The genetic basis of resistance and matching-allele interactions of a host-parasite system: The Daphnia magna-Pasteuria ramosa model

Bento, Gilberto; Routtu, Jarkko; Fields, Peter D.; Bourgeois, Yann; Pasquier, Louis Du; Ebert, Dieter

doi:10.1371/journal.pgen.1006596

Cited by 67 publications

(171 citation statements)

References 35 publications

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“…The infection genetics in this system is therefore consistent with the matching allele (MA) model of infection (Grosberg and Hart ; Bento et al. ) and is known to exhibit FS dynamics in the long term (Decaestecker et al. ).…”

Section: Discussionsupporting

confidence: 70%

Environmental variation causes different (co) evolutionary routes to the same adaptive destination across parasite populations

Auld

Brand

2017

Evolution Letters

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Epidemics are engines for host‐parasite coevolution, where parasite adaptation to hosts drives reciprocal adaptation in host populations. A key challenge is to understand whether parasite adaptation and any underlying evolution and coevolution is repeatable across ecologically realistic populations that experience different environmental conditions, or if each population follows a completely unique evolutionary path. We established twenty replicate pond populations comprising an identical suite of genotypes of crustacean host, Daphnia magna, and inoculum of their parasite, Pasteuria ramosa. Using a time‐shift experiment, we compared parasite infection traits before and after epidemics and linked patterns of parasite evolution with shifts in host genotype frequencies. Parasite adaptation to the sympatric suite of host genotypes came at a cost of poorer performance on foreign genotypes across populations and environments. However, this consistent pattern of parasite adaptation was driven by different types of frequency‐dependent selection that was contingent on an ecologically relevant environmental treatment (whether or not there was physical mixing of water within ponds). In unmixed ponds, large epidemics drove rapid and strong host‐parasite coevolution. In mixed ponds, epidemics were smaller and host evolution was driven mainly by the mixing treatment itself; here, host evolution and parasite evolution were clear, but coevolution was absent. Population mixing breaks an otherwise robust coevolutionary cycle. These findings advance our understanding of the repeatability of (co)evolution across noisy, ecologically realistic populations.

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Section: Discussionsupporting

confidence: 70%

Environmental variation causes different (co) evolutionary routes to the same adaptive destination across parasite populations

Auld

Brand

2017

Evolution Letters

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“…Only the attachment step, however, perfectly correlated with the genotype of the host. Our results confirm previous studies that the ability of the pathogen to attach to the host oesophagus was an all‐or‐nothing affair (Bento et al, ; Duneau et al, ), with all individuals of a host recombinant line either allowing complete parasite attachment or not (Figure a). In contrast, variation in the subsequent success of the pathogen in establishing infection was more variable (Figure b), with the probability of all replicates of a susceptible host line becoming infected varying continuously between 0 and 1 (mean = 0.80; SD = 0.21).…”

Section: Resultssupporting

confidence: 92%

“…Here, we use a quantitative trait locus (QTL) approach to identify the chromosomal regions underlying the components of the defence cascade, from the initial contact between the host and pathogen, to the transmission of the pathogen at host death. Work on the highly specific step underlying host susceptibility has already revealed that this attachment step is under the control of a limited number of regions in the host genome (Bento et al, ; Luijckx, Fienberg, Duneau, & Ebert, ; Metzger, Luijckx, Bento, Mariadassou, & Ebert, ), consistent with the idea that the genetic architecture of this step is unlike other complex traits. Yet, the other components of the infection process remain uncharacterized.…”

Section: Introductionsupporting

confidence: 54%

“…For each of these traits, there was little evidence for QTL hotspots (Breitling et al, ), arising from either tight linkage or pleiotropy, except for QTL at the end of linkage group 10 that contributed to body size, castration, fecundity and lifespan (Figure ). Epistasis effect sizes were also comparatively low, further reinforcing the largely independent effects of the qualitative and qualitative alleles underlying each metric of susceptibility—but see Hall and Ebert () for why this is more a reflection of the genotypes used for the QTL panel, rather than a true assessment of additive versus epistatic variance in a population; and see also Metzger et al () and Bento et al ().…”

Section: Discussionmentioning

confidence: 90%

“…First, whenever variation at a single step of infection is controlled for, as we did here by phenotyping only individuals which allow attachment of the pathogen, then any loci that are in linkage disequilibrium with the QTL underlying this step (LD, due to physical linkage or coselection) will be hidden. In our study this bias is likely to be small, as the single major effect locus underlying host susceptibility lies on a relatively small 130‐kb haplotype block at the very end of linkage group 3 (Bento et al, ), and as part of the F 2 intercross mapping design we experimentally induced recombination to break up LD amongst the parental alleles. Nevertheless, the potential for LD to mask the signal of additional loci within the susceptible fraction of host genotypes needs to be considered when mapping the individual steps of infection in this or other systems, particularly when using a genome‐wide association approach in which recombination is not induced.…”

Section: Discussionmentioning

confidence: 99%

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Dissecting the genetic architecture of a stepwise infection process

2019

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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.

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Contrasting patterns of divergence at the regulatory and sequence level in European Daphnia galeata natural populations

Ravindran

Herrmann

Cordellier

2019

Ecology and Evolution

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Understanding the genetic basis of local adaptation has long been a focus of evolutionary biology. Recently, there has been increased interest in deciphering the evolutionary role of Daphnia 's plasticity and the molecular mechanisms of local adaptation. Using transcriptome data, we assessed the differences in gene expression profiles and sequences in four European Daphnia galeata populations. In total, ~33% of 32,903 transcripts were differentially expressed between populations. Among 10,280 differentially expressed transcripts, 5,209 transcripts deviated from neutral expectations and their population‐specific expression pattern is likely the result of local adaptation processes. Furthermore, a SNP analysis allowed inferring population structure and distribution of genetic variation. The population divergence at the sequence level was comparatively higher than the gene expression level by several orders of magnitude consistent with strong founder effects and lack of gene flow between populations. Using sequence homology, the candidate transcripts were annotated using a comparative genomics approach. Additionally, we also performed a weighted gene co‐expression analysis to identify population‐specific regulatory patterns of transcripts in D. galeata . Thus, we identified candidate transcriptomic regions for local adaptation in this key species of aquatic ecosystems in the absence of any laboratory‐induced stressor.

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The genetic basis of resistance and matching-allele interactions of a host-parasite system: The Daphnia magna-Pasteuria ramosa model

Cited by 67 publications

References 35 publications

Environmental variation causes different (co) evolutionary routes to the same adaptive destination across parasite populations

Environmental variation causes different (co) evolutionary routes to the same adaptive destination across parasite populations

Dissecting the genetic architecture of a stepwise infection process

Contrasting patterns of divergence at the regulatory and sequence level in European Daphnia galeata natural populations

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