Predicting gut microbiota dynamics in obese individuals from cross-sectional data
Obesity affects approximately 39% of adults worldwide. While gut microbiota has been linked to obesity, most research has focused on static taxonomic composition rather than the dynamic interactions between microbial taxa. We applied BEEM-Static, a generalized Lotka-Volterra model, to cross-sectiona...
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| Published in | Frontiers in cellular and infection microbiology Vol. 15; p. 1485791 |
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| Language | English |
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| Abstract | Obesity affects approximately 39% of adults worldwide. While gut microbiota has been linked to obesity, most research has focused on static taxonomic composition rather than the dynamic interactions between microbial taxa.
We applied BEEM-Static, a generalized Lotka-Volterra model, to cross-sectional 16S rRNA gut microbiome data from six public datasets, comprising 2,435 profiles from lean and obese individuals.
A total of 57 significant microbial interactions were identified in obese individuals (79% negative), compared to 37 in lean individuals (92% negative). For example, Bacteroidetes showed a stronger inhibitory effect on Firmicutes in obese individuals (-0.41) than in lean ones (-0.26). Firmicutes and Proteobacteria exhibited consistently higher carrying capacities in obese populations.
These findings suggest that microbial interaction networks-not just taxonomic abundance-play a key role in obesity-related dysbiosis. Our approach enables the inference of microbiota dynamics from a single time point, paving the way for tailored dietary interventions, which we refer to as
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| AbstractList | IntroductionObesity affects approximately 39% of adults worldwide. While gut microbiota has been linked to obesity, most research has focused on static taxonomic composition rather than the dynamic interactions between microbial taxa.MethodsWe applied BEEM-Static, a generalized Lotka-Volterra model, to cross-sectional 16S rRNA gut microbiome data from six public datasets, comprising 2,435 profiles from lean and obese individuals.ResultsA total of 57 significant microbial interactions were identified in obese individuals (79% negative), compared to 37 in lean individuals (92% negative). For example, Bacteroidetes showed a stronger inhibitory effect on Firmicutes in obese individuals (−0.41) than in lean ones (−0.26). Firmicutes and Proteobacteria exhibited consistently higher carrying capacities in obese populations.DiscussionThese findings suggest that microbial interaction networks—not just taxonomic abundance—play a key role in obesity-related dysbiosis. Our approach enables the inference of microbiota dynamics from a single time point, paving the way for tailored dietary interventions, which we refer to as Optibiomics. Obesity affects approximately 39% of adults worldwide. While gut microbiota has been linked to obesity, most research has focused on static taxonomic composition rather than the dynamic interactions between microbial taxa. We applied BEEM-Static, a generalized Lotka-Volterra model, to cross-sectional 16S rRNA gut microbiome data from six public datasets, comprising 2,435 profiles from lean and obese individuals. A total of 57 significant microbial interactions were identified in obese individuals (79% negative), compared to 37 in lean individuals (92% negative). For example, Bacteroidetes showed a stronger inhibitory effect on Firmicutes in obese individuals (-0.41) than in lean ones (-0.26). Firmicutes and Proteobacteria exhibited consistently higher carrying capacities in obese populations. These findings suggest that microbial interaction networks-not just taxonomic abundance-play a key role in obesity-related dysbiosis. Our approach enables the inference of microbiota dynamics from a single time point, paving the way for tailored dietary interventions, which we refer to as . Obesity affects approximately 39% of adults worldwide. While gut microbiota has been linked to obesity, most research has focused on static taxonomic composition rather than the dynamic interactions between microbial taxa.IntroductionObesity affects approximately 39% of adults worldwide. While gut microbiota has been linked to obesity, most research has focused on static taxonomic composition rather than the dynamic interactions between microbial taxa.We applied BEEM-Static, a generalized Lotka-Volterra model, to cross-sectional 16S rRNA gut microbiome data from six public datasets, comprising 2,435 profiles from lean and obese individuals.MethodsWe applied BEEM-Static, a generalized Lotka-Volterra model, to cross-sectional 16S rRNA gut microbiome data from six public datasets, comprising 2,435 profiles from lean and obese individuals.A total of 57 significant microbial interactions were identified in obese individuals (79% negative), compared to 37 in lean individuals (92% negative). For example, Bacteroidetes showed a stronger inhibitory effect on Firmicutes in obese individuals (-0.41) than in lean ones (-0.26). Firmicutes and Proteobacteria exhibited consistently higher carrying capacities in obese populations.ResultsA total of 57 significant microbial interactions were identified in obese individuals (79% negative), compared to 37 in lean individuals (92% negative). For example, Bacteroidetes showed a stronger inhibitory effect on Firmicutes in obese individuals (-0.41) than in lean ones (-0.26). Firmicutes and Proteobacteria exhibited consistently higher carrying capacities in obese populations.These findings suggest that microbial interaction networks-not just taxonomic abundance-play a key role in obesity-related dysbiosis. Our approach enables the inference of microbiota dynamics from a single time point, paving the way for tailored dietary interventions, which we refer to as Optibiomics.DiscussionThese findings suggest that microbial interaction networks-not just taxonomic abundance-play a key role in obesity-related dysbiosis. Our approach enables the inference of microbiota dynamics from a single time point, paving the way for tailored dietary interventions, which we refer to as Optibiomics. |
| Author | Starcevic, Antonio Soljic, Irena Vuckovic, Tea Allen, Andrew P. Melvan, Ena |
| AuthorAffiliation | 3 Faculty of Food Technology and Biotechnology, University of Zagreb , Zagreb , Croatia 2 Research and Development Department , Metabelly, Split , Croatia 1 School of Natural Sciences, Macquarie University , Sydney, NSW , Australia |
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| Keywords | GLV method dietary interventions microbial interactions microbiome dynamics gut microbiota obesity personalized nutrition |
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| SubjectTerms | Adult Bacteria - classification Bacteria - genetics Bacteria - isolation & purification Bacteroidetes Cellular and Infection Microbiology Cross-Sectional Studies dietary interventions Dysbiosis - microbiology Female Firmicutes - genetics Firmicutes - isolation & purification Gastrointestinal Microbiome GLV method gut microbiota Humans Male Microbial Interactions obesity Obesity - microbiology personalized nutrition RNA, Ribosomal, 16S - genetics |
| Title | Predicting gut microbiota dynamics in obese individuals from cross-sectional data |
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