Dissecting the genetic architecture of a stepwise infection process

Abstract

How a host fights infection depends on an ordered sequence of steps, beginning with attempts to prevent a pathogen from establishing an infection, through to steps that mitigate a pathogen's control of host resources or minimize the damage caused during infection. Yet empirically characterizing the genetic basis of these steps remains challenging. Although each step is likely to have a unique genetic and environmental signature, and may therefore respond to selection in different ways, events that occur earlier in the infection process can mask or overwhelm the contributions of subsequent steps. In this study, we dissect the genetic architecture of a stepwise infection process using a quantitative trait locus (QTL) mapping approach. We control for variation at the first line of defence against a bacterial pathogen and expose downstream genetic variability related to the host's ability to mitigate the damage pathogens cause. In our model, the water‐flea Daphnia magna, we found a single major effect QTL, explaining 64% of the variance, that is linked to the host's ability to completely block pathogen entry by preventing their attachment to the host oesophagus; this is consistent with the detection of this locus in previous studies. In susceptible hosts allowing attachment, however, a further 23 QTLs, explaining between 5% and 16% of the variance, were mapped to traits related to the expression of disease. The general lack of pleiotropy and epistasis for traits related to the different stages of the infection process, together with the wide distribution of QTLs across the genome, highlights the modular nature of a host's defence portfolio, and the potential for each different step to evolve independently. We discuss how isolating the genetic basis of individual steps can help to resolve discussion over the genetic architecture of host resistance.

Among-Strain Variation in Resistance of Paramecium caudatum to the Endonuclear Parasite Holospora undulata: Geographic and Lineage-Specific Patterns

“…However, other studies using more than 60-fold higher dosages of IFs than in our present study also reveal substantial among-strain variation in resistance (Fels et al, 2008). Quantitative variation may thus reflect more complex mechanisms of resistance, for example if multiple host factors (and possibly interactions with multiple parasite factors) or a multi-step process (perhaps involving multiple signaling steps) determine infection success (Agrawal and Lively, 2003;Hall et al, 2019). It appears that nearly all P. caudatum strains examined here are potentially "colonizable" in that the defenses could be breached, allowing the parasite to enter the cell and invade the nucleus.…”

Rapid Evolution of Parasite Resistance via Improved Recognition and Accelerated Immune Activation and Deactivation

“…Taking a stepwise approach, and experimentally breaking apart different phases of hostparasite interactions, has the potential to be a powerful strategy for understanding the causes of variation in infection outcomes, and for partitioning the genetic and environmental contributions to parasite resistance or susceptibility. To date, this approach has largely been confined to functional cell and molecular biology studies, but has the potential to greatly enhance our understanding of host-parasite coevolution at larger scales (Hall et al 2017(Hall et al , 2019. By using this approach, and experimentally partitioning different phases of the host response to infection through time, we provide a detailed look at the evolution of resistance in closely related populations of threespine stickleback that vary in their response to the tapeworm parasite,…”

Energetic scaling across different host densities and its consequences for pathogen proliferation

“…It is naturally infected by the endospore forming, gram‐positive bacteria, Pasteuria ramosa Metchnikoff 1888 . P. ramosa is horizontally transmitted and infection causes severe loss of fecundity, an increase in body size (gigantism) and reduced survival (Clerc et al., 2015; Hall et al., 2019). Both host life history and pathogen performance are influenced by host density and resource intake, with host fecundity and pathogen spore production reduced when food is limited or densities are high (Pulkkinen & Ebert, 2004; Cressler et al., 2014; Nørgaard et al., 2019; although responses can be genotype specific, see Michel et al., 2016).…”