Systems biology approach to functionally assess the Clostridioides difficile pangenome reveals genetic diversity with discriminatory power.

Combatting Clostridioides difficile infections, a dominant cause of hospital-associated infections with incidence and resulting deaths increasing worldwide, is complicated by the frequent emergence of new virulent strains. Here, we employ whole-genome sequencing, high-throughput phenotypic screenings, and genome-scale models of metabolism to evaluate the genetic diversity of 451 strains of C. difficile. Constructing the C. difficile pangenome based on this set revealed 9,924 distinct gene clusters, of which 2,899 (29%) are defined as core, 2,968 (30%) are defined as unique, and the remaining 4,057 (41%) are defined as accessory. We develop a strain typing method, sequence typing by accessory genome (STAG), that identifies 176 genetically distinct groups of strains and allows for explicit interrogation of accessory gene content. Thirty-five strains representative of the overall set were experimentally profiled on 95 different nutrient sources, revealing 26 distinct growth profiles and unique nutrient preferences; 451 strain-specific genome scale models of metabolism were constructed, allowing us to computationally probe phenotypic diversity in 28,864 unique conditions. The models create a mechanistic link between the observed phenotypes and strain-specific genetic differences and exhibit an ability to correctly predict growth in 76% of measured cases. The typing and model predictions are used to identify and contextualize discriminating genetic features and phenotypes that may contribute to the emergence of new problematic strains.

Gut Microbiota From Sjögren syndrome Patients Causes Decreased T Regulatory Cells in the Lymphoid Organs and Desiccation-Induced Corneal Barrier Disruption in Mice.

Sjögren syndrome (SS) is an autoimmune inflammatory disorder characterized by secretory dysfunction in the eye and mouth; in the eye, this results in tear film instability, reduced tear production, and corneal barrier disruption. A growing number of studies show that homeostasis of the ocular surface is impacted by the intestinal microbiome, and several 16S sequencing studies have demonstrated dysbiosis of the intestinal microbiota in SS patients. In this study, we utilized metagenomic sequencing to perform a deeper analysis of the intestinal microbiome using stools collected from sex- and age-matched healthy (n = 20), dry eye (n = 4) and SS (n = 7) subjects. The observed Operational Taxonomic Units (OTUs) and Shannon alpha diversity were significantly decreased in SS compared to healthy controls, and there was a significant inverse correlation between observed OTUs and ocular severity score. We also identified specific bacterial strains that are differentially modulated in SS vs. healthy subjects. To investigate if the differential composition of intestinal microbiome would have an impact on the immune and eye phenotype, we performed functional studies using germ-free mice colonized with human intestinal microbiota from SS patients and healthy controls. Flow cytometry analysis demonstrated reduced frequency of CD4+ FOXP3+ cells in ocular draining cervical lymph nodes (CLN) in mice colonized with SS patient intestinal microbiota 4 weeks post-colonization. We also found that offspring of SS-humanized mice also have fewer CD4+FOXP3+ cells in the CLN as well as spleen, demonstrating vertical transmission. SS-humanized mice subjected to desiccating stress exhibited greater corneal barrier disruption as compared to healthy control humanized mice under the same conditions. Taken together, these data support the hypothesis that the intestinal microbiota can modulate ocular surface health, possibly by influencing development of CD4+ FOXP3+ regulatory T cells (Tregs) in the ocular draining lymph nodes.

Impact of a human gut microbe on Vibrio cholerae host colonization through biofilm enhancement.

Recent studies indicate that the human intestinal microbiota could impact the outcome of infection by Vibrio cholerae, the etiological agent of the diarrheal disease cholera. A commensal bacterium, Paracoccus aminovorans, was previously identified in high abundance in stool collected from individuals infected with V. cholerae when compared to stool from uninfected persons. However, if and how P. aminovorans interacts with V. cholerae has not been experimentally determined; moreover, whether any association between this bacterium alters the behaviors of V. cholerae to affect the disease outcome is unclear. Here, we show that P. aminovorans and V. cholerae together form dual-species biofilm structure at the air-liquid interface, with previously uncharacterized novel features. Importantly, the presence of P. aminovorans within the murine small intestine enhances V. cholerae colonization in the same niche that is dependent on the Vibrio exopolysaccharide and other major components of mature V. cholerae biofilm. These studies illustrate that multispecies biofilm formation is a plausible mechanism used by a gut microbe to increase the virulence of the pathogen, and this interaction may alter outcomes in enteric infections.

Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity.

Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.

AMiGA: Software for Automated Analysis of Microbial Growth Assays.

The analysis of microbial growth is one of the central methods in the field of microbiology. Microbial growth dynamics can be characterized by meaningful parameters, including carrying capacity, exponential growth rate, and growth lag. However, microbial assays with clinical isolates, fastidious organisms, or microbes under stress often produce atypical growth shapes that do not follow the classical microbial growth pattern. Here, we introduce the analysis of microbial growth assays (AMiGA) software, which streamlines the analysis of growth curves without any assumptions about their shapes. AMiGA can pool replicates of growth curves and infer summary statistics for biologically meaningful growth parameters. In addition, AMiGA can quantify death phases and characterize diauxic shifts. It can also statistically test for differential growth under distinct experimental conditions. Altogether, AMiGA streamlines the organization, analysis, and visualization of microbial growth assays. IMPORTANCE Our current understanding of microbial physiology relies on the simple method of measuring microbial populations' sizes over time and under different conditions. Many advances have increased the throughput of those assays and enabled the study of nonlab-adapted microbes under diverse conditions that widely affect their growth dynamics. Our software provides an all-in-one tool for estimating the growth parameters of microbial cultures and testing for differential growth in a high-throughput and user-friendly fashion without any underlying assumptions about how microbes respond to their growth conditions.

Predicting Vibrio cholerae Infection and Disease Severity Using Metagenomics in a Prospective Cohort Study.

BACKGROUND: Susceptibility to Vibrio cholerae infection is affected by blood group, age, and preexisting immunity, but these factors only partially explain who becomes infected. A recent study used 16S ribosomal RNA amplicon sequencing to quantify the composition of the gut microbiome and identify predictive biomarkers of infection with limited taxonomic resolution. METHODS: To achieve increased resolution of gut microbial factors associated with V. cholerae susceptibility and identify predictors of symptomatic disease, we applied deep shotgun metagenomic sequencing to a cohort of household contacts of patients with cholera. RESULTS: Using machine learning, we resolved species, strains, gene families, and cellular pathways in the microbiome at the time of exposure to V. cholerae to identify markers that predict infection and symptoms. Use of metagenomic features improved the precision and accuracy of prediction relative to 16S sequencing. We also predicted disease severity, although with greater uncertainty than our infection prediction. Species within the genera Prevotella and Bifidobacterium predicted protection from infection, and genes involved in iron metabolism were also correlated with protection. CONCLUSION: Our results highlight the power of metagenomics to predict disease outcomes and suggest specific species and genes for experimental testing to investigate mechanisms of microbiome-related protection from cholera.

Systems biology analysis of the Clostridioides difficile core-genome contextualizes microenvironmental evolutionary pressures leading to genotypic and phenotypic divergence.

Hospital acquired Clostridioides (Clostridium) difficile infection is exacerbated by the continued evolution of C. difficile strains, a phenomenon studied by multiple laboratories using stock cultures specific to each laboratory. Intralaboratory evolution of strains contributes to interlaboratory variation in experimental results adding to the challenges of scientific rigor and reproducibility. To explore how microevolution of C. difficile within laboratories influences the metabolic capacity of an organism, three different laboratory stock isolates of the C. difficile 630 reference strain were whole-genome sequenced and profiled in over 180 nutrient environments using phenotypic microarrays. The results identified differences in growth dynamics for 32 carbon sources including trehalose, fructose, and mannose. An updated genome-scale model for C. difficile 630 was constructed and used to contextualize the 28 unique mutations observed between the stock cultures. The integration of phenotypic screens with model predictions identified pathways enabling catabolism of ethanolamine, salicin, arbutin, and N-acetyl-galactosamine that differentiated individual C. difficile 630 laboratory isolates. The reconstruction was used as a framework to analyze the core-genome of 415 publicly available C. difficile genomes and identify areas of metabolism prone to evolution within the species. Genes encoding enzymes and transporters involved in starch metabolism and iron acquisition were more variable while C. difficile distinct metabolic functions like Stickland fermentation were more consistent. A substitution in the trehalose PTS system was identified with potential implications in strain virulence. Thus, pairing genome-scale models with large-scale physiological and genomic data enables a mechanistic framework for studying the evolution of pathogens within microenvironments and will lead to predictive modeling to combat pathogen emergence.

A comparative study of gut microbiomes in captive nocturnal strepsirrhines.

Feeding strategy and diet are increasingly recognized for their roles in governing primate gut microbiome (GMB) composition. Whereas feeding strategy reflects evolutionary adaptations to a host's environment, diet is a more proximate measure of food intake. Host phylogeny, which is intertwined with feeding strategy, is an additional, and often confounding factor that shapes GMBs across host lineages. Nocturnal strepsirrhines are an intriguing and underutilized group in which to examine the links between these three factors and GMB composition. Here, we compare GMB composition in four species of captive, nocturnal strepsirrhines with varying feeding strategies and phylogenetic relationships, but nearly identical diets. We use 16S rRNA sequences to determine gut bacterial composition. Despite similar husbandry conditions, including diet, we find that GMB composition varies significantly across host species and is linked to host feeding strategy and phylogeny. The GMBs of the omnivorous and the frugivorous species were significantly more diverse than were those of the insectivorous and exudativorous species. Across all hosts, GMBs were enriched for bacterial taxa associated with the macronutrient resources linked to the host's respective feeding strategy. Ultimately, the reported variation in microbiome composition suggests that the impacts of captivity and concurrent diet do not overshadow patterns of feeding strategy and phylogeny. As our understanding of primate GMBs progresses, populations of captive primates can provide insight into the evolution of host-microbe relationships, as well as inform future captive management protocols that enhance primate health and conservation.

Antibiotic-induced changes in the microbiota disrupt redox dynamics in the gut.

How host and microbial factors combine to structure gut microbial communities remains incompletely understood. Redox potential is an important environmental feature affected by both host and microbial actions. We assessed how antibiotics, which can impact host and microbial function, change redox state and how this contributes to post-antibiotic succession. We showed gut redox potential increased within hours of an antibiotic dose in mice. Host and microbial functioning changed under treatment, but shifts in redox potentials could be attributed specifically to bacterial suppression in a host-free ex vivo human gut microbiota model. Redox dynamics were linked to blooms of the bacterial family Enterobacteriaceae. Ecological succession to pre-treatment composition was associated with recovery of gut redox, but also required dispersal from unaffected gut communities. As bacterial competition for electron acceptors can be a key ecological factor structuring gut communities, these results support the potential for manipulating gut microbiota through managing bacterial respiration.

Human Gut Microbiota Predicts Susceptibility to Vibrio cholerae Infection.

Background: Cholera is a public health problem worldwide, and the risk factors for infection are only partially understood. Methods: We prospectively studied household contacts of patients with cholera to compare those who were infected to those who were not. We constructed predictive machine learning models of susceptibility, using baseline gut microbiota data. We identified bacterial taxa associated with susceptibility to Vibrio cholerae infection and tested these taxa for interactions with V. cholerae in vitro. Results: We found that machine learning models based on gut microbiota, as well as models based on known clinical and epidemiological risk factors, predicted V. cholerae infection. A predictive gut microbiota of roughly 100 bacterial taxa discriminated between contacts who developed infection and those who did not. Susceptibility to cholera was associated with depleted levels of microbes from the phylum Bacteroidetes. By contrast, a microbe associated with cholera by our modeling framework, Paracoccus aminovorans, promoted the in vitro growth of V. cholerae. Gut microbiota structure, clinical outcome, and age were also linked. Conclusion: These findings support the hypothesis that abnormal gut microbial communities are a host factor related to V. cholerae susceptibility.

The Microbiome Stress Project: Toward a Global Meta-Analysis of Environmental Stressors and Their Effects on Microbial Communities.

Microbial community structure is highly sensitive to natural (e.g., drought, temperature, fire) and anthropogenic (e.g., heavy metal exposure, land-use change) stressors. However, despite an immense amount of data generated, systematic, cross-environment analyses of microbiome responses to multiple disturbances are lacking. Here, we present the Microbiome Stress Project, an open-access database of environmental and host-associated 16S rRNA amplicon sequencing studies collected to facilitate cross-study analyses of microbiome responses to stressors. This database will comprise published and unpublished datasets re-processed from the raw sequences into exact sequence variants using our standardized computational pipeline. Our database will provide insight into general response patterns of microbiome diversity, structure, and stability to environmental stressors. It will also enable the identification of cross-study associations between single or multiple stressors and specific microbial clades. Here, we present a proof-of-concept meta-analysis of 606 microbiomes (from nine studies) to assess microbial community responses to: (1) one stressor in one environment: soil warming across a variety of soil types, (2) a range of stressors in one environment: soil microbiome responses to a comprehensive set of stressors (incl. temperature, diesel, antibiotics, land use change, drought, and heavy metals), (3) one stressor across a range of environments: copper exposure effects on soil, sediment, activated-sludge reactors, and gut environments, and (4) the general trends of microbiome stressor responses. Overall, we found that stressor exposure significantly decreases microbiome alpha diversity and increases beta diversity (community dispersion) across a range of environments and stressor types. We observed a hump-shaped relationship between microbial community resistance to stressors (i.e., the average pairwise similarity score between the control and stressed communities) and alpha diversity. We used Phylofactor to identify microbial clades and individual taxa as potential bioindicators of copper contamination across different environments. Using standardized computational and statistical methods, the Microbiome Stress Project will leverage thousands of existing datasets to build a general framework for how microbial communities respond to environmental stress.

The importance of accurately correcting for the natural abundance of stable isotopes.

The use of isotopically labeled tracer substrates is an experimental approach for measuring in vivo and in vitro intracellular metabolic dynamics. Stable isotopes that alter the mass but not the chemical behavior of a molecule are commonly used in isotope tracer studies. Because stable isotopes of some atoms naturally occur at non-negligible abundances, it is important to account for the natural abundance of these isotopes when analyzing data from isotope labeling experiments. Specifically, a distinction must be made between isotopes introduced experimentally via an isotopically labeled tracer and the isotopes naturally present at the start of an experiment. In this tutorial review, we explain the underlying theory of natural abundance correction of stable isotopes, a concept not always understood by metabolic researchers. We also provide a comparison of distinct methods for performing this correction and discuss natural abundance correction in the context of steady state 13C metabolic flux, a method increasingly used to infer intracellular metabolic flux from isotope experiments.