Metabonomics and diabetes mellitus

Morchella esculenta mushroom polysaccharide attenuates diabetes and modulates intestinal permeability and gut microbiota in a type 2 diabetic mice model

Type 2 diabetes mellitus (T2DM) is a health issue that causes serious worldwide economic problems. It has previously been reported that natural polysaccharides have been studied with regard to regulating the gut microbiota, which plays an important role in T2DM. Here, we investigate the effects of Morchella esculenta polysaccharide (MEP) on a high-fat diet (HFD) and streptozotocin (STZ)-induced T2DM in BALB/c mice. The administration of MEP effectively regulated hyperglycemia and hyperlipidemia and improved insulin sensitivity. We also determined an improvement in gut microbiota composition by 16sRNA pyrosequencing. Treatment with MEP showed an increase in beneficial bacteria, i.e., Lactobacillus and Firmicutes, while the proportion of the opportunistic bacteria Actinobacteria, Corynebacterium, and Facklamia decreased. Furthermore, the treatment of T2DM mice with MEP resulted in reduced endotoxemia and insulin resistance-related pro-inflammatory cytokines interleukin 1β (IL-1β), tumor necrosis factor-alpha (TNF-α), and interleukin 6 (IL-6). Moreover, MEP treatment improved intestinal permeability by modulating the expression of the colon tight-junction proteins zonula occludens-1 (ZO-1), occludin, claudin-1, and mucin-2 protein (MUC2). Additionally, MEP administration affects the metagenome of microbial communities in T2DM mice by altering the functional metabolic pathways. All these findings suggested that MEP is a beneficial prebiotic associated with ameliorating the gut microbiota and its metabolites in T2DM.

Cutaneous Thermal Injury Modulates Blood and Skin Metabolomes Differently in a Murine Model

As the field of metabolomics develops further, investigations of how the metabolome is affected following thermal injury may be helpful to inform diagnostics and guide treatments. In this study, changes to the metabolome were tested and validated in a murine burn injury model. After a 30% total body surface scald injury or sham procedure sera and skin biopsies were collected at 1, 2, 6, or 24 hr.

Burn-specific changes in the metabolome were detected compared to sham animals. The sera metabolome exhibited a more rapid response to burn injury than that of the skin and it peaked more proximal to injury (6 vs 24 hr). Progression of metabolic response in the skin was less synchronous and showed a higher overlap of the significantly modified metabolites (SMMs) among tested time-points. Top affected pathways identified by SMMs of skin included inositol phosphate metabolism, ascorbate and alderate metabolism, caffeine metabolism, and the pentose phosphate pathway. Future research is warranted in human and larger animal models to further elucidate the role of metabolomic perturbations and the pathophysiology following burn injury.

The total flavonoids from Selaginella tamariscina (beauv.) Spring improve glucose and lipid metabolism in db/db mice

Objectives

This study aimed to investigate the glucose and lipid metabolism improving effect of the total flavonoids from Selaginella tamariscina (Beauv.) Spring (TFST) on db/db mice, and to study its mechanism of action.

Materials and Methods

The db/db mice were divided into 5 groups: the normal group (NC), the diabetic group (DM), the gliclazide group (GZ), the DM+TFST (110 mg/kg), and the DM+TFST (220 mg/kg). The body weight, blood glucose, INS, GC, TC, TG, LDL, and HDL were detected. HE staining was used to observe the liver and pancreas. Urine was tested by UPLC-QTOF-MS to study the metabolic differences of each group, coupled with SIMCA-P13.0 for PCA and OPLS-DA analysis, to identify potential biomarkers, find the metabolic pathway. Western blot was used to examine liver tissue of mice for studying effect of TFST on the PPAR-γ/PI3K/GLU4 pathway.

Results

TFST can reduce the weight and levels of TC, TG, and LDL-C, increase the level of GC in blood, and reduce the fat accumulation and inflammation in the liver, and repair the islet cell. 13 biomarkers were identified, they are mainly involved in amino acid metabolism, and purine and pyrimidine metabolism. The results of Western blot show TFST can improve the utilization rate of GLU4 by regulating PPAR-γ and PI3K expression in the liver of db/db mice.

Conclusion

TFST can improve glucose and lipid metabolism of DM, which relates to regulation of the PPAR-γ/PI3K/GLU4 signaling pathway, and affect the amino acid metabolism, purine, and pyrimidine metabolism.

Identification of Potential Serum Metabolic Biomarkers of Diabetic Kidney Disease: A Widely Targeted Metabolomics Study

Background and Objectives. Diabetic kidney disease is a leading cause of chronic kidney disease and end-stage renal disease across the world. Early identification of DKD is vitally important for the effective prevention and control of it. However, the available indicators are doubtful in the early diagnosis of DKD. This study is aimed at determining novel sensitive and specific biomarkers to distinguish DKD from their counterparts effectively based on the widely targeted metabolomics approach. Materials and Method. This case-control study involved 44 T2DM patients. Among them, 24 participants with DKD were defined as the cases and another 20 without DKD were defined as the controls. The ultraperformance liquid chromatography-electrospray ionization-tandem mass spectrometry system was applied for the assessment of the serum metabolic profiles. Comprehensive analysis of metabolomics characteristics was conducted to detect the candidate metabolic biomarkers and assess their capability and feasibility.

RESULT

A total of 11 differential metabolites, including Hexadecanoic Acid (C16:0), Linolelaidic Acid (C18:2N6T), Linoleic Acid (C18:2N6C), Trans-4-Hydroxy-L-Proline, 6-Aminocaproic Acid, L-Dihydroorotic Acid, 6-Methylmercaptopurine, Piperidine, Azoxystrobin Acid, Lysopc 20:4, and Cuminaldehyde, were determined as the potential biomarkers for the DKD early identification, based on the multivariable generalized linear regression model and receiver operating characteristic analysis.

CONCLUSION

Serum metabolites might act as sensitive and specific biomarkers for DKD early detection. Further longitudinal studies are needed to confirm our findings.

Characterization of the gut microbiota in leptin deficient obese mice - Correlation to inflammatory and diabetic parameters

Gut microbiota have been implicated as a relevant factor in the development of type 2 diabetes mellitus (T2DM), and its diversity might be a cause of variation in animal models of T2DM. In this study, we aimed to characterise the gut microbiota of a T2DM mouse model with a long term vision of being able to target the gut microbiota to reduce the number of animals used in experiments. Male B6.V-Lep(ob)/J mice were characterized according to a number of characteristics related to T2DM, inflammation and gut microbiota. All findings were thereafter correlated to one another in a linear regression model. The total gut microbiota profile correlated to glycated haemoglobin, and high proportions of Prevotellaceae and Lachnospiraceae correlated to impaired or improved glucose intolerance, respectively. In addition, Akkermansia muciniphila disappeared with age as glucose intolerance worsened. A high proportion of regulatory T cells correlated to the gut microbiota and improved glucose tolerance. Furthermore, high levels of IL-10, IL-12 and TNF-α correlated to impaired glucose tolerance, blood glucose or glycated haemoglobin. The findings indicate that gut microbiota may contribute to variation in various disease read-outs in the B6.V-Lep(ob)/J model and considering them in both quality assurance and data evaluation for the B6.V-Lep(ob)/J model may have a reducing impact on the inter-individual variation.

Systemic and characteristic metabolites in the serum of streptozotocin-induced diabetic rats at different stages as revealed by a (1)H-NMR based metabonomic approach

Diabetes mellitus is a typical heterogeneous metabolic disorder characterized by abnormal metabolism of carbohydrates, lipids, and proteins. Investigating the changes in metabolic pathways during the evolution of diabetes mellitus may contribute to the understanding of its metabolic features and pathogenesis. In this study, serum samples were collected from diabetic rats and age-matched controls at different time points: 1 and 9 weeks after streptozotocin (STZ) treatment. (1)H nuclear magnetic resonance ((1)H NMR)-based metabonomics with quantitative analysis was performed to study the metabolic changes. The serum samples were also subjected to clinical chemistry analysis to verify the metabolic changes observed by metabonomics. Partial least squares discriminant analysis (PLS-DA) demonstrated that the levels of serum metabolites in diabetic rats are different from those in control rats. These findings indicate that the metabolic characteristics of the two groups are markedly different at 1 and 9 weeks. Quantitative analysis showed that the levels of some metabolites, such as pyruvate, lactate, citrate, acetone, acetoacetate, acetate, glycerol, and valine, varied in a time-dependent manner in diabetic rats. These results suggest that serum metabolites related to glycolysis, the tricarboxylic acid cycle, gluconeogenesis, fatty acid β-oxidation, branched-chain amino acid metabolism, and the tyrosine metabolic pathways are involved in the evolution of diabetes. The metabolic changes represent potential features and promote a better understanding of the mechanisms involved in the development of diabetes mellitus. This work further suggests that (1)H NMR metabonomics is a valuable approach for providing novel insights into the pathogenesis of diabetes mellitus and its complications.

Systemic perturbations of key metabolites in diabetic rats during the evolution of diabetes studied by urine metabonomics

BACKGROUND

Elucidation of metabolic profiles during diabetes progression helps understand the pathogenesis of diabetes mellitus. In this study, urine metabonomics was used to identify time-related metabolic changes that occur during the development of diabetes mellitus and characterize the biochemical process of diabetes on a systemic, metabolic level.

METHODOLOGY/PRINCIPAL FINDINGS

Urine samples were collected from diabetic rats and age-matched controls at different time points: 1, 5, 10, and 15 weeks after diabetes modeling. (1)H nuclear magnetic resonance ((1)H NMR) spectra of the urine samples were obtained and analyzed by multivariate data analysis and quantitative statistical analysis. The metabolic patterns of diabetic groups are separated from the controls at each time point, suggesting that the metabolic profiles of diabetic rats were markedly different from the controls. Moreover, the samples from the diabetic 1-wk group are closely associated, whereas those of the diabetic 15-wk group are scattered, suggesting that the presence of various of complications contributes significantly to the pathogenesis of diabetes. Quantitative analysis indicated that urinary metabolites related to energy metabolism, tricarboxylic acid (TCA) cycle, and methylamine metabolism are involved in the evolution of diabetes.

CONCLUSIONS/SIGNIFICANCE

The results highlighted that the numbers of metabolic changes were related to diabetes progression, and the perturbed metabolites represent potential metabolic biomarkers and provide clues that can elucidate the mechanisms underlying the generation and development of diabetes as well as its complication.

Metabolomics techniques in nanotoxicology studies

The rapid growth in the development of nanoparticles for uses in a variety of applications including targeted drug delivery, cancer therapy, imaging, and as biological sensors has led to questions about potential toxicity of such particles to humans. High-throughput methods are necessary to evaluate the potential toxicity of nanoparticles. The omics technologies are particularly well suited to evaluate toxicity in both in vitro and in vivo systems. Metabolomics, specifically, can rapidly screen for biomarkers related to predefined pathways or processes in biofluids and tissues. Specifically, oxidative stress has been implicated as a potential mechanism of toxicity in nanoparticles and is generally difficult to measure by conventional methods. Furthermore, metabolomics can provide mechanistic insight into nanotoxicity. This chapter focuses on the application of both LC/MS and NMR-based metabolomics approaches to study the potential toxicity of nanoparticles.

1H NMR-based metabonomic analysis of metabolic changes in streptozotocin-induced diabetic rats

Diabetes mellitus is a complex metabolic disorder characterized by chronic hyperglycemia, hypoinsulinemia, and ketosis. To access the biochemical process of diabetes, we applied quantitative (1)H NMR-based metabonomics to analyze urine, serum, and liver extracts from streptozotocin-induced diabetic rats. Principle component analysis (PCA) of (1)H NMR spectra disclosed metabolic pattern differences between diabetic and control rats, and identified the related metabolic changes. The PCA scores plot demonstrated that the diabetic group could be distinguished from the control group, indicating that the metabolic characteristics of the two groups were markedly different. Our work reveals the accumulation of triglycerides, fatty acids and acetoacetate in diabetic rats, and may provide an efficient, convenient way for evaluating the pathological state and biochemical process of diabetes mellitus.

Metabonomics research of diabetic nephropathy and type 2 diabetes mellitus based on UPLC-oaTOF-MS system

Ultra performance liquid chromatography (UPLC) coupled with orthogonal acceleration time-of-flight (oaTOF) mass spectrometry has showed great potential in diabetes research. In this paper, a UPLC-oaTOF-MS system was employed to distinguish the global serum profiles of 8 diabetic nephropathy (DN) patients, 33 type 2 diabetes mellitus (T2DM) patients and 25 healthy volunteers, and tried to find potential biomarkers. The UPLC system produced information-rich chromatograms with typical measured peak widths of 4 s, generating peak capacities of 225 in 15 min. Furthermore, principal component analysis (PCA) was used for group differentiation and marker selection. As shown in the scores plot, the distinct clustering between the patients and controls was observed, and DN and T2DM patients were also separated into two individual groups. Several compounds were tentatively identified based on accurate mass, isotopic pattern and MS/MS information. In addition, significant changes in the serum level of leucine, dihydrosphingosine and phytosphingosine were noted, indicating the perturbations of amino acid metabolism and phospholipid metabolism in diabetic diseases, which having implications in clinical diagnosis and treatment.