Objective Bacterial translocation to various organs including human adipose tissue (AT) due to increased intestinal permeability remains poorly understood. We hypothesised that: (1) bacterial presence is highly tissue specific and (2) related in composition and quantity to immune inflammatory and metabolic burden. Design We quantified and sequenced the bacterial 16S rRNA gene in blood and AT samples (omental, mesenteric and subcutaneous) of 75 subjects with obesity with or without type 2 diabetes (T2D) and used catalysed reporter deposition (CARD) – fluorescence in situ hybridisation (FISH) to detect bacteria in AT. Results Under stringent experimental and bioinformatic control for contaminants, bacterial DNA was detected in blood and omental, subcutaneous and mesenteric AT samples in the range of 0.1 to 5 pg/µg DNA isolate. Moreover, CARD-FISH allowed the detection of living, AT-borne bacteria. Proteobacteria and Firmicutes were the predominant phyla, and bacterial quantity was associated with immune cell infiltration, inflammatory and metabolic parameters in a tissue-specific manner. Bacterial composition differed between subjects with and without T2D and was associated with related clinical measures, including systemic and tissues-specific inflammatory markers. Finally, treatment of adipocytes with bacterial DNA in vitro stimulated the expression of TNFA and IL6 . Conclusions Our study provides contaminant aware evidence for the presence of bacteria and bacterial DNA in several ATs in obesity and T2D and suggests an important role of bacteria in initiating and sustaining local AT subclinical inflammation and therefore impacting metabolic sequelae of obesity.
Abstract Background Studies on DNA methylation following bariatric surgery have primarily focused on blood cells, while it is unclear to which extend it may reflect DNA methylation profiles in specific metabolically relevant organs such as adipose tissue (AT). Here, we investigated whether adipose tissue depots specific methylation changes after bariatric surgery are mirrored in blood. Methods Using Illumina 850K EPIC technology, we analysed genome-wide DNA methylation in paired blood, subcutaneous and omental visceral AT (SAT/OVAT) samples from nine individuals with severe obesity pre- and post-surgery. Findings The numbers and effect sizes of differentially methylated regions (DMRs) post-bariatric surgery were more pronounced in AT (SAT: 12,865 DMRs from -11.5 to 10.8%; OVAT: 14,632 DMRs from -13.7 to 12.8%) than in blood (9,267 DMRs from -8.8 to 7.7%). Cross-tissue DMRs implicated immune-related genes. Among them, 49 regions could be validated with similar methylation changes in blood from independent individuals. Fourteen DMRs correlated with differentially expressed genes in AT post bariatric surgery, including downregulation of PIK3AP1 in both SAT and OVAT. DNA methylation age acceleration was significantly higher in AT compared to blood, but remained unaffected after surgery. Interpretation Concurrent methylation pattern changes in blood and AT, particularly in immune-related genes, suggest blood DNA methylation mirrors inflammatory state of AT post-bariatric surgery.
In this study, we show that DNA methylation changes are associated with differential gene expression and the phenotypic improvements after bariatric surgery. Further studies are ongoing addressing long-term methylation changes of candidate sites after surgery, which helps to explore the role of DNA methylation on beneficial effects of bariatric surgery.
******************************************************************* MetaDrugs workflow ******************************************************************* Data analysis pipeline for investigating drug-host-microbiome relationships in cardiometabolic disease (MetaCardis cohort). For questions and requests, please contact: Sofia K. Forslund (sofia.forslund@mdc-berlin.de) and Till Birkner (till.birkner@mdc-berlin.de) ******************************************************************* Contents: ------------------------------------------------------------------- Data files: metadata.tar.gz - archived cohort metadata files input_features.tar.gz - archived preprocessed serum and urine metabolome and gut microbiome features output_complete.tar.gz - archived example analysis output files for each of the input feature file output_rerun.tar.gz - archived empty directory for generating test output files as described in this document ------------------------------------------------------------------- Text files: archived in feature_names.tar.gz: atcs_names - full names for atcs drug compounds contrast_names - full names for disease comparison groups file_names - brief description of the files in input_features folder gmm_names - full names of GMM modules kegg_names - full names of KEGG modules ko_names - full names of KO modules metadata_names - full names of metadata features mOTU_names - species names for metagenomics data taxon_names - taxon names for metagenomics data ------------------------------------------------------------------- Scripts: ------------------------------------------------------------------- runFrame.r - main wrapper script envoking the analysis pipeline ------------------------------------------------------------------- runFrame_rel_comb.r - script calculating drug combination effects runFrame_rel.r - script calculating dosage effects testCombPresenceSeparate.r - testing of significant drug combination effects beyond single drug effects testDosagePresenceSeparate.pl - testing of significant drug dosage effects beyond single drug effects testDosagePresenceSeparateNegative.pl - testing of unique drug dosage effects beyond single drug effects ------------------------------------------------------------------- prettifyResults_uncollapsed.pl - wrapper scripts to create and format a single analysis output file makeTables.r - wrapper script to make excel tables with analysis results ------------------------------------------------------------------- Example output file: ------------------------------------------------------------------- output_all_formatted_noc_uncollapsed_complete.tsv - contains all disease-drug-host-microbiome feature analysis results in one place. *******************************************************************
Abstract Objectives The host-microbiota co-metabolite trimethylamine N -oxide (TMAO) is linked to increased thrombotic and cardiovascular risks. Here we, sought to i) characterize which host variables contribute to fasting serum TMAO levels in real-life settings ii) identify potential actionable therapeutic means related to circulating TMAO. Design We applied “explainable” machine learning, univariate-, multivariate- and mediation analyses of fasting plasma TMAO concentration and a multitude of bioclinical phenotypes in 1,741 adult Europeans of the MetaCardis study. We expanded and validated our epidemiological findings in mechanistic studies in human renal fibroblasts and a murine model of kidney fibrosis following TMAO exposure. Results Next to age, kidney function was the primary variable predicting circulating TMAO in MetaCardis, with microbiota composition and diet playing minor, albeit significant roles. Mediation analysis revealed a causal relationship between TMAO and kidney function decline that strengthened at more severe stages of cardiometabolic disease. We corroborated our findings in preclinical models where TMAO exposure augmented conversion of human renal fibroblasts into myofibroblasts and increased kidney scarring in vivo . Mechanistically, TMAO aggravated kidney fibrosis due to ERK1/2 hyperactivation synergistically with TGF-β1 signaling. Consistent with our findings, patients receiving next-generation glucose-lowering drugs with reno-protective properties, had significantly lower circulating TMAO when compared to propensity-score matched control individuals. Conclusion After age, kidney function is the major determinant of fasting circulating TMAO in adults. Our findings of lower TMAO levels in individuals medicated with reno-protective anti-diabetic drugs suggests a clinically actionable intervention for decreasing TMAO-associated excess cardiovascular risk that merits urgent investigation in human trials. Data availability statement Raw shotgun sequencing data that support the findings of this study have been deposited in the European Nucleotide Archive with accession codes PRJEB37249, PRJEB38742, PRJEB41311 and PRJEB46098. Serum NMR and urine NMR metabolome data have been uploaded to Metabolights with accession number MTBLS3429; serum GC-MS and isotopically quantified serum metabolites (UPLC–MS/MS) are available from MassIVE with accession numbers MSV000088042 and MSV000088043, respectively.
Abstract Background The microbiome has emerged as an environmental factor contributing to obesity and type 2 diabetes (T2D). While the majority of studies have focused on associations between the gut microbiome and metabolic disease, increasing evidence suggests links between circulating bacterial components (i.e. bacterial DNA) and cardiometabolic disease as well as blunted response to metabolic interventions such as bariatric surgery. In this aspect, thorough next generation sequencing based and contaminant aware approaches are lacking. To address these points, we tested whether bacterial DNA could be amplified in the blood of subjects with obesity and high metabolic risk under strict experimental and analytical control to minimize bacterial contamination. Moreover we characterized a bacterial signature associated with the individual metabolic risk and explored its dynamics alongside metabolic improvement after bariatric surgery. Methods Subjects undergoing elective bariatric surgery were recruited into sex and BMI matched subgroups with (n=24) or without T2D (n=24). Bacterial DNA in the blood was quantified and prokaryotic 16S rRNA gene amplicons were sequenced. A contaminant aware approach was applied to derive a compositional microbial signature from bacterial sequences in subjects with and without T2D and within subjects at baseline and at three and twelve months post bariatric surgery. We modelled associations between bacterial load and composition with host metabolic and anthropometric markers. We further tested whether compositional shifts were related to weight loss response and T2D remission after bariatric surgery. Lastly, Catalyzed Reporter Deposition (CARD) - Fluorescence In Situ Hybridization (FISH) was employed to visualize bacteria in blood samples. Results Contaminant aware classification of bacterial 16S rRNA sequences allowed the derivation of a blood bacterial signature, which was associated with metabolic health. Based on bacterial phyla and genera detected in the blood samples, a metabolic syndrome classification index score was derived and shown to robustly classify subjects along their actual clinical group. T2D was characterized by decreased bacterial richness and a loss of genera associated with improved metabolic health. Moreover, circulating bacterial load was significantly associated with metabolic health and increased after bariatric surgery. Weight loss and metabolic improvement following bariatric surgery were associated with an early and stable increase of these genera in parallel with improvements in key cardiometabolic risk parameters. CARD-FISH allowed the detection of living Bacteria in blood samples in obesity. Conclusions We show that the circulating bacterial signature reflects metabolic disease and its improvement after bariatric surgery. Our work provides contaminant aware evidence for the presence of living bacteria in the blood and suggests a putative crosstalk between components of the blood and metabolism in metabolic health regulation.
******************************************************************* MetaDrugs workflow ******************************************************************* Data analysis pipeline for investigating drug-host-microbiome relationships in cardiometabolic disease (MetaCardis cohort). For questions and requests, please contact: Sofia K. Forslund (sofia.forslund@mdc-berlin.de) and Till Birkner (till.birkner@mdc-berlin.de) ******************************************************************* Contents: ------------------------------------------------------------------- Data files: metadata.tar.gz - archived cohort metadata files* input_features.tar.gz - archived preprocessed serum and urine metabolome and gut microbiome features output_complete.tar.gz - archived example analysis output files for each of the input feature file output_rerun.tar.gz - archived empty directory for generating test output files as described in this document
*Please note: Due to conflicts with Danish Data Protection laws, metadata from the Danish subset of the cohort were removed in this repository. Please reach out for a potential case-by-case access request for access to the complete set of metadata. ------------------------------------------------------------------- Text files: archived in feature_names.tar.gz: atcs_names - full names for atcs drug compounds contrast_names - full names for disease comparison groups file_names - brief description of the files in input_features folder gmm_names - full names of GMM modules kegg_names - full names of KEGG modules ko_names - full names of KO modules metadata_names - full names of metadata features mOTU_names - species names for metagenomics data taxon_names - taxon names for metagenomics data ------------------------------------------------------------------- Scripts: ------------------------------------------------------------------- runFrame.r - main wrapper script envoking the analysis pipeline ------------------------------------------------------------------- runFrame_rel_comb.r - script calculating drug combination effects runFrame_rel.r - script calculating dosage effects testCombPresenceSeparate.r - testing of significant drug combination effects beyond single drug effects testDosagePresenceSeparate.pl - testing of significant drug dosage effects beyond single drug effects testDosagePresenceSeparateNegative.pl - testing of unique drug dosage effects beyond single drug effects ------------------------------------------------------------------- prettifyResults_uncollapsed.pl - wrapper scripts to create and format a single analysis output file makeTables.r - wrapper script to make excel tables with analysis results ------------------------------------------------------------------- Example output file: ------------------------------------------------------------------- output_all_formatted_noc_uncollapsed_complete.tsv - contains all disease-drug-host-microbiome feature analysis results in one place. *******************************************************************
