Data from: Gut microbiome critically impacts PCB-induced changes in metabolic fingerprints and the hepatic transcriptome in mice
Lim, Joe et al. (2020), Data from: Gut microbiome critically impacts PCB-induced changes in metabolic fingerprints and the hepatic transcriptome in mice, Dryad, Dataset, https://doi.org/10.5061/dryad.wwpzgmsgf
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.
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.
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.
National Institutes of Health, Award: R01 ES025708,R01 ES030197,R01 GM111381,R01 ES031098,T32 ES007032,ES013661,ES005605
University of Washington Center for Exposures, Diseases, Genomics, and Environment, Award: NIH P30 ES0007033