Single-strain behavior predicts responses to environmental pH and osmolality in the gut microbiota

Abstract

Changes to gut environmental factors such as pH and osmolality due to disease or drugs correlate with major shifts in microbiome composition; however, we currently cannot predict which species can tolerate such changes or how the community will be affected. Here, we assessed the growth of 92 representative human gut bacterial strains spanning 28 families across multiple pH values and osmolalities in vitro. The ability to grow in extreme pH or osmolality conditions correlated with the availability of known stress response genes in many cases, but not all, indicating that novel pathways may participate in protecting against acid or osmotic stresses. Machine learning analysis uncovered genes or subsystems that are predictive of differential tolerance in either acid or osmotic stress. For osmotic stress, we corroborated the increased abundance of these genes in vivo during osmotic perturbation. The growth of specific taxa in limiting conditions in isolation in vitro correlated with survival in complex communities in vitro and in an in vivo mouse model of diet-induced intestinal acidification. Our data show that in vitro stress tolerance results are generalizable and that physical parameters may supersede interspecies interactions in determining the relative abundance of community members. This study provides insight into the ability of the microbiota to respond to common perturbations that may be encountered in the gut and provides a list of genes that correlate with increased ability to survive in these conditions. IMPORTANCE To achieve greater predictability in microbiota studies, it is crucial to consider physical environmental factors such as pH and particle concentration, as they play a pivotal role in influencing bacterial function and survival. For example, pH is significantly altered in various diseases, including cancers, inflammatory bowel disease, as well in the case of over-the-counter drug use. Additionally, conditions like malabsorption can affect particle concentration. In our study, we investigate how changes in environmental pH and osmolality can serve as predictive indicators of bacterial growth and abundance. Our research provides a comprehensive resource for anticipating shifts in microbial composition and gene abundance during complex perturbations. Moreover, our findings underscore the significance of the physical environment as a major driver of bacterial composition. Finally, this work emphasizes the necessity of incorporating physical measurements into animal and clinical studies to better understand the factors influencing shifts in microbiota abundance.

Keywords: acid stress; culturomics; machine learning; microbiota; osmolality; single-strain culture.

Fig 1

Phylogenetic overview and experimental setup of characterized intestinal bacterial strains. (A) 16S rRNA sequences from each member of the strain library were acquired from the SILVA database and used to generate a phylogenetic tree. (B) Experimental design and workflow for the characterization of growth of bacterial strains under different physical conditions. (C) Heatmap of PATRIC annotations of characterized strains within the subcategories of the Stress Response, Defense, and Virulence gene categories.

Fig 2

Osmotic and pH stress responses lead to phenotype variations. (A) Heatmap displaying normalized growth rate and OD₆₀₀ of 92 characterized bacterial strains in osmotic conditions ordered by phylogenetic relatedness. The growth rates and OD were normalized to maximum growth rate and OD values within a characterized strain across osmolality and pH conditions. The growth curves characterized in conditions of varying osmolality and pH demonstrate general trends of tolerance across bacterial taxa. (B–D) ML analysis of PATRIC annotations and growth profile data of characterized strains obtained via a one-level decision tree regression model. (B) Boxplot of the presence (red) or absence (blue) of bacterial species possessing features in glutathione biosynthesis and the gamma-glutamyl cycle as a function of the maximum OD across osmolality conditions of ~440, 890, 1,176, and 1,800 mOsm/kg (left). This pathway is found to be enriched in vivo during osmotic perturbation (right). (C) Boxplot of characterized bacterial species encoding (red) or missing (blue) a gene for the choline uptake and conversion to betaine cluster as a function of the maximum OD under varying osmolality conditions (left). This pathway is found to be enriched in vivo during osmotic perturbation (right). (D) Boxplot highlighting the presence (red) or absence (blue) of at least 10 osmotic stress genes as a function of the maximum OD in varying osmotic conditions (left). This pathway is found to be enriched in vivo during osmotic perturbation (right). **P < 0.01, ***P < 0.001, ****P < 0.0001.

Fig 3

The relative abundance of bacterial families correlates with osmolality and pH across multiple complex microbiota communities in vitro. (A) Bar plot of relative abundance of bacterial taxa isolated from in vitro cultures of human fecal samples (n =6) subjected to ranges of pH and osmolalities identical to in vitro single-strain cultures. The relative abundance and composition of bacterial families were determined via 16S rRNA sequencing. (B) OD₆₀₀ at 48 h, measured to determine the effect of osmolality and pH on community growth. (C) Pearson’s correlations of bacterial families in complex communities of human fecal samples characterized in vitro, with negative correlations between relative abundance and osmolality (left) and pH (right) highlighted in blue and positive correlations in red. (D) Plot of relative abundance as a function of increasing pH for Bifidobacteriaceae, demonstrating a negative correlation between pH and relative abundance (r=−0.86). (E) Plot of relative abundance and increasing osmolality for Enterococcaceae, demonstrating a positive correlation between osmolality and Enterococcaceae abundance (r =0.71).

Fig 4

Humanized mice on a guar gum diet demonstrate a significant drop in cecal pH and shifts in bacterial family composition. (A) Experimental schematic of germ-free (GF) SW mice, detailing the timeline of humanization and the switch to a guar gum diet (n = 5) versus a control diet (n = 3). The bottom figure depicts segments of the gastrointestinal tract collected for pH measurements, 16S sequencing, and SCFA measurements. (B) Relative bacterial abundance of humanized mice on the guar gum or control diet, highlighting the gut microbial composition at the family taxonomic level. (C) Quantification of pH along the gastrointestinal tract of humanized mice on the guar gum or control diet. (D) SCFA concentrations in cecal contents from humanized mice on the guar gum or control diet.