Characterizing poorly controlled type 2 diabetes using 1H-NMR metabolomics.

INTRODUCTION: The prevalence of type 2 diabetes has surged to epidemic proportions and despite treatment administration/adherence, some individuals experience poorly controlled diabetes. While existing literature explores metabolic changes in type 2 diabetes, understanding metabolic derangement in poorly controlled cases remains limited. OBJECTIVE: This investigation aimed to characterize the urine metabolome of poorly controlled type 2 diabetes in a South African cohort. METHOD: Using an untargeted proton nuclear magnetic resonance metabolomics approach, urine samples from 15 poorly controlled type 2 diabetes patients and 25 healthy controls were analyzed and statistically compared to identify differentiating metabolites. RESULTS: The poorly controlled type 2 diabetes patients were characterized by elevated concentrations of various metabolites associated with changes to the macro-fuel pathways (including carbohydrate metabolism, ketogenesis, proteolysis, and the tricarboxylic acid cycle), autophagy and/or apoptosis, an uncontrolled diet, and kidney and liver damage. CONCLUSION: These results indicate that inhibited cellular glucose uptake in poorly controlled type 2 diabetes significantly affects energy-producing pathways, leading to apoptosis and/or autophagy, ultimately contributing to kidney and mild liver damage. The study also suggests poor dietary compliance as a cause of the patient's uncontrolled glycemic state. Collectively these findings offer a first-time comprehensive overview of urine metabolic changes in poorly controlled type 2 diabetes and its association with secondary diseases, offering potential insights for more targeted treatment strategies to prevent disease progression, treatment efficacy, and diet/treatment compliance.

Tuberculosis is associated with sputum metabolome variations, irrespective of patient sex or HIV status: an untargeted GCxGC-TOFMS study.

INTRODUCTION: Various studies have identified TB-induced metabolome variations. However, in most of these studies, a large degree of variation exists between individual patients. OBJECTIVES: To identify differential metabolites for TB, independent of patients' sex or HIV status. METHODS: Untargeted GCxGC/TOF-MS analyses were applied to the sputum of 31 TB + and 197 TB- individuals. Univariate statistics were used to identify metabolites which are significantly different between TB + and TB- individuals (a) irrespective of HIV status, and (b) with a HIV + status. Comparisons a and b were repeated for (i) all participants, (ii) males only and (iii) females only. RESULTS: Twenty-one compounds were significantly different between the TB + and TB- individuals within the female subgroup (11% lipids; 10% carbohydrates; 1% amino acids, 5% other and 73% unannotated), and 6 within the male subgroup (20% lipids; 40% carbohydrates; 6% amino acids, 7% other and 27% unannotated). For the HIV + patients (TB + vs. TB-), a total of 125 compounds were significant within the female subgroup (16% lipids; 8% carbohydrates; 12% amino acids, 6% organic acids, 8% other and 50% unannotated), and 44 within the male subgroup (17% lipids; 2% carbohydrates; 14% amino acids related, 8% organic acids, 9% other and 50% unannotated). Only one annotated compound, 1-oleoyl lysophosphaditic acid, was consistently identified as a differential metabolite for TB, irrespective of sex or HIV status. The potential clinical application of this compound should be evaluated further. CONCLUSIONS: Our findings highlight the importance of considering confounders in metabolomics studies in order to identify unambiguous disease biomarkers.

Evaluation of BAYESIL for automated annotation of 1H NMR data using limited sample volumes: application to African elephant serum.

INTRODUCTION: Technological advancements enabled the analyses of limited sample volumes on 1H NMR. Manual spectral profiling of the data is, however, complex, and timely. OBJECTIVE: To evaluate the performance of BAYESIL for automated identification and quantification of 1H NMR spectra of limited volume samples. METHOD: Aliquots of a pooled African elephant serum sample were analyzed using standard and reduced volumes. Performance was evaluated on confidence scores, non-detects and laboratory CV. RESULTS: Of the 47 compounds detected, 28 had favorable performances. The approach could differentiate samples based on biological variation. CONCLUSIONS: BAYESIL is valuable for limited sample 1H NMR data analyses.

Association of the metabolic syndrome with PAI 1act and clot lysis time over a 10-year follow up in an African population.

BACKGROUND AND AIMS: The association between the metabolic syndrome (MetS) and plasminogen activator inhibitor-1 (PAI-1) has been well established in cross-sectional studies. It is less clear whether this translates into decreased clot lysis rates and very little information is available on non-European populations. Little is known regarding prospective associations and whether clot lysis progressively worsens in MetS individuals over time. We determined the prospective association of MetS with PAI-1 activity (PAI-1act) and clot lysis time (CLT) over a 10-year period. METHODS AND RESULTS: As many as 2010 African men and women aged ≥30 years were stratified according to MetS status and number of MetS criteria (0-5). We also determined the contribution of the PAI-1 4G/5G polymorphism to these associations and identified which MetS criteria had the strongest associations with PAI-1act and CLT. Both PAI-1act and CLT remained consistently elevated in individuals with MetS throughout the 10-year period. PAI-1act and CLT did not increase more over time in MetS individuals than in controls. The 4G/5G genotype did not influence the association of PAI-1act or clot lysis with MetS. Increased waist circumference, increased triglycerides and decreased HDL-C were the main predictors of PAI-1act and CLT. CONCLUSIONS: Black South Africans with MetS had increased PAI-1act and longer CLTs than individuals without MetS. The inhibited clot lysis in MetS did, however, not deteriorate over time compared to controls. Of the MetS criteria, obesity and altered lipids were the main predictors of PAI-1act and CLT and are thus potential targets for prevention strategies to decrease thrombotic risk.

The metabolomics of a protein kinase C delta (PKCδ) knock-out mouse model.

INTRODUCTION: PKCδ is ubiquitously expressed in mammalian cells and its dysregulation plays a key role in the onset of several incurable diseases and metabolic disorders. However, much remains unknown about the metabolic pathways and disturbances induced by PKC deficiency, as well as the metabolic mechanisms involved. OBJECTIVES: This study aims to use metabolomics to further characterize the function of PKC from a metabolomics standpoint, by comparing the full serum metabolic profiles of PKC deficient mice to those of wild-type mice. METHODS: The serum metabolomes of PKCδ knock-out mice were compared to that of a wild-type strain using a GCxGC-TOFMS metabolomics research approach and various univariate and multivariate statistical analyses. RESULTS: Thirty-seven serum metabolite markers best describing the difference between PKCδ knock-out and wild-type mice were identified based on a PCA power value > 0.9, a t-test p-value < 0.05, or an effect size > 1. XERp prediction was also done to accurately select the metabolite markers within the 2 sample groups. Of the metabolite markers identified, 78.4% (29/37) were elevated and 48.65% of these markers were fatty acids (18/37). It is clear that a total loss of PKCδ functionality results in an inhibition of glycolysis, the TCA cycle, and steroid synthesis, accompanied by upregulation of the pentose phosphate pathway, fatty acids oxidation, cholesterol transport/storage, single carbon and sulphur-containing amino acid synthesis, branched-chain amino acids (BCAA), ketogenesis, and an increased cell signalling via N-acetylglucosamine. CONCLUSION: The charaterization of the dysregulated serum metabolites in this study, may represent an additional tool for the early detection and screening of PKCδ-deficiencies or abnormalities.

M. tuberculosis curli pili (MTP) is associated with alterations in carbon, fatty acid and amino acid metabolism in a THP-1 macrophage infection model.

The initial host-pathogen interaction is crucial for the establishment of infection. An improved understanding of the pathophysiology of Mycobacterium tuberculosis (M. tuberculosis) during macrophage infection can aid the development of intervention therapeutics against tuberculosis. M. tuberculosis curli pili (MTP) is a surface located adhesin, involved in the first point-of-contact between pathogen and host. This study aimed to better understand the role of MTP in modulating the intertwined metabolic pathways of M. tuberculosis and its THP-1 macrophage host. Metabolites were extracted from pelleted wet cell mass of THP-1 macrophages infected with M. tuberculosis wild-type V9124 (WT), Δmtp-deletion mutant and the mtp-complemented strains, respectively, via a whole metabolome extraction method using a 1:3:1 ratio of chloroform:methanol:water. Metabolites were detected by two-dimensional gas chromatography time-of-flight mass spectrometry. Significant metabolites were determined through univariate and multivariate statistical tests and online pathway databases. Relative to the WT, a total of nine and ten metabolites were significantly different in the Δmtp and complement strains, respectively. All nine significant metabolites were found in elevated levels in the Δmtp relative to the WT. Additionally, of the ten significant metabolites, eight were detected in lower levels and two were detected in higher levels in the complement relative to the WT. The absence of the MTP adhesin resulted in reduced virulence of M. tuberculosis leading to alterations in metabolites involved in carbon, fatty acid and amino acid metabolism during macrophage infection, suggesting that MTP plays an important role in the modulation of host metabolic activity. These findings support the prominent role of the MTP adhesin as a virulence factor as well as a promising biomarker for possible diagnostic and therapeutic intervention.

A laboratory approach for characterizing chronic fatigue: what does metabolomics tell us?

INTRODUCTION: Manifestations of fatigue range from chronic fatigue up to a severe syndrome and myalgic encephalomyelitis. Fatigue grossly affects the functional status and quality of life of affected individuals, prompting the World Health Organization to recognize it as a chronic non-communicable condition. OBJECTIVES: Here, we explore the potential of urinary metabolite information to complement clinical criteria of fatigue, providing an avenue towards an objective measure of fatigue in patients presenting with the full spectrum of fatigue levels. METHODS: The experimental group consisted of 578 chronic fatigue female patients. The measurement design was composed of (1) existing clinical fatigue scales, (2) a hepatic detoxification challenge test, and (3) untargeted proton nuclear magnetic resonance (1H-NMR) procedure to generate metabolomics data. Data analysed via an in-house Matlab script that combines functions from a Statistics and a PLS Toolbox. RESULTS: Multivariate analysis of the original 459 profiled 1H-NMR bins for the low (control) and high (patient) fatigue groups indicated complete separation following the detoxification experimental challenge. Important bins identified from the 1H-NMR spectra provided quantitative metabolite information on the detoxification challenge for the fatigue groups. CONCLUSIONS: Untargeted 1H-NMR metabolomics proved its applicability as a global profiling tool to reveal the impact of toxicological interventions in chronic fatigue patients. No clear potential biomarker emerged from this study, but the quantitative profile of the phase II biotransformation products provide a practical visible effect directing to up-regulation of crucial phase II enzyme systems in the high fatigue group in response to a high xenobiotic-load.

The GC-MS metabolomics signature in patients with fibromyalgia syndrome directs to dysbiosis as an aspect contributing factor of FMS pathophysiology.

INTRODUCTION: Fibromyalgia syndrome (FMS) is a chronic pain syndrome. Previous analyses of untargeted metabolomics data indicated altered metabolic profile in FMS patients. OBJECTIVES: We report a semi-targeted explorative metabolomics study on the urinary metabolite profile of FMS patients; exploring the potential of urinary metabolite information to augment existing medical diagnosis. METHODS: All cases were females. Patients had a medical history of persistent FMS (n = 18). Control groups were first-generation family members of the patients (n = 11), age-related individuals without indications of FMS (n = 10), and healthy, young (18-22 years) individuals (n = 41). The biofluid investigated was early morning urine samples. Data generation was done through gas chromatography-mass spectrometry (GC-MS) analysis and data processing and analyses were performed using Matlab, R, SPSS and SAS software. RESULTS: Quantitative analysis revealed the presence of 196 metabolites. Unsupervised and supervised multivariate analyses distinguished all three control groups and the FMS patients, which could be related to 14 significantly increased metabolites. These metabolites are associated with energy metabolism, digestion and metabolism of carbohydrates and other host and gut metabolites. CONCLUSIONS: Overall, urinary metabolite profiles in the FMS patients suggest: (1) energy utilization is a central aspect of this pain disorder, (2) dysbiosis seems to prevail in FMS patients, indicated by disrupted microbiota metabolites, supporting the model that microbiota may alter brain function through the gut-brain axis, with the gut being a gateway to generalized pain, and (3) screening of urine from FMS is an avenue to explore for adding non-invasive clinical information for diagnosis and treatment of FMS.

Metabolomics variable selection and classification in the presence of observations below the detection limit using an extension of ERp.

BACKGROUND: ERp is a variable selection and classification method for metabolomics data. ERp uses minimized classification error rates, based on data from a control and experimental group, to test the null hypothesis of no difference between the distributions of variables over the two groups. If the associated p-values are significant they indicate discriminatory variables (i.e. informative metabolites). The p-values are calculated assuming a common continuous strictly increasing cumulative distribution under the null hypothesis. This assumption is violated when zero-valued observations can occur with positive probability, a characteristic of GC-MS metabolomics data, disqualifying ERp in this context. This paper extends ERp to address two sources of zero-valued observations: (i) zeros reflecting the complete absence of a metabolite from a sample (true zeros); and (ii) zeros reflecting a measurement below the detection limit. This is achieved by allowing the null cumulative distribution function to take the form of a mixture between a jump at zero and a continuous strictly increasing function. The extended ERp approach is referred to as XERp. RESULTS: XERp is no longer non-parametric, but its null distributions depend only on one parameter, the true proportion of zeros. Under the null hypothesis this parameter can be estimated by the proportion of zeros in the available data. XERp is shown to perform well with regard to bias and power. To demonstrate the utility of XERp, it is applied to GC-MS data from a metabolomics study on tuberculosis meningitis in infants and children. We find that XERp is able to provide an informative shortlist of discriminatory variables, while attaining satisfactory classification accuracy for new subjects in a leave-one-out cross-validation context. CONCLUSION: XERp takes into account the distributional structure of data with a probability mass at zero without requiring any knowledge of the detection limit of the metabolomics platform. XERp is able to identify variables that discriminate between two groups by simultaneously extracting information from the difference in the proportion of zeros and shifts in the distributions of the non-zero observations. XERp uses simple rules to classify new subjects and a weight pair to adjust for unequal sample sizes or sensitivity and specificity requirements.

A diagnostic biomarker profile for fibromyalgia syndrome based on an NMR metabolomics study of selected patients and controls.

BACKGROUND: Fibromyalgia syndrome (FMS) is a chronic pain syndrome. A plausible pathogenesis of the disease is uncertain and the pursuit of measurable biomarkers for objective identification of affected individuals is a continuing endeavour in FMS research. Our objective was to perform an explorative metabolomics study (1) to elucidate the global urinary metabolite profile of patients suffering from FMS, and (2) to explore the potential of this metabolite information to augment existing medical practice in diagnosing the disease. METHODS: We selected patients with a medical history of persistent FMS (n = 18), who described their recent state of the disease through the Fibromyalgia Impact Questionnaire (FIQR) and an in-house clinical questionnaire (IHCQ). Three control groups were used: first-generation family members of the patients (n = 11), age-related individuals without any indications of FMS or related conditions (n = 10), and healthy young (18-22 years) individuals (n = 20). All subjects were female and the biofluid under investigation was urine. Correlation analysis of the FIQR showed the FMS patients represented a well-defined disease group for this metabolomics study. Spectral analyses of urine were conducted using a 500 MHz 1H nuclear magnetic resonance (NMR) spectrometer; data processing and analyses were performed using Matlab, R, SPSS and SAS software. RESULTS AND DISCUSSION: Unsupervised and supervised multivariate analyses distinguished all three control groups and the FMS patients, and significant increases in metabolites related to the gut microbiome (hippuric, succinic and lactic acids) were observed. We have developed an algorithm for the diagnosis of FMS consisting of three metabolites - succinic acid, taurine and creatine - that have a good level of diagnostic accuracy (Receiver Operating Characteristic (ROC) analysis - area under the curve 90%) and on the pain and fatigue symptoms for the selected FMS patient group. CONCLUSION: Our data and comparative analyses indicated an altered metabolic profile of patients with FMS, analytically detectable within their urine. Validation studies may substantiate urinary metabolites to supplement information from medical assessment, tender-point measurements and FIQR questionnaires for an improved objective diagnosis of FMS.

Variable selection for binary classification using error rate p-values applied to metabolomics data.

BACKGROUND: Metabolomics datasets are often high-dimensional though only a limited number of variables are expected to be informative given a specific research question. The important task of selecting informative variables can therefore become complex. In this paper we look at discriminating between two groups. Two tasks need to be performed: (i) finding variables which differ between the two groups; and (ii) determining how the selected variables can be used to classify new subjects. We introduce an approach using minimum classification error rates as test statistics to find discriminatory and therefore informative variables. The thresholds resulting in the minimum error rates can be used to classify new subjects. This approach transforms error rates into p-values and is referred to as ERp. RESULTS: We show that non-parametric hypothesis testing, based on minimum classification error rates as test statistics, can find statistically significantly shifted variables. The discriminatory ability of variables becomes more apparent when error rates are evaluated based on their corresponding p-values, as relatively high error rates can still be statistically significant. ERp can handle unequal and small group sizes, as well as account for the cost of misclassification. ERp retains (if known) or reveals (if unknown) the shift direction, aiding in biological interpretation. The threshold resulting in the minimum error rate can immediately be used to classify new subjects. We use NMR generated metabolomics data to illustrate how ERp is able to discriminate subjects diagnosed with Mycobacterium tuberculosis infected meningitis from a control group. The list of discriminatory variables produced by ERp contains all biologically relevant variables with appropriate shift directions discussed in the original paper from which this data is taken. CONCLUSIONS: ERp performs variable selection and classification, is non-parametric and aids biological interpretation while handling unequal group sizes and misclassification costs. All this is achieved by a single approach which is easy to perform and interpret. ERp has the potential to address many other characteristics of metabolomics data. Future research aims to extend ERp to account for a large proportion of observations below the detection limit, as well as expand on interactions between variables.

Trends in malaria admissions at the Mbakong Health Centre of the North West Region of Cameroon: a retrospective study.

BACKGROUND: Malaria is the leading cause of death worldwide. It is urgent to assess the impact of interventions and scaled-up control efforts. Despite reported reduction in malaria prevalence in Africa, the trends in Cameroon are not yet fully understood. The aim of this study was to investigate the trends in malaria admissions among febrile patients seeking treatment over a seven-year period (2006-2012) in an endemic area in Cameroon, hypothesizing a declining trend. This period followed changes in malaria treatment policy. The objectives were to identify possible trends in malaria admissions and to evaluate the impact of changes to treatment guidelines on the prevalence. METHODS: Data was collected through consultation and perusal of laboratory and prescription registers of the Mbakong Health Centre. Data analysis was conducted using SPSS and SAS Statistics. RESULTS: Analysis revealed that 4,230 febrile patients were received from 2006-2012. Of these febrile cases, 29.30% were confirmed positive. Between 2006 and 2012 confirmed malaria positive cases of those tested fluctuated, dropping from 53.21% in 2006 to 17.20% in 2008; then rising to 35.00% in 2011 and, finally, dropping to 18.2% of those tested in 2012. The prevalence in females and males across all age groups were similar: a slightly higher risk of males to have malaria (OR = 1.08, 95% CI 0.94-1.25) were not practically significant. Of those tested, the 5 to < 15 years and the 1 to < 5 years age groups were the hardest hit by malaria in the area. A practically visible and significant association was observed between the age and gender with regards to the number of malaria positive results (Pearson ×2 = 153.675, p < 0.00001, Cramer's V = 0.352). Malaria prevalence exhibited a fluctuating yet declining trend, as observed over the 28 quarters between January, 2006 and December, 2012. CONCLUSIONS: The changes to the treatment guidelines appear to result in a declining trend as was observed between 2006 and 2008. However, malaria admissions fluctuated between 2008 and 2012. There is, therefore, a need to step up control efforts of especially the vulnerable groups, such as the very young.

Metabolic Alterations in Mothers Living with HIV and Their HIV-Exposed, Uninfected Infants.

HIV-exposed, uninfected (HEU) children present with suboptimal growth and a greater susceptibility to infection in early life when compared to HIV-unexposed, uninfected (HUU) children. The reasons for these findings are poorly understood. We used a metabolomics approach to investigate the metabolic differences between pregnant women living with HIV (PWLWH) and their HEU infants compared to the uninfected and unexposed controls. Untargeted metabolomic profiling was performed using 1H-NMR spectroscopy on maternal plasma at 28 weeks' gestation and infant plasma at birth, 6/10 weeks, and 6 months. PWLWH were older but, apart from a larger 28 week mid-upper-arm circumference, anthropometrically similar to the controls. At all the time points, HEU infants had a significantly reduced growth compared to HUU infants. PWLWH had lower plasma 3-hydroxybutyric acid, acetoacetic acid, and acetic acid levels. In infants at birth, threonine and myo-inositol levels were lower in the HEU group while formic acid levels were higher. At 6/10 weeks, betaine and tyrosine levels were lower in the HEU group. Finally, at six months, 3-hydroxyisobutyric acid levels were lower while glycine levels were higher in the HEU infants. The NMR analysis has provided preliminary information indicating differences between HEU and HUU infants' plasma metabolites involved in energy utilization, growth, and protection from infection.

Urinary metabolic characterization of advanced tuberculous meningitis cases in a South African paediatric population.

Tuberculous meningitis (TBM) is a severe form of tuberculosis with high neuro-morbidity and mortality, especially among the paediatric population (aged ≤12 years). Little is known of the associated metabolic changes. This study aimed to identify characteristic metabolic markers that differentiate severe cases of paediatric TBM from controls, through non-invasive urine collection. Urine samples selected for this study were from two paediatric groups. Group 1: controls (n = 44): children without meningitis, no neurological symptoms and from the same geographical region as group 2. Group 2: TBM cases (n = 13): collected from paediatric patients that were admitted to Tygerberg Hospital in South Africa on the suspicion of TBM, mostly severely ill; with a later confirmation of TBM. Untargeted 1H NMR-based metabolomics data of urine were generated, followed by statistical analyses via MetaboAnalyst (v5.0), and the identification of important metabolites. Twenty nine urinary metabolites were identified as characteristic of advanced TBM and categorized in terms of six dysregulated metabolic pathways: 1) upregulated tryptophan catabolism linked to an altered vitamin B metabolism; 2) perturbation of amino acid metabolism; 3) increased energy production-metabolic burst; 4) disrupted gut microbiota metabolism; 5) ketoacidosis; 6) increased nitrogen excretion. We also provide original biological insights into this biosignature of urinary metabolites that can be used to characterize paediatric TBM patients in a South African cohort.

Phenylalanine metabolism and tetrahydrobiopterin bio-availability in COVID-19 and HIV.

Various metabolomics studies have reported increased phenylalanine serum concentrations in SARS-CoV-2 positive cases and have correlated increased phenylalanine with COVID-19 severity. In this study, we report similar results based upon metabolomics analysis of serum collected from a South African cohort of adults with confirmed COVID-19. The novelty of this study is the inclusion of HIV positive cases in the African context. We found that pre-existing HIV co-infection exacerbates the disruption of phenylalanine metabolism in COVID-19. What is lacking in literature is biological context and deeper understanding of perturbed phenylalanine metabolism in COVID-19. We delve deep into the metabolism of phenylalanine in COVID-19 and posit new insights for COVID-19 cases co-infected with HIV; namely, HIV-COVID-19 co-infected individuals do not have sufficient bioavailability of tetrahydrobiopterin (BH4). Hence, we identify BH4 as a potential supplement to alleviate/lessen COVID-19 symptoms.

M. tuberculosis curli pili (MTP) facilitates a reduction of microbicidal activity of infected THP-1 macrophages during early stages of infection.

Mycobacterium tuberculosis (M. tuberculosis) curli pili (MTP) is a surface located adhesin, which is involved in the initial point-of-contact between the pathogen and the host. Host-pathogen interaction is essential for establishing infection. M. tuberculosis has the ability to infect various host lung cell types, which includes both the epithelial cells and macrophages, and subsequent differences in their cellular function will be evident in their metabolic profiles. Understanding the differences between these cell types and their individual metabolic response to M. tuberculosis infection, with and without the presence of the MTP, will aid to better elucidate the role of this adhesin in modulating metabolic pathways during infection. This may further contribute to the development of improved diagnostic and therapeutic interventions, much needed at present in order to improve control the global tuberculosis (TB) epidemic. This study used a two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) metabolomics approach to compare the metabolite profiles of A549 epithelial cells to that of THP-1 macrophages, infected with M. tuberculosis, in the presence and absence of MTP. Significant metabolites were identified using various univariate and multivariate statistical analysis. A total of 44, 40, 50 and 34 metabolites were differentially detected when comparing the (a) uninfected A549 epithelial cells to uninfected THP-1 macrophages, (b) wild-type infected A549 epithelial cells to wild-type infected THP-1 macrophages, (c) ∆mtp-infected A549 epithelial cells to ∆mtp-infected THP-1 macrophages (d) complement-infected A549 epithelial cells to complement-infected THP-1 macrophages, respectively. These included metabolites that were involved in amino acid metabolism, fatty acid metabolism, general central carbon metabolism, and nucleic acid metabolism. In the absence of the M. tuberculosis MTP adhesin, the THP-1 macrophages predominantly displayed higher concentrations of amino acids and their metabolic intermediates, than the A549 epithelial cells. The deletion of MTP from M. tuberculosis in the host infection models potentially elicited a pro-inflammatory phenotype, particularly in the macrophage model. In the presence of MTP, the metabolite profile changes indicate potential regulation of host defence mechanisms, accompanied by a reduction in microbicidal abilities of host cells. Hence MTP can be considered a virulence factor of M. tuberculosis. Therefore, blocking MTP interaction with the host may facilitate a faster pathogen clearance during the initial stages of infection, and potentially enhance current therapeutic interventions.

Chronological Metabolic Response to Intensive Phase TB Therapy in Patients with Cured and Failed Treatment Outcomes.

Despite the arguable success of the standardized tuberculosis (TB) treatment regime, a significant number of patients still present with treatment failure. To improve on current TB treatment strategies, we sought to gain a better understanding of the hosts' response to TB therapy. A targeted metabolomics approach was used to compare the urinary acylcarnitine and amino acid profiles of eventually cured TB patients with those of patients presenting with a failed treatment outcome, comparing these patient groups at the time of diagnosis and at weeks 1, 2, and 4 of treatment. Among the significant metabolites identified were histidine, isoleucine, leucine, methionine, valine, proline, tyrosine, alanine, serine, and γ-aminobutyric acid. In general, metabolite fluctuations in time followed a similar pattern for both groups for most compounds but with a delayed onset or shift of the pattern in the successfully treated patient group. These time-trends detected in both groups could potentially be ascribed to a vitamin B6 deficiency and fluctuations in the oxidative stress levels and urea cycle intermediates, linked to the drug-induced inhibition and stimulation of various enzymes. The earlier onset of observed trends in the failed patients is proposed to relate to genotypic and phenotypic variations in drug metabolizing enzymes, subsequently leading to a poor treatment efficiency either due to the rise of adverse drug reactions or to insufficient concentrations of the active drug metabolites. This study emphasizes the need for a more personalized TB treatment approach, by including enzyme phenotyping and the monitoring of oxidative stress and vitamin B6 levels, for example.

Beetroot juice - a suitable post-marathon metabolic recovery supplement?

BACKGROUND: Red beetroot (Beta vulgaris L.) is a multifunctional functional food that reportedly exhibits potent anti-inflammatory, antioxidant, vasodilation, and cellular regulatory properties. This vegetable has gained a fair amount of scientific attention as a possible cost-effective supplement to enhance performance and expedite recovery after physical exercise. To date, no study has investigated the effects of incremental beetroot juice ingestion on the metabolic recovery of athletes after an endurance race. Considering this, as well as the beneficial glucose and insulin regulatory roles of beetroot, this study investigated the effects of beetroot juice supplementation on the metabolic recovery trend of athletes within 48 h after completing a marathon. METHODS: By employing an untargeted two-dimensional gas chromatography time-of-flight mass spectrometry approach, serum samples (collected pre-, post-, 24 h post-, and 48 h post-marathon) of 31 marathon athletes that ingested a series (n = 7; 250 ml) of either beetroot juice (n = 15 athletes) or isocaloric placebo (n = 16 athletes) supplements within 48 h post-marathon, were analysed and statistically compared. RESULTS: The metabolic profiles of the beetroot-ingesting cohort recovered to a pre-marathon-related state within 48 h post-marathon, mimicking the metabolic recovery trend observed in the placebo cohort. Since random inter-individual variation was observed immediately post-marathon, only metabolites with large practical significance (p-value ≤0.05 and d-value ≥0.5) within 24 h and 48 h post-marathon were considered representative of the effects of beetroot juice on metabolic recovery. These (n = 4) mainly included carbohydrates (arabitol and xylose) and odd-chain fatty acids (nonanoate and undecanoate). The majority of these were attributed to beetroot content and possible microbial fermentation thereof. CONCLUSION: Apart from the global metabolic recovery trends of the two opposing cohorts, it appears that beetroot ingestion did not expedite metabolic recovery in athletes within 48 h post-marathon.

Metallothionein 1 Overexpression Does Not Protect Against Mitochondrial Disease Pathology in Ndufs4 Knockout Mice.

Mitochondrial diseases (MD), such as Leigh syndrome (LS), present with severe neurological and muscular phenotypes in patients, but have no known cure and limited treatment options. Based on their neuroprotective effects against other neurodegenerative diseases in vivo and their positive impact as an antioxidant against complex I deficiency in vitro, we investigated the potential protective effect of metallothioneins (MTs) in an Ndufs4 knockout mouse model (with a very similar phenotype to LS) crossed with an Mt1 overexpressing mouse model (TgMt1). Despite subtle reductions in the expression of neuroinflammatory markers GFAP and IBA1 in the vestibular nucleus and hippocampus, we found no improvement in survival, growth, locomotor activity, balance, or motor coordination in the Mt1 overexpressing Ndufs4-/- mice. Furthermore, at a cellular level, no differences were detected in the metabolomics profile or gene expression of selected one-carbon metabolism and oxidative stress genes, performed in the brain and quadriceps, nor in the ROS levels of macrophages derived from these mice. Considering these outcomes, we conclude that MT1, in general, does not protect against the impaired motor activity or improve survival in these complex I-deficient mice. The unexpected absence of increased oxidative stress and metabolic redox imbalance in this MD model may explain these observations. However, tissue-specific observations such as the mildly reduced inflammation in the hippocampus and vestibular nucleus, as well as differential MT1 expression in these tissues, may yet reveal a tissue- or cell-specific role for MTs in these mice.

Metabolic characterization of tuberculous meningitis in a South African paediatric population using 1H NMR metabolomics.

OBJECTIVE: To better characterize the cerebrospinal fluid (CSF) metabolic profile of tuberculous meningitis (TBM) cases using a South African paediatric cohort. METHODS: 1H NMR metabolomics was used to analyse the CSF of a South African paediatric cohort. Univariate and multivariate statistical analyses were performed to compare a homogeneous control group with a well-defined TBM group. RESULTS: Twenty metabolites were identified to discriminate TBM cases from controls. As expected, reduced glucose and elevated lactate were the dominating discriminators. A closer investigation of the CSF metabolic profile yielded 18 metabolites of statistical significance. Ten metabolites (acetate, alanine, choline, citrate, creatinine, isoleucine, lysine, myo-inositol, pyruvate and valine) overlapped with two other prior investigations. Eight metabolites (2-hydroxybutyrate, carnitine, creatine, creatine phosphate, glutamate, glutamine, guanidinoacetate and proline) were unique to our paediatric TBM cohort. CONCLUSIONS: Through strict exclusion criteria, quality control checks and data filtering, eight unique CSF metabolites associated with TBM were identified for the first time and linked to: uncontrolled glucose metabolism, upregulated proline and creatine metabolism, detoxification and disrupted glutamate-glutamine cycle in the TBM samples. Associated with oxidative stress and chronic neuroinflammation, our findings collectively imply destabilization, and hence increased permeability, of the blood-brain barrier in the TBM cases.