Inferring dynamic signatures of microbes in complex host ecosystems

The effects of astragaloside IV on gut microbiota and serum metabolism in a mice model of intracerebral hemorrhage

BACKGROUND

Astragaloside IV (AS-IV) is the main active component of "Astragalus membranaceus (Fisch.) Bunge, a synonym of Astragalus propinquus Schischkin (Fabaceae)", which demonstrated to be useful for the treatment of intracerebral hemorrhage (ICH). However, due to the low bioavailability and barrier permeability of AS-IV, the gut microbiota may be an important key regulator for AS-IV to work.

OBJECTIVE

To explore the influences of gut microbiota on the effects of AS-IV on ICH.

METHODS

Mice were randomly divided into five groups: sham, ICH, and AS-IV-treated groups (25 mg/kg, 50 mg/kg, and 100 mg/kg). Behavioral tests, brain histopathology, and immunohistochemistry analysis were used to evaluate the degree of brain injury. Western blot was employed to verify peri‑hematoma inflammation. The plasma lipopolysaccharide (LPS) leakage, the fluorescein isothiocyanate-dextran permeability, the colonic histopathology, and immunohistochemistry were detected to evaluate the barrier function of intestinal mucosal. Moreover, 16S rDNA sequencing and metabolomic analysis was applied to screen differential bacteria and metabolites, respectively. The correlation analysis was adopted to determine the potential relationship between differential bacteria and critical metabolites or neurological deficits.

RESULTS

AS-IV alleviated neurological deficits, neuronal injury and apoptosis, and blood-brain barrier disruption. This compound reduced tumor necrosis factor (TNF)-α expression, increased arginase (Arg)-1 and interleukin (IL)-33 levels around the hematoma. Next, 16S rRNA sequencing indicated that AS-IV altered the gut microbiota, and inhibited the production of conditional pathogenic bacteria. Metabolomic analysis demonstrated that AS-IV regulated the serum metabolic profiles, especially the aminoacid metabolism and peroxisome proliferator-activated receptor (PPAR) signaling pathway. Additionally, AS-IV mitigated intestinal barrier damage and LPS leakage.

CONCLUSION

This study provides a new perspective on the use of AS-IV for the treatment of ICH. Among them, gut microbiota and its metabolites may be the key regulator of AS-IV in treating ICH.

Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions

Microbiomes interact dynamically with their environment to perform exploitable functions such as production of valuable metabolites and degradation of toxic metabolites for a wide range of applications in human health, agriculture, and environmental cleanup. Developing computational models to predict the key bacterial species and environmental factors to build and optimize such functions are crucial to accelerate microbial community engineering. However, there is an unknown web of interactions that determine the highly complex and dynamic behavior of these systems, which precludes the development of models based on known mechanisms. By contrast, entirely data-driven machine learning models can produce physically unrealistic predictions and often require significant amounts of experimental data to learn system behavior. We develop a physically-constrained recurrent neural network that preserves model flexibility but is constrained to produce physically consistent predictions and show that it can outperform existing machine learning methods in the prediction of certain experimentally measured species abundance and metabolite concentrations. Further, we present a closed-loop, Bayesian experimental design algorithm to guide data collection by selecting experimental conditions that simultaneously maximize information gain and target microbial community functions. Using a bioreactor case study, we demonstrate how the proposed framework can be used to efficiently navigate a large design space to identify optimal operating conditions. The proposed methodology offers a flexible machine learning approach specifically tailored to optimize microbiome target functions through the sequential design of informative experiments that seek to explore and exploit community functions.

coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies

BACKGROUND

One of the main challenges of microbiome analysis is its compositional nature that if ignored can lead to spurious results. Addressing the compositional structure of microbiome data is particularly critical in longitudinal studies where abundances measured at different times can correspond to different sub-compositions.

RESULTS

We developed coda4microbiome, a new R package for analyzing microbiome data within the Compositional Data Analysis (CoDA) framework in both, cross-sectional and longitudinal studies. The aim of coda4microbiome is prediction, more specifically, the method is designed to identify a model (microbial signature) containing the minimum number of features with the maximum predictive power. The algorithm relies on the analysis of log-ratios between pairs of components and variable selection is addressed through penalized regression on the "all-pairs log-ratio model", the model containing all possible pairwise log-ratios. For longitudinal data, the algorithm infers dynamic microbial signatures by performing penalized regression over the summary of the log-ratio trajectories (the area under these trajectories). In both, cross-sectional and longitudinal studies, the inferred microbial signature is expressed as the (weighted) balance between two groups of taxa, those that contribute positively to the microbial signature and those that contribute negatively. The package provides several graphical representations that facilitate the interpretation of the analysis and the identified microbial signatures. We illustrate the new method with data from a Crohn's disease study (cross-sectional data) and on the developing microbiome of infants (longitudinal data).

CONCLUSIONS

coda4microbiome is a new algorithm for identification of microbial signatures in both, cross-sectional and longitudinal studies. The algorithm is implemented as an R package that is available at CRAN ( https://cran.r-project.org/web/packages/coda4microbiome/ ) and is accompanied with a vignette with a detailed description of the functions. The website of the project contains several tutorials: https://malucalle.github.io/coda4microbiome/.

Analysis of Postmortem Intestinal Microbiota Successional Patterns with Application in Postmortem Interval Estimation

Microorganisms play a vital role in the decomposition of vertebrate remains in natural nutrient cycling, and the postmortem microbial succession patterns during decomposition remain unclear. The present study used hierarchical clustering based on Manhattan distances to analyze the similarities and differences among postmortem intestinal microbial succession patterns based on microbial 16S rDNA sequences in a mouse decomposition model. Based on the similarity, seven different classes of succession patterns were obtained. Generally, the normal intestinal flora in the cecum was gradually decreased with changes in the living conditions after death, while some facultative anaerobes and obligate anaerobes grew and multiplied upon oxygen consumption. Furthermore, a random forest regression model was developed to predict the postmortem interval based on the microbial succession trend dataset. The model demonstrated a mean absolute error of 20.01 h and a squared correlation coefficient of 0.95 during 15-day decomposition. Lactobacillus, Dubosiella, Enterococcus, and the Lachnospiraceae NK4A136 group were considered significant biomarkers for this model according to the ranked list. The present study explored microbial succession patterns in terms of relative abundances and variety, aiding in the prediction of postmortem intervals and offering some information on microbial behaviors in decomposition ecology.

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.

Complexity-stability trade-off in empirical microbial ecosystems

May's stability theory, which holds that large ecosystems can be stable up to a critical level of complexity, a product of the number of resident species and the intensity of their interactions, has been a central paradigm in theoretical ecology. So far, however, empirically demonstrating this theory in real ecological systems has been a long-standing challenge with inconsistent results. Especially, it is unknown whether this theory is pertinent in the rich and complex communities of natural microbiomes, mainly due to the challenge of reliably reconstructing such large ecological interaction networks. Here we introduce a computational framework for estimating an ecosystem's complexity without relying on a priori knowledge of its underlying interaction network. By applying this method to human-associated microbial communities from different body sites and sponge-associated microbial communities from different geographical locations, we found that in both cases the communities display a pronounced trade-off between the number of species and their effective connectance. These results suggest that natural microbiomes are shaped by stability constraints, which limit their complexity.

Using Community Ecology Theory and Computational Microbiome Methods To Study Human Milk as a Biological System

Human milk is a complex and dynamic biological system that has evolved to optimally nourish and protect human infants. Yet, according to a recent priority-setting review, “our current understanding of human milk composition and its individual components and their functions fails to fully recognize the importance of the chronobiology and systems biology of human milk in the context of milk synthesis, optimal timing and duration of feeding, and period of lactation” (P. Christian et al., Am J Clin Nutr 113:1063–1072, 2021, https://doi.org/10.1093/ajcn/nqab075). We attribute this critical knowledge gap to three major reasons as follows. (i) Studies have typically examined each subsystem of the mother-milk-infant “triad” in isolation and often focus on a single element or component (e.g., maternal lactation physiology or milk microbiome or milk oligosaccharides or infant microbiome or infant gut physiology). This undermines our ability to develop comprehensive representations of the interactions between these elements and study their response to external perturbations. (ii) Multiomics studies are often cross-sectional, presenting a snapshot of milk composition, largely ignoring the temporal variability during lactation. The lack of temporal resolution precludes the characterization and inference of robust interactions between the dynamic subsystems of the triad. (iii) We lack computational methods to represent and decipher the complex ecosystem of the mother-milk-infant triad and its environment. In this review, we advocate for longitudinal multiomics data collection and demonstrate how incorporating knowledge gleaned from microbial community ecology and computational methods developed for microbiome research can serve as an anchor to advance the study of human milk and its many components as a “system within a system.”

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.

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.

A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types

Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics, and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical, or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens, and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g., sparse, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial community data and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modeling to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

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/ ).

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 regulatory T (Treg) cells expressing the transcription factor ROR-γt in a MyD88-dependent manner, which were 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.

Different Dimensions in Microbial Community Adaptation and Function

With the omics tool, the challenges in understanding the microbial community functions are becoming more intriguing. It is the environment created scenario, which demands alignment of the different members of the community for the desired output leading to common condition for their survival. The resultant community pathways provide a broad umbrella of metabolic options giving the desired plasticity, which plays decision making role in the adaptation process. The initial step in community characterization must involve the discovery of key and core member of the community and monitoring the fluctuations in functional abundance over the space and time. The concept of entropy and metabolic fluxes must reflect the inner metabolic machinery of the taxon selection and route of functional operation in a community. The segregation of member based on their functional role and hierarchical level in the community must be an essential step to be followed by interaction mapping and measurement of metabolic fluxes to derive the flow of metabolites within the community. This conceptual framework and integrated omics tools with supported statistical modeling algorithm can help in bringing out finer details in the process of community functional adaptation in any given scenario.

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.

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.

Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference

IMPACT STATEMENT

This review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome's role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.

Gnotobiotic and Conventional Mouse Systems to Support Microbiota Based Studies

Animal models are essential to dissect host-microbiota interactions that impact health and the development of disease. In addition to providing pre-clinical models for the development of novel therapeutics and diagnostic biomarkers, mouse systems actively support microbiome studies by defining microbial contributions to normal development and homeostasis, and as well as their role in promoting diseases such as inflammatory auto-immune diseases, diabetes, metabolic syndromes, and susceptibilities to infectious agents. Mice provide a genetically tenable host that can be reared under gnotobiotic (germfree) conditions, allowing colonization studies with human or mouse-origin defined or complex microbial communities to define specific in vivo effects. The protocols and background information detail key aspects to consider in designing host-microbiome experiments with mouse models, and to develop robust systems that leverage gnotobiotic mice, microbial consortia, and specific environmental perturbations to identify causal effects in vivo.

Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing

Motivation

The number of microbial and metagenomic studies has increased drastically due to advancements in next-generation sequencing-based measurement techniques. Statistical analysis and the validity of conclusions drawn from (time series) 16S rRNA and other metagenomic sequencing data is hampered by the presence of significant amount of noise and missing data (sampling zeros). Accounting uncertainty in microbiome data is often challenging due to the difficulty of obtaining biological replicates. Additionally, the compositional nature of current amplicon and metagenomic data differs from many other biological data types adding another challenge to the data analysis.

Results

To address these challenges in human microbiome research, we introduce a novel probabilistic approach to explicitly model overdispersion and sampling zeros by considering the temporal correlation between nearby time points using Gaussian Processes. The proposed Temporal Gaussian Process Model for Compositional Data Analysis (TGP-CODA) shows superior modeling performance compared to commonly used Dirichlet-multinomial, multinomial and non-parametric regression models on real and synthetic data. We demonstrate that the nonreplicative nature of human gut microbiota studies can be partially overcome by our method with proper experimental design of dense temporal sampling. We also show that different modeling approaches have a strong impact on ecological interpretation of the data, such as stationarity, persistence and environmental noise models.

Availability and implementation

A Stan implementation of the proposed method is available under MIT license at https://github.com/tare/GPMicrobiome.

Contact

taijo@flatironinstitute.org or rb113@nyu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Mapping the ecological networks of microbial communities

Mapping the ecological networks of microbial communities is a necessary step toward understanding their assembly rules and predicting their temporal behavior. However, existing methods require assuming a particular population dynamics model, which is not known a priori. Moreover, those methods require fitting longitudinal abundance data, which are often not informative enough for reliable inference. To overcome these limitations, here we develop a new method based on steady-state abundance data. Our method can infer the network topology and inter-taxa interaction types without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental data sets. Our method represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

Hypothesis Testing and Statistical Analysis of Microbiome

After the initiation of Human Microbiome Project in 2008, various biostatistic and bioinformatic tools for data analysis and computational methods have been developed and applied to microbiome studies. In this review and perspective, we discuss the research and statistical hypotheses in gut microbiome studies, focusing on mechanistic concepts that underlie the complex relationships among host, microbiome, and environment. We review the current available statistic tools and highlight recent progress of newly developed statistical methods and models. Given the current challenges and limitations in biostatistic approaches and tools, we discuss the future direction in developing statistical methods and models for the microbiome studies.

Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis

A system-level framework of complex microbe–microbe and host–microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.

The metabolic interactions between gut microbes and host cells play roles in human health. Here, Sung et al. present a literature-curated metabolic network of the human gut microbiota and three human cell types, together with a mathematical approach to identify distinct microbial and metabolic features in gut microbiomes.

Western diets, gut dysbiosis, and metabolic diseases: Are they linked?

Obesity afflicts 36.5% of the US population and 600 million individuals world-wide. Thus, it is imperative to understand the risk factors underlying metabolic disease including diet, activity level, sleep, and genetics. Another key contributory factor is the gut microbiota given its widely reported role in the development of metabolic disease. The gut microbiota, particularly its structure and function, is heavily influenced by Western style diets rich in a complex mixture of fats and high in simple sugars. In this review, the profound impact of obesity and Western diets on the gut microbiota will be illustrated, and the following research questions will be addressed: 1) to what extent do high fat diets (HFDs) alter community membership and function and does this depend upon the amount or type of fat consumed?, 2) how rapidly do dietary shifts alter gut microbial communities?, 3) are these alterations sustained or can the microbiome recover from dietary stress?, 4) how does diet drive host-microbe interactions leading to obesity?, and 5) what can be done to restore the detrimental impact of HFD on the gut microbiota? The goal of this review is to address these questions by parsing out the effects and underlying mechanisms of how Western diets impact the gut microbiota and host. By doing so, potential avenues for further exploration and strategies for microbiome-based interventions to prevent or treat diet-induced obesity may become more apparent.

Features of the bronchial bacterial microbiome associated with atopy, asthma, and responsiveness to inhaled corticosteroid treatment

BACKGROUND

Compositional differences in the bronchial bacterial microbiota have been associated with asthma, but it remains unclear whether the findings are attributable to asthma, to aeroallergen sensitization, or to inhaled corticosteroid treatment.

OBJECTIVES

We sought to compare the bronchial bacterial microbiota in adults with steroid-naive atopic asthma, subjects with atopy but no asthma, and nonatopic healthy control subjects and to determine relationships of the bronchial microbiota to phenotypic features of asthma.

METHODS

Bacterial communities in protected bronchial brushings from 42 atopic asthmatic subjects, 21 subjects with atopy but no asthma, and 21 healthy control subjects were profiled by using 16S rRNA gene sequencing. Bacterial composition and community-level functions inferred from sequence profiles were analyzed for between-group differences. Associations with clinical and inflammatory variables were examined, including markers of type 2-related inflammation and change in airway hyperresponsiveness after 6 weeks of fluticasone treatment.

RESULTS

The bronchial microbiome differed significantly among the 3 groups. Asthmatic subjects were uniquely enriched in members of the Haemophilus, Neisseria, Fusobacterium, and Porphyromonas species and the Sphingomonodaceae family and depleted in members of the Mogibacteriaceae family and Lactobacillales order. Asthma-associated differences in predicted bacterial functions included involvement of amino acid and short-chain fatty acid metabolism pathways. Subjects with type 2-high asthma harbored significantly lower bronchial bacterial burden. Distinct changes in specific microbiota members were seen after fluticasone treatment. Steroid responsiveness was linked to differences in baseline compositional and functional features of the bacterial microbiome.

CONCLUSION

Even in subjects with mild steroid-naive asthma, differences in the bronchial microbiome are associated with immunologic and clinical features of the disease. The specific differences identified suggest possible microbiome targets for future approaches to asthma treatment or prevention.

Assessing the Intestinal Microbiota in the SHINE Trial

Advances in DNA sequencing technology now allow us to explore the dynamics and functions of the microbes that inhabit the human body, the microbiota. Recent studies involving experimental animal models suggest a role of the gut microbiota in growth. However, the specific changes in the human gut microbiota that contribute to growth remain unclear, and studies investigating the gut microbiota as a determinant of environmental enteric dysfunction (EED) and child stunting are lacking. In this article, we review the evidence for a link between the developing infant gut microbiota, infant feeding, EED, and stunting, and discuss the potential causal pathways relating these variables. We outline the analytic approaches we will use to investigate these relationships, by capitalizing on the longitudinal design and randomized interventions of the Sanitation Hygiene Infant Nutrition Efficacy trial in Zimbabwe.

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.

Resilience of the intestinal microbiota following pathogenic bacterial infection is independent of innate immunity mediated by NOD1 or NOD2

The innate immune receptors, NOD1 and NOD2, are key regulators of intestinal homeostasis. NOD2 deficiency is linked to increased risk for Crohn's disease, a type of inflammatory bowel disease characterized by chronic inflammatory pathology and dysbiosis within resident microbial communities. However, the relationship between NOD protein-regulated immune functions and dysbiosis remains unclear. We hypothesized that the relationship between NOD1 or NOD2 deficiency and altered community structure during chronic disease may arise via NOD-dependent impairment of community resilience over time. Using the Salmonella ΔaroA model of chronic colitis with littermate mice to control for environmental influences on the microbiota, we show that NOD proteins exert a relatively minor impact on the chronic inflammatory environment and do not significantly contribute to bacterial abundance or community resilience following infection. Rather, temporal shifts in relative abundance of targeted bacterial groups correlated with inflammatory phenotype driven by presence of the pathogen and the ensuing complex immune response.

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.

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.

Diet dominates host genotype in shaping the murine gut microbiota

Mammals exhibit marked interindividual variations in their gut microbiota, but it remains unclear if this is primarily driven by host genetics or by extrinsic factors like dietary intake. To address this, we examined the effect of dietary perturbations on the gut microbiota of five inbred mouse strains, mice deficient for genes relevant to host-microbial interactions (MyD88(-/-), NOD2(-/-), ob/ob, and Rag1(-/-)), and >200 outbred mice. In each experiment, consumption of a high-fat, high-sugar diet reproducibly altered the gut microbiota despite differences in host genotype. The gut microbiota exhibited a linear dose response to dietary perturbations, taking an average of 3.5 days for each diet-responsive bacterial group to reach a new steady state. Repeated dietary shifts demonstrated that most changes to the gut microbiota are reversible, while also uncovering bacteria whose abundance depends on prior consumption. These results emphasize the dominant role that diet plays in shaping interindividual variations in host-associated microbial communities.

Biotic interactions and temporal dynamics of the human gastrointestinal microbiota

The human gastrointestinal (GI) microbiota is important to human health and imbalances or shifts in the gut microbial community have been linked to many diseases. Most studies of the GI microbiota only capture snapshots of this dynamic community at one or a few time points. Although this is valuable in terms of providing knowledge of community composition and variability between individuals, it does not provide the foundation for going beyond descriptive studies and toward truly predictive ecological models. In order to achieve this goal, we need longitudinal data of appropriate temporal and taxonomic resolution, so that established time series analysis tools for identifying and quantifying putative interactions among community members can be used. Here, we present new analyses of existing data to illustrate the potential usefulness of this approach. We discuss challenges related to sampling and data processing, as well as analytical approaches and considerations for future studies of the GI microbiota and other complex microbial systems.

Dynamics of the microbiota in response to host infection

Longitudinal studies of the microbiota are important for discovering changes in microbial communities that affect the host. The complexity of these ecosystems requires rigorous integrated experimental and computational methods to identify temporal signatures that promote physiologic or pathophysiologic responses in vivo. Employing a murine model of infectious colitis with the pathogen Citrobacter rodentium, we generated a 2-month time-series of 16S rDNA gene profiles, and quantitatively cultured commensals, from multiple intestinal sites in infected and uninfected mice. We developed a computational framework to discover time-varying signatures for individual taxa, and to automatically group signatures to identify microbial sub-communities within the larger gut ecosystem that demonstrate common behaviors. Application of this model to the 16S rDNA dataset revealed dynamic alterations in the microbiota at multiple levels of resolution, from effects on systems-level metrics to changes across anatomic sites for individual taxa and species. These analyses revealed unique, time-dependent microbial signatures associated with host responses at different stages of colitis. Signatures included a Mucispirillum OTU associated with early disruption of the colonic surface mucus layer, prior to the onset of symptomatic colitis, and members of the Clostridiales and Lactobacillales that increased with successful resolution of inflammation, after clearance of the pathogen. Quantitative culture data validated findings for predominant species, further refining and strengthening model predictions. These findings provide new insights into the complex behaviors found within host ecosystems, and define several time-dependent microbial signatures that may be leveraged in studies of other infectious or inflammatory conditions.

Selecting protein families for environmental features based on manifold regularization

Recently, statistics and machine learning have been developed to identify functional or taxonomic features of environmental features or physiological status. Important proteins (or other functional and taxonomic entities) to environmental features can be potentially used as biosensors. A major challenge is how the distribution of protein and gene functions embodies the adaption of microbial communities across environments and host habitats. In this paper, we propose a novel regularization method for linear regression to adapt the challenge. The approach is inspired by local linear embedding (LLE) and we call it a manifold-constrained regularization for linear regression (McRe). The novel regularization procedure also has potential to be used in solving other linear systems. We demonstrate the efficiency and the performance of the approach in both simulation and real data.

The impact of antibiotics on growth in children in low and middle income countries: systematic review and meta-analysis of randomised controlled trials

OBJECTIVES

To determine whether antibiotic treatment leads to improvements in growth in prepubertal children in low and middle income countries, to determine the magnitude of improvements in growth, and to identify moderators of this treatment effect.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Medline, Embase, Scopus, the Cochrane central register of controlled trials, and Web of Science.

STUDY SELECTION

Randomised controlled trials conducted in low or middle income countries in which an orally administered antibacterial agent was allocated by randomisation or minimisation and growth was measured as an outcome. Participants aged 1 month to 12 years were included. Control was placebo or non-antimicrobial intervention.

RESULTS

Data were pooled from 10 randomised controlled trials representing 4316 children, across a variety of antibiotics, indications for treatment, treatment regimens, and countries. In random effects models, antibiotic use increased height by 0.04 cm/month (95% confidence interval 0.00 to 0.07) and weight by 23.8 g/month (95% confidence interval 4.3 to 43.3). After adjusting for age, effects on height were larger in younger populations and effects on weight were larger in African studies compared with other regions.

CONCLUSION

Antibiotics have a growth promoting effect in prepubertal children in low and middle income countries. This effect was more pronounced for ponderal than for linear growth. The antibiotic growth promoting effect may be mediated by treatment of clinical or subclinical infections or possibly by modulation of the intestinal microbiota. Better definition of the mechanisms underlying this effect will be important to inform optimal and safe approaches to achieving healthy growth in vulnerable populations.

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 dynamic microbiome

While our genomes are essentially static, our microbiomes are inherently dynamic. The microbial communities we harbor in our bodies change throughout our lives due to many factors, including maturation during childhood, alterations in our diets, travel, illnesses, and medical treatments. Moreover, there is mounting evidence that our microbiomes change us, by promoting health through their beneficial actions or by increasing our susceptibility to diseases through a process termed dysbiosis. Recent technological advances are enabling unprecedentedly detailed studies of the dynamics of the microbiota in animal models and human populations. This review will highlight key areas of investigation in the field, including establishment of the microbiota during early childhood, temporal variability of the microbiome in healthy adults, responses of the microbiota to intentional perturbations such as antibiotics and dietary changes, and prospective analyses linking changes in the microbiota to host disease status. Given the importance of computational methods in the field, this review will also discuss issues and pitfalls in the analysis of microbiome time-series data, and explore several promising new directions for mathematical model and algorithm development.

Environmental triggers for IBD

The fundamental elucidation of how environmental influences provoke the initiation of disease as well as flares of inflammatory bowel disease (IBD) remains incomplete. The current understanding of these diseases suggests that ulcerative colitis (UC) and Crohn's disease (CD) result from poorly defined interactions between genetic and environmental factors which culminate in the pathologic effects and clinical manifestations of these diseases. The genetic variant appears not sufficient itself to lead to the development of the clinical disease, but likely must combine with the environmental factors. The intestinal microbiome is pivotal to IBD development. A greater understanding of the contribution of these factors to dysbiosis is critical, and we aspire to restoring a healthy microbiome to treat flares and ideally prevent the development of IBD and its complications. This article aims to place the environmental influences in the context of their potential contribution to the development of the pathophysiology of IBD.

Mathematical modeling of primary succession of murine intestinal microbiota

Understanding the nature of interpopulation interactions in host-associated microbial communities is critical to understanding gut colonization, responses to perturbations, and transitions between health and disease. Characterizing these interactions is complicated by the complexity of these communities and the observation that even if populations can be cultured, their in vitro and in vivo phenotypes differ significantly. Dynamic models are the cornerstone of computational systems biology and a key objective of computational systems biologists is the reconstruction of biological networks (i.e., network inference) from high-throughput data. When such computational models reflect biology, they provide an opportunity to generate testable hypotheses as well as to perform experiments that are impractical or not feasible in vivo or in vitro. We modeled time-series data for murine microbial communities using statistical approaches and systems of ordinary differential equations. To obtain the dense time-series data, we sequenced the 16S ribosomal RNA (rRNA) gene from DNA isolated from the fecal material of germfree mice colonized with cecal contents of conventionally raised animals. The modeling results suggested a lack of mutualistic interactions within the community. Among the members of the Bacteroidetes, there was evidence for closely related pairs of populations to exhibit parasitic interactions. Among the Firmicutes, the interactions were all competitive. These results suggest future animal and in silico experiments. Our modeling approach can be applied to other systems to provide a greater understanding of the dynamics of communities associated with health and disease.

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.

Colonization resistance and microbial ecophysiology: using gnotobiotic mouse models and single-cell technology to explore the intestinal jungle

The highly diverse intestinal microbiota forms a structured community engaged in constant communication with itself and its host and is characterized by extensive ecological interactions. A key benefit that the microbiota affords its host is its ability to protect against infections in a process termed colonization resistance (CR), which remains insufficiently understood. In this review, we connect basic concepts of CR with new insights from recent years and highlight key technological advances in the field of microbial ecology. We present a selection of statistical and bioinformatics tools used to generate hypotheses about synergistic and antagonistic interactions in microbial ecosystems from metagenomic datasets. We emphasize the importance of experimentally testing these hypotheses and discuss the value of gnotobiotic mouse models for investigating specific aspects related to microbiota-host-pathogen interactions in a well-defined experimental system. We further introduce new developments in the area of single-cell analysis using fluorescence in situ hybridization in combination with metabolic stable isotope labeling technologies for studying the in vivo activities of complex community members. These approaches promise to yield novel insights into the mechanisms of CR and intestinal ecophysiology in general, and give researchers the means to experimentally test hypotheses in vivo at varying levels of biological and ecological complexity.