Combinatorial, additive and dose-dependent drug-microbiome associations.

During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery1-5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease.

Statin therapy is associated with lower prevalence of gut microbiota dysbiosis.

Microbiome community typing analyses have recently identified the Bacteroides2 (Bact2) enterotype, an intestinal microbiota configuration that is associated with systemic inflammation and has a high prevalence in loose stools in humans1,2. Bact2 is characterized by a high proportion of Bacteroides, a low proportion of Faecalibacterium and low microbial cell densities1,2, and its prevalence varies from 13% in a general population cohort to as high as 78% in patients with inflammatory bowel disease2. Reported changes in stool consistency3 and inflammation status4 during the progression towards obesity and metabolic comorbidities led us to propose that these developments might similarly correlate with an increased prevalence of the potentially dysbiotic Bact2 enterotype. Here, by exploring obesity-associated microbiota alterations in the quantitative faecal metagenomes of the cross-sectional MetaCardis Body Mass Index Spectrum cohort (n = 888), we identify statin therapy as a key covariate of microbiome diversification. By focusing on a subcohort of participants that are not medicated with statins, we find that the prevalence of Bact2 correlates with body mass index, increasing from 3.90% in lean or overweight participants to 17.73% in obese participants. Systemic inflammation levels in Bact2-enterotyped individuals are higher than predicted on the basis of their obesity status, indicative of Bact2 as a dysbiotic microbiome constellation. We also observe that obesity-associated microbiota dysbiosis is negatively associated with statin treatment, resulting in a lower Bact2 prevalence of 5.88% in statin-medicated obese participants. This finding is validated in both the accompanying MetaCardis cardiovascular disease dataset (n = 282) and the independent Flemish Gut Flora Project population cohort (n = 2,345). The potential benefits of statins in this context will require further evaluation in a prospective clinical trial to ascertain whether the effect is reproducible in a randomized population and before considering their application as microbiota-modulating therapeutics.

Cardiomuscular Biomarkers in the Diagnosis and Prognostication of Immune Checkpoint Inhibitor Myocarditis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are approved for multiple cancers but can result in ICI-associated myocarditis, an infrequent but life-threatening condition. Elevations in cardiac biomarkers, specifically troponin-I (cTnI), troponin-T (cTnT), and creatine kinase (CK), are used for diagnosis. However, the association between temporal elevations of these biomarkers with disease trajectory and outcomes has not been established. METHODS: We analyzed the diagnostic accuracy and prognostic performances of cTnI, cTnT, and CK in patients with ICI myocarditis (n=60) through 1-year follow-up in 2 cardio-oncology units (APHP Sorbonne, Paris, France and Heidelberg, Germany). A total of 1751 (1 cTnT assay type), 920 (4 cTnI assay types), and 1191 CK sampling time points were available. Major adverse cardiomyotoxic events (MACE) were defined as heart failure, ventricular arrhythmia, atrioventricular or sinus block requiring pacemaker, respiratory muscle failure requiring mechanical ventilation, and sudden cardiac death. Diagnostic performance of cTnI and cTnT was also assessed in an international ICI myocarditis registry. RESULTS: Within 72 hours of admission, cTnT, cTnI, and CK were increased compared with upper reference limits (URLs) in 56 of 57 (98%), 37 of 42 ([88%] P=0.03 versus cTnT), and 43 of 57 ([75%] P<0.001 versus cTnT), respectively. This increased rate of positivity for cTnT (93%) versus cTnI ([64%] P<0.001) on admission was confirmed in 87 independent cases from an international registry. In the Franco-German cohort, 24 of 60 (40%) patients developed ≥1 MACE (total, 52; median time to first MACE, 5 [interquartile range, 2-16] days). The highest value of cTnT:URL within the first 72 hours of admission performed best in terms of association with MACE within 90 days (area under the curve, 0.84) than CK:URL (area under the curve, 0.70). A cTnT:URL ≥32 within 72 hours of admission was the best cut-off associated with MACE within 90 days (hazard ratio, 11.1 [95% CI, 3.2-38.0]; P<0.001), after adjustment for age and sex. cTnT was increased in all patients within 72 hours of the first MACE (23 of 23 [100%]), whereas cTnI and CK values were less than the URL in 2 of 19 (11%) and 6 of 22 (27%) of patients (P<0.001), respectively. CONCLUSIONS: cTnT is associated with MACE and is sensitive for diagnosis and surveillance in patients with ICI myocarditis. A cTnT:URL ratio <32 within 72 hours of diagnosis is associated with a subgroup at low risk for MACE. Potential differences in diagnostic and prognostic performances between cTnT and cTnI as a function of the assays used deserve further evaluation in ICI myocarditis.

Automatic digitization of paper electrocardiograms - A systematic review.

The digitization of electrocardiogram paper records is an essential step to preserve and analyze cardiac data. This digitization process is not flawless as it involves several challenges, such as skew correction, binarization, and signal extraction. Various approaches have been proposed to address these challenges and recent studies have introduced innovative solutions, such as deep learning models and automation processes. Although existing approaches have shown promising results, there is a lack of common databases and metrics where authors could evaluate and compare their methods. Furthermore, the limited accessibility of code or software hinders the comparison process. Overall, while digitization of paper ECG recordings is important in advancing cardiology research, additional efforts are needed to standardize the evaluation process while improving code accessibility. This article provides a systematic review of this process.

Impairment of gut microbial biotin metabolism and host biotin status in severe obesity: effect of biotin and prebiotic supplementation on improved metabolism.

OBJECTIVES: Gut microbiota is a key component in obesity and type 2 diabetes, yet mechanisms and metabolites central to this interaction remain unclear. We examined the human gut microbiome's functional composition in healthy metabolic state and the most severe states of obesity and type 2 diabetes within the MetaCardis cohort. We focused on the role of B vitamins and B7/B8 biotin for regulation of host metabolic state, as these vitamins influence both microbial function and host metabolism and inflammation. DESIGN: We performed metagenomic analyses in 1545 subjects from the MetaCardis cohorts and different murine experiments, including germ-free and antibiotic treated animals, faecal microbiota transfer, bariatric surgery and supplementation with biotin and prebiotics in mice. RESULTS: Severe obesity is associated with an absolute deficiency in bacterial biotin producers and transporters, whose abundances correlate with host metabolic and inflammatory phenotypes. We found suboptimal circulating biotin levels in severe obesity and altered expression of biotin-associated genes in human adipose tissue. In mice, the absence or depletion of gut microbiota by antibiotics confirmed the microbial contribution to host biotin levels. Bariatric surgery, which improves metabolism and inflammation, associates with increased bacterial biotin producers and improved host systemic biotin in humans and mice. Finally, supplementing high-fat diet-fed mice with fructo-oligosaccharides and biotin improves not only the microbiome diversity, but also the potential of bacterial production of biotin and B vitamins, while limiting weight gain and glycaemic deterioration. CONCLUSION: Strategies combining biotin and prebiotic supplementation could help prevent the deterioration of metabolic states in severe obesity. TRIAL REGISTRATION NUMBER: NCT02059538.

Human gut microbes impact host serum metabolome and insulin sensitivity.

Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.

Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome.

AIMS: Congenital long-QT syndromes (cLQTS) or drug-induced long-QT syndromes (diLQTS) can cause torsade de pointes (TdP), a life-threatening ventricular arrhythmia. The current strategy for the identification of drugs at the high risk of TdP relies on measuring the QT interval corrected for heart rate (QTc) on the electrocardiogram (ECG). However, QTc has a low positive predictive value. METHODS AND RESULTS: We used convolutional neural network (CNN) models to quantify ECG alterations induced by sotalol, an IKr blocker associated with TdP, aiming to provide new tools (CNN models) to enhance the prediction of drug-induced TdP (diTdP) and diagnosis of cLQTS. Tested CNN models used single or multiple 10-s recordings/patient using 8 leads or single leads in various cohorts: 1029 healthy subjects before and after sotalol intake (n = 14 135 ECGs); 487 cLQTS patients (n = 1083 ECGs: 560 type 1, 456 type 2, 67 type 3); and 48 patients with diTdP (n = 1105 ECGs, with 147 obtained within 48 h of a diTdP episode). CNN models outperformed models using QTc to identify exposure to sotalol [area under the receiver operating characteristic curve (ROC-AUC) = 0.98 vs. 0.72, P ≤ 0.001]. CNN models had higher ROC-AUC using multiple vs. single 10-s ECG (P ≤ 0.001). Performances were comparable for 8-lead vs. single-lead models. CNN models predicting sotalol exposure also accurately detected the presence and type of cLQTS vs. healthy controls, particularly for cLQT2 (AUC-ROC = 0.9) and were greatest shortly after a diTdP event and declining over time (P ≤ 0.001), after controlling for QTc and intake of culprit drugs. ECG segment analysis identified the J-Tpeak interval as the best discriminator of sotalol intake. CONCLUSION: CNN models applied to ECGs outperform QTc measurements to identify exposure to drugs altering the QT interval, congenital LQTS, and are greatest shortly after a diTdP episode.

Intestinal alteration of α-gustducin and sweet taste signaling pathway in metabolic diseases is partly rescued after weight loss and diabetes remission.

Carbohydrates and sweeteners are detected by the sweet taste receptor in enteroendocrine cells (EECs). This receptor is coupled to the gustducin G-protein, which α-subunit is encoded by GNAT3 gene. In intestine, the activation of sweet taste receptor triggers a signaling pathway leading to GLP-1 secretion, an incretin hormone. In metabolic diseases, GLP-1 concentration and incretin effect are reduced while partly restored after Roux-en-Y gastric bypass (RYGB). We wondered if the decreased GLP-1 secretion in metabolic diseases is caused by an intestinal defect in sweet taste transduction pathway. In our RNA-sequencing of EECs, GNAT3 expression is decreased in patients with obesity and type 2 diabetes compared with normoglycemic obese patients. This prompted us to explore sweet taste signaling pathway in mice with metabolic deteriorations. During obesity onset in mice, Gnat3 expression was downregulated in EECs. After metabolic improvement with enterogastro anastomosis surgery in mice (a surrogate of the RYGB in humans), the expression of Gnat3 increased in the new alimentary tract and glucose-induced GLP-1 secretion was improved. To evaluate if high-fat diet-induced dysbiotic intestinal microbiota could explain the changes in the expression of sweet taste α-subunit G-protein, we performed a fecal microbiota transfer in mice. However, we could not conclude if dysbiotic microbiota impacted or not intestinal Gnat3 expression. Our data highlight that metabolic disorders were associated with altered gene expression of sweet taste signaling in intestine. This could contribute to impaired GLP-1 secretion that is partly rescued after metabolic improvement.NEW & NOTEWORTHY Our data highlighted 1) the sweet taste transduction pathway in EECs plays pivotal role for glucose homeostasis at least at gene expression level; 2) metabolic disorders lead to altered gene expression of sweet taste signaling pathway in intestine contributing to impaired GLP-1 secretion; and 3) after surgical intestinal modifications, increased expression of GNAT3, encoding α-gustducin contributed to metabolic improvement.

Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.

In recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported. In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals. Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D). Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis. Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin. We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species. Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa. These in turn cause functional microbiome shifts, in part alleviated by metformin-induced changes. Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication.

Phosphatidylglycerols are induced by gut dysbiosis and inflammation, and favorably modulate adipose tissue remodeling in obesity.

Lipidomic techniques can improve our understanding of complex lipid interactions that regulate metabolic diseases. Here, a serum phospholipidomics analysis identified associations between phosphatidylglycerols (PGs) and gut microbiota dysbiosis. Compared with the other phospholipids, serum PGs were the most elevated in patients with low microbiota gene richness, which were normalized after a dietary intervention that restored gut microbial diversity. Serum PG levels were positively correlated with metagenomic functional capacities for bacterial LPS synthesis and host markers of low-grade inflammation; transcriptome databases identified PG synthase, the first committed enzyme in PG synthesis, as a potential mediator. Experiments in mice and cultured human-derived macrophages demonstrated that LPS induces PG release. Acute PG treatment in mice altered adipose tissue gene expression toward remodeling and inhibited ex vivo lipolysis in adipose tissue, suggesting that PGs favor lipid storage. Indeed, several PG species were associated with the severity of obesity in mice and humans. Finally, despite enrichment in PGs in bacterial membranes, experiments employing gnotobiotic mice colonized with recombinant PG overproducing Lactococcus lactis showed limited direct contribution of microbial PGs to the host. In summary, PGs are inflammation-responsive lipids indirectly regulated by the gut microbiota via endotoxins and regulate adipose tissue homeostasis in obesity.-Kayser, B. D., Lhomme, M., Prifti, E., Da Cunha, C., Marquet, F., Chain, F., Naas, I., Pelloux, V., Dao, M.-C., Kontush, A., Rizkalla, S. W., Aron-Wisnewsky, J., Bermúdez-Humarán, L. G., Oakley, F., Langella, P., Clément, K., Dugail, I. Phosphatidylglycerols are induced by gut dysbiosis and inflammation, and favorably modulate adipose tissue remodeling in obesity.

Alterations of the human gut microbiome in liver cirrhosis.

Liver cirrhosis occurs as a consequence of many chronic liver diseases that are prevalent worldwide. Here we characterize the gut microbiome in liver cirrhosis by comparing 98 patients and 83 healthy control individuals. We build a reference gene set for the cohort containing 2.69 million genes, 36.1% of which are novel. Quantitative metagenomics reveals 75,245 genes that differ in abundance between the patients and healthy individuals (false discovery rate < 0.0001) and can be grouped into 66 clusters representing cognate bacterial species; 28 are enriched in patients and 38 in control individuals. Most (54%) of the patient-enriched, taxonomically assigned species are of buccal origin, suggesting an invasion of the gut from the mouth in liver cirrhosis. Biomarkers specific to liver cirrhosis at gene and function levels are revealed by a comparison with those for type 2 diabetes and inflammatory bowel disease. On the basis of only 15 biomarkers, a highly accurate patient discrimination index is created and validated on an independent cohort. Thus microbiota-targeted biomarkers may be a powerful tool for diagnosis of different diseases.

Major microbiota dysbiosis in severe obesity: fate after bariatric surgery.

OBJECTIVES: Decreased gut microbial gene richness (MGR) and compositional changes are associated with adverse metabolism in overweight or moderate obesity, but lack characterisation in severe obesity. Bariatric surgery (BS) improves metabolism and inflammation in severe obesity and is associated with gut microbiota modifications. Here, we characterised severe obesity-associated dysbiosis (ie, MGR, microbiota composition and functional characteristics) and assessed whether BS would rescue these changes. DESIGN: Sixty-one severely obese subjects, candidates for adjustable gastric banding (AGB, n=20) or Roux-en-Y-gastric bypass (RYGB, n=41), were enrolled. Twenty-four subjects were followed at 1, 3 and 12 months post-BS. Gut microbiota and serum metabolome were analysed using shotgun metagenomics and liquid chromatography mass spectrometry (LC-MS). Confirmation groups were included. RESULTS: Low gene richness (LGC) was present in 75% of patients and correlated with increased trunk-fat mass and comorbidities (type 2 diabetes, hypertension and severity). Seventy-eight metagenomic species were altered with LGC, among which 50% were associated with adverse body composition and metabolic phenotypes. Nine serum metabolites (including glutarate, 3-methoxyphenylacetic acid and L-histidine) and functional modules containing protein families involved in their metabolism were strongly associated with low MGR. BS increased MGR 1 year postsurgery, but most RYGB patients remained with low MGR 1 year post-BS, despite greater metabolic improvement than AGB patients. CONCLUSIONS: We identified major gut microbiota alterations in severe obesity, which include decreased MGR and related functional pathways linked with metabolic deteriorations. The lack of full rescue post-BS calls for additional strategies to improve the gut microbiota ecosystem and microbiome-host interactions in severe obesity. TRIAL REGISTRATION NUMBER: NCT01454232.

Akkermansia muciniphila abundance is lower in severe obesity, but its increased level after bariatric surgery is not associated with metabolic health improvement.

The gut bacterial species Akkermansia muciniphila is associated with a healthier clinical profile. The purpose of this study was to determine the association between A. muciniphila and glucose homeostasis in patients undergoing bariatric surgery (BS): gastric banding (GB) or Roux-en-Y gastric bypass (RYGB). This nonrandomized prospective study included 65 women with severe obesity. Longitudinal analysis included subjects for whom A. muciniphila data were available at follow-up [1, 3, and 12 mo; GB (n = 10) or RYGB (n = 11)]. Glucose homeostasis markers were measured under fasting conditions (glucose, insulin, and HbA1c) or during an oral glucose tolerance test. Fecal microbiota was analyzed using shotgun metagenomics, and A. muciniphila relative abundance was assessed with 16S rRNA quantitative PCR. A. muciniphila relative abundance was significantly lower in severe obesity [mean body mass index, 45.7 kg/m2 (SD 5.4)] than in moderate obesity [33.2 kg/m2 (SD 3.8)] but not associated with glucose homeostasis markers. A significant increase in A. muciniphila relative abundance after RYGB was not correlated with metabolic improvement. Baseline A. muciniphila abundance was correlated with bacterial gene richness and was highest in the high-richness Ruminococcaceae enterotype. A. muciniphila increased in relative abundance after BS in patients with low baseline A. muciniphila abundance, especially those with a Bacteroides type 2 enterotype classification. Although decreased in severe obesity, relative abundance of A. muciniphila was not associated with glucose homeostasis before or after BS. A certain level of A. muciniphila abundance might be required to observe a beneficial link to health. The severity of obesity and gut dysbiosis may partly explain the discrepancy with previous findings in less obese populations.

Elevated serum ceramides are linked with obesity-associated gut dysbiosis and impaired glucose metabolism.

INTRODUCTION: Low gut microbiome richness is associated with dyslipidemia and insulin resistance, and ceramides and other sphingolipids are implicated in the development of diabetes. OBJECTIVES: Determine whether circulating sphingolipids, particularly ceramides, are associated with alterations in the gut microbiome among obese patients with increased diabetes risk. METHODS: This was a cross-sectional and longitudinal retrospective analysis of a dietary/weight loss intervention. Fasted serum was collected from 49 participants (41 women) and analyzed by HPLC-MS/MS to quantify 45 sphingolipids. Shotgun metagenomic sequencing of stool was performed to profile the gut microbiome. RESULTS: Confirming the link to deteriorated glucose homeostasis, serum ceramides were positively correlated with fasting glucose, but inversely correlated with fasting and OGTT-derived measures of insulin sensitivity and ß-cell function. Significant associations with gut dysbiosis were demonstrated, with SM and ceramides being inversely correlated with gene richness. Ceramides with fatty acid chain lengths of 20-24 carbons were the most associated with low richness. Diet-induced weight loss, which improved gene richness, decreased most sphingolipids. Thirty-one MGS, mostly corresponding to unidentified bacteria species, were inversely correlated with ceramides, including a number of Bifidobacterium and Methanobrevibacter smithii. Higher ceramide levels were also associated with increased metagenomic modules for lipopolysaccharide synthesis and flagellan synthesis, two pathogen-associated molecular patterns, and decreased enrichment of genes involved in methanogenesis and bile acid metabolism. CONCLUSION: This study identifies an association between gut microbiota richness, ceramides, and diabetes risk in overweight/obese humans, and suggests that the gut microbiota may contribute to dysregulation of lipid metabolism in metabolic disorders.

Dietary intervention impact on gut microbial gene richness.

Complex gene-environment interactions are considered important in the development of obesity. The composition of the gut microbiota can determine the efficacy of energy harvest from food and changes in dietary composition have been associated with changes in the composition of gut microbial populations. The capacity to explore microbiota composition was markedly improved by the development of metagenomic approaches, which have already allowed production of the first human gut microbial gene catalogue and stratifying individuals by their gut genomic profile into different enterotypes, but the analyses were carried out mainly in non-intervention settings. To investigate the temporal relationships between food intake, gut microbiota and metabolic and inflammatory phenotypes, we conducted diet-induced weight-loss and weight-stabilization interventions in a study sample of 38 obese and 11 overweight individuals. Here we report that individuals with reduced microbial gene richness (40%) present more pronounced dys-metabolism and low-grade inflammation, as observed concomitantly in the accompanying paper. Dietary intervention improves low gene richness and clinical phenotypes, but seems to be less efficient for inflammation variables in individuals with lower gene richness. Low gene richness may therefore have predictive potential for the efficacy of intervention.

Richness of human gut microbiome correlates with metabolic markers.

We are facing a global metabolic health crisis provoked by an obesity epidemic. Here we report the human gut microbial composition in a population sample of 123 non-obese and 169 obese Danish individuals. We find two groups of individuals that differ by the number of gut microbial genes and thus gut bacterial richness. They contain known and previously unknown bacterial species at different proportions; individuals with a low bacterial richness (23% of the population) are characterized by more marked overall adiposity, insulin resistance and dyslipidaemia and a more pronounced inflammatory phenotype when compared with high bacterial richness individuals. The obese individuals among the lower bacterial richness group also gain more weight over time. Only a few bacterial species are sufficient to distinguish between individuals with high and low bacterial richness, and even between lean and obese participants. Our classifications based on variation in the gut microbiome identify subsets of individuals in the general white adult population who may be at increased risk of progressing to adiposity-associated co-morbidities.

Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology.

OBJECTIVE: Individuals with obesity and type 2 diabetes differ from lean and healthy individuals in their abundance of certain gut microbial species and microbial gene richness. Abundance of Akkermansia muciniphila, a mucin-degrading bacterium, has been inversely associated with body fat mass and glucose intolerance in mice, but more evidence is needed in humans. The impact of diet and weight loss on this bacterial species is unknown. Our objective was to evaluate the association between faecal A. muciniphila abundance, faecal microbiome gene richness, diet, host characteristics, and their changes after calorie restriction (CR). DESIGN: The intervention consisted of a 6-week CR period followed by a 6-week weight stabilisation diet in overweight and obese adults (N=49, including 41 women). Faecal A. muciniphila abundance, faecal microbial gene richness, diet and bioclinical parameters were measured at baseline and after CR and weight stabilisation. RESULTS: At baseline A. muciniphila was inversely related to fasting glucose, waist-to-hip ratio and subcutaneous adipocyte diameter. Subjects with higher gene richness and A. muciniphila abundance exhibited the healthiest metabolic status, particularly in fasting plasma glucose, plasma triglycerides and body fat distribution. Individuals with higher baseline A. muciniphila displayed greater improvement in insulin sensitivity markers and other clinical parameters after CR. These participants also experienced a reduction in A. muciniphila abundance, but it remained significantly higher than in individuals with lower baseline abundance. A. muciniphila was associated with microbial species known to be related to health. CONCLUSIONS: A. muciniphila is associated with a healthier metabolic status and better clinical outcomes after CR in overweight/obese adults. The interaction between gut microbiota ecology and A. muciniphila warrants further investigation. TRIAL REGISTRATION NUMBER: NCT01314690.

Spectral consensus strategy for accurate reconstruction of large biological networks.

BACKGROUND: The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with few or no experimentally proven interactions. A striking example lies in the recent gut bacterial studies that provided researchers with a plethora of information sources. Despite a deeper knowledge of microbiome composition, inferring bacterial interactions remains a critical step that encounters significant issues, due in particular to high-dimensional settings, unknown gut bacterial taxa and unavoidable noise in sparse datasets. Such data type make any a priori choice of a learning method particularly difficult and urge the need for the development of new scalable approaches. RESULTS: We propose a consensus method based on spectral decomposition, named Spectral Consensus Strategy, to reconstruct large networks from high-dimensional datasets. This novel unsupervised approach can be applied to a broad range of biological networks and the associated spectral framework provides scalability to diverse reconstruction methods. The results obtained on benchmark datasets demonstrate the interest of our approach for high-dimensional cases. As a suitable example, we considered the human gut microbiome co-presence network. For this application, our method successfully retrieves biologically relevant relationships and gives new insights into the topology of this complex ecosystem. CONCLUSIONS: The Spectral Consensus Strategy improves prediction precision and allows scalability of various reconstruction methods to large networks. The integration of multiple reconstruction algorithms turns our approach into a robust learning method. All together, this strategy increases the confidence of predicted interactions from high-dimensional datasets without demanding computations.