Carolus J. Reinecke

A hypothetical astrocyte–microglia lactate shuttle derived from a 1H NMR metabolomics analysis of cerebrospinal fluid from a cohort of South African children with tuberculous meningitis

Tuberculosis meningitis (TBM) is the most severe form of extra-pulmonary tuberculosis and is particularly intense in small children; there is no universally accepted algorithm for the diagnosis and substantiation of TB infection, which can lead to delayed intervention, a high risk factor for morbidity and mortality. In this study a proton magnetic resonance (1H NMR)-based metabolomics analysis and several chemometric methods were applied to data generated from lumber cerebrospinal fluid (CSF) samples from three experimental groups: (1) South African infants and children with confirmed TBM, (2) non-meningitis South African infants and children as controls, and (3) neurological controls from the Netherlands. A total of 16 NMR-derived CSF metabolites were identified, which clearly differentiated between the controls and TBM cases under investigation. The defining metabolites were the combination of perturbed glucose and highly elevated lactate, common to some other neurological disorders. The remaining 14 metabolites of the host’s response to TBM were likewise mainly energy-associated indicators. We subsequently generated a hypothesis expressed as an “astrocyte–microglia lactate shuttle” (AMLS) based on the host’s response, which emerged from the NMR-metabolomics information. Activation of microglia, as implied by the AMLS hypothesis, does not, however, present a uniform process and involves intricate interactions and feedback loops between the microglia, astrocytes and neurons that hamper attempts to construct basic and linear cascades of cause and effect; TBM involves a complex integration of the responses from the various cell types present within the CNS, with microglia and the astrocytes as main players.

The Th1/Th2/Th17 cytokine profile of HIV-infected individuals: a multivariate cytokinomics approach

HIV infection causes the dysregulation of cytokine production. A cytokinomics approach employing cytometric bead array (CBA) technology, flow cytometry and multivariate analysis was applied to the investigation of HIV-induced T helper cell type 1 (Th1), Th2 and Th17 cytokine changes in the sera of treatment naive individuals. Stepwise linear discriminant analysis (LDA) and logistic regression identified interleukin (IL)-6 to be discriminatory for HIV infection with 74.6% and 71.2% of the cases correctly classified. Analysis of variance (ANOVA) confirmed IL-6 and IL-10 concentrations to be significantly (p=0.001 and p=0.025) different between the groups. A scatter plot of the log IL-6 and IL-10 concentrations for the groups largely overlapped, with improved differentiation where patients were advancing to the acquired immunodeficiency syndrome (AIDS). IL-17A levels were higher than other cytokines but did not significantly distinguish the groups suggesting that the HIV- and HIV+ individuals had similar immune profiles. This possibility was supported by other clinical indicators. Taken together, the measured cytokines (IL-6, 10 and 17) have potential prognostic value.

Cerebrospinal fluid in tuberculous meningitis exhibits only the L-enantiomer of lactic acid

BackgroundThe defining feature of the cerebrospinal fluid (CSF) collected from infants and children with tuberculous meningitis (TBM), derived from an earlier untargeted nuclear magnetic resonance (NMR) metabolomics study, was highly elevated lactic acid. Undetermined was the contribution from host response (L-lactic acid) or of microbial origin (D-lactic acid), which was set out to be determined in this study.MethodsIn this follow-up study, we used targeted ultra-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (UPLC–ESI–MS/MS) to determine the ratio of the L and D enantiomers of lactic acid in these CSF samples.ResultsHere we report for the first time that the lactic acid observed in the CSF of confirmed TBM cases was in the L-form and solely a response from the host to the infection, with no contribution from any bacteria. The significance of elevated lactic acid in TBM appears to be that it is a crucial energy substrate, used preferentially over glucose by microglia, and exhibits neuroprotective capabilities.ConclusionThese results provide experimental evidence to support our conceptual astrocyte–microglia lactate shuttle model formulated from our previous NMR-based metabolomics study — highlighting the fact that lactic acid plays an important role in neuroinflammatory diseases such as TBM. Furthermore, this study reinforces our belief that the determination of enantiomers of metabolites corresponding to infectious diseases is of critical importance in substantiating the clinical significance of disease markers.

Concurrent class analysis identifies discriminatory variables from metabolomics data on isovaleric acidemia

Metabolomics data are typically complex and high dimensional. Multivariate dimension-reducing techniques have thus been developed for analysing metabolomics data to disclose underlying relationships, with principal component analysis (PCA) as the technique mostly applied. Despite its widespread use in metabolomics, PCA has shortcomings that limit its applicability. Several approaches have been made to overcome these limitations and we describe an advanced disjoint PCA (DPCA) model, termed concurrent class analysis and abbreviated as CONCA. CONCA is a new model, and is unique in linking DPCA models to a traditional PCA model. This is accomplished by restructuring the input data matrix, applying DPCA group models to the restructured data, and combining the DPCA models in order to replicate a traditional PCA. We applied the CONCA model to a metabolomics data set on isovaleric acidaemia (IVA), a rare inherited metabolic disorder. The outcome showed that three of the variables with high discrimination value identified through the CONCA analysis are prominent organic acid biomarkers for IVA. Moreover, three further minor metabolites associated with the disease, and two as a consequence of treatment, were likewise identified as important discriminatory variables. The benefit of the CONCA model thus is its ability to disclose information concerning each individual group and to identify the variables important in discrimination (VIDs) which are also responsible for group separation.

Contribution towards a metabolite profile of the detoxification of benzoic acid through glycine conjugation: an intervention study

Benzoic acid is widely used as a preservative in food products and is detoxified in humans through glycine conjugation. Different viewpoints prevail on the physiological significance of the glycine conjugation reaction and concerns have been raised on potential public health consequences following uncontrolled benzoic acid ingestion. We performed a metabolomics study which used commercial benzoic acid containing flavored water as vehicle for designed interventions, and report here on the controlled consumption of the benzoic acid by 21 cases across 6 time points for a total of 126 time points. Metabolomics data from urinary samples analyzed by nuclear magnetic resonance spectroscopy were generated in a time-dependent cross-over study. We used ANOVA-simultaneous component analysis (ASCA), repeated measures analysis of variance (RM-ANOVA) and unfolded principal component analysis (unfolded PCA) to supplement conventional statistical methods to uncover fully the metabolic perturbations due to the xenobiotic intervention, encapsulated in the metabolomics tensor (three-dimensional matrices having cases, spectral areas and time as axes). Identification of the biologically important metabolites by the novel combination of statistical methods proved the power of this approach for metabolomics studies having complex data structures in general. The study disclosed a high degree of inter-individual variation in detoxification of the xenobiotic and revealed metabolic information, indicating that detoxification of benzoic acid through glycine conjugation to hippuric acid does not indicate glycine depletion, but is supplemented by ample glycine regeneration. The observations lend support to the view of maintenance of glycine homeostasis during detoxification. The study indicates also that time-dependent metabolomics investigations, using designed interventions, provide a way of interpreting the variation induced by the different factors of a designed experiment–an approach with potential to advance significantly our understanding of normal and pathophysiological perturbations of endogenous or exogenous origin.

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

BackgroundMetabolomics 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.ResultsWe 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.ConclusionsERp 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.

Cerebrospinal fluid amino acid profiling of pediatric cases with tuberculous meningitis

Background: In Africa, tuberculosis is generally regarded as persisting as one of the most devastating infectious diseases. The pediatric population is particularly vulnerable, with infection of the brain in the form of tuberculous meningitis (TBM) being the most severe manifestation. TBM is often difficult to diagnose in its early stages because of its non-specific clinical presentation. Of particular concern is that late diagnosis, and subsequent delayed treatment, leads to high risk of long-term neurological sequelae, and even death. Using advanced technology and scientific expertise, we are intent on further describing the biochemistry behind this devastating neuroinflammatory disease, with the goal of improving upon its early diagnosis. Method: We used the highly sensitive analytical platform of gas chromatography-mass spectrometry (GC-MS) to analyze amino acid profiles of cerebrospinal fluid (CSF) collected from a cohort of 33 South African pediatric TBM cases, compared to 34 controls. Results: Through the use of a stringent quality assurance procedure and various statistical techniques, we were able to confidently identify five amino acids as being significantly elevated in TBM cases, namely, alanine, asparagine, glycine, lysine, and proline. We found also in an earlier untargeted metabolomics investigation that alanine can be attributed to increased CSF lactate levels, and lysine as a marker of lipid peroxidation. Alanine, like glycine, is an inhibitory neurotransmitter in the brain. Asparagine, as with proline, is linked to the glutamate-glutamine cycle. Asparagine is associated with the removal of increased nitrites in the brain, whereas elevated proline coincides with the classic biochemical marker of increased CSF protein in TBM. All five discriminatory amino acids are linked to ammonia due to increased nitrites in TBM. Conclusion: A large amount of untapped biochemical information is present in CSF of TBM cases, of which amino acid profiling through GC-MS has potential in aiding in earlier diagnosis, and hence crucial earlier treatment.

1H NMR spectral identification of medication in cerebrospinal fluid of pediatric meningitis

&NA; Exploratory metabolomics studies of cerebrospinal fluid (CSF), using proton nuclear magnetic resonance (1H NMR) spectroscopy, hold major potential application in neurodiagnostics. Such studies, however, rely upon established databases of known metabolites. Here we address the ‘unknowns’ in the 1H NMR spectra of CSF from treated pediatric meningitis cases. Through knowledge of the clinical information given by the pediatrician and analytical application of 1H NMR spectroscopy on pure reference compounds of the medication used, we identified four of the previously unknown compounds in the 1H NMR CSF spectra — the drugs pyrazinamide, isoniazid, acyclovir, and sulfamethoxazole. We report on the one‐ and two‐dimensional 1H NMR spectral data and chemical information of these four compounds. By expanding our knowledge of 1H NMR CSF spectra from treated meningitis cases, we are able to bring 1H NMR closer to the forefront of neurodiagnostics. HighlightsIdentification of previously unknown peaks as pyrazinamide, isoniazid, acyclovir, and sulfamethoxazole in cerebrospinal fluid of treated pediatric meningitis.1H NMR spectral information and corresponding chemical information.Support of efficiency of isoniazid and pyrazinamide in crossing inflamed meninges as first‐line anti‐tuberculosis treatment.

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

BackgroundERp 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.ResultsXERp 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.ConclusionXERp 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.

Metabolic risks of neonates at birth following in utero exposure to HIV-ART: the amino acid profile of cord blood

IntroductionUntargeted metabolomics of cord blood indicated that antiretroviral therapy to HIV-infected mothers (HIV-ART) did not compromise the exposed neonates with regard to the stress of neonatal hypoglycaemia at birth. However, identified biomarkers reflected stress in their energy metabolism, raising concern over developmental risks in some newborns exposed to ART.ObjectivesThis study addresses the concern over HIV-ART-induced metabolic perturbations by expanding the metabolomics study to the amino acid profiles in cord blood collected at birth from newborns either exposed or unexposed to HIV-ART in utero.MethodsAmino acid profiles derived from liquid chromatographic triple quadruple spectra of cord blood from neonates exposed and unexposed to HIV-ART (cohort 1) were investigated using a metabolomics approach. Amino acid data, generated by ultra performance liquid chromatography–tandem mass spectrometry from similar cases (cohort 2), were included for comparison.ResultsMultivariate and supporting statistics indicated differentiation between the exposed and unexposed neonates in both cohorts, caused by a general decrease or downregulation of amino acid concentrations in the cord blood samples from the exposed cases. Specifically, significant upregulation of aspartic acid in both cohorts and downregulation of arginine, and of threonine, tryptophan and lysine in cohorts 1 and 2, respectively, were observed.ConclusionsThe benefits of ART for HIV-infected pregnant women are well established. However, the amino acid profile of cord blood, obtained from the two independent cohorts, adds to observed metabolic risks of in utero HIV-ART-exposed newborns. These risks could potentially have adverse consequences for the future health of some exposed infants.