DOI: 10.5061/dryad.9zw3r22mb
title: Domestication shapes the pig gut microbiome and immune traits from the scale of lineage to population

description: Animal ecology and evolution have long been known to shape host
 physiology, but more recently, the gut microbiome has been identified as a
 mediator between animal ecology and evolution and health. The gut
 microbiome has been shown to differ between wild and domestic animals, but
 the role of these differences for domestic animal evolution remains
 unknown. Gut microbiome responses to new animal genotypes and local
 environmental change during domestication may promote specific host
 phenotypes that are adaptive (or not) to the domestic environment. Because
 the gut microbiome supports host immune function, understanding the
 effects of animal ecology and evolution on the gut microbiome and immune
 phenotypes is critical. We investigated how domestication affects the gut
 microbiome and host immune state in multiple pig populations across five
 domestication contexts representing domestication status and current
 living conditions: free-ranging wild, captive wild, free-ranging domestic,
 captive domestic in research or industrial settings. We observed that
 domestication context explained much of the variation in gut microbiome
 composition, pathogen abundances, and immune markers, yet the main
 differences in the repertoire of metabolic genes found in the gut
 microbiome were between the wild and domestic genetic lineages. We also
 documented population-level effects within domestication contexts,
 demonstrating that fine scale environmental variation also shaped host and
 microbe features. Our findings highlight that understanding which gut
 microbiome and immune traits respond to host genetic lineage and/or scales
 of local ecology could inform targeted interventions that manipulate the
 gut microbiome to achieve beneficial health outcomes.
description: # Domestication shapes the pig gut microbiome and immune traits from the
 scale of lineage to population.
 [https://doi.org/10.5061/dryad.9zw3r22mb](https://doi.org/10.5061/dryad.9zw3r22mb) Description: We investigated how domestication affects the gut microbiome and host immune state in multiple pig populations across five domestication contexts representing domestication status and current living conditions: free-ranging wild, captive wild, free-ranging domestic, captive domestic in research or industrial settings. We observed that domestication context explained much of the variation in gut microbiome composition, pathogen abundances, and immune markers, yet the main differences in the repertoire of metabolic genes found in the gut microbiome were between the wild and domestic genetic lineages. We also documented population-level effects within domestication contexts, demonstrating that fine scale environmental variation also shaped host and microbe features. Our findings highlight that understanding which gut microbiome and immune traits respond to host genetic lineage and/or scales of local ecology could inform targeted interventions that manipulate the gut microbiome to achieve beneficial health outcomes. These files will allow you to replicate the bioinformatic and statistical analyses we used to assess the impact of domestication on the pig gut microbiome and immune state. ## Description of the data and file structure For 16S rRNA gene sequencing analyses, there is an R markdown file (16S\_analyses.Rmd), a metadata file for each sample with covariates such as location where sample was collected and domestication group (fecal\_metadata\_Apr2022.xlsx), and files generated from read processing: the number and type of ASVs assigned to each sample (ASV\_counts\_decontam.tsv), a table that details the taxonomic lineage of each ASV from Kingdom to Species (ASV\_taxonomy\_decontam.tsv), and a phylogenetic tree (tree.nwk). Some samples in the metadata file will have N/A values for some covariates as we do not have data for that particular covariate. For cytokine measurements, some samples will be marked as "below\_detection" if their values were below the detection limit. For shotgun metagenomic analyses, there is an R markdown file (pig\_metagenomics\_clean.Rmd) and a metadata file (dom\_cat\_metagenomic\_metadata.xlsx). The R markdown files will allow you to replicate any statistical analyses and visualizations using output files from the steps detailed in Code information below. The SNP.R file along with the plink.vcf, PIG2.gds, and SNP\_metadata.xlsx files can be used to replicate all SNP analyses. ## Sharing/Access information All sequencing data are available in NCBI SRA (accession numbers PRJNA926635 for 16S and PRJNA926638 for metagenomics) with associated metadata files. ## Code information The following R files should be used in order to process raw 16S rRNA gene sequencing data: 1. Cutadapt to remove primers (cutadapt.txt) 2. Dada2 to learn errors, dereplicate, infer ASVs, merge amplicons, make sequence tables, and assign taxonomy (dada2.txt) 3. Decontam to remove contaminants, mitochondria, and chloroplasts (decontam.txt) 4. Tables to create final counts and taxonomy tables (tables.txt) 5. Tree to create a phylogenetic tree (qiime\_tree.txt) The output will be an ASV decontaminated counts table, an ASV taxonomy table, and a phylogenetic tree. The following files should be used in order to process raw shotgun metagenomic data: 1. Quality filtering and trimming using Fastp (fastp.txt) 2. Map sequences against reference genome (map\_to\_hostgenome.txt) 3. Assemble to contigs using megahit (megahit.txt) 4. Gene prediction with prodigal (prodigal.txt) 5. Dereplication with cd-hit (derep.txt) 6. Align to UniProt and taxonomic classification (align\_uniprot.txt) 7. Annotation with EggNog, CARD, and VFDB (eggnog.txt, card.txt, vfdb.txt) 8. Salmon gene quantification (salmon.txt) Outputs will be annotation files and quantification files.