Xenobiotics shape the physiology and gene expression of the active human gut microbiome

Abstract

The human gut contains trillions of microorganisms that influence our health by metabolizing xenobiotics, including host-targeted drugs and antibiotics. Recent efforts have characterized the diversity of this host-associated community, but it remains unclear which microorganisms are active and what perturbations influence this activity. Here, we combine flow cytometry, 16S rRNA gene sequencing, and metatranscriptomics to demonstrate that the gut contains a distinctive set of active microorganisms, primarily Firmicutes. Short-term exposure to a panel of xenobiotics significantly affected the physiology, structure, and gene expression of this active gut microbiome. Xenobiotic-responsive genes were found across multiple bacterial phyla, encoding antibiotic resistance, drug metabolism, and stress response pathways. These results demonstrate the power of moving beyond surveys of microbial diversity to better understand metabolic activity, highlight the unintended consequences of xenobiotics, and suggest that attempts at personalized medicine should consider interindividual variations in the active human gut microbiome.

Figure 1. The human gut microbiota is highly active with substantial proportions of damaged cells

Also see Figures S1, S2 and Table S3. (A) Cellular targets of the fluorescent dyes. The nucleic acid dye Pi enters cells with compromised membranes; DiBAC binds to intracellular lipid-containing material of depolarized cells; and SybrGreen stains the nucleic acids of all bacteria irrespective of their membrane status, identifying two clusters of cells: the low- (LNA) and high- (HNA) nucleic acid containing cells. (B) Average proportions of damaged cells (Pi⁺ and DiBAC⁺) and cells with a low (LNA) or high nucleic acid content (HNA) in three unrelated individuals (n=5–10 samples/individual). Values are mean±sem. (C) SybrGreen and Pi dual-staining in 3 unrelated individuals. Pi^- cells are in grey, Pi⁺ cells are in pink, with the HNA (solid) and LNA (stripes) subsets indicated. (D) Pi and DiBAC dual-staining in the same individuals. Pi^- cells are in grey, Pi⁺ cells are in purple, with the DiBAC^- (stripes) and DiBAC⁺ (solid) fractions indicated.

Figure 2. The human gut microbiota contains a distinctive active subset

Also see Figures S3, S4 and Tables S1, S4, S5. (A) Number of abundant genera (relative abundance ≥1% in at least one sample) found in the Pi⁺, DiBAC⁺, LNA, HNA, and control fractions. Values are mean±sem. (B) Unweighted UniFrac clustering of community membership across each subgroup. Samples are labeled by individual in panels B–D: red (individual A), green (individual B), and blue (individual C). (C) Comparisons of microbial community structure, using the Bray-Curtis distance metric. The first principal component is shown across each subgroup and individual. (D) The active and damaged subsets of the gut microbiota are distinctive from the overall unsorted community (Control). UPGMA clustering based on Bray-Curtis distance is shown on the left: replicate samples are collapsed into wedges and three outliers have been removed (see Figure S3A for the full tree). The relative abundance of the major bacterial families in the gut microbiota: Firmicutes (blue), Bacteroidetes (orange), and Actinobacteria (Bifidobacteriaceae; purple). Remaining taxa have been grouped into ‘Other’.

Figure 3. Antibiotics, but not host-targeted drugs, alter microbial physiology

Also see Figure S5 and Tables S2, S3. Top to bottom: Average proportions of cells with loss of membrane integrity, altered polarity, and high levels of activity across 3 unrelated individuals, determined after incubations with xenobiotics. Control incubations consisted of un-amended BHI medium, BHI medium with the drug vehicle (ethanol), and low pH. Treatments with a significant impact relative to controls are noted (p<0.05, Dunn’s test). Values are mean±sem.

Figure 4. The physiological response of microbial communities to antibiotics varies over time within each individual

Also see Figure S5 and Tables S2, S3. Top to bottom: Proportions of cells with loss of membrane integrity, altered polarity, and high levels of activity after antibiotics exposure. Values for vehicle controls are indicated with asterisks. When visible, error bars represent staining replicates. Values are mean±sem.

Figure 5. Antibiotics targeting cell wall biosynthesis consistently increase cell damage, and change the structure of the active and damaged gut microbiota

Also see Figure S5 and Tables S1, S2, S4. (A) Average proportions of cells with loss of membrane integrity, altered polarity, and high levels of activity following exposure to antibiotics targeting the bacterial cell wall and membrane (2 time-points/individual). Values are mean±sem. (B) Changes to the highly active (HNA), less active (LNA), and damaged (Pi⁺) gut microbiota following exposure to ampicillin, digoxin, and low pH. Each experiment represents FACS-Seq analysis of samples from a single individual with or without treatment. Bars represent the average across all technical replicates (n=2–3 replicates).

Figure 6. Impact of xenobiotics on the expression of KEGG categories

Also see Figure S6 and Table S2, S4, S7. Expression levels of genes assigned to high-level KEGG categories following short-term exposure to xenobiotics. (A) The % of normalized counts assigned to each category is shown for the 14 treatments and their vehicle controls. (B) Principal component analysis of KEGG category expression levels for the 14 treatments and controls. The mean expression profile for each group was clustered using the “princomp” function in Matlab.

Figure 7. Short-term exposure to antibiotics and host-targeted drugs results in altered community-wide gene expression

Also see Figures S6 and Tables S2, S4, S6, S7. COGs up- or down-regulated after exposure to xenobiotics are labeled red and blue, respectively (adjusted p-value<0.01, AIC<300). COGs with significantly different expression between controls collected on separate days were excluded. Treatments and COGs were clustered using the “clusterGram” function in Matlab. The four major treatment groups are noted by colored boxes on top of the heatmap.