Deep learning methods in metagenomics: a review.

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

Functional alterations and predictive capacity of gut microbiome in type 2 diabetes.

The gut microbiome plays a significant role in the development of Type 2 Diabetes Mellitus (T2DM), but the functional mechanisms behind this association merit deeper investigation. Here, we used the nanopore sequencing technology for metagenomic analyses to compare the gut microbiome of individuals with T2DM from the United Arab Emirates (n = 40) with that of control (n = 44). DMM enterotyping of the cohort resulted concordantly with previous results, in three dominant groups Bacteroides (K1), Firmicutes (K2), and Prevotella (K3) lineages. The diversity analysis revealed a high level of diversity in the Firmicutes group (K2) both in terms of species richness and evenness (Wilcoxon rank-sum test, p value < 0.05 vs. K1 and K3 groups), consistent with the Ruminococcus enterotype described in Western populations. Additionally, functional enrichment analyses of KEGG modules showed significant differences in abundance between individuals with T2DM and controls (FDR < 0.05). These differences include modules associated with the degradation of amino acids, such as arginine, the degradation of urea as well as those associated with homoacetogenesis. Prediction analysis with the Predomics approach suggested potential biomarkers for T2DM, including a balance between a depletion of Enterococcus faecium and Blautia lineages with an enrichment of Absiella spp or Eubacterium limosum in T2DM individuals, highlighting the potential of metagenomic analysis in predicting predisposition to diabetic cardiomyopathy in T2DM patients.

Evidence of a causal and modifiable relationship between kidney function and circulating trimethylamine N-oxide.

The host-microbiota co-metabolite trimethylamine N-oxide (TMAO) is linked to increased cardiovascular risk but how its circulating levels are regulated remains unclear. We applied "explainable" machine learning, univariate, multivariate and mediation analyses of fasting plasma TMAO concentration and a multitude of phenotypes in 1,741 adult Europeans of the MetaCardis study. Here we show that next to age, kidney function is the primary variable predicting circulating TMAO, with microbiota composition and diet playing minor, albeit significant, roles. Mediation analysis suggests a causal relationship between TMAO and kidney function that we corroborate in preclinical models where TMAO exposure increases kidney scarring. Consistent with our findings, patients receiving glucose-lowering drugs with reno-protective properties have significantly lower circulating TMAO when compared to propensity-score matched control individuals. Our analyses uncover a bidirectional relationship between kidney function and TMAO that can potentially be modified by reno-protective anti-diabetic drugs and suggest a clinically actionable intervention for decreasing TMAO-associated excess cardiovascular risk.

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.

Association of N-Acetyl Asparagine with QTc in Diabetes: A Metabolomics Study.

Changes in the cardio-metabolomics profile and hormonal status have been associated with long QT syndrome, sudden cardiac death and increased mortality. The mechanisms underlying QTc duration are not fully understood. Therefore, an identification of novel markers that complement the diagnosis in these patients is needed. In the present study, we performed untargeted metabolomics on the sera of diabetic patients at a high risk of cardiovascular disease, followed up for 2.55 [2.34-2.88] years (NCT02431234), with the aim of identifying the metabolomic changes associated with QTc. We used independent weighted gene correlation network analysis (WGCNA) to explore the association between metabolites clusters and QTc at T1 (baseline) and T2 (follow up). The overlap of the highly correlated modules at T1 and T2 identified N-Acetyl asparagine as the only metabolite in common, which was involved with the urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine. This analysis was confirmed by applying mixed models, further highlighting its association with QTc. In the current study, we were able to identify a metabolite associated with QTc in diabetic patients at two chronological time points, suggesting a previously unrecognized potential role of N-Acetyl asparagine in diabetic patients suffering from long QTc.

Microbiome and metabolome features of the cardiometabolic disease spectrum.

Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages-acute coronary syndrome, chronic IHD and IHD with heart failure-and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.

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.

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.

Exploring Semi-Quantitative Metagenomic Studies Using Oxford Nanopore Sequencing: A Computational and Experimental Protocol.

The gut microbiome plays a major role in chronic diseases, of which several are characterized by an altered composition and diversity of bacterial communities. Large-scale sequencing projects allowed for characterizing the perturbations of these communities. However, translating these discoveries into clinical applications remains a challenge. To facilitate routine implementation of microbiome profiling in clinical settings, portable, real-time, and low-cost sequencing technologies are needed. Here, we propose a computational and experimental protocol for whole-genome semi-quantitative metagenomic studies of human gut microbiome with Oxford Nanopore sequencing technology (ONT) that could be applied to other microbial ecosystems. We developed a bioinformatics protocol to analyze ONT sequences taxonomically and functionally and optimized preanalytic protocols, including stool collection and DNA extraction methods to maximize read length. This is a critical parameter for the sequence alignment and classification. Our protocol was evaluated using simulations of metagenomic communities, which reflect naturally occurring compositional variations. Next, we validated both protocols using stool samples from a bariatric surgery cohort, sequenced with ONT, Illumina, and SOLiD technologies. Results revealed similar diversity and microbial composition profiles. This protocol can be implemented in a clinical or research setting, bringing rapid personalized whole-genome profiling of target microbiome species.

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.

Protein supplementation during an energy-restricted diet induces visceral fat loss and gut microbiota amino acid metabolism activation: a randomized trial.

Interactions between diet and gut microbiota are critical regulators of energy metabolism. The effects of fibre intake have been deeply studied but little is known about the impact of proteins. Here, we investigated the effects of high protein supplementation (Investigational Product, IP) in a double blind, randomised placebo-controled intervention study (NCT01755104) where 107 participants received the IP or an isocaloric normoproteic comparator (CP) alongside a mild caloric restriction. Gut microbiota profiles were explored in a patient subset (n = 53) using shotgun metagenomic sequencing. Visceral fat decreased in both groups (IP group: - 20.8 ± 23.2 cm2; CP group: - 14.5 ± 24.3 cm2) with a greater reduction (p < 0.05) with the IP supplementation in the Per Protocol population. Microbial diversity increased in individuals with a baseline low gene count (p < 0.05). The decrease in weight, fat mass and visceral fat mass significantly correlated with the increase in microbial diversity (p < 0.05). Protein supplementation had little effects on bacteria composition but major differences were seen at functional level. Protein supplementation stimulated bacterial amino acid metabolism (90% amino-acid synthesis functions enriched with IP versus 13% in CP group (p < 0.01)). Protein supplementation alongside a mild energy restriction induces visceral fat mass loss and an activation of gut microbiota amino-acid metabolism.Clinical trial registration: NCT01755104 (24/12/2012). https://clinicaltrials.gov/ct2/show/record/NCT01755104?term=NCT01755104&draw=2&rank=1 .

Gut microbiota changes after metabolic surgery in adult diabetic patients with mild obesity: a randomised controlled trial.

BACKGROUND: Roux-en-Y gastric bypass (RYGB) surgery is one of the most efficient procedures for the treatment of obesity, also improving metabolic and inflammatory status, in patients with mild obesity. The underlying mechanisms have not been fully understood, but gut microbiota is hypothesized to play a key role. Our aim was to evaluate the association between gut microbiota changes and anthropometric, metabolic and inflammatory profiles after metabolic surgery compared with medical therapy, in type 2 diabetic (T2DM) adults with mild obesity (BMI 30-35 kg/m2). METHODS: DM2 was an open-label, randomised controlled clinical trial (RCT: ISRCTN53984585) with 2 arms: (i) surgical, and (ii) medical. The main outcome was gut microbiota changes after: metabolic surgery (Roux-en-Y gastric bypass-RYGB) versus standard medical therapy. Secondary outcomes included anthropometric, metabolic and inflammatory profiles. Clinical visits, blood workup, and stool samples were collected at baseline and months (M)1, 3, 6, 12. Gut microbiota was profiled using 16S rRNA targeted sequencing. RESULTS: Twenty patients were included: 10 in surgical and 10 in medical arm. Anthropometric and metabolic comparative analysis favoured RYGB over medical arm. At M12, the percentage of weight loss was 25.5 vs. 4.9% (p < 0.001) and HbA1c was 6.2 vs. 7.7% (p < 0.001) respectively. We observed a continuous increase of genus richness after RYGB up until M12. In the medical arm, genus richness ended-up being significantly lower at M12. Composition analysis indicated significant changes of the overall microbial ecosystem (permanova p = 0.004, [R2 = 0.17]) during the follow-up period after RYGB. There was a strong association between improvement of anthropometric/metabolic/inflammatory biomarkers and increase in microbial richness and Proteobacterial lineages. CONCLUSIONS: This was the first RCT studying composite clinical, analytic, and microbiome changes in T2DM patients with class 1 obesity after RYGB versus standard medical therapy. The remarkable phenotypic improvement after surgery occurred concomitantly with changes in the gut microbiome, but at a lower level. TRIAL REGISTRATION: ISRCTN53984585.

Altered subcutaneous adipose tissue parameters after switching ART-controlled HIV+ patients to raltegravir/maraviroc.

OBJECTIVE: To evaluate the effect on anthropometric, metabolic and adipose tissue parameters of switching ART-controlled persons living with HIV (PLWH) from a protease inhibitor regimen to raltegravir/maraviroc. DESIGN: Sub-study of the ANRS157 ROCnRAL study with the investigation of subcutaneous abdominal adipose tissue (SCAT) biopsy at inclusion and study end. METHODS: We performed lipoaspiration of paired SCAT samples, histology on fresh/fixed samples and examined the transcriptomic profile analyzed using Illumina microarrays after RNA extraction. Statistical analyses used the Wilcoxon-paired test. RESULTS: The patients (n = 8) were mainly male (7/8), aged (mean ±â€Šstandard error of the mean) 54.9 ±â€Š1.2 years, BMI 26.1 ±â€Š1.2 kg/m2, CD4+ 699 ±â€Š56 cells/mm3, all viral load (VL) <50 copies/ml. After a follow-up of 6 ±â€Š0.5 months, all PLWH remained with VL <50 copies/ml. BMI, trunk and limb fat amounts were unchanged yet systemic insulin resistance increased. Adipose tissue histology was unchanged except for borderline increased adipocyte diameter (P = 0.1). Among the 16 094 RNA transcripts, 458 genes were up-regulated and 244 were down-regulated. Analyses of the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology databases, evaluating modifications in the main functional pathways, revealed that genes related to immune recognition/function were less expressed as were genes encoding T-cell receptor and receptor signaling pathways. The gene expression profiles indicated decreased inflammation but genes involved in adipogenesis and insulin resistance were overexpressed. CONCLUSION: After 6 months of raltegravir/maraviroc, adipogenesis-related gene profile was enhanced in SCAT, in agreement with a tendency for increased adipocyte size. Enhanced SCAT insulin resistance-related profile was concordant with higher systemic insulin resistance. However, the immune activation/inflammation profile was globally lowered. We propose that raltegravir/maraviroc might favor SCAT gain but reduce inflammation/immune activation.

From correlation to causality: the case of Subdoligranulum.

Gut microbes are considered as major factors contributing to human health. Nowadays, the vast majority of the data available in the literature are mostly exhibiting negative or positive correlations between specific bacteria and metabolic parameters. From these observations, putative detrimental or beneficial effects are then inferred. Akkermansia muciniphila is one of the unique examples for which the correlations with health benefits have been causally validated in vivo in rodents and humans. In this study, based on available metagenomic data in overweight/obese population and clinical variables that we obtained from two cohorts of individuals (n = 108) we identified several metagenomic species (MGS) strongly associated with A. muciniphila with one standing out: Subdoligranulum. By analyzing both qPCR and shotgun metagenomic data, we discovered that the abundance of Subdoligranulum was correlated positively with microbial richness and HDL-cholesterol levels and negatively correlated with fat mass, adipocyte diameter, insulin resistance, levels of leptin, insulin, CRP, and IL6 in humans. Therefore, to further explore whether these strong correlations could be translated into causation, we investigated the effects of the unique cultivated strain of Subdoligranulum (Subdoligranulum variabile DSM 15176 T) in obese and diabetic mice as a proof-of-concept. Strikingly, there were no significant difference in any of the hallmarks of obesity and diabetes measured (e.g., body weight gain, fat mass gain, glucose tolerance, liver weight, plasma lipids) at the end of the 8 weeks of treatment. Therefore, the absence of effect following the supplementation with S. variabile indicates that increasing the intestinal abundance of this bacterium is not translated into beneficial effects in mice. In conclusion, we demonstrated that despite the fact that numerous strong correlations exist between a given bacteria and health, proof-of-concept experiments are required to be further validated or not in vivo. Hence, an urgent need for causality studies is warranted to move from human observations to preclinical validations.

Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology.

Microbiota-host-diet interactions contribute to the development of metabolic diseases. Imidazole propionate is a novel microbially produced metabolite from histidine, which impairs glucose metabolism. Here, we show that subjects with prediabetes and diabetes in the MetaCardis cohort from three European countries have elevated serum imidazole propionate levels. Furthermore, imidazole propionate levels were increased in subjects with low bacterial gene richness and Bacteroides 2 enterotype, which have previously been associated with obesity. The Bacteroides 2 enterotype was also associated with increased abundance of the genes involved in imidazole propionate biosynthesis from dietary histidine. Since patients and controls did not differ in their histidine dietary intake, the elevated levels of imidazole propionate in type 2 diabetes likely reflects altered microbial metabolism of histidine, rather than histidine intake per se. Thus the microbiota may contribute to type 2 diabetes by generating imidazole propionate that can modulate host inflammation and metabolism.

Effect of congenital adrenal hyperplasia treated by glucocorticoids on plasma metabolome: a machine-learning-based analysis.

BACKGROUND: Congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency leads to impaired cortisol biosynthesis. Treatment includes glucocorticoid supplementation. We studied the specific metabolomics signatures in CAH patients using two different algorithms. METHODS: In a case-control study of CAH patients matched on sex and age with healthy control subjects, two metabolomic analyses were performed: one using MetaboDiff, a validated differential metabolomic analysis tool and the other, using Predomics, a novel machine-learning algorithm. RESULTS: 168 participants were included (84 CAH patients). There was no correlation between plasma cortisol levels during glucocorticoid supplementation and metabolites in CAH patients. Indoleamine 2,3-dioxygenase enzyme activity was correlated with ACTH (rho coefficient = -0.25, p-value = 0.02), in CAH patients but not in controls subjects. Overall, 33 metabolites were significantly altered in CAH patients. Main changes came from: purine and pyrimidine metabolites, branched aminoacids, tricarboxylic acid cycle metabolites and associated pathways (urea, glucose, pentose phosphates). MetaboDiff identified 2 modules that were significantly different between both groups: aminosugar metabolism and purine metabolism. Predomics found several interpretable models which accurately discriminated the two groups (accuracy of 0.86 and AUROC of 0.9). CONCLUSION: CAH patients and healthy control subjects exhibit significant differences in plasma metabolomes, which may be explained by glucocorticoid supplementation.