Metabolic Profiling and Phenotyping of Central Nervous System Diseases: Metabolites Bring Insights into Brain Dysfunctions

Metabolic Profiling of Aromatic Compounds

Metabolic profiling is a powerful modern tool in searching for novel biomarkers and indicators of normal or pathological processes in the body [...].

Metabolomic Signatures of Alzheimer's Disease Indicate Brain Region-Specific Neurodegenerative Progression

Pathological mechanisms contributing to Alzheimer's disease (AD) are still elusive. Here, we identified the metabolic signatures of AD in human post-mortem brains. Using 1H NMR spectroscopy and an untargeted metabolomics approach, we identified (1) metabolomic profiles of AD and age-matched healthy subjects in post-mortem brain tissue, and (2) region-common and region-unique metabolome alterations and biochemical pathways across eight brain regions revealed that BA9 was the most affected. Phenylalanine and phosphorylcholine were mainly downregulated, suggesting altered neurotransmitter synthesis. N-acetylaspartate and GABA were upregulated in most regions, suggesting higher inhibitory activity in neural circuits. Other region-common metabolic pathways indicated impaired mitochondrial function and energy metabolism, while region-unique pathways indicated oxidative stress and altered immune responses. Importantly, AD caused metabolic changes in brain regions with less well-documented pathological alterations that suggest degenerative progression. The findings provide a new understanding of the biochemical mechanisms of AD and guide biomarker discovery for personalized risk prediction and diagnosis.

Metabolomic Profiling of Recombinant Erythropoietin (rHuEpo) in Trained Caucasian Athletes

OBJECTIVE

Recombinant human erythropoietin (rHuEpo) is prohibited by the World Anti-Doping Agency but remains the drug of choice for many cheating athletes wishing to evade detection using current methods. The aim of this study was to identify a robust metabolomics signature of rHuEpo using an untargeted approach in blood (plasma and serum) and urine.

DESIGN

Longitudinal study.

SETTING

University of Glasgow.

PARTICIPANTS

Eighteen male participants regularly engaged in predominantly endurance-based activities, such as running, cycling, swimming, triathlon, and team sports, were recruited.

INTERVENTIONS

Each participant received 50 IU·kg -1 body mass of rHuEpo subcutaneously every 2 days for 4 weeks. Samples were collected at baseline, during rHuEpo administration (over 4 weeks) and after rHuEpo administration (week 7-10). The samples were analyzed using hydrophilic interaction liquid chromatography mass spectrometry.

MAIN OUTCOME MEASURES

Significant metabolic signatures of rHuEpo administration were identified in all biofluids tested in this study.

RESULTS

Regarding metabolomics data, 488 plasma metabolites, 694 serum metabolites, and 1628 urinary metabolites were identified. Reproducible signatures of rHuEpo administration across all biofluids included alterations of pyrimidine metabolism (orotate and dihydroorotate) and acyl-carnitines (palmitoyl-carnitine and elaidic carnitine), metabolic pathways that are associated with erythropoiesis or erythrocyte membrane function, respectively.

CONCLUSIONS

Preliminary metabolic signatures of rHuEpo administration were identified. Future studies will be required to validate these encouraging results in independent cohorts and with orthogonal techniques, such as integration of our data with signatures derived from other "omics" analyses of rHuEpo administration (eg, transcriptomics).

Using metabolomics to predict severe traumatic brain injury outcome (GOSE) at 3 and 12 months

BACKGROUND

Prognostication is very important to clinicians and families during the early management of severe traumatic brain injury (sTBI), however, there are no gold standard biomarkers to determine prognosis in sTBI. As has been demonstrated in several diseases, early measurement of serum metabolomic profiles can be used as sensitive and specific biomarkers to predict outcomes.

METHODS

We prospectively enrolled 59 adults with sTBI (Glasgow coma scale, GCS ≤ 8) in a multicenter Canadian TBI (CanTBI) study. Serum samples were drawn for metabolomic profiling on the 1st and 4th days following injury. The Glasgow outcome scale extended (GOSE) was collected at 3- and 12-months post-injury. Targeted direct infusion liquid chromatography-tandem mass spectrometry (DI/LC-MS/MS) and untargeted proton nuclear magnetic resonance spectroscopy (¹H-NMR) were used to profile serum metabolites. Multivariate analysis was used to determine the association between serum metabolomics and GOSE, dichotomized into favorable (GOSE 5-8) and unfavorable (GOSE 1-4), outcomes.

RESULTS

Serum metabolic profiles on days 1 and 4 post-injury were highly predictive (Q² > 0.4-0.5) and highly accurate (AUC > 0.99) to predict GOSE outcome at 3- and 12-months post-injury and mortality at 3 months. The metabolic profiles on day 4 were more predictive (Q² > 0.55) than those measured on day 1 post-injury. Unfavorable outcomes were associated with considerable metabolite changes from day 1 to day 4 compared to favorable outcomes. Increased lysophosphatidylcholines, acylcarnitines, energy-related metabolites (glucose, lactate), aromatic amino acids, and glutamate were associated with poor outcomes and mortality.

DISCUSSION

Metabolomic profiles were strongly associated with the prognosis of GOSE outcome at 3 and 12 months and mortality following sTBI in adults. The metabolic phenotypes on day 4 post-injury were more predictive and significant for predicting the sTBI outcome compared to the day 1 sample. This may reflect the larger contribution of secondary brain injury (day 4) to sTBI outcome. Patients with unfavorable outcomes demonstrated more metabolite changes from day 1 to day 4 post-injury. These findings highlighted increased concentration of neurobiomarkers such as N-acetylaspartate (NAA) and tyrosine, decreased concentrations of ketone bodies, and decreased urea cycle metabolites on day 4 presenting potential metabolites to predict the outcome. The current findings strongly support the use of serum metabolomics, that are shown to be better than clinical data, in determining prognosis in adults with sTBI in the early days post-injury. Our findings, however, require validation in a larger cohort of adults with sTBI to be used for clinical practice.

Cerebrospinal Fluid-Basic Concepts Review

Cerebrospinal fluid plays a crucial role in protecting the central nervous system (CNS) by providing mechanical support, acting as a shock absorber, and transporting nutrients and waste products. It is produced in the ventricles of the brain and circulates through the brain and spinal cord in a continuous flow. In the current review, we presented basic concepts related to cerebrospinal fluid history, cerebrospinal fluid production, circulation, and its main components, the role of the blood-brain barrier and the blood-cerebrospinal fluid barrier in the maintenance of cerebrospinal fluid homeostasis, and the utility of Albumin Quotient (Q_Alb) evaluation in the diagnosis of CNS diseases. We also discussed the collection of cerebrospinal fluid (type, number of tubes, and volume), time of transport to the laboratory, and storage conditions. Finally, we briefly presented the role of cerebrospinal fluid examination in CNS disease diagnosis of various etiologies and highlighted that research on identifying cerebrospinal fluid biomarkers indicating disease presence or severity, evaluating treatment effectiveness, and enabling understanding of pathogenesis and disease mechanisms is of great importance. Thus, in our opinion, research on cerebrospinal fluid is still necessary for both the improvement of CNS disease management and the discovery of new treatment options.

Serum metabolic profiling of targeted bile acids reveals potentially novel biomarkers for primary biliary cholangitis and autoimmune hepatitis

BACKGROUND

Primary biliary cholangitis (PBC) and autoimmune hepatitis (AIH) are two unexplained immune diseases. The golden standard for diagnosis of these diseases requires a liver biopsy. Liver biopsy is not widely accepted by patients because of its invasive nature, and atypical liver histology can confuse diagnosis. In view of the lack of effective diagnostic markers for PBC and AIH, combined with the increasingly mature metabolomics technologies, including full-contour metabolomics and target.

AIM

To determine non-invasive, reliable, and sensitive biochemical markers for the differential diagnosis of PBC and AIH.

METHODS

Serum samples from 54 patients with PBC, 26 patients with AIH and 30 healthy controls were analyzed by Ultra-high performance liquid chromatography-tandem mass spectrometry serum metabolomics. The metabolites and metabolic pathways were identified, and the metabolic changes, metabolic pathways and inter-group differences between PBC and AIH were analyzed. Fifteen kinds of target metabolites of bile acids (BAs) were quantitatively analyzed by SRM, and the differential metabolites related to the diagnosis of PBC were screened by receiver operating characteristic curve analysis.

RESULTS

We found the changes in the levels of amino acids, BAs, organic acids, phospholipids, choline, sugar, and sugar alcohols in patients with PBC and AIH. Furthermore, the SRM assay of BAs revealed the increased levels of chenodeoxycholic acid, lithocholic acid (LCA), taurolithocholic acid (TLCA), and LCA + TLCA in the PBC group compared with those in the AIH group. The levels of BAs may be used as biomarkers to differentiate PBC from AIH diseases. The levels of glycochenodeoxycholic acid, glycochenodeoxycholic sulfate, and taurodeoxycholic acid were gradually elevated with the increase of Child-Pugh class, which was correlated with the severity of disease.

CONCLUSION

The results demonstrated that the levels of BAs could serve as potential biomarkers for the early diagnosis and assessment of the severity of PBC and AIH.

Metabolomic profiling relates tianeptine effectiveness with hippocampal GABA, myo-inositol, cholesterol, and fatty acid metabolism restoration in socially isolated rats

RATIONALE

Discovering biomarkers of major depressive disorder (MDD) can give a deeper understanding of this mood disorder and improve the ability to screen for, diagnose, and treat MDD.

OBJECTIVES

In this study, metabolomics was used in unraveling metabolite fluctuations of MDD and drug outcome by creating specific metabolomic fingerprints. We report metabolomic patterns of change of the hippocampus of adult male Wistar rats following chronic social isolation (CSIS) (6 weeks), an animal model of depression, and/or chronic tianeptine (Tian) treatment (10 mg kg^-1 per day) (lasting 3 weeks of 6-week CSIS), monitored by using comprehensive GC × GC-MS.

RESULTS

The comparative metabolomic analysis highlighted the role of gamma aminobutyric acid (GABA), iso-allocholate, and unsaturated fatty acid metabolism alterations following the CSIS, which was corroborated with moderate to strong negative Pearson's correlation of GABA, docosahexaenoic, 9-hexadecenoic acid, 5,8,11,14-eicosatetraynoic, and arachidonic acids with immobility behavior in the forced swim test. The antidepressant effect of Tian restored GABA levels, which was absent in Tian resilient rats. Tian decreased myo-inositol and increased TCA cycle intermediates, amino acids, and cholesterol and its metabolite. As key molecules of divergence between Tian effectiveness and resilience, metabolomics revealed myo-inositol, GABA, cholesterol, and its metabolite. A significant moderate positive correlation between myo-inositol and immobility was revealed. Tian probably acted by upregulating NMDAR's and α₂ adrenergic receptors (AR) or norepinephrine transporter in both control and stressed animals.

CONCLUSION

Metabolomics revealed several dysregulations underlying CSIS-induced depressive-like behavior and responsiveness to Tian, predominantly converging into NMDAR-mediated glutamate and myo-inositol signalization and GABA inhibitory pathways.

Cortex metabolome and proteome analysis reveals chronic arsenic exposure via drinking water induces developmental neurotoxicity through hnRNP L mediated mitochondrial dysfunction in male rats

Lots of people are at the risk of arsenic-contaminated drinking water. Arsenic exposure was confirmed to be closely linked to neurocognitive deficits, particularly during childhood. The multi-omics approaches are known be well suitable for toxicological research. Thus, this study aimed to explore the molecular mechanisms of arsenic-induced learning and memory function impairments through the integrative proteome and metabolome analysis of cortex in rats. The weaned rats were exposed to arsenic-contaminated drinking water for six months to mimic the developmental exposure. 220 differential proteins and 19 differential metabolites were identified in the cortex, and nine potential biomarkers were found to be related to impaired Morris water maze (MWM) indicators. Chronic arsenic exposure affected the cognitive function by inducing the overproduction of amyloid-β (Aβ) peptides and the redox imbalance in the mitochondria. Glycolysis and tricarboxylic acid (TCA) cycle enhancement driven by the increased heterogeneous nuclear ribonucleoprotein L (hnRNP L) is a low-dose protective mechanism against arsenic-induced ATP deficiency and oxidative stress. Moreover, apoptosis is another important pathway of arsenic-induced neurotoxicity. This study provides new evidence about the alterations of proteins and metabolites in the cortex of the exposed rats under arsenic toxicity. These findings suggest hnRNP L could be a potential target for the treatment of arsenic-induced neurotoxicity.

1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophy

Background

5q spinal muscular atrophy (SMA) is a disabling and life-limiting neuromuscular disease. In recent years, novel therapies have shown to improve clinical outcomes. Yet, the absence of reliable biomarkers renders clinical assessment and prognosis of possibly already affected newborns with a positive newborn screening result for SMA imprecise and difficult. Therapeutic decisions and stratification of individualized therapies remain challenging, especially in symptomatic children. The aim of this proof-of-concept and feasibility study was to explore the value of ¹H-nuclear magnetic resonance (NMR)-based metabolic profiling in identifying non-invasive diagnostic and prognostic urinary fingerprints in children and adolescents with SMA.

Results

Urine samples were collected from 29 treatment-naïve SMA patients (5 pre-symptomatic, 9 SMA 1, 8 SMA 2, 7 SMA 3), 18 patients with Duchenne muscular dystrophy (DMD) and 444 healthy controls. Using machine-learning algorithms, we propose a set of prediction models built on urinary fingerprints that showed potential diagnostic value in discriminating SMA patients from controls and DMD, as well as predictive properties in separating between SMA types, allowing predictions about phenotypic severity. Interestingly, preliminary results of the prediction models suggest additional value in determining biochemical onset of disease in pre-symptomatic infants with SMA identified by genetic newborn screening and furthermore as potential therapeutic monitoring tool.

Conclusions

This study provides preliminary evidence for the use of ¹H-NMR-based urinary metabolic profiling as diagnostic and prognostic biomarker in spinal muscular atrophy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-021-02075-x.

Cerebrospinal fluid metabolomics: detection of neuroinflammation in human central nervous system disease

The high morbidity and mortality of neuroinflammatory diseases drives significant interest in understanding the underlying mechanisms involved in the innate and adaptive immune response of the central nervous system (CNS). Diagnostic biomarkers are important to define treatable neuroinflammation. Metabolomics is a rapidly evolving research area offering novel insights into metabolic pathways, and elucidation of reliable metabolites as biomarkers for diseases. This review focuses on the emerging literature regarding the detection of neuroinflammation using cerebrospinal fluid (CSF) metabolomics in human cohort studies. Studies of classic neuroinflammatory disorders such as encephalitis, CNS infection and multiple sclerosis confirm the utility of CSF metabolomics. Additionally, studies in neurodegeneration and neuropsychiatry support the emerging potential of CSF metabolomics to detect neuroinflammation in common CNS diseases such as Alzheimer's disease and depression. We demonstrate metabolites in the tryptophan-kynurenine pathway, nitric oxide pathway, neopterin and major lipid species show moderately consistent ability to differentiate patients with neuroinflammation from controls. Integration of CSF metabolomics into clinical practice is warranted to improve recognition and treatment of neuroinflammation.

Imaging of Neurotransmitters and Small Molecules in Brain Tissues Using Laser Desorption/Ionization Mass Spectrometry Assisted with Zinc Oxide Nanoparticles

Inorganic nanostructured materials such as silicon, carbon, metals, and metal oxides have been explored as matrices of low-background signals to assist the laser desorption/ionization (LDI) mass spectrometric (MS) analysis of small molecules, but their applications for imaging of small molecules in biological tissues remain limited in the literature. Titanium dioxide is one of the known nanoparticles (NP) that can effectively assist LDI MS imaging of low molecular weight molecules (LMWM). TiO₂ NP is commercially available as dispersions, which can be applied using a chemical solution sprayer. However, aggregation of NP can occur in the dispersions, and the aggregated NP can slowly clog the sprayer nozzle. In this work, the use of zinc oxide (ZnO) NP for LDI MS imaging is investigated as a superior alternative due to its dissolution in acidic pH. ZnO NP was found to deliver similar or better results in the imaging of LMWM in comparison to TiO₂ NP. The regular acid washes were effective in minimizing clogging and maintaining high reproducibility. High-quality images of mouse sagittal and rat coronal tissue sections were obtained. Ions were detected predominately as Na⁺ or K⁺ adducts in the positive ion mode. The number of LMWM detected with ZnO NP was similar to that obtained with TiO₂ NP, and only a small degree of specificity was observed.

Major ceRNA regulation and key metabolic signature analysis of intervertebral disc degeneration

Background and objective

Intervertebral disc degeneration (IDD) is a complex multifactorial and irreversible pathological process. In IDD, multiple competing endogenous RNAs (ceRNA, including mRNA, lncRNA, and pseudogenes) can compete to bind with miRNAs. However, the potential metabolic signatures in nucleus pulposus (NP) cells remain poorly understood. This study investigated key metabolic genes and the ceRNA regulatory mechanisms in the pathogenesis of IDD based on microarray datasets.

Methods

We retrieved and downloaded four independent IDD microarray datasets from the Gene Expression Omnibus. Combining the predicted interactions from online databases (miRcode, miRDB, miRTarBase, and TargetScan), differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were identified. A ceRNA network was constructed and annotated using GO and KEGG pathway enrichment analyses. Moreover, we searched the online metabolic gene set and used support vector machine (SVM) to find the critical metabolic DEmRNA(s) and other DERNAs. Differential gene expression was validated with a merged dataset.

Results

A total of 45 DEmRNAs, 36 DElncRNAs, and only one DEmiRNA (miR-338-3p) were identified in the IDD microarray datasets. GO and KEGG pathway enrichment analyses revealed that the DEmRNAs were predominantly enriched in the PI3K-Akt signaling pathway, MAPK signaling pathway, IL-17 signaling pathway, apoptosis, and cellular response to oxidative stress. Based on SVM screening, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFK/FBPase) 2 is the critical metabolic gene with lower expression in IDD, and AC063977.6 is the key lncRNA with lower expression in IDD. The ceRNA hypothesis suggests that AC063977.6, miR-338-3p (high expression), and PFKFB2 are dysregulated as an axis in IDD.

Conclusions

The results suggest that lncRNA AC063977.6 correlate with PFKFB2, the vital metabolic signature gene, via targeting miR-338-3p during IDD pathogenesis. The current study may shed light on unraveling the pathogenesis of IDD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-021-04109-8.

Stop the rot. Enzyme inactivation at brain harvest prevents artifacts: A guide for preservation of the in vivo concentrations of brain constituents

Post-mortem metabolism is widely recognized to cause rapid and prolonged changes in the concentrations of multiple classes of compounds in brain, that is, they are labile. Post-mortem changes from levels in living brain include components of pathways of metabolism of glucose and energy compounds, amino acids, lipids, signaling molecules, neuropeptides, phosphoproteins, and proteins. Methods that stop enzyme activity at brain harvest were developed almost 50 years ago and have been extensively used in studies of brain functions and diseases. Unfortunately, these methods are not commonly used to harvest brain tissue for mass spectrometry-based metabolomic studies or for imaging mass spectrometry studies (IMS, also called mass spectrometry imaging, MSI, or matrix-assisted laser desorption/ionization-MSI, MALDI-MSI). Instead these studies commonly kill animals, decapitate, dissect out brain and regions of interest if needed, then 'snap' freeze the tissue to stop enzymatic activity after harvest, with post-mortem intervals typically ranging from ~0.5 to 3 min. To increase awareness of the importance of stopping metabolism at harvest and preventing the unnecessary complications of not doing so, this commentary provides examples of labile metabolites and the magnitudes of their post-mortem changes in concentrations during brain harvest. Brain harvest methods that stop metabolism at harvest eliminate post-mortem enzymatic activities and can improve characterization of normal and diseased brain. In addition, metabolomic studies would be improved by reporting absolute units of concentration along with normalized peak areas or fold changes. Then reported values can be evaluated and compared with the extensive neurochemical literature to help prevent reporting of artifactual data.

Proteomics for Target Identification in Psychiatric and Neurodegenerative Disorders

Psychiatric and neurodegenerative disorders such as schizophrenia (SCZ), Parkinson's disease (PD), and Alzheimer's disease (AD) continue to grow around the world with a high impact on health, social, and economic outcomes for the patient and society. Despite efforts, the etiology and pathophysiology of these disorders remain unclear. Omics technologies have contributed to the understanding of the molecular mechanisms that underlie these complex disorders and have suggested novel potential targets for treatment and diagnostics. Here, we have highlighted the unique and common pathways shared between SCZ, PD, and AD and highlight the main proteomic findings over the last 5 years using in vitro models, postmortem brain samples, and cerebrospinal fluid (CSF) or blood of patients. These studies have identified possible therapeutic targets and disease biomarkers. Further studies including target validation, the use of large sample sizes, and the integration of omics findings with bioinformatics tools are required to provide a better comprehension of pharmacological targets.

Systems biology approaches to study lipidomes in health and disease

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

Physical activity reduces anxiety and regulates brain fatty acid synthesis

Physical activity impacts brain functions, but the direct mechanisms of this effect are not fully recognized or understood. Among multidimensional changes induced by physical activity, brain fatty acids (FA) appear to play an important role; however, the knowledge in this area is particularly scarce. Here we performed global metabolomics profiling of the hippocampus and the frontal cortex (FC) in a model of voluntary running in mice. Examined brain structures responded differentially to physical activity. Specifically, the markers of the tricarboxylic acid (TCA) cycle were downregulated in the FC, whereas glycolysis was enhanced in the hippocampus. Physical activity stimulated production of myristic, palmitic and stearic FA; i.e., the primary end products of de novo lipogenesis in the brain, which was accompanied by increased expression of hippocampal fatty acid synthase (FASN), suggesting stimulation of lipid synthesis. The changes in the brain fatty acid profile were associated with reduced anxiety level in the running mice. Overall, the study examines exercise-related metabolic changes in the brain and links them to behavioral outcomes.

Ancestral stress programs sex-specific biological aging trajectories and non-communicable disease risk

The incidence of non-communicable diseases (NCDs) is rising globally but their causes are generally not understood. Here we show that cumulative ancestral stress leads to premature aging and raises NCD risk in a rat population. This longitudinal study revealed that cumulative multigenerational prenatal stress (MPS) across four generations (F0-F3) raises age- and sex-dependent adverse health outcomes in F4 offspring. MPS accelerated biological aging processes and exacerbated sex-specific incidences of respiratory and kidney diseases, inflammatory processes and tumors. Unbiased deep sequencing of frontal cortex revealed that MPS altered expression of microRNAs and their target genes involved in synaptic plasticity, stress regulation, immune function and longevity. Multi-layer top-down deep learning metabolite enrichment analysis of urine markers revealed altered metabolic homeodynamics in MPS males. Thus, peripheral metabolic signatures may provide sensitive biomarkers of stress vulnerability and disease risk. Programming by MPS appears to be a significant determinant of lifetime mental health trajectories, physical wellbeing and vulnerability to NCDs through altered epigenetic regulation.

Metabolomics and Biomarker Discovery in Traumatic Brain Injury

Traumatic brain injury (TBI) is one of the leading causes of disability and mortality worldwide. The TBI pathogenesis can induce broad pathophysiological consequences and clinical outcomes attributed to the complexity of the brain. Thus, the diagnosis and prognosis are important issues for the management of mild, moderate, and severe forms of TBI. Metabolomics of readily accessible biofluids is a promising tool for establishing more useful and reliable biomarkers of TBI than using clinical findings alone. Metabolites are an integral part of all biochemical and pathophysiological pathways. Metabolomic processes respond to the internal and external stimuli resulting in an alteration of metabolite concentrations. Current high-throughput and highly sensitive analytical tools are capable of detecting and quantifying small concentrations of metabolites, allowing one to measure metabolite alterations after a pathological event when compared to a normal state or a different pathological process. Further, these metabolic biomarkers could be used for the assessment of injury severity, discovery of mechanisms of injury, and defining structural damage in the brain in TBI. Metabolic biomarkers can also be used for the prediction of outcome, monitoring treatment response, in the assessment of or prognosis of post-injury recovery, and potentially in the use of neuroplasticity procedures. Metabolomics can also enhance our understanding of the pathophysiological mechanisms of TBI, both in primary and secondary injury. Thus, this review presents the promising application of metabolomics for the assessment of TBI as a stand-alone platform or in association with proteomics in the clinical setting.

ASIC3-dependent metabolomics profiling of serum and urine in a mouse model of fibromyalgia

Fibromyalgia (FM) is characterized by chronic widespread pain. The pathogenesis of FM remains unclear. No specific biomarkers are available. Animal models of FM may provide an opportunity to explore potential biomarkers in a relative homogenous disease condition. Here, we probed the metabolomics profiles of serum and urine in a mouse model of FM induced by intermittent cold stress (ICS). We focused on the role of acid-sensing ion channel 3 (ASIC3) in the metabolomics profiling because ICS treatment induced chronic widespread muscle pain lasting for 1 month in wild-type (Asic3^+/+) but not Asic3-knockout (Asic3^−/−) mice. Serum and urine samples were collected from both genotypes at different ICS stages, including before ICS (basal level) and post-ICS at days 10 (middle phase, P10) and 40 (recovery phase, P40). Control naïve mice and ICS-induced FM mice differed in ¹H-NMR- and LC-MS-based metabolomics profiling. On pathway analysis, the leading regulated pathways in Asic3^+/+ mice were taurine and hypotaurine, cysteine and methionine, glycerophospholipid, and ascorbate and aldarate metabolisms, and the major pathways in Asic3^−/− mice involved amino acid-related metabolism. Finally, we developed an algorithm for the impactful metabolites in the FM model including cis-aconitate, kynurenate, taurine, pyroglutamic acid, pyrrolidonecarboxylic acid, and 4-methoxyphenylacetic acid in urine as well as carnitine, deoxycholic acid, lysoPC(16:0), lysoPC(20:3), oleoyl-L-carnitine, and trimethylamine N-oxide in serum. Asic3^−/− mice were impaired in only muscle allodynia development but not other pain symptoms in the ICS model, so the ASIC3-dependent metabolomics changes could be useful for developing diagnostic biomarkers specific to chronic widespread muscle pain, the core symptom of FM. Further pharmacological validations are needed to validate these metabolomics changes as potential biomarkers for FM diagnosis and/or treatment responses.

Serum metabolome and targeted bile acid profiling reveals potential novel biomarkers for drug-induced liver injury

This study aims to determine the non-invasive, reliable and sensitive biochemical parameters for the diagnosis of drug-induced liver injury (DILI).Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) and selected reaction monitoring (SRM) were used to profile the serum metabolome and quantify 15 targeted bile acid metabolites, respectively, in samples obtained from 38 DILI patients and 30 healthy controls.A comparison of the resulting serum metabolome profiles of the study participants revealed significant differences between DILI patients and healthy controls. Specifically, serum palmitic acid, taurochenodeoxycholic acid, glycocholic acid (GCA), and tauroursodeoxycholic acid (TUDCA) levels were significantly higher, and serum lysophosphatidylethanolamine levels were significantly lower in DILI patients vs healthy controls (P < .001). Furthermore, the SRM assay of bile acids revealed that the increase in GCA, taurocholic acid (TCA), TUDCA, glycochenodeoxycholic acid (GCDCA), glycochenodeoxycholic sulfate (GCDCS), and taurodeoxycholic acid (TDCA) corresponded to a higher degree of liver damage. These results also indicate that serum concentrations of chenodeoxycholic acid (CDCA), deoxycholic acid (DCA) and lithocholic acid (LCA) were significantly lower in patients with severe DILI, when compared to healthy controls, and that this decrease was closely correlated to the severity of liver damage.Taken together, these results demonstrate that bile acids could serve as potential biomarkers for the early diagnosis and severity of DILI.

Metabolic profiling of listeria rhombencephalitis in small ruminants by 1 H high-resolution magic angle spinning NMR spectroscopy

Listeria rhombencephalitis is caused by infection with Listeria monocytogenes and is associated with a high mortality rate in humans and ruminants. Little is known about the metabolic changes associated with neurolisteriosis in particular and infectious central nervous system (CNS) diseases in general. The purpose of our study was to investigate the metabolic changes associated with listeria rhombencephalitis in small ruminants (goats and sheep) as a model for inflammatory CNS disease by ¹ H high-resolution magic angle spinning nuclear magnetic resonance (¹ H HR-MAS NMR) spectroscopy of brain biopsies obtained from the brainstem and thalamus. Statistical analysis revealed distinct differences in the metabolic profile of brainstem biopsies, the primary location of listeria rhombencephalitis with moderate or severe inflammatory changes. N-Acetylaspartate (NAA), N-acetylaspartylglutamate, choline, myo-inositol and scyllo-inositol were decreased, and glycine, phosphocholine, taurine and lactate were increased, in the diseased group (n = 13) in comparison with the control group (n = 12). In the thalamus, which showed no or only mild inflammatory changes in the majority of animals, no statistically significant metabolic changes were observed. However, trends for metabolic alterations were partly the same as those found in the brainstem, including NAA, choline and lactate. This may be an indicator of metabolic changes occurring in the early stages of the disease. Therefore, further research with a larger number of animals is needed to evaluate the presence of subtle metabolic changes associated with mild inflammatory changes in the thalamus. In conclusion, ¹ H HR-MAS NMR investigation of listeria rhombencephalitis identified brain metabolite changes, offering new insights into the disease pathophysiology.

Frequency and association of mitochondrial genetic variants with neurological disorders

Mitochondria are small cytosolic organelles and the main source of energy production for the cells, especially in the brain. This organelle has its own genome, the mitochondrial DNA (mtDNA), and genetic variants in this molecule can alter the normal energy metabolism in the brain, contributing to the development of a wide assortment of Neurological Disorders (ND), including neurodevelopmental syndromes, neurodegenerative diseases and neuropsychiatric disorders. These ND are comprised by a heterogeneous group of syndromes and diseases that encompass different cognitive phenotypes and behavioral disorders, such as autism, Asperger's syndrome, pervasive developmental disorder, attention deficit hyperactivity disorder, Huntington disease, Leigh Syndrome and bipolar disorder. In this work we carried out a Systematic Literature Review (SLR) to identify and describe the mitochondrial genetic variants associated with the occurrence of ND. Most of genetic variants found in mtDNA were associated with Single Nucleotide Polimorphisms (SNPs), ~79%, with ~15% corresponding to deletions, ~3% to Copy Number Variations (CNVs), ~2% to insertions and another 1% included mtDNA replication problems and genetic rearrangements. We also found that most of the variants were associated with coding regions of mitochondrial proteins but were also found in regulatory transcripts (tRNA and rRNA) and in the D-Loop replication region of the mtDNA. After analysis of mtDNA deletions and CNV, none of them occur in the D-Loop region. This SLR shows that all transcribed mtDNA molecules have mutations correlated with ND. Finally, we describe that all mtDNA variants found were associated with deterioration of cognitive (dementia) and intellectual functions, learning disabilities, developmental delays, and personality and behavior problems.

Current technical approaches to brain energy metabolism

Neuroscience is a technology-driven discipline and brain energy metabolism is no exception. Once satisfied with mapping metabolic pathways at organ level, we are now looking to learn what it is exactly that metabolic enzymes and transporters do and when, where do they reside, how are they regulated, and how do they relate to the specific functions of neurons, glial cells, and their subcellular domains and organelles, in different areas of the brain. Moreover, we aim to quantify the fluxes of metabolites within and between cells. Energy metabolism is not just a necessity for proper cell function and viability but plays specific roles in higher brain functions such as memory processing and behavior, whose mechanisms need to be understood at all hierarchical levels, from isolated proteins to whole subjects, in both health and disease. To this aim, the field takes advantage of diverse disciplines including anatomy, histology, physiology, biochemistry, bioenergetics, cellular biology, molecular biology, developmental biology, neurology, and mathematical modeling. This article presents a well-referenced synopsis of the technical side of brain energy metabolism research. Detail and jargon are avoided whenever possible and emphasis is given to comparative strengths, limitations, and weaknesses, information that is often not available in regular articles.

Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics

INTRODUCTION

CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

Identification of altered brain metabolites associated with TNAP activity in a mouse model of hypophosphatasia using untargeted NMR-based metabolomics analysis

Tissue non-specific alkaline phosphatase (TNAP) is a key player of bone mineralization and TNAP gene (ALPL) mutations in human are responsible for hypophosphatasia (HPP), a rare heritable disease affecting the mineralization of bones and teeth. Moreover, TNAP is also expressed by brain cells and the severe forms of HPP are associated with neurological disorders, including epilepsy and brain morphological anomalies. However, TNAP's role in the nervous system remains poorly understood. To investigate its neuronal functions, we aimed to identify without any a priori the metabolites regulated by TNAP in the nervous tissue. For this purpose we used ¹ H- and ³¹ P NMR to analyze the brain metabolome of Alpl (Akp2) mice null for TNAP function, a well-described model of infantile HPP. Among 39 metabolites identified in brain extracts of 1-week-old animals, eight displayed significantly different concentration in Akp2^-/- compared to Akp2^+/+ and Akp2^+/- mice: cystathionine, adenosine, GABA, methionine, histidine, 3-methylhistidine, N-acetylaspartate (NAA), and N-acetyl-aspartyl-glutamate, with cystathionine and adenosine levels displaying the strongest alteration. These metabolites identify several biochemical processes that directly or indirectly involve TNAP function, in particular through the regulation of ecto-nucleotide levels and of pyridoxal phosphate-dependent enzymes. Some of these metabolites are involved in neurotransmission (GABA, adenosine), in myelin synthesis (NAA, NAAG), and in the methionine cycle and transsulfuration pathway (cystathionine, methionine). Their disturbances may contribute to the neurodevelopmental and neurological phenotype of HPP.

Stress transgenerationally programs metabolic pathways linked to altered mental health

Stress is among the primary causes of mental health disorders, which are the most common reason for disability worldwide. The ubiquity of these disorders, and the costs associated with them, lends a sense of urgency to the efforts to improve prediction and prevention. Down-stream metabolic changes are highly feasible and accessible indicators of pathophysiological processes underlying mental health disorders. Here, we show that remote and cumulative ancestral stress programs central metabolic pathways linked to mental health disorders. The studies used a rat model consisting of a multigenerational stress lineage (the great-great-grandmother and each subsequent generation experienced stress during pregnancy) and a transgenerational stress lineage (only the great-great-grandmother was stressed during pregnancy). Urine samples were collected from adult male F4 offspring and analyzed using 1H NMR spectroscopy. The results of variable importance analysis based on random variable combination were used for unsupervised multivariate principal component analysis and hierarchical clustering analysis, as well as metabolite set enrichment analysis (MSEA) and pathway analysis. We identified distinct metabolic profiles associated with the multigenerational and transgenerational stress phenotype, with consistent upregulation of hippurate and downregulation of tyrosine, threonine, and histamine. MSEA and pathway analysis showed that these metabolites are involved in catecholamine biosynthesis, immune responses, and microbial host interactions. The identification of metabolic signatures linked to ancestral programming assists in the discovery of gene targets for future studies of epigenetic regulation in pathogenic processes. Ultimately, this research can lead to biomarker discovery for better prediction and prevention of mental health disorders.

Quantitative in vivo neurochemical profiling in humans: where are we now?

Proton nuclear magnetic resonance spectroscopy of biofluids has become one of the key techniques for metabolic profiling and phenotyping. This technique has been widely used in a number of epidemiological studies and in a variety of health disorders. However, its utilization in brain disorders is limited due to the blood-brain barrier, which not only protects the brain from unwanted substances in the blood, but also substantially limits the potential of finding biomarkers for neurological disorders in serum. This review article focuses on the potential of localized in vivo proton magnetic resonance spectroscopy (¹H-MRS) for non-invasive neurochemical profiling in the human brain. First, methodological aspects of ¹H-MRS (data acquisition, processing and metabolite quantification) that are essential for reliable non-invasive neurochemical profiling are described. Second, the power of ¹H-MRS-based neurochemical profiling is demonstrated using some examples of its application in neuroscience and neurology. Finally, the authors present their vision and propose necessary steps to establish ¹H-MRS as a method suitable for large-scale neurochemical profiling in epidemiological research.

Metabolomics studies identify novel diagnostic and prognostic indicators in patients with alcoholic hepatitis

AIM: To identify plasma analytes using metabolomics that correlate with the diagnosis and severity of liver disease in patients with alcoholic hepatitis (AH).

METHODS: We prospectively recruited patients with cirrhosis from AH (n = 23) and those with cirrhosis with acute decompensation (AD) from etiologies other than alcohol (n = 25). We used mass spectrometry to identify 29 metabolic compounds in plasma samples from fasted subjects. A receiver operating characteristics analysis was performed to assess the utility of biomarkers in distinguishing acute AH from alcoholic cirrhosis. Logistic regression analysis was performed to build a predictive model for AH based on clinical characteristics. A survival analysis was used to construct Kaplan Meier curves evaluating transplant-free survival.

RESULTS: A comparison of model for end-stage liver disease (MELD)-adjusted metabolomics levels between cirrhosis patients who had AD or AH showed that patients with AH had significantly higher levels of betaine, and lower creatinine, phenylalanine, homocitrulline, citrulline, tyrosine, octenoyl-carnitine, and symmetric dimethylarginine. When considering combined levels, betaine and citrulline were highly accurate predictors for differentiation between AH and AD (area under receiver operating characteristics curve = 0.84). The plasma levels of carnitine [0.54 (0.18, 0.91); P = 0.005], homocitrulline [0.66 (0.34, 0.99); P < 0.001] and pentanoyl-carnitine [0.53 (0.16, 0.90); P = 0.007] correlated with MELD scores in patients diagnosed with AH. Increased levels of many biomarkers (carnitine P = 0.005, butyrobetaine P = 0.32, homocitrulline P = 0.002, leucine P = 0.027, valine P = 0.024, phenylalanine P = 0.037, tyrosine P = 0.012, acetyl-carnitine P = 0.006, propionyl-carnitine P = 0.03, butyryl-carnitine P = 0.03, trimethyl-lisine P = 0.034, pentanoyl-carnitine P = 0.03, hexanoyl-carnitine P = 0.026) were associated with increased mortality in patients with AH.

CONCLUSION: Metabolomics plasma analyte levels might be used to diagnose of AH or help predict patient prognoses.

The Role of Metabolomics in Brain Metabolism Research

This special edition of the Journal of Neuroimmune Pharmacology focuses on the leading edge of metabolomics in brain metabolism research. The topics covered include a metabolomic field overview and the challenges in neuroscience metabolomics. The workflow and utility of different analytical platforms to profile complex biological matrices that include biofluids, brain tissue and cells, are shown in several case studies. These studies demonstrate how global and targeted metabolite profiling can be applied to distinguish disease stages and to understand the effects of drug action on the central nervous system (CNS). Finally, we discuss the importance of metabolomics to advance the understanding of brain function that includes ligand-receptor interactions and new insights into the mechanisms of CNS disorders.