Proton nuclear magnetic resonance-based metabonomics for rapid diagnosis of meningitis and ventriculitis

The metabolic fingerprint of chronic hepatitis C progression: Metabolome shifts and cutting-edge diagnostic options

Hepatitis C virus infection causes chronic diseases such as cirrhosis and hepatocellular carcinoma. Metabolomics research has been shown to be linked to pathophysiologic pathways in liver illnesses. The aim of this study was to investigate the serum metabolic profile of patients with chronic hepatitis C (CHC) infection and to identify underlying mechanisms as well as potential biomarkers associated with the disease. Nuclear magnetic resonance (NMR) was used to evaluate the sera of 83 patients with CHC virus and 52 healthy control volunteers (NMR). Then, multivariate statistical analysis was used to find distinguishing metabolites between the two groups. Sixteen out of 40 metabolites including include 3-HB, betaine, carnitine, creatinine, fucose, glutamine, glycerol, isopropanol, lysine, mannose, methanol, methionine, ornithine, proline, serine, and valine-were shown to be significantly different between the CHC and normal control (NC) groups (variable importance in projection >1 and p < 0.05). All the metabolic perturbations in this disease are associated with pathways of Glycine, serine, and threonine metabolism, glycerolipid metabolism, arginine and proline metabolism, aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, alanine, aspartate, and glutamate metabolism. Multivariate statistical analysis constructed using these expressed metabolites showed CHC patients can be discriminated from NCs with high sensitivity (90%) and specificity (99%). The metabolomics approach may expand the diagnostic armamentarium for patients with CHC while contributing to a comprehensive understanding of disease mechanisms.

1H NMR Using Metabolic Study in Body Fluids for Diagnosis of Cryptococcal Meningitis in Adults

Background

Cryptococcal meningitis is considered to affect HIV patients and those with impaired immune systems. Early identification and treatment are the keys to decreasing morbidity and mortality related to CM. Using 1H NMR spectroscopy, a prospective case-control study will assess the metabolic profile of adults' serum, urine, and CSF.

Methodology

The present multicentric study was conducted at Lucknow. The study included 150 participants, out of which there were 31 cryptococcal meningitis cases, 34 positive meningitis controls, and the rest, 85, were disease controls.

Result

The discriminant function analysis (DFA) of the three biofluids was used to find significant metabolites between the cases and the control group collectively. A group categorization between control group and the cases in serum, urine, and CSF samples was also made possible by the NMR spectral bin-based orthogonal signal correction and principal component analysis score plots of important metabolites produced from DFA. The cases group had a higher proportion of patients with higher CSF protein levels than the positive control group (BM and TM). Acetone was found among urine samples in both control samples, i.e., positive and negative.

Conclusion

This is the first study to explore biomarkers in serum, urine, and CSF in addition to radiological features and clinical symptoms. Hence, a quick, non-invasive prognosis and diagnosis of cryptococcal meningitis in adults can be made using clinical and microbiological investigation, as well as metabolomic analysis of urine samples. This study shows that urine can be used as a biofluid to differentiate between Cryptococcus meningitis in adults. However, when compared to the negative control, our sample size was significantly smaller, necessitating further confirmation on a larger sample size.

NMR based CSF metabolomics in tuberculous meningitis: correlation with clinical and MRI findings

We report the potential role of ¹H Nuclear Magnetic Resonance (NMR) based metabolomics in tuberculous meningitis (TBM). We also correlate the significant metabolites with clinical-radiological parameters. Forty-three patients with TBM were included, and their severity of meningitis was graded as stages I to III, and patients with positive Mycobacterium tuberculosis or its nucleic acid was considered as definite TBM. ¹H NMR-based metabolomic study was performed on (CSF) samples, and the significant metabolites compared to healthy controls were identified. Outcome at three months was defined as death, poor and good based on the modified Rankin Scale. These metabolites were compared between definite and probable groups of TBM, and also correlated with MRI findings. About 11 metabolites were found to be significant for distinguishing TBM from the controls. In TBM, lactate, glutamate, alanine, arginine, 2-hydroxyisobutyrate, formate, and cis-aconitate were upregulated, and glucose, fructose, glutamine, and myo-inositol were downregulated compared to the controls. For differentiating TBM from the controls, the AUC of the ROC curve generated using these significant metabolites was 0.99, with a 95% confidence interval from 0.96 to 1, demonstrating that these metabolites were able to classify cases with good sensitivity and specificity. Lactate concentration in CSF correlated with hemoglobin, CSF glucose, and infarction. The outcome did not correlate with metabolomics parameters. NMR-based CSF metabolomics have a potential role in differentiating TBM from the controls.

CSF Metabolomics of Tuberculous Meningitis: A Review

From the World Health Organization's global TB report for 2020, it is estimated that in 2019 at least 80,000 children (a particularly vulnerable population) developed tuberculous meningitis (TBM)-an invariably fatal disease if untreated-although this is likely an underestimate. As our latest technologies have evolved-with the unprecedented development of the various "omics" disciplines-a mountain of new data on infectious diseases have been created. However, our knowledge and understanding of infectious diseases are still trying to keep pace. Metabolites offer much biological information, but the insights they permit can be difficult to derive. This review summarizes current metabolomics studies on cerebrospinal fluid (CSF) from TBM cases and collates the metabolic data reported. Collectively, CSF metabolomics studies have identified five classes of metabolites that characterize TBM: amino acids, organic acids, nucleotides, carbohydrates, and "other". Taken holistically, the information given in this review serves to promote the mechanistic action of hypothesis generation that will drive and direct future studies on TBM.

Derivation of a metabolic signature associated with bacterial meningitis in infants

BACKGROUND

Diagnosis of bacterial meningitis (BM) is challenging in newborn infants. Presently, biomarkers of BM have limited diagnostic accuracy. Analysis of cerebrospinal fluid (CSF) metabolites may be a useful diagnostic tool in BM.

METHODS

In a nested case-control study, we examined >400 metabolites in CSF of uninfected infants and infants with culture-confirmed BM using gas and liquid chromatography mass spectrometry. Preterm and full-term infants in a Level III or IV Neonatal Intensive Care Unit were prospectively enrolled when evaluated for serious bacterial infection.

RESULTS

Over 200 CSF metabolites significantly differed in uninfected infants and infants with BM. Using machine learning, we found that as few as 6 metabolites distinguished infants with BM from uninfected infants in this pilot cohort. Further analysis demonstrated three metabolites associated with Group B Streptococcal meningitis.

CONCLUSIONS

We report the first comprehensive metabolic analysis of CSF in infants with BM. In our pilot cohort, we derived a metabolic signature that predicted the presence or absence of BM, irrespective of gestational age, postnatal age, sex, race and ethnicity, presence of neurosurgical hardware, white blood cell count in CSF, and red blood cell contamination in CSF. Metabolic analysis may aid diagnosis of BM and facilitate clinical decision-making in infants.

IMPACT

In a pilot cohort, metabolites in cerebrospinal fluid distinguished infants with bacterial meningitis from uninfected infants.We report the first comprehensive metabolic analysis of cerebrospinal fluid in infants with bacterial meningitis.Our findings may be used to improve diagnosis of bacterial meningitis and to offer mechanistic insights into the pathophysiology of bacterial meningitis in infants.

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.

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics.

Factors and measures predicting external CSF drain-associated ventriculitis

Objective

To determine the diagnostic value of clinical factors and biochemical or microbiological measures for diagnosing a drain-associated ventriculitis, we summarized the available evidence.

Methods

We performed a systematic review and meta-analysis of studies of patients with external ventricular CSF drains who developed drain-associated ventriculitis by searching MEDLINE, EMBASE, and CENTRAL electronic database. We reported the occurrence of abnormal test results in patients with and without drain-associated ventriculitis. For continuous variables, we recalculated mean values presented in multiple studies.

Results

We identified 42 articles published between 1984 and 2018 including 3,035 patients with external CSF drains of whom 697 (23%) developed drain-associated bacterial ventriculitis. Indications for drain placement were subarachnoid, intraventricular or cerebral hemorrhage or hemorrhage not further specified (69%), traumatic brain injury (13%), and obstructive hydrocephalus secondary to a brain tumor (10%). Fever was present in 116 of 162 patients with ventriculitis (72%) compared with 80 of 275 (29%) patients without ventriculitis. The CSF cell count was increased for 74 of 80 patients (93%) with bacterial ventriculitis and 30 of 95 patients (32%) without ventriculitis. CSF culture was positive in 125 of 156 episodes classified as ventriculitis (80%), and CSF Gram stain was positive in 44 of 81 patients (54%). In patients with ventriculitis, PCR on ribosomal RNA was positive on 54 of 78 CSF samples (69%).

Conclusion

Clinical factors and biochemical and microbiological measures have limited diagnostic value in differentiating between ventriculitis and sterile inflammation in patients with external CSF drains. Prospective well-designed diagnostic accuracy studies in drain-associated ventriculitis are needed.

Biomarkers of Infection and Sepsis

The role of biomarkers for detection of sepsis has come a long way. Molecular biomarkers are taking front stage at present, but machine learning and other computational measures using bigdata sets are promising. Clinical research in sepsis is hampered by lack of specificity of the diagnosis; sepsis is a syndrome with no uniformly agreed definition. This lack of diagnostic precision means there is no gold standard for this diagnosis. The final conclusion is expert opinion, which is not bad but not perfect. Perhaps machine learning will displace expert opinion as the final and most accurate definition for sepsis.

NMR metabolomics of cerebrospinal fluid differentiates inflammatory diseases of the central nervous system

BACKGROUND

Myriad infectious and noninfectious causes of encephalomyelitis (EM) have similar clinical manifestations, presenting serious challenges to diagnosis and treatment. Metabolomics of cerebrospinal fluid (CSF) was explored as a method of differentiating among neurological diseases causing EM using a single CSF sample.

METHODOLOGY/PRINCIPAL FINDINGS

1H NMR metabolomics was applied to CSF samples from 27 patients with a laboratory-confirmed disease, including Lyme disease or West Nile Virus meningoencephalitis, multiple sclerosis, rabies, or Histoplasma meningitis, and 25 controls. Cluster analyses distinguished samples by infection status and moderately by pathogen, with shared and differentiating metabolite patterns observed among diseases. CART analysis predicted infection status with 100% sensitivity and 93% specificity.

CONCLUSIONS/SIGNIFICANCE

These preliminary results suggest the potential utility of CSF metabolomics as a rapid screening test to enhance diagnostic accuracies and improve patient outcomes.

Metabolic Profiling of Central Nervous System Disease in Trypanosoma brucei rhodesiense Infection

Background

The progression of human African trypanosomiasis from the early hemolymphatic stage to the late meningoencephalitic stage is of critical diagnostic importance as it determines the choice of potentially toxic drug regimens. Current diagnostic criteria involving analysis of cerebrospinal fluid (CSF) for parasites and/or pleocytosis are sensitive, but recent evidence suggests that specificity may be poor.

Methods

We used an untargeted global metabolic profiling approach for the discovery of novel candidate stage-diagnostic markers in CSF from patients infected with Trypanosoma brucei rhodesiense, using ¹H nuclear magnetic resonance (NMR) spectroscopy.

Results

Metabolic markers did not distinguish between early and late-stage cases but were associated with neuroinflammatory responses and the presentation of neurological disturbances. In particular, increased concentrations of 3-hydroxybutyrate and alanine and reduced concentrations of mannose and urea were discriminatory for the presentation of daytime somnolence and gait ataxia.

Conclusions

CSF metabolite concentrations provide markers for neuroinflammatory responses during central nervous system (CNS) invasion by trypanosomes and are associated with the presentation of neurological disturbances independently of disease stage determined by current criteria. This suggests that applying a dichotomous-stage diagnosis on the basis of CSF pleocytosis does not accurately reflect the biological changes occurring as parasites invade the CNS and has implications for biomarker discovery strategies.

Proton NMR metabolic profiling of CSF reveals distinct differentiation of meningitis from negative controls

BACKGROUND

Cerebrospinal fluid (CSF) is an essential bio-fluid of the central nervous system (CNS), playing a vital role in the protection of CNS and performing neuronal function regulation. The chemical composition of CSF varies during onset of meningitis, neurodegenerative disorders (positive controls) and in traumatic cases (negative controls).

METHODS

The study design was broadly categorized into meningitis cases, negative controls and positive controls. Further differentiation among the three groups was carried out using Principal Component Analysis (PCA) followed by supervised Partial Least Square Discriminant Analysis (PLS-DA).

RESULTS

The statistical analysis of meningitis vs. negative controls using PLS-DA model resulted in R² of 0.97 and Q² of 0.85. There was elevation in the levels of ketone bodies, total free amino acids, glutamine, creatine, citrate and choline containing compounds (choline and GPC) in meningitis cases. Similarly, meningitis vs. positive controls resulted in R² of 0.80 and Q² of 0.60 and showed elevation in the levels of total free amino acids, glutamine, creatine/creatinine and citrate in the meningitis group. Four cases of HIV were identified by PLS-DA model as well as by clinical investigations.

CONCLUSION

On the basis of metabolic profile it was found that negative control CSF samples are more appropriate for differentiation of meningitis than positive control CSF samples.

Cerebrospinal fluid metabolomic profiling in tuberculous and viral meningitis: Screening potential markers for differential diagnosis

BACKGROUND

Tuberculous meningitis (TBM) is the most severe and frequent form of central nervous system tuberculosis. The current lack of efficient diagnostic tests makes it difficult to differentiate TBM from other common types of meningitis, especially viral meningitis (VM). Metabolomics is an important tool to identify disease-specific biomarkers. However, little metabolomic information is available on adult TBM.

METHODS

We used ¹H nuclear magnetic resonance-based metabolomics to investigate the metabolic features of the CSF from 18 TBM and 20 VM patients. Principal component analysis and orthogonal signal correction-partial least squares-discriminant analysis (OSC-PLS-DA) were applied to analyze profiling data. Metabolites were identified using the Human Metabolome Database and pathway analysis was performed with MetaboAnalyst 3.0.

RESULTS

The OSC-PLS-DA model could distinguish TBM from VM with high reliability. A total of 25 key metabolites that contributed to their discrimination were identified, including some, such as betaine and cyclohexane, rarely reported before in TBM. Pathway analysis indicated that amino acid and energy metabolism was significantly different in the CSF of TBM compared with VM.

CONCLUSIONS

Twenty-five key metabolites identified in our study may be potential biomarkers for TBM differential diagnosis and are worthy of further investigation.

Neonatal Meningitis: Overcoming Challenges in Diagnosis, Prognosis, and Treatment with Omics

Neonatal meningitis is a devastating condition. Prognosis has not improved in decades, despite the advent of improved antimicrobial therapy and heightened index of suspicion among clinicians caring for affected infants. One in ten infants die from meningitis, and up to half of survivors develop significant lifelong complications, including seizures, impaired hearing and vision, and delayed or arrested development of such basic skills as talking and walking. At present, it is not possible to predict which infants will suffer poor outcomes. Early treatment is critical to promote more favorable outcomes, though diagnosis of meningitis in infants is technically challenging, time-intensive, and invasive. Profound neuronal injury has long been described in the setting of neonatal meningitis, as has elevated levels of many pro- and anti-inflammatory cytokines. Mechanisms of the host immune response that drive clearance of the offending organism and underlie brain injury due to meningitis are not well understood, however. In this review, we will discuss challenges in diagnosis, prognosis, and treatment of neonatal meningitis. We will highlight transcriptomic, proteomic, and metabolomic data that contribute to suggested mechanisms of inflammation and brain injury in this setting with a view toward fruitful areas for future investigation.

Nuclear Magnetic Resonance Spectroscopy-Based Identification of Yeast

Rapid and robust high-throughput identification of environmental, industrial, or clinical yeast isolates is important whenever relatively large numbers of samples need to be processed in a cost-efficient way. Nuclear magnetic resonance (NMR) spectroscopy generates complex data based on metabolite profiles, chemical composition and possibly on medium consumption, which can not only be used for the assessment of metabolic pathways but also for accurate identification of yeast down to the subspecies level. Initial results on NMR based yeast identification where comparable with conventional and DNA-based identification. Potential advantages of NMR spectroscopy in mycological laboratories include not only accurate identification but also the potential of automated sample delivery, automated analysis using computer-based methods, rapid turnaround time, high throughput, and low running costs.We describe here the sample preparation, data acquisition and analysis for NMR-based yeast identification. In addition, a roadmap for the development of classification strategies is given that will result in the acquisition of a database and analysis algorithms for yeast identification in different environments.

Potential Metabolic Biomarkers to Identify Interstitial Lung Abnormalities

Determining sensitive biomarkers in the peripheral blood to identify interstitial lung abnormalities (ILAs) is essential for the simple early diagnosis of ILAs. This study aimed to determine serum metabolic biomarkers of ILAs and the corresponding pathogenesis. Three groups of subjects undergoing health screening, including healthy subjects, subjects with ILAs, and subjects who were healthy initially and with ILAs one year later (Healthy→ILAs), were recruited for this study. The metabolic profiles of all of the subjects’ serum were analyzed by liquid chromatography quadruple time-of-flight mass spectrometry. The metabolic characteristics of the ILAs subjects were discovered, and the corresponding biomarkers were predicted. The metabolomic data from the Healthy→ILAs subjects were collected for further verification. The results indicated that five serum metabolite alterations (up-regulated phosphatidylcholine, phosphatidic acid, betaine aldehyde and phosphatidylethanolamine, as well as down-regulated 1-acylglycerophosphocholine) were sensitive and reliable biomarkers for identifying ILAs. Perturbation of the corresponding biological pathways (RhoA signaling, mTOR/P70S6K signaling and phospholipase C signaling) might be at least partially responsible for the pathogenesis of ILAs. This study may provide a good template for determining the early diagnostic markers of subclinical disease status and for obtaining a better understanding of their pathogenesis.

Tuberculous meningitis in infants and children: Insights from nuclear magnetic resonance metabolomics

Abstract Tuberculous meningitis (TBM) is a prevalent form of central nervous system tuberculosis (CNS-TB) and the most severe common form of bacterial meningitis in infants and children below the age of 13 years, especially in the Western Cape Province of South Africa. Research to identify markers for timely and accurate diagnosis and treatment outcomes remains high on the agenda for TBM, in respect of which the field of metabolomics is as yet largely unexploited. However, the national Department of Science and Technology (DST) recently established several biotechnology platforms at South African institutions, including one for metabolomics hosted at North-West University. We introduce this national platform for nuclear magnetic resonance (NMR) metabolomics by providing an overview of work on TBM. We focus on selected collaborative multidisciplinary approaches to this disease and conclude with the outcomes of an untargeted NMR metabolomics study of cerebrospinal fluid from TBM patients. This study enabled the formulation of a conceptual shuttle representing the unique metabolic plasticity of CNS metabolism towards the energy requirements for the microglia-driven neuroinflammatory responses, of which TBM is one example. From insights generated by this explorative NMR metabolomics investigation, we propose directions for future in-depth research strategies to address this devastating disease. In our view, the timely initiative of the DST, the operational expertise in metabolomics now available and the potential for involving national and international networks in this field of research offers remarkable opportunities for the future of metabolomics in South Africa and for an ever greater understanding of disease mechanisms.

Metabonomics and intensive care

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency medicine 2016. Other selected articles can be found online at http://www.biomedcentral.com/collections/annualupdate2016. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

Vaginal microbiome and metabolome highlight specific signatures of bacterial vaginosis

In this study, we sought to find novel bacterial and metabolic hallmarks for bacterial vaginosis (BV). We studied the vaginal microbiome and metabolome of vaginal fluids from BV-affected patients (n = 43) and healthy controls (n = 37) by means of an integrated approach based on quantitative polymerase chain reaction (qPCR) and proton nuclear magnetic resonance ((1)H-NMR). The correlations between the clinical condition and vaginal bacterial communities were investigated by principal component analysis (PCA). To define the metabolomics signatures of BV, 100 discriminant analysis by projection on latent structure (PLS-DA) models were calculated. Bacterial signatures distinguishing the health condition and BV were identified by qPCR. Lactobacillus crispatus strongly featured the healthy vagina, while increased concentrations of Prevotella, Atopobium and Mycoplasma hominis specifically marked the infection. (1)H-NMR analysis has led to the identification and quantification of 17 previously unreported molecules. BV was associated with changes in the concentration of metabolites belonging to the families of amines, organic acids, short chain fatty acids, amino acids, nitrogenous bases and monosaccharides. In particular, maltose, kynurenine and NAD(+) primarily characterised the healthy status, while nicotinate, malonate and acetate were the best metabolic hallmarks of BV. This study helps to better understand the role of the vaginal microbiota and metabolome in the development of BV infection. We propose a molecular approach for the diagnosis of BV based on quantitative detection in the vaginal fluids of Atopobium, Prevotella and M. hominis, and nicotinate, malonate and acetate by combining qPCR and (1)H-NMR.

Rabies: Rare Human Infection - Common Questions

Rabies is an acute, rapidly progressive encephalitis that is almost always fatal. Prophylaxis is highly effective but economics limits disease control. The mechanism of death from rabies is unclear. It is poorly cytopathic and poorly inflammatory. Rabies behaves like an acquired metabolic disorder. There may be a continuum of disease severity. History of animal bite is rare. The diagnosis is often missed. Intermittent encephalopathy, dysphagia, hydrophobia and aerophobia, and focal paresthesias or myoclonic jerks suggest rabies. Laboratory diagnosis is cumbersome but sensitive. Treatment is controversial but survivors are increasingly reported, with good outcomes in 4 of 8 survivors.

Metabolomics for characterization of gender differences in patients infected with dengue virus

OBJECTIVE

To determine the metabolic response associate with dengue infection based on human gender metabolic differences by means of (1)H NMR-spectrometry.

METHODS

The mid-stream urine collected from both male and female patients diagnosed with dengue fever at Penang General Hospital and fourty-three healthy individuals were analyzed with (1)H NMR spectroscopy, followed by chemometric multivariate analysis. NMR signals which highlighted in the OPLS-DA S-plot were further selected and identified using Human Metabolome Database, Chenomx Profiler.

RESULTS

The results pointed out that NMR urine profiling was able to capture human gender metabolic differences that are important for the distinction of classes of individuals of similar physiological conditions; infected with dengue. Distinct differences between dengue infected patients versus healthy individuals and subtle differences in male versus female infected with dengue were found to be related to the metabolism of amino acid and tricarboxylic acid intermediates cycle.

CONCLUSIONS

The (1)H NMR metabolomic investigation combined with appropriate algorithms and pattern recognition procedures, gave an evidence for the existence of distinct metabolic differentiation of individuals, according to their gender, modulates with the infection risk.

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.

Metabolism in chronic fatigue syndrome

Chronic fatigue syndrome (CFS) is a poorly understood condition that presents as long-term physical and mental fatigue with associated symptoms of pain and sensitivity across a broad range of systems in the body. The poor understanding of the disorder comes from the varying clinical diagnostic definitions as well as the broad array of body systems from which its symptoms present. Studies on metabolism and CFS suggest irregularities in energy metabolism, amino acid metabolism, nucleotide metabolism, nitrogen metabolism, hormone metabolism, and oxidative stress metabolism. The overwhelming body of evidence suggests an oxidative environment with the minimal utilization of mitochondria for efficient energy production. This is coupled with a reduced excretion of amino acids and nitrogen in general. Metabolomics is a developing field that studies metabolism within a living system under varying conditions of stimuli. Through its development, there has been the optimisation of techniques to do large-scale hypothesis-generating untargeted studies as well as hypothesis-testing targeted studies. These techniques are introduced and show an important future direction for research into complex illnesses such as CFS.

Rifaximin modulates the vaginal microbiome and metabolome in women affected by bacterial vaginosis

Bacterial vaginosis (BV) is a common vaginal disorder characterized by the decrease of lactobacilli and overgrowth of Gardnerella vaginalis and resident anaerobic vaginal bacteria. In the present work, the effects of rifaximin vaginal tablets on vaginal microbiota and metabolome of women affected by BV were investigated by combining quantitative PCR and a metabolomic approach based on (1)H nuclear magnetic resonance. To highlight the general trends of the bacterial communities and metabolomic profiles in response to the antibiotic/placebo therapy, a multivariate statistical strategy was set up based on the trajectories traced by vaginal samples in a principal component analysis space. Our data demonstrated the efficacy of rifaximin in restoring a health-like condition in terms of both bacterial communities and metabolomic features. In particular, rifaximin treatment was significantly associated with an increase in the lactobacillus/BV-related bacteria ratio, as well as with an increase in lactic acid concentration and a decrease of a pool of metabolites typically produced by BV-related bacteria (acetic acid, succinate, short-chain fatty acids, and biogenic amines). Among the tested dosages of rifaximin (100 and 25 mg for 5 days and 100 mg for 2 days), 25 mg for 5 days was found to be the most effective.

Biomarkers of Brain Injury in Cerebral Infections

BACKGROUND

Central nervous system (CNS) infections present a major burden of disease worldwide and are associated with high rates of mortality and morbidity. Swift diagnosis and initiation of appropriate treatment are vital to minimize the risk of poor outcome; however, tools are lacking to accurately diagnose infection, assess injury severity, and predict outcome. Biomarkers of structural neurological injury could provide valuable information in addressing some of these challenges.

CONTENT

In this review, we summarize experimental and clinical research on biomarkers of neurological injury in a range of CNS infectious diseases. Data suggest that in both adults and children, the biomarkers S100B and neuron-specific enlose (NSE), among others, can provide insight into the pathophysiology of CNS infection and injury severity, evolution, and response to treatment. Research into the added utility of combining a panel of biomarkers and in assessing biomarker association with clinical and radiological outcomes warrants further work. Various factors, including age, the establishment of normative values, and comparison of biomarker concentrations across different testing platforms still present challenges in biomarker application.

SUMMARY

Research regarding the value of biomarkers in CNS infections is still in its infancy. However, early evidence supports their utility in diagnosis and prognosis, and potentially as effective surrogate end points in the assessment of novel interventions.

A metabolomic perspective on coeliac disease

Metabolomics is an "omic" science that is now emerging with the purpose of elaborating a comprehensive analysis of the metabolome, which is the complete set of metabolites (i.e., small molecules intermediates) in an organism, tissue, cell, or biofluid. In the past decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. A metabolomics investigation of coeliac disease (CD) revealed that a metabolic fingerprint for CD can be defined, which accounts for three different but complementary components: malabsorption, energy metabolism, and alterations in gut microflora and/or intestinal permeability. In this review, we will discuss the major advancements in metabolomics of CD, in particular with respect to the role of gut microbiome and energy metabolism.

Cerebrospinal fluid lactate in post-neurosurgical bacterial meningitis diagnosis

BACKGROUND

Differential diagnosis between post-neurosurgical bacterial meningitis (PNBM) and aseptic meningitis is difficult. Inflammatory and biochemical cerebrospinal fluid (CSF) changes mimic those classically observed after CNS surgery. CSF lactate assay has therefore been proposed as a useful PNBM marker.

OBJECTIVE

To evaluate the diagnostic accuracy of CSF lactate as a PNBM marker in patients hospitalized after a neurosurgical procedure.

METHODS

Between July 2005 and June 2009, a prospective clinical study, in which all patients with clinical suspicion of PNBM were enrolled, was conducted at our neurosurgical Intensive Care Unit. PNBM diagnosis was categorized as proven, probable or negative before the analysis.

RESULTS

Seventy-nine patients, 51 males with a mean age of 50 years (range 32-68 years) were included. Surgery was elective in 76% patients, mostly for brain tumors (57%); thirty PNBM episodes were identified. CSF parameters were significantly different in glucose concentration (27 mg% vs. 73 mg%, p<0.001), lactate (8 mmol/L vs. 2.8 mmol/L, p<0.001), CSF neutrophil pleocytosis (850 mm(-3) vs. 10mm(-3), p<0.001), and protein levels (449 mg% vs. 98 mg%) between the PNBM and non-PNBM groups. The ROC curve that best fits PNBM diagnosis is lactate.

CONCLUSION

Increased CSF lactate is a useful PNBM marker, with better predictive value than CSF hypoglycorrhachia or pleocytosis. Lactate levels ≥ 4 mmol/L showed 97% sensitivity and 78% specificity, with a 97% negative predictive value.

Nuclear magnetic resonance based profiling of biofluids reveals metabolic dysregulation in HIV-infected persons and those on anti-retroviral therapy

BACKGROUND

Although HIV causes immune deficiency by infection and depletion of immunocytes, metabolic alterations with clinical manifestations are also reported in HIV/AIDS patients. Here we aimed to profile metabolite changes in the plasma, urine, and saliva of HIV/AIDS patients, including those on anti-retroviral therapy (ART).

METHODS

Metabolic profiling of biofluids collected from treatment naïve HIV/AIDS patients and those receiving ART was done with solution-state nuclear magnetic resonance (NMR) spectroscopy followed by statistical analysis and annotation.

RESULTS

In Principal Component Analysis (PCA) of the NMR spectra, Principal Component 1 (PC1) alone accounted for 99.3%, 87.2% and 78.8% variations in plasma, urine, and saliva, respectively. Partial least squares discriminant analysis (PLS-DA) was applied to generate three-component models, which showed plasma and urine to be better than saliva in discriminating between patients and healthy controls, and between ART-naïve patients and those receiving therapy. Twenty-six metabolites were differentially altered in any or two types of samples. Our results suggest that urinary Neopterin, and plasma Choline and Sarcosine could be used as metabolic biomarkers of HIV/AIDS infection. Pathway analysis revealed significant alternations in 12 metabolic pathways.

CONCLUSIONS

This study catalogs differentially regulated metabolites in biofluids, which helped classify subjects as healthy controls, HIV/AIDS patients, and those on ART. It also underscores the importance of further studying the consequences of HIV infection on host metabolism and its implications for pathogenesis.

Metabolomics of cerebrospinal fluid from humans treated for rabies

Rabies is a rapidly progressive lyssavirus encephalitis that is statistically 100% fatal. There are no clinically effective antiviral drugs for rabies. An immunologically naïve teenager survived rabies in 2004 through improvised supportive care; since then, 5 additional survivors have been associated with use of the so-called Milwaukee Protocol (MP). The MP applies critical care focused on the altered metabolic and physiologic states associated with rabies. The aim of this study was to examine the metabolic profile of cerebrospinal fluid (CSF) from rabies patients during clinical progression of rabies encephalitis in survivors and nonsurvivors and to compare these samples with control CSF samples. Unsupervised clustering algorithms distinguished three stages of rabies disease and identified several metabolites that differentiated rabies survivors from those who subsequently died, in particular, metabolites related to energy metabolism and cell volume control. Moreover, for those patients who survived, the trajectory of their metabolic profile tracked toward the control profile and away from the rabies profile. NMR metabolomics of human rabies CSF provide new insights into the mechanisms of rabies pathogenesis, which may guide future therapy of this disease.

Gram-negative and Gram-positive bacterial infections give rise to a different metabolic response in a mouse model

Metabolomics has become an important tool to study host-pathogen interactions and to discover potential novel therapeutic targets. In an attempt to develop a better understanding of the process of pathogenesis and the associated host response we have used a quantitative ¹H NMR approach to study the metabolic response to different bacterial infections. Here we describe that metabolic changes found in serum of mice that were infected with Staphylococcus aureus, Streptococcus pneumoniae, Escherichia coli and Pseudomonas aeruginosa can distinguish between infections caused by Gram-positive and Gram-negative bacterial strains. By combining the results of the mouse study with those of bacterial footprinting culture experiments, bacterially secreted metabolites could be identified as potential bacterium-specific biomarkers for P. aeruginosa infections but not for the other strains. Multivariate statistical analysis revealed correlations between metabolic, cytokine and physiological responses. In TLR4 and TLR2 knockout mice, host-response pathway correlated metabolites could be identified and allowed us for the first time to distinguish between bacterial- and host-induced metabolic changes. Since Gram-positive and Gram-negative bacteria activate different receptor pathways in the host, our results suggest that it may become possible in the future to use a metabolomics approach to improve on current clinical microbiology diagnostic methods.

Biofluid metabonomics using (1)H NMR spectroscopy: the road to biomarker discovery in gastroenterology and hepatology

Metabolic profiling or 'metabonomics' is an investigatory method that allows metabolic changes associated with the presence of an underlying pathological process to be investigated. Various biofluids can be utilized in the process but urine, serum and fecal extract are most pertinent to the investigation of gastrointestinal and hepatological disease. Nuclear magnetic resonance spectroscopy-based metabonomic research has the potential to generate novel noninvasive diagnostic tests, based on biomarkers of disease, which are simple and cost effective yet retain high sensitivity and specificity characteristics. The process involves a number of key steps, including sample collection, data acquisition, chemometric techniques and, finally, validation. This technique-driven review aims to demystify the metabonomics pathway, while also illustrating the potential of this technique with recent examples of its application in hepato-gastroenterological disease.

Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks

(1)H NMR metabolic profiling of urine, serum and plasma has been used to monitor the impact of the pre-analytical steps on the sample quality and stability in order to propose standard operating procedures (SOPs) for deposition in biobanks. We analyzed the quality of serum and plasma samples as a function of the elapsed time (t = 0-4 h) between blood collection and processing and of the time from processing to freezing (up to 24 h). The stability of the urine metabolic profile over time (up to 24 h) at various storage temperatures was monitored as a function of the different pre-analytical treatments like pre-storage centrifugation, filtration, and addition of the bacteriostatic preservative sodium azide. Appreciable changes in the profiles, reflecting changes in the concentration of a number of metabolites, were detected and discussed in terms of chemical and enzymatic reactions for both blood and urine samples. Appropriate procedures for blood derivatives collection and urine preservation/storage that allow maintaining as much as possible the original metabolic profile of the fresh samples emerge, and are proposed as SOPs for biobanking.

Hepatitis C virus infection diagnosis using metabonomics

Metabonomics based on nuclear magnetic resonance (NMR) can reveal the profile of endogenous metabolites of low molecular weight in biofluids related to disease. The profile is identified a 'metabolic fingerprint' like from the pathological process, why this metabonomics has been used as a diagnostic method. The aim of the present study was to apply metabonomics to identify patients infected with the hepatitis C virus (HCV) through an analysis of ¹H NMR spectra of urine samples associated with multivariate statistical methods. A pilot study was carried out for the diagnostic test evaluation, involving two groups: (i) 34 patients positive for anti-HCV and HCV-RNA and negative for anti-HBc (disease group); and (ii) 32 individuals positive for anti-HBc and negative for HBsAg and anti-HCV. The urine samples were analyzed through ¹H NMR, applying principal component analysis and discriminant analysis for classification. The metabonomics model was capable of identifying 32 of the 34 patients in the disease group as positive and 31 of the 32 individuals in the control group as negative, demonstrating 94% sensitivity and specificity of 97% as well as positive and negative predictive values of 97% and 94%, respectively, and 95% accuracy (P < 0.001). In conclusion, the metabonomics model based on ¹H NMR spectra of urine samples in this preliminary study discriminated patients with HCV infection with high sensitivity and specificity, thereby demonstrating this model to be a potential tool for use in medical practice in the near future.

Genomic and metabolomic advances in the identification of disease and adverse event biomarkers

Incomplete knowledge of tissue pathogenesis is hampering the identification of biomarkers for the appropriate therapeutic targets to prevent or inhibit disease processes, and the prediction and diagnosis of injury due to disease and adverse events of drug therapy. The revolution in genomics and metabolomics, combined with advanced bioinformatics and computational methods for mining such large, complex data sets, are beginning to provide critical insights into tissue injury. Such results will move us closer to the promise of personalized medicine.

Computational approaches to metabolomics

This chapter is intended to familiarize readers with the field of metabolomics and some of the algorithms, data analysis strategies, and computer programs used to analyze or interpret metabolomic data. Specifically, this chapter provides a brief overview of the experimental approaches and applications of metabolomics followed by a description of the spectral and statistical analysis tools for metabolomics. The chapter concludes with a discussion of the resources that can be used to interpret and analyze metabolomic data at a biological or clinical level. Emerging needs, challenges, and recent progress being made in these areas are also discussed.

Chemometrics in metabolomics--a review in human disease diagnosis

Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.

Metabolomics, a New Promising Technology for Toxicological Research

Metabolomics which deals with the biological metabolite profile produced in the body and its relation to disease state is a relatively recent research area for drug discovery and biological sciences including toxicology and pharmacology. Metabolomics, based on analytical method and multivariate analysis, has been considered a promising technology because of its advantage over other toxicogenomic and toxicoproteomic approaches. The application of metabolomics includes the development of biomarkers associated with the pathogenesis of various diseases, alternative toxicity tests, high-throughput screening (HTS), and risk assessment, allowing the simultaneous acquisition of multiple biochemical parameters in biological samples. The metabolic profile of urine, in particular, often shows changes in response to exposure to xenobiotics or disease-induced stress, because of the biological system's attempt to maintain homeostasis. In this review, we focus on the most recent advances and applications of metabolomics in toxicological research.

Rapid etiological classification of meningitis by NMR spectroscopy based on metabolite profiles and host response

Bacterial meningitis is an acute disease with high mortality that is reduced by early treatment. Identification of the causative microorganism by culture is sensitive but slow. Large volumes of cerebrospinal fluid (CSF) are required to maximise sensitivity and establish a provisional diagnosis.

We have utilised nuclear magnetic resonance (NMR) spectroscopy to rapidly characterise the biochemical profile of CSF from normal rats and animals with pneumococcal or cryptococcal meningitis. Use of a miniaturised capillary NMR system overcame limitations caused by small CSF volumes and low metabolite concentrations. The analysis of the complex NMR spectroscopic data by a supervised statistical classification strategy included major, minor and unidentified metabolites.

Reproducible spectral profiles were generated within less than three minutes, and revealed differences in the relative amounts of glucose, lactate, citrate, amino acid residues, acetate and polyols in the three groups. Contributions from microbial metabolism and inflammatory cells were evident. The computerised statistical classification strategy is based on both major metabolites and minor, partially unidentified metabolites. This data analysis proved highly specific for diagnosis (100% specificity in the final validation set), provided those with visible blood contamination were excluded from analysis; 6–8% of samples were classified as indeterminate.

This proof of principle study suggests that a rapid etiologic diagnosis of meningitis is possible without prior culture. The method can be fully automated and avoids delays due to processing and selective identification of specific pathogens that are inherent in DNA-based techniques.

[Recent development of metabonomics and its applications in clinical research]

In the post-genomic era, systems biology is central to the biological sciences. Functional genomics such as transcriptomics and proteomics can simultaneous determine massive gene or protein expression changes following drug treatment or other intervention. However, these changes can't be coupled directly to changes in biological function. As a result, metabonomics and its many pseudonyms (metabolomics, metabolic profiling, etc.) have exploded onto the scientific scene in the past several years. Metabonomics is a rapidly growing research area and a system approach for comprehensive and quantitative analysis of the global metabolites in a biological matrix. Analytical chemistry approach is necessary for the development of comprehensive metabonomics investigations. Fundamentally, there are two types of metabonomics approaches: mass-spectrometry (MS) based and nuclear magnetic resonance (NMR) methodologies. Metabonomics measurements provide a wealth of data information and interpretation of these data relies mainly on chemometrics approaches to perform large-scale data analysis and data visualization, such as principal and independent component analysis, multidimensional scaling, a variety of clustering techniques, and discriminant function analysis, among many others. In this review, the recent development of analytical and statistical techniques used in metabonomics is summarized. Major applications of metabonomics relevant to clinical and preclinical study are then reviewed. The applications of metabonomics in study of liver diseases, cancers and other diseases have proved useful both as an experimental tool for pathogenesis mechanism re-search and ultimately a tool for diagnosis and monitoring treatment response of these diseases. Next, the applications of metabonomics in preclinical toxicology are discussed and the role that metabonomics might do in pharmaceutical research and development is explained with special reference to the aims and achievements of the Consortium for Metabonomic Toxicology (COMET), and the concept of pharmacometabonomics as a way of predicting an individual's response to treatment is highlighted. Finally, the role of metabonomics in elucidating the function of the unknown or novel enzyme is mentioned.

The metabonomic signature of celiac disease

Celiac disease (CD) is a multifactorial disorder involving genetic and environmental factors, thus, having great potential impact on metabolism. This study aims at defining the metabolic signature of CD through Nuclear Magnetic Resonance (NMR) of urine and serum samples of CD patients. Thirty-four CD patients at diagnosis and 34 healthy controls were examined by (1)H NMR of their serum and urine. A CD patients' subgroup was also examined after a gluten-free diet (GFD). Projection to Latent Structures provided data reduction and clustering, and Support Vector Machines provided pattern recognition and classification. The classification accuracy of CD and healthy control groups was 79.7-83.4% for serum and 69.3% for urine. Sera of CD patients were characterized by lower levels (P < 0.01) of several metabolites such as amino acids, lipids, pyruvate and choline, and by higher levels of glucose and 3-hydroxybutyric acid, while urines showed altered levels (P < 0.05) of, among others, indoxyl sulfate, meta-[hydroxyphenyl]propionic acid and phenylacetylglycine. After 12 months of GFD, all but one of the patients were classified as healthy by the same statistical analysis. NMR thus reveals a characteristic metabolic signature of celiac disease. Altered serum levels of glucose and ketonic bodies suggest alterations of energy metabolism, while the urine data point to alterations of gut microbiota. Metabolomics may thus provide further hints on the biochemistry of the disease.