results with other researchers and putative stakeholders through a workshop, where the broader value of gene-level research in ecology, conservation and environmental sustainability will be explored Thus, we hope that this project will nurture innovative and long-term collaborations between computational scientists and ecologists both locally and in the wider scientific community.