Gut Microbiome Perturbations Induced by Bacterial Infection Affect Arsenic Biotransformation

Exposure to arsenic affects large human populations worldwide and has been associated with a long list of human diseases, including skin, bladder, lung, and liver cancers, diabetes, and cardiovascular disorders. In addition, there are large individual differences in susceptibility to arsenic-induced diseases, which are frequently associated with different patterns of arsenic metabolism. Several underlying mechanisms, such as genetic polymorphisms and epigenetics, have been proposed, as these factors closely impact the individuals’ capacity to metabolize arsenic. In this context, the role of the gut microbiome in directly metabolizing arsenic and triggering systemic responses in diverse organs raises the possibility that perturbations of the gut microbial communities affect the spectrum of metabolized arsenic species and subsequent toxicological effects. In this study, we used an animal model with an altered gut microbiome induced by bacterial infection, 16S rRNA gene sequencing, and inductively coupled plasma mass spectrometry-based arsenic speciation to examine the effect of gut microbiome perturbations on the biotransformation of arsenic. Metagenomics sequencing revealed that bacterial infection significantly perturbed the gut microbiome composition in C57BL/6 mice, which in turn resulted in altered spectra of arsenic metabolites in urine, with inorganic arsenic species and methylated and thiolated arsenic being perturbed. These data clearly illustrated that gut microbiome phenotypes significantly affected arsenic metabolic reactions, including reduction, methylation, and thiolation. These findings improve our understanding of how infectious diseases and environmental exposure interact and may also provide novel insight regarding the gut microbiome composition as a new risk factor of individual susceptibility to environmental chemicals

Additional file 1: of Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction

Figure S1. BioAnalyzer traces for samples used in the study. Left: Intact UHR, Right: Heat degraded UHR RNA. (PPTX 127 kb

Additional file 4: of Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction

Figure S3. Insert size distribution for RNAseq libraries from intact RNA. The insert size for each library passing the 50% rRNA filter was calculated for reads with convergent reads that were separated by < 1000 bp. Kit abbreviations: RZ = RiboZero Gold, LX = Lexogen RiboCop, NE = NEBNext rRNA Depletion, K=Kapa RiboErase, CR = Clontech Ribogone, CZ = SMARTer Pico total RNA. Top: length of the 90th percentile of inserts reads. Middle: length of the median insert read. Bottom: length of the 10th percentile of inserts read. (PPTX 82 kb

Additional file 2: of Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction

Contains the following: Describes normalization of kit protocols and any individual site deviations from this normalization. Figure S1. – BioAnalyzer traces for samples used in the study. Left: Intact UHR, Right: Heat degraded UHR RNA. Figure S2. – Clustering of differentially detected genes. Top 50 most differentially detected genes, as measured by variance of log2RPKM across all samples, were clustered based on their differential expression. TOP: Hierarchical tree of clustering based on a complete linkage function using Euclidean distance. 2ND LINE: Intact/Degraded status is shown. Intact samples are indicated in white while degraded samples are indicated in grey. 3RD LINE: Kit. Dark Blue = RZ|RiboZero Gold, Yellow = LX|Lexogen RiboCop, Aqua = NE|NEBNext rRNA Depletion, Green = Q|Qiagen, Grey = K|Kapa RiboErase, Blue = CR|Clontech Ribogone, Orange = CZ|SMARTer Pico total RNA. HEAT MAP: Red indicate higher level of absolute expression. Scale shown to right. White lines indicate the highest branches within the hierarchical tree. Figure S3. – Insert size distribution for RNAseq libraries from intact RNA. The insert size for each library passing the 50% rRNA filter was calculated for reads with convergent reads that were separated by < 1000 bp. Kit abbreviations: RZ = RiboZero Gold, LX = Lexogen RiboCop, NE = NEBNext rRNA Depletion, K=Kapa RiboErase, CR = Clontech Ribogone, CZ = SMARTer Pico total RNA. Top: length of the 90th percentile of inserts reads. Middle: length of the median insert read. Bottom: length of the 10th percentile of inserts read. Figure S4. - Relative ratio of reads mapping to ERCCs. Fraction of reads mapping to each ERCC mRNA is shown for each replicate. Light horizontal lines show 2-fold changes in fraction observed (log scale). Each expected concentration is shown in a different color. Data sets ordered by intact/degraded status followed by site within each kit left to right. Table S1. Catalog numbers and manual versions for protocols used. Table S2. Comparison of RNA library preparation chemistry variables. (DOCX 21 kb

Additional file 5: of Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction

Figure S4. Fraction of reads mapping to each ERCC mRNA is shown for each replicate. Light horizontal lines show 2-fold changes in fraction observed (log scale). Each expected concentration is shown in a different color. Data sets ordered by intact/degraded status followed by site within each kit left to right. (PPTX 165 kb