Mapping the Inner Workings of the Microbiome: Genomic- and Metagenomic-Based Study of Metabolism and Metabolic Interactions in the Human Microbiome

Manor, Ohad; Levy, Roie; Borenstein, Elhanan

doi:10.1016/j.cmet.2014.07.021

Cited by 79 publications

(67 citation statements)

References 142 publications

(164 reference statements)

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“…A downside of this method is the inherent assumption that one species would sacrifice its own growth to increase the growth rate of another species (49). Another alternative would be fixing the growth rates of the included microbes at experimentally determined ratios (49).…”

Section: Discussionmentioning

confidence: 99%

Anoxic Conditions Promote Species-Specific Mutualism between Gut Microbes In Silico

Heinken

Thiele

2015

Appl Environ Microbiol

117

100

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bThe human gut is inhabited by thousands of microbial species, most of which are still uncharacterized. Gut microbes have adapted to each other's presence as well as to the host and engage in complex cross feeding. Constraint-based modeling has been successfully applied to predicting microbe-microbe interactions, such as commensalism, mutualism, and competition. Here, we apply a constraint-based approach to model pairwise interactions between 11 representative gut microbes. Microbe-microbe interactions were computationally modeled in conjunction with human small intestinal enterocytes, and the microbe pairs were subjected to three diets with various levels of carbohydrate, fat, and protein in normoxic or anoxic environments. Each microbe engaged in species-specific commensal, parasitic, mutualistic, or competitive interactions. For instance, Streptococcus thermophilus efficiently outcompeted microbes with which it was paired, in agreement with the domination of streptococci in the small intestinal microbiota. Under anoxic conditions, the probiotic organism Lactobacillus plantarum displayed mutualistic behavior toward six other species, which, surprisingly, were almost entirely abolished under normoxic conditions. This finding suggests that the anoxic conditions in the large intestine drive mutualistic cross feeding, leading to the evolvement of an ecosystem more complex than that of the small intestinal microbiota. Moreover, we predict that the presence of the small intestinal enterocyte induces competition over host-derived nutrients. The presented framework can readily be expanded to a larger gut microbial community. This modeling approach will be of great value for subsequent studies aiming to predict conditions favoring desirable microbes or suppressing pathogens.T he human intestine is inhabited by a complex ecosystem consisting of thousands of microbial species. Its collective genome (the microbiome) contains more than 150 times as many genes as our own genome (1). The community of gut microbes has coevolved with humans and has adapted to the competitive conditions in the intestine. Gut microbes differ in their metabolic potential to exploit various environmental conditions and to persist in the intestine (2). They respond differently to the availability of dietary nutrients (3). Moreover, the gut microbiota has to contend with a steep oxygen gradient, which ranges from a partial O 2 pressure of approximately 80 mm Hg to nearly anoxic conditions (4). The diverse physiology of the small intestine creates a wide range of local oxygen microenvironments that favor particular groups of bacteria (4). Furthermore, metabolic interaction patterns between microbes affect microbial growth. Generally, six types of speciesspecies interactions can be distinguished (5) (Fig. 1a). In the case of neutralism, two organisms do not depend on each other for growth. If shared resources become scarce, this condition leads to competition (5). In the gut, competition over fermentable carbohydrates typically arises (6). If only one...

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Section: Discussionmentioning

confidence: 99%

Anoxic Conditions Promote Species-Specific Mutualism between Gut Microbes In Silico

Heinken

Thiele

2015

Appl Environ Microbiol

117

100

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“…Advantages: Network interactions between microbiome components and their potential effects on the host can be elucidated [87]. Disadvantages: Analyses can be costly and time-consuming for whole-community metagenomics because deep sequencing is needed to capture contributions of rare community members, which can have important effects on microbiome function [88].…”

Section: Box 1 Principal Approaches To Investigate Microbiome Functionmentioning

confidence: 99%

Engineering Microbiomes to Improve Plant and Animal Health

Mueller

Sachs

2015

Trends in Microbiology

535

393

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“…This information would allow identification of diagnostic biomarkers and may provide insight into the role of the gut microbiota in disease progression Qin et al, 2014;Zeller et al, 2014). A predictive systems-level model of the human gut microbiome is required to elucidate causalities and quantify the interactions between microbes, host, and diet (Greenblum et al, 2013;Manor et al, 2014;Shoaie and Nielsen, 2014).…”

Section: Introductionmentioning

confidence: 99%

Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome

Shoaie¹,

Ghaffari²,

et al. 2015

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The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.

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Mapping the Inner Workings of the Microbiome: Genomic- and Metagenomic-Based Study of Metabolism and Metabolic Interactions in the Human Microbiome

Cited by 79 publications

References 142 publications

Anoxic Conditions Promote Species-Specific Mutualism between Gut Microbes In Silico

Anoxic Conditions Promote Species-Specific Mutualism between Gut Microbes In Silico

Engineering Microbiomes to Improve Plant and Animal Health

Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome

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