Translational activity is uncoupled from nucleic acid content in bacterial cells of the human gut microbiota

Abstract

Changes in bacterial diversity in the human gut have been associated with many conditions, despite not always reflecting changes in bacterial activity. Methods linking bacterial identity to function are needed for improved understanding of how bacterial communities adapt and respond to their environment, including the gut. Here, we optimized bioorthogonal non-canonical amino acid tagging (BONCAT) for the gut microbiota and combined it with fluorescently activated cell sorting and sequencing (FACS-Seq) to identify the translationally active members of the community. We then used this novel technique to compare with other bulk community measurements of activity and viability: relative nucleic acid content and membrane damage. The translationally active bacteria represent about half of the gut microbiota, and are not distinct from the whole community. The high nucleic acid content bacteria also represent half of the gut microbiota, but are distinct from the whole community and correlate with the damaged subset. Perturbing the community with xenobiotics previously shown to alter bacterial activity but not diversity resulted in stronger changes in the distinct physiological fractions than in the whole community. BONCAT is a suitable method to probe the translationally active members of the gut microbiota, and combined with FACS-Seq, allows for their identification. The high nucleic acid content bacteria are not necessarily the protein-producing bacteria in the community; thus, further work is needed to understand the relationship between nucleic acid content and bacterial metabolism in the human gut. Considering physiologically distinct subsets of the gut microbiota may be more informative than whole-community profiling.

Keywords: BONCAT; Gut microbiome; bacterial activity; bacterial physiology; flow cytometry; single-cell.

Figure 1.

Optimizingin-vitro BONCAT incubation conditions for the human gut microbiota. a) HPG concentrations between 1 and 5 mM tested with varying concentrations of fecal supernatant between 10 and 50% show no significant differences in incubation based on HPG concentration. b) Growth curves with HPG, methionine, or neither. Bacteria were diluted 1/10 in 50% supernatant and incubated at anaerobically at 37°C in the dark, with shaking. c) Family level 16S rRNA gene sequencing of the gut microbiota with and without HPG or methionine in 50% supernatant for 1, 3, or 5 hours (n = 1). Labels indicate time (hours), followed by No addition (N), Methionine (M), or HPG (H)

Figure 2.

Abundance and diversity of physiological fractions per individual. (a) Relative abundance of cells in each physiological fraction (n = 10). b) Correlations between the proportion of PI+ bacteria and BONCAT+ bacteria to the proportion of HNA. C) PCoA of weighted UniFrac distances of regularized log transformed data indicate significant clustering by individual. There is significant clustering by (c) individual

Figure 3.

Beta diversity by physiological fractions. (a) PCoA of weighted UniFrac distances of regularized log transformed data indicate significant clustering by physiology. As there is large variation across individuals, the first (b) and second (c) principle components were plotted by individual to show trends in clustering by physiology

Figure 4.

Taxonomic overview of the physiologically distinct subpopulations of the human gut microbiota. Relative abundance at the (a) phylum level and (b) top ten genera across physiological groups and by individual (c) and (d). Pairwise Adonis tests, with FDR adjustment

Figure 5.

Similarity of physiologically distinct fractions across individuals. a) Weighted UniFrac distances for each pair of samples of rlog transformed data. b) Distribution of core, unique and shared taxa across physiological fractions. Red stars represent significantly different dispersion, black stars represent significantly differential abundance as per corncob models

Figure 6.

Differences of physiological groups after xenobiotic incubations. PCoA of rlog transformed weighted UniFrac distances broken down by physiology and treatment for a) individual 1 and b) individual 2. The circle for treatment C represent the control, the triangle for treatment D represents digoxin, the square for treatment G represents glucose, and the plus sign for treatment N represents nizatidine. Cellular relative abundance of (c) HNA and LNA, (e) BONCAT+, and (g) PI+ bacteria in Individual 1. The relative abundance of d) HNA and LNA, f) BONCAT+, and h) PI+ bacteria in Individual 2. Ex-vivo incubations were performed anaerobically at 37°C for 2 hours. N = 3 incubation replicates, error bars represent S.D, One-way ANOVA compared to the control group, with FDR correction