A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment.

The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.

MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics.

Longitudinal microbiome data sets are being generated with increasing regularity, and there is broad recognition that these studies are critical for unlocking the mechanisms through which the microbiome impacts human health and disease. However, there is a dearth of computational tools for analyzing microbiome time-series data. To address this gap, we developed an open-source software package, Microbiome Differentiable Interpretable Temporal Rule Engine (MDITRE), which implements a new highly efficient method leveraging deep-learning technologies to derive human-interpretable rules that predict host status from longitudinal microbiome data. Using semi-synthetic and a large compendium of publicly available 16S rRNA amplicon and metagenomics sequencing data sets, we demonstrate that in almost all cases, MDITRE performs on par with or better than popular uninterpretable machine learning methods, and orders-of-magnitude faster than the prior interpretable technique. MDITRE also provides a graphical user interface, which we show through case studies can be used to derive biologically meaningful interpretations linking patterns of microbiome changes over time with host phenotypes. IMPORTANCE The human microbiome, or collection of microbes living on and within us, changes over time. Linking these changes to the status of the human host is crucial to understanding how the microbiome influences a variety of human diseases. Due to the large scale and complexity of microbiome data, computational methods are essential. Existing computational methods for linking changes in the microbiome to the status of the human host are either unable to scale to large and complex microbiome data sets or cannot produce human-interpretable outputs. We present a new computational method and software package that overcomes the limitations of previous methods, allowing researchers to analyze larger and more complex data sets while producing easily interpretable outputs. Our method has the potential to enable new insights into how changes in the microbiome over time maintain health or lead to disease in humans and facilitate the development of diagnostic tests based on the microbiome.

Gut metabolites predict Clostridioides difficile recurrence.

BACKGROUND: Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the USA, with recurrence rates > 15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Moreover, previous studies have not shown that microbial abundances in the gut measured by 16S rRNA amplicon sequencing alone can accurately predict CDI recurrence. RESULTS: We conducted a prospective, longitudinal study of 53 non-immunocompromised participants with primary CDI. Stool sample collection began pre-CDI antibiotic treatment at the time of diagnosis, and continued up to 8 weeks post-antibiotic treatment, with weekly or twice weekly collections. Samples were analyzed using (1) 16S rRNA amplicon sequencing, (2) liquid chromatography/mass-spectrometry metabolomics measuring 1387 annotated metabolites, and (3) short-chain fatty acid profiling. The amplicon sequencing data showed significantly delayed recovery of microbial diversity in recurrent participants, and depletion of key anaerobic taxa at multiple time-points, including Clostridium cluster XIVa and IV taxa. The metabolomic data also showed delayed recovery in recurrent participants, and moreover mapped to pathways suggesting distinct functional abnormalities in the microbiome or host, such as decreased microbial deconjugation activity, lowered levels of endocannabinoids, and elevated markers of host cell damage. Further, using predictive statistical/machine learning models, we demonstrated that the metabolomic data, but not the other data sources, can accurately predict future recurrence at 1 week (AUC 0.77 [0.71, 0.86; 95% interval]) and 2 weeks (AUC 0.77 [0.69, 0.85; 95% interval]) post-treatment for primary CDI. CONCLUSIONS: The prospective, longitudinal, and multi-omic nature of our CDI recurrence study allowed us to uncover previously unrecognized dynamics in the microbiome and host presaging recurrence, and, in particular, to elucidate changes in the understudied gut metabolome. Moreover, we demonstrated that a small set of metabolites can accurately predict future recurrence. Our findings have implications for development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence, by providing candidate metabolic biomarkers for diagnostics development, as well as offering insights into the complex microbial and metabolic alterations that are protective or permissive for recurrence. Video Abstract.

In vivo commensal control of Clostridioides difficile virulence.

Leveraging systems biology approaches, we illustrate how metabolically distinct species of Clostridia protect against or worsen Clostridioides difficile infection in mice by modulating the pathogen's colonization, growth, and virulence to impact host survival. Gnotobiotic mice colonized with the amino acid fermenter Paraclostridium bifermentans survive infection with reduced disease severity, while mice colonized with the butyrate-producer, Clostridium sardiniense, succumb more rapidly. Systematic in vivo analyses revealed how each commensal alters the gut-nutrient environment to modulate the pathogen's metabolism, gene regulatory networks, and toxin production. Oral administration of P. bifermentans rescues conventional, clindamycin-treated mice from lethal C. difficile infection in a manner similar to that of monocolonized animals, thereby supporting the therapeutic potential of this commensal species. Our findings lay the foundation for mechanistically informed therapies to counter C. difficile disease using systems biology approaches to define host-commensal-pathogen interactions in vivo.

High-resolution temporal profiling of the human gut microbiome reveals consistent and cascading alterations in response to dietary glycans.

BACKGROUND: Dietary glycans, widely used as food ingredients and not directly digested by humans, are of intense interest for their beneficial roles in human health through shaping the microbiome. Characterizing the consistency and temporal responses of the gut microbiome to glycans is critical for rationally developing and deploying these compounds as therapeutics. METHODS: We investigated the effect of two chemically distinct glycans (fructooligosaccharides and polydextrose) through three clinical studies conducted with 80 healthy volunteers. Stool samples, collected at dense temporal resolution (~ 4 times per week over 10 weeks) and analyzed using shotgun metagenomic sequencing, enabled detailed characterization of participants' microbiomes. For analyzing the microbiome time-series data, we developed MC-TIMME2 (Microbial Counts Trajectories Infinite Mixture Model Engine 2.0), a purpose-built computational tool based on nonparametric Bayesian methods that infer temporal patterns induced by perturbations and groups of microbes sharing these patterns. RESULTS: Overall microbiome structure as well as individual taxa showed rapid, consistent, and durable alterations across participants, regardless of compound dose or the order in which glycans were consumed. Significant changes also occurred in the abundances of microbial carbohydrate utilization genes in response to polydextrose, but not in response to fructooligosaccharides. Using MC-TIMME2, we produced detailed, high-resolution temporal maps of the microbiota in response to glycans within and across microbiomes. CONCLUSIONS: Our findings indicate that dietary glycans cause reproducible, dynamic, and differential alterations to the community structure of the human microbiome.

Clinical Predictors of Recurrence After Primary Clostridioides difficile Infection: A Prospective Cohort Study.

BACKGROUND: Recurrent Clostridioides difficile infection (CDI) is a major public health threat. While clinical prediction tools exist, they do not incorporate the newest Infectious Diseases Society of America guidelines. METHODS: This was a prospective longitudinal study of patients experiencing their first episode of uncomplicated CDI. Patients were followed from diagnosis through 8 weeks post-completion of their anti-CDI therapy to assess recurrence. Stool was collected at diagnosis and weekly for 8 weeks following treatment. Recurrence was defined as diarrhea as well as a positive stool test by toxin EIA (EIA) for C. difficile. Fisher's exact test for binary variables and Student's t test for continuous variables were performed. Cox regression was performed to assess for predictors of CDI recurrence. RESULTS: Seventy-five patients were enrolled between August 1, 2015, and September 1, 2018. Mean age 58.1 years ± 15.5, 69.3% female, 74.7% were white, 11.3% had baseline irritable bowel syndrome, and 54.7% were actively using PPIs. Over the 8-week follow-up period, 22 patients developed a confirmed CDI recurrence. Univariate predictors of recurrence included treatment with metronidazole (40.9% vs 15.1%, p = 0.03), initially diagnosis by EIA (77.3% vs 43.4%, p = 0.007) and platelet count (206 ± 72.1 vs 270.9 ± 114.8, p = 0.03). A Cox regression model revealed primary diagnosis by EIA (HR 3.39, 95% CI 1.23, 9.31, p = 0.018) and treatment with metronidazole (HR 3.27 95% CI 1.31-8.19, p = 0.01) remain predictors for CDI recurrence. CONCLUSION: In a large prospective longitudinal cohort of uncomplicated CDI patients, treatment with metronidazole and diagnosis via EIA were the most robust predictors of CDI recurrence.

MITRE: inferring features from microbiota time-series data linked to host status.

Longitudinal studies are crucial for discovering causal relationships between the microbiome and human disease. We present MITRE, the Microbiome Interpretable Temporal Rule Engine, a supervised machine learning method for microbiome time-series analysis that infers human-interpretable rules linking changes in abundance of clades of microbes over time windows to binary descriptions of host status, such as the presence/absence of disease. We validate MITRE's performance on semi-synthetic data and five real datasets. MITRE performs on par or outperforms conventional difficult-to-interpret machine learning approaches, providing a powerful new tool enabling the discovery of biologically interpretable relationships between microbiome and human host ( https://github.com/gerberlab/mitre/ ).

Engineered Interspecies Amino Acid Cross-Feeding Increases Population Evenness in a Synthetic Bacterial Consortium.

In nature, microbes interact antagonistically, neutrally, or beneficially. To shed light on the effects of positive interactions in microbial consortia, we introduced metabolic dependencies and metabolite overproduction into four bacterial species. While antagonistic interactions govern the wild-type consortium behavior, the genetic modifications alleviated antagonistic interactions and resulted in beneficial interactions. Engineered cross-feeding increased population evenness, a component of ecological diversity, in different environments, including in a more complex gnotobiotic mouse gut environment. Our findings suggest that metabolite cross-feeding could be used as a tool for intentionally shaping microbial consortia in complex environments.IMPORTANCE Microbial communities are ubiquitous in nature. Bacterial consortia live in and on our body and in our environment, and more recently, biotechnology is applying microbial consortia for bioproduction. As part of our body, bacterial consortia influence us in health and disease. Microbial consortium function is determined by its composition, which in turn is driven by the interactions between species. Further understanding of microbial interactions will help us in deciphering how consortia function in complex environments and may enable us to modify microbial consortia for health and environmental benefits.

Author Correction: Microbiota therapy acts via a regulatory T cell MyD88/RORγt pathway to suppress food allergy.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Microbiota therapy acts via a regulatory T cell MyD88/RORγt pathway to suppress food allergy.

The role of dysbiosis in food allergy (FA) remains unclear. We found that dysbiotic fecal microbiota in FA infants evolved compositionally over time and failed to protect against FA in mice. Infants and mice with FA had decreased IgA and increased IgE binding to fecal bacteria, indicative of a broader breakdown of oral tolerance than hitherto appreciated. Therapy with Clostridiales species impacted by dysbiosis, either as a consortium or as monotherapy with Subdoligranulum variabile, suppressed FA in mice as did a separate immunomodulatory Bacteroidales consortium. Bacteriotherapy induced expression by regulatory T (Treg) cells of the transcription factor ROR-γt in a MyD88-dependent manner, which was deficient in FA infants and mice and ineffectively induced by their microbiota. Deletion of Myd88 or Rorc in Treg cells abrogated protection by bacteriotherapy. Thus, commensals activate a MyD88/ROR-γt pathway in nascent Treg cells to protect against FA, while dysbiosis impairs this regulatory response to promote disease.

Dynamic Modulation of the Gut Microbiota and Metabolome by Bacteriophages in a Mouse Model.

The human gut microbiome is comprised of densely colonizing microorganisms including bacteriophages, which are in dynamic interaction with each other and the mammalian host. To address how bacteriophages impact bacterial communities in the gut, we investigated the dynamic effects of phages on a model microbiome. Gnotobiotic mice were colonized with defined human gut commensal bacteria and subjected to predation by cognate lytic phages. We found that phage predation not only directly impacts susceptible bacteria but also leads to cascading effects on other bacterial species via interbacterial interactions. Metabolomic profiling revealed that shifts in the microbiome caused by phage predation have a direct consequence on the gut metabolome. Our work provides insight into the ecological importance of phages as modulators of bacterial colonization, and it additionally suggests the potential impact of gut phages on the mammalian host with implications for their therapeutic use to precisely modulate the microbiome.

Impaired glucose metabolism and altered gut microbiome despite calorie restriction of ob/ob mice.

BACKGROUND: Growing evidence supports the role of gut microbiota in obesity and its related disorders including type 2 diabetes. Ob/ob mice, which are hyperphagic due to leptin deficiency, are commonly used models of obesity and were instrumental in suggesting links between gut microbiota and obesity. Specific changes in their gut microbiota such as decreased microbial diversity and increased Firmicutes to Bacteroidetes ratio have been suggested to contribute to obesity via increased microbiota capacity to harvest energy. However, the differential development of ob/ob mouse gut microbiota compared to wild type microbiota and the role of hyperphagia in their metabolic impairment have not been investigated thoroughly. RESULTS: We performed a 10-week long study in ob/ob (n = 12) and wild type control (n = 12) mice fed ad libitum. To differentiate effects of leptin deficiency from hyperphagia, we pair-fed an additional group of ob/ob mice (n = 11) based on the food consumption of control mice. Compared to control mice, ob/ob mice fed ad libitum exhibited compromised glucose metabolism and increased body fat percentage. Pair-fed ob/ob mice exhibited even more compromised glucose metabolism and maintained strikingly similar high body fat percentage at the cost of lean body mass. Acclimatization of the microbiota to our facility took up to 5 weeks. Leptin deficiency impacted gut microbial composition, explaining 18.3% of the variance. Pair-feeding also altered several taxa, although the overall community composition at the end of the study was not significantly different. We found 24 microbial taxa associations with leptin deficiency, notably enrichment of members of Lactobacillus and depletion of Akkermansia muciniphila. Microbial metabolic functions related to energy harvest, including glycan degradation, phosphotransferase systems and ABC transporters, were enriched in the ob/ob mice. Taxa previously reported as relevant for obesity were associated with body weight, including Oscillibacter and Alistipes (both negatively correlated) and Prevotella (positively correlated). CONCLUSIONS: Leptin deficiency caused major changes in the mouse gut microbiota composition. Several microbial taxa were associated with body composition. Pair-fed mice maintained a pre-set high proportion of body fat despite reduced calorie intake, and exhibited more compromised glucose metabolism, with major implications for treatment options for genetically obese individuals.

Quorum Sensing Can Be Repurposed To Promote Information Transfer between Bacteria in the Mammalian Gut.

The gut microbiome is intricately involved with establishing and maintaining the health of the host. Engineering of gut microbes aims to add new functions and expand the scope of control over the gut microbiome. To create systems that can perform increasingly complex tasks in the gut, it is necessary to harness the ability of the bacteria to communicate in the gut environment. Interestingly, acyl-homoserine lactone (acyl-HSL)-mediated Gram-negative bacterial quorum sensing, a widely used mode of intercellular signaling system in nature, has not been identified in normal healthy mammalian gut. It remains unknown whether the gut bacteria that do not natively use quorum sensing can be engineered to successfully signal to other bacteria using acyl-HSLs in the gut environment. Here, we repurposed quorum sensing to create an information transfer system between native gut Escherichia coli and attenuated Salmonella enterica serovar Typhimurium. Specifically, we functionalized one species with inducible signal production and the other with signal detection and recording using genomically integrated circuits. The information transfer system demonstrated successful intra- and interspecies signaling in the murine gut. This study provides a basis for further understanding of interbacterial interactions in an otherwise hard-to-study environment as well as a basis for further investigation of the potential of acyl-HSLs as intercellular signaling molecules of engineered gut consortia.

Computer-guided design of optimal microbial consortia for immune system modulation.

Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome compositicon and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (Treg) data from germ-free mice, we estimate the contributions of twelve Clostridia strains with known immune-modulating effect to Treg induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting Treg activation and rank them by the Treg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured Treg. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics.

Macrophage dysfunction initiates colitis during weaning of infant mice lacking the interleukin-10 receptor.

Infants with defects in the interleukin 10 receptor (IL10R) develop very early onset inflammatory bowel disease. Whether IL10R regulates lamina propria macrophage function during infant development in mice and whether macrophage-intrinsic IL10R signaling is required to prevent colitis in infancy is unknown. Here we show that although signs of colitis are absent in IL10R-deficient mice during the first two weeks of life, intestinal inflammation and macrophage dysfunction begin during the third week of life, concomitant with weaning and accompanying diversification of the intestinal microbiota. However, IL10R did not directly regulate the microbial ecology during infant development. Interestingly, macrophage depletion with clodronate inhibited the development of colitis, while the absence of IL10R specifically on macrophages sensitized infant mice to the development of colitis. These results indicate that IL10R-mediated regulation of macrophage function during the early postnatal period is indispensable for preventing the development of murine colitis.

Engineered bacteria can function in the mammalian gut long-term as live diagnostics of inflammation.

Bacteria can be engineered to function as diagnostics or therapeutics in the mammalian gut but commercial translation of technologies to accomplish this has been hindered by the susceptibility of synthetic genetic circuits to mutation and unpredictable function during extended gut colonization. Here, we report stable, engineered bacterial strains that maintain their function for 6 months in the mouse gut. We engineered a commensal murine Escherichia coli strain to detect tetrathionate, which is produced during inflammation. Using our engineered diagnostic strain, which retains memory of exposure in the gut for analysis by fecal testing, we detected tetrathionate in both infection-induced and genetic mouse models of inflammation over 6 months. The synthetic genetic circuits in the engineered strain were genetically stable and functioned as intended over time. The durable performance of these strains confirms the potential of engineered bacteria as living diagnostics.

Regulation of Glucose Uptake and Enteroendocrine Function by the Intestinal Epithelial Insulin Receptor.

Insulin receptors (IRs) and IGF-I receptors (IGF-IR) are major regulators of metabolism and cell growth throughout the body; however, their roles in the intestine remain controversial. Here we show that genetic ablation of the IR or IGF-IR in intestinal epithelial cells of mice does not impair intestinal growth or development or the composition of the gut microbiome. However, the loss of IRs alters intestinal epithelial gene expression, especially in pathways related to glucose uptake and metabolism. More importantly, the loss of IRs reduces intestinal glucose uptake. As a result, mice lacking the IR in intestinal epithelium retain normal glucose tolerance during aging compared with controls, which show an age-dependent decline in glucose tolerance. Loss of the IR also results in a reduction of glucose-dependent insulinotropic polypeptide (GIP) expression from enteroendocrine K-cells and decreased GIP release in vivo after glucose ingestion but has no effect on glucagon-like peptide 1 expression or secretion. Thus, the IR in the intestinal epithelium plays important roles in intestinal gene expression, glucose uptake, and GIP production, which may contribute to pathophysiological changes in individuals with diabetes, metabolic syndrome, and other insulin-resistant states.

Alterations of the human gut microbiome in multiple sclerosis.

The gut microbiome plays an important role in immune function and has been implicated in several autoimmune disorders. Here we use 16S rRNA sequencing to investigate the gut microbiome in subjects with multiple sclerosis (MS, n=60) and healthy controls (n=43). Microbiome alterations in MS include increases in Methanobrevibacter and Akkermansia and decreases in Butyricimonas, and correlate with variations in the expression of genes involved in dendritic cell maturation, interferon signalling and NF-kB signalling pathways in circulating T cells and monocytes. Patients on disease-modifying treatment show increased abundances of Prevotella and Sutterella, and decreased Sarcina, compared with untreated patients. MS patients of a second cohort show elevated breath methane compared with controls, consistent with our observation of increased gut Methanobrevibacter in MS in the first cohort. Further study is required to assess whether the observed alterations in the gut microbiome play a role in, or are a consequence of, MS pathogenesis.

MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses.

Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE's utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations.

Computational Pathology: A Path Ahead.

CONTEXT: We define the scope and needs within the new discipline of computational pathology, a discipline critical to the future of both the practice of pathology and, more broadly, medical practice in general. OBJECTIVE: To define the scope and needs of computational pathology. DATA SOURCES: A meeting was convened in Boston, Massachusetts, in July 2014 prior to the annual Association of Pathology Chairs meeting, and it was attended by a variety of pathologists, including individuals highly invested in pathology informatics as well as chairs of pathology departments. CONCLUSIONS: The meeting made recommendations to promote computational pathology, including clearly defining the field and articulating its value propositions; asserting that the value propositions for health care systems must include means to incorporate robust computational approaches to implement data-driven methods that aid in guiding individual and population health care; leveraging computational pathology as a center for data interpretation in modern health care systems; stating that realizing the value proposition will require working with institutional administrations, other departments, and pathology colleagues; declaring that a robust pipeline should be fostered that trains and develops future computational pathologists, for those with both pathology and nonpathology backgrounds; and deciding that computational pathology should serve as a hub for data-related research in health care systems. The dissemination of these recommendations to pathology and bioinformatics departments should help facilitate the development of computational pathology.