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

The Relationship between Exposome and Microbiome

Currently, exposome studies include a raft of different monitoring tools, including remote sensors, smartphones, omics analyses, distributed lag models, etc. The similarity in structure between the exposome and the microbiota plus their functions led us to pose three pertinent questions from this viewpoint, looking at the actual relationship between the exposome and the microbiota. In terms of the exposome, a bistable equilibrium between health and disease depends on constantly dealing with an ever-changing totality of exposures that together shape an individual from conception to death. Regarding scientific knowledge, the exposome is still lagging in certain areas, like the importance of microorganisms in the equation. The human microbiome is defined as an aggregate assemblage of gut commensals that are hosted by our surfaces related to the external environment. Commensals' resistance to a variety of environmental exposures, such as antibiotic administration, confirms that a layer of these organisms is protected within the host. The exposome is a conceptual framework defined as the environmental component of the science-inspired systems ideology that shifts from a specificity-based medical approach to reasoning in terms of complexity. A parallel concept in population health research and precision public health is the human flourishing index, which aims to account for the numerous environmental factors that affect individual and population well-being beyond ambient pollution.

Microbial diversity and ecological complexity emerging from environmental variation and horizontal gene transfer in a simple mathematical model

BACKGROUND

Microbiomes are generally characterized by high diversity of coexisting microbial species and strains, and microbiome composition typically remains stable across a broad range of conditions. However, under fixed conditions, microbial ecology conforms with the exclusion principle under which two populations competing for the same resource within the same niche cannot coexist because the less fit population inevitably goes extinct. Therefore, the long-term persistence of microbiome diversity calls for an explanation.

RESULTS

To explore the conditions for stabilization of microbial diversity, we developed a simple mathematical model consisting of two competing populations that could exchange a single gene allele via horizontal gene transfer (HGT). We found that, although in a fixed environment, with unbiased HGT, the system obeyed the exclusion principle, in an oscillating environment, within large regions of the phase space bounded by the rates of reproduction and HGT, the two populations coexist. Moreover, depending on the parameter combination, all three major types of symbiosis were obtained, namely, pure competition, host-parasite relationship, and mutualism. In each of these regimes, certain parameter combinations provided for synergy, that is, a greater total abundance of both populations compared to the abundance of the winning population in the fixed environment.

CONCLUSIONS

The results of this modeling study show that basic phenomena that are universal in microbial communities, namely, environmental variation and HGT, provide for stabilization and persistence of microbial diversity, and emergence of ecological complexity.

Microbial diversity and ecological complexity emerging from environmental variation and horizontal gene transfer in a simple mathematical model

Microbiomes are generally characterized by high diversity of coexisting microbial species and strains that remains stable within a broad range of conditions. However, under fixed conditions, microbial ecology conforms with the exclusion principle under which two populations competing for the same resource within the same niche cannot coexist because the less fit population inevitably goes extinct. To explore the conditions for stabilization of microbial diversity, we developed a simple mathematical model consisting of two competing populations that could exchange a single gene allele via horizontal gene transfer (HGT). We found that, although in a fixed environment, with unbiased HGT, the system obeyed the exclusion principle, in an oscillating environment, within large regions of the phase space bounded by the rates of reproduction and HGT, the two populations coexist. Moreover, depending on the parameter combination, all three major types of symbiosis obtained, namely, pure competition, host-parasite relationship and mutualism. In each of these regimes, certain parameter combinations provided for synergy, that is, a greater total abundance of both populations compared to the abundance of the winning population in the fixed environments. These findings show that basic phenomena that are universal in microbial communities, environmental variation and HGT, provide for stabilization of microbial diversity and ecological complexity.

Commensal antimicrobial resistance mediates microbiome resilience to antibiotic disruption

Despite their therapeutic benefits, antibiotics exert collateral damage on the microbiome and promote antimicrobial resistance. However, the mechanisms governing microbiome recovery from antibiotics are poorly understood. Treatment of Mycobacterium tuberculosis, the world's most common infection, represents the longest antimicrobial exposure in humans. Here, we investigate gut microbiome dynamics over 20 months of multidrug-resistant tuberculosis (TB) and 6 months of drug-sensitive TB treatment in humans. We find that gut microbiome dynamics and TB clearance are shared predictive cofactors of the resolution of TB-driven inflammation. The initial severe taxonomic and functional microbiome disruption, pathobiont domination, and enhancement of antibiotic resistance that initially accompanied long-term antibiotics were countered by later recovery of commensals. This resilience was driven by the competing evolution of antimicrobial resistance mutations in pathobionts and commensals, with commensal strains with resistance mutations reestablishing dominance. Fecal-microbiota transplantation of the antibiotic-resistant commensal microbiome in mice recapitulated resistance to further antibiotic disruption. These findings demonstrate that antimicrobial resistance mutations in commensals can have paradoxically beneficial effects by promoting microbiome resilience to antimicrobials and identify microbiome dynamics as a predictor of disease resolution in antibiotic therapy of a chronic infection.

Association between microbiome and the development of adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure

Patients exposed to trauma often experience high rates of adverse post-traumatic neuropsychiatric sequelae (APNS). The biological mechanisms promoting APNS are currently unknown, but the microbiota-gut-brain axis offers an avenue to understanding mechanisms as well as possibilities for intervention. Microbiome composition after trauma exposure has been poorly examined regarding neuropsychiatric outcomes. We aimed to determine whether the gut microbiomes of trauma-exposed emergency department patients who develop APNS have dysfunctional gut microbiome profiles and discover potential associated mechanisms. We performed metagenomic analysis on stool samples (n = 51) from a subset of adults enrolled in the Advancing Understanding of RecOvery afteR traumA (AURORA) study. Two-, eight- and twelve-week post-trauma outcomes for post-traumatic stress disorder (PTSD) (PTSD checklist for DSM-5), normalized depression scores (PROMIS Depression Short Form 8b) and somatic symptom counts were collected. Generalized linear models were created for each outcome using microbial abundances and relevant demographics. Mixed-effect random forest machine learning models were used to identify associations between APNS outcomes and microbial features and encoded metabolic pathways from stool metagenomics. Microbial species, including Flavonifractor plautii, Ruminococcus gnavus and, Bifidobacterium species, which are prevalent commensal gut microbes, were found to be important in predicting worse APNS outcomes from microbial abundance data. Notably, through APNS outcome modeling using microbial metabolic pathways, worse APNS outcomes were highly predicted by decreased L-arginine related pathway genes and increased citrulline and ornithine pathways. Common commensal microbial species are enriched in individuals who develop APNS. More notably, we identified a biological mechanism through which the gut microbiome reduces global arginine bioavailability, a metabolic change that has also been demonstrated in the plasma of patients with PTSD.

Mathematical models of cystic fibrosis as a systemic disease

Cystic fibrosis (CF) is widely known as a disease of the lung, even though it is in truth a systemic disease, whose symptoms typically manifest in gastrointestinal dysfunction first. CF ultimately impairs not only the pancreas and intestine but also the lungs, gonads, liver, kidneys, bones, and the cardiovascular system. It is caused by one of several mutations in the gene of the epithelial ion channel protein CFTR. Intense research and improved antimicrobial treatments during the past eight decades have steadily increased the predicted life expectancy of a person with CF (pwCF) from a few weeks to over 50 years. Moreover, several drugs ameliorating the sequelae of the disease have become available in recent years, and notable treatments of the root cause of the disease have recently generated substantial improvements in health for some but not all pwCF. Yet, numerous fundamental questions remain unanswered. Complicating CF, for instance in the lung, is the fact that the associated insufficient chloride secretion typically perturbs the electrochemical balance across epithelia and, in the airways, leads to the accumulation of thick, viscous mucus and mucus plaques that cannot be cleared effectively and provide a rich breeding ground for a spectrum of bacterial and fungal communities. The subsequent infections often become chronic and respond poorly to antibiotic treatments, with outcomes sometimes only weakly correlated with the drug susceptibility of the target pathogen. Furthermore, in contrast to rapidly resolved acute infections with a single target pathogen, chronic infections commonly involve multi-species bacterial communities, called "infection microbiomes," that develop their own ecological and evolutionary dynamics. It is presently impossible to devise mathematical models of CF in its entirety, but it is feasible to design models for many of the distinct drivers of the disease. Building upon these growing yet isolated modeling efforts, we discuss in the following the feasibility of a multi-scale modeling framework, known as template-and-anchor modeling, that allows the gradual integration of refined sub-models with different granularity. The article first reviews the most important biomedical aspects of CF and subsequently describes mathematical modeling approaches that already exist or have the potential to deepen our understanding of the multitude aspects of the disease and their interrelationships. The conceptual ideas behind the approaches proposed here do not only pertain to CF but are translatable to other systemic diseases. This article is categorized under: Congenital Diseases > Computational Models.

A computational method to dissect colonization resistance of the gut microbiota against pathogens

The mammalian gut microbiome protects the host through colonization resistance (CR) against the incursion of exogenous and often harmful microorganisms, but identifying the exact microbes responsible for the gut microbiota-mediated CR against a particular pathogen remains a challenge. To address this limitation, we developed a computational method: generalized microbe-phenotype triangulation (GMPT). We first systematically validated GMPT using a classical population dynamics model in community ecology and demonstrated its superiority over baseline methods. We then tested GMPT on simulated data generated from the ecological network inferred from a real community (GnotoComplex microflora) and real microbiome data on two mouse studies on Clostridioides difficile infection. We demonstrated GMPT's ability to streamline the discovery of microbes that are potentially responsible for microbiota-mediated CR against pathogens. GMPT holds promise to advance our understanding of CR mechanisms and facilitate the rational design of microbiome-based therapies for preventing and treating enteric infections.

Defining the Benefits of Antibiotic Resistance in Commensals and the Scope for Resistance Optimization

Antibiotic resistance is a major medical and public health challenge, characterized by global increases in the prevalence of resistant strains. The conventional view is that all antibiotic resistance is problematic, even when not in pathogens. Resistance in commensal bacteria poses risks, as resistant organisms can provide a reservoir of resistance genes that can be horizontally transferred to pathogens or may themselves cause opportunistic infections in the future. While these risks are real, we propose that commensal resistance can also generate benefits during antibiotic treatment of human infection, by promoting continued ecological suppression of pathogens. To define and illustrate this alternative conceptual perspective, we use a two-species mathematical model to identify the necessary and sufficient ecological conditions for beneficial resistance. We show that the benefits are limited to species (or strain) interactions where commensals suppress pathogen growth and are maximized when commensals compete with, rather than prey on or otherwise exploit pathogens. By identifying benefits of commensal resistance, we propose that rather than strictly minimizing all resistance, resistance management may be better viewed as an optimization problem. We discuss implications in two applied contexts: bystander (nontarget) selection within commensal microbiomes and pathogen treatment given polymicrobial infections. IMPORTANCE Antibiotic resistance is commonly viewed as universally costly, regardless of which bacterial cells express resistance. Here, we derive an opposing logic, where resistance in commensal bacteria can lead to reductions in pathogen density and improved outcomes on both the patient and public health scales. We use a mathematical model of commensal-pathogen interactions to define the necessary and sufficient conditions for beneficial resistance, highlighting the importance of reciprocal ecological inhibition to maximize the benefits of resistance. More broadly, we argue that determining the benefits as well as the costs of resistances in human microbiomes can transform resistance management from a minimization to an optimization problem. We discuss applied contexts and close with a review of key resistance optimization dimensions, including the magnitude, spectrum, and mechanism of resistance.

Controlling the human microbiome

We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays important roles in human physiology and diseases. We have acquired extensive knowledge of the organismal compositions and metabolic functions of the human microbiome. However, the ultimate proof of our understanding of the human microbiome is reflected in our ability to manipulate it for health benefits. To facilitate the rational design of microbiome-based therapies, there are many fundamental questions to be addressed at the systems level. Indeed, we need a deep understanding of the ecological dynamics associated with such a complex ecosystem before we rationally design control strategies. In light of this, this review discusses progress from various fields, e.g., community ecology, network science, and control theory, that are helping us make progress toward the ultimate goal of controlling the human microbiome.

Ecological dynamics imposes fundamental challenges in community-based microbial source tracking

Quantifying the contributions of possible environmental sources ("sources") to a specific microbial community ("sink") is a classical problem in microbiology known as microbial source tracking (MST). Solving the MST problem will not only help us understand how microbial communities were formed, but also have far-reaching applications in pollution control, public health, and forensics. MST methods generally fall into two categories: target-based methods (focusing on the detection of source-specific indicator species or chemicals); and community-based methods (using community structure to measure similarity between sink samples and potential source environments). As next-generation sequencing becomes a standard community-assessment method in microbiology, numerous community-based computational methods, referred to as MST solvers hereafter have been developed and applied to various real datasets to demonstrate their utility across different contexts. Yet, those MST solvers do not consider microbial interactions and priority effects in microbial communities. Here, we revisit the performance of several representative MST solvers. We show compelling evidence that solving the MST problem using existing MST solvers is impractical when ecological dynamics plays a role in community assembly. In particular, we clearly demonstrate that the presence of either microbial interactions or priority effects will render the MST problem mathematically unsolvable for MST solvers. We further analyze data from fecal microbiota transplantation studies, finding that the state-of-the-art MST solvers fail to identify donors for most of the recipients. Finally, we perform community coalescence experiments to demonstrate that the state-of-the-art MST solvers fail to identify the sources for most of the sinks. Our findings suggest that ecological dynamics imposes fundamental challenges in MST. Interpretation of results of existing MST solvers should be done cautiously.

The impact of mass drug administration of antibiotics on the gut microbiota of target populations

Antibiotics have become a mainstay of healthcare in the past century due to their activity against pathogens. This manuscript reviews the impact of antibiotic use on the intestinal microbiota in the context of mass drug administration (MDA). The importance of the gut microbiota to human metabolism and physiology is now well established, and antibiotic exposure may impact host health via collateral effects on the microbiota and its functions. To gain further insight into how gut microbiota respond to antibiotic perturbation and the implications for public health, factors that influence the impact of antibiotic exposure on the microbiota, potential health outcomes of antibiotic-induced microbiota alterations, and strategies that have the potential to ameliorate these wider antibiotic-associated microbiota perturbations are also reviewed.

Implications of Gut Microbiota in Neurodegenerative Diseases

The morbidity associated with neurodegenerative diseases (NDs) is increasing, posing a threat to the mental and physical quality of life of humans. The crucial effect of microbiota on brain physiological processes is mediated through a bidirectional interaction, termed as the gut–brain axis (GBA), which is being investigated in studies. Many clinical and laboratory trials have indicated the importance of microbiota in the development of NDs via various microbial molecules that transmit from the gut to the brain across the GBA or nervous system. In this review, we summarize the implications of gut microbiota in ND, which will be beneficial for understanding the etiology and progression of NDs that may in turn help in developing ND interventions and clinical treatments for these diseases.

Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models

Microbial communities and their functions are shaped by complex networks of interactions among microbes and with their environment. While the critical roles microbial communities play in numerous environments have become increasingly appreciated, we have a very limited understanding of their interactions and how these interactions combine to generate community-level behaviors. This knowledge gap hinders our ability to predict community responses to perturbations and to design interventions that manipulate these communities to our benefit. Dynamic models are promising tools to address these questions. We review existing modeling techniques to construct dynamic models of microbial communities at different scales and suggest ways to leverage multiple types of models and data to facilitate our understanding and engineering of microbial communities.

Specialized metabolic functions of keystone taxa sustain soil microbiome stability

BACKGROUND

The relationship between biodiversity and soil microbiome stability remains poorly understood. Here, we investigated the impacts of bacterial phylogenetic diversity on the functional traits and the stability of the soil microbiome. Communities differing in phylogenetic diversity were generated by inoculating serially diluted soil suspensions into sterilized soil, and the stability of the microbiome was assessed by detecting community variations under various pH levels. The taxonomic features and potential functional traits were detected by DNA sequencing.

RESULTS

We found that bacterial communities with higher phylogenetic diversity tended to be more stable, implying that microbiomes with higher biodiversity are more resistant to perturbation. Functional gene co-occurrence network and machine learning classification analyses identified specialized metabolic functions, especially "nitrogen metabolism" and "phosphonate and phosphinate metabolism," as keystone functions. Further taxonomic annotation found that keystone functions are carried out by specific bacterial taxa, including Nitrospira and Gemmatimonas, among others.

CONCLUSIONS

This study provides new insights into our understanding of the relationships between soil microbiome biodiversity and ecosystem stability and highlights specialized metabolic functions embedded in keystone taxa that may be essential for soil microbiome stability. Video abstract.

Modelling the effect of birth and feeding modes on the development of human gut microbiota

The human gut microbiota is transmitted from mother to infant through vaginal birth and breastfeeding. Bifidobacterium, a genus that dominates the infants' gut, is adapted to breast milk in its ability to metabolize human milk oligosaccharides; it is regarded as a mutualist owing to its involvement in the development of the immune system. The composition of microbiota, including the abundance of Bifidobacteria, is highly variable between individuals and some microbial profiles are associated with diseases. However, whether and how birth and feeding practices contribute to such variation remains unclear. To understand how early events affect the establishment of microbiota, we develop a mathematical model of two types of Bifidobacteria and a generic compartment of commensal competitors. We show how early events affect competition between mutualists and commensals and microbe-host-immune interactions to cause long-term alterations in gut microbial profiles. Bifidobacteria associated with breast milk can trigger immune responses with lasting effects on the microbial community structure. Our model shows that, in response to a change in birth environment, competition alone can produce two distinct microbial profiles post-weaning. Adding immune regulation to our competition model allows for variations in microbial profiles in response to different feeding practices. This analysis highlights the importance of microbe-microbe and microbe-host interactions in shaping the gut populations following different birth and feeding modes.

Synthetic gut microbiome: Advances and challenges

An exponential rise in studies regarding the association among human gut microbial communities, human health, and diseases is currently attracting the attention of researchers to focus on human gut microbiome research. However, even with the ever-growing number of studies on the human gut microbiome, translation into improved health is progressing slowly. This hampering is due to the complexities of the human gut microbiome, which is composed of >1,000 species of microorganisms, such as bacteria, archaea, viruses, and fungi. To overcome this complexity, it is necessary to reduce the gut microbiome, which can help simplify experimental variables to an extent, such that they can be deliberately manipulated and controlled. Reconstruction of synthetic or established gut microbial communities would make it easier to understand the structure, stability, and functional activities of the complex microbial community of the human gut. Here, we provide an overview of the developments and challenges of the synthetic human gut microbiome, and propose the incorporation of multi-omics and mathematical methods in a better synthetic gut ecosystem design, for easy translation of microbiome information to therapies.

Metabolic multistability and hysteresis in a model aerobe-anaerobe microbiome community

Major changes in the microbiome are associated with health and disease. Some microbiome states persist despite seemingly unfavorable conditions, such as the proliferation of aerobe-anaerobe communities in oxygen-exposed environments in wound infections or small intestinal bacterial overgrowth. Mechanisms underlying transitions into and persistence of these states remain unclear. Using two microbial taxa relevant to the human microbiome, we combine genome-scale mathematical modeling, bioreactor experiments, transcriptomics, and dynamical systems theory to show that multistability and hysteresis (MSH) is a mechanism describing the shift from an aerobe-dominated state to a resilient, paradoxically persistent aerobe-anaerobe state. We examine the impact of changing oxygen and nutrient regimes and identify changes in metabolism and gene expression that lead to MSH and associated multi-stable states. In such systems, conceptual causation-correlation connections break and MSH must be used for analysis. Using MSH to analyze microbiome dynamics will improve our conceptual understanding of stability of microbiome states and transitions between states.

Strain-level analysis of gut-resident pro-inflammatory viridans group Streptococci suppressed by long-term cotrimoxazole prophylaxis among HIV-positive children in Zimbabwe

Antimicrobials have become a mainstay of healthcare in the past century due to their activity against pathogens. More recently, it has become clear that they can also affect health via their impact on the microbiota and inflammation. This may explain some of their clinical benefits despite global increases in antimicrobial resistance (AMR) and reduced antimicrobial effectiveness. We showed in a randomized controlled trial of stopping versus continuing cotrimoxazole prophylaxis among HIV-positive Zimbabwean children taking antiretroviral therapy (ART), that continuation of cotrimoxazole persistently suppressed gut-resident viridans group streptococcal species (VGS) that were associated with intestinal inflammation. In this addendum, we provide a broader overview of how antibiotics can shape the microbiota and use high read-depth whole metagenome sequencing data from our published study to investigate whether (i) the impact of cotrimoxazole on gut VGS and (ii) VGS associated inflammation, is attributable to strain-level variability. We focus on S. salivarius, the VGS species that was most prevalent in the cohort and for which there was sufficient genome coverage to differentiate strains. We demonstrate that suppression of S. salivarius by cotrimoxazole is not strain specific, nor did stool concentration of the pro-inflammatory mediator myeloperoxidase vary by S. salivarius strain. We also show that gut-resident S. salivarius strains present in this study population are distinct from common oral strains. This is the first analysis of how cotrimoxazole prophylaxis used according to international treatment guidelines for children living with HIV influences the gut microbiome at the strain-level. We also provide a detailed review of the literature on the mechanisms by which suppression of VGS may act synergistically with cotrimoxazole's anti-inflammatory effects to reduce gut inflammation. A greater understanding of the sub-clinical effects of antibiotics offers new insights into their responsible clinical use.

Gut microbiota in ulcerative colitis: insights on pathogenesis and treatment

Gut microbiota constitute the largest reservoir of the human microbiome and are an abundant and stable ecosystem-based on its diversity, complexity, redundancy, and host interactions This ecosystem is indispensable for human development and health. The integrity of the intestinal mucosal barrier depends on its interactions with gut microbiota. The commensal bacterial community is implicated in the pathogenesis of inflammatory bowel disease (IBD), including ulcerative colitis (UC). The dysbiosis of microbes is characterized by reduced biodiversity, abnormal composition of gut microbiota, altered spatial distribution, as well as interactions among microbiota, between different strains of microbiota, and with the host. The defects in microecology, with the related metabolic pathways and molecular mechanisms, play a critical role in the innate immunity of the intestinal mucosa in UC. Fecal microbiota transplantation (FMT) has been used to treat many diseases related to gut microbiota, with the most promising outcome reported in antibiotic-associated diarrhea, followed by IBD. This review evaluated the results of various reports of FMT in UC. The efficacy of FMT remains highly controversial, and needs to be regularized by integrated management, standardization of procedures, and individualization of treatment.

Emerging Priorities for Microbiome Research

Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology. From June 19-23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. This review is inspired by some of the topics that arose as priority areas from this unique, interactive workshop. The goal of this review is to summarize the Innovation Lab's findings by introducing the reader to emerging challenges, exciting potential, and current directions in microbiome research. The review is broken into five key topic areas: (1) interactions between microbes and the human body, (2) evolution and ecology of microbes, including the role played by the environment and microbe-microbe interactions, (3) analytical and mathematical methods currently used in microbiome research, (4) leveraging knowledge of microbial composition and interactions to develop engineering solutions, and (5) interventional approaches and engineered microbiota that may be enabled by selectively altering microbial composition. As such, this review seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today and provide inspiration for future investigation and multi-disciplinary collaboration.

Modelling microbiome recovery after antibiotics using a stability landscape framework

Treatment with antibiotics is one of the most extreme perturbations to the human microbiome. Even standard courses of antibiotics dramatically reduce the microbiome's diversity and can cause transitions to dysbiotic states. Conceptually, this is often described as a 'stability landscape': the microbiome sits in a landscape with multiple stable equilibria, and sufficiently strong perturbations can shift the microbiome from its normal equilibrium to another state. However, this picture is only qualitative and has not been incorporated in previous mathematical models of the effects of antibiotics. Here, we outline a simple quantitative model based on the stability landscape concept and demonstrate its success on real data. Our analytical impulse-response model has minimal assumptions with three parameters. We fit this model in a Bayesian framework to data from a previous study of the year-long effects of short courses of four common antibiotics on the gut and oral microbiomes, allowing us to compare parameters between antibiotics and microbiomes, and further validate our model using data from another study looking at the impact of a combination of last-resort antibiotics on the gut microbiome. Using Bayesian model selection we find support for a long-term transition to an alternative microbiome state after courses of certain antibiotics in both the gut and oral microbiomes. Quantitative stability landscape frameworks are an exciting avenue for future microbiome modelling.

Competitive lottery-based assembly of selected clades in the human gut microbiome

BACKGROUND

While the composition of the gut microbiome has now been well described by several large-scale studies, models that can account for the range of microbiome compositions that have been observed are still lacking. One model that has been well studied in macro communities and that could be useful for understanding microbiome assembly is the competitive lottery model. This model posits that groups of organisms from a regional pool of species are able to colonize the same niche and that the first species to arrive will take over the entire niche, excluding other group members.

RESULTS

Here, we examined whether this model also plays a role in the assembly of the human gut microbiome, defining measures to identify groups of organisms whose distribution across samples conforms to the competitive lottery schema. Applying this model to multiple datasets with thousands of human gut microbiome samples, we identified several taxonomic groups that exhibit a lottery-like distribution, including the Akkermansia, Dialister, and Phascolarctobacterium genera. We validated that these groups exhibit lottery-like assembly in multiple independent microbiome datasets confirming that this assembly schema is universal and not cohort specific. Examining the distribution of species from these groups in the gut microbiome of developing infants, we found that the initial lottery winner can be replaced by a different member of the group. We further found that species from lottery-like groups tend to have fewer genes in their genomes, suggesting more specialized species that are less able to engage in niche differentiation.

CONCLUSIONS

Combined, our findings highlight the complex and dynamic process through which microbial communities assemble and suggest that different phylogenetic groups may follow different models during this process.

A coexistence theory in microbial communities

Microbes are widespread in natural ecosystems where they create complex communities. Understanding the functions and dynamics of such microbial communities is a very important theme not only for ecology but also for humankind because microbes can play major roles in our health. Yet, it remains unclear how such complex ecosystems are maintained. Here, we present a simple theory on the dynamics of a microbial community. Bacteria preferring a particular pH in their environment indirectly inhibit the growth of the other types of bacteria by changing the pH to their optimum value. This pH-driven interaction always causes a state of bistability involving different types of bacteria that can be more or less abundant. Furthermore, a moderate abundance ratio of different types of bacteria can confer enhanced resilience to a specific equilibrium state, particularly when a trade-off relationship exists between growth and the ability of bacteria to change the pH of their environment. These results suggest that the balance of the composition of microbiota plays a critical role in maintaining microbial communities.

No appendix necessary: Fecal transplants and antibiotics can resolve Clostridium difficile infection

The appendix has been hypothesized to protect the colon against Clostridium difficile infection (CDI) by providing a continuous source of commensal bacteria that crowd out the potentially unhealthy bacteria and/or by contributing to defensive immune dynamics. Here, a series of deterministic systems comprised of ordinary differential equations, which treat the system as an ecological community of microorganisms, model the dynamics of colon microbiome. The first model includes migration of commensal bacteria from the appendix to the gut, while the second model expands this to also include immune dynamics. Simulations and simple analytic techniques are used to explore dynamics under biologically relevant parameters values. Both models exhibited bistability with steady states of a healthy state and of fulminant CDI. However, we find that the appendix size was much too small for migration to affect the stability of the system. Both models affirm the use of fecal transplants in conjunction with antibiotic use for CDI treatment, while the second model also suggests that anti-inflammatory drugs may protect against CDI. Ultimately, in general neither the appendiceal migration rate of commensal microbiota nor the boost to antibody production could exert an appreciable impact on the stability of the system, thus failing to support the proposed protective role of the appendix against CDI.

Studying microbial functionality within the gut ecosystem by systems biology

Humans are not autonomous entities. We are all living in a complex environment, interacting not only with our peers, but as true holobionts; we are also very much in interaction with our coexisting microbial ecosystems living on and especially within us, in the intestine. Intestinal microorganisms, often collectively referred to as intestinal microbiota, contribute significantly to our daily energy uptake by breaking down complex carbohydrates into simple sugars, which are fermented to short-chain fatty acids and subsequently absorbed by human cells. They also have an impact on our immune system, by suppressing or enhancing the growth of malevolent and beneficial microbes. Our lifestyle can have a large influence on this ecosystem. What and how much we consume can tip the ecological balance in the intestine. A "western diet" containing mainly processed food will have a different effect on our health than a balanced diet fortified with pre- and probiotics. In recent years, new technologies have emerged, which made a more detailed understanding of microbial communities and ecosystems feasible. This includes progress in the sequencing of PCR-amplified phylogenetic marker genes as well as the collective microbial metagenome and metatranscriptome, allowing us to determine with an increasing level of detail, which microbial species are in the microbiota, understand what these microorganisms do and how they respond to changes in lifestyle and diet. These new technologies also include the use of synthetic and in vitro systems, which allow us to study the impact of substrates and addition of specific microbes to microbial communities at a high level of detail, and enable us to gather quantitative data for modelling purposes. Here, we will review the current state of microbiome research, summarizing the computational methodologies in this area and highlighting possible outcomes for personalized nutrition and medicine.

Multi-stability and the origin of microbial community types

The study of host-associated microbial community composition has suggested the presence of alternative community types. We discuss three mechanisms that could explain these observations. The most commonly invoked mechanism links community types to a response to environmental change; alternatively, community types were shown to emerge from interactions between members of local communities sampled from a metacommunity. Here, we emphasize multi-stability as a third mechanism, giving rise to different community types in the same environmental conditions. We illustrate with a toy model how multi-stability can generate community types and discuss the consequences of multi-stability for data interpretation.

Sociomicrobiology and Pathogenic Bacteria

The study of microbial pathogenesis has been primarily a reductionist science since Koch's principles. Reductionist approaches are essential to identify the causal agents of infectious disease, their molecular mechanisms of action, and potential drug targets, and much of medicine's success in the treatment of infectious disease stems from that approach. But many bacteria-caused diseases cannot be explained by a single bacterium. Several aspects of bacterial pathogenesis will benefit from a more holistic approach that takes into account social interaction among bacteria of the same species and between species in consortia such as the human microbiome. The emerging discipline of sociomicrobiology provides a framework to dissect microbial interactions in single and multi-species communities without compromising mechanistic detail. The study of bacterial pathogenesis can benefit greatly from incorporating concepts from other disciplines such as social evolution theory and microbial ecology, where communities, their interactions with hosts, and with the environment play key roles.

Which games are growing bacterial populations playing?

Microbial communities display complex population dynamics, both in frequency and absolute density. Evolutionary game theory provides a natural approach to analyse and model this complexity by studying the detailed interactions among players, including competition and conflict, cooperation and coexistence. Classic evolutionary game theory models typically assume constant population size, which often does not hold for microbial populations. Here, we explicitly take into account population growth with frequency-dependent growth parameters, as observed in our experimental system. We study the in vitro population dynamics of the two commensal bacteria (Curvibacter sp. (AEP1.3) and Duganella sp. (C1.2)) that synergistically protect the metazoan host Hydra vulgaris (AEP) from fungal infection. The frequency-dependent, nonlinear growth rates observed in our experiments indicate that the interactions among bacteria in co-culture are beyond the simple case of direct competition or, equivalently, pairwise games. This is in agreement with the synergistic effect of anti-fungal activity observed in vivo. Our analysis provides new insight into the minimal degree of complexity needed to appropriately understand and predict coexistence or extinction events in this kind of microbial community dynamics. Our approach extends the understanding of microbial communities and points to novel experiments.

Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations

BACKGROUND

Intestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes.

METHODOLOGY/PRINCIPAL FINDINGS

In this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery.

CONCLUSION/SIGNIFICANCE

The model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.

Longitudinal Prediction of the Infant Gut Microbiome with Dynamic Bayesian Networks

Sequencing of the 16S rRNA gene allows comprehensive assessment of bacterial community composition from human body sites. Previously published and publicly accessible data on 58 preterm infants in the Neonatal Intensive Care Unit who underwent frequent stool collection was used. We constructed Dynamic Bayesian Networks from the data and analyzed predictive performance and network characteristics. We constructed a DBN model of the infant gut microbial ecosystem, which explicitly captured specific relationships and general trends in the data: increasing amounts of Clostridia, residual amounts of Bacilli, and increasing amounts of Gammaproteobacteria that then give way to Clostridia. Prediction performance of DBNs with fewer edges were overall more accurate, although less so on harder-to-predict subjects (p = 0.045). DBNs provided quantitative likelihood estimates for rare abruptions events. Iterative prediction was less accurate (p < 0.001), but showed remarkable insensitivity to initial conditions and predicted convergence to a mix of Clostridia, Gammaproteobacteria, and Bacilli. DBNs were able to identify important relationships between microbiome taxa and predict future changes in microbiome composition from measured or synthetic initial conditions. DBNs also provided likelihood estimates for sudden, dramatic shifts in microbiome composition, which may be useful in guiding further analysis of those samples.

The ecology of the microbiome: Networks, competition, and stability

The human gut harbors a large and complex community of beneficial microbes that remain stable over long periods. This stability is considered critical for good health but is poorly understood. Here we develop a body of ecological theory to help us understand microbiome stability. Although cooperating networks of microbes can be efficient, we find that they are often unstable. Counterintuitively, this finding indicates that hosts can benefit from microbial competition when this competition dampens cooperative networks and increases stability. More generally, stability is promoted by limiting positive feedbacks and weakening ecological interactions. We have analyzed host mechanisms for maintaining stability-including immune suppression, spatial structuring, and feeding of community members-and support our key predictions with recent data.

Computational Studies of the Intestinal Host-Microbiota Interactome

A large and growing body of research implicates aberrant immune response and compositional shifts of the intestinal microbiota in the pathogenesis of many intestinal disorders. The molecular and physical interaction between the host and the microbiota, known as the host-microbiota interactome, is one of the key drivers in the pathophysiology of many of these disorders. This host-microbiota interactome is a set of dynamic and complex processes, and needs to be treated as a distinct entity and subject for study. Disentangling this complex web of interactions will require novel approaches, using a combination of data-driven bioinformatics with knowledge-driven computational modeling. This review describes the computational approaches for investigating the host-microbiota interactome, with emphasis on the human intestinal tract and innate immunity, and highlights open challenges and existing gaps in the computation methodology for advancing our knowledge about this important facet of human health.

The demographic determinants of human microbiome health

The human microbiome is a vast reservoir of microbial diversity and increasingly recognized to have a fundamental role in human health. In polymicrobial communities, the presence of one species can modulate the demography (i.e., growth and distribution) of other species. These demographic impacts generate feedbacks in multispecies interactions, which can be magnified in spatially structured populations (e.g., host-associated communities). Here, we argue that demographic feedbacks between species are central to microbiome development, shaping whether and how potential metabolic interactions come to be realized between expanding lineages of bacteria. Understanding how demographic feedbacks tune metabolic interactions and in turn shape microbiome structure and function is now a key challenge to our abilities to better manage microbiome health.

Emergence of collective territorial defense in bacterial communities: horizontal gene transfer can stabilize microbiomes

Multispecies bacterial communities such as the microbiota of the gastrointestinal tract can be remarkably stable and resilient even though they consist of cells and species that compete for resources and also produce a large number of antimicrobial agents. Computational modeling suggests that horizontal transfer of resistance genes may greatly contribute to the formation of stable and diverse communities capable of protecting themselves with a battery of antimicrobial agents while preserving a varied metabolic repertoire of the constituent species. In other words horizontal transfer of resistance genes makes a community compatible in terms of exoproducts and capable to maintain a varied and mature metagenome. The same property may allow microbiota to protect a host organism, or if used as a microbial therapy, to purge pathogens and restore a protective environment.

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.

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.

Recent advances in DNA sequencing and metagenomics are opening a window into the human microbiome revealing novel associations between certain microbial consortia and disease. However, most of these studies are cross-sectional and lack a mechanistic understanding of this ecosystem's structure and its response to external perturbations, therefore not allowing accurate temporal predictions. In this article, we develop a method to analyze temporal community data accounting also for time-dependent external perturbations. In particular, this method combines the classical Lotka–Volterra model of population dynamics with regression techniques to obtain mechanistically descriptive coefficients which can be further used to construct predictive models of ecosystem dynamics. Using then data from a mouse experiment under antibiotic perturbations, we are able to predict and recover the microbiota temporal dynamics and study the concept of alternative stable states and antibiotic-induced transitions. As a result, our method reveals a group of commensal microbes that potentially protect against infection by the pathogen Clostridium difficile and proposes a possible mechanism how the antibiotic makes the host more susceptible to infection.

Computational meta'omics for microbial community studies

Complex microbial communities are an integral part of the Earth's ecosystem and of our bodies in health and disease. In the last two decades, culture-independent approaches have provided new insights into their structure and function, with the exponentially decreasing cost of high-throughput sequencing resulting in broadly available tools for microbial surveys. However, the field remains far from reaching a technological plateau, as both computational techniques and nucleotide sequencing platforms for microbial genomic and transcriptional content continue to improve. Current microbiome analyses are thus starting to adopt multiple and complementary meta'omic approaches, leading to unprecedented opportunities to comprehensively and accurately characterize microbial communities and their interactions with their environments and hosts. This diversity of available assays, analysis methods, and public data is in turn beginning to enable microbiome-based predictive and modeling tools. We thus review here the technological and computational meta'omics approaches that are already available, those that are under active development, their success in biological discovery, and several outstanding challenges.

Biofilms 2012: new discoveries and significant wrinkles in a dynamic field

The ASM 6th Conference on Biofilms was held in Miami, Florida, 29 September to 4 October, 2012. The conference provided an opportunity for the exchange of new findings and ideas with regard to biofilm research. A wide range of findings, spanning applied biology, evolution, ecology, physiology, and molecular biology, were presented at the conference. This review summarizes the presentations with regard to emerging biofilm-related themes.

The evolution of mutualism in gut microbiota via host epithelial selection

A computer model of the gut shows how a host can readily select friendly bacteria over harmful bacteria through a process called “selectivity amplification.”

The human gut harbours a large and genetically diverse population of symbiotic microbes that both feed and protect the host. Evolutionary theory, however, predicts that such genetic diversity can destabilise mutualistic partnerships. How then can the mutualism of the human microbiota be explained? Here we develop an individual-based model of host-associated microbial communities. We first demonstrate the fundamental problem faced by a host: The presence of a genetically diverse microbiota leads to the dominance of the fastest growing microbes instead of the microbes that are most beneficial to the host. We next investigate the potential for host secretions to influence the microbiota. This reveals that the epithelium–microbiota interface acts as a selectivity amplifier: Modest amounts of moderately selective epithelial secretions cause a complete shift in the strains growing at the epithelial surface. This occurs because of the physical structure of the epithelium–microbiota interface: Epithelial secretions have effects that permeate upwards through the whole microbial community, while lumen compounds preferentially affect cells that are soon to slough off. Finally, our model predicts that while antimicrobial secretion can promote host epithelial selection, epithelial nutrient secretion will often be key to host selection. Our findings are consistent with a growing number of empirical papers that indicate an influence of host factors upon microbiota, including growth-promoting glycoconjugates. We argue that host selection is likely to be a key mechanism in the stabilisation of the mutualism between a host and its microbiota.

The cells of our bodies are greatly outnumbered by the bacteria that live on us and, in particular, in our gut. It is now clear that many gut bacteria are highly beneficial, protecting us from pathogens and helping us with digestion. But what prevents beneficial bacteria from going bad? Why don't bacteria evolve to shirk on the help that they provide and simply use us as a food source? Here we explore this problem using a computer model that reduces the problem to its key elements. We first illustrate the basic problem faced by a host: Whenever beneficial bacteria grow slowly, the host will lose them to fast-growing species that provide no benefit. We then propose a solution to the host's problem: The host can use secretions—nutrients and toxins—to control the bacteria that grow on the epithelial cell layer of the gut. In particular, our model predicts that the epithelial surface acts as a “selectivity amplifier”. The host can thereby maintain beneficial bacteria with only small amounts of weakly selective secretions. Our model fits with a growing body of experimental data showing that hosts have diverse and important influences on their gut bacteria.