The role of metabolomics in neurological disease

Potential Metabolite Biomarkers of Multiple Sclerosis from Multiple Biofluids

Multiple sclerosis (MS) is a chronic and progressive neurological disorder without a cure, but early intervention can slow disease progression and improve the quality of life for MS patients. Obtaining an accurate diagnosis for MS is an arduous and error-prone task that requires a combination of a detailed medical history, a comprehensive neurological exam, clinical tests such as magnetic resonance imaging, and the exclusion of other possible diseases. A simple and definitive biofluid test for MS does not exist, but is highly desirable. To address this need, we employed NMR-based metabolomics to identify potentially unique metabolite biomarkers of MS from a cohort of age and sex-matched samples of cerebrospinal fluid (CSF), serum, and urine from 206 progressive MS (PMS) patients, 46 relapsing-remitting MS (RRMS) patients, and 99 healthy volunteers without a MS diagnosis. We identified 32 metabolites in CSF that varied between the control and PMS patients. Utilizing patient-matched serum samples, we were able to further identify 31 serum metabolites that may serve as biomarkers for PMS patients. Lastly, we identified 14 urine metabolites associated with PMS. All potential biomarkers are associated with metabolic processes linked to the pathology of MS, such as demyelination and neuronal damage. Four metabolites with identical profiles across all three biofluids were discovered, which demonstrate their potential value as cross-biofluid markers of PMS. We further present a case for using metabolic profiles from PMS patients to delineate biomarkers of RRMS. Specifically, three metabolites exhibited a variation from healthy volunteers without MS through RRMS and PMS patients. The consistency of metabolite changes across multiple biofluids, combined with the reliability of a receiver operating characteristic classification, may provide a rapid diagnostic test for MS.

Serum amino acid profiles in patients with myasthenia gravis

Myasthenia gravis (MG) is an autoimmune disease characterized by weakness and rapid fatigue. Diagnostic methods used for myasthenia gravis are not conclusive and satisfactory, therefore it is necessary to develop reliable tools to help diagnose myasthenia gravis as early as possible. The aim of the study was to use HPLC-MS in conjunction with multivariate statistical analyses to investigate changes in the amino acid metabolic profiles between myasthenia gravis patients compared and controls. In addition, the effect of treatment regimens and myasthenia gravis type, on the observed changes in amino acid metabolic profiles were assessed. Serum levels of 29 amino acids were determined in 2 groups of individuals-28 patients with myasthenia gravis and 53 control subjects (CS). The results of our study indicate that serum levels of several amino acids in patients with myasthenia gravis changed significantly compared to the control group. Statistical analysis revealed differences between amino acids concentration in patients with different therapeutic scheme. In conclusion, amino acids may be involved in mechanisms underlying myasthenia gravis pathogenesis as well as may be potential biomarkers in MG patients diagnosis. However, considering the multifactorial, heterogenous and complex nature of this disease, validation on a larger study sample in further research is required before application into diagnostic practice.

GC/MS-Based Urine Metabolomics Study on the Ameliorative Effect of Xanthoceras sorbifolia Extract on Alzheimer’s Disease in Mice

The cause of Alzheimer's disease, the most common type of dementia today, is still unclear, and in current research, there are no drugs that work relatively well. Therefore, the study for new drugs to treat Alzheimer's disease is an urgent research need. Research on the improvement of Alzheimer's disease with extracts of Xanthoceras sorbifolia has been increasing in recent years, but the mechanism is not fully understood. The experiments were conducted to validate the model and analyze the treatment effect through D-galactose and Aβ_25–35 induced dementia model mice, using the Morris water maze, to detect the learning behavior and brain tissue section to observe the hippocampal tissue structure of mice. We performed a nontargeted metabolomic analysis of the urine obtained from different groups of mice using gas chromatography-mass spectrometry. Fourteen potential biomarkers were identified in the mice's urine, outlining five metabolic pathways of interest. It was shown that the extracts of Xanthoceras sorbifolia may exert protective effects on mice in dementia models through energy metabolism, neuroinflammation, and antioxidants. This study reveals the potential pathogenesis of Alzheimer's disease and the possible therapeutic mechanism of Xanthoceras sorbifolia, suggests relevant biomarkers, and provides an additional basis for the clinical application of Xanthoceras sorbifolia.

Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ_1–42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

Metabolomics in degenerative brain diseases

Among the most studied diseases that affect the central nervous system are Parkinson's, Alzheimer's, and Huntington's diseases, but the lack of effective biomarkers, accurate diagnosis, and precise treatment for each of them is currently an issue. Due to the contribution of biomarkers in supporting diagnosis, many recent efforts have focused on their identification and validation at the beginning or during the progression of the mental illness. Metabolome reveals the metabolic processes that result from protein activities under the guided gene expression and environmental factors, either in healthy or pathological conditions. In this context, metabolomics has proven to be a valuable approach. Currently, magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the most commonly used bioanalytical techniques for metabolomics. MS-assisted profiling is considered the most versatile technique, and the NMR is the most reproductive. However, each one of them has its drawbacks. In this review, we summarized several alterations in metabolites that have been reported for these three classic brain diseases using MS and NMR-based research, which might suggest some possible biomarkers to support the diagnosis and/or new targets for their treatment.

Contribution of Metabolomics to Multiple Sclerosis Diagnosis, Prognosis and Treatment

Metabolomics-based technologies map in vivo biochemical changes that may be used as early indicators of pathological abnormalities prior to the development of clinical symptoms in neurological conditions. Metabolomics may also reveal biochemical pathways implicated in tissue dysfunction and damage and thus assist in the development of novel targeted therapeutics for neuroinflammation and neurodegeneration. Metabolomics holds promise as a non-invasive, high-throughput and cost-effective tool for early diagnosis, follow-up and monitoring of treatment response in multiple sclerosis (MS), in combination with clinical and imaging measures. In this review, we offer evidence in support of the potential of metabolomics as a biomarker and drug discovery tool in MS. We also use pathway analysis of metabolites that are described as potential biomarkers in the literature of MS biofluids to identify the most promising molecules and upstream regulators, and show novel, still unexplored metabolic pathways, whose investigation may open novel avenues of research.

Biomarkers for the Clinical Diagnosis of Alzheimer's Disease: Metabolomics Analysis of Brain Tissue and Blood

With an increase in aging populations worldwide, age-related diseases such as Alzheimer's disease (AD) have become a global concern. At present, a cure for neurodegenerative disease is lacking. There is an urgent need for a biomarker that can facilitate the diagnosis, classification, prognosis, and treatment response of AD. The recent emergence of highly sensitive mass-spectrometry platforms and high-throughput technology can be employed to discover and catalog vast datasets of small metabolites, which respond to changed status in the body. Metabolomics analysis provides hope for a better understanding of AD as well as the subsequent identification and analysis of metabolites. Here, we review the state-of-the-art emerging candidate biomarkers for AD.

Multiple Sclerosis Biomarker Discoveries by Proteomics and Metabolomics Approaches

Multiple sclerosis (MS) is an autoimmune inflammatory disorder of the central nervous system (CNS) resulting in demyelination and axonal loss in the brain and spinal cord. The precise pathogenesis and etiology of this complex disease are still a mystery. Despite many studies that have been aimed to identify biomarkers, no protein marker has yet been approved for MS. There is urgently needed for biomarkers, which could clarify pathology, monitor disease progression, response to treatment, and prognosis in MS. Proteomics and metabolomics analysis are powerful tools to identify putative and novel candidate biomarkers. Different human compartments analysis using proteomics, metabolomics, and bioinformatics approaches has generated new information for further clarification of MS pathology, elucidating the mechanisms of the disease, finding new targets, and monitoring treatment response. Overall, omics approaches can develop different therapeutic and diagnostic aspects of complex disorders such as multiple sclerosis, from biomarker discovery to personalized medicine.

Metabolomics markers in Neurology: current knowledge and future perspectives for therapeutic targeting

INTRODUCTION

Metabolomics is an emerging approach providing new insights into the metabolic changes and underlying mechanisms involved in the pathogenesis of neurological disorders.

AREAS COVERED

Here, the authors present an overview of the current knowledge of metabolic profiling (metabolomics) to provide critical insight on the role of biochemical markers and metabolic alterations in neurological diseases.

EXPERT OPINION

Elucidation of characteristic metabolic alterations in neurological disorders is crucial for a better understanding of their pathogenesis, and for identifying potential biomarkers and drug targets. Nevertheless, discrepancies in diagnostic criteria, sample handling protocols, and analytical methods still affect the generalizability of current study results.

The Use of Metabolomics to Identify Biological Signatures of Manganese Exposure

Objectives

Manganese (Mn) is a known neurotoxicant, and given its health effects and ubiquitous nature in metal-working settings, identification of a valid and reproducible biomarker of Mn exposure is of interest. Here, global metabolomics is utilized to determine metabolites that differ between groups defined by Mn exposure status, with the goal being to help inform a potential metabolite biomarker of Mn exposure.

Methods

Mn exposed subjects were recruited from a Mn steel foundry and Mn unexposed subjects were recruited from crane operators at a metal recycling facility. Over the course of a work day, each subject wore a personal inhalable dust sampler (IOM), and provided an end of shift urine sample that underwent global metabolomics profiling. Both exposed and unexposed subjects were divided into a training set and demographically similar validation set. Using a two-sided adjusted t-test, relative abundances of all metabolites found were compared between Mn exposed and unexposed training sets, and those with a false discovery rates (FDR) <0.1 were further tested in the validation sets.

Results

Fifteen ions were found to be significantly different (FDR < 0.1) between the exposed and unexposed training sets, and nine of these ions remained significantly different between the exposed and unexposed validation set as well. When further dividing exposure status into 'lower exposure' and 'higher exposure', several of these nine ions exhibited an apparent exposure-response relationship.

Conclusions

This is the first time that metabolomics has been used to distinguish between Mn exposure status in an occupational cohort, though additional work should be done to replicate these findings with a larger cohort. With metabolite identification by name, empirical formula, or pathway, a better understanding of the relationship between Mn exposure and neurotoxic effects could be elucidated, and a potential metabolite biomarker of Mn exposure could be determined.

Metabolomics As a Tool for the Characterization of Drug-Resistant Epilepsy

Purpose

Drug resistance is a critical issue in the treatment of epilepsy, contributing to clinical emergencies and increasing both serious social and economic burdens on the health system. The wide variety of potential drug combinations followed by often failed consecutive attempts to match drugs to an individual patient may mean that this treatment stage may last for years with suboptimal benefit to the patient. Given these challenges, it is valuable to explore the availability of new methodologies able to shorten the period of determining a rationale pharmacologic treatment. Metabolomics could provide such a tool to investigate possible markers of drug resistance in subjects with epilepsy.

Methods

Blood samples were collected from (1) controls (C) (n = 35), (2) patients with epilepsy “responder” (R) (n = 18), and (3) patients with epilepsy “non-responder” (NR) (n = 17) to the drug therapy. The samples were analyzed using nuclear magnetic resonance spectroscopy, followed by multivariate statistical analysis.

Key findings

A different metabolic profile based on metabolomics analysis of the serum was observed between C and patients with epilepsy and also between R and NR patients. It was possible to identify the discriminant metabolites for the three classes under investigation. Serum from patients with epilepsy were characterized by increased levels of 3-OH-butyrate, 2-OH-valerate, 2-OH-butyrate, acetoacetate, acetone, acetate, choline, alanine, glutamate, scyllo-inositol (C < R < NR), and decreased concentration of glucose, lactate, and citrate compared to C (C > R > NR).

Significance

In conclusion, metabolomics may represent an important tool for discovery of differences between subjects affected by epilepsy responding or resistant to therapies and for the study of its pathophysiology, optimizing the therapeutic resources and the quality of life of patients.

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

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.

NMR-based metabolomic approach to study urine samples of chronic inflammatory rheumatic disease patients

The nuclear magnetic resonance (NMR)-based metabolomic approach was used as analytical methodology to study the urine samples of chronic inflammatory rheumatic disease (CIRD) patients. The urine samples of CIRD patients were compared to the ones of both healthy subjects and patients with multiple sclerosis (MS), another immuno-mediated disease. Urine samples collected from 39 CIRD patients, 25 healthy subjects, and 26 MS patients were analyzed using ¹H NMR spectroscopy, and the NMR spectra were examined using partial least squares-discriminant analysis (PLS-DA). PLS-DA models were validated by a double cross-validation procedure and randomization tests. Clear discriminations between CIRD patients and healthy controls (average diagnostic accuracy 83.5 ± 1.9%) as well as between CIRD patients and MS patients (diagnostic accuracy 81.1 ± 1.9%) were obtained. Leucine, alanine, 3-hydroxyisobutyric acid, hippuric acid, citric acid, 3-hydroxyisovaleric acid, and creatinine contributed to the discrimination; all of them being in a lower concentration in CIRD patients as compared to controls or to MS patients. The application of NMR metabolomics to study these still poorly understood diseases can be useful to better clarify the pathologic mechanisms; moreover, as a holistic approach, it allowed the detection of, by means of anomalous metabolic traits, the presence of other pathologies or pharmaceutical treatments not directly connected to CIRDs, giving comprehensive information on the general health state of individuals. Graphical abstract NMR-based metabolomic approach as a tool to study urine samples in CIRD patients with respect to MS patients and healthy controls.

Recent advancements in diagnostic tools in mitochondrial energy metabolism diseases

The involvement of mitochondrial energy metabolism in human disease ranges from rare monogenic disease to common diseases and aging with a genetic and/or lifestyle/environmental cause. This wide ranging involvement is due to the central role played by mitochondrion in cellular metabolism, its role in cellular perception of threats and its role in effecting responses to these threats. Investigating mitochondrial function/dysfunction or mitochondria-associated cell-biological responses have thus become a common finding where the pathogenic processes are investigated. Although, such investigations are warranted, it is not always clear if mitochondria can indeed be associated with cause or merely playing a responsive role in disease pathology. As this key question is also essential to disease progression and therapy, it should be recognized in investigative design. We herewith, present an overview of the current approaches and technologies used and the practicalities around these technologies.

Analytical pitfalls and challenges in clinical metabolomics

Metabolomics-based strategies have become an integral part of modern clinical research, allowing for a better understanding of pathophysiological conditions and disease mechanisms, as well as providing innovative tools for more adequate diagnostic and prognosis approaches. Metabolomics is considered an essential tool in precision medicine, which aims for personalized prevention and tailor-made treatments. Nevertheless, multiple pitfalls may be encountered in clinical metabolomics during the entire workflow, hampering the quality of the data and, thus, the biological interpretation. This review describes the challenges underlying metabolomics-based experiments, discussing step by step the potential pitfalls of the analytical process, including study design, sample collection, storage, as well as preparation, chromatographic and electrophoretic separation, detection and data analysis. Moreover, it offers practical solutions and strategies to tackle these challenges, ensuring the generation of high-quality data.

Urinary NMR-based metabolomic analysis of rats possessing variable sperm count following orally administered Eurycoma longifolia extracts of different quassinoid levels

ETHNOPHARMACOLOGICAL RELEVANCE

Eurycoma longifolia (Tongkat Ali, TA) roots have been ethnically used as a remedy to boost male sexual desire, libido, energy and fertility.

AIM OF THE STUDY

The study evaluated the effect of TA extracts with different quassinoid levels on rats sperm count and examined corresponding post-treatment urinary metabolic changes.

MATERIALS AND METHODS

Twenty-four male Sprague-Dawley rats, categorized into 4 groups of 6 rats each, were orally administered for 48 days with water for the control (group 1), 125mg/kg of TA water extract (TAW, group 2), 125mg/kg of TA quassinoid-poor extract (TAQP, group 3) and 21mg/kg of TA quassinoid-rich extract (TAQR, group 4). Upon completion of the 48-day treatment, the urine samples were analyzed by NMR and the animals were subsequently sacrificed for sperm count analysis. The urine profiles were categorized according to sperm count level.

RESULTS

The results showed that the sperm count in TAW- and TAQR-treated groups was significantly higher compared to the TAQP-administered and control groups. The orthogonal partial least squares discriminant analysis (OPLS-DA) model indicated a clear separation among the urine profiles with respect to sperm count level. Urine (1)H-NMR profiles of the high-sperm count group contained higher concentrations of trigonelline, alanine, benzoic acid and higher intensity of a signal at 3.42ppm, while ethanol was at higher concentration in the normal-sperm count group.

CONCLUSIONS

The results proved the efficacy of quassinoids on sperm count increase in rats and provided quantitative markers in urine suitable for analysis of sperm profile and male fertility status.

NMR-Based Metabolomics Separates the Distinct Stages of Disease in a Chronic Relapsing Model of Multiple Sclerosis

Relapsing experimental allergic encephalomyelitis (Cr-EAE) is commonly used to explore the pathogenesis and efficacy of new therapies for MS, but it is unclear whether the metabolome of Cr-EAE is comparable to human multiple sclerosis (MS). For MS, the diagnosis and staging can be achieved by metabolomics on blood using a combination of magnetic resonance spectroscopy and partial least squares discriminant analysis (PLS-DA). Here, we sought to discover whether this approach could be used to differentiate between sequential disease states in Cr-EAE and whether the same metabolites would be discriminatory. Urine and plasma samples were obtained at different time-points from a clinically relevant model of MS. Using PLS-DA modelling for the urine samples furnished some predictive models, but could not discriminate between all disease states. However, PLS-DA modelling of the plasma samples was able to distinguish between animals with clinically silent disease (day 10, 28) and animals with active disease (day 14, 38). We were also able to distinguish Cr-EAE mice from naive mice at all-time points and control mice, treated with complete Freund's adjuvant alone, at day 14 and 38. Key metabolites that underpin these models included fatty acids, glucose and taurine. Two of these metabolites, fatty acids and glucose, were also key metabolites in separating relapsing-remitting MS from secondary-progressive MS in the human study. These results demonstrate the sensitivity of this metabolomics approach for distinguishing between different disease states. Furthermore, some, but not all, of the changes in metabolites were conserved in humans and the mouse model, which could be useful for future drug development.

Recovery correction technique for NMR spectroscopy of perchloric acid extracts using DL-valine-2,3-d2: validation and application to 5-fluorouracil-induced brain damage

PURPOSE

We explored a recovery correction technique that can correct metabolite loss during perchloric acid (PCA) extraction and minimize inter-assay variance in quantitative (1)H nuclear magnetic resonance (NMR) spectroscopy of the brain and evaluated its efficacy in 5-fluorouracil (5-FU)- and saline-administered rats.

METHODS

We measured the recovery of creatine and dl-valine-2,3-d2 from PCA extract containing both compounds (0.5 to 8 mM). We intravenously administered either 5-FU for 4 days (total, 100 mg/kg body weight) or saline into 2 groups of 11 rats each. We subsequently performed PCA extraction of the whole brain on Day 9, externally adding 7 µmol of dl-valine-2,3-d2. We estimated metabolite concentrations using an NMR spectrometer with recovery correction, correcting metabolite concentrations based on the recovery factor of dl-valine-2,3-d2. For each metabolite concentration, we calculated the coefficient of variation (CEV) and compared differences between the 2 groups using unpaired t-test.

RESULTS

Equivalent recoveries of dl-valine-2,3-d2 (89.4 ± 3.9%) and creatine (89.7 ± 3.9%) in the PCA extract of the mixed solution indicated the suitability of dl-valine-2,3-d2 as an internal reference. In the rat study, recovery of dl-valine-2,3-d2 was 90.6 ± 9.2%. Nine major metabolite concentrations adjusted by recovery of dl-valine-2,3-d2 in saline-administered rats were comparable to data in the literature. CEVs of these metabolites were reduced from 10 to 17% before to 7 to 16% after correction. The significance of differences in alanine and taurine between the 5-FU- and saline-administered groups was determined only after recovery correction (0.75 ± 0.12 versus 0.86 ± 0.07 for alanine; 5.17 ± 0.59 versus 5.66 ± 0.42 for taurine [µmol/g brain tissue]; P < 0.05).

CONCLUSION

A new recovery correction technique corrected metabolite loss during PCA extraction, minimized inter-assay variance in quantitative (1)H NMR spectroscopy of brain tissue, and effectively detected inter-group differences in concentrations of brain metabolites between 5-FU- and saline-administered rats.

Pushing CT and MR imaging to the molecular level for studying the "omics": current challenges and advancements

During the past decade, medical imaging has made the transition from anatomical imaging to functional and even molecular imaging. Such transition provides a great opportunity to begin the integration of imaging data and various levels of biological data. In particular, the integration of imaging data and multiomics data such as genomics, metabolomics, proteomics, and pharmacogenomics may open new avenues for predictive, preventive, and personalized medicine. However, to promote imaging-omics integration, the practical challenge of imaging techniques should be addressed. In this paper, we describe key challenges in two imaging techniques: computed tomography (CT) and magnetic resonance imaging (MRI) and then review existing technological advancements. Despite the fact that CT and MRI have different principles of image formation, both imaging techniques can provide high-resolution anatomical images while playing a more and more important role in providing molecular information. Such imaging techniques that enable single modality to image both the detailed anatomy and function of tissues and organs of the body will be beneficial in the imaging-omics field.

Biomarker Discovery and Translation in Metabolomics

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Metabolomic approach to human brain spectroscopy identifies associations between clinical features and the frontal lobe metabolome in multiple sclerosis

Proton magnetic resonance spectroscopy ((1)H-MRS) is capable of noninvasively detecting metabolic changes that occur in the brain tissue in vivo. Its clinical utility has been limited so far, however, by analytic methods that focus on independently evaluated metabolites and require prior knowledge about which metabolites to examine. Here, we applied advanced computational methodologies from the field of metabolomics, specifically partial least squares discriminant analysis and orthogonal partial least squares, to in vivo (1)H-MRS from frontal lobe white matter of 27 patients with relapsing-remitting multiple sclerosis (RRMS) and 14 healthy controls. We chose RRMS, a chronic demyelinating disorder of the central nervous system, because its complex pathology and variable disease course make the need for reliable biomarkers of disease progression more pressing. We show that in vivo MRS data, when analyzed by multivariate statistical methods, can provide reliable, distinct profiles of MRS-detectable metabolites in different patient populations. Specifically, we find that brain tissue in RRMS patients deviates significantly in its metabolic profile from that of healthy controls, even though it appears normal by standard MRI techniques. We also identify, using statistical means, the metabolic signatures of certain clinical features common in RRMS, such as disability score, cognitive impairments, and response to stress. This approach to human in vivo MRS data should promote understanding of the specific metabolic changes accompanying disease pathogenesis, and could provide biomarkers of disease progression that would be useful in clinical trials.

NMR HRMAS spectroscopy of lung biopsy samples: Comparison study between human, pig, rat, and mouse metabolomics

PURPOSE

Using the metabolomics by NMR high-resolution magic angle spinning spectroscopy, we assessed the lung metabolome of various animal species in order to identify the animal model that could be substituted to human lung in studies on fresh lung biopsies.

METHODS

The experiments were conducted on intact lung biopsy samples of pig, rat, mouse, and human using a Bruker Advance III 500 spectrometer. Thirty-five to 39 metabolites were identified and 23 metabolites were quantified. Principal component analysis, partial least-squares discriminant analysis, and analysis of variance tests were performed in order to compare the metabolic profiles of each animal lung biopsies to those of the human lung.

RESULTS

The metabolic composition between human and pig lung was similar. However, human lung was distinguishable from mouse and rat regarding: Trimethylamine N-oxide and betaïne which were present in rodents but not in human lung, carnitine, and glycerophosphocholine which were present in mouse but not in human lung. Conversely, succinic acid was undetected in rat lung. Furthermore, fatty acids concentration was significantly higher in rodent lungs compared to human lung.

CONCLUSION

Using the metabolomics by NMR high-resolution magic angle spinning spectroscopy on lung biopsy, samples allowed to highlight that pig lung seems to be close to human lung as regarding its metabolite composition with more similarities than dissimilarities.

Computational tools for the secondary analysis of metabolomics experiments

Metabolomics experiments have become commonplace in a wide variety of disciplines. By identifying and quantifying metabolites researchers can achieve a systems level understanding of metabolism. These studies produce vast swaths of data which are often only lightly interpreted due to the overwhelmingly large amount of variables that are measured. Recently, a number of computational tools have been developed which enable much deeper analysis of metabolomics data. These data have been difficult to interpret as understanding the connections between dozens of altered metabolites has often relied on the biochemical knowledge of researchers and their speculations. Modern biochemical databases provide information about the interconnectivity of metabolism which can be automatically polled using metabolomics secondary analysis tools. Starting with lists of altered metabolites, there are two main types of analysis: enrichment analysis computes which metabolic pathways have been significantly altered whereas metabolite mapping contextualizes the abundances and significances of measured metabolites into network visualizations. Many different tools have been developed for one or both of these applications. In this review the functionality and use of these software is discussed. Together these novel secondary analysis tools will enable metabolomics researchers to plumb the depths of their data and produce farther reaching biological conclusions than ever before.