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Early life exposure to environmental contaminants (BDE-47, TBBPA, and BPS) produced persistent gut dysbiosis in adult male mice

Citation

Cui, Julia Yue et al. (2020), Early life exposure to environmental contaminants (BDE-47, TBBPA, and BPS) produced persistent gut dysbiosis in adult male mice, Dryad, Dataset, https://doi.org/10.5061/dryad.m905qftzn

Abstract

The gut microbiome is a pivotal player in toxicological responses.  We investigated the effects of maternal exposure to 3 human health-relevant toxicants (BDE-47, TBBPA, and BPS) on the composition and metabolite levels (bile acids [BAs] and short chain fatty acids [SCFAs]) of the gut microbiome in adult pups.  CD-1 mouse dams were orally exposed to vehicle (corn oil, 10ml/kg), BDE-47 (0.2 mg/kg), TBBPA (0.2 mg/kg), or BPS (0.2 mg/kg) once daily from gestational day 8 to the end of lactation (postnatal day 21). 16S rRNA sequencing and targeted metabolomics were performed in fecal DNA of 12-week-old adult male pups (n=14-23/group).  BPS had the most prominent effect on the beta-diversity of the fecal microbiome compared to TBPPA and BDE-47 (QIIME).  Seventy-three taxa were persistently altered by at least 1 chemical, and 12 taxa were commonly regulated by all chemicals (most of which were from the Clostridia class and were decreased).  The most distinct microbial biomarkers were S24-7 for BDE-47, Rikenellaceae for TBPPA, and Lactobacillus for BPS (LefSe).  The community-wide contributions to the shift in microbial pathways  were predicted using FishTaco.   Fecal BA output was persistently increased by all chemicals (LC-MS).  TBBPA increased propionic acid and succinate, whereas BPS decreased acetic acid (GC-MS.  In conclusion, maternal exposure to these toxicants persistently modified fecal microbiome and metabolites later in life, and dysbiosis may contribute to the mechanisms of developmental origins of adult-onset of toxic outcomes.

Methods

Fecal DNA isolation and 16S rDNA sequencing. DNA was isolated and extracted using the E.Z.N.A. stool kit (OMEGA Bio-tek, Inc., Norcross, GA) following the manufacturer’s protocol as we described previously (Cheng et al. 2018; Dempsey et al. 2019; Li et. al. 2018; Scoville et. al. 2019).  DNA concentration was determined by a Qubit 2.0 Fluorometer (Thermo Fisher Scientific , Waltham, MA).  Amplification and sequencing of the hypervariable V4 region of bacterial 16S rDNA was done using a HiSeq-2500 sequencing system (250bp paired-end; n=15-24 per group; Novogene, Sacramento, CA).

Data analysis for the 16S rDNA sequencing data.  The quality of raw reads from the de-multiplexed FASTQ files was examined using FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/), and all reads were kept for further analysis using Quantitative Insights Into Microbial Ecology (QIIME) version 1.9.1 (Coparaso et al. 2010).  Specifically, the paired forward and reverse reads of the same sample were joined using “join_paired_ends.py”.  The joined FASTQ files were then each uniquely labeled and then merged together into a FASTA file using “split_libraries.fastq.py”.  Operational Taxonomic Units (OTUs) were assigned using “pick_open_reference_otus.py” against the 99_otus.fasta reference database (Version 13.8, Greengenes Database Consortium) (DeSantis et. al. 2006), enabling both the forward and the reverse strand matches.  The OTU tables were then sorted and summarized from the phylum (L2) to species (L7) levels. Alpha diversity was determined using “alpha_rarefaction.py” and beta diversity with “jackknifed_beta_diversity.py”.  Functional profiles (KEGG pathways) of microbial communities were predicted using PICRUSt (Phylogenic Investigation of Communities by Reconstruction of Unobserved States) (Langille et. al. 2013) using the following scripts: normalize by copy number.py, predict metagenomes.py, and categorize by function.py. FishTaco was used to predict the taxa that contribute to the functional shifts in the microbiome (http://elbo.gs.washington.edu/software_fishtaco.html) (Manor and Borenstein 2017) with taxonomic and functional abundance profiles as well as inferred genomic information.

Funding

National Institutes of Health, Award: R01 ES025708; R01 ES030197; R01 GM111381, R25 ES025503; P30 ES0007033

University of Washington, Award: Sheldon Murphy Endowment