The genetic basis for variation in resistance to infection in the Drosophila melanogaster genetic reference panel

Abstract

Individuals vary extensively in the way they respond to disease but the genetic basis of this variation is not fully understood. We found substantial individual variation in resistance and tolerance to the fungal pathogen Metarhizium anisopliae Ma549 using the Drosophila melanogaster Genetic Reference Panel (DGRP). In addition, we found that host defense to Ma549 was correlated with defense to the bacterium Pseudomonas aeruginosa Pa14, and several previously published DGRP phenotypes including oxidative stress sensitivity, starvation stress resistance, hemolymph glucose levels, and sleep indices. We identified polymorphisms associated with differences between lines in both their mean survival times and microenvironmental plasticity, suggesting that lines differ in their ability to adapt to variable pathogen exposures. The majority of polymorphisms increasing resistance to Ma549 were sex biased, located in non-coding regions, had moderately large effect and were rare, suggesting that there is a general cost to defense. Nevertheless, host defense was not negatively correlated with overall longevity and fecundity. In contrast to Ma549, minor alleles were concentrated in the most Pa14-susceptible as well as the most Pa14-resistant lines. A pathway based analysis revealed a network of Pa14 and Ma549-resistance genes that are functionally connected through processes that encompass phagocytosis and engulfment, cell mobility, intermediary metabolism, protein phosphorylation, axon guidance, response to DNA damage, and drug metabolism. Functional testing with insertional mutagenesis lines indicates that 12/13 candidate genes tested influence susceptibility to Ma549. Many candidate genes have homologs identified in studies of human disease, suggesting that genes affecting variation in susceptibility are conserved across species.

A review of progress toward field application of transgenic mosquitocidal entomopathogenic fungi

“…In contrast to mosquitoes, Drosophila is a very tractable model system. Individual fruit flies vary extensively in their ability to control and tolerate fungal and bacterial pathogens during infection, and resistance correlated with several unexpected phenotypes, including sleep . However, there is reason for optimism that resistance will not evolve against toxin‐expressing strains for many years because in every realistic scenario, genetically modified (GM) Metarhizium spp.…”

Isolation of a natural DNA virus ofDrosophila melanogaster, and characterisation of host resistance and immune responses

“…The DGRP (Mackay et al, 2012) have previously been used to dissect genetic variation underlying resistance and tolerance to bacterial, fungal, and viral pathogens (Magwire et al, 2012;Bou Sleiman et al, 2015;Wang et al, 2017;Howick and Lazzaro, 2017 variance (Houle, 1992). Genetic variation in resistance has also been measured in response to (2012) inferred narrow sense heritability (h 2 ) as half VG and accounted for the homozygosity of inbred lines when inferring CVG, it is clear that VG for resistance to KV is closer to the VG for resistance to other native fly viruses than to non-native ones, at least for survival.…”

“…Second, if our aim is to understand the coevolutionary process itself, then the standing diversity in both host and virus populations may be fundamentally altered in coevolving as opposed to naïve pairs. For example, heritable variation for host resistance was detectable for two natural viruses of D. melanogaster, but not for two non-natural viruses (Magwire et al, 2012;Wang et al, 2017). This difference was in part due to large-effect segregating polymorphisms for resistance to the natural viruses, which are predicted to result from active coevolutionary dynamics (Contamine et al, 1989;Magwire et al, 2011;Magwire et al, 2012;Cogni et al, 2016).…”

Creating outbred and inbred populations of haplodiploid mites to measure adaptive responses in the lab

“…Importantly, inbred lines can also be used to assess genetic correlations and trade-offs between different traits (e.g. Travers et al 2015, Howick and Lazzaro 2017, Wang et al 2017, Lafuente et al 2018, Everman et al 2019), including those measured in different environments (Howick and Lazzaro 2014, Unckless et al 2015, Orsted et al 2018. Indeed, because all individuals of a given inbred line represent roughly the same genotype, responses of each genotype can be measured in different contexts.…”