DOI: 10.5061/dryad.wwpzgmsgf
title: Data from: Gut microbiome critically impacts PCB-induced changes in metabolic fingerprints and the hepatic transcriptome in mice

description: Polychlorinated biphenyls (PCBs) are ubiquitously detected in the
 environment and have been linked to metabolic diseases. The liver serves
 as a central hub for the metabolism of xenobiotics and endogenous
 metabolites. Gut dysbiosis is recognized as a critical regulator of
 disease susceptibility, however, little is known regarding how PCBs and
 gut microbiome interact to modulate the interface between xenobiotic and
 intermediary metabolism. We hypothesized that the gut microbiome regulates
 PCBs-mediated changes in the metabolic fingerprints and hepatic
 transcriptome. Ninety-day-old female conventional (CV) and germ-free (GF)
 C57BL/6 mice were orally exposed to the PCB Fox River Mixture (synthetic
 PCB mixture, 6 or 30 mg/kg) or corn oil (vehicle control, 10 ml/kg), once
 daily for 3 consecutive days. Organs were collected 24 hours after the
 final dose. RNA-Seq was conducted on liver, and endogenous aqueous
 metabolites (amino acids, carbohydrates, and nucleotides) were measured in
 liver and serum by LC-MS. The primary factor in clustering the
 transcriptomic and metabolomic signatures within the same exposure was by
 enterotype. The numbers of PCB-regulated genes were higher in CV than in
 GF conditions. The prototypical target genes of the major
 xenobiotic-sensing transcription factors AhR, PXR, and CAR were more
 readily up-regulated by PCBs in CV than in GF conditions, indicating the
 effect of PCBs on the hepatic transcriptome act partly through the gut
 microbiome. Xenobiotic and steroid metabolism pathways were up-regulated,
 whereas response to incorrect proteins pathway was down-regulated by PCBs
 in a gut microbiome-dependent manner. At the high PCB dose, NADP and
 arginine appear to interact with drug-metabolizing enzymes (Cyp1-3 family,
 DhcR7, and Nqo1), which are highly correlated with Anaerotruncus and
 Roseburia in CV mice, providing a novel explanation of gut-liver
 interaction in toxicant exposures. In GF exposure groups, hepatic glucose
 was down-regulated, whereas fructose 6-phosphate and glucose 6-phosphate
 were up-regulated, indicating increased glucose utilization potentiated by
 lack of gut microbiota. Through querying the LINCS L1000 chemical
 database, Enrichr predicted that therapeutic drugs targeting the
 anti-inflammatory and ER stress pathways are potential remedies to
 mitigate PCB toxicity. In conclusion, our findings demonstrate that
 habitation of the gut microbiota drives PCBs-mediated hepatic responses,
 possibly due to crosstalk between gut and liver.
description: Mice were exposed to corn oil (vehicle control; 10 ml/kg body weight), or Fox River mixture low dose (6 mg/kg body weight) or high dose (30 mg/kg body weight), once daily between 8 and 10 AM for 3 consecutive days.  Tissues were harvested 24 hours after the final dose. Large intestinal contents was flushed out in 15 ml of ice-cold phosphate-buffered saline (PBS) that contains 0.1% dithiothreitol (DTT) (Sigma Aldrich, St. Louis, Missouri), and immediately frozen on dry ice Microbial DNA was extracted using a with a method as previously (Cheng et al., 2018). All tissues were immediately frozen in liquid nitrogen and stored at −80°C until further analysis. The large intestinal content in PBS/DTT solution from CV mice was thawed overnight at 4°C, and the large intestinal pellet (LIP) was obtained by centrifugation at 10 000 g at 4°C for 1 h. Bacterial DNA was isolated from the LIP using an OMEGA E.Z.N.A. Stool DNA Kit (OMEGA Biotech Inc., Norcross, Georgia). The concentration of DNA was determined using Qubit (Thermo Fisher Scientific, Waltham, Massachusetts). The integrity and quantity of all DNA samples were confirmed using an Agilent 2100 Bioanalyzer (Agilent Technologies Inc., Santa Clara, California). Bacterial 16S rRNA V4 amplicon sequencing was performed using a HiSeq 2500 second-generation sequencer (250 bp paired-end) (Beijing Genome Institute Americans Corporation, Cambridge, Massachusetts) (n = 3 per group). Total RNA was extracted from frozen livers using RNA-Bee reagent (Tel-Test Inc., Friendswood, Texas) following the manufacturer’s protocol.  RNA concentrations were quantified using a NanoDrop 1000 Spectrophotometer (Thermo Scientific, Waltham, MA) at 260 nm.  The integrity of total RNA samples was evaluated by agarose gel electrophoresis with visualization of 18S and 28S rRNA bands under UV light, and confirmed by an Agilent 2100 Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA). Samples with RNA integrity numbers above 8.0 were used for RNA-Seq in triplicates.  The cDNA library was constructed using a ribosomal depletion method, and reads were sequenced using a 75 bp paired end sequencing per the Illumina manufacturer’s protocol.  FASTQ files were de-multiplexed and concatenated for each sample.  Quality control the FASTQ files was performed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).  Sequenced reads from the FASTQ files were then mapped to the mouse reference genome (National Center for Biotechnology Information [NCBI GRCm38/mm10]) using HISAT2 version 2.1 (Kim et al., 2019).  The sequencing alignment/map (SAM) files were converted to binary alignment/map (BAM) format using SAMtools version 1.8 (Li et al., 2009) and were analyzed by Cufflinks version 2.2.1 to estimate the transcript abundance (Trapnell et al., 2012) using Gencode mouse version 22 (vM22) gene transfer format (GTF).  The abundance was expressed as fragments per kilobase of transcript per million mapped reads (FPKM) and was converted to transcripts per million (TPM).  Genes were considered expressed if the transcripts per million (TPM) of each gene were greater than the total sample number and if the variance was greater than 1. 
description: Please see "metadata.tsv" for sample information for
 16s data. "RNA-seq_metadata.xlsx" contains sample information
 for RNA-seq. The samples are ordered in the two metadata to contain
 information of the same sample. For example, the first sample in the
 bacteria metadata is the same mouse as the first sample in the RNA-seq
 data.