Plasma glutamine and glutamic acid are potential biomarkers for predicting diabetic retinopathy

Introduction

Diabetic patients with a long disease duration usually accompanied complication such as diabetic retinopathy, but in some patients had no complication.

Objectives

We analyzed differences in plasma metabolites according to the presence or absence of diabetic retinopathy (DR) in type 2 diabetic (T2D) patients with disease duration?≥?15 years.

Methods

A cohort of 183 T2D patients was established. Their biospecimens and clinical information were collected in accordance with the guidelines of the National Biobank of Korea, and the Korean Diabetes Association. DR phenotypes of the subjects were verified by ophthalmologic specialists. Plasma metabolites were analyzed using gas chromatography time-of-flight mass spectrometry and ultra-performance liquid chromatography–quadrupole time-of-flight mass spectrometry. And these results were analyzed using multivariate statistics.

Results

For metabolomic study, propensity score matched case and control subjects were chosen. Mean age of the subjects was 66.4 years and mean T2D duration was 22.2 years. Metabolomic identification revealed various carbohydrates, amino acids, and organic compounds that distinguished between age- and sex-matched non-diabetic controls and T2D subjects. Among these, glutamine and glutamic acid were suggested as the most distinctive metabolites for the presence of DR. Receiver operating characteristics curves showed an excellent diagnostic value of combined (AUC?=?0.739) and the ratio (AUC?=?0.742) of glutamine and glutamic acid for DR. And these results were consistent in validation analyses.

Conclusion

Our results imply that plasma glutamine, glutamic acid, and their ratio may be valuable as novel biomarkers for anticipating DR in T2D subjects.

     

Postprandial metabolite profiles associated with type 2 diabetes clearly stratify individuals with impaired fasting glucose

Introduction

Fasting metabolite profiles have been shown to distinguish type 2 diabetes (T2D) patients from normal glucose tolerance (NGT) individuals.

Objectives

We investigated whether, besides fasting metabolite profiles, postprandial metabolite profiles associated with T2D can stratify individuals with impaired fasting glucose (IFG) by their similarities to T2D.

Methods

Three groups of individuals (age 45–65 years) without any history of IFG or T2D were selected from the Netherlands Epidemiology of Obesity study and stratified by baseline fasting glucose concentrations (NGT (n?=?176), IFG (n?=?186), T2D (n?=?171)). 163 metabolites were measured under fasting and postprandial states (150 min after a meal challenge). Metabolite profiles specific for a high risk of T2D were identified by LASSO regression for fasting and postprandial states. The selected profiles were utilised to stratify IFG group into high (T2D probability?≥?0.7) and low (T2D probability?≤?0.5) risk subgroups. The stratification performances were compared with clinically relevant metabolic traits.

Results

Two metabolite profiles specific for T2D (n_fasting = 12 metabolites, n_postprandial = 4 metabolites) were identified, with all four postprandial metabolites also being identified in the fasting state. Stratified by the postprandial profile, the high-risk subgroup of IFG individuals (n?=?72) showed similar glucose concentrations to the low-risk subgroup (n?=?57), yet a higher BMI (difference: 3.3 kg/m² (95% CI 1.7–5.0)) and postprandial insulin concentrations (21.5 mU/L (95% CI 1.8–41.2)).

Conclusion

Postprandial metabolites identified T2D patients as good as fasting metabolites and exhibited enhanced signals for IFG stratification, which offers a proof of concept that metabolomics research should not focus on the fasting state alone.

     

Altered plasma acylcarnitine and amino acid profiles in type 2 diabetic kidney disease

Introduction

Dysregulation of acylcarnitines (AcylCNs) and amino acids metabolism have implicated in abnormality of fatty acid oxidation in type 2 diabetes (T2D). However, it is not well known whether altered plasma AcylCN, and amino acid profiles are associated with albuminuria or diabetic nephropathy (DN) in T2D.

Objective

The aim of this study was to elucidate alterations in plasma levels of AcylCNs and amino acids with respect to the T2D patients with various stages of albuminuria.

Methods

We recruited 52 healthy subjects as control, and 156 T2D patients which were divided into 52 normoalbuminuria, 52 microalbuminuria, and 52 macroalbuminuria. Plasma 37 AcylCNs and 12 amino acids were analyzed by tandem mass spectrometry.

Results

We found that T2D with normoalbuminuria and microalbuminuria had lower shot-, medium-, and long-chain AcylCNs, whereas T2D with macroalbuminuria had higher short-and medium-chain AcylCNs and lower long-chain AcylCNs than healthy subjects. Moreover, estimated glomerular filtration rate (eGFR) was a negative, independent and significant predictor of albumin to creatinine ratio (ACR) levels (β = ?0.376, P < 0.001), whereas plasma Low-density lipoprotein cholesterol (LDL-C) was significantly and positively associated with ACR levels (β = 0.169, P = 0.049). Furthermore, multivariate ordinal logistic regression analysis revealed that isobutyrylcarnitine (C4) was a positive, independent, and significant predictor of ACR levels with higher odds of having T2D patients with progression normoalbuminuria to microalbuminuria [OR = 9.93, 95 % CI (3.51–28.05), P < 0.001].

Conclusions

The findings suggest that plasma C4 may serve as a potential biomarker for the early stages of DN.

     

NMR-based metabolic characterization of chicken tissues and biofluids: a model for avian research

Introduction

Poultry is one of the most consumed meat in the world and its related industry is always looking for ways to improve animal welfare and productivity. It is therefore essential to understand the metabolic response of the chicken to new feed formulas, various supplements, infections and treatments.

Objectives

As a basis for future research investigating the impact of diet and infections on chicken’s metabolism, we established a high-resolution proton nuclear magnetic resonance (NMR)-based metabolic atlas of the healthy chicken (Gallus gallus).

Methods

Metabolic extractions were performed prior to ¹H-NMR and 2D NMR spectra acquisition on twelve biological matrices: liver, kidney, spleen, plasma, egg yolk and white, colon, caecum, faecal water, ileum, pectoral muscle and brain of 6 chickens. Metabolic profiles were then exhaustively characterized.

Results

Nearly 80 metabolites were identified. A cross-comparison of these matrices was performed to determine metabolic variations between and within each section and highlighted that only eight core metabolites were systematically found in every matrice.

Conclusion

This work constitutes a database for future NMR-based metabolomic investigations in relation to avian production and health.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

A metabolomics guided exploration of marine natural product chemical space

Introduction

Natural products from culture collections have enormous impact in advancing discovery programs for metabolites of biotechnological importance. These discovery efforts rely on the metabolomic characterization of strain collections.

Objective

Many emerging approaches compare metabolomic profiles of such collections, but few enable the analysis and prioritization of thousands of samples from diverse organisms while delivering chemistry specific read outs.

Method

In this work we utilize untargeted LC–MS/MS based metabolomics together with molecular networking to inventory the chemistries associated with 1000 marine microorganisms.

Result

This approach annotated 76 molecular families (a spectral match rate of 28 %), including clinically and biotechnologically important molecules such as valinomycin, actinomycin D, and desferrioxamine E. Targeting a molecular family produced primarily by one microorganism led to the isolation and structure elucidation of two new molecules designated maridric acids A and B.

Conclusion

Molecular networking guided exploration of large culture collections allows for rapid dereplication of know molecules and can highlight producers of uniques metabolites. These methods, together with large culture collections and growing databases, allow for data driven strain prioritization with a focus on novel chemistries.

     

Stability of targeted metabolite profiles of urine samples under different storage conditions

Introduction

Few studies have investigated the influence of storage conditions on urine samples and none of them used targeted mass spectrometry (MS).

Objectives

We investigated the stability of metabolite profiles in urine samples under different storage conditions using targeted metabolomics.

Methods

Pooled, fasting urine samples were collected and stored at ?80 °C (biobank standard), ?20 °C (freezer), 4 °C (fridge), ~9 °C (cool pack), and ~20 °C (room temperature) for 0, 2, 8 and 24 h. Metabolite concentrations were quantified with MS using the AbsoluteIDQ? p150 assay. We used the Welch-Satterthwaite-test to compare the concentrations of each metabolite. Mixed effects linear regression was used to assess the influence of the interaction of storage time and temperature.

Results

The concentrations of 63 investigated metabolites were stable at ?20 and 4 °C for up to 24 h when compared to samples immediately stored at ?80 °C. When stored at ~9 °C for 24 h, few amino acids (Arg, Val and Leu/Ile) significantly decreased by 40% in concentration (P < 7.9E?04); for an additional three metabolites (Ser, Met, Hexose H1) when stored at ~20 °C reduced up to 60% in concentrations. The concentrations of four more metabolites (Glu, Phe, Pro, and Thr) were found to be significantly influenced when considering the interaction between exposure time and temperature.

Conclusion

Our findings indicate that 78% of quantified metabolites were stable for all examined storage conditions. Particularly, some amino acid concentrations were sensitive to changes after prolonged storage at room temperature. Shipping or storing urine samples on cool packs or at room temperature for more than 8 h and multiple numbers of freeze and thaw cycles should be avoided.

     

Complement C4 induces regulatory T cells differentiation through dendritic cell in systemic lupus erythematosus

Background

Systemic lupus erythematosus (SLE) is a prototypic systemic autoimmune disease. Complement component 4 (C4) has be proved to play a role in pathogenesis of SLE. In the present study, we investigated the effect of C4 on T cells differentiation.

Methods

Thirty SLE patients were included in this study. CD4+ T cells were isolated from healthy subjects, and dendritic cells (DCs) were isolated from healthy subjects or SLE patients. C4 was supplemented to co-incubate with T cells and DCs.

Results

Serum C4 concentration was positively correlated with regulatory T cell (Treg) percentage (R² = 0.5907, p < 0.001) and TGFβ concentration (R² = 0.5641, p < 0.001) in SLE patients. Different concentrations of C4 had no effect on T cells differentiation. Co-incubated T cells with DCs and C4 for 7 days, the Treg percentage and TGF-β concentration were significantly elevated. In addition, pre-treated DCs (from healthy subjects or SLE patients) with C4 and then co-incubated with T cells, the increases of Treg percentage and TGF-β concentration were also observed.

Conclusion

C4 takes part in T cells differentiation to Treg cells via DCs.

     

Treatment of lean and diet-induced obesity (DIO) mice with a novel stable obestatin analogue alters plasma metabolite levels as detected by untargeted LC–MS metabolomics

Introduction

Obestatin is a controversial gastrointestinal peptide purported to have metabolic actions.

Objectives

This study investigated whether treatment with a stable obestatin analogue (PEG-OB(Cys¹⁰, Cys¹³)) changed plasma metabolite levels firstly in lean and subsequently in diet-induced obesity (DIO) C57BL6/J mice.

Methods

Untargeted LC-HRMS metabolomics experiments were carried out in ESI + mode with plasma extracts from both groups of animals. Data were normalised, multivariate and univariate statistical analysis performed and metabolites of interest putatively identified.

Results

In lean mice, 39 metabolites were significantly changed by obestatin treatment and the majority of these were increased, including various C16 and C18 moieties of phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine and monoacylglycerol, along with vitamin A, vitamin D3, tyrosine, acetylcarnitine and 2α-(hydroxymethyl)-5α-androstane-3β,17β-diol. Decreased concentrations of glycolithocholic acid, 3-dehydroteasterone and various phospholipids were observed. In DIO mice, 25 metabolites were significantly affected and strikingly, the magnitudes of changes here were generally much greater in DIO mice than in lean mice, and in contrast, the majority of metabolite changes were decreases. Four metabolites affected in both groups included glycolithocholic acid, and three different long-chain (C18) phospholipid molecules (phosphatidylethanolamine, platelet activating factor (PAF), and monoacylglycerol). Metabolites exclusively affected in DIO mice included various phosphatidylcholines, lysophosphatidylcholines and fatty acyls, as well as creatine and oxidised glutathione.

Conclusion

This investigation demonstrates that obestatin treatment affects phospholipid turnover and influences lipid homeostasis, whilst providing convincing evidence that obestatin may be acting to ameliorate diet-induced impairments in lipid metabolism, and it may influence steroid, bile acid, PAF and glutathione metabolism.

     

The association of fecal microbiota and fecal,blood serum and urine metabolites in myalgic encephalomyelitis/chronic fatigue syndrome

Introduction

The human gut microbiota has the ability to modulate host metabolism. Metabolic profiling of the microbiota and the host biofluids may determine associations significant of a host–microbe relationship. Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a long-term disorder of fatigue that is poorly understood, but has been linked to gut problems and altered microbiota.

Objectives

Find changes in fecal microbiota and metabolites in ME/CFS and determine their association with blood serum and urine metabolites.

Methods

A workflow was developed that correlates microbial counts with fecal, blood serum and urine metabolites quantitated by high-throughput ¹H NMR spectroscopy. The study consists of thirty-four females with ME/CFS (34.9?±?1.8 SE years old) and twenty-five non-ME/CFS female (33.0?±?1.6 SE years old).

Results

The workflow was validated using the non-ME/CFS cohort where fecal short chain fatty acids (SCFA) were associated with serum and urine metabolites indicative of host metabolism changes enacted by SCFA. In the ME/CFS cohort a decrease in fecal lactate and an increase in fecal butyrate, isovalerate and valerate were observed along with an increase in Clostridium spp. and a decrease in Bacteroides spp. These differences were consistent with an increase in microbial fermentation of fiber and amino acids to produce SCFA in the gut of ME/CFS patients. Decreased fecal amino acids positively correlated with substrates of gluconeogenesis and purine synthesis in the serum of ME/CFS patients.

Conclusion

Increased production of SCFA by microbial fermentation in the gut of ME/CFS patients may be associated with deleterious effects on the host energy metabolism.

     

Human amylin proteotoxicity impairs protein biosynthesis,and alters major cellular signaling pathways in the heart,brain and liver of humanized diabetic rat model in vivo

Introduction

Chronic hypersecretion of the 37 amino acid amylin is common in type 2 diabetics (T2D). Recent studies implicate human amylin aggregates cause proteotoxicity (cell death induced by misfolded proteins) in both the brain and the heart.

Objectives

Identify systemic mechanisms/markers by which human amylin associated with cardiac and brain defects might be identified.

Methods

We investigated the metabolic consequences of amyloidogenic and cytotoxic amylin oligomers in heart, brain, liver, and plasma using non-targeted metabolomics analysis in a rat model expressing pancreatic human amylin (HIP model).

Results

Four metabolites were significantly different in three or more of the four compartments (heart, brain, liver, and plasma) in HIP rats. When compared to a T2D rat model, HIP hearts uniquely had significant DECREASES in five amino acids (lysine, alanine, tyrosine, phenylalanine, serine), with phenylalanine decreased across all four tissues investigated, including plasma. In contrast, significantly INCREASED circulating phenylalanine is reported in diabetics in multiple recent studies.

Conclusion

DECREASED phenylalanine may serve as a unique marker of cardiac and brain dysfunction due to hyperamylinemia that can be differentiated from alterations in T2D in the plasma. While the deficiency in phenylalanine was seen across tissues including plasma and could be monitored, reduced tyrosine was seen only in the brain. The 50 % reduction in phenylalanine and tyrosine in HIP brains is significant given their role in supporting brain chemistry as a precursor for catecholamines (dopamine, norepinephrine, epinephrine), which may contribute to the increased morbidity and mortality in diabetics at a multi-system level beyond the effects on glucose metabolism.

     

Optimized GC–MS metabolomics for the analysis of kidney tissue metabolites

Introduction

Metabolomics is a promising approach for discovery of relevant biomarkers in cells, tissues, organs, and biofluids for disease identification and prediction. The field has mostly relied on blood-based biofluids (serum, plasma, urine) as non-invasive sources of samples as surrogates of tissue or organ-specific conditions. However, the tissue specificity of metabolites pose challenges in translating blood metabolic profiles to organ-specific pathophysiological changes, and require further downstream analysis of the metabolites.

Objectives

As part of this project, we aim to develop and optimize an efficient extraction protocol for the analysis of kidney tissue metabolites representative of key primate metabolic pathways.

Methods

Kidney cortex and medulla tissues of a baboon were homogenized and extracted using eight different extraction protocols including methanol/water, dichloromethane/methanol, pure methanol, pure water, water/methanol/chloroform, methanol/chloroform, methanol/acetonitrile/water, and acetonitrile/isopropanol/water. The extracts were analyzed by a two-dimensional gas chromatography time-of-flight mass-spectrometer (2D GC–ToF-MS) platform after methoximation and silylation.

Results

Our analysis quantified 110 shared metabolites in kidney cortex and medulla tissues from hundreds of metabolites found among the eight different solvent extractions spanning low to high polarities. The results revealed that medulla is metabolically richer compared to the cortex. Dichloromethane and methanol mixture (3:1) yielded highest number of metabolites across both the tissue types. Depending on the metabolites of interest, tissue type, and the biological question, different solvents can be used to extract specific groups of metabolites.

Conclusion

This investigation provides insights into selection of extraction solvents for detection of classes of metabolites in renal cortex and medulla, which is fundamentally important for identification of prognostic and diagnostic metabolic kidney biomarkers for future therapeutic applications.

     

Automated metabolite identification from biological fluid <Superscript>1</Superscript>H NMR spectra

Introduction

Metabolite identification in biological samples using Nuclear Magnetic Resonance (NMR) spectra is a challenging task due to the complexity of the biological matrices.

Objectives

This paper introduces a new, automated computational scheme for the identification of metabolites in 1D ¹H NMR spectra based on the Human Metabolome Database.

Methods

The methodological scheme comprises of the sequential application of preprocessing, data reduction, metabolite screening and combination selection.

Results

The proposed scheme has been tested on the 1D ¹H NMR spectra of: (a) an amino acid mixture, (b) a serum sample spiked with the amino acid mixture, (c) 20 blood serum, (d) 20 human amniotic fluid samples, (e) 160 serum samples from publicly available database. The methodological scheme was compared against widely used software tools, exhibiting good performance in terms of correct assignment of the metabolites.

Conclusions

This new robust scheme accomplishes to automatically identify peak resonances in ¹H-NMR spectra with high accuracy and less human intervention with a wide range of applications in metabolic profiling.

     

Intensive determination of storage condition effects on human plasma metabolomics

Introduction

Human plasma metabolomics offer powerful tools for understanding disease mechanisms and identifying clinical biomarkers for diagnosis, efficacy prediction and patient stratification. Although storage conditions can affect the reliability of data from metabolites, strict control of these conditions remains challenging, particularly when clinical samples are included from multiple centers. Therefore, it is necessary to consider stability profiles of each analyte.

Objectives

The purpose of this study was to extract unstable metabolites from vast metabolome data and identify factors that cause instability.

Method

Plasma samples were obtained from five healthy volunteers, were stored under ten different conditions of time and temperature and were quantified using leading-edge metabolomics. Instability was evaluated by comparing quantitation values under each storage condition with those obtained after ?80 °C storage.

Result

Stability profiling of the 992 metabolites showed time- and temperature-dependent increases in numbers of significantly changed metabolites. This large volume of data enabled comparisons of unstable metabolites with their related molecules and allowed identification of causative factors, including compound-specific enzymatic activity in plasma and chemical reactivity. Furthermore, these analyses indicated extreme instability of 1-docosahexaenoylglycerol, 1-arachidonoylglycerophosphate, cystine, cysteine and N⁶-methyladenosine.

Conclusion

A large volume of data regarding storage stability was obtained. These data are a contribution to the discovery of biomarker candidates without misselection based on unreliable values and to the establishment of suitable handling procedures for targeted biomarker quantification.

     

Type 2 diabetes increases oocyte mtDNA mutations which are eliminated in the offspring by bottleneck effect

Background

Diabetes induces many complications including reduced fertility and low oocyte quality, but whether it causes increased mtDNA mutations is unknown.

Methods

We generated a T2D mouse model by using high-fat-diet (HFD) and Streptozotocin (STZ) injection. We examined mtDNA mutations in oocytes of diabetic mice by high-throughput sequencing techniques.

Results

T2D mice showed glucose intolerance, insulin resistance, low fecundity compared to the control group. T2D oocytes showed increased mtDNA mutation sites and mutation numbers compared to the control counterparts. mtDNA mutation examination in F1 mice showed that the mitochondrial bottleneck could eliminate mtDNA mutations.

Conclusions

T2D mice have increased mtDNA mutation sites and mtDNA mutation numbers in oocytes compared to the counterparts, while these adverse effects can be eliminated by the bottleneck effect in their offspring. This is the first study using a small number of oocytes to examine mtDNA mutations in diabetic mothers and offspring.

     

Metabolomic analysis of tomato seed germination

Introduction

Seed germination is inherently related to seed metabolism, which changes throughout its maturation, desiccation and germination processes. The metabolite content of a seed and its ability to germinate are determined by underlying genetic architecture and environmental effects during development.

Objective

This study aimed to assess an integrative approach to explore genetics modulating seed metabolism in different developmental stages and the link between seed metabolic- and germination traits.

Methods

We have utilized gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) metabolite profiling to characterize tomato seeds during dry and imbibed stages. We describe, for the first time in tomato, the use of a so-called generalized genetical genomics (GGG) model to study the interaction between genetics, environment and seed metabolism using 100 tomato recombinant inbred lines (RILs) derived from a cross between Solanum lycopersicum and Solanum pimpinellifolium.

Results

QTLs were found for over two-thirds of the metabolites within several QTL hotspots. The transition from dry to 6 h imbibed seeds was associated with programmed metabolic switches. Significant correlations varied among individual metabolites and the obtained clusters were significantly enriched for metabolites involved in specific biochemical pathways.

Conclusions

Extensive genetic variation in metabolite abundance was uncovered. Numerous identified genetic regions that coordinate groups of metabolites were detected and these will contain plausible candidate genes. The combined analysis of germination phenotypes and metabolite profiles provides a strong indication for the hypothesis that metabolic composition is related to germination phenotypes and thus to seed performance.

     

A metabolomics approach to uncover effects of different exercise modalities in type 1 diabetes

Introduction

Exercise-associated metabolism in type 1 diabetes (T1D) remains under-studied due to the complex interplay between exogenous insulin, counter-regulatory hormones and insulin-sensitivity.

Objective

To identify the metabolic differences induced by two exercise modalities in T1D using ultra high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC–HRMS) based metabolomics.

Methods

Twelve T1D adults performed intermittent high-intensity (IHE) and continuous-moderate-intensity (CONT) exercise. Serum samples were analysed by UHPLC–HRMS.

Results

Metabolic profiling of IHE and CONT highlighted exercise-induced changes in purine and acylcarnitine metabolism.

Conclusion

IHE may increase beta-oxidation through higher ATP-turnover. UHPLC–HRMS based metabolomics as a data-driven approach without an a priori hypothesis may help uncover distinctive metabolic effects during exercise in T1D.Clinical trial registration number is www.clinicaltrials.gov: NCT02068638.

     

Strategies to assess and optimize stability of endogenous amines during cerebrospinal fluid sampling

Introduction

Metabolic profiling of cerebrospinal fluid (CSF) is a promising technique for studying brain diseases. Measurements should reflect the in vivo situation, so ex vivo metabolism should be avoided.

Objective

To investigate the effects of temperature (room temperature vs. 4 °C), centrifugation and ethanol, as anti-enzymatic additive during CSF sampling on concentrations of glutamic acid, glutamine and other endogenous amines.

Methods

CSF samples from 21 individuals were processed using five different protocols. Isotopically-labeled alanine, isoleucine, glutamine, glutamic acid and dopamine were added prior to sampling to trace any degradation. Metabolomics analysis of endogenous amines, isotopically-labeled compounds and degradation products was performed with a validated LC–MS method.

Results

Thirty-six endogenous amines were quantified. There were no statistically significant differences between sampling protocols for 31 out of 36 amines. For GABA there was primarily an effect of temperature (higher concentrations at room temperature than at 4 °C) and a small effect of ethanol (lower concentrations if added) due to possible degradation. O-phosphoethanolamine concentrations were also lower when ethanol was added. Degradation of isotopically-labeled compounds (e.g. glutamine to glutamic acid) was minor with no differences between protocols.

Conclusion

Most amines can be considered stable during sampling, provided that samples are cooled immediately to 4 °C, centrifuged, and stored at ??80 °C within 2 h. The effect of ethanol addition for more unstable metabolites needs further investigation. This was the first time that labeled compounds were used to monitor ex vivo metabolism during sampling. This is a useful strategy to study the stability of other metabolites of interest.

     

A putative urinary biosignature for diagnosis and follow-up of tuberculous meningitis in children: outcome of a metabolomics study disclosing host–pathogen responses

Introduction

Tuberculous meningitis (TBM) is a severe manifestation of tuberculosis, presenting with high morbidity and mortality in children. Existing diagnostic methods for TBM are invasive and time-consuming and the need for highly sensitive and selective diagnosis remains high on the TBM agenda.

Objective

Our aim was to exploit metabolomics as an approach to identify metabolites as potential diagnostic predictors for children with TBM through a non-invasive means.

Methods

Urine samples selected for this study were from three paediatric groups: patients with confirmed TBM (n = 12), patients clinically suspected with TBM but later confirmed to be negative (n = 19) and age-matched controls (n = 29). Metabolomics data were generated through gas chromatography–mass spectrometry analysis and important metabolites were identified according to standard statistical procedures used for metabolomics data.

Results

A global metabolite profile that characterized TBM was developed from the data, reflecting the host and microbial responses. Nine different logistic regression models were fitted to selected metabolites for the best combination as predictors for TBM. Four metabolites—methylcitric, 2-ketoglutaric, quinolinic and 4-hydroxyhippuric acids—showed excellent diagnostic ability and provided prognostic insight into our TBM patients.

Conclusions

This study is the first to illustrate holistically the metabolic complexity of TBM and provided proof-of-concept that a biosignature of urinary metabolites can be defined for non-invasive diagnosis and prognosis of paediatric TBM patients. The biosignature should be developed and validated through future prospective studies to generate a medical algorithm for diagnosis in the initial stages of the disease and for monitoring of treatment strategies.

     

Metabolite signatures of diabetes with cardiovascular disease: a pilot investigation

Introduction

Type 2 diabetes (T2D) is an independent risk factor in the development of cardiovascular disease. However, there are significant limitations in the detection of the metabolic disturbances in hyperglycemia that lead to vascular dysfunction.

Objectives

The goals of the study were: (i) to identify circulating metabolites discriminating T2D and normoglycemia, and (ii) to assess phenotypic correlations of identified metabolites with other cardiometabolic risk traits (CMTs).

Methods

We have generated global and targeted metabolomic profiles using AB Sciex TripleTOF 5600 and Thermo Scientific Q Exactive Plus using serum samples of patients and healthy controls from a Punjabi population from India.

Results

In global profiling, we identified eight unknown molecules that currently do not match to any spectra in public databases. Additionally, serum levels of pyroglutamate, imidazole-4-acetate, tyramine-O-sulphate and 2,3-diphosphoglycerate were significantly elevated (2–5 fold) and betaine-aldehyde was reduced (fourfold) in patients. In targeted screening of amino acids and sugars, increased concentrations of serine, inositol, and threonine strongly correlated with T2D in both genders, while N-acetyl-l-alanine was reduced (58 fold) in men and glutamine was increased (fourfold) in women. Using random forest and ROC (AUC) analyses, we further cross-validated the predictive abilities of these molecules. Inositol, serine and threonine were among the top informative biomarkers in both genders while N-acetyl-l-alanine was highly confined to men.

Conclusions

Our study has identified several metabolites whose concentrations were altered in T2D. Although further study is needed in larger datasets, the identified metabolites (unknown or known) point towards shared etiological pathways underlie diabetes and vascular disease which can be targeted for potential therapeutics or biomarkers discovery.