Dataset Information

Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort.

ABSTRACT:

Background

The gut microbiome is a complex and metabolically active community that directly influences host phenotypes. In this study, we profile gut microbiota using 16S rRNA gene sequencing in 531 well-phenotyped Finnish men from the Metabolic Syndrome In Men (METSIM) study.

Results

We investigate gut microbiota relationships with a variety of factors that have an impact on the development of metabolic and cardiovascular traits. We identify novel associations between gut microbiota and fasting serum levels of a number of metabolites, including fatty acids, amino acids, lipids, and glucose. In particular, we detect associations with fasting plasma trimethylamine N-oxide (TMAO) levels, a gut microbiota-dependent metabolite associated with coronary artery disease and stroke. We further investigate the gut microbiota composition and microbiota-metabolite relationships in subjects with different body mass index and individuals with normal or altered oral glucose tolerance. Finally, we perform microbiota co-occurrence network analysis, which shows that certain metabolites strongly correlate with microbial community structure and that some of these correlations are specific for the pre-diabetic state.

Conclusions

Our study identifies novel relationships between the composition of the gut microbiota and circulating metabolites and provides a resource for future studies to understand host-gut microbiota relationships.

SUBMITTER: Org E

PROVIDER: S-EPMC5390365 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort.

Org Elin E Blum Yuna Y Kasela Silva S Mehrabian Margarete M Kuusisto Johanna J Kangas Antti J AJ Soininen Pasi P Wang Zeneng Z Ala-Korpela Mika M Hazen Stanley L SL Laakso Markku M Lusis Aldons J AJ

Genome biology 20170413 1

<h4>Background</h4>The gut microbiome is a complex and metabolically active community that directly influences host phenotypes. In this study, we profile gut microbiota using 16S rRNA gene sequencing in 531 well-phenotyped Finnish men from the Metabolic Syndrome In Men (METSIM) study.<h4>Results</h4>We investigate gut microbiota relationships with a variety of factors that have an impact on the development of metabolic and cardiovascular traits. We identify novel associations between gut microbi ...[more]

PMID: 28407784

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Project description:ObjectiveGut dysbiosis and metabolic abnormalities have been implicated in HIV infection. However, the exact causal relationships among the gut microbiota, metabolites, and HIV infection remain poorly understood. Our study involving Mendelian randomization (MR) and mediation analysis aims to unveil these causalities.MethodsGenetic instrumental variables for the gut microbiota were retrieved from MiBioGen consortium (n = 18,340). Metabolism-related genetic variants were sourced from the CLSA cohort (n = 8299). GWAS summary statistics for symptomatic HIV infection were derived from the FinnGen study (n = 309,154), and the UK Biobank (n = 208,808). We performed the bidirectional two-sample MR to assess causalities with the inverse-variance weighted (IVW) method as the primary analysis. Moreover, we executed a mediation analysis using two-step MR methods.ResultsCompared to the causal effects of HIV infection on gut microbiota (or metabolites), those of gut microbiota (or plasma metabolites) on the risk of HIV infection were more substantial. Phylum Proteobacteria (OR: 2.114, 95% CI 1.042-4.288, P = 0.038), and genus Ruminococcaceae UCG013 (OR: 2.127, 95% CI 1.080-4.191, P = 0.029) exhibited an adverse causal effect on HIV infection, whereas genus Clostridium sensu stricto 1(OR: 0.491, 95% CI 0.252-0.956, P = 0.036) and family Erysipelotrichaceae (OR: 0.399, 95% CI 0.193-0.827, P = 0.013) acted as significant protective factors for HIV. The salicyluric glucuronide level (OR = 2.233, 95% CI 1.120-4.453, P = 0.023) exhibited a considerably adverse causal effect on HIV infection. Conversely, the salicylate-to-citrate ratio (OR: 0.417, 95% CI 0.253-0.688, P = 0.001) was identified as a protective factor for HIV. We identified only one bidirectional causality between 1-palmitoyl-GPI and HIV infection. Mechanistically, genus Haemophilus mediated the causal effects of three phospholipids on HIV infection risk: 1-palmitoyl-GPI (mediation proportion = 33.7%, P = 0.018), 1-palmitoyl-2-arachidonoyl-GPI (mediation proportion = 18.3%, P = 0.019), and 1-linoleoyl-2-linolenoyl-GPC (mediation proportion = 20.3%, P = 0.0216). Additionally, 5-Dodecenoylcarnitine (C12:1) mediated the causal effect of genus Sellimonas on the risk of HIV infection (mediation proportion = 13.7%, P = 0.0348).ConclusionOur study revealed that gut microbiota and metabolites causally influence HIV infection risk more substantially than the reverse. We identified the bidirectional causality between 1-palmitoyl-GPI (16:0) and HIV infection, and elucidated four mediation relationships. These findings provide genetic insights into prediction, prevention, and personalized medicine of HIV infection.

Project description:In humans, the metabolic and immune changes occurring during perinatal period also describe metabolic syndrome. Gut microbiota can cause symptoms of metabolic syndrome in pregnant women. Increased gut permeability is also involved in metabolic disorders in non-pregnant hosts. However, longitudinal studies investigating the changes in metabolic characteristics, gut microbiota, and gut permeability of sows throughout pregnancy and lactation are lacking. The correlation between gut microbiota and metabolic status of sows is also poorly known. The present study was conducted to investigate the temporal variations in sow metabolic characteristics, gut microbiota, gut permeability, and gut inflammation at days 30 (G30) and 109 (G109) of gestation and days 3 (L3) and 14 (L14) of lactation. Results showed that insulin sensitivity was decreased in L3. Circulating concentrations of pro-inflammatory cytokine IL-6 increased in G109 and L3. 16S rRNA gene sequencing of the V3-V4 region showed that gut microbiota changed dramatically across different reproductive stages. The bacterial abundance and alpha diversity in L3 were the lowest. The phyla Proteobacteria and Fusobacteria exhibited the highest relative abundance in L3. Among the genera, Bacteroides, Escherichia_Shigella, and Fusobacterium were highest, but Oscillospira the lowest, in relative abundance in L3. The fecal levels of acetate and total short-chain fatty acids were increased in G109, but fecal butyrate concentrations were markedly decreased in L3. The plasma zonulin concentrations, a biomarker for gut permeability, were increased in G109 and L3. The plasma endotoxin concentrations were increased in L3. Furthermore, levels of fecal lipocalin-2 and pro-inflammatory cytokines IL-6 and TNF-α were increased in G109 and L3. In contrast, fecal levels of anti-inflammatory cytokine IL-10 were significantly decreased in G109 and L3. Additionally, the increased relative abundances of Fusobacterium in L3 were positively correlated with plasma zonulin and fecal endotoxin but negatively correlated with fecal IL-10. These findings indicate that the mother sow exhibits a metabolic syndrome and dramatical changes in gut microbiota during perinatal period, especially in early lactation. Besides, increased gut permeability and plasma endotoxin concentrations caused by negative microbial changes would possibly be the potential mechanisms under which sow's metabolic disorders and inflammatory status were exacerbated during early lactation.

Project description:BackgroundThe prevalence of metabolic syndrome is on the rise globally. Understanding the etiology and discovering potential treatment target have become a priority. Observational data have linked gut microbiota with metabolic syndrome and its comprising traits. However, whether these relations underlie causal effects remains unclear.MethodsUsing Inver Variance Weighted (IVW) as primary analysis method, we performed two-sample Mendelian Randomization (MR) analyses to explore the causal relationship between gut microbiota and metabolic syndrome with its comprising traits. Methods including MR-Egger regression, MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO), Weighted Mode, and Weighted Median were chosen for additional MR analysis to test the robustness of MR results. Cochran's IVW Q test and leave-one-out IVW analysis tested the heterogeneity among instrumental variables (IVs). Steiger filtering was utilized to exclude all IVs with reverse causality. Genome-wide association study (GWAS) data used in this study were all from the largest respective GWAS studies available.ResultsOut of 1172 tests, a total of 16 associations with evidence of causality were identified after sensitivity analyses, but only 3 remained after multiple testing correction. Class Melainabacteria (β = 0.02, adjusted P = 0.01) with affiliated order Gastranaerophilales (β = 0.02, adjusted P = 1.20*10- 3) and genus Eubacterium hallii (β = 0.03, adjusted P = 0.03) showed a positive effect on abdominal obesity. All effect sizes were small (abs(β) < 0.1). All causal relationships identified were unidirectional.ConclusionsGiven the study's limitations, we found little evidence supporting a large causal effect, i.e. absolute effect size > 0.1, of gut microbial taxa abundance on metabolic syndrome and its comprising traits. This study also suggests that previously reported associations between gut microbiota and metabolic syndrome with its comprising traits may not necessarily lead to causal relations.Clinical trial numberNot applicable.

Project description:Berberine is an isoquinoline alkaloid found in plants. It presents a wide range of pharmacological activities, including anti-inflammatory and antioxidant properties, despite a low oral bioavailability. Growing evidence suggests that the gut microbiota is the target of berberine, and that the microbiota metabolizes berberine to active metabolites, although little evidence exists in the specific species involved in its therapeutic effects. This study was performed to detail the bidirectional interactions of berberine with the broiler chicken gut microbiota, including the regulation of gut microbiota composition and metabolism by berberine and metabolization of berberine by the gut microbiota, and how they contribute to berberine-mediated effects on gut health. As previous evidence showed that high concentrations of berberine may induce dysbiosis, low (0.1 g/kg feed), middle (0.5 g/kg feed) and high (1 g/kg feed) doses were here investigated. Low and middle doses of in-feed berberine stimulated potent beneficial bacteria from the Lachnospiraceae family in the large intestine of chickens, while middle and high doses tended to increase villus length in the small intestine. Plasma levels of the berberine-derived metabolites berberrubine, thalifendine and demethyleneberberine were positively correlated with the villus length of chickens. Berberrubine and thalifendine were the main metabolites of berberine in the caecum, and they were produced in vitro by the caecal microbiota, confirming their microbial origin. We show that members of the genus Blautia could demethylate berberine into mainly thalifendine, and that this reaction may stimulate the production of short-chain fatty acids (SCFAs) acetate and butyrate, via acetogenesis and cross-feeding respectively. We hypothesize that acetogens such as Blautia spp. are key bacteria in the metabolization of berberine, and that berberrubine, thalifendine and SCFAs play a significant role in the biological effect of berberine.

Project description:Background and purposeWe investigated the causal relationships between the gut microbiota (GM), stroke, and potential metabolite mediators using Mendelian randomization (MR).MethodsWe leveraged the summary statistics of GM (n=18,340 in the MiBioGen consortium), blood metabolites (n=115,078 in the UK Biobank), and stroke (cases n=60,176 and controls n=1,310,725 in the Global Biobank Meta-Analysis Initiative) from the largest genome-wide association studies to date. We performed bidirectional MR analyses to explore the causal relationships between the GM and stroke, and two mediation analyses, two-step MR and multivariable MR, to discover potential mediating metabolites.ResultsTen taxa were causally associated with stroke, and stroke led to changes in 27 taxa. In the two-step MR, Bifidobacteriales order, Bifidobacteriaceae family, Desulfovibrio genus, apolipoprotein A1 (ApoA1), phospholipids in high-density lipoprotein (HDL_PL), and the ratio of apolipoprotein B to ApoA1 (ApoB/ApoA1) were causally associated with stroke (all P<0.044). The causal associations between Bifidobacteriales order, Bifidobacteriaceae family and stroke were validated using the weighted median method in an independent cohort. The three GM taxa were all positively associated with ApoA1 and HDL_PL, whereas Desulfovibrio genus was negatively associated with ApoB/ApoA1 (all P<0.010). Additionally, the causal associations between the three GM taxa and ApoA1 remained significant after correcting for the false discovery rate (all q-values <0.027). Multivariable MR showed that the associations between Bifidobacteriales order, Bifidobacteriaceae family and stroke were mediated by ApoA1 and HDL_PL, each accounting for 6.5% (P=0.028) and 4.6% (P=0.033); the association between Desulfovibrio genus and stroke was mediated by ApoA1, HDL_PL, and ApoB/ApoA1, with mediated proportions of 7.6% (P=0.019), 4.2% (P=0.035), and 9.1% (P=0.013), respectively.ConclusionThe current MR study provides evidence supporting the causal relationships between several specific GM taxa and stroke and potential mediating metabolites.

Dataset Information

Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort.

Background

Results

Conclusions

Publications

Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort.

Similar Datasets

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