Cross-sectional associations between the gut microbe Ruminococcus gnavus and features of the metabolic syndrome: the HUNT study

Grahnemo, Louise; Nethander, Maria; Coward, Eivind; Gabrielsen, Maiken Elvestad; Sree, Satya; Billod, Jean-Marc; Engstrand, Lars; Abrahamsson, Sanna; Hveem, Kristian; Ohlsson, Claes

doi:10.1016/s2213-8587(22)00113-9

Cited by 43 publications

(36 citation statements)

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“…We observed that the abundance of Ruminococcus-1 , Akkermansia , Lachnospiraceae_NK4A136 _group , Christensenellaceae were decreased in the simple obesity group in our study. The latest research suggests that Ruminococcus gnavus was positively associated with percent body fat [ 18 ], inconsistent with our research. Contrary results may be related to different recruitment criteria.…”

Section: Discussioncontrasting

confidence: 99%

“…The limitation is that the analysis was unable to provide species-level information. Almost at the same time, a large cohort was employed to analyze the abundance of 50 prevalent and well-characterized gut microbes using a validated quantitative PCR method [ 18 ]. The limitation of this study is that analysis by bioinformatics methods to identify specific genes and pathways was not performed.…”

Section: Introductionmentioning

confidence: 99%

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Gut Microbiota Characteristics of People with Obesity by Meta-Analysis of Existing Datasets

et al. 2022

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Obesity is a complex chronic, relapsing, progressive disease. Association studies have linked microbiome alterations with obesity and overweight. However, the results are not always consistent. An integrated analysis of 4282 fecal samples (2236 control (normal weight) group, 1152 overweight, and 894 simple obesity) was performed to identify obesity-associated microbial markers. Based on a random effects model and a fixed effects model, we calculated the odds ratios of the metrics, including bacterial alpha-diversity, beta-diversity, Bacteroidetes/Firmicutes ratio, common genera, and common pathways, between the simple obesity and control groups as well as the overweight and control groups. The random forest model was trained based on a single dataset at the genus level. Feature selection based on feature importance ranked by mean decrease accuracy and leave-one-out cross-validation was conducted to improve the predictive performance of the models. Chao1 and evenness possessed significant ORs higher than 1.0 between the obesity and control groups. Significant bacterial community differences were observed between the simple obesity and the control. The ratio of Bacteroidetes/Firmicutes was significantly higher in simple obesity patients. The relative abundance of Lachnoclostridium and Faecalitalea were higher in people with simple obesity, while 23 genera, including Christensenellaceae_R-7_group, Akkermansia, Alistipes, and Butyricimonas, etc., were significantly lower. The random forest model achieved a high accuracy (AUC = 0.83). The adenine and adenosine salvage pathway (PWY-6609) and the L-histidine degradation I pathway (HISDEG-PWY) were clustered in obese patients, while amino acid biosynthesis and degradation pathways (HISDEG-PWY, DAPLYSINESYN-PWY) were decreased. This study identified obesity microbial biomarkers, providing fertile targets for the management of obesity.

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Section: Discussioncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gut Microbiota Characteristics of People with Obesity by Meta-Analysis of Existing Datasets

et al. 2022

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“…Several CRC-related gut microbiome research shows it has a significantly higher abundance in healthy individuals [38, 39]. R. gnavus is a well-known inflammatory polysaccharide producer associated with inflammatory bowel disease, and metabolic syndrome [40, 41]. The abundance of R. gnavus was reported to have a significant negative correlation with colorectal tumor numbers [42].…”

Section: Resultsmentioning

confidence: 99%

GutMeta: online microbiome analysis and interactive visualization with build-in curated human gut microbiome database

Jiang

Wang

Che

et al. 2022

Preprint

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Background: The human gut microbiome is associated with numerous human diseases. The whole-genome shotgun metagenomics sequencing helps accumulate a massive amount of gut microbiome data. However, few curated integrated platforms are available to explore the vast dataset. Advances in data generation pose new challenges to researchers attempting to analyze, visualize, and reuse published data. Result: GutMeta (human GUT whole-genome shotgun METAgenomics data analysis platform) is a one-stop online human gut metagenomic research platform that integrates a curated database, analyses, and visualizations. First, we built the Human Gut Metagenomics Database (HGMD), which contained taxonomy profiling and metadata of the metagenomics. HGMD collected the published human gut microbiome samples with whole metagenome shotgun (WMGS) sequencing data and consistently performed taxonomy classification using MetaPhlan3 for each sample. The various related metadata information was curated, and phenotypes were according to the MeSH ID. At this moment, HGMD contains 20,898 samples from 91 projects related to 65 diseases. Embedded tools could help users to explore the samples by keywords. Second, GutMeta provides researchers with user-friendly metagenomics analysis modules, including community diversity calculation, differential testing, dimension reduction, disease classifier construction, etc. Then, GutMeta provides corresponding interactive visualizations which can download as Scalable Vector Graphics (SVG), providing high-quality images. Further, GutMeta supplies two additional visualizations for the multi-level taxonomy overview for advanced investigations. GutMeta also supports online editing, including attribute adjustment, recoloring, reordering, and drag-and-drop. Third, GutMeta supports users in building their metagenomics analysis workspaces, including standard profiles uploading and built-in HGMD data import for online customized analyses and visualization. Conclusion: GutMeta offers a solution to improve reproducibility in metagenomic research, with the standardized procedure from input data to downstream analysis and visualization. GutMeta is a free access analysis platform that integrates human gut WMGS sequencing data, nine online bioinformatics analysis and data visualization modules/pipelines, and a customized workspace. GutMeta is avaiable at https://GutMeta.deepomics.org.

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“…Hydrogen sulfide produced by B. wadsworthia is a well-known pro-inflammatory factor ( 59 , 60 ). Ruminococcus gnavus is connected to Crohn's disease through its pro-inflammatory product, glucorhamnan polysaccharide ( 61 ), and is associated with metabolic syndrome ( 62 ). By contrast, the microbiome of esports players and students was enriched in beneficial Bacteroides species: B. thetaiotaomicron contributes to an increase in mucus production and strengthening of the epithelial barrier ( 63 ), and has anti-inflammatory properties that inhibit the development of Crohn's disease ( 64 ), while B. uniformis is negatively correlated with physical activity in older adults ( 65 ), alleviates obesity in conjunction with an appropriate diet ( 66 ) and enhances endurance exercise performance ( 67 ).…”

Section: Discussionmentioning

confidence: 99%

Characteristics of the gut microbiome in esports players compared with those in physical education students and professional athletes

et al. 2023

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IntroductionEsports is a category of competitive video games that, in many aspects, may be similar to traditional sports; however, the gut microbiota composition of players has not been yet studied.Materials and methodsHere, we investigated the composition and function of the gut microbiota, as well as short chain fatty acids (SCFAs), and amino acids, in a group of 109 well-characterized Polish male esports players. The results were compared with two reference groups: 25 endurance athletes and 36 healthy students of physical education. DNA and metabolites isolated from fecal samples were analyzed using shotgun metagenomic sequencing and mass spectrometry, respectively. Physical activity and nutritional measures were evaluated by questionnaire.ResultsAlthough anthropometric, physical activity and nutritional measures differentiated esports players from students, there were no differences in bacterial diversity, the Bacteroidetes/Firmicutes ratio, the composition of enterotype clusters, metagenome functional content, or SCFA concentrations. However, there were significant differences between esports players and students with respect to nine bacterial species and nine amino acids. By contrast, all of the above-mentioned measures differentiated professional athletes from esports players and students, with 45 bacteria differentiating professional athletes from the former and 31 from the latter. The only species differentiating all three experimental groups was Parabacteroides distasonis, showing the lowest and highest abundance in esports players and athletes, respectively.ConclusionOur study confirms the marked impact of intense exercise training on gut microbial structure and function. Differences in lifestyle and dietary habits between esports players and physical education students appear to not have a major effect on the gut microbiota.

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Cross-sectional associations between the gut microbe Ruminococcus gnavus and features of the metabolic syndrome: the HUNT study

Cited by 43 publications

References 10 publications

Gut Microbiota Characteristics of People with Obesity by Meta-Analysis of Existing Datasets

Gut Microbiota Characteristics of People with Obesity by Meta-Analysis of Existing Datasets

GutMeta: online microbiome analysis and interactive visualization with build-in curated human gut microbiome database

Characteristics of the gut microbiome in esports players compared with those in physical education students and professional athletes

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