A large-scale metagenomic survey dataset of the post-weaning piglet gut lumen

Abstract

Background Early weaning and intensive farming practices predispose piglets to the development of infectious and often lethal diseases, against which antibiotics are used. Besides contributing to the build-up of antimicrobial resistance, antibiotics are known to modulate the gut microbial composition. As an alternative to antibiotic treatment, studies have previously investigated the potential of probiotics for the prevention of postweaning diarrhea. In order to describe the post-weaning gut microbiota, and to study the effects of two probiotics formulations and of intramuscular antibiotic treatment on the gut microbiota, we sampled and processed over 800 faecal time-series samples from 126 piglets and 42 sows. Results Here we report on the largest shotgun metagenomic dataset of the pig gut lumen microbiome to date, consisting of >8 Tbp of shotgun metagenomic sequencing data. The animal trial, the workflow from sample collection to sample processing, and the preparation of libraries for sequencing, are described in detail. We provide a preliminary analysis of the dataset, centered on a taxonomic profiling of the samples, and a 16S-based beta diversity analysis of the mothers and the piglets in the first 5 weeks after weaning. Conclusions This study was conducted to generate a publicly available databank of the faecal metagenome of weaner piglets aged between 3 and 9 weeks old, treated with different probiotic formulations and intramuscular antibiotic treatment. Besides investigating the effects of the probiotic and intramuscular antibiotic treatment, the dataset can be explored to assess a wide range of ecological questions with regards to antimicrobial resistance, host-associated microbial and phage communities, and their dynamics during the aging of the host.

Hackflex: low-cost, high-throughput, Illumina Nextera Flex library construction

“…In the barcode v0 design, i5 oligos are the tandem complement sequences of the i7 oligos. Although the tandem design of v0 barcodes did not significantly impact the quality of the data on a MiSeq instrument, a significant degradation of index read two quality (i5) was observed when such barcode combinations were applied to generate data for a large-scale, porcine-gut, metagenomics project [17, 18]. It was noted by Illumina technical support that the tandem complement design could lead to significantly reduced quality scores for the i5 index read on the NovaSeq instrument (Illumina technical support, personal communication).…”

“…In order to increase the library complexity, the Hackflex method could be tweaked to decrease the transposase dilution. However, Hackflex is designed to enable large-scale sequencing projects for low-complexity genomes such as bacteria, including metagenomic projects with high sample numbers [17, 18] or DNA surveillance projects, where thousands of bacterial genomes must be sequenced.…”

Phylogenetic diversity analysis of shotgun metagenomic reads describes gut microbiome development and treatment effects in the post-weaned pig

“…Post-weaning piglets ( n =126) derived from a large commercial pig herd were transferred to the study facility in January 2017. Piglets, aged 22.5±2.5 days at the start of the trial, consisted of 4 main cross-breed types: “Duroc × Landrace” ( n =46), “Duroc × Large White” ( n =59), “Landrace × cross bred (LW × D)” ( n =9), “Large White × Duroc” ( n =12); and three pig lines (line 319: n =9; line 316: n =46; line 326: n =71) 54 . The study facility consisted of 4 environmentally controlled rooms (Rooms 1–4) with air conditioning, concrete slatted block flooring with underground drainage, and open rung steel pens.…”

“…A thorough description of the animal trial, and the metadata containing behaviour, weight, and faecal consistency scores recorded over the 6-week period of the trial, is available in our previous work 54 .…”

“…All samples were included in the analysis, except for the analysis of treatment effect, where, in order to minimize the effect of age as a confounding factor, we excluded animals born on 2017-01-06 and on 2017-01-07. In this manner the largest age gap between animals was 3 days, with animals born on 2017-01-08 (n=24), 2017-01-09 (n=24), 2017-01-10 (n=23), and 2017-01- 54 . Briefly, raw reads were mapped against the rRNA reference database silvabac-16s-id90.fasta (parameters: --fastx --blast 1 --num_alignments 1) (script: sortmerna.sh) and filtered based on e-value (e-value <= 1e-30), identity (identity ≥ 80%), and alignment length (length ≥ 100 bp) (script: sortmerna_filter.sh).…”