Dataset Information

Two dynamic regimes in the human gut microbiome.

ABSTRACT: The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)-a multivariate method developed for econometrics-to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes.

SUBMITTER: Gibbons SM

PROVIDER: S-EPMC5340412 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Two dynamic regimes in the human gut microbiome.

Gibbons Sean M SM Kearney Sean M SM Smillie Chris S CS Alm Eric J EJ

PLoS computational biology 20170221 2

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most o ...[more]

PMID: 28222117

Similar Datasets

Project description:Precision medicine is currently a topic of great interest in clinical and intervention science. A key component of precision medicine is that it is evidence-based, i.e., data-driven, and consequently there has been tremendous interest in estimation of precision medicine strategies using observational or randomized study data. One way to formalize precision medicine is through a treatment regime, which is a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended treatment. An optimal treatment regime is defined as maximizing the mean of some cumulative clinical outcome if applied to a population of interest. It is well-known that even under simple generative models an optimal treatment regime can be a highly nonlinear function of patient information. Consequently, a focal point of recent methodological research has been the development of flexible models for estimating optimal treatment regimes. However, in many settings, estimation of an optimal treatment regime is an exploratory analysis intended to generate new hypotheses for subsequent research and not to directly dictate treatment to new patients. In such settings, an estimated treatment regime that is interpretable in a domain context may be of greater value than an unintelligible treatment regime built using 'black-box' estimation methods. We propose an estimator of an optimal treatment regime composed of a sequence of decision rules, each expressible as a list of "if-then" statements that can be presented as either a paragraph or as a simple flowchart that is immediately interpretable to domain experts. The discreteness of these lists precludes smooth, i.e., gradient-based, methods of estimation and leads to non-standard asymptotics. Nevertheless, we provide a computationally efficient estimation algorithm, prove consistency of the proposed estimator, and derive rates of convergence. We illustrate the proposed methods using a series of simulation examples and application to data from a sequential clinical trial on bipolar disorder.

Project description:BackgroundMicrobiome studies suggest the presence of an interaction between the human gut microbiome and soil-transmitted helminth. Upon deworming, a complex interaction between the anthelminthic drug, helminths and microbiome composition might occur. To dissect this, we analyse the changes that take place in the gut bacteria profiles in samples from a double blind placebo controlled trial conducted in an area endemic for soil transmitted helminths in Indonesia.MethodsEither placebo or albendazole were given every three months for a period of one and a half years. Helminth infection was assessed before and at 3 months after the last treatment round. In 150 subjects, the bacteria were profiled using the 454 pyrosequencing. Statistical analysis was performed cross-sectionally at pre-treatment to assess the effect of infection, and at post-treatment to determine the effect of infection and treatment on microbiome composition using the Dirichlet-multinomial regression model.ResultsAt a phylum level, at pre-treatment, no difference was seen in microbiome composition in terms of relative abundance between helminth-infected and uninfected subjects and at post-treatment, no differences were found in microbiome composition between albendazole and placebo group. However, in subjects who remained infected, there was a significant difference in the microbiome composition of those who had received albendazole and placebo. This difference was largely attributed to alteration of Bacteroidetes. Albendazole was more effective against Ascaris lumbricoides and hookworms but not against Trichuris trichiura, thus in those who remained infected after receiving albendazole, the helminth composition was dominated by T. trichiura.DiscussionWe found that overall, albendazole does not affect the microbiome composition. However, there is an interaction between treatment and helminths as in subjects who received albendazole and remained infected there was a significant alteration in Bacteroidetes. This helminth-albendazole interaction needs to be studied further to fully grasp the complexity of the effect of deworming on the microbiome.Trial registrationISRCTN Registy, ISRCTN83830814.

Project description:Iron is an essential micronutrient for life. In mammals, dietary iron is absorbed primarily in the small intestine. Currently, the impacts of dietary iron on the taxonomic structure and function of the gut microbiome and reciprocal effects on the animal host are not well understood. Here, we establish a mouse model of low-iron challenge in which intestinal biomarkers and reduced fecal iron reveal iron stress while serum iron and mouse behavioral markers indicate maintenance of iron homeostasis. We show that the diversity of the gut microbiome in conventional C57BL/6 mice changes dramatically during 2 weeks on a low-iron diet. We also show the effects of a low-iron diet on microbiome diversity are long lasting and not easily recovered when iron is returned to the diet. Finally, after optimizing taxon association methods, we show that some bacteria are unable to fully recover after the low-iron challenge and appear to be extirpated from the gut entirely. In particular, operational taxonomic units (OTUs) from the Prevotellaceae and Porphyromonadaceae families and Bacteroidales order are highly sensitive to low-iron conditions, while other seemingly insensitive OTUs recover. These results provide new insights into the iron requirements of gut microbiome members and add to the growing understanding of mammalian iron cycling.IMPORTANCE All cells need iron. Both too much and too little iron lead to diseases and unwanted outcomes. Although the impact of dietary iron on human cells and tissues has been well studied, there is currently a lack of understanding about how different levels of iron influence the abundant and diverse members of the human microbiome. This study develops a well-characterized mouse model for studying low-iron levels and identifies key groups of bacteria that are most affected. We found that the microbiome undergoes large changes when iron is removed from the diet but that many individual bacteria are able to rebound when iron levels are changed back to normal. That said, a select few members, referred to as iron-sensitive bacteria, seem to be lost. This study begins to identify individual members of the mammalian microbiome most affected by changes in dietary iron levels.

Dataset Information

Two dynamic regimes in the human gut microbiome.

Publications

Two dynamic regimes in the human gut microbiome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets