Lanthanide-dependent cross-feeding of methane-derived carbon is linked by microbial community interactions

The utilization of methane, a potent greenhouse gas, is an important component of local and global carbon cycles that is characterized by tight linkages between methane-utilizing (methanotrophic) and nonmethanotrophic bacteria. It has been suggested that the methanotroph sustains these nonmethanotrophs by cross-feeding, because subsequent products of the methane oxidation pathway, such as methanol, represent alternative carbon sources. We established cocultures in a microcosm model system to determine the mechanism and substrate that underlay the observed cross-feeding in the environment. Lanthanum, a rare earth element, was applied because of its increasing importance in methylotrophy. We used co-occurring strains isolated from Lake Washington sediment that are involved in methane utilization: a methanotroph and two nonmethanotrophic methylotrophs. Gene-expression profiles and mutant analyses suggest that methanol is the dominant carbon and energy source the methanotroph provides to support growth of the nonmethanotrophs. However, in the presence of the nonmethanotroph, gene expression of the dominant methanol dehydrogenase (MDH) shifts from the lanthanide-dependent MDH (XoxF)-type, to the calcium-dependent MDH (MxaF)-type. Correspondingly, methanol is released into the medium only when the methanotroph expresses the MxaF-type MDH. These results suggest a cross-feeding mechanism in which the nonmethanotrophic partner induces a change in expression of methanotroph MDHs, resulting in release of methanol for its growth. This partner-induced change in gene expression that benefits the partner is a paradigm for microbial interactions that cannot be observed in studies of pure cultures, underscoring the importance of synthetic microbial community approaches to understand environmental microbiomes.

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Gene expression profiles based flux balance model to predict the carbon source for Bacillus subtilis

10.1101/842518 ◽

2019 ◽

Author(s):

Kulwadee Thanamit ◽

Franziska Hoerhold ◽

Marcus Oswald ◽

Rainer Koenig

Keyword(s):

Gene Expression ◽

Carbon Source ◽

Drug Targets ◽

Expression Profiles ◽

Carbon Sources ◽

Gene Expression Profiles ◽

Fast Method ◽

Flux Balance ◽

Metabolic Fluxes

ABSTRACTFinding drug targets for antimicrobial treatment is a central focus in biomedical research. To discover new drug targets, we developed a method to identify which nutrients are essential for microorganisms. Using 13C labeled metabolites to infer metabolic fluxes is the most informative way to infer metabolic fluxes to date. However, the data can get difficult to acquire in complicated environments, for example, if the pathogen homes in host cells. Although data from gene expression profiling is less informative compared to metabolic tracer derived data, its generation is less laborious, and may still provide the relevant information. Besides this, metabolic fluxes have been successfully predicted by flux balance analysis (FBA). We developed an FBA based approach using the stoichiometric knowledge of the metabolic reactions of a cell combining them with expression profiles of the coding genes. We aimed to identify essential drug targets for specific nutritional uptakes of microorganisms. As a case study, we predicted each single carbon source out of a pool of eight different carbon sources for B. subtilis based on gene expression profiles. The models were in good agreement to models basing on 13C metabolic flux data of the same conditions. We could well predict every carbon source. Later, we applied successfully the model to unseen data from a study in which the carbon source was shifted from glucose to malate and vice versa. Technically, we present a new and fast method to reduce thermodynamically infeasible loops, which is a necessary preprocessing step for such model-building algorithms.SIGNIFICANCEIdentifying metabolic fluxes using 13C labeled tracers is the most informative way to gain insight into metabolic fluxes. However, obtaining the data can be laborious and challenging in a complex environment. Though transcriptional data is an indirect mean to estimate the fluxes, it can help to identify this. Here, we developed a new method employing constraint-based modeling to predict metabolic fluxes embedding gene expression profiles in a linear regression model. As a case study, we used the data from Bacillus subtilis grown under different carbon sources. We could well predict the correct carbon source. Additionally, we established a novel and fast method to remove thermodynamically infeasible loops.

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The influence of rosuvastatin on the gastrointestinal microbiota and host gene expression profiles

AJP Gastrointestinal and Liver Physiology ◽

10.1152/ajpgi.00149.2016 ◽

2017 ◽

Vol 312 (5) ◽

pp. G488-G497 ◽

Cited By ~ 17

Author(s):

J. A. Nolan ◽

P. Skuse ◽

K. Govindarajan ◽

E. Patterson ◽

N. Konstantinidou ◽

...

Keyword(s):

Gene Expression ◽

Community Structure ◽

Bile Acid ◽

Microbial Community ◽

Gut Microbiota ◽

Acid Metabolism ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Bile Acid Metabolism ◽

Gastrointestinal Microbiota

Statins are the most widely prescribed medications worldwide for the treatment of hypercholesterolemia. They inhibit the activity of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMG-R), an enzyme involved in cholesterol synthesis in higher organisms and in isoprenoid biosynthesis in some bacteria. We hypothesized that statins may influence the microbial community in the gut through either direct inhibition or indirect mechanisms involving alterations to host responses. We therefore examined the impact of rosuvastatin (RSV) on the community structure of the murine gastrointestinal microbiota. RSV was orally administered to mice and the effects on the gut microbiota, host bile acid profiles, and markers of inflammation were analyzed. RSV significantly influenced the microbial community in both the cecum and feces, causing a significant decrease in α-diversity in the cecum and resulting in a reduction of several physiologically relevant bacterial groups. RSV treatment of mice significantly affected bile acid metabolism and impacted expression of inflammatory markers known to influence microbial community structure (including RegIIIγ and Camp) in the gut. This study suggests that a commonly used statin (RSV) leads to an altered gut microbial composition in normal mice with attendant impacts on local gene expression profiles, a finding that should prompt further studies to investigate the implications of statins for gut microbiota stability and health in humans. NEW & NOTEWORTHY This work demonstrates that rosuvastatin administration in mice affects the gastrointestinal microbiota, influences bile acid metabolism, and alters transcription of genes encoding factors involved in gut homeostasis and immunity in the gastrointestinal tract.

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