Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models

Revealing quality chemicals of Tetrastigma hemsleyanum roots in different geographical origins using untargeted metabolomics and random-forest based spectrum-effect analysis

Tetrastigma hemsleyanum root is a popular functional food in China, and the price varies based on the origin of the product. The link between the origin, metabolic profile, and bioactivity of T. hemsleyanum must be investigated. This study compares the metabolic profiles of 254 samples collected from eight different areas with 49 potential key chemical markers using plant metabolomics. The metabolic pathways of the five critical flavonoid metabolites were annotated and enriched using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Moreover, a random forest model aiding the spectrum-effect relationship analysis was developed for the first time indicating catechin and darendoside B as potential quality markers of antioxidant activity. The findings of this study provide a comprehensive understanding of the chemical composition and bioactive compounds of T. hemsleyanum as well as valuable information on the evaluation of the quality of various samples and products in the market.

Metabolomics of male infertility

This review explores the use of metabolomics in male infertility. Metabolomics, an evolving omics technology that targets the products of cellular metabolism, is valuable for elucidating underlying pathophysiology of many disorders including male infertility. The identification of reliable biomarkers is essential for accurate diagnosis and for developing precision therapeutics for those afflicted by reproductive dysfunction. Unfortunately, despite significant progress to date, the intricate relationships between these metabolic pathways and male infertility remain elusive. It is clear, however, that additional research is required to more fully characterize the role of metabolomics in this disorder and in the potential development of targeted therapies for precision medicine.

Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome

Direct diagnosis and accurate assessment of metabolic syndrome (MetS) allow for prompt clinical interventions. However, traditional diagnostic strategies overlook the complex heterogeneity of MetS. Here, we perform metabolomic analysis in 13,554 participants from the natural cohort and identify 26 hub plasma metabolic fingerprints (PMFs) associated with MetS and its early identification (pre-MetS). By leveraging machine-learning algorithms, we develop robust diagnostic models for pre-MetS and MetS with convincing performance through independent validation. We utilize these PMFs to assess the relative contributions of the four major MetS risk factors in the general population, ranked as follows: hyperglycemia, hypertension, dyslipidemia, and obesity. Furthermore, we devise a personalized three-dimensional plasma metabolic risk (PMR) stratification, revealing three distinct risk patterns. In summary, our study offers effective screening tools for identifying pre-MetS and MetS patients in the general community, while defining the heterogeneous risk stratification of metabolic phenotypes in real-world settings.

Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models

Purpose

The objective of this study was to employ machine learning (ML) models utilizing non-invasive factors to achieve early and low-cost identification of MetS in a large physical examination population.

Patients and Methods

The study enrolled 9171 participants who underwent physical examinations at Northern Jiangsu People's Hospital in 2009 and 2019, to determine MetS based on criteria established by the Chinese Diabetes Society. Non-invasive characteristics such as gender, age, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected and used as input variables to train and evaluate ML models for MetS identification. Several ML models were used for MetS identification, including logistic regression (LR), k-nearest neighbors algorithm (k-NN), naive bayesian (NB), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM).

Results

Our ML models all showed good performance in the 10-fold cross-validation except for the SVM model. In the external validation, the NB model exhibited the best performance with an AUC of 0.976, accuracy of 0.923, sensitivity of 98.32%, and specificity of 91.32%.

Conclusion

This study proposed a new non-invasive method for early and low-cost identification of MetS by using ML models. This approach has the potential to serve as a highly sensitive, convenient, and cost-effective tool for large-scale MetS screening.

Insight from untargeted metabolomics: Revealing the potential marker compounds changes in refrigerated pork based on random forests machine learning algorithm

Data on changes in non-volatile components and metabolic pathways during pork storage were inadequately investigated. Herein, an untargeted metabolomics coupled with random forests machine learning algorithm was proposed to identify the potential marker compounds and their effects on non-volatile production during pork storage by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS/MS). A total of 873 differential metabolites were identified based on analysis of variance (ANOVA). Bioinformatics analysis shows that the key metabolic pathways for protein degradation and amino acid transport are amino acid metabolism and nucleotide metabolism. Finally, 40 potential marker compounds were screened using the random forest regression model, innovatively proposing the key role of pentose-related metabolism in pork spoilage. Multiple linear regression analysis revealed that d-xylose, xanthine, and pyruvaldehyde could be key marker compounds related to the freshness of refrigerated pork. Therefore, this study could provide new ideas for the identification of marker compounds in refrigerated pork.

Serum metabolomics profiling of improved metabolic syndrome is characterized by decreased pro-inflammatory biomarkers: A longitudinal study in Chinese male adults

Metabolic syndrome (MetS) is a serious global health concern. The objective of this study is to dynamically investigate the changes of metabolic profiles and metabolites in Chinese male MetS subjects after an 18 months diet and exercise intervention. Fifty male MetS patients defined according to International Diabetes Federation 2005 guidelines were subjected to diet and exercise counseling for 18 months. Serum samples were taken at baseline, 12 months, and 18 months, respectively, for clinical evaluation and metabolomics analyses. Diet and exercise intervention for 18 months achieved significant improvements in the metabolic profiles of all participants. Nineteen subjects (38.0%) exhibited MetS remission at the end of the study. A total of 812 relative features were characterized and 61 were successfully identified. Furthermore, 17 differential metabolites were of significance at both time points (baseline-12 months, baseline-18 months) and presented nonlinear trends through time. Eight metabolites (47.1%) were predominantly converged to inflammation and oxidative stress. Pro-inflammatory biomarkers were remarkably decreased after 18 months of intervention, and prostaglandin E2, neuroprotectin D1, and taxiphyllin in combination were firstly found to demonstrate a fair discriminative power (area under curve = 0.911) to predict the improvement of MetS undergone diet and exercise intervention. The significant shift of metabolomic profiling after 18 months of lifestyle counseling provide a novel insight and reveal that earlier inflammation control may be of potential benefit in MetS management.

New Advances in Metabolic Syndrome, from Prevention to Treatment: The Role of Diet and Food

The definition of metabolic syndrome (MetS) has undergone several changes over the years due to the difficulty in establishing universal criteria for it. Underlying the disorders related to MetS is almost invariably a pro-inflammatory state related to altered glucose metabolism, which could lead to elevated cardiovascular risk. Indeed, the complications closely related to MetS are cardiovascular diseases (CVDs) and type 2 diabetes (T2D). It has been observed that the predisposition to metabolic syndrome is modulated by complex interactions between human microbiota, genetic factors, and diet. This review provides a summary of the last decade of literature related to three principal aspects of MetS: (i) the syndrome's definition and classification, pathophysiology, and treatment approaches; (ii) prediction and diagnosis underlying the biomarkers identified by means of advanced methodologies (NMR, LC/GC-MS, and LC, LC-MS); and (iii) the role of foods and food components in prevention and/or treatment of MetS, demonstrating a possible role of specific foods intake in the development of MetS.

Insulin resistance in Alzheimer's disease: The genetics and metabolomics links

Alzheimer's disease (AD) is a neurodegenerative disease with significant socioeconomic burden worldwide. Although genetics and environmental factors play a role, AD is highly associated with insulin resistance (IR) disorders such as metabolic syndrome (MS), obesity, and type two diabetes mellitus (T2DM). These findings highlight a shared pathogenesis. The use of metabolomics as a downstream systems' biology (omics) approach can help to identify these shared metabolic traits and assist in the early identification of at-risk groups and potentially guide therapy. Targeting the shared AD-IR metabolic trait with lifestyle interventions and pharmacological treatments may offer promising AD therapeutic approach. In this narrative review, we reviewed the literature on the AD-IR pathogenic link, the shared genetics and metabolomics biomarkers between AD and IR disorders, as well as the lifestyle interventions and pharmacological treatments which target this pathogenic link.

Validation of a Microwave-Assisted Derivatization Gas Chromatography-Mass Spectrometry Method for the Quantification of 2-Hydroxybutyrate in Human Serum as an Early Marker of Diabetes Mellitus

Circulating levels of 2-hydroxybutyrate (2HB) are highly related to glycemic status in different metabolomic studies. According to recent evidence, 2HB is an early biomarker of the future development of dysglycemia and type 2 diabetes mellitus and may be causally related to the progression of normal subjects to impaired fasting glucose or insulin resistance. In the present study, we developed and validated a simple, specific and sensitive gas chromatography-mass spectrometry (GC-MS) method specifically intended to quantify serum levels of 2HB. Liquid–liquid extraction with ethyl acetate was followed by 2 min of microwave-assisted derivatization. The method presented acceptable accuracy, precision and recovery, and the limit of quantification was 5 µM. Levels of 2HB were found to be stable in serum after three freeze-thaw cycles, and at ambient temperature and at a temperature of 4 °C for up to 24 h. Extracts derivatized under microwave irradiation were stable for up to 96 h. No differences were found in 2HB concentrations measured in serum or plasma EDTA samples. In summary, the method is useful for a rapid, precise and accurate quantification of 2HB in serum samples assessed for the evaluation of dysglycemia and diabetes mellitus.

Machine learning-aided risk prediction for metabolic syndrome based on 3 years study

Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding risk factors. In this study, we investigate the risk prediction for MetS using a data set of 67,730 samples with physical examination records of three consecutive years provided by the Department of Health Management, Nanfang Hospital, Southern Medical University, P.R. China. Specifically, the prediction for MetS takes the numerical features of examination records as well as the differential features by using the examination records over the past two consecutive years, namely, the differential numerical feature (DNF) and the differential state feature (DSF), and the risk factors of the above features w.r.t different ages and genders are statistically analyzed. From numerical results, it is shown that the proposed DSF in addition to the numerical feature of examination records, significantly contributes to the risk prediction of MetS. Additionally, the proposed scheme, by using the proposed features, yields a superior performance to the state-of-the-art MetS prediction model, which provides the potential of effective prescreening the occurrence of MetS.

Which Role Plays 2-Hydroxybutyric Acid on Insulin Resistance?

Type 2 Diabetes Mellitus (T2D) is defined as a chronic condition caused by beta cell loss and/or dysfunction and insulin resistance (IR). The discovering of novel biomarkers capable of identifying T2D and other metabolic disorders associated with IR in a timely and accurate way is critical. In this review, 2-hydroxybutyric acid (2HB) is presented as that upheaval biomarker with an unexplored potential ahead. Due to the activation of other metabolic pathways during IR, 2HB is synthesized as a coproduct of protein metabolism, being the progression of IR intrinsically related to the increasing of 2HB levels. Hence, the focus of this review will be on the 2HB metabolite and its involvement in glucose homeostasis. A literature review was conducted, which comprised an examination of publications from different databases that had been published over the previous ten years. A total of 19 articles fulfilled the intended set of criteria. The use of 2HB as an early indicator of IR was separated into subjects based on the number of analytes examined simultaneously. In terms of the association between 2HB and IR, it has been established that increasing 2HB levels can predict the development of IR. Thus, 2HB has demonstrated considerable promise as a clinical monitoring molecule, not only as an IR biomarker, but also for disease follow-up throughout IR treatment.

Adaptations Supporting Plasma Methionine on a Limited-Methionine, High-Cystine Diet Alter the Canine Plasma Metabolome Consistent with Interventions that Extend Life Span in Other Species

BACKGROUND

Using indicator amino acid oxidation methodology, the mean dietary requirement of adult dogs for methionine (Met) was estimated to be ∼66% of the current recommended allowance. Dogs fed a diet formulated to provide the estimated mean Met requirement for 32 wk maintained plasma Met, seemingly supported by betaine oxidation.

OBJECTIVE

To gain a better understanding of the metabolic changes that were associated with supporting plasma Met when dogs were fed a limited Met diet over 32 wk, we analyzed plasma samples taken from that study using a data-driven metabolomics approach.

METHODS

Labrador retrievers (20 females/13 males; mean age: 4.9 y; range: 2.0-7.9 y) were fed semi-purified, nutritionally complete diets. After 4 wk of feeding a control diet (DL-Met; 1.37 g/1000 kcal), 17 dogs remained on this diet and 16 were transitioned to a test diet formulated to the estimated mean Met requirement (0.55 g/1000 kcal), with dietary total sulfur amino acid maintained with additional l-cystine (Cys). Dogs were individually fed diets to maintain a stable body weight at an ideal body condition score for 32 wk. Plasma samples from fasted blood collected at baseline and 8 and 32 wk were analyzed using untargeted metabolic profiling.

RESULTS

Analysis of metabolites (n = 593) confirmed our primary findings (increased Met, betaine, and dimethylglycine). Metabolite changes consistent with repartitioning choline to support Met cycling included reduced pools of lipids derived via phosphatidylethanolamine N-methyltransferase and enhanced fatty acid oxidation. Some changes were consistent with metabolomics studies reported in other species that used interventions known to extend life span (caloric- and Met-restricted diets or feeding strategy).

CONCLUSIONS

Changes in the plasma metabolome were consistent with reported adaptations to support Met-dependent activities. We propose that feeding a limited-Met, high-Cys diet using the estimated mean Met requirement in adult Labrador retrievers alters regulation of the Met cycle, thereby altering metabolism, similar to interventions that extend life span.

Comparative Untargeted Metabolomic Profiling of Induced Mitochondrial Fusion in Pancreatic Cancer

Mitochondria are dynamic organelles that constantly alter their shape through the recruitment of specialized proteins, like mitofusin-2 (Mfn2) and dynamin-related protein 1 (Drp1). Mfn2 induces the fusion of nearby mitochondria, while Drp1 mediates mitochondrial fission. We previously found that the genetic or pharmacological activation of mitochondrial fusion was tumor suppressive against pancreatic ductal adenocarcinoma (PDAC) in several model systems. The mechanisms of how these different inducers of mitochondrial fusion reduce pancreatic cancer growth are still unknown. Here, we characterized and compared the metabolic reprogramming of these three independent methods of inducing mitochondrial fusion in KPC cells: overexpression of Mfn2, genetic editing of Drp1, or treatment with leflunomide. We identified significantly altered metabolites via robust, orthogonal statistical analyses and found that mitochondrial fusion consistently produces alterations in the metabolism of amino acids. Our unbiased methodology revealed that metabolic perturbations were similar across all these methods of inducing mitochondrial fusion, proposing a common pathway for metabolic targeting with other drugs.

Plasma metabolomics are associated with metabolic syndrome: A targeted approach

OBJECTIVES

Advances in metabolomic tools have allowed us to gain a more comprehensive understanding of metabolic syndrome (MetS). The aim of this study was to evaluate the association between plasma metabolomic profiles and MetS.

METHODS

For this study, adults without diabetes, chronic kidney disease, stroke, heart disease, or cancer and with full metabolomics, biochemical, and dietetic data available, representing a subsample of the Health Survey of Sao Paulo study (ISA-Capital; N = 130), were included. The joint interim statement consensus criteria were used for diagnosing MetS. Absolute quantification (µmol/L) of blood metabolites was achieved by targeted quantitative profiling of annotated metabolites by electrospray ionization tandem mass spectrometry in plasma samples. Mean differences in the compounds for MetS were evaluated by linear regression adjusted for confounding factors.

RESULTS

Serine was inversely associated with MetS (β = -15.04; P = 0.014). In glycerophospholipids with acyl-alkyl bonds, there was an inverse association with MetS, including phosphatidylcholine (PC) ae C42:5 (β = -0.15; P = 0.040), PC ae C44:5 (β = -0.15; P = 0.046), PC ae C40:4 (β = -0.21; P = 0.014) and PC ae C44:4 (β = -0.04; P = 0.032).

CONCLUSION

Plasma metabolomic profiles were associated with MetS, especially the amino acid serine and some acyl-alkyl PCs.

Metabolomics applications in coronary artery disease personalized medicine

Coronary artery disease (CAD), the most common cardiovascular disease (CVD), contributes to significant mortality worldwide. CAD is a multifactorial disease wherein various factors contribute to its pathogenesis often complicating management. Biomarker based personalized medicine may provide a more effective way to individualize therapy in multifactorial diseases in general and CAD specifically. Systems' biology "Omics" biomarkers have been investigated for this purpose. These biomarkers provide a more comprehensive understanding on pathophysiology of the disease process and can help in identifying new therapeutic targets and tailoring therapy to achieve optimum outcome. Metabolomics biomarkers usually reflect genetic and non-genetic factors involved in the phenotype. Metabolomics analysis may provide better understanding of the disease pathogenesis and drug response variation. This will help in guiding therapy, particularly for multifactorial diseases such as CAD. In this chapter, advances in metabolomics analysis and its role in personalized medicine will be reviewed with comprehensive focus on CAD. Assessment of risk, diagnosis, complications, drug response and nutritional therapy will be discussed. Together, this chapter will review the current application of metabolomics in CAD management and highlight areas that warrant further investigation.

UHPLC-MS/MS-Based Nontargeted Metabolomics Analysis Reveals Biomarkers Related to the Freshness of Chilled Chicken

To identify metabolic biomarkers related to the freshness of chilled chicken, ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) was used to obtain profiles of the metabolites present in chilled chicken stored for different lengths of time. Random forest regression analysis and stepwise multiple linear regression were used to identify key metabolic biomarkers related to the freshness of chilled chicken. A total of 265 differential metabolites were identified during storage of chilled chicken. Of these various metabolites, 37 were selected as potential biomarkers by random forest regression analysis. Receiver operating characteristic (ROC) curve analysis indicated that the biomarkers identified using random forest regression analysis showed a strong correlation with the freshness of chilled chicken. Subsequently, stepwise multiple linear regression analysis based on the biomarkers identified by using random forest regression analysis identified indole-3-carboxaldehyde, uridine monophosphate, s-phenylmercapturic acid, gluconic acid, tyramine, and Serylphenylalanine as key metabolic biomarkers. In conclusion, our study characterized the metabolic profiles of chilled chicken stored for different lengths of time and identified six key metabolic biomarkers related to the freshness of chilled chicken. These findings can contribute to a better understanding of the changes in the metabolic profiles of chilled chicken during storage and provide a basis for the further development of novel detection methods for the freshness of chilled chicken.

A GC-MS-Based Metabolomics Investigation of the Protective Effect of Liu-Wei-Di-Huang-Wan in Type 2 Diabetes Mellitus Mice

Materials and Methods

MKR mice were used for the development of diabetes with high-fat diet feeding. These mice were further injected with streptozocin (STZ) to aggravate kidney failure. Fasting blood glucose (FBG) and urinary albumin-to-creatinine ratio (ACR values) were determined to validate the successful establishment of diabetic models with desired kidney dysfunction. Metabolomics approach coupled with gas chromatography-mass spectrometry (GC-MS) and random forest (RF) algorithm was proposed to discover the metabolic differences among model group and control group as well as to examine the therapeutic efficacy of traditional Chinese medicine, Liu-Wei-Di-Huang-Wan (LWDHW), in diabetes and associated kidney failure.

Results

Some metabolites such as 3-hydroxybutyric acid, citric acid, hexadecanoic acid, and octadecanoic acid showed significant differences between the control group and model group. Treatment with LWDHW resulted in a significant decrease in FBG and ACR values. These results suggested that LWDHW could have beneficial effects in diabetes-associated renal failure.

Targeted Metabolomics for Plasma Amino Acids and Carnitines in Patients with Metabolic Syndrome Using HPLC-MS/MS

Metabolic syndrome (MetS) is a health disorder characterized by metabolic abnormalities that predict an increased risk to develop cardiovascular disease (CVD) and type 2 diabetes. Biomarkers can provide an insight into the novel mechanism for MetS and can be potentially used for personalized response to therapies. We exploited a targeted HPLC-MS/MS method to characterize plasma amino acids and carnitine metabolic profile in MetS patients. A training set (40 cases and 40 controls) and validation set (80 MetS patients and 80 healthy controls) were carried out to find the metabolic profiles. We discovered two carnitine metabolites including hydroxydecanoyl carnitine and methylglutarylcarnitine. Our results indicated that the decreased level of hydroxydecanoyl carnitine and methylglutarylcarnitine may be associated with the risk of MetS. These biomarkers may improve the risk prediction and provide a novel tool for monitoring of the progression of disease and response to treatment in MetS patients.

Discovery of metabolite profiles of metabolic syndrome using untargeted and targeted LC-MS based lipidomics approach

Metabolic syndrome (MetS) is an important risk factor for type 2 diabetes, cardiovascular diseases and all-cause morbidity and mortality. Biomarkers can provide insight into the mechanism, facilitate early detection, and monitor progression of MetS and its response to therapeutic interventions. To identify potential biomarkers, we applied a non-targeted and targeted lipidomics method to characterize plasma metabolic profile in MetS patients. Metabolic profiling was performed on a non-target set (40 cases and 40 controls) on UHPLC-Q-TOF/MS and target set (80 MetS patients and 80 healthy controls) on UHPLC-Q-orbitrap MS. Using comprehensive screening and validation workflow, we identified a panel of three metabolites including PC(18:1/P-16:0), PC(o-22:3/22:3), PC(P-18:1/16:1). Our results indicated that the identified biomarkers may improve the risk prediction and provide a novel tool for monitoring of the progression of disease and response to treatment in MetS patients.

Current Progress of Lipid Analysis in Metabolic Diseases by Mass Spectrometry Methods

BACKGROUND

Obesity, insulin resistance, diabetes, and metabolic syndrome are associated with lipid alterations, and they affect the risk of long-term cardiovascular disease. A reliable analytical instrument to detect changes in the composition or structures of lipids and the tools allowing to connect changes in a specific group of lipids with a specific disease and its progress, is constantly lacking. Lipidomics is a new field of medicine based on the research and identification of lipids and lipid metabolites present in human organism. The primary aim of lipidomics is to search for new biomarkers of different diseases, mainly civilization diseases.

OBJECTIVE

We aimed to review studies reporting the application of mass spectrometry for lipid analysis in metabolic diseases.

METHOD

Following an extensive search of peer-reviewed articles on the mass spectrometry analysis of lipids the literature has been discussed in this review article.

RESULTS

The lipid group contains around 1.7 million species; they are totally different, in terms of the length of aliphatic chain, amount of rings, additional functional groups. Some of them are so complex that their complex analyses are a challenge for analysts. Their qualitative and quantitative analysis of is based mainly on mass spectrometry.

CONCLUSION

Mass spectrometry techniques are excellent tools for lipid profiling in complex biological samples and the combination with multivariate statistical analysis enables the identification of potential diagnostic biomarkers.

Mathematical models of amino acid panel for assisting diagnosis of children acute leukemia

BACKGROUND

The altered concentrations of amino acids were found in the bone marrow or blood of leukemia patients. Metabolomics technology combining mathematical model of biomarkers could be used for assisting the diagnosis of pediatric acute leukemia (AL).

METHODS

The concentrations of 17 amino acids was measured by targeted liquid chromatograph-tandem mass spectrometry in periphery blood collected using dried blood spots. After evaluation, the mathematical models were further evaluated by prospective clinical validation cohort for AL diagnosis.

RESULTS

The concentrations of 13 in 17 amino acids were statistically different between the periphery blood dried serum dots measured by targeted LC-MS/MS. The receiver operating characteristic analysis for the models of amino acid panel showed that the area under curve for AL diagnosis were 0.848, 0.834 and 0.856 by SVM, RF and XGBoost. The Kappa values in further prospectively evaluated clinical cohort were 0.697, 0.703 and 0.789 (p > 0.05) respectively, and the accuracies for the models were 84.86%, 85.20% and 89.46% respectively with further clinical validation.

CONCLUSIONS

The established mathematical model is a faster, cheaper and more convenient way than conventional methods, and no significant difference on the effect of diagnosis comparing with conventional methods. The mathematical model can be clinically useful for assisting pediatric AL diagnosis.

Alteration of Serum Free Fatty Acids are Indicators for Progression of Pre-leukaemia Diseases to Leukaemia

Acute Leukaemia (AL) is a neoplasm of WBCs (white blood cells). Being an important class of metabolites, alteration in free fatty acids (FFAs) levels play a key role in cancer development and progression. As they involve in cell signaling, maintain membrane integrity, regulate homeostasis and effect cell and tissue functions. Considering this fact, a comprehensive analysis of FFAs was conducted to monitor their alteration in AL, pre-leukaemic diseases and healthy control. Fifteen FFAs were analyzed in 179 serum samples of myelodysplastic syndrome (MDS), aplastic anemia (APA), acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML) and healthy control using gas chromatography-multiple reaction monitoring-mass spectrometry (GC-MRM-MS). A multivariate statistical method of random forest (RF) was employed for chemometric analysis. Serum level of two FFAs including C18:0 and C14:0 were found discriminative among all five groups, and between ALL and AML, respectively. Moreover, C14:0 was identified as differentiated FFAs for systematic progression of pre-leukaemic conditions towards AML. C16:0 came as discriminated FFAs between APA and MDS/AML. Over all it was identified that FFAs profile not only become altered in leukaemia but also in pre-leukaemic diseases.

Application of near infrared spectroscopy for rapid determination the geographical regions and polysaccharides contents of Lentinula edodes

In this study, a calibration model based on Near-infrared spectroscopy (NIR) technique and chemometrics method was developed for rapid and non-destructive detecting the polysaccharide contents of lentinula edodes samples collected from different regions. The polysaccharide contents of these samples were firstly determined by standard phenol-sulphruic acid method. Then, NIR spectra of these samples were collected by using Fourier transform infrared spectrometry. Based on these experimental data, a random forest method was further used to distinguish the regions of these samples, with a classification accuracy of 96.6%. After that, a rapid, accurate, and quantitative model was established for predicting the polysaccharide contents of these samples. In the model establishing process, some signal pre-treatment methods were optimized, and the validation results with highest determination coefficient (R²) and low root mean square errors of prediction (RMSEP) were, 0.925 and 0.720, respectively. These results showed that combined NIR technique with chemometrics was an effective and green method for lentinula edodes quality control.

Impact of hydroxyurea therapy on serum fatty acids of β-thalassemia patients

INTRODUCTION AND OBJECTIVE

Fatty acids (FAs) influence cell and tissue metabolism, function, responsiveness to hormonal and other signals in addition to maintenance of membrane integrity of cells. β-Thalassemia is a prevalent inherited blood disorder characterized by abnormal red cell membrane structure and function. Induction of HbF by hydroxyurea (HU) is an enduring therapeutic intervention to manage this. Therefore, in the present study we have carried out the quantification of thirteen free fatty acids to disclose the prognosis of HU in β-thalassemia.

METHODS

FAs quantification was carried out using GC-MRM-MS method in the serum of 98 cases of β-thalassemia patients and out of which samples from 34 patients were collected before and after treatment with HU in addition to healthy controls (n = 31).

RESULTS

Using the combination of random forest (RF) with GC-MRM-MS we were able to establish a classification and prediction model that can discriminate the β-thalassemia from healthy as well as from HU treated group. Docosanoic acid (C-22:0) was most significantly altered in β-thalassemia as compared to healthy at p-value of 8.3 × 10^-09 while erucic acid (C-22:1 Δ^cis-13) can be used as potential marker of HU prognosis because its level became significantly dissimilar at p-value of 3.7 × 10^-04 in same patients in response to HU. However, nervonic acid (C-24:1 Δ^cis-15) was found to be the key player in effectively separating three groups.

CONCLUSION

In inference, we have noticed that HU therapy also rectifies the serum fatty acid profile in addition to its reported affect i.e. HbF induction in β-thalassemia patients.

Metabolic profiling putatively identifies plasma biomarkers of male infertility using UPLC-ESI-IT-TOFMS

Male infertility has become a global health problem. Currently, the diagnosis of male infertility depends on the results of semen quality or requires invasive surgical intervention. The process is complex and time-consuming. Metabolomics is an emerging platform with unique advantages in disease diagnosis and pathological mechanism research. In this study, ultra-performance liquid chromatography-electrospray ionization-ion trap-time of flight mass spectrometry (UPLC-ESI-IT-TOFMS) combined with chemometrics methods was used to discover potential biomarkers of male infertility based on non-targeted plasma metabolomics. Plasma samples from healthy controls (HC, n = 43) and various types of infertile patients, i.e., patients having oligozoospermia (OS, n = 36), asthenospermia (AS, n = 56) and erectile dysfunction (ED, n = 45) were analyzed by UPLC-ESI-IT-TOFMS. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed. The results of OPLS-DA showed that HCs could be discriminated from infertile patients including OS (R² = 0.903, Q² = 0.617, AUC = 0.992), AS (R² = 0.985, Q² = 0.658, AUC = 0.999) or ED (R² = 0.942, Q² = 0.500, AUC = 0.998). Some potential biomarkers were successfully discovered by variable selection methods and variable important in the projection (VIP) in combination with the T-test. Statistical significance was set at p < 0.05; the Benjamini–Hochberg false discovery rate was used to reduce type 1 errors resulting from multiple comparisons. The identified biomarkers were associated with energy consumption, hormone regulation and antioxidant defenses in spermatogenesis. To elucidate the pathophysiology of male infertility, relative metabolic pathways were studied. It was found that male infertility is closely related to disturbed phospholipid metabolism, amino acid metabolism, steroid hormone biosynthesis metabolism, metabolism of fatty acids and products of carnitine acylation, and purine and pyrimidine metabolisms. Plasma metabolomics provides a novel strategy for the diagnosis of male infertility and offers a new insight to study pathogenesis mechanism.

Ultra-performance liquid chromatography-electrospray ionization-ion trap-time of flight mass spectrometry combined with chemometrics methods was used to discover potential biomarkers of male infertility based on untargeted plasma metabolomics.

The in silico identification of human bile salt export pump (ABCB11) inhibitors associated with cholestatic drug-induced liver injury

Drug-induced liver injury (DILI) is one of the major causes of drug attrition and failure. Currently, there is increasing evidence that direct inhibition of the human bile salt export pump (BSEP/ABCB11) by drugs and/or metabolites is one of the most important mechanisms of cholestatic DILI. In the present study, we employ two in silico methods, random forest (RF) and the pharmacophore method, to recognize potential BSEP inhibitors that could cause cholestatic DILI, with the aim of mitigating the risk of cholestatic DILI to some extent. The RF model achieved the best prediction performance, producing AUC (area under receiver operating characteristic curve) values of 0.901, 0.929 and 0.996 for leave-one-out cross-validation, the test set and the external test set, respectively, indicating that the built RF model has a satisfactory identification ability. As a complement to the RF model, the pharmacophore model was also built and was proved to be reliable with good predictive performance based on the internal and external validation results. Further analysis indicates that hydrophobicity, molecular size and polarity are important factors that influence the inhibitory activity of BSEP. Furthermore, the two models are applied to screen FDA-approved small molecule drugs, among which the drugs with the potential risk of cholestatic DILI are reported. In conclusion, the RF and pharmacophore models that we present can be considered as integrated screening tools to indicate the potential risk of cholestatic DILI by inhibition of BSEP.

Identification of potential diagnostic biomarkers of acute pancreatitis by serum metabolomic profiles

Acute pancreatitis (AP) is defined as an acute inflammation of pancreas that may cause damage to other tissues and organs depending upon the severity of symptoms. The diagnosis of AP is usually made by detection of raised circulating pancreatic enzyme levels, but there are occasional false positive and false negative diagnoses and such tests are often normal in delayed presentations. More accurate biomarkers would help in such situations. In this study, the global metabolites' changes of AP patients (APP) were profiled by using gas chromatography-mass spectrometry (GC-MS). Multivariate pattern recognition techniques were used to establish the classification models to distinguish APP from healthy participants (HP). Some significant metabolites including 3-hydroxybutyric acid, phosphoric acid, glycerol, citric acid, d-galactose, d-mannose, d-glucose, hexadecanoic acid and serotonin were selected as potential biomarkers for helping clinical diagnosis of AP. Furthermore, the metabolite changes in APP with severe and mild symptoms were also analyzed. Based on the selected biomarkers, some relevant pathways were also identified. Our results suggested that GC-MS based serum metabolomics method can be used in the clinical diagnosis of AP by profiling potential biomarkers.

Metabolomic investigation into molecular mechanisms of a clinical herb prescription against metabolic syndrome by a systematic approach

This study provided an effective and comprehensive approach for understanding the pathophysiological mechanisms of Mets and therapeutic mechanisms of WDD in treatment of Mets.

Genomic and Metabolomic Profile Associated to Clustering of Cardio-Metabolic Risk Factors

Background

To identify metabolomic and genomic markers associated with the presence of clustering of cardiometabolic risk factors (CMRFs) from a general population.

Methods and Findings

One thousand five hundred and two subjects, Caucasian, > 18 years, representative of the general population, were included. Blood pressure measurement, anthropometric parameters and metabolic markers were measured. Subjects were grouped according the number of CMRFs (Group 1: <2; Group 2: 2; Group 3: 3 or more CMRFs). Using SNPlex, 1251 SNPs potentially associated to clustering of three or more CMRFs were analyzed. Serum metabolomic profile was assessed by ¹H NMR spectra using a Brucker Advance DRX 600 spectrometer. From the total population, 1217 (mean age 54±19, 50.6% men) with high genotyping call rate were analysed. A differential metabolomic profile, which included products from mitochondrial metabolism, extra mitochondrial metabolism, branched amino acids and fatty acid signals were observed among the three groups. The comparison of metabolomic patterns between subjects of Groups 1 to 3 for each of the genotypes associated to those subjects with three or more CMRFs revealed two SNPs, the rs174577_AA of FADS2 gene and the rs3803_TT of GATA2 transcription factor gene, with minimal or no statistically significant differences. Subjects with and without three or more CMRFs who shared the same genotype and metabolomic profile differed in the pattern of CMRFS cluster. Subjects of Group 3 and the AA genotype of the rs174577 had a lower prevalence of hypertension compared to the CC and CT genotype. In contrast, subjects of Group 3 and the TT genotype of the rs3803 polymorphism had a lower prevalence of T2DM, although they were predominantly males and had higher values of plasma creatinine.

Conclusions

The results of the present study add information to the metabolomics profile and to the potential impact of genetic factors on the variants of clustering of cardiometabolic risk factors.

Serum Metabolic Profiling Reveals Altered Metabolic Pathways in Patients with Post-traumatic Cognitive Impairments

Cognitive impairment, the leading cause of traumatic brain injury (TBI)-related disability, adversely affects the quality of life of TBI patients, and exacts a personal and economic cost that is difficult to quantify. The underlying pathophysiological mechanism is currently unknown, and an effective treatment of the disease has not yet been identified. This study aimed to advance our understanding of the mechanism of disease pathogenesis; thus, metabolomics based on gas chromatography/mass spectrometry (GC-MS), coupled with multivariate and univariate statistical methods were used to identify potential biomarkers and the associated metabolic pathways of post-TBI cognitive impairment. A biomarker panel consisting of nine serum metabolites (serine, pyroglutamic acid, phenylalanine, galactose, palmitic acid, arachidonic acid, linoleic acid, citric acid, and 2,3,4-trihydroxybutyrate) was identified to be able to discriminate between TBI patients with cognitive impairment, TBI patients without cognitive impairment and healthy controls. Furthermore, associations between these metabolite markers and the metabolism of amino acids, lipids and carbohydrates were identified. In conclusion, our study is the first to identify several serum metabolite markers and investigate the altered metabolic pathway that is associated with post-TBI cognitive impairment. These markers appear to be suitable for further investigation of the disease mechanisms of post-TBI cognitive impairment.

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.

HydPred: a novel method for the identification of protein hydroxylation sites that reveals new insights into human inherited disease

HydPred was presented as the most reliable tool up to now for the identification of protein hydroxylation sites with a user-friendly web server at http://lishuyan.lzu.edu.cn/hydpred/.

A Metabolic Signature of Mitochondrial Dysfunction Revealed through a Monogenic Form of Leigh Syndrome

A decline in mitochondrial respiration represents the root cause of a large number of inborn errors of metabolism. It is also associated with common age-associated diseases and the aging process. To gain insight into the systemic, biochemical consequences of respiratory chain dysfunction, we performed a case-control, prospective metabolic profiling study in a genetically homogenous cohort of patients with Leigh syndrome French Canadian variant, a mitochondrial respiratory chain disease due to loss-of-function mutations in LRPPRC. We discovered 45 plasma and urinary analytes discriminating patients from controls, including classic markers of mitochondrial metabolic dysfunction (lactate and acylcarnitines), as well as unexpected markers of cardiometabolic risk (insulin and adiponectin), amino acid catabolism linked to NADH status (α-hydroxybutyrate), and NAD⁺ biosynthesis (kynurenine and 3-hydroxyanthranilic acid). Our study identifies systemic, metabolic pathway derangements that can lie downstream of primary mitochondrial lesions, with implications for understanding how the organelle contributes to rare and common diseases.

A potential tool for diagnosis of male infertility: Plasma metabolomics based on GC-MS

Male infertility has become an important public health problem worldwide. Nowadays the diagnosis of male infertility frequently depends on the results of semen quality or requires more invasive surgical intervention. Therefore, it is necessary to develop a novel approach for early diagnosis of male infertility. According to the presence or absence of normal sexual function, the male infertility is classified into two phenotypes, erectile dysfunction (ED) and semen abnormalities (SA). The aim of this study was to investigate the GC-MS plasma profiles of infertile male having erectile dysfunction (ED) and having semen abnormalities (SA) and discover the potential biomarkers. The plasma samples from healthy controls (HC) (n=61) and infertility patients with ED (n=26) or with SA (n=44) were analyzed by gas chromatography-mass spectrometry (GC-MS) for discrimination and screening potential biomarkers. The partial least squares-discriminant analysis (PLS-DA) was performed on GC-MS dataset. The results showed that HC could be discriminated from infertile cases having SA (AUC=86.96%, sensitivity=78.69%, specificity=84.09%, accuracy=80.95%) and infertile cases having ED (AUC=94.33%, sensitivity=80.33%, specificity=100%, accuracy=87.36%). Some potential biomarkers were successfully discovered by two commonly used variable selection methods, variable importance on projection (VIP) and original coefficients of PLS-DA (β). 1,5-Anhydro-sorbitol and α-hydroxyisovaleric acid were identified as the potential biomarkers for distinguishing HC from the male infertility patients. Meanwhile, lactate, glutamate and cholesterol were the found to be the important variables to distinguish between patients with erectile dysfunction from those with semen abnormalities. The plasma metabolomics may be developed as a novel approach for fast, noninvasive, and acceptable diagnosis and characterization of male infertility.

Supervised learning methods in modeling of CD4+ T cell heterogeneity

Background

Modeling of the immune system – a highly non-linear and complex system – requires practical and efficient data analytic approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types, such as neutrophils, eosinophils, macrophages, dendritic cells, T cells, and B cells. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. For example, CD4+ T cells can be differentiated into Th1, Th2, Th17, Th9, Th22, Treg, Tfh, as well as Tr1. Each subset plays different roles in the immune system. To study molecular mechanisms of cell differentiation, computational systems biology approaches can be used to represent these processes; however, the latter often requires building complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. Furthermore, studying the immune system entails integration of complex processes which occur at different time and space scales.

Methods

This study presents and compares four supervised learning methods for modeling CD4+ T cell differentiation: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Linear Regression (LR). Application of supervised learning methods could reduce the complexity of Ordinary Differential Equations (ODEs)-based intracellular models by only focusing on the input and output cytokine concentrations. In addition, this modeling framework can be efficiently integrated into multiscale models.

Results

Our results demonstrate that ANN and RF outperform the other two methods. Furthermore, ANN and RF have comparable performance when applied to in silico data with and without added noise. The trained models were also able to reproduce dynamic behavior when applied to experimental data; in four out of five cases, model predictions based on ANN and RF correctly predicted the outcome of the system. Finally, the running time of different methods was compared, which confirms that ANN is considerably faster than RF.

Conclusions

Using machine learning as opposed to ODE-based method reduces the computational complexity of the system and allows one to gain a deeper understanding of the complex interplay between the different related entities.

Distinguishing the serum metabolite profiles differences in breast cancer by gas chromatography mass spectrometry and random forest method

In this study, we proposed a metabolomics strategy to distinguish different metabolic characters of healthy controls, breast benign (BE) patients, and breast malignant (BC) patients by using the GC-MS and random forest method (RF).

Unraveling biochemical pathways affected by mitochondrial dysfunctions using metabolomic approaches

Mitochondrial dysfunction(s) (MDs) can be defined as alterations in the mitochondria, including mitochondrial uncoupling, mitochondrial depolarization, inhibition of the mitochondrial respiratory chain, mitochondrial network fragmentation, mitochondrial or nuclear DNA mutations and the mitochondrial accumulation of protein aggregates. All these MDs are known to alter the capacity of ATP production and are observed in several pathological states/diseases, including cancer, obesity, muscle and neurological disorders. The induction of MDs can also alter the secretion of several metabolites, reactive oxygen species production and modify several cell-signalling pathways to resolve the mitochondrial dysfunction or ultimately trigger cell death. Many metabolites, such as fatty acids and derived compounds, could be secreted into the blood stream by cells suffering from mitochondrial alterations. In this review, we summarize how a mitochondrial uncoupling can modify metabolites, the signalling pathways and transcription factors involved in this process. We describe how to identify the causes or consequences of mitochondrial dysfunction using metabolomics (liquid and gas chromatography associated with mass spectrometry analysis, NMR spectroscopy) in the obesity and insulin resistance thematic.