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

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.

肠道微生物组在毒理学应答过程中发挥核心作用。本研究探究了母体暴露于3种与人类健康相关的有毒物质（BDE-47、TBBPA和BPS）对成年子代小鼠肠道微生物组组成及代谢物水平（胆汁酸[bile acids, BAs]与短链脂肪酸[short chain fatty acids, SCFAs]）的影响。实验中，CD-1小鼠孕鼠自妊娠第8天至哺乳期结束（产后第21天），每日经口给予赋形剂（玉米油，10ml/kg）、BDE-47（0.2mg/kg）、TBBPA（0.2mg/kg）或BPS（0.2mg/kg）。对12周龄成年雄性子代小鼠的粪便DNA开展16S rRNA测序与靶向代谢组学检测（每组n=14-23）。

与TBBPA和BDE-47相比，BPS对粪便微生物组的β多样性影响最为显著（QIIME分析）。共有73个分类群经至少一种受试化学物质处理后发生持续改变，12个分类群被所有3种化学物质共同调控（其中多数隶属于梭菌纲[Clostridia]且丰度下调）。最具特异性的微生物生物标志物分别为：BDE-47对应S24-7科、TBBPA对应理研菌科（Rikenellaceae）以及BPS对应乳杆菌属（Lactobacillus）（LefSe分析）。本研究采用FishTaco预测了群落整体对微生物通路偏移的贡献。所有受试化学物质均使粪便胆汁酸输出量持续升高（LC-MS检测）。TBBPA可增加丙酸与琥珀酸含量，而BPS则降低乙酸含量（GC-MS检测）。综上，母体暴露于上述有毒物质可在子代成年后持续改变其粪便微生物组与代谢物谱，菌群失调可能参与成年期毒性结局的发育起源机制。

### 粪便DNA提取与16S rDNA测序
粪便DNA的提取与纯化采用E.Z.N.A.粪便试剂盒（OMEGA Bio-tek公司，美国佐治亚州诺克罗斯市），严格遵循制造商说明书操作，具体流程参照我们此前发表的研究（Cheng et al. 2018; Dempsey et al. 2019; Li et. al. 2018; Scoville et. al. 2019）。DNA浓度采用Qubit 2.0荧光计（赛默飞世尔科技公司，美国马萨诸塞州沃尔瑟姆市）进行定量。采用HiSeq-2500测序系统（250bp双端测序；每组n=15-24；Novogene，美国加利福尼亚州萨克拉门托市）对细菌16S rDNA的高变V4区进行扩增与测序。

### 16S rDNA测序数据分析
对双端拆分后的FASTQ原始测序读长使用FastQC（https://www.bioinformatics.babraham.ac.uk/projects/fastqc/）进行质量评估，所有读长均保留用于后续分析，分析工具采用微生物生态学定量洞察（Quantitative Insights Into Microbial Ecology, QIIME）版本1.9.1（Coparaso et al. 2010）。具体步骤如下：首先使用"join_paired_ends.py"工具将同一样本的正向与反向读长进行拼接；随后将拼接后的FASTQ文件进行唯一标记，并通过"split_libraries.fastq.py"工具合并为FASTA文件。使用"pick_open_reference_otus.py"工具，基于99_otus.fasta参考数据库（版本13.8，Greengenes Database Consortium）（DeSantis et. al. 2006）进行操作分类单元（Operational Taxonomic Units, OTUs）聚类，同时支持正、反向链匹配。随后将OTU表格从门水平（L2）到种水平（L7）进行分级整理与汇总。使用"alpha_rarefaction.py"计算α多样性，使用"jackknifed_beta_diversity.py"计算β多样性。微生物群落的功能预测（KEGG通路）采用PICRUSt（通过重建未观测状态实现群落的系统发育分析，Phylogenic Investigation of Communities by Reconstruction of Unobserved States）（Langille et. al. 2013）完成，具体脚本包括：normalize by copy number.py、predict metagenomes.py及categorize by function.py。使用FishTaco（Manor and Borenstein 2017；http://elbo.gs.washington.edu/software_fishtaco.html），结合分类学与功能丰度谱及推断的基因组信息，预测驱动微生物群落功能偏移的分类群。