Advanced Glycation End Products (AGEs) in Diet and Skin in Relation to Stool Microbiota: The Rotterdam Study.

BACKGROUND: Advanced glycation end products (AGEs) are involved in age-related diseases, but the interaction of gut microbiota with dietary AGEs (dAGEs) and tissue AGEs in the population is unknown. OBJECTIVE: Our objective was to investigate the association of dietary and tissue AGEs with gut microbiota in the population-based Rotterdam Study, using skin AGEs as a marker for tissue accumulation and stool microbiota as a surrogate for gut microbiota. DESIGN: Dietary intake of three AGEs (dAGEs), namely carboxymethyl-lysine (CML), N-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MGH1), and carboxyethyl-lysine (CEL), was quantified at baseline from food frequency questionnaires. Following up after a median of 5.7 years, skin AGEs were measured using skin autofluorescence (SAF), and stool microbiota samples were sequenced (16S rRNA) to measure microbial composition (including alpha-diversity, beta-dissimilarity, and taxonomic abundances) as well as predict microbial metabolic pathways. Associations of both dAGEs and SAF with microbial measures were investigated using multiple linear regression models in 1052 and 718 participants, respectively. RESULTS: dAGEs and SAF were not associated with either the alpha-diversity or beta-dissimilarity of the stool microbiota. After multiple-testing correction, dAGEs were not associated with any of the 188 genera tested, but were nominally inversely associated with the abundance of Barnesiella, Colidextribacter, Oscillospiraceae UCG-005, and Terrisporobacter, in addition to being positively associated with Coprococcus, Dorea, and Blautia. A higher abundance of Lactobacillus was associated with a higher SAF, along with several nominally significantly associated genera. dAGEs and SAF were nominally associated with several microbial pathways, but none were statistically significant after multiple-testing correction. CONCLUSIONS: Our findings did not solidify a link between habitual dAGEs, skin AGEs, and overall stool microbiota composition. Nominally significant associations with several genera and functional pathways suggested a potential interaction between gut microbiota and AGE metabolism, but validation is required. Future studies are warranted, to investigate whether gut microbiota modifies the potential impact of dAGEs on health.

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals.

Importance: Metabolomics reflect the net effect of genetic and environmental influences and thus provide a comprehensive approach to evaluating the pathogenesis of complex diseases, such as depression. Objective: To identify the metabolic signatures of major depressive disorder (MDD), elucidate the direction of associations using mendelian randomization, and evaluate the interplay of the human gut microbiome and metabolome in the development of MDD. Design, Setting and Participants: This cohort study used data from participants in the UK Biobank cohort (n = 500 000; aged 37 to 73 years; recruited from 2006 to 2010) whose blood was profiled for metabolomics. Replication was sought in the PREDICT and BBMRI-NL studies. Publicly available summary statistics from a 2019 genome-wide association study of depression were used for the mendelian randomization (individuals with MDD = 59 851; control individuals = 113 154). Summary statistics for the metabolites were obtained from OpenGWAS in MRbase (n = 118 000). To evaluate the interplay of the metabolome and the gut microbiome in the pathogenesis of depression, metabolic signatures of the gut microbiome were obtained from a 2019 study performed in Dutch cohorts. Data were analyzed from March to December 2021. Main Outcomes and Measures: Outcomes were lifetime and recurrent MDD, with 249 metabolites profiled with nuclear magnetic resonance spectroscopy with the Nightingale platform. Results: The study included 6811 individuals with lifetime MDD compared with 51 446 control individuals and 4370 individuals with recurrent MDD compared with 62 508 control individuals. Individuals with lifetime MDD were younger (median [IQR] age, 56 [49-62] years vs 58 [51-64] years) and more often female (4447 [65%] vs 2364 [35%]) than control individuals. Metabolic signatures of MDD consisted of 124 metabolites spanning the energy and lipid metabolism pathways. Novel findings included 49 metabolites, including those involved in the tricarboxylic acid cycle (ie, citrate and pyruvate). Citrate was significantly decreased (ß [SE], -0.07 [0.02]; FDR = 4 × 10-04) and pyruvate was significantly increased (ß [SE], 0.04 [0.02]; FDR = 0.02) in individuals with MDD. Changes observed in these metabolites, particularly lipoproteins, were consistent with the differential composition of gut microbiota belonging to the order Clostridiales and the phyla Proteobacteria/Pseudomonadota and Bacteroidetes/Bacteroidota. Mendelian randomization suggested that fatty acids and intermediate and very large density lipoproteins changed in association with the disease process but high-density lipoproteins and the metabolites in the tricarboxylic acid cycle did not. Conclusions and Relevance: The study findings showed that energy metabolism was disturbed in individuals with MDD and that the interplay of the gut microbiome and blood metabolome may play a role in lipid metabolism in individuals with MDD.

The gut microbiome and child mental health: A population-based study.

The link between the gut microbiome and the brain has gained increasing scientific and public interest for its potential to explain psychiatric risk. While differences in gut microbiome composition have been associated with several mental health problems, evidence to date has been largely based on animal models and human studies with modest sample sizes. In this cross-sectional study in 1,784 ten-year-old children from the multi-ethnic, population-based Generation R Study, we aimed to characterize associations of the gut microbiome with child mental health problems. Gut microbiome was assessed from stool samples using 16S rRNA sequencing. We focused on overall psychiatric symptoms as well as with specific domains of emotional and behavioral problems, assessed via the maternally rated Child Behavior Checklist. While we observed lower gut microbiome diversity in relation to higher overall and specific mental health problems, associations were not significant. Likewise, we did not identify any taxonomic feature associated with mental health problems after multiple testing correction, although suggestive findings indicated depletion of genera previously associated with psychiatric disorders, including Hungatella, Anaerotruncus and Oscillospiraceae. The identified compositional abundance differences were found to be similar across all mental health problems. Finally, we did not find significant enrichment for specific microbial functions in relation to mental health problems. In conclusion, based on the largest sample examined to date, we do not find clear evidence of associations between gut microbiome diversity, taxonomies or functions and mental health problems in the general pediatric population. In future, the use of longitudinal designs with repeated measurements of microbiome and psychiatric outcomes will be critical to identify whether and when associations between the gut microbiome and mental health emerge across development and into adulthood.

Gut microbiome-wide association study of depressive symptoms.

Depression is one of the most poorly understood diseases due to its elusive pathogenesis. There is an urgency to identify molecular and biological mechanisms underlying depression and the gut microbiome is a novel area of interest. Here we investigate the relation of fecal microbiome diversity and composition with depressive symptoms in 1,054 participants from the Rotterdam Study cohort and validate these findings in the Amsterdam HELIUS cohort in 1,539 subjects. We identify association of thirteen microbial taxa, including genera Eggerthella, Subdoligranulum, Coprococcus, Sellimonas, Lachnoclostridium, Hungatella, Ruminococcaceae (UCG002, UCG003 and UCG005), LachnospiraceaeUCG001, Eubacterium ventriosum and Ruminococcusgauvreauiigroup, and family Ruminococcaceae with depressive symptoms. These bacteria are known to be involved in the synthesis of glutamate, butyrate, serotonin and gamma amino butyric acid (GABA), which are key neurotransmitters for depression. Our study suggests that the gut microbiome composition may play a key role in depression.

The gut microbiota and depressive symptoms across ethnic groups.

The gut microbiome is thought to play a role in depressive disorders, which makes it an attractive target for interventions. Both the microbiome and depressive symptom levels vary substantially across ethnic groups. Thus, any intervention for depression targeting the microbiome requires understanding of microbiome-depression associations across ethnicities. Analysing data from the HELIUS cohort, we characterize the gut microbiota and its associations with depressive symptoms in 6 ethnic groups (Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish, Moroccan; N = 3211), living in the same urban area. Diversity of the gut microbiota, both within (α-diversity) and between individuals (ß-diversity), predicts depressive symptom levels, taking into account demographic, behavioural, and medical differences. These associations do not differ between ethnic groups. Further, ß-diversity explains 29%-18% of the ethnic differences in depressive symptoms. Bacterial genera associated with depressive symptoms belong to mulitple families, prominently including the families Christensenellaceae, Lachnospiraceae, and Ruminococcaceae. In summary, the results show that the gut microbiota are linked to depressive symptom levels and that this association generalizes across ethnic groups. Moreover, the results suggest that ethnic differences in the gut microbiota may partly explain parallel disparities in depression.

Association of Insulin Resistance and Type 2 Diabetes With Gut Microbial Diversity: A Microbiome-Wide Analysis From Population Studies.

Importance: Previous studies have indicated that gut microbiome may be associated with development of type 2 diabetes. However, these studies are limited by small sample size and insufficient for confounding. Furthermore, which specific taxa play a role in the development of type 2 diabetes remains unclear. Objective: To examine associations of gut microbiome composition with insulin resistance and type 2 diabetes in a large population-based setting controlling for various sociodemographic and lifestyle factors. Design, Setting, and Participants: This cross-sectional analysis included 2166 participants from 2 Dutch population-based prospective cohorts: the Rotterdam Study and the LifeLines-DEEP study. Exposures: The 16S ribosomal RNA method was used to measure microbiome composition in stool samples collected between January 1, 2012, and December 31, 2013. The α diversity (Shannon, richness, and Inverse Simpson indexes), ß diversity (Bray-Curtis dissimilarity matrix), and taxa (from domain to genus level) were identified to reflect gut microbiome composition. Main Outcomes and Measures: Associations among α diversity, ß diversity, and taxa with the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and with type 2 diabetes were examined. Glucose and insulin were measured to calculate the HOMA-IR. Type 2 diabetes cases were identified based on glucose levels and medical records from January 2012 to December 2013. Analyses were adjusted for technical covariates, lifestyle, sociodemographic, and medical factors. Data analysis was performed from January 1, 2018, to December 31, 2020. Results: There were 2166 participants in this study: 1418 from the Rotterdam Study (mean [SD] age, 62.4 [5.9] years; 815 [57.5%] male) and 748 from the LifeLines-DEEP study (mean [SD] age, 44.7 [13.4] years; 431 [57.6%] male); from this total, 193 type 2 diabetes cases were identified. Lower microbiome Shannon index and richness were associated with higher HOMA-IR (eg, Shannon index, -0.06; 95% CI, -0.10 to -0.02), and patients with type 2 diabetes had a lower richness than participants without diabetes (odds ratio [OR], 0.93; 95% CI, 0.88-0.99). The ß diversity (Bray-Curtis dissimilarity matrix) was associated with insulin resistance (R2 = 0.004, P = .001 in the Rotterdam Study and R2 = 0.005, P = .002 in the LifeLines-DEEP study). A total of 12 groups of bacteria were associated with HOMA-IR or type 2 diabetes. Specifically, a higher abundance of Christensenellaceae (ß = -0.08; 95% CI, -0.12 to -0.03: P < .001), Christensenellaceae R7 group (ß = -0.07; 95% CI, -0.12 to -0.03; P < .001), Marvinbryantia (ß = -0.07; 95% CI, -0.11 to -0.03; P < .001), Ruminococcaceae UCG005 (ß = -0.09; 95% CI, -0.13 to -0.05; P < .001), Ruminococcaceae UCG008 (ß = -0.07; 95% CI, -0.11 to -0.03; P < .001), Ruminococcaceae UCG010 (ß = -0.08; 95% CI, -0.12 to -0.04; P < .001), or Ruminococcaceae NK4A214 group (ß = -0.09; 95% CI, -0.13 to -0.05; P < .001) was associated with lower HOMA-IR. A higher abundance of Clostridiaceae 1 (OR, 0.51; 95% CI, 0.41-0.65; P < .001), Peptostreptococcaceae (OR, 0.56; 95% CI, 0.45-0.70; P < .001), C sensu stricto 1 (OR, 0.51; 95% CI, 0.40-0.65; P < .001), Intestinibacter (OR, 0.60; 95% CI, 0.48-0.76; P < .001), or Romboutsia (OR, 0.55; 95% CI, 0.44-0.70; P < .001) was associated with less type 2 diabetes. These bacteria are all known to produce butyrate. Conclusions and Relevance: In this cross-sectional study, higher microbiome α diversity, along with more butyrate-producing gut bacteria, was associated with less type 2 diabetes and with lower insulin resistance among individuals without diabetes. These findings could help provide insight into the etiology, pathogenesis, and treatment of type 2 diabetes.

A population-based study on associations of stool microbiota with atopic diseases in school-age children.

BACKGROUND: Infants with less diverse gut microbiota seem to have higher risks of atopic diseases in early life, but any associations at school age are unclear. OBJECTIVES: This study sought to examine the associations of diversity, relative abundance, and functional pathways of stool microbiota with atopic diseases in school-age children. METHODS: We performed a cross-sectional study within an existing population-based prospective cohort among 1440 children 10 years of age. On stool samples, 16S ribosomal RNA gene sequencing was performed, and taxonomic and functional tables were produced. Physician-diagnosed eczema, allergy, and asthma were measured by questionnaires, allergic sensitization by skin prick tests, and lung function by spirometry. RESULTS: The α-diversity of stool microbiota was associated with a decreased risk of eczema (odds ratio [OR], 0.98; 95% CI, 0.97, 1.00), and ß-diversity was associated with physician-diagnosed inhalant allergy (R2 = 0.001; P = .047). Lachnospiraceae, Ruminococcaceae_UCG-005, and Christensenellaceae_R-7_group species were associated with decreased risks of eczema, inhalant allergic sensitization, and physician-diagnosed inhalant allergy (OR range, 0.88-0.94; 95% CI range, 0.79-0.96 to 0.88-0.98), while Agathobacter species were associated with an increased risk of physician-diagnosed inhalant allergy (OR, 1.23; 95% CI, 1.08-1.42). Functional pathways related to heme and terpenoid biosynthesis were associated with decreased risks of physician-diagnosed inhalant allergy and asthma (OR range, 0.89-0.86; 95% CI range, 0.80-0.99 to 0.73-1.02). No associations of stool microbiota with lung function were observed. CONCLUSIONS: The diversity, relative abundance and functional pathways of stool microbiota were most consistently associated with physician-diagnosed inhalant allergy in school-age children and less consistently with other atopic diseases.

Large-scale association analyses identify host factors influencing human gut microbiome composition.

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10-8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10-20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10-10 < P < 5 × 10-8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.

Microbiomics, Metabolomics, Predicted Metagenomics, and Hepatic Steatosis in a Population-Based Study of 1,355 Adults.

BACKGROUND AND AIMS: Previous small studies have appraised the gut microbiome (GM) in steatosis, but large-scale studies are lacking. We studied the association of the GM diversity and composition, plasma metabolites, predicted functional metagenomics, and steatosis. APPROACH AND RESULTS: This is a cross-sectional analysis of the prospective population-based Rotterdam Study. We used 16S ribosomal RNA gene sequencing and determined taxonomy using the SILVA reference database. Alpha diversity and beta diversity were calculated using the Shannon diversity index and Bray-Curtis dissimilarities. Differences were tested across steatosis using permutational multivariate analysis of variance. Hepatic steatosis was diagnosed by ultrasonography. We subsequently selected genera using regularized regression. The functional metagenome was predicted based on the GM using Kyoto Encyclopedia of Genes and Genomes pathways. Serum metabolomics were assessed using high-throughput proton nuclear magnetic resonance. All analyses were adjusted for age, sex, body mass index, alcohol, diet, and proton-pump inhibitors. We included 1,355 participants, of whom 472 had steatosis. Alpha diversity was lower in steatosis (P = 1.1∙10-9 ), and beta diversity varied across steatosis strata (P = 0.001). Lasso selected 37 genera of which three remained significantly associated after adjustment (Coprococcus3: ß = -65; Ruminococcus Gauvreauiigroup: ß = 62; and Ruminococcus Gnavusgroup: ß = 45, Q-value = 0.037). Predicted metagenome analyses revealed that pathways of secondary bile-acid synthesis and biotin metabolism were present, and D-alanine metabolism was absent in steatosis. Metabolic profiles showed positive associations for aromatic and branched chain amino acids and glycoprotein acetyls with steatosis and R. Gnavusgroup, whereas these metabolites were inversely associated with alpha diversity and Coprococcus3. CONCLUSIONS: We confirmed, on a large-scale, the lower microbial diversity and association of Coprococcus and Ruminococcus Gnavus with steatosis. We additionally showed that steatosis and alpha diversity share opposite metabolic profiles.

Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas.

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).

Diversity, compositional and functional differences between gut microbiota of children and adults.

The gut microbiota has been shown to play diverse roles in human health and disease although the underlying mechanisms have not yet been fully elucidated. Large cohort studies can provide further understanding into inter-individual differences, with more precise characterization of the pathways by which the gut microbiota influences human physiology and disease processes. Here, we aimed to profile the stool microbiome of children and adults from two population-based cohort studies, comprising 2,111 children in the age-range of 9 to 12 years (the Generation R Study) and 1,427 adult individuals in the range of 46 to 88 years of age (the Rotterdam Study). For the two cohorts, 16S rRNA gene profile datasets derived from the Dutch population were generated. The comparison of the two cohorts showed that children had significantly lower gut microbiome diversity. Furthermore, we observed higher relative abundances of genus Bacteroides in children and higher relative abundances of genus Blautia in adults. Predicted functional metagenome analysis showed an overrepresentation of the glycan degradation pathways, riboflavin (vitamin B2), pyridoxine (vitamin B6) and folate (vitamin B9) biosynthesis pathways in children. In contrast, the gut microbiome of adults showed higher abundances of carbohydrate metabolism pathways, beta-lactam resistance, thiamine (vitamin B1) and pantothenic (vitamin B5) biosynthesis pathways. A predominance of catabolic pathways in children (valine, leucine and isoleucine degradation) as compared to biosynthetic pathways in adults (valine, leucine and isoleucine biosynthesis) suggests a functional microbiome switch to the latter in adult individuals. Overall, we identified compositional and functional differences in gut microbiome between children and adults in a population-based setting. These microbiome profiles can serve as reference for future studies on specific human disease susceptibility in childhood, adulthood and specific diseased populations.

Relationship between gut microbiota and circulating metabolites in population-based cohorts.

Gut microbiota has been implicated in major diseases affecting the human population and has also been linked to triglycerides and high-density lipoprotein levels in the circulation. Recent development in metabolomics allows classifying the lipoprotein particles into more details. Here, we examine the impact of gut microbiota on circulating metabolites measured by Nuclear Magnetic Resonance technology in 2309 individuals from the Rotterdam Study and the LifeLines-DEEP cohort. We assess the relationship between gut microbiota and metabolites by linear regression analysis while adjusting for age, sex, body-mass index, technical covariates, medication use, and multiple testing. We report an association of 32 microbial families and genera with very-low-density and high-density subfractions, serum lipid measures, glycolysis-related metabolites, ketone bodies, amino acids, and acute-phase reaction markers. These observations provide insights into the role of microbiota in host metabolism and support the potential of gut microbiota as a target for therapeutic and preventive interventions.

Intestinal microbiome composition and its relation to joint pain and inflammation.

Macrophage-mediated inflammation is thought to have a causal role in osteoarthritis-related pain and severity, and has been suggested to be triggered by endotoxins produced by the gastrointestinal microbiome. Here we investigate the relationship between joint pain and the gastrointestinal microbiome composition, and osteoarthritis-related knee pain in the Rotterdam Study; a large population based cohort study. We show that abundance of Streptococcus species is associated with increased knee pain, which we validate by absolute quantification of Streptococcus species. In addition, we replicate these results in 867 Caucasian adults of the Lifelines-DEEP study. Finally we show evidence that this association is driven by local inflammation in the knee joint. Our results indicate the microbiome is a possible therapeutic target for osteoarthritis-related knee pain.

Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials.

Correct identification of different human epithelial materials such as from skin, saliva and vaginal origin is relevant in forensic casework as it provides crucial information for crime reconstruction. However, the overlap in human cell type composition between these three epithelial materials provides challenges for their differentiation and identification when using previously proposed human cell biomarkers, while their microbiota composition largely differs. By using validated 16S rRNA gene massively parallel sequencing data from the Human Microbiome Project of 1636 skin, oral and vaginal samples, 50 taxonomy-independent deep learning networks were trained to classify these three tissues. Validation testing was performed in de-novo generated high-throughput 16S rRNA gene sequencing data using the Ion Torrent™ Personal Genome Machine from 110 test samples: 56 hand skin, 31 saliva and 23 vaginal secretion specimens. Body-site classification accuracy of these test samples was very high as indicated by AUC values of 0.99 for skin, 0.99 for oral, and 1 for vaginal secretion. Misclassifications were limited to 3 (5%) skin samples. Additional forensic validation testing was performed in mock casework samples by de-novo high-throughput sequencing of 19 freshly-prepared samples and 22 samples aged for 1 up to 7.6 years. All of the 19 fresh and 20 (91%) of the 22 aged mock casework samples were correctly tissue-type classified. Moreover, comparing the microbiome results with outcomes from previous human mRNA-based tissue identification testing in the same 16 aged mock casework samples reveals that our microbiome approach performs better in 12 (75%), similarly in 2 (12.5%), and less good in 2 (12.5%) of the samples. Our results demonstrate that this new microbiome approach allows for accurate tissue-type classification of three human epithelial materials of skin, oral and vaginal origin, which is highly relevant for future forensic investigations.

Differences in Systemic IgA Reactivity and Circulating Th Subsets in Healthy Volunteers With Specific Microbiota Enterotypes.

Changes in the intestinal microbiota have been associated with the development of immune-mediated diseases in humans. Additionally, the introduction of defined bacterial species into the mouse intestinal microbiota has been shown to impact on the adaptive immune response. However, how much impact the intestinal microbiota composition actually has on regulating adaptive immunity remains poorly understood. Therefore, we studied aspects of the adaptive immunity in healthy adults possessing distinct intestinal microbiota profiles. The intestinal microbiota composition was determined via Illumina sequencing of bacterial 16S rRNA genes extracted from the feces of 35 individuals. Blood B-cell and T-cell subsets from the same individuals were studied using flow cytometry. Finally, the binding of fecal and plasma Immunoglobulin A (IgA) to intestinal bacteria (associated with health and disease) Bacteroides fragilis, Prevotella copri, Bifidobacterium longum, Clostridium difficile, and Escherichia coli was analyzed using ELISA. Unsupervised clustering of microbiota composition revealed the presence of three clusters within the cohort. Cluster 1 and 2 were similar to previously-described enterotypes with a predominance of Bacteroides in Cluster 1 and Prevotella in Cluster 2. The bacterial diversity (Shannon index) and bacterial richness of Cluster 3 was significantly higher than observed in Clusters 1 and 2, with the Ruminococacceae tending to predominate. Within circulating B- and T-cell subsets, only Th subsets were significantly different between groups of distinct intestinal microbiota. Individuals of Cluster 3 have significantly fewer Th17 and Th22 circulating cells, while Th17.1 cell numbers were increased in individuals of Cluster 1. IgA reactivity to intestinal bacteria was higher in plasma than feces, and individuals of Cluster 1 had significant higher plasma IgA reactivity against B. longum than individuals of Cluster 2. In conclusion, we identified three distinct fecal microbiota clusters, of which two clusters resembled previously-described "enterotypes". Global T-cell and B-cell immunity seemed unaffected, however, circulating Th subsets and plasma IgA reactivity were significantly different between Clusters. Hence, the impact of intestinal bacteria composition on human B cells, T cells and IgA reactivity appears limited in genetically-diverse and environmentally-exposed humans, but can skew antibody reactivity and Th cell subsets.

Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative.

BACKGROUND: In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture. RESULTS: Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories. CONCLUSION: We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed.

Microbiome measurement: Possibilities and pitfalls.

Microbiome research is an emerging field in medical sciences. Several studies have made headways in understanding the influence of microbes on our health and disease states. Further progress in mapping microbiome populations across different body sites and understanding the underlying mechanisms of microbiome-host interactions depends critically on study design, collection protocols, analytical genetic techniques, and reference databases. In particular, a shift has appeared going from small sample collections to large-scale population studies (with extensive phenotypic information including disease status) which calls for some adaptions. In this review we will focus on gut microbiome profiling using the 16S ribosomal RNA approach in the setting of large-scale population studies, and discuss some novel developments.