Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus

Exploring accurate mass measurements in pixel-based chemometrics: Advancing coffee classification with GC-HRMS-A proof of concept study

This paper presents a study that assesses the application of chemometrics for classifying coffee samples in a quality control context. High-resolution and accurate mass measurements were utilized as input for pixel-based orthogonal partial least squares discriminant analysis (OPLS-DA) models. The compositional data were acquired through a fully automated workflow combining headspace solid-phase microextraction and gas chromatography-high-resolution mass spectrometry (GC-HRMS) using an FT-Orbitrap® mass analyzer. A workflow centered on accurate mass measurements was successfully utilized for group-type analysis, offering an alternative to methods relying solely on MS similarity searches. The predictive models underwent thorough evaluation, demonstrating robust multivariate classification performance. Five key coffee attributes, bitterness, acidity, body, intensity, and roasting level were successfully predicted using GC-HRMS data. The results revealed strong predictive accuracy across all models, ranging from 88.9 % (bitterness) to 94.4 % (roasting level). This study represents a significant advancement in automating methods for coffee quality control, notably increasing the predictive ability of the models compared to existing literature.

Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

Activation of GFRAL+ neurons induces hypothermia and glucoregulatory responses associated with nausea and torpor

GFRAL-expressing neurons actuate aversion and nausea, are targets for obesity treatment, and may mediate metformin effects by long-term GDF15-GFRAL agonism. Whether GFRAL+ neurons acutely regulate glucose and energy homeostasis is, however, underexplored. Here, we report that cell-specific activation of GFRAL+ neurons using a variety of techniques causes a torpor-like state, including hypothermia, the release of stress hormones, a shift from glucose to lipid oxidation, and impaired insulin sensitivity, glucose tolerance, and skeletal muscle glucose uptake but augmented glucose uptake in visceral fat. Metabolomic analysis of blood and transcriptomics of muscle and fat indicate alterations in ketogenesis, insulin signaling, adipose tissue differentiation and mitogenesis, and energy fluxes. Our findings indicate that acute GFRAL+ neuron activation induces endocrine and gluco- and thermoregulatory responses associated with nausea and torpor. While chronic activation of GFRAL signaling promotes weight loss in obesity, these results show that acute activation of GFRAL+ neurons causes hypothermia and hyperglycemia.

Microbiota-mediated metabolic perturbations in the gut and brain of mice after microplastic exposure

Microplastics (MPs), emerging environmental toxicants, have drawn attention because of their wide distribution in the environment. Exposure to MPs induces gut microbiota dysbiosis, intestinal barrier dysfunction, metabolic perturbations, and neurotoxicity in different rodents. However, the relationship between MPs, gut microbiota, and the metabolome of the gut and brain in mice remains unclear. In this study, female C57BL/6 mice were orally gavaged with vehicle, 200 nm MP, and 800 nm MP three times per week for four weeks. Cecal contents were collected for gut microbiota analysis using 16S rRNA gene sequencing. Intestinal and brain tissues from mice were used to determine metabolic profiles using liquid chromatography-mass spectrometry (LC-MS). The results showed that MP altered microbiota composition, accompanied by metabolic perturbations in the mouse gut and brain. Specifically, Firmicutes and Bacteroidetes were suggested to be important phyla for MP exposure, partially dominating further metabolite alterations. Simultaneously, MP-induced metabolic profiles were associated with energy homeostasis and bile acid, nucleotide, and carnitine metabolic pathways. The results of the mediation analysis further revealed an MP-microbiota-metabolite relationship. Our results indicate that MPs can induce gut dysbiosis and disturb metabolic dysfunction in the mouse brain and/or intestine. Integrative omics approaches have the potential to monitor MP-induced molecular responses in various organs and systematically elucidate the complex mechanisms of human health effects.

Metabolomic Profile, Plasmatic Levels of Losartan and EXP3174, Blood Pressure Control in Hypertensive Patients and Their Correlation with COVID-19

Systemic arterial hypertension (SAH) is one of the most prevalent chronic diseases worldwide and is related to serious health complications. It has been pointed out as a major risk factor for COVID-19. This study aimed to determine the impact of COVID-19 on the metabolomic profile, the correlation with the plasmatic levels of losartan and its active metabolite (EXP3174), biochemical markers, and blood pressure (BP) control in hypertensive patients. 1H NMR metabolomic profiles of hypertensive and normotensive patients with and without previous COVID-19 diagnosis were identified. Plasmatic levels of LOS and EXP3174 were correlated with BP, biochemical markers, and the metabolomic fingerprint of the groups. Biomarkers linked to important aspects of SAH and COVID-19 were identified, such as glucose, glutamine, arginine, creatinine, alanine, choline, erythritol, homogentisate, 0-tyrosine, and 2-hydroxybutyrate. Those metabolites are indicative of metabolic alterations, kidney damage, pulmonary dysfunction, and persistent inflammation, which can be found in both diseases. Some hypertensive patients did not reach the therapeutic levels of LOS and EXP3174, while the BP control was also limited among the normotensive patients with previous COVID-19 diagnoses. Metabolomics proved to be an important tool for assessing the effectiveness of losartan pharmacotherapy and the damage caused by SAH and COVID-19 in hypertensive patients.

Metabolomic Profiling of Bipolar Disorder by 1H-NMR in Serbian Patients

Bipolar disorder (BD) is a brain disorder that causes changes in a person's mood, energy, and ability to function. It has a prevalence of 60 million people worldwide, and it is among the top 20 diseases with the highest global burden. The complexity of this disease, including diverse genetic, environmental, and biochemical factors, and diagnoses based on the subjective recognition of symptoms without any clinical test of biomarker identification create significant difficulties in understanding and diagnosing BD. A ¹H-NMR-based metabolomic study applying chemometrics of serum samples of Serbian patients with BD (33) and healthy controls (39) was explored, providing the identification of 22 metabolites for this disease. A biomarker set including threonine, aspartate, gamma-aminobutyric acid, 2-hydroxybutyric acid, serine, and mannose was established for the first time in BD serum samples by an NMR-based metabolomics study. Six identified metabolites (3-hydroxybutyric acid, arginine, lysine, tyrosine, phenylalanine, and glycerol) are in agreement with the previously determined NMR-based sets of serum biomarkers in Brazilian and/or Chinese patient samples. The same established metabolites (lactate, alanine, valine, leucine, isoleucine, glutamine, glutamate, glucose, and choline) in three different ethnic and geographic origins (Serbia, Brazil, and China) might have a crucial role in the realization of a universal set of NMR biomarkers for BD.

A Metabolomics-Based Study on the Discriminative Classification Models and Toxicological Mechanism of Estazolam Fatal Intoxication

Fatal intoxication with sedative-hypnotic drugs is increasing yearly. However, the plasma drug concentration data for fatal intoxication involving these substances are not systematic and even overlap with the intoxication group. Therefore, developing a more precise and trustworthy approach to determining the cause of death is necessary. This study analyzed mice plasma and brainstem samples using the liquid chromatography-high resolution tandem mass spectrometry (LC-HR MS/MS)-based metabolomics method to create discriminative classification models for estazolam fatal intoxication (EFI). The most perturbed metabolic pathway between the EFI and EIND (estazolam intoxication non-death) was examined, Both EIND and EFI groups were administered 500 mg of estazolam per 100 g of body weight. Mice that did not die beyond 8 hours were treated with cervical dislocation and were classified into the EIND groups; the lysine degradation pathway was verified by qPCR (Quantitative Polymerase Chain Reaction), metabolite quantitative and TEM (transmission electron microscopy) analysis. Non-targeted metabolomics analysis with EFI were the experimental group and four hypoxia-related non-drug-related deaths (NDRDs) were the control group. Mass spectrometry data were analyzed with Compound Discoverer (CD) 3.1 software and multivariate statistical analyses were performed using the online software MetaboAnalyst 5.0. After a series of analyses, the results showed the discriminative classification model in plasma was composed of three endogenous metabolites: phenylacetylglycine, creatine and indole-3-lactic acid, and in the brainstem was composed of palmitic acid, creatine, and indole-3-lactic acid. The specificity validation results showed that both classification models distinguished between the other four sedatives-hypnotics, with an area under ROC curve (AUC) of 0.991, and the classification models had an extremely high specificity. When comparing different doses of estazolam, the AUC value of each group was larger than 0.80, and the sensitivity was also high. Moreover, the stability results showed that the AUC value was equal to or very close to 1 in plasma samples stored at 4 °C for 0, 1, 5, 10 and 15 days; the predictive power of the classification model was stable within 15 days. The results of lysine degradation pathway validation revealed that the EFI group had the highest lysine and saccharopine concentrations (mean (ng/mg) = 1.089 and 1.2526, respectively) when compared to the EIND and control group, while the relative expression of SDH (saccharopine dehydrogenase) showed significantly lower in the EFI group (mean = 1.206). Both of these results were statistically significant. Furthermore, TEM analysis showed that the EFI group had the more severely damaged mitochondria. This work gives fresh insights into the toxicological processes of estazolam and a new method for identifying EFI-related causes of mortality.

PARAFAC2×N: Coupled decomposition of multi-modal data with drift in N modes

Analysis of GC×GC-TOFMS data for large numbers of poorly-resolved peaks, and for large numbers of samples remains an enduring problem that hinders the widespread application of the technique. For multiple samples, GC×GC-TOFMS data for specific chromatographic regions manifests as a 4^th order tensor of I mass spectral acquisitions, J mass channels, K modulations, and L samples. Chromatographic drift is common along both the first-dimension (modulations), and along the second-dimension (mass spectral acquisitions), while drift along the mass channel is for all practical purposes nonexistent. A number of solutions to handling GC×GC-TOFMS data have been proposed: these involve reshaping the data to make it amenable to either 2^nd order decomposition techniques based on Multivariate Curve Resolution (MCR), or 3^rd order decomposition techniques such as Parallel Factor Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic drift along one mode, which has enabled its use for robust decomposition of multiple GC-MS experiments. Although extensible, it is not straightforward to implement a PARAFAC2 model that accounts for drift along multiple modes. In this submission, we demonstrate a new approach and a general theory for modelling data with drift along multiple modes, for applications in multidimensional chromatography with multivariate detection. The proposed model captures over 99.9% of variance for a synthetic data set, presenting an extreme example of peak drift and co-elution across two modes of separation.

Metabolomic profiling of glucose homeostasis in African Americans: the Insulin Resistance Atherosclerosis Family Study (IRAS-FS)

INTRODUCTION

African Americans are at increased risk for type 2 diabetes.

OBJECTIVES

This work aimed to examine metabolomic signature of glucose homeostasis in African Americans.

METHODS

We used an untargeted liquid chromatography-mass spectrometry metabolomic approach to comprehensively profile 727 plasma metabolites among 571 African Americans from the Insulin Resistance Atherosclerosis Family Study (IRAS-FS) and investigate the associations between these metabolites and both the dynamic (S_I, insulin sensitivity; AIR, acute insulin response; DI, disposition index; and S_G, glucose effectiveness) and basal (HOMA-IR and HOMA-B) measures of glucose homeostasis using univariate and regularized regression models. We also compared the results with our previous findings in the IRAS-FS Mexican Americans.

RESULTS

We confirmed increased plasma metabolite levels of branched-chain amino acids and their metabolic derivatives, 2-aminoadipate, 2-hydroxybutyrate, glutamate, arginine and its metabolic derivatives, carbohydrate metabolites, and medium- and long-chain fatty acids were associated with insulin resistance, while increased plasma metabolite levels in the glycine, serine and threonine metabolic pathway were associated with insulin sensitivity. We also observed a differential ancestral effect of glutamate on glucose homeostasis with significantly stronger effects observed in African Americans than those previously observed in Mexican Americans.

CONCLUSION

We extended the observations that metabolites are useful biomarkers in the identification of prediabetes in individuals at risk of type 2 diabetes in African Americans. We revealed, for the first time, differential ancestral effect of certain metabolites (i.e., glutamate) on glucose homeostasis traits. Our study highlights the need for additional comprehensive metabolomic studies in well-characterized multiethnic cohorts.

Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review

BACKGROUND

Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed.

OBJECTIVE

We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction.

METHODS

We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes etiology on September 19th, 2022. Exposures were classified into specific external, general external, or internal exposures. We reviewed machine learning and data mining methods and characterized the scope of environmental exposures studied in the etiology of general diabetes, type 1 diabetes, type 2 diabetes, and other types of diabetes.

RESULTS

We identified 44 articles for inclusion. Specific external exposures were the most common exposures studied, and supervised models were the most common methods used. Well-established specific external exposures of low physical activity, high cholesterol, and high triglycerides were predictive of general diabetes, type 2 diabetes, and prediabetes, while novel metabolic and gut microbiome biomarkers were implicated in type 1 diabetes.

DISCUSSION

The use of machine learning and data mining methods to elucidate environmental triggers of diabetes was largely limited to well-established risk factors identified using easily explainable and interpretable models. Future studies should seek to leverage machine learning and data mining to explore the temporality and co-occurrence of multiple exposures and further evaluate the role of general external and internal exposures in diabetes etiology.

Dysbiotic human oral microbiota alters systemic metabolism via modulation of gut microbiota in germ-free mice

Background

The effect of oral microbiota on the intestinal microbiota has garnered growing attention as a mechanism linking periodontal diseases to systemic diseases. However, the salivary microbiota is diverse and comprises numerous bacteria with a largely similar composition in healthy individuals and periodontitis patients.

Aim

We explored how health-associated and periodontitis-associated salivary microbiota differently colonized the intestine and their subsequent systemic effects.

Methods

The salivary microbiota was collected from a healthy individual and a periodontitis patient and gavaged into C57BL/6NJcl[GF] mice. Gut microbial communities, hepatic gene expression profiles, and serum metabolites were analyzed.

Results

The gut microbial composition was significantly different between periodontitis-associated microbiota-administered (PAO) and health-associated oral microbiota-administered (HAO) mice. The hepatic gene expression profile demonstrated a distinct pattern between the two groups, with higher expression of lipid and glucose metabolism-related genes. Disease-associated metabolites such as 2-hydroxyisobutyric acid and hydroxybenzoic acid were elevated in PAO mice. These metabolites were significantly correlated with characteristic gut microbial taxa in PAO mice. Conversely, health-associated oral microbiota were associated with higher levels of beneficial serum metabolites in HAO mice.

Conclusion

The multi-omics approach used in this study revealed that periodontitis-associated oral microbiota is associated with the induction of disease phenotype when they colonized the gut of germ-free mice.

Metabolic profiles of ultra-processed food consumption and their role in obesity risk in British children

BACKGROUND & AIMS

Higher consumption of ultra-processed foods (UPF) has been associated with childhood obesity, but underlying mechanisms remain unclear. We investigated plasma nuclear magnetic resonance metabolic profiles of higher UPF consumption and their role in obesity risk in the British ALSPAC cohort.

METHODS

We performed cross-sectional and prospective metabolome wide association analyses of UPF, calculated from food diaries using the NOVA classification. In cross-sectional analysis, we tested the association between UPF consumption and metabolic profile at 7 years (N = 4528), and in the prospective analysis we tested the association between UPF consumption at 13 years and metabolic profile at 17 years (N = 3086). Effects of UPF-associated metabolites at 7 years on subsequent fat mass accumulation were assessed using growth curve models.

RESULTS

At 7 years, UPF was associated with 115 metabolic traits including lower levels of branched-chain and aromatic amino acids and higher levels of citrate, glutamine, and monounsaturated fatty acids, which were also associated with greater fat mass accumulation. Reported intake of nutrients mediated associations with most metabolites, except for citrate.

CONCLUSIONS

UPF consumption among British children is associated with perturbation of multiple metabolic traits, many of which contribute to child obesity risk.

Alterations in Microbiota and Metabolites Related to Spontaneous Diabetes and Pre-Diabetes in Rhesus Macaques

Spontaneous type 2 diabetes mellitus (T2DM) macaques are valuable resources for our understanding the pathological mechanism of T2DM. Based on one month’s fasting blood glucose survey, we identified seven spontaneous T2DM macaques and five impaired glucose regulation (IGR) macaques from 1408 captive individuals. FPG, HbA1c, FPI and IR values were significant higher in T2DM and IGR than in controls. 16S rRNA sequencing of fecal microbes showed the significantly greater abundance of Oribacterium, bacteria inhibiting the production of secondary bile acids, and Phascolarctobacterium, bacteria producing short-chain fatty acids was significantly lower in T2DM macaques. In addition, several opportunistic pathogens, such as Mogibacterium and Kocuria were significantly more abundant in both T2DM and IGR macaques. Fecal metabolites analysis based on UHPLC-MS identified 50 differential metabolites (DMs) between T2DM and controls, and 26 DMs between IGR and controls. The DMs were significantly enriched in the bile acids metabolism, fatty acids metabolism and amino acids metabolism pathways. Combining results from physiochemical parameters, microbiota and metabolomics, we demonstrate that the imbalance of gut microbial community leading to the dysfunction of glucose, bile acids, fatty acids and amino acids metabolism may contribute to the hyperglycaemia in macaques, and suggest several microbes and metabolites are potential biomarkers for T2DM and IGR macaques.

NMR Metabolomics in Serum Fingerprinting of Schizophrenia Patients in a Serbian Cohort

Schizophrenia is a widespread mental disorder that leads to significant functional impairments and premature death. The state of the art indicates gaps in the understanding and diagnosis of this disease, but also the need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response. In order to fulfill existing gaps, the establishment of a universal set of disorder biomarkers is a necessary step. Metabolomic investigations of serum samples of Serbian patients with schizophrenia (51) and healthy controls (39), based on NMR analyses associated with chemometrics, led to the identification of 26 metabolites/biomarkers for this disorder. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models with prediction accuracies of 0.9718 and higher were accomplished during chemometric analysis. The established biomarker set includes aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols, which are identified for the first time in schizophrenia serum samples by NMR experiments. The other 22 identified metabolites in the Serbian samples are in accordance with the previously established NMR-based serum biomarker sets of Brazilian and/or Chinese patient samples. Thirteen metabolites (lactate/lactic acid, threonine, leucine, isoleucine, valine, glutamine, asparagine, alanine, gamma-aminobutyric acid, choline, glucose, glycine and tyrosine) that are common for three different ethnic and geographic origins (Serbia, Brazil and China) could be a good start point for the setup of a universal NMR serum biomarker set for schizophrenia.

Guide to Metabolomics Analysis: A Bioinformatics Workflow

Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

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.

A high-fat High-fructose Diet Dysregulates the Homeostatic Crosstalk Between Gut Microbiome, Metabolome and Immunity in an Experimental Model of Obesity

SCOPE

Ample evidence supports the prominent role of gut-liver axis in perpetuating pathological networks of high-fat high-fructose (HFF) diet induced metabolic disorders, however, the molecular mechanisms are still not fully understood. Herein, we aim to present a holistic delineation and scientific explanation for the crosstalk between the gut and liver, including the potential mediators involved in orchestrating the metabolic and immune systems.

METHODS

An experimental obesity associated metaflammation rat model was induced with a HFF diet. An integrative multi-omics analysis was then performed. Following the clues illustrated by the multi-omics discoveries, putative pathways were subsequently validated by RT-qPCR and Western blotting.

RESULTS

HFF diet led to obese phenotypes in rats, as well as histopathological changes. Integrated omics analysis showed there existed a strong interdependence among gut microbiota composition, intestinal metabolites and innate immunity regulation in the liver. Some carboxylic acids might contribute to gut-liver communication. Moreover, activation of the hepatic LPS-TLR4 pathway in obesity was confirmed.

CONCLUSIONS

HFF-intake disturbs gut flora homeostasis. Crosstalk between gut microbiota and innate immune system mediated hepatic metaflammation in obese rats, associated with LPS-TLR4 signaling pathway activation. Moreover, α-hydroxyisobutyric acid and some other organic acids may play a role as messengers in the liver-gut axis. High-fat high-fructose diet (HFF) induces obesity associated chronic inflammation; HFF dysregulates the rat intestinal metabolome and gut microbiota composition; HFF impacts hepatic expression of genes involved in innate immunity; Modulation of gut microbiota composition and innate immunity are connected partly via TLR4 signalling; Small molecular carboxylic acids are potential mediators of gut-liver axis communication in chronic obesity. This article is protected by copyright. All rights reserved.

Using Metabolomics in Diabetes Management with Traditional Chinese Medicine: A Review

The incidence of diabetes worldwide continues to rise, placing a huge economic and medical burden on human society. More than 90% of diabetic cases are type 2 diabetes (T2D). At present, the pathogenesis of T2D is not yet fully understood. Metabolomics uses high-resolution analytical techniques (typically NMR and MS) to help identify biomarkers associated with the risk of T2D and reveal potential pathogenesis. Many metabolites such as branched-chain amino acids (BCAAs), aromatic amino acids, glycine, 2-hydroxybutyric acid (2-HB), lysophosphatidylcholine (LPC) (18:2), and trehalose have proven to be biomarkers of T2D. Insulin resistance (IR) induced by BCAA in T2D mice is related to the activation of mammalian target of rapamycin (mTOR) and phosphorylation of insulin receptor substrate-1 (IRS1). Incomplete LCFA [Formula: see text]-oxidation promote acylcarnitine byproduct accumulation and stimulates proinflammatory NF[Formula: see text]B-related pathways to inhibit insulin action. Traditional Chinese Medicine (TCM) presents unique advantages in the treatment of T2D. Multiple metabolites and metabolic pathways have been identified in the treatment of TCM, providing valuable biomarkers and novel targets for drug therapy and pharmacological mechanism. Therefore, this paper reviews the modern achievements of metabolomics in T2D research and the progress of TCM management in recent years, in order to provide valuable information for related research.

Enantioselective metabolomics by liquid chromatography-mass spectrometry

Metabolomics strives to capture the entirety of the metabolites in a biological system by comprehensive analysis, often by liquid chromatography hyphenated to mass spectrometry. A particular challenge thereby is the differentiation of structural isomers. Common achiral targeted and untargeted assays do not distinguish between enantiomers. This may lead to information loss. An increasing number of publications demonstrate that the enantiomeric ratio of certain metabolites can be meaningful biomarkers of certain diseases emphasizing the importance of introducing enantioselective analytical procedures in metabolomics. In this work, the state-of-the-art in the field of LC-MS based metabolomics is summarized with focus on developments in the recent decade. Methodologies, tagging strategies, workflows and general concepts are outlined. Selected biological applications in which enantioselective metabolomics has documented its usefulness are briefly discussed. In general, targeted enantioselective metabolomics assays are often based on a direct approach using chiral stationary phases (CSP) with polysaccharide derivatives, macrocyclic antibiotics, chiral crown ethers, chiral ion exchangers, donor-acceptor phases as chiral selectors. Rarely, these targeted assays focus on more than 20 analytes and usually are restricted to a certain metabolite class. In a variety of cases, pre-column derivatization of metabolites has been performed, especially for amino acids, to improve separation and detection sensitivity. Triple quadrupole instruments are the detection methods of first choice in targeted assays. Here, issues like matrix effect, absence of blank matrix impair accuracy of results. In selected applications, multiple heart cutting 2D-LC (RP followed by chiral separation) has been pursued to overcome this problem and alleviate bias due to interferences. Non-targeted assays, on the other hand, are based on indirect approach involving tagging with a chiral derivatizing agent (CDA). Besides classical CDAs numerous innovative reagents and workflows have been proposed and are discussed. Thereby, a critical issue for the accuracy is often neglected, viz. the validation of the enantiomeric impurity in the CDA. The majority of applications focus on amino acids, hydroxy acids, oxidized fatty acids and oxylipins. Some potential clinical applications are highlighted.

Alterations of Gut Microbiome and Metabolite Profiles Associated With Anabatic Lipid Dysmetabolism in Thyroid Cancer

BACKGROUND

Currently, the high morbidity of individuals with thyroid cancer (TC) is an increasing health care burden worldwide. The aim of our study was to investigate the relationship among the gut microbiota community, metabolites, and the development of differentiated thyroid cancer.

METHODS

16S rRNA gene sequencing and an integrated LC-MS-based metabolomics approach were performed to obtain the components and characteristics of fecal microbiota and metabolites from 50 patients with TC and 58 healthy controls (HCs).

RESULTS

The diversity and richness of the gut microbiota in the TC patients were markedly decreased. The composition of the gut microbiota was significantly altered, and the Bacteroides enterotype was the dominant enterotype in TC patients. Additionally, the diagnostic validity of the combined model (three genera and eight metabolites) and the metabolite model (six metabolites) were markedly higher than that of the microbial model (seven genera) for distinguishing TC patients from HCs. LEfSe analysis demonstrated that genera (g_Christensenellaceae_R-7_group, g_Eubacterium_coprostanoligenes_group) and metabolites [27-hydroxycholesterol (27HC), cholesterol] closely related to lipid metabolism were greatly reduced in the TC group. In addition, a clinical serum indicator (total cholesterol) and metabolites (27HC and cholesterol) had the strongest influence on the sample distribution. Furthermore, functional pathways related to steroid biosynthesis and lipid digestion were inhibited in the TC group. In the microbiota-metabolite network, 27HC was significantly related to metabolism-related microorganisms (g_Christensenellaceae_R-7_group).

CONCLUSIONS

Our research explored the characteristics of the gut microecology of patients with TC. The findings of this study will help to discover risk factors that affect the occurrence and development of TC in the intestinal microecology.

Application of Metabolomics in Various Types of Diabetes

Metabolomics is the analysis of numerous small molecules known as metabolites. Over the past few years, with the continuous development in metabolomics, it has been widely used in the detection, diagnosis, and treatment of diabetes and has demonstrated great benefits. At the same time, studies on diabetes and its complications have discovered the metabolic markers that are characteristic of diabetes. However, the pathogenesis of diabetes has yet to be clarified, as well as no complete cure. The mechanism of diabetes has not been completely elucidated, and its eradication treatment is not available. Thus, prevention of the onset of the disease and its treatment have become very important. In this review, we focused on the recent progress in the use of metabolites in diabetes and their complications, as well as understanding the impact of diabetes metabolites.

Mechanistic details of the actinobacterial lyase-catalyzed degradation reaction of 2-hydroxyisobutyryl-CoA

Actinobacterial 2-hydroxyacyl-CoA lyase reversibly catalyzes the thiamine diphosphate-dependent cleavage of 2-hydroxyisobutyryl-CoA to formyl-CoA and acetone. This enzyme has great potential for use in synthetic one-carbon assimilation pathways for sustainable production of chemicals, but lacks details of substrate binding and reaction mechanism for biochemical reengineering. We determined crystal structures of the tetrameric enzyme in the closed conformation with bound substrate, covalent postcleavage intermediate, and products, shedding light on active site architecture and substrate interactions. Together with molecular dynamics simulations of the covalent precleavage complex, the complete catalytic cycle is structurally portrayed, revealing a proton transfer from the substrate acyl Cβ hydroxyl to residue E493 that returns it subsequently to the postcleavage Cα-carbanion intermediate. Kinetic parameters obtained for mutants E493A, E493Q, and E493K confirm the catalytic role of E493 in the WT enzyme. However, the 10- and 50-fold reduction in lyase activity in the E493A and E493Q mutants, respectively, compared with WT suggests that water molecules may contribute to proton transfer. The putative catalytic glutamate is located on a short α-helix close to the active site. This structural feature appears to be conserved in related lyases, such as human 2-hydroxyacyl-CoA lyase 2. Interestingly, a unique feature of the actinobacterial 2-hydroxyacyl-CoA lyase is a large C-terminal lid domain that, together with active site residues L127 and I492, restricts substrate size to ≤C5 2-hydroxyacyl residues. These details about the catalytic mechanism and determinants of substrate specificity pave the ground for designing tailored catalysts for acyloin condensations for one-carbon and short-chain substrates in biotechnological applications.

Targeted metabolomic analysis identifies increased serum levels of GABA and branched chain amino acids in canine diabetes

INTRODUCTION

Dogs with naturally occurring diabetes mellitus represent a potential model for human type 1 diabetes, yet significant knowledge voids exist in terms of the pathogenic mechanisms underlying the canine disorder. Untargeted metabolomic studies from a limited number of diabetic dogs identified similarities to humans with the disease.

OBJECTIVE

To expand and validate earlier metabolomic studies, identify metabolites that differ consistently between diabetic and healthy dogs, and address whether certain metabolites might serve as disease biomarkers.

METHODS

Untargeted metabolomic analysis via liquid chromatography-mass spectrometry was performed on serum from diabetic (n = 15) and control (n = 15) dogs. Results were combined with those of our previously published studies using identical methods (12 diabetic and 12 control dogs) to identify metabolites consistently different between the groups in all 54 dogs. Thirty-two candidate biomarkers were quantified using targeted metabolomics. Biomarker concentrations were compared between the groups using multiple linear regression (corrected P < 0.0051 considered significant).

RESULTS

Untargeted metabolomics identified multiple persistent differences in serum metabolites in diabetic dogs compared with previous studies. Targeted metabolomics showed increases in gamma amino butyric acid, valine, leucine, isoleucine, citramalate, and 2-hydroxyisobutyric acid in diabetic versus control dogs while indoxyl sulfate, N-acetyl-L-aspartic acid, kynurenine, anthranilic acid, tyrosine, glutamine, and tauroursodeoxycholic acid were decreased.

CONCLUSION

Several of these findings parallel metabolomic studies in both human diabetes and other animal models of this disease. Given recent studies on the role of GABA and branched chain amino acids in human diabetes, the increase in serum concentrations in canine diabetes warrants further study of these metabolites as potential biomarkers, and to identify similarity in mechanisms underlying this disease in humans and dogs.

Metabolomic analysis reveals potential biomarkers and serum metabolomic profiling in spontaneous intracerebral hemorrhage patients using UPLC/quadrupole time-of-flight MS

Spontaneous intracerebral hemorrhage (ICH) accounts for 10-20% of all strokes and contributes to higher mortalities and severe disabilities. The aims of this study were, therefore, to characterize novel biomarkers, metabolic disruptions, and mechanisms involving ICH. A total 30 ICH patients and 30 controls were enrolled in the study, and their clinical characteristics were analyzed. Nontargeted metabolomic analysis was conducted using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF). Multivariate statistical analysis and receiver operating characteristic curve analysis were used for screening and evaluating the predictive ability of biomarkers. ICH patients showed significantly higher systolic blood pressure, diastolic blood pressure, blood glucose levels, white blood cell counts, neutrophil count, percentage of neutrophils and globulin and a lower albumin/globin ratio when compared with controls. In sum, 11 important metabolites were identified, which were associated with disruption of fatty acid oxidation and sphingolipid and phospholipid metabolism, as well as increased inflammation, oxidative stress, and vascular pathologies. Further multiple logistic regression analyses of these metabolites showed that l-carnitine and phosphatidylcholine (20:3/22:6) have potential as biomarkers of ICH, and the area under the curve, sensitivity, specificity were 0.974, 90%, and 93%, respectively. These findings provide insights into the pathogenesis, early prevention, and diagnosis of ICH.

Metabolomics in Bariatric Surgery: Towards Identification of Mechanisms and Biomarkers of Metabolic Outcomes

Bariatric surgery has been widely performed for the treatment of obesity and type 2 diabetes. Efforts have been made to investigate the mechanisms underlying the metabolic effects achieved by bariatric surgery and to identify candidates who will benefit from this surgery. Metabolomics, which includes comprehensive profiling of metabolites in biological samples, has been utilized for various disease entities to discover pathophysiological metabolic pathways and biomarkers predicting disease progression or prognosis. Over the last decade, metabolomic studies on patients undergoing bariatric surgery have identified significant biomarkers related to metabolic effects. This review describes the significance, progress, and challenges for the future of metabolomics in the area of bariatric surgery.

Advances in the Study of Metabolomics and Metabolites in Some Species Interactions

In the natural environment, interactions between species are a common natural phenomena. The mechanisms of interaction between different species are mainly studied using genomic, transcriptomic, proteomic, and metabolomic techniques. Metabolomics is a crucial part of system biology and is based on precision instrument analysis. In the last decade, the emerging field of metabolomics has received extensive attention. Metabolomics not only provides a qualitative and quantitative method for studying the mechanisms of interactions between different species, but also helps clarify the mechanisms of defense between the host and pathogen, and to explore new metabolites with various biological activities. This review focuses on the methods and progress of interspecies metabolomics. Additionally, the prospects and challenges of interspecies metabolomics are discussed.

Lipids: biomarkers of healthy aging

Life expectancy, and longevity have been increasing in recent years. However, this is, in most cases, accompanied by age-related diseases. Thus, it became essential to better understand the mechanisms inherent to aging, and to establish biomarkers that characterize this physiological process. Among all biomolecules, lipids appear to be a good target for the study of these biomarkers. In fact, some lipids have already been associated with age-related diseases. With the development of analytical techniques such as Mass Spectrometry, and Nuclear Magnetic Resonance, Lipidomics has been increasingly used to study pathological, and physiological states of an organism. Thus, the study of serum, and plasma lipidome in centenarians, and elderly individuals without age-related diseases can be a useful tool for the identification of aging biomarkers, and to understand physiological aging, and longevity. This review focus on the importance of lipids as biomarkers of aging, and summarize the changes in the lipidome that have been associated with aging, and longevity.

Non-targeted Serum Metabolomics Identifies Candidate Biomarkers Panels Associated with Nonalcoholic Fatty Liver Disease: A Pilot Study in Russian Male Patients

Aims:

The aim of the present study was to explore changes in the serum metabolome of patients with NAFLD relative to healthy controls to identify biomarkers associated with steatosis or Non-Alcoholic Steatohepatitis (NASH).

Background:

The serum metabolome reflects changes at the organismal level. This is especially important in Non-Alcoholic Liver Disease (NAFLD), where changes in hormones, cytokines, enzymes and other metabolic alterations can affect the liver, as well as adipose tissue, skeletal muscle and other systems.

Objective:

The objectives were to conduct non-targeted serum metabolomics, data processing, and identification of candidate biomarkers, as well as panels and assessment of their prognostic value.

Materials and Methods:

Non-targeted metabolomic analysis of blood serum samples from 21 male patients with NAFLD (simple steatosis or NASH) and seven male Control group was performed using gas chromatography-mass spectrometry.

Results:

A total of 319 serum metabolites were detected in NAFLD and Control groups, several of which differed significantly between groups. The most discriminating biomarkers were 3-hydroxybutyric acid, 2-hydroxybutyric acid, 2,3-dihydroxybutyric acid, arabitol and 3-methyl-2-oxovaleric acid. Using a panel of three, four or more markers could distinguish patients with NAFLD from controls, and patients with NASH from those with simple steatosis.

Conclusion:

We identified candidate biomarkers for simple steatosis and NASH. Since NAFLD is a multifactorial disease, it is preferable to use a marker panel rather than individual metabolites. Markers may not only result from dysregulation of metabolic pathways in patients with NAFLD, they may also reflect adaptive responses to disease, including functional changes in the intestinal microbiota.

Multi-omics analyses reveal relationships among dairy consumption, gut microbiota and cardiometabolic health

BACKGROUND

Little is known about the interplay among dairy intake, gut microbiota and cardiometabolic health in human prospective cohort studies.

METHODS

The present study included 1780 participants from the Guangzhou Nutrition and Health Study. We examined the prospective association between habitual dairy consumption (total dairy, milk, yogurt) and gut microbial composition using linear regression after adjusting for socio-demographic, lifestyle and dietary factors. The cross-sectional association of dairy-associated microbial features with cardiometabolic risk factors was examined with a linear regression model, adjusting for potential confounders. Serum metabolomic profiles were analyzed by partial correlation analysis.

FINDINGS

There was a significant overall difference in gut microbial community structure (β-diversity) comparing the highest with the lowest category for each of total dairy, milk and yogurt (P < 0.05). We observed that dairy-associated microbes and α-diversity indices were inversely associated with blood triglycerides, while positively associated with high-density lipoprotein cholesterol. A follow-up metabolomics analysis revealed the association of targeted serum metabolites with dairy-microbial features and cardiometabolic traits. Specifically, 2-hydroxy-3-methylbutyric acid, 2-hydroxybutyric acid and L-alanine were inversely associated with dairy-microbial score, while positively associated with triglycerides (FDR-corrected P < 0.1).

INTERPRETATION

Dairy consumption is associated with the gut microbial composition and a higher α-diversity, which provides new insights into the understanding of dairy-gut microbiota interactions and their relationship with cardiometabolic health.

FUNDING

This work was supported by the National Natural Science Foundation of China, Zhejiang Ten-thousand Talents Program, Westlake University and the 5010 Program for Clinical Researches of the Sun Yat-sen University.

Volatile Metabolic Markers for Monitoring Pectobacterium carotovorum subsp. carotovorum Using Headspace Solid-Phase Microextraction Coupled with Gas Chromatography-Mass Spectrometry

Identifying the extracellular metabolites of microorganisms in fresh vegetables is industrially useful for assessing the quality of processed foods. Pectobacterium carotovorum subsp. carotovorum (PCC) is a plant pathogenic bacterium that causes soft rot disease in cabbages. This microbial species in plant tissues can emit specific volatile molecules with odors that are characteristic of the host cell tissues and PCC species. In this study, we used headspace solid-phase microextraction followed by gas chromatography coupled with mass spectrometry (HS-SPME-GC-MS) to identify volatile compounds (VCs) in PCC-inoculated cabbage at different storage temperatures. HS-SPME-GC-MS allowed for recognition of extracellular metabolites in PCC-infected cabbages by identifying specific volatile metabolic markers. We identified 4-ethyl-5-methylthiazole and 3-butenyl isothiocyanate as markers of fresh cabbages, whereas 2,3-butanediol and ethyl acetate were identified as markers of soft rot in PCC-infected cabbages. These analytical results demonstrate a suitable approach for establishing non-destructive plant pathogen-diagnosis techniques as alternatives to standard methods, within the framework of developing rapid and efficient analytical techniques for monitoring plant-borne bacterial pathogens. Moreover, our techniques could have promising applications in managing the freshness and quality control of cabbages.

Metabolite Triplet in Serum Improves the Diagnostic Accuracy of Prediabetes and Diabetes Screening

Large-scale population screenings are not feasible by applying laborious oral glucose tolerance tests, but using fasting blood glucose (FPG) and glycated hemoglobin (HbA_1c), a considerable number of diagnoses are missed. A novel marker is urgently needed to improve the diagnostic accuracy of broad-scale diabetes screening in easy-to-collect blood samples. In this study, by applying a novel knowledge-based, multistage discovery and validation strategy, we scaled down from 108 diabetes-associated metabolites to a diagnostic metabolite triplet (Met-T), namely hexose, 2-hydroxybutyric/2-hydroxyisobutyric acid, and phenylalanine. Met-T showed in two independent cohorts, each comprising healthy controls, prediabetic, and diabetic individuals, distinctly higher diagnostic sensitivities for diabetes screening than FPG alone (>79.6 vs <68%). Missed diagnoses decreased from >32% using fasting plasma glucose down to <20.4%. Combining Met-T and fasting plasma glucose further improved the diagnostic accuracy. Additionally, a positive association of Met-T with future diabetes risk was found (odds ratio: 1.41; p = 1.03 × 10^-6). The results reveal that missed prediabetes and diabetes diagnoses can be markedly reduced by applying Met-T alone or in combination with FPG and it opens perspectives for higher diagnostic accuracy in broad-scale diabetes-screening approaches using easy to collect sample materials.

Urinary Metabolomic Profiling Reveals Biological Pathways and Predictive Signatures Associated with Childhood Asthma

Background

Despite considerable efforts, the pathogenic mechanisms of asthma are still incompletely understood, due to its heterogeneous nature. However, metabolomics can offer a global view of a biological system, making it a valuable tool for further elucidation of mechanisms and biomarker discovery in asthma.

Methods

GC-MS-based metabolomic analysis was conducted for comparison of urine metabolic profiles between asthmatic children (n=30) and healthy controls (n=30).

Results

An orthogonal projections to latent structures discriminant-analysis model revealed a clear separation of the asthma and control groups (R ² _x =0.137, R ² _y =0.947, Q ²=0.82). A total of 20 differential metabolites were identified as discriminant factors, of which eleven were significantly increased and nine decreased in the asthma group compared to the control group. Pathway-enrichment analysis based on these differential metabolites indicated that sphingolipid metabolism, protein biosynthesis, and citric acid cycle were strongly associated with asthma. Among the identified metabolites, 2-hydroxybutanoic acid showed excellent discriminatory performance for distinguishing asthma from healthy controls, with an AUC of 0.969.

Conclusion

Our study revealed significant changes in the urine metabolome of asthma patients. Several perturbed pathways (eg, sphingolipid metabolism and citric acid cycle) may be related to asthma pathogenesis, and 2-hydroxybutanoic acid could serve as a potential biomarker for asthma diagnosis.

Plasma metabolomics analysis of the effects of drinking soda water on hyperglycemia mice using ultrahigh-pressure liquid chromatography-quadrupole-time of flight-mass spectrometry

The aim of this study was to evaluate the effects of a natural soda water [Shi Han Quan (SHQ)] on hyperglycemia and plasma metabolic profiling and explore the mechanism using metabolomics techniques. Kun-Ming mice weighing 26 ± 2 g were used for the hyperglycemia animal model with alloxan and divided into control, hyperglycemia (HG), and HG + SHQ soda water (SHQ) groups. The experiment lasted for 30 days. The plasma metabolomic profiling of mice was determined using ultrahigh-pressure liquid chromatography-quadrupole-time of flight-mass spectrometry. After the mice drank SHQ soda water, the levels of insulin and blood glucose were significantly lower in the SHQ group compared with the control group, and the level of insulin sensitivity [insulin sensitivity index (ISI)] was significantly higher in the SHQ group compared with the HG group. The mice in the different groups after SHQ intervention could be separated into distinct clusters, and nine major plasma metabolites with significant differences between groups were found closely associated with blood glucose and ISI. The metabolic pathway analysis of these metabolites involved abnormal fatty acid oxidation and phospholipid, acylcarnitine, and corticoid metabolism. The results suggested the metabolic changes and possible mechanism of SHQ improving the alloxan-induced HG, and the findings provided insights into the prevention and control of HG and diabetes.

Metabolomic signatures for visceral adiposity and dysglycaemia in Asian Chinese and Caucasian European adults: the cross-sectional TOFI_Asia study

BACKGROUND

Asian Chinese are more susceptible to deposition of visceral adipose tissue (VAT) and type 2 diabetes (T2D) development than European Caucasians when matched for gender, age and body mass index (BMI). Our aims were: (i) characterise the ethnicity-specific metabolomic signature of visceral adiposity measured by dual energy X-ray absorptiometry (DXA) and fasting plasma glucose (FPG), and (ii) identify individuals susceptible to worse metabolic health outcomes.

METHODS

Fasting plasma samples from normoglycaemic (n = 274) and prediabetic (n = 83) participants were analysed with liquid chromatography-mass spectrometry using untargeted metabolomics. Multiple linear regression adjusting for age, gender and BMI was performed to identify metabolites associated with FPG and VAT calculated as percentage of total body fat (%VAT_TBF) in each ethnic group. Metabolic risk groups in each ethnicity were stratified based on the joint metabolomic signature for FPG and %VAT_TBF and clinically characterised using partial least squares-discriminant analysis (PLS-DA) and t-tests.

RESULTS

FPG was correlated with 40 and 110 metabolites in Caucasians and Chinese respectively, with diglyceride DG(38:5) (adjusted β = 0.29, p = 3.00E-05) in Caucasians and triglyceride TG(54:4) (adjusted β = 0.28, p = 2.02E-07) in Chinese being the most significantly correlated metabolite based on the p-value. %VAT_TBF was correlated with 85 and 119 metabolites in Caucasians and Chinese respectively, with TG(56:2) (adjusted β = 0.3, p = 8.25E-09) in Caucasians and TG(58:3) (adjusted β = 0.25, p = 2.34E-08) in Chinese being the most significantly correlated. 24 metabolites associated with FPG were common to both ethnicities including glycerolipid species. 67 metabolites associated with %VAT_TBF were common to both ethnicities including positive correlations with dihydroceramide, sphingomyelin, glycerolipid, phosphatidylcholine, phosphatidylethnolamine, and inverse correlations with ether-linked phosphatidylcholine. Participant re-stratification found greater total and central adiposity, worse clinical lipid profiles, higher serum glucoregulatory peptides and liver enzymes in normal fasting glucose (NFG) individuals with a prediabetic metabolomic profile than NFG individuals with a normoglycaemic metabolomic profile in both ethnicities.

CONCLUSIONS

Untargeted metabolomics identified common and disparate metabolites associated with FPG and %VAT_TBF, with an ethnic-dimorphic signature for these metabolic traits. These signatures could improve risk stratification and identify NFG individuals with an adverse cardiometabolic and T2D risk profile.

Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches

This mini-review aims to discuss the development and applications of mass spectrometry (MS)-based hybrid approaches in metabolomics. Several recently developed hybrid approaches are introduced. Then, the overall workflow, frequently used instruments, data handling strategies, and applications are compared and their pros and cons are summarized. Overall, the improved repeatability and quantitative capability in large-scale MS-based metabolomics studies are demonstrated, in comparison to either targeted or untargeted metabolomics approaches alone. In summary, we expect this review to serve as a first attempt to highlight the development and applications of emerging hybrid approaches in metabolomics, and we believe that hybrid metabolomics approaches could have great potential in many future studies.

Specificity of metabolic colorectal cancer biomarkers in serum through effect size

INTRODUCTION

Colorectal cancer is one of the most diagnosed cancers, leading to numerous deaths. In addition to existing screening methods, metabolic profiling could help both to diagnose and to understand the various states of the disease.

OBJECTIVES

Find specific candidate biomarkers (CB) in serum of patients with colorectal cancer (CRC), in comparison to the situation after remission (R-CRC), evaluated on distinct patients.

METHODS

All serum samples were analyzed using comprehensive two-dimensional gas chromatography (GC × GC) coupled to high resolution time of flight mass spectrometry (TOF-MS) through an optimized and validated untargeted analytical method regulated by a quality control (QC) system. First, we used a specific multi-approaches data (pre)processing workflow to highlight, annotate and assess the performances of the most altered metabolites between CRC patients (n = 18) and healthy control samples (HC, n = 19) specifically matched for age and gender, two of the most influential confounding factors. On the contrary, due to the difficulty to control for all clinical and demographic traits when sampling small cohorts, the samples from patients in remission (n = 17) were not matched. Because of the consequent risk of bias, the usual null hypothesis significance tests (NHST) could not be applied reliably. Therefore, we compared the R-CRC samples to another specifically matched group of healthy controls (R-HC, n = 17), and used this comparison to indirectly address the difference between patients with colorectal cancer and patients in remission through a measure called effect size (ES) whose methodological aspects were investigated.

RESULTS

24 candidate biomarkers were found significantly altered and able to discriminate the CRC and HC samples efficiently (Receiver Operating Characteristic (ROC) area under the curve (AUC) of 0.86, sensitivity and specificity of 0.72 and 0.78). 10 of those were found to have signals close to healthy levels in the R-CRC samples and were therefore specific to colorectal cancer. In the point-biserial case studied here, r-like (strength of association) and d-like (standardized mean difference) ES were directly convertible and only linear and rank-based ES were different. We therefore used and recommend Hedges' g, Spearman's rho and Kendall's tau, along with an unstandardized ES. The confidence intervals, that quantify the uncertainty of the measure, were well represented through scatterplots and distribution curves.

CONCLUSION

The candidate biomarkers found, along with their specificity, could help for the detection of colorectal cancer, the diagnosis of remission, and for the understanding of its pathophysiology, after proper validation on independent cohorts. The effect size, here applied on a MS global profiling data set, is an ideal complement to NHST and a useful tool to compare and combine distinct cohorts, within a study as well as between studies (meta-analysis).

A lipidome-wide association study of the lipoprotein insulin resistance index

Background

The lipoprotein insulin resistance (LPIR) score was shown to predict insulin resistance (IR) and type 2 diabetes (T2D) in healthy adults. However, the molecular basis underlying the LPIR utility for classification remains unclear.

Objective

To identify small molecule lipids associated with variation in the LPIR score, a weighted index of lipoproteins measured by nuclear magnetic resonance, in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study (n = 980).

Methods

Linear mixed effects models were used to test the association between the LPIR score and 413 lipid species and their principal component analysis-derived groups. Significant associations were tested for replication with homeostatic model assessment-IR (HOMA-IR), a phenotype correlated with the LPIR score (r = 0.48, p <  0.001), in the Heredity and Phenotype Intervention (HAPI) Heart Study (n = 590).

Results

In GOLDN, 319 lipids were associated with the LPIR score (false discovery rate-adjusted p-values ranging from 4.59 × 10^− 161 to 49.50 × 10^− 3). Factors 1 (triglycerides and diglycerides/storage lipids) and 3 (mixed lipids) were positively (β = 0.025, p = 4.52 × 10^− 71 and β = 0.021, p = 5.84 × 10^− 41, respectively) and factor 2 (phospholipids/non-storage lipids) was inversely (β = − 0.013, p = 2.28 × 10^− 18) associated with the LPIR score. These findings were replicated for HOMA-IR in the HAPI Heart Study (β = 0.10, p = 1.21 × 10^− 02 for storage, β = − 0.13, p = 3.14 × 10^− 04 for non-storage, and β = 0.19, p = 8.40 × 10^− 07 for mixed lipids).

Conclusions

Non-storage lipidomics species show a significant inverse association with the LPIR metabolic dysfunction score and present a promising focus for future therapeutic and prevention studies.

Metabolomic identification of pregnancy-specific biomarkers in blood plasma of BOS TAURUS beef cattle after transfer of in vitro produced embryos

Blood biomarkers may help to predict pregnancy in recipients of in vitro produced (IVP) embryos. Using ¹H nuclear magnetic resonance, we quantified 36 metabolites in the blood plasma of recipients (90% heifers, healthy, 1.95 years on average at the time of 1st embryo transfer -ET-) collected at Day-0 (estrus) and Day-7 (before ET time). First, IVP embryos were transferred to Asturiana de los Valles recipients as fresh (F) (N = 26) and vitrified/warmed (V/W) (N = 48) (discovery groups). Only at estrus, we discovered 4, 11, and 5 (F-ET), and 2, 2, and 4 (V/W-ET) metabolites that predicted pregnancy on Day-40, Day-62 and calving time, respectively (ROC-AUC > 0.700; P < .05). Thereafter, validation was performed in independent samples (N = 67 F and N = 63 V/W) of three cattle breeds by an index of overall classification accuracy (OCA>0.650, P < .05). The numbers of candidate biomarkers validated were 2, 9 and 1 (F-ET) and 2, 2, and 3 (V/W-ET) on Day 40, Day-62 and calving time. Relevant metabolites were validated at the three (2-Oxoglutaric acid (F-ET), and 2-Hydroxybutyric acid and Dimethylamine (V/W-ET)) and two pregnancy endpoints (Ketoleucine (F-ET); Day-40 and Day-62) analysed. Fatty acid degradation and oxidative metabolism were enriched in pregnant recipients. The candidate biomarkers identified can improve embryo-recipient selection. SIGNIFICANCE: We identified, for the first time, reliable pregnancy and birth candidate metabolite biomarkers for fresh and vitrified IVP embryos in blood of beef cattle recipients. Our findings can help to improve embryo-recipient selection, which is usually carried out in a way that females that will not become pregnant are not well differentiated.

Maternal plasma metabolic markers of neonatal adiposity and associated maternal characteristics: The GUSTO study

Infant adiposity may be related to later metabolic health. Maternal metabolite profiling reflects both genetic and environmental influences and allows elucidation of metabolic pathways associated with infant adiposity. In this multi-ethnic Asian cohort, we aimed to (i) identify maternal plasma metabolites associated with infant adiposity and other birth outcomes and (ii) investigate the maternal characteristics associated with those metabolites. In 940 mother-offspring pairs, we performed gas chromatography-mass spectrometry and identified 134 metabolites in maternal fasting plasma at 26–28 weeks of gestation. At birth, neonatal triceps and subscapular skinfold thicknesses were measured by trained research personnel, while weight and length measures were abstracted from delivery records. Gestational age was estimated from first-trimester dating ultrasound. Associations were assessed by multivariable linear regression, with p-values corrected using the Benjamini-Hochberg approach. At a false discovery rate of 5%, we observed associations between 28 metabolites and neonatal sum of skinfold thicknesses (13 amino acid-related, 4 non-esterified fatty acids, 6 xenobiotics, and 5 unknown compounds). Few associations were observed with gestational duration, birth weight, or birth length. Maternal ethnicity, pre-pregnancy BMI, and diet quality during pregnancy had the strongest associations with the specific metabolome related to infant adiposity. Further studies are warranted to replicate our findings and to understand the underlying mechanisms.

Actinobacterial Degradation of 2-Hydroxyisobutyric Acid Proceeds via Acetone and Formyl-CoA by Employing a Thiamine-Dependent Lyase Reaction

The tertiary branched short-chain 2-hydroxyisobutyric acid (2-HIBA) has been associated with several metabolic diseases and lysine 2-hydroxyisobutyrylation seems to be a common eukaryotic as well as prokaryotic post-translational modification in proteins. In contrast, the underlying 2-HIBA metabolism has thus far only been detected in a few microorganisms, such as the betaproteobacterium Aquincola tertiaricarbonis L108 and the Bacillus group bacterium Kyrpidia tusciae DSM 2912. In these strains, 2-HIBA can be specifically activated to the corresponding CoA thioester by the 2-HIBA-CoA ligase (HCL) and is then isomerized to 3-hydroxybutyryl-CoA in a reversible and B₁₂-dependent mutase reaction. Here, we demonstrate that the actinobacterial strain Actinomycetospora chiangmaiensis DSM 45062 degrades 2-HIBA and also its precursor 2-methylpropane-1,2-diol via acetone and formic acid by employing a thiamine pyrophosphate-dependent lyase. The corresponding gene is located directly upstream of hcl, which has previously been found only in operonic association with the 2-hydroxyisobutyryl-CoA mutase genes in other bacteria. Heterologous expression of the lyase gene from DSM 45062 in E. coli established a 2-hydroxyisobutyryl-CoA lyase activity in the latter. In line with this, analysis of the DSM 45062 proteome reveals a strong induction of the lyase-HCL gene cluster on 2-HIBA. Acetone is likely degraded via hydroxylation to acetol catalyzed by a MimABCD-related binuclear iron monooxygenase and formic acid appears to be oxidized to CO₂ by selenium-dependent dehydrogenases. The presence of the lyase-HCL gene cluster in isoprene-degrading Rhodococcus strains and Pseudonocardia associated with tropical leafcutter ant species points to a role in degradation of biogenic short-chain ketones and highly branched organic compounds.

Exploring the Diversity of Sugar Compounds in Healthy, Prediabetic, and Diabetic Volunteers

SCOPE

Diabetes is thought to primarily represent a disturbance of carbohydrate metabolism; however, population studies employing metabolomics have mainly identified plasma amino acids and lipids, or their products, as biomarkers. In this pilot study, the aim is to analyze a wide spectrum of sugar compounds in the fasting state and during an oral glucose tolerance test (OGTT) in healthy, prediabetic, and type 2 diabetic volunteers.

METHODS AND RESULTS

The three volunteer groups underwent a standard OGTT. Plasma samples obtained in the fasting state, 30 and 90 min after the OGTT, are subjected to a semitargeted GC-MS (gas chromatography-mass spectrometry) sugar profiling. Overall, 40 sugars are detected in plasma, of which some are yet unknown to change during an OGTT. Several sugars (e.g., trehalose) reveal significant differences between the volunteer groups both in fasting plasma and in distinct time courses after the OGTT. This suggests an endogenous production from orally absorbed glucose and/or an insulin-dependent production/removal from plasma.

CONCLUSION

It is demonstrated that more sugars than expected can be found in human plasma. Since some of these show characteristic differences depending on health status, it may be worthwhile to assess their usability as biomarkers for diagnosing early-stage insulin resistance and type 2 diabetes.

Metabolic Signatures Differentiate Rett Syndrome From Unaffected Siblings

Rett syndrome (RTT, OMIM 312750), a severe neurodevelopmental disorder characterized by regression with loss of spoken language and hand skills, development of characteristic hand stereotypies, and gait dysfunction, is primarily caused by de novo mutations in the X-linked gene Methyl-CpG-binding protein 2 (MECP2). Currently, treatment options are limited to symptomatic management, however, reversal of disease phenotype is possible in mouse models by restoration of normal MECP2 gene expression. A significant challenge is the lack of biomarkers of disease state, disease severity, or treatment response. Using a non-targeted metabolomic approach we evaluated metabolite profiles in plasma from thirty-four people with RTT compared to thirty-seven unaffected age- and gender-matched siblings. We identified sixty-six significantly altered metabolites that cluster broadly into amino acid, nitrogen handling, and exogenous substance pathways. RTT disease metabolite and metabolic pathways abnormalities point to evidence of oxidative stress, mitochondrial dysfunction, and alterations in gut microflora. These observed changes provide insight into underlying pathological mechanisms and the foundation for biomarker discovery of disease severity biomarkers.

The Natural Metabolite 4-Cresol Improves Glucose Homeostasis and Enhances β-Cell Function

Exposure to natural metabolites contributes to the risk of cardiometabolic diseases (CMDs). Through metabolome profiling, we identify the inverse correlation between serum concentrations of 4-cresol and type 2 diabetes. The chronic administration of non-toxic doses of 4-cresol in complementary preclinical models of CMD reduces adiposity, glucose intolerance, and liver triglycerides, enhances insulin secretion in vivo, stimulates islet density and size, and pancreatic β-cell proliferation, and increases vascularization, suggesting activated islet enlargement. In vivo insulin sensitivity is not affected by 4-cresol. The incubation of mouse isolated islets with 4-cresol results in enhanced insulin secretion, insulin content, and β-cell proliferation of a magnitude similar to that induced by GLP-1. In both CMD models and isolated islets, 4-cresol is associated with the downregulated expression of the kinase DYRK1A, which may mediate its biological effects. Our findings identify 4-cresol as an effective regulator of β-cell function, which opens up perspectives for therapeutic applications in syndromes of insulin deficiency.

Insulin resistance and metabonomics analysis of fatty liver haemorrhagic syndrome in laying hens induced by a high-energy low-protein diet

Fatty liver haemorrhagic syndrome (FLHS) is a widespread metabolic disease in laying hens that causes a decrease in egg production and even death. Insulin resistance is a major contributor to the pathogenesis of nonalcoholic fatty liver disease. However, the relationship between FLHS and the insulin resistance mechanisms underlying FLHS is not well elucidated. Therefore, we established an FLHS model induced by feeding a high-energy low-protein diet. In the current study, we found that the fasting glucose and insulin concentrations were elevated in the FLHS group compared with the control group during the experimental period. The results of the oral glucose tolerance test (OGTT) and insulin sensitivity test (IST) showed a high level of insulin resistance in the FLHS model. InsR, 4EBP-1, Glut-1 and Glut-3 mRNA expression were decreased, and TOR, S6K1, and FOXO1 were elevated (P < 0.05). Metabolomic analysis with GC/MS identified 46 differentially expressed metabolites between these two groups, and of these, 14 kinds of metabolism molecules and 32 kinds of small metabolism molecules were decreased (P < 0.05). Further investigation showed that glucose, lipid and amino acid metabolism blocks in the progression of FLHS by GO functional and pathway analysis. Overall, these results suggest that insulin resistance participated in FLHS; comprehensively, metabolites participated in the dysregulated biological process.