Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes

TIGAR exacerbates obesity by triggering LRRK2-mediated defects in macroautophagy and chaperone-mediated autophagy in adipocytes

Obesity is one of the most common metabolic diseases around the world, which is distinguished by the abnormal buildup of triglycerides within adipose cells. Recent research has revealed that autophagy regulates lipid mobilization to maintain energy balance. TIGAR (Trp53 induced glycolysis regulatory phosphatase) has been identified as a glycolysis inhibitor, whether it plays a role in the metabolism of lipids is unknown. Here, we found that TIGAR transgenic (TIGAR+/+) mice exhibited increased fat mass and trended to obesity phenotype. Non-target metabolomics showed that TIGAR caused the dysregulation of the metabolism profile. The quantitative transcriptome sequencing identified an increased levels of LRRK2 and RAB7B in the adipose tissue of TIGAR+/+ mice. It was confirmed in vitro that TIGAR overexpression increased the levels of LRRK2 by inhibiting polyubiquitination degradation, thereby suppressing macroautophagy and chaperone-mediated autophagy (CMA) while increasing lipid accumulation which were reversed by the LRRK2 inhibitor DNL201. Furthermore, TIGAR drove LRRK2 to interact with RAB7B for suppressing lysosomal degradation of lipid droplets, while the increased lipid droplets in adipocytes were blocked by the RAB7B inhibitor ML282. Additionally, fat-specific TIGAR knockdown of TIGAR+/+ mice alleviated the symptoms of obesity, and adipose tissues-targeting superiority DNL201 nano-emulsion counteracted the obesity phenotype in TIGAR+/+ mice. In summary, the current results indicated that TIGAR performed a vital function in the lipid metabolism through LRRK2-mediated negative regulation of macroautophagy and CMA in adipocyte. The findings suggest that TIGAR has the potential to serve as a viable therapeutic target for treating obesity and its associated metabolic dysfunction.

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

Linking MS1 and MS2 signals in positive and negative modes of LC-HRMS in untargeted metabolomics using the ROIMCR approach

Data-independent acquisition (DIA) mode in liquid chromatography (LC) high-resolution mass spectrometry (HRMS) has emerged as a powerful strategy in untargeted metabolomics for detecting a broad range of metabolites. However, the use of this approach also represents a challenge in the analysis of the large datasets generated. The regions of interest (ROI) multivariate curve resolution (MCR) approach can help in the identification and characterization of unknown metabolites in their mixtures by linking their MS1 and MS2 DIA spectral signals. In this study, it is proposed for the first time the analysis of MS1 and MS2 DIA signals in positive and negative electrospray ionization modes simultaneously to increase the coverage of possible metabolites present in biological systems. In this work, this approach has been tested for the detection and identification of the amino acids present in a standard mixture solution and in fish embryo samples. The ROIMCR analysis allowed for the identification of all amino acids present in the analyzed mixtures in both positive and negative modes. The methodology allowed for the direct linking and correspondence between the MS signals in their different acquisition modes. Overall, this approach confirmed the advantages and possibilities of performing the proposed ROIMCR simultaneous analysis of mass spectrometry signals in their differing acquisition modes in untargeted metabolomics studies.

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: "Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal" (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214-0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: "Cer(d18:1/24:1) i2", "PC(20:3-OH/P-18:1)", "Ganoderic acid C2", "TG(16:0/17:1/18:1)" and "GPEtn(18:0/20:4)".

Potential progression biomarkers of diabetic kidney disease determined using comprehensive machine learning analysis of non-targeted metabolomics

Diabetic kidney disease is the main cause of end-stage renal disease worldwide. The prediction of the clinical course of patients with diabetic kidney disease remains difficult, despite the identification of potential biomarkers; therefore, novel biomarkers are needed to predict the progression of the disease. We conducted non-targeted metabolomics using plasma and urine of patients with diabetic kidney disease whose estimated glomerular filtration rate was between 30 and 60 mL/min/1.73 m². We analyzed how the estimated glomerular filtration rate changed over time (up to 30 months) to detect rapid decliners of kidney function. Conventional logistic analysis suggested that only one metabolite, urinary 1-methylpyridin-1-ium (NMP), was a promising biomarker. We then applied a deep learning method to identify potential biomarkers and physiological parameters to predict the progression of diabetic kidney disease in an explainable manner. We narrowed down 3388 variables to 50 using the deep learning method and conducted two regression models, piecewise linear and handcrafted linear regression, both of which examined the utility of biomarker combinations. Our analysis, based on the deep learning method, identified systolic blood pressure and urinary albumin-to-creatinine ratio, six identified metabolites, and three unidentified metabolites including urinary NMP, as potential biomarkers. This research suggests that the machine learning method can detect potential biomarkers that could otherwise escape identification using the conventional statistical method.

Microbiome risk profiles as biomarkers for inflammatory and metabolic disorders

The intestine harbours a complex array of microorganisms collectively known as the gut microbiota. The past two decades have witnessed increasing interest in studying the gut microbiota in health and disease, largely driven by rapid innovation in high-throughput multi-omics technologies. As a result, microbial dysbiosis has been linked to many human pathologies, including type 2 diabetes mellitus and inflammatory bowel disease. Integrated analyses of multi-omics data, including metagenomics and metabolomics along with measurements of host response and cataloguing of bacterial isolates, have identified many bacteria and bacterial products that are correlated with disease. Nevertheless, insight into the mechanisms through which microbes affect intestinal health requires going beyond correlation to causation. Current understanding of the contribution of the gut microbiota to disease causality remains limited, largely owing to the heterogeneity of microbial community structures, interindividual differences in disease evolution and incomplete understanding of the mechanisms that integrate microbiota-derived signals into host signalling pathways. In this Review, we provide a broad insight into the microbiome signatures linked to inflammatory and metabolic disorders, discuss outstanding challenges in this field and propose applications of multi-omics technologies that could lead to an improved mechanistic understanding of microorganism-host interactions.

Omics era in type 2 diabetes: From childhood to adulthood

Parallel to the dramatic rise of pediatric obesity, estimates reported an increased prevalence of type 2 diabetes (T2D) already in childhood. The close relationship between obesity and T2D in children is mainly sustained by insulin resistance (IR). In addition, the cardiometabolic burden of T2D including nonalcoholic fatty liver disease, cardiovascular disease and metabolic syndrome is also strictly related to IR. Although T2D pathophysiology has been largely studied in an attempt to improve therapeutic options, molecular mechanisms are still not fully elucidated. In this perspective, omics approaches (including lipidomics, metabolomics, proteomics and metagenomics) are providing the most attractive therapeutic options for T2D. In particular, distinct both lipids and metabolites are emerging as potential therapeutic tools. Of note, among lipid classes, the pathogenic role of ceramides in T2D context has been supported by several data. Thus, selective changes of ceramides expression might represent innovative therapeutic strategies for T2D treatment. More, distinct metabolomics pathways have been also found to be associated with higher T2D risk, by providing novel potential T2D biomarkers. Taken together, omics data are responsible for the expanding knowledge of T2D pathophysiology, by providing novel insights to improve therapeutic strategies for this tangled disease. We aimed to summarize the most recent evidence in the intriguing field of the omics approaches in T2D both in adults and children.