Evolutionary gain and loss of a pathological immune response to parasitism
Data files
Jun 27, 2022 version files 2.73 MB
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AllSchisto.csv
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Bolnick2009LakeSummaryData.csv
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BrockColorMalesFibrosis2012.csv
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chrXXI.sync_pop.csv
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F2_fillGeno.7.csv
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F2_Parent_pheno_all_edited.csv
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F2_ROS_data.csv
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FibrosisFreq.csv
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Figure1_Cross_trait_data_all.csv
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Figure1_Cross_trait_data_F2s.csv
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GBC_fillGeno.7.csv
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GBC_ROS_data.csv
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Hund_Wild_Parasites_Data_2018.csv
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PU1_allelefreq_transpose.csv
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PU1_fst.csv
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PU1ExpDesign.csv
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RBC_fillGeno.7.csv
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RBC_ROS_data.csv
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README_AllSchisto.rtf
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README_Bolnick2009LakeSummaryData.rtf
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README_BrockColorMalesFibrosis2012.rtf
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README_chrXXIsyncpop.rtf
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README_F2_fillGeno7.rtf
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README_F2_Parent_pheno_all_edited.rtf
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README_FibrosisFreq.rtf
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README_Figure1_Cross_trait_data_all.rtf
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README_Figure1_Cross_trait_data_F2s.rtf
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README_GBC_fillGeno7.rtf
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README_Hund_Wild_parasites_Data_2018.rtf
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README_PU1_allelefreq_transpose.rtf
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README_PU1_fst.rtf
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README_PU1ExpDesign.rtf
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README_RBC_fillGeno7.rtf
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README_ShimFibrosis2016.rtf
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README_Stuart2013_Lake_Stream_Fibrosis.rtf
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README.txt
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ShimFibrosis2016.csv
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Stuart2013_LakeStream_Fibrosis.csv
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Abstract
Parasites impose fitness costs on their hosts. Biologists often assume that natural selection favors infection-resistant hosts. Yet, when the immune response itself is costly, theory suggests selection may instead favor loss of resistance. Intraspecific variation in immune costs are rarely surveyed in a manner that tests evolutionary patterns, and there are few examples of adaptive loss of resistance. Here, we show that when marine threespine stickleback colonized freshwater lakes they gained resistance to the freshwater-associated tapeworm, Schistocephalus solidus. Extensive peritoneal fibrosis and inflammation is a commonly observed phenotype that contributes to suppression of cestode growth and viability, but also impose a substantial cost of reduced fecundity. Combining genetic mapping and population genomics, we find that opposing selection generates immune system differences between tolerant and resistant populations, consistent with divergent optimization.
Data were collected as described in Weber et al (https://www.biorxiv.org/content/10.1101/2021.08.04.455160v1). The repository includes diverse datasets involving laboratory experimental infections, flow cytometry, gene expression, population genomics, and multiple field samples of infection rates and immune traits.