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Aims/hypothesis Drug and surgical-based therapies in type 2 diabetes are associated with altered gut microbiota architecture. Here we investigated the role of the gut microbiome in improved glucose homeostasis following bariatric surgery. Methods We carried out gut microbiome analyses in gastrectomised (by vertical sleeve gastrectomy [VSG]) rats of the Goto–Kakizaki (GK) non-obese model of spontaneously occurring type 2 diabetes, followed by physiological studies in the GK rat. Results VSG in the GK rat led to permanent improvement of glucose tolerance associated with minor changes in the gut microbiome, mostly characterised by significant enrichment of caecal Prevotella copri. Gut microbiota enrichment with P. copri in GK rats through permissive antibiotic treatment, inoculation of gut microbiota isolated from gastrectomised GK rats, and direct inoculation of P. copri, resulted in significant improvement of glucose tolerance, independent of changes in body weight. Plasma bile acids were increased in GK rats following inoculation with P. copri and P. copri-enriched microbiota from VSG-treated rats; the inoculated GK rats then showed increased liver glycogen and upregulated expression of Fxr (also known as Nr1h4), Srebf1c, Chrebp (also known as Mlxipl) and Il10 and downregulated expression of Cyp7a1. Conclusions Our data underline the impact of intestinal P. copri on improved glucose homeostasis through enhanced bile acid metabolism and farnesoid X receptor (FXR) signalling, which may represent a promising opportunity for novel type 2 diabetes therapeutics.

Non-alcoholic fatty liver disease (NAFLD) is often associated with obesity and type 2 diabetes. To disentangle etiological relationships between these conditions and identify genetically-determined metabolites involved in NAFLD processes, we mapped 1H nuclear magnetic resonance (NMR) metabolomic and disease-related phenotypes in a mouse F2 cross derived from strains showing resistance (BALB/c) and increased susceptibility (129S6) to these diseases. Quantitative trait locus (QTL) analysis based on single nucleotide polymorphism (SNP) genotypes identified diet responsive QTLs in F2 mice fed control or high fat diet (HFD). In HFD fed F2 mice we mapped on chromosome 18 a QTL regulating liver micro- and macrovesicular steatosis and inflammation, independently from glucose intolerance and adiposity, which was linked to chromosome 4. Linkage analysis of liver metabolomic profiling data identified a QTL for octopamine, which co-localised with the QTL for liver histopathology in the cross. Functional relationship between these two QTLs was validated in vivo in mice chronically treated with octopamine, which exhibited reduction in liver histopathology and metabolic benefits, underlining its role as a mechanistic biomarker of fatty liver with potential therapeutic applications.

Figure S1. Representative total ion current (TIC) chromatogram profiles in positive (A) & negative (B) ion modes. Data were obtained following untargeted liquid chromatography mass spectrometry (LC-MS) analysis with a CSH C18 columnof a quality control samples representing a pool of plasma samples. Retention times (X-axis) are plotted against relative intensity of the spectral peaks (Y-axis). Approximate positions of the families of lipid compounds in the chromatogram are shown: CE Ceramides, DG Diacyglycerol, FA Fatty Acids, PC Phosphatidylcholines, PE Phosphatidylethanolamines, PG Phosphatidylglycerolipids, PI phosphatidylinositols, PS phosphatidylserines, SM sphingomyelins, TG Triacylglycerol. Figure S2. 3-D Principal component analysis of mass spectrometry data in the cohort. Metabolome data from plasma samples of the cohort processed with a CSH C18 column were analyzed after filtering and normalization for the positive mode (A) and the negative mode (B). Figure S3. Metabolome-wide association between metabolic features and plasma LDL (A, B) and total (C, D) cholesterol. Metabolomic data were obtained by liquid chromatography mass spectrometry (LC-MS) with a CSH C18 column.Spectral features referenced by their mass to charge ratio (X-axis) are plotted against the statistical significance of the association to plasma LDL and total cholesterol (Y-axis). Association results are shown for data in negative (A, C) and positive (B, D) ionization modes. (PPTX 579 kb)

ObjectivesDecreased 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.DesignSixty-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.ResultsLow 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 (includingglutarate,3-methoxyphenylacetic acidandL-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.ConclusionsWe 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 numberNCT01454232.

Background Lipoproteins are major players in the development and progression of atherosclerotic plaques leading to coronary stenosis and myocardial infarction. Epidemiological, genetic and experimental observations have implicated the association of sphingolipids and intermediates of sphingolipid synthesis in atherosclerosis. We aimed to investigate relationships between quantitative changes in serum sphingolipids, the regulation of the metabolism of lipoproteins (LDL, HDL), and endophenotypes of coronary artery disease (CAD). Methods We carried out untargeted liquid chromatography – mass spectrometry (UPLC-MS) lipidomics of serum samples of subjects belonging to a cross-sectional study and recruited on the basis of absence or presence of angiographically-defined CAD, and extensively characterized for clinical and biochemical phenotypes. Results Among the 2998 spectral features detected in the serum samples, 1328 metabolic features were significantly correlated with at least one of the clinical or biochemical phenotypes measured in the cohort. We found evidence of significant associations between 34 metabolite signals, corresponding to a set of sphingomyelins, and serum HDL cholesterol. Many of these metabolite associations were also observed with serum LDL and total cholesterol levels but not as much with serum triglycerides. Conclusion Among patients with CAD, sphingolipids in the form of sphingomyelins are directly correlated with serum levels of lipoproteins and total cholesterol. Results from this study support the fundamental role of sphingolipids in modulating lipid serum levels, highlighting the importance to identify novel targets in the sphingolipid metabolic pathway for anti-atherogenic therapies. Electronic supplementary material The online version of this article (10.1186/s12944-018-0948-5) contains supplementary material, which is available to authorized users.

SUMMARYGut microbiota changes are associated with increased risk of Type 2 diabetes (T2D) and obesity. Through serum metabolome profiling in patients with cardiometabolic disease (CMD) we identified significant inverse correlation between the microbial metabolite 4-cresol and T2D. Chronic administration of non toxic dose of 4-cresol in two complementary preclinical models of CMD reduced adiposity, glucose intolerance and liver triglycerides, and enhanced insulin secretion in vivo, which may be explained by markedly increased pancreas weight, augmented islet density and size, and enhanced vascularisation suggesting activated islet neogenesis. Incubation of isolated islets with 4-cresol enhanced insulin secretion, insulin content and cell proliferation. In both CMD models 4-cresol treatment in vivo was associated with altered expression of SIRT1 and the kinase DYRK1A, which may contribute to mediate its biological effects. Our findings identify 4-cresol as an effective regulator of β-cell function and T2D endophenotypes, which opens therapeutic perspectives in syndromes of insulin deficiency.

High-density lipoprotein (HDL) possesses multiple biological activities; small, dense HDL3c particles displaying distinct lipidomic composition exert potent antiatherogenic activities which can be compromised in dyslipidemic, hyperglycemic insulin-resistant states. However, it remains indeterminate (i) whether such functional HDL deficiency is related to altered HDL composition, and (ii) whether it originates from atherogenic dyslipidemia, dysglycemia, or both. In the present work we analyzed compositional characteristics of HDL subpopulations and functional activity of small, dense HDL3c particles in treatment-naïve patients with well-controlled (n = 10) and poorly-controlled (n = 8) type 2 diabetes (T2D) and in normolipidemic age- and sex-matched controls (n = 11). Our data reveal that patients with both well- and poorly-controlled T2D displayed dyslipidemia and low-grade inflammation associated with altered HDL composition. Such compositional alterations in small, dense HDL subfractions were specifically correlated with plasma HbA1c levels. Further analysis using a lipidomic approach revealed that small, dense HDL3c particles from T2D patients with poor glycemic control displayed additional modifications of their chemical composition. In parallel, antioxidative activity of HDL3c towards oxidation of low-density lipoprotein was diminished. These findings indicate that defective functionality of small, dense HDL particles in patients with T2D is not only affected by the presence of atherogenic dyslipidemia, but also by the level of glycemic control, reflecting compositional alterations of HDL. Fil: Gomez Rosso, Leonardo Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; Argentina. Inserm; Francia. Universite Pierre et Marie Curie; Francia Fil: Lhomme, Marie. Institute of Cardiometabolism and Nutrition; Francia Fil: Meroño, Tomás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; Argentina Fil: Dellepiane, Ana. Centro Ramón Carrillo; Argentina Fil: Sorroche, Patricia Beatriz. Hospital Italiano; Argentina Fil: Hedjazi, Lyamine. Institute of Cardiometabolism and Nutrition; Francia. Centro Ramón Carrillo; Argentina Fil: Zakiev, Emile. Inserm; Francia. Universite Pierre et Marie Curie; Francia. Russian Academy of Medical Sciences; Rusia Fil: Sukhorukov, Vasily. Inserm; Francia. Universite Pierre et Marie Curie; Francia. Russian Academy of Medical Sciences; Rusia Fil: Orekhov, Alexander. Russian Academy of Medical Sciences; Rusia. Skolkovo Innovative Center; Rusia Fil: Gasparri, Julieta. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; Argentina Fil: Chapman, M. John. Inserm; Francia. Universite Pierre et Marie Curie; Francia Fil: Brites, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; Argentina Fil: Kontush, Anatol. Inserm; Francia. Universite Pierre et Marie Curie; Francia

Methylamines are biologically-active metabolites present in serum and urine samples, which play complex roles in metabolic diseases. Methylamines can be detected by proton nuclear magnetic resonance (NMR), but specific methods remain to be developed for their routine assay in human serum in clinical settings. Here we developed and validated a novel reliable “methylamine panel” method for simultaneous quantitative analysis of trimethylamine (TMA), its major detoxification metabolite trimethylamine- N -oxide (TMAO), and precursors choline, betaine and l -carnitine in human serum using Ultra Performance Liquid Chromatography (UPLC) coupled to High Resolution Mass Spectrometry (HRMS). Metabolite separation was carried out on a HILIC stationary phase. For all metabolites, the assay was linear in the range of 0.25–12.5 μmol/L and enabled to reach limit of detection of about 0.10 μmol/L. Relative standard deviations were below 16% for the three levels of concentrations. We demonstrated the strong reliability and robustness of the method, which was applied to serum samples from healthy individuals to establish the range of concentrations of the metabolites and their correlation relationships and detect gender differences. Our data provide original information for implementing in a clinical environment a MS-based diagnostic method with potential for targeted metabolic screening of patients at risk of cardiometabolic diseases.

Human knowledge about monitoring process variables is usually incomplete. To deal with this partial knowledge many types of representation other than the quantitative one are used to describe process variables (qualitative, symbolic interval). Thus, the development of automatic reasoning mechanisms about the process is faced with this problem of multiple data representations. In this paper, a unified principle for reasoning about heterogeneous data is introduced. This principle is based on a simultaneous mapping of data from initially heterogeneous spaces into only one homogeneous space based on a relative measure using appropriate characteristic functions. Once the heterogeneous data are represented in a unified space, a single processing for various analysis purposes can be performed using simple reasoning mechanisms. An application of this principle within a fuzzy logic framework is performed here to demonstrate its effectiveness. We show that simple fuzzy reasoning mechanisms can be used to reason in a unified way about heterogeneous data in three well known machine learning problems.

Background Personalized medicine has become a priority in breast cancer patient management. In addition to the routinely used clinicopathological characteristics, clinicians will have to face an increasing amount of data derived from tumor molecular profiling. The aims of this study were to develop a new gene selection method based on a fuzzy logic selection and classification algorithm, and to validate the gene signatures obtained on breast cancer patient cohorts. Methods We analyzed data from four published gene expression datasets for breast carcinomas. We identified the best discriminating genes by comparing molecular expression profiles between histologic grade 1 and 3 tumors for each of the training datasets. The most pertinent probes were selected and used to define fuzzy molecular grade 1-like (good prognosis) and fuzzy molecular grade 3-like (poor prognosis) profiles. To evaluate the prognostic performance of the fuzzy grade signatures in breast cancer tumors, a Kaplan-Meier analysis was conducted to compare the relapse-free survival deduced from histologic grade and fuzzy molecular grade classification. Results We applied the fuzzy logic selection on breast cancer databases and obtained four new gene signatures. Analysis in the training public sets showed good performance of these gene signatures for grade (sensitivity from 90% to 95%, specificity 67% to 93%). To validate these gene signatures, we designed probes on custom microarrays and tested them on 150 invasive breast carcinomas. Good performance was obtained with an error rate of less than 10%. For one gene signature, among 74 histologic grade 3 and 18 grade 1 tumors, 88 cases (96%) were correctly assigned. Interestingly histologic grade 2 tumors (n = 58) were split in these two molecular grade categories. Conclusion We confirmed the use of fuzzy logic selection as a new tool to identify gene signatures with good reliability and increased classification power. This method based on artificial intelligence algorithms was successfully applied to breast cancers molecular grade classification allowing histologic grade 2 classification into grade 1 and grade 2 like to improve patients prognosis. It opens the way to further development for identification of new biomarker combinations in other applications such as prediction of treatment response. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0077-1) contains supplementary material, which is available to authorized users.

In this paper we propose a feature selection method for symbolic interval data based on similarity margin. In this method, classes are parameterized by an interval prototype based on an appropriate learning process. A similarity measure is defined in order to estimate the similarity between the interval feature value and each class prototype. Then, a similarity margin concept has been introduced. The heuristic search is avoided by optimizing an objective function to evaluate the importance (weight) of each interval feature in a similarity margin framework. The experimental results show that the proposed method selects meaningful features for interval data. In particular, the method we propose yields a significant improvement on classification task of three real-world datasets.

Definition of membership-margin based objective function.Weighting the antecedents of fuzzy if-then rules to improve classification performance.Evaluate the feature importance within membership-margin framework.Feature selection algorithm for mixed-type and high dimensional data.The effectiveness of the approach is proved through an extensive experimental study. The present paper describes a new feature weighting method based on a membership margin. Distinctive properties of the proposed method include its capability to process problems characterized by mixed-type data (quantitative, qualitative and interval) as well as a huge number of features. The key idea is to map simultaneously all the features of different types into a common space; the membership space. Once all features are represented in a homogeneous space, a feature weighting task can be performed in unified way. This weighting approach is integrated here within a fuzzy classifier through a fuzzy rule weighted concept in order to improve its performance. Each antecedent fuzzy set in the fuzzy if-then rule is weighted to characterize the importance of each proposition and therefore its corresponding feature. Weight estimation process is based on membership margin maximization to estimate a fuzzy weight of each feature in the membership space. Experiments on low and high dimensional real-world datasets demonstrate that the proposed approach can improve significantly the performance of the fuzzy rule-based as well as other state of the art classifiers and can even outperform classical feature weighting approaches. In particular, we show that this approach can yield meaningful results on two real-world applications for cancer prognosis and industrial process diagnosis.

High-throughput (1)H nuclear magnetic resonance (NMR) is an increasingly popular robust approach for qualitative and quantitative metabolic profiling, which can be used in conjunction with genomic techniques to discover novel genetic associations through metabotype quantitative trait locus (mQTL) mapping. There is therefore a crucial necessity to develop specialized tools for an accurate detection and unbiased interpretability of the genetically determined metabolic signals. Here we introduce and implement a combined chemoinformatic approach for objective and systematic analysis of untargeted (1)H NMR-based metabolic profiles in quantitative genetic contexts. The R/Bioconductor mQTL.NMR package was designed to (i) perform a series of preprocessing steps restoring spectral dependency in collinear NMR data sets to reduce the multiple testing burden, (ii) carry out robust and accurate mQTL mapping in human cohorts as well as in rodent models, (iii) statistically enhance structural assignment of genetically determined metabolites, and (iv) illustrate results with a series of visualization tools. Built-in flexibility and implementation in the powerful R/Bioconductor framework allow key preprocessing steps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific problems. The mQTL.NMR package is freely available with its source code through the Comprehensive R/Bioconductor repository and its own website ( http://www.ican-institute.org/tools/ ). It represents a significant advance to facilitate untargeted metabolomic data processing and quantitative analysis and their genetic mapping.

Microarray profiling has recently generated the hope to gain new insights into breast cancer biology and thereby improve the performance of current prognostic tools. However, it also poses several serious challenges to classical data analysis techniques related to the characteristics of resulting data, mainly high dimensionality and low signal-to-noise ratio. Despite the tremendous research work performed to handle the first challenge in the feature selection framework, very little attention has been directed to address the second one. We propose in this article to address both issues simultaneously based on symbolic data analysis capabilities in order to derive more accurate genetic marker–based prognostic models. In particular, interval data representation is employed to model various uncertainties in microarray measurements. A recent feature selection algorithm that handles symbolic interval data is used then to derive a genetic signature. The predictive value of the derived signature is then assessed by following a rigorous experimental setup and compared with existing prognostic approaches in terms of predictive performance and estimated survival probability. It is shown that the derived signature (GenSym) performs significantly better than other prognostic models, including the 70-gene signature, St. Gallen, and National Institutes of Health criteria.

Process monitoring and fault diagnosis are of great importance for operation safety and efficiency of complex industrial plants. The present article proposes a novel methodology to address the sensor location problem for fault detection. Firstly, all the process situations are identified based on a fuzzy learning algorithm using measurements generated from the whole available set of sensors. Then, a fuzzy feature selection approach is used to select the optimal number of sensors that characterize accurately the set of process situations (abnormal and normal). This method optimizes the performance of the learning algorithm within a membership margin framework, and thereby, it is capable to address correlation and redundancy issues. A behavioral pattern of the process is constructed with the selected sensors and is used to associate new online observations to previously characterized process situations. The proposed strategy has been applied for fault diagnosis to a pharmaceutical synthesis carried out in a new intensified heat-exchanger reactor.