Vanni Bucci

Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile

The gastrointestinal tracts of mammals are colonized by hundreds of microbial species that contribute to health, including colonization resistance against intestinal pathogens. Many antibiotics destroy intestinal microbial communities and increase susceptibility to intestinal pathogens. Among these, Clostridium difficile, a major cause of antibiotic-induced diarrhoea, greatly increases morbidity and mortality in hospitalized patients. Which intestinal bacteria provide resistance to C. difficile infection and their in vivo inhibitory mechanisms remain unclear. Here we correlate loss of specific bacterial taxa with development of infection, by treating mice with different antibiotics that result in distinct microbiota changes and lead to varied susceptibility to C. difficile. Mathematical modelling augmented by analyses of the microbiota of hospitalized patients identifies resistance-associated bacteria common to mice and humans. Using these platforms, we determine that Clostridium scindens, a bile acid 7α-dehydroxylating intestinal bacterium, is associated with resistance to C. difficile infection and, upon administration, enhances resistance to infection in a secondary bile acid dependent fashion. Using a workflow involving mouse models, clinical studies, metagenomic analyses, and mathematical modelling, we identify a probiotic candidate that corrects a clinically relevant microbiome deficiency. These findings have implications for the rational design of targeted antimicrobials as well as microbiome-based diagnostics and therapeutics for individuals at risk of C. difficile infection.

Intestinal Microbiota Containing Barnesiella Species Cures Vancomycin-Resistant Enterococcus faecium Colonization

ABSTRACT Bacteria causing infections in hospitalized patients are increasingly antibiotic resistant. Classical infection control practices are only partially effective at preventing spread of antibiotic-resistant bacteria within hospitals. Because the density of intestinal colonization by the highly antibiotic-resistant bacterium vancomycin-resistant Enterococcus (VRE) can exceed 109 organisms per gram of feces, even optimally implemented hygiene protocols often fail. Decreasing the density of intestinal colonization, therefore, represents an important approach to limit VRE transmission. We demonstrate that reintroduction of a diverse intestinal microbiota to densely VRE-colonized mice eliminates VRE from the intestinal tract. While oxygen-tolerant members of the microbiota are ineffective at eliminating VRE, administration of obligate anaerobic commensal bacteria to mice results in a billionfold reduction in the density of intestinal VRE colonization. 16S rRNA gene sequence analysis of intestinal bacterial populations isolated from mice that cleared VRE following microbiota reconstitution revealed that recolonization with a microbiota that contains Barnesiella correlates with VRE elimination. Characterization of the fecal microbiota of patients undergoing allogeneic hematopoietic stem cell transplantation demonstrated that intestinal colonization with Barnesiella confers resistance to intestinal domination and bloodstream infection with VRE. Our studies indicate that obligate anaerobic bacteria belonging to the Barnesiella genus enable clearance of intestinal VRE colonization and may provide novel approaches to prevent the spread of highly antibiotic-resistant bacteria.

Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota.

The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka–Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.

Cutting through the complexity of cell collectives

Via strength in numbers, groups of cells can influence their environments in ways that individual cells cannot. Large-scale structural patterns and collective functions underpinning virulence, tumour growth and bacterial biofilm formation are emergent properties of coupled physical and biological processes within cell groups. Owing to the abundance of factors influencing cell group behaviour, deriving general principles about them is a daunting challenge. We argue that combining mechanistic theory with theoretical ecology and evolution provides a key strategy for clarifying how cell groups form, how they change in composition over time, and how they interact with their environments. Here, we review concepts that are critical for dissecting the complexity of cell collectives, including dimensionless parameter groups, individual-based modelling and evolutionary theory. We then use this hybrid modelling approach to provide an example analysis of the evolution of cooperative enzyme secretion in bacterial biofilms.

Emergence of spatial structure in the tumor microenvironment due to the Warburg effect

Significance Cancer cells undergo dramatic metabolic alterations, such as the Warburg effect where glucose is consumed independently of oxygen, leading to high lactic acid production. Although these alterations can give growth advantages to cancer cells, they have a profound effect in the extracellular environment, and thus it is not clear how they affect healthy cells. Here we show that lactic acid accumulation can impair the survival of tumor-associated macrophages. Using a multidisciplinary combination of computational and experimental methods, we show that this decreased survival can lead to spatial patterns of macrophage localization that resemble how tumor-associated macrophages distribute in real tumors. Spatial patterns can potentiate tumor growth, and thus understanding how they are formed may bring therapeutic insights. Drastic metabolic alterations, such as the Warburg effect, are found in most if not all types of malignant tumors. Emerging evidence shows that cancer cells benefit from these alterations, but little is known about how they affect noncancerous stromal cells within the tumor microenvironment. Here we show that cancer cells are better adapted to metabolic changes in the microenvironment, leading to the emergence of spatial structure. A clear example of tumor spatial structure is the localization of tumor-associated macrophages (TAMs), one of the most common stromal cell types found in tumors. TAMs are enriched in well-perfused areas, such as perivascular and cortical regions, where they are known to potentiate tumor growth and invasion. However, the mechanisms of TAM localization are not completely understood. Computational modeling predicts that gradients—of nutrients, gases, and metabolic by-products such as lactate—emerge due to altered cell metabolism within poorly perfused tumors, creating ischemic regions of the tumor microenvironment where TAMs struggle to survive. We tested our modeling prediction in a coculture system that mimics the tumor microenvironment. Using this experimental approach, we showed that a combination of metabolite gradients and differential sensitivity to lactic acid is sufficient for the emergence of macrophage localization patterns in vitro. This suggests that cancer metabolic changes create a microenvironment where tumor cells thrive over other cells. Understanding differences in tumor-stroma sensitivity to these alterations may open therapeutic avenues against cancer.

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.

Towards Predictive Models of the Human Gut Microbiome

The intestinal microbiota is an ecosystem susceptible to external perturbations such as dietary changes and antibiotic therapies. Mathematical models of microbial communities could be of great value in the rational design of microbiota-tailoring diets and therapies. Here, we discuss how advances in another field, engineering of microbial communities for wastewater treatment bioreactors, could inspire development of mechanistic mathematical models of the gut microbiota. We review the state of the art in bioreactor modeling and current efforts in modeling the intestinal microbiota. Mathematical modeling could benefit greatly from the deluge of data emerging from metagenomic studies, but data-driven approaches such as network inference that aim to predict microbiome dynamics without explicit mechanistic knowledge seem better suited to model these data. Finally, we discuss how the integration of microbiome shotgun sequencing and metabolic modeling approaches such as flux balance analysis may fulfill the promise of a mechanistic model.

The Evolution of Bacteriocin Production in Bacterial Biofilms

Bacteriocin production is a spiteful behavior of bacteria that is central to the competitive dynamics of many human pathogens. Social evolution predicts that bacteriocin production is favored when bacteriocin-producing cells are mixed at intermediate frequency with their competitors and when competitive neighborhoods are localized. Both predictions are supported by biofilm experiments. However, the means by which physical and biological processes interact to produce conditions that favor the evolution of bacteriocin production remain to be investigated. Here we fill this gap using analytical and computational approaches. We identify and collapse key parameters into a single number, the critical bacteriocin range, that measures the threshold distance from a focal bacteriocin-producing cell within which its fitness is higher than that of a sensitive cell. We develop an agent-based model to test our predictions and confirm that bacteriocin production is most favored when relatedness is intermediate and competition is local. We then use invasion analysis to determine evolutionarily stable strategies for bacteriocin production. Finally, we perform long-term evolutionary simulations to analyze how the critical bacteriocin range and genetic lineage segregation affect biodiversity in multistrain biofilms. We find that biodiversity is maintained in highly segregated biofilms for a wide array of critical bacteriocin ranges. However, under conditions of high nutrient penetration leading to well-mixed biofilms, biodiversity rapidly decreases and becomes sensitive to the critical bacteriocin range.

Social interaction, noise and antibiotic-mediated switches in the intestinal microbiota.

The intestinal microbiota plays important roles in digestion and resistance against entero-pathogens. As with other ecosystems, its species composition is resilient against small disturbances but strong perturbations such as antibiotics can affect the consortium dramatically. Antibiotic cessation does not necessarily restore pre-treatment conditions and disturbed microbiota are often susceptible to pathogen invasion. Here we propose a mathematical model to explain how antibiotic-mediated switches in the microbiota composition can result from simple social interactions between antibiotic-tolerant and antibiotic-sensitive bacterial groups. We build a two-species (e.g. two functional-groups) model and identify regions of domination by antibiotic-sensitive or antibiotic-tolerant bacteria, as well as a region of multistability where domination by either group is possible. Using a new framework that we derived from statistical physics, we calculate the duration of each microbiota composition state. This is shown to depend on the balance between random fluctuations in the bacterial densities and the strength of microbial interactions. The singular value decomposition of recent metagenomic data confirms our assumption of grouping microbes as antibiotic-tolerant or antibiotic-sensitive in response to a single antibiotic. Our methodology can be extended to multiple bacterial groups and thus it provides an ecological formalism to help interpret the present surge in microbiome data.

Antibiotic treatment for Tuberculosis induces a profound dysbiosis of the microbiome that persists long after therapy is completed

Mycobacterium tuberculosis, the cause of Tuberculosis (TB), infects one third of the world’s population and causes substantial mortality worldwide. In its shortest format, treatment of TB requires six months of multidrug therapy with a mixture of broad spectrum and mycobacterial specific antibiotics, and treatment of multidrug resistant TB is longer. The widespread use of this regimen makes this one of the largest exposures of humans to antimicrobials, yet the effects of TB treatment on intestinal microbiome composition and long-term stability are unknown. We compared the microbiome composition, assessed by both 16S rDNA and metagenomic DNA sequencing, of TB cases during antimycobacterial treatment and following cure by 6 months of antibiotics. TB treatment does not perturb overall diversity, but nonetheless dramatically depletes multiple immunologically significant commensal bacteria. The microbiomic perturbation of TB therapy can persist for at least 1.2 years, indicating that the effects of TB treatment are long lasting. These results demonstrate that TB treatment has dramatic effects on the intestinal microbiome and highlight unexpected durable consequences of treatment for the world’s most common infection on human ecology.