Plasma Metabolite Markers of Parkinson’s Disease and Atypical Parkinsonism

Differentiating between Parkinson’s disease (PD) and the atypical Parkinsonian disorders of multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) is difficult clinically due to overlapping symptomatology, especially at early disease stages. Consequently, there is a need to identify metabolic markers for these diseases and to develop them into viable biomarkers. In the present investigation, solution nuclear magnetic resonance and mass spectrometry metabolomics were used to quantitatively characterize the plasma metabolomes (a total of 167 metabolites) of a cohort of 94 individuals comprising 34 PD, 12 MSA, and 17 PSP patients, as well as 31 control subjects. The distinct and statistically significant differences observed in the metabolite concentrations of the different disease and control groups enabled the identification of potential plasma metabolite markers of each disorder and enabled the differentiation between the disorders. These group-specific differences further implicate disturbances in specific metabolic pathways. The two metabolites, formic acid and succinate, were altered similarly in all three disease groups when compared to the control group, where a reduced level of formic acid suggested an effect on pyruvate metabolism, methane metabolism, and/or the kynurenine pathway, and an increased succinate level suggested an effect on the citric acid cycle and mitochondrial dysfunction.

Arterial hypertension: modern advances in metabolomics

Early diagnosis and effective pharmacotherapy of arterial hypertension are urgent problems, a significant contribution to the solution of which can be made by metabolomics. The etiology of hypertension remains unknown for the majority of patients with high blood pressure; the diagnosis for 90% is defined as essential (primary) hypertension. This population is characterized by disturbance of the metabolic pathways of lipids, glucose, biogenic amines and amino acids, which may manifest with hyperlipidemia, hyperglycemia, and insulin resistance with the possible subsequent development of type II diabetes mellitus. The study of the metabolomic signature can provide a clue to the identification of biomarkers of hypertension and contribute to the effective development of preclinical diagnosis and identification of risk groups, as well as a more complete understanding of the etiological and pathogenetic mechanisms of increased blood pressure. Published studies indicate the existence of metabolome characteristic of hypertensive patients, distinguishing them from normotensive subjects. The most typical are changes involving amino acids, polyunsaturated fatty acids, carnitines, phosphatidylcholines, and acylglycerols.The variability of the response to antihypertensive therapy does not allow achieving effective control of blood pressure in a significant proportion of patients. The peculiarities of changes in the metabolome under the use of various pharmacological groups can be used to identify metabolite markers of the response to the main classes of antihypertensive drugs, as well as markers of the development of side effects of drug therapy. Thus, individualization of the pharmacotherapeutic approach based on pharmacometabolomics can significantly increase the efficacy and safety of antihypertensive therapy.This review aims to study the main groups of metabolites identified in published trials as predictors of the development of hypertension, as well as metabolite markers of response to antihypertensive therapy.

Identification of Metabolomic Biomarkers of Seed Vigor and Aging in Hybrid Rice

BackgroundSeed deterioration during rice seed storage will lead to seed vigor loss, which adversely affects agricultural production, the long-term preservation of germplasm resources, and the conservation of species diversity. However, the mechanisms underlying seed vigor maintenance remain largely unknown. ResultsIn this study, 16 hybrid rice combinations were selected from four sterile lines and four restorer lines. Following artificial aging and natural aging treatments, the metabolite markers that could accurately reflect the aging degree of the hybrid rice seeds were identified based on the germination percentage and metabolomics analysis by gas chromatography-mass spectrometry. Significantly differences in the degree of seed deterioration were observed among the 16 hybrid rice combinations tested, with each restorer and sterile lines after storage having the different germination percentage. The hybrid rice combination with the storage-resistant restorer line Guanghui122 exhibited the highest germination percentage under both natural and artificial storage. A total of 89 metabolic peaks and 56 metabolites were identified, most of which were related to primary metabolism. Interestingly, the content of galactose, gluconic acid, fructose and glycerol in the seeds increased significantly during the aging process. Absolute quantification indicated that galactose and gluconic acid were very significantly negatively correlated with the germination percentage of the seeds under the different aging treatments. The galactose content was significantly positively correlated with gluconic acid content. Additionally, while the relative content of raffinose did not change much during storage, a significant positive correlation between raffinose and the germination rate of the artificially aged seeds before storage was detected.ConclusionBased on the metabolomics, metabolite markers which could accurately reflect the aging degree of hybrid rice seeds were identified. Galactose and gluconic acid were very significantly negatively correlated with the germination percentage of the seeds which suggested that these metabolites could constitute potential metabolic markers of seed aging. These findings are of great significance for the rapid and accurate evaluation of seed aging, the determination of seed quality, and the development of molecular breeding approaches for high-vigor rice seeds.

In Silico Metabolic Modeling Reveals Potential Muscle Metabolite Markers of Insulin Resistance in Newly Diagnosed Diabetic Patients

Background Type 2 diabetes mellitus (T2DM) is a challenging and globally ubiquitous metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a great role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose hemostasis and the development of insulin resistance and T2DM. Here, we attempted to find metabolic dysregulations that are associated with the onset of T2DM. Besides, metabolite markers of T2DM were explored. Methods We reconstructed a human muscle-specific metabolic model and applied it to perform metabolic analysis in newly diagnosed T2DM patients. We investigated the metabolism reprogramming by using two topology-based and constraint-based approach. Moreover, we applied a machine learning method to predict potential metabolite markers of insulin resistance in muscle.Results Our results showed that metabolic alterations have occurred in carbohydrate, fatty acids, lipids, amino acids, and inositol phosphate metabolisms as well as pathways implicated in building extracellular matrix (ECM). Also, dysregulation of coenzyme Q10 metabolism was observed. Moreover, 13 exchange metabolites were predicted as the potential metabolite markers of insulin resistance in skeletal muscle. The efficiency of these markers in detecting insulin-resistant muscle was validated using a separate muscle gene expression data from another diabetes-related study. Conclusion In this study, the most updated muscle-specific metabolic model was generated and successfully was validated. This model was used for the investigation of metabolic disturbances at the onset of T2DM. Our results indicated the significance of ECM metabolites in insulin resistance, and reinforce the role of coenzyme Q10 as a candidate for further research in insulin resistance and T2DM treatment. The model is freely available and can be used for other muscle metabolic studies. We also predicted metabolite markers of insulin resistance in the skeletal muscle, which can be considered for further empirical investigations.

Metabolic Modeling Reveals Potential Muscle Metabolite Markers of Insulin Resistance in Newly Diagnosed Diabetic Patients

BackgroundType 2 diabetes mellitus (T2DM) is a challenging and globally ubiquitous metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a great role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose hemostasis and the development of insulin resistance and T2DM. Here, we attempted to find metabolic dysregulations that are associated with the onset of T2DM. Besides, metabolite markers of T2DM were explored. MethodsWe reconstructed a human muscle-specific metabolic model and applied it to perform metabolic analysis in newly diagnosed T2DM patients. We investigated the metabolism reprogramming by using two topology-based and constraint-based approach. Moreover, we applied a machine learning method to predict potential metabolite markers of insulin resistance in muscle.ResultsOur results showed that metabolic alterations have occurred in carbohydrate, fatty acids, lipids, amino acids, and inositol phosphate metabolisms as well as pathways implicated in building extracellular matrix (ECM). Also, dysregulation of coenzyme Q10 metabolism was observed. Moreover, 13 exchange metabolites were predicted as the potential metabolite markers of insulin resistance in skeletal muscle. The efficiency of these markers in detecting insulin-resistant muscle was validated using a separate muscle gene expression data from another diabetes-related study. ConclusionIn this study, the most updated muscle-specific metabolic model was generated and successfully was validated. This model was used for the investigation of metabolic disturbances at the onset of T2DM. Our results indicated the significance of ECM metabolites in insulin resistance, and reinforce the role of coenzyme Q10 as a candidate for further research in insulin resistance and T2DM treatment. The model is freely available and can be used for other muscle metabolic studies. We also predicted metabolite markers of insulin resistance in the skeletal muscle, which can be considered for further empirical investigations.

In Silico Metabolic Modeling Reveals Muscle Metabolite Markers of Insulin Resistance In Newly Diagnosed Diabetic Patients

BackgroundType 2 diabetes mellitus (T2DM) is a challenging and globally ubiquitous metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a great role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose hemostasis and the development of insulin resistance and T2DM. Here, we attempted to find metabolic dysregulations that are associated with the onset of T2DM. Besides, metabolite markers of T2DM were explored. ResultsWe reconstructed a human muscle-specific metabolic model and applied it to perform metabolic analysis in newly diagnosed T2DM patients. We investigated the metabolism reprogramming by using two topology-based and constraint-based approach. Our results showed that metabolic alterations have occurred in carbohydrate, fatty acids, lipids, amino acids, and inositol phosphate metabolisms as well as pathways implicated in building extracellular matrix (ECM). Also, dysregulation of coenzyme Q10 metabolism was observed. Moreover, we applied a machine learning method to predict potential metabolite markers of insulin resistance in muscle. 13 exchange metabolites were predicted as the potential metabolite markers of insulin resistance in skeletal muscle. The efficiency of these markers in detecting insulin-resistant muscle was validated using a separate muscle gene expression data from another diabetes-related study. ConclusionIn this study, the most updated muscle-specific metabolic model was generated and successfully was validated. This model was used for the investigation of metabolic disturbances at the onset of T2DM. Our results indicated the significance of ECM metabolites in insulin resistance, and reinforce the role of coenzyme Q10 as a candidate for further research in insulin resistance and T2DM treatment. The model is freely available and can be used for other muscle metabolic studies.

Differential Metabolites Markers from Trunking and Stressed NonTrunking Sago Palm (Metroxylon sagu Rottb.)

Background: Sago palm is an important agricultural starch-producing crop in Malaysia. The trunk of sago palm is responsible for the production of the starch reaching maturity for harvesting after ten years. However, there are sago palms that failed to develop its trunk after 17 years being planted. This is known as a stressed “non-trunking” sago palm, which eliminates the economic value of the palms. Objective: The study was initiated to compare the differences in metabolite expression between trunking and non-trunking sago palm and secondly to determine the potential metabolite-makers that are related to differential phenotypes of sago palms. Method: Metabolites were extracted using various solvents and analysed using NMR spectroscopy and GC-MS spectrometry. Data obtained were subjected to principal component analysis. Results: The study determined that differential metabolites expression were detected in the leaf extracts of normal trunking sago palm compared to the non-trunking palms. Metabolite groups which are differently expressed between trunking and non-trunking sago palm are oils and waxes, haloalkanes, sulfite esters, phosphonates, phosphoric acid, thiophene ester, terpenes and tocopherols. GC-MS analysis of Jones & Kinghorn extraction method determined two sets of metabolite markers which explains the differences in metabolites expression of trunking and non-trunking sago palm in ethyl acetate and methanol extract of 89.55% comprising sulfurous ester compounds and 87.04% comprising sulfurous ester, sulfurous acid and cyclohexylmethyl hexyl ester respectively. Conclusion: Two sets of metabolite markers were expressed in the trunking and non-trunking sago palm. These metabolites can potentially be used as markers for identifying normal and stressed plants.