An NMR metabolomic investigation of early metabolic disturbances following traumatic brain injury in a mammalian model

Simulating Cerebral Edema and Ischemia After Traumatic Acute Subdural Hematoma Using Triphasic Swelling Biomechanics

Poor outcome following traumatic acute subdural hematoma (ASDH) is associated with the severity of the primary injury and secondary injury including cerebral edema and ischemia. However, the underlying secondary injury mechanism contributing to elevated intracranial pressure (ICP) and high mortality rate remains unclear. Cerebral edema occurs in response to the exposure of the intracellular fixed charge density (FCD) after cell death, causing ICP to increase. The increased ICP from swollen tissue compresses blood vessels in adjacent tissue, restricting blood flow and leading to ischemic damage. We hypothesize that the mass occupying effect of ASDH exacerbates the ischemic injury, leading to ICP elevation, which is an indicator of high mortality rate in the clinic. Using FEBio (febio.org) and triphasic swelling biomechanics, this study modeled clinically relevant ASDHs and simulated post-traumatic brain swelling and ischemia to predict ICP. Results showed that common convexity ASDH significantly increased ICP by exacerbating ischemic injury, and surgical removal of the convexity ASDH may control ICP by preventing ischemia progression. However, in cases where the primary injury is very severe, surgical intervention alone may not effectively decrease ICP, as the contribution of the hematoma to the elevated ICP is insignificant. In addition, interhemispheric ASDH, located between the cerebral hemispheres, does not significantly exacerbate ischemia, supporting the conservative surgical management generally recommended for interhemispheric ASDH. The joint effect of the mass occupying effect of the blood clot and resulting ischemia contributes to elevated ICP which may increase mortality. Our novel approach may improve the fidelity of predicting patient outcome after motor vehicle crashes and traumatic brain injuries due to other causes.

Acute and sub-acute metabolic change in different brain regions induced by moderate blunt traumatic brain injury

The objective of the study was to observe the effect of moderate closed-head injury on hippocampal, thalamic, and striatal tissue metabolism with time. Closed head injury is responsible for metabolic changes. These changes can be permanent or temporary, depending on the injury's impact. For the experiment, 20 rats were randomly divided into four groups, each containing five animals. Animals were subjected to injury using a modified Marmarou's weight drop device; hippocampal, thalamic, and striatal tissue samples were collected after 1 day, 3 days, and 7 days of injury. NMR spectra were acquired following sample processing. Changes in myo-inositol, creatine, glutamate, succinate, lactate, and N-acetyl aspartic acid in hippocampal tissues were observed at day 3 PI. The tyrosine level in the hippocampus was altered at day 7 PI. While thalamic and striatal tissue samples showed altered levels of branched-chain amino acids and myo-inositol at day 1PI. Taurine, gamma amino butyric acid (GABA), choline, and alpha keto-glutarate levels were found to be significantly altered in striatal tissues at days 1 and 3PI. Acetate and GABA levels were altered in the thalamus on day 1 PI. The choline level in the thalamus was found to alter at all-time points after injury. The alteration in these metabolites may be due to the alteration in their respective pathways. Neurotransmitter and energy metabolism pathways were found to be altered in all three brain regions after TBI. This study may help better understand the effect of injury on the metabolic balance of a specific brain region and recovery.

Whole-Blood Metabolomics of a Rat Model of Repetitive Concussion

Mild traumatic brain injury (mTBI) and repetitive mTBI (RmTBI) are silent epidemics, and so far, there is no objective diagnosis. The severity of the injury is solely based on the Glasgow Coma Score (GCS) scale. Most patients suffer from one or more behavioral abnormalities, such as headache, amnesia, cognitive decline, disturbed sleep pattern, anxiety, depression, and vision abnormalities. Additionally, most neuroimaging modalities are insensitive to capture structural and functional alterations in the brain, leading to inefficient patient management. Metabolomics is one of the established omics technologies to identify metabolic alterations, mostly in biofluids. NMR-based metabolomics provides quantitative metabolic information with non-destructive and minimal sample preparation. We employed whole-blood NMR analysis to identify metabolic markers using a high-field NMR spectrometer (800 MHz). Our approach involves chemical-free sample pretreatment and minimal sample preparation to obtain a robust whole-blood metabolic profile from a rat model of concussion. A single head injury was given to the mTBI group, and three head injuries to the RmTBI group. We found significant alterations in blood metabolites in both mTBI and RmTBI groups compared with the control, such as alanine, branched amino acid (BAA), adenosine diphosphate/adenosine try phosphate (ADP/ATP), creatine, glucose, pyruvate, and glycerphosphocholine (GPC). Choline was significantly altered only in the mTBI group and formate in the RmTBI group compared with the control. These metabolites corroborate previous findings in clinical and preclinical cohorts. Comprehensive whole-blood metabolomics can provide a robust metabolic marker for more accurate diagnosis and treatment intervention for a disease population.

Progress Toward a Multiomic Understanding of Traumatic Brain Injury: A Review

Traumatic brain injury (TBI) is not a single disease state but describes an array of conditions associated with insult or injury to the brain. While some individuals with TBI recover within a few days or months, others present with persistent symptoms that can cause disability, neuropsychological trauma, and even death. Understanding, diagnosing, and treating TBI is extremely complex for many reasons, including the variable biomechanics of head impact, differences in severity and location of injury, and individual patient characteristics. Because of these confounding factors, the development of reliable diagnostics and targeted treatments for brain injury remains elusive. We argue that the development of effective diagnostic and therapeutic strategies for TBI requires a deep understanding of human neurophysiology at the molecular level and that the framework of multiomics may provide some effective solutions for the diagnosis and treatment of this challenging condition. To this end, we present here a comprehensive review of TBI biomarker candidates from across the multiomic disciplines and compare them with known signatures associated with other neuropsychological conditions, including Alzheimer’s disease and Parkinson’s disease. We believe that this integrated view will facilitate a deeper understanding of the pathophysiology of TBI and its potential links to other neurological diseases.

NMR-Based Metabolomics of Rat Hippocampus, Serum, and Urine in Two Models of Autism

Autism spectrum disorders (ASDs) are increasingly diagnosed as developmental disabilities of unclear etiology related to genetic, epigenetic, or environmental factors. The diagnosis of ASD in children is based on the recognition of typical behavioral symptoms, while no reliable biomarkers are available. Rats in whom ASD-like symptoms are due to maternal administration of the teratogenic drugs valproate or thalidomide on critical day 11 of pregnancy are widely used models in autism research. The present studies, aimed at detecting changes in the levels of hydrophilic and hydrophobic metabolites, were carried out on 1-month-old rats belonging to the abovementioned two ASD models and on a control group. Analysis of both hydrophilic and hydrophobic metabolite levels gives a broader view of possible mechanisms involved in the pathogenesis of autism. Hippocampal proton magnetic resonance (MRS) spectroscopy and ex vivo nuclear magnetic resonance (NMR) analysis of serum and urine samples were used. The results were analyzed using advanced statistical tests. Both the results of our present MRS studies of the hippocampus and of the NMR studies of body fluids in both ASD models, particularly from the THAL model, appeared to be consistent with previously published NMR results of hippocampal homogenates and data from the literature on autistic children. We detected symptoms of disturbances in neurotransmitter metabolism, energy deficit, and oxidative stress, as well as intestinal malfunction, which shed light on the pathogenesis of ASD and could be used for diagnostic purposes. These results confirm the usefulness of the noninvasive techniques used in ASD studies.

Temporal profile of serum metabolites and inflammation following closed head injury in rats is associated with HPA axis hyperactivity

INTRODUCTION

Closed head injury (CHI) causes neurological disability along with systemic alterations that can activate neuro-endocrine response through hypothalamic-pituitary-adrenal (HPA) axis activation. A dysregulated HPA axis function can lead to relocation of energy substrates and alteration in metabolic pathways and inflammation at the systemic level.

OBJECTIVES

Assessment of time-dependent changes in serum metabolites and inflammation after both mild and moderate CHI. Along with this, serum corticosterone levels and hypothalamic microglial response were observed.

METHODS

Rats underwent mild and moderate weight-drop injury and their serum and hypothalamus were assessed at acute, sub-acute and chronic timepoints. Changes in serum metabolomics were determined using high resolution NMR spectroscopy. Serum inflammatory cytokine, corticosterone levels and hypothalamic microglia were assessed at all timepoints.

RESULTS

Metabolites including lactate, choline and branched chain amino acids were found as the classifiers that helped distinguish between control and injured rats during acute, sub-acute and chronic timepoints. While, increased αglucose: βglucose and TMAO: choline ratios after acute and sub-acute timepoints of mild injury differentiated from moderate injured rats. The injured rats also showed distinct inflammatory profile where IL-1β and TNF-α levels were upregulated in moderate injured rats while IL-10 levels were downregulated in mild injured rats. Furthermore, injury specific alterations in serum metabolic and immunologic profile were found to be associated with hyperactive HPA axis, with consistent increase in serum corticosterone concentration post injury. The hypothalamic microglia showed a characteristic activated de-ramified cellular morphology in both mild and moderate injured rats.

CONCLUSION

The study suggests that HPA axis hyperactivity along with hypothalamic microglial activation led to temporal changes in the systemic metabolism and inflammation. These time dependent changes in the metabolite profile of rats can further strengthen the knowledge of diagnostic markers and help distinguish injury related outcomes after TBI.

Alterations in Urine Metabolomics Following Sport-Related Concussion: A 1H NMR-Based Analysis

Objective: Millions of sport-related concussions (SRC) occur annually in North America, and current diagnosis of concussion is based largely on clinical evaluations. The objective of this study was to determine whether urinary metabolites are significantly altered post-SRC compared to pre-injury.

Setting: Outpatient sports medicine clinic.

Participants: Twenty-six male youth sport participants.

Methods: Urine was analyzed pre-injury and after SRC by ¹H NMR spectroscopy. Data were analyzed using multivariate statistics, pairwise t-test, and metabolic pathway analysis. Variable importance analysis based on random variable combination (VIAVC) was applied to the entire data set and resulted in a panel of 18 features. Partial least square discriminant analysis was performed exploring the separation between pre-injury and post-SRC groups. Pathway topography analysis was completed to identify biological pathway involvement. Spearman correlations provide support for the relationships between symptom burden and length of return to play and quantifiable metabolic changes in the human urinary metabolome.

Results: Phenylalanine and 3-indoxysulfate were upregulated, while citrate, propylene glycol, 1-methylhistidine, 3-methylhistidine, anserine, and carnosine were downregulated following SRC. A receiver operator curve (ROC) tool constructed using the 18-feature classifier had an area under the curve (AUC) of 0.887. A pairwise t-test found an additional 19 altered features, 7 of which overlapped with the VIAVC analysis. Pathway topology analysis indicated that aminoacyl-tRNA biosynthesis and beta-alanine metabolism were the two pathways most significantly changed. There was a significant positive correlation between post-SRC 2-hydroxybutyrate and the length of return to play (ρ = 0.482, p = 0.02) as well as the number of symptoms and post-SRC lactose (ρ = 0.422, p = 0.036).

Conclusion: We found that ¹H NMR metabolomic urinary analysis can identify a set of metabolites that can correctly classify SRC with an accuracy of 81.6%, suggesting potential for a more objective method of characterizing SRC. Correlations to both the number of symptoms and length of return to play indicated that 2-hydroxybutyrate and lactose may have potential applications as biomarkers for sport-related concussion.

Metabolomics in postmortem cerebrospinal fluid diagnostics: a state-of-the-art method to interpret central nervous system-related pathological processes

In the last few years, quantitative analysis of metabolites in body fluids using LC/MS has become an established method in laboratory medicine and toxicology. By preparing metabolite profiles in biological specimens, we are able to understand pathophysiological mechanisms at the biochemical and thus the functional level. An innovative investigative method, which has not yet been used widely in the forensic context, is to use the clinical application of metabolomics. In a metabolomic analysis of 41 samples of postmortem cerebrospinal fluid (CSF) samples divided into cohorts of four different causes of death, namely, cardiovascular fatalities, isoIated torso trauma, traumatic brain injury, and multi-organ failure, we were able to identify relevant differences in the metabolite profile between these individual groups. According to this preliminary assessment, we assume that information on biochemical processes is not gained by differences in the concentration of individual metabolites in CSF, but by a combination of differently distributed metabolites forming the perspective of a new generation of biomarkers for diagnosing (fatal) TBI and associated neuropathological changes in the CNS using CSF samples.

Machine learning assistive rapid, label-free molecular phenotyping of blood with two-dimensional NMR correlational spectroscopy

Translation of the findings in basic science and clinical research into routine practice is hampered by large variations in human phenotype. Developments in genotyping and phenotyping, such as proteomics and lipidomics, are beginning to address these limitations. In this work, we developed a new methodology for rapid, label-free molecular phenotyping of biological fluids (e.g., blood) by exploiting the recent advances in fast and highly efficient multidimensional inverse Laplace decomposition technique. We demonstrated that using two-dimensional T1-T2 correlational spectroscopy on a single drop of blood (<5 μL), a highly time- and patient-specific 'molecular fingerprint' can be obtained in minutes. Machine learning techniques were introduced to transform the NMR correlational map into user-friendly information for point-of-care disease diagnostic and monitoring. The clinical utilities of this technique were demonstrated through the direct analysis of human whole blood in various physiological (e.g., oxygenated/deoxygenated states) and pathological (e.g., blood oxidation, hemoglobinopathies) conditions.

Metabolomics Analysis of Hippocampus and Cortex in a Rat Model of Traumatic Brain Injury in the Subacute Phase

Traumatic brain injury (TBI) is a complex and serious disease as its multifaceted pathophysiological mechanisms remain vague. The molecular changes of hippocampal and cortical dysfunction in the process of TBI are poorly understood, especially their chronic effects on metabolic profiles. Here we utilize metabolomics-based liquid chromatography coupled with tandem mass spectrometry coupled with bioinformatics method to assess the perturbation of brain metabolism in rat hippocampus and cortex on day 7. The results revealed a signature panel which consisted of 13 identified metabolites to facilitate targeted interventions for subacute TBI discrimination. Purine metabolism change in cortical tissue and taurine and hypotaurine metabolism change in hippocampal tissue were detected. Furthermore, the associations between the metabolite markers and the perturbed pathways were analyzed based on databases: 64 enzyme and one pathway were evolved in TBI. The findings represented significant profiling changes and provided unique metabolite-protein information in a rat model of TBI following the subacute phase. This study may inspire scientists and doctors to further their studies and provide potential therapy targets for clinical interventions.

Serum and urine metabolomics based on UPLC-Q-TOF/MS reveals the antipyretic mechanism of Reduning injection in a rat model

ETHNOPHARMACOLOGICAL RELEVANCE

Reduning injection (RDN), a patented traditional Chinese medicine, has the obvious antipyretic effect and has been widely used in China. Although some previous studies proved its antipyretic effect by animal efficacy experiment or clinical observation, its holistic mechanism in vivo was still unclear.

AIM OF THE STUDY

To comprehensively elucidate the antipyretic mechanism of RDN, the investigation of fever-related potential biomarkers and metabolic pathways in the rat fever model is described in this paper.

MATERIALS AND METHODS

Rat fever model was established by dry yeast. A large number of endogenous metabolites in serum and urine were detected by UPLC-Q-TOF/MS, and fever-related potential biomarkers were screened and identified by multivariate analysis and metabolite databases. The reliability and biological significance of the largely disturbed biomarkers was verified by the metabolic network and the correlation with pharmacodynamic indicators, which contained IL-1β, IL-6, TNF-α, PGE₂ and cAMP.

RESULTS

The established UPLC-Q-TOF/MS analytical method afforded satisfactory results in terms of precision, repeatability and stability, which met the requirements of biological sample determination. A total of 32 potential biomarkers associated with fever were screened and identified, among which 22 species could be adjusted by RDN. The metabolism pathway analysis revealed that valine, leucine and isoleucine biosynthesis, and sphingolipid metabolism were greatly disturbed. Their biomarkers involved L-leucine, L-valine, sphinganine and phytosphingosine, all of which showed a callback trend after RDN was given. These 4 biomarkers had a certain correlation with some known fever-related small molecules and pharmacodynamic indicators, which indicated that the selected fever-related biomarkers had certain reliability and biological significance.

CONCLUSIONS

RDN has a good regulation of the metabolic disorder of endogenous components in dry yeast-induced fever rats. Its antipyretic mechanism is mainly related to the regulation of amino acid, lipid and energy metabolism. The study is useful to better understand and analyze the pharmacodynamic mechanism of complex systems, such as traditional Chinese medicine.

Metabolomics analysis of the hippocampus in a rat model of traumatic brain injury during the acute phase

BACKGROUND

Traumatic brain injury (TBI) has increased in rank among traumatic injuries worldwide. Traumatic brain injury is a serious obstacle given that its complex pathology represents a long-term process. Recently, systems biology strategies such as metabolomics to investigate the multifactorial nature of TBI have facilitated attempts to find biomarkers and probe molecular pathways for its diagnosis and therapy.

METHODS

This study included a group of 20 rats with controlled cortical impact and a group of 20 sham rats. We utilized mNSS tests to investigate neurological metabolic impairments on day 1 and day 3. Furthermore, we applied metabolomics and bioinformatics to determine the metabolic perturbation caused by TBI during the acute period in the hippocampus tissue of controlled cortical impact (CCI) rats. Notably, TBI-protein-metabolite subnetworks identified from a database were assessed for associations between metabolites and TBI by the dysregulation of related enzymes and transporters.

RESULTS

Our results identified 7 and 8 biomarkers on day 1 and day 3, respectively. Additionally, related pathway disorders showed effects on arginine and proline metabolism as well as taurine and hypotaurine metabolism on day 3 in acute TBI. Furthermore, according to metabolite-protein database searches, 25 metabolite-protein pairs were established as causally associated with TBI. Further, bioinformation indicated that these TBI-associated proteins mainly take part in 5'-nucleotidase activity and carboxylic acid transmembrane transport. In addition, interweaved networks were constructed to show that the development of TBI might be affected by metabolite-related proteins and their protein pathways.

CONCLUSION

The overall results show that acute TBI is susceptible to metabolic disorders, and the joint metabolite-protein network analysis provides a favorable prediction of TBI pathogenesis mechanisms in the brain. The signatures in the hippocampus might be promising for the development of biomarkers and pathways relevant to acute TBI and could further guide testable predictions of the underlying mechanism of TBI.

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.

Plasma metabolite profiling reveals potential biomarkers of giant cell tumor of bone by using NMR-based metabolic profiles: A cross-sectional study

Giant cell tumor (GCT) of bone is a locally aggressive bone tumor, which accounts for 4% to 5% of all primary bone tumors. At present, the early diagnosis and postoperative recurrence monitoring are still more difficult due to the lack of effective biomarkers in GCT. As an effective tool, metabolomics has played an essential role in the biomarkers research of many tumors. However, there has been no related study of the metabolomics of GCT up to now. The purpose of this study was to identify several key metabolites as potential biomarkers for GCT by using nuclear magnetic resonance (NMR)-based metabolic profiles.Patients with GCT in our hospital were recruited in this study and their plasma was collected as the research sample, and plasma collected from healthy subjects was considered as the control. NMR was then utilized to detect all samples. Furthermore, based on correlation coefficients, variable importance for the projection values and P values of metabolites obtained from multidimensional statistical analysis, the most critical metabolites were selected as potential biomarkers of GCT. Finally, relevant metabolic pathways involved in these potential biomarkers were determined by database retrieval, based on which the metabolic pathways were plotted.Finally, 28 GCT patients and 26 healthy volunteers agreed to participate in the study. In the multidimensional statistical analysis, all results showed that there was obvious difference between the GCT group and the control group. Ultimately, 18 metabolites with significant differences met the selection condition, which were identified as potential biomarkers. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Human Metabolome Database (HMD) database searching and literature review, these metabolites were found to be mainly correlated with glucose metabolism, fat metabolism, amino acid metabolism, and intestinal microbial metabolism. These metabolic disorders might, in turn, reflect important pathological processes such as proliferation and migration of tumor cells and immune escape in GCT.Our work showed that these potential biomarkers identified appeared to have early diagnostic and relapse monitoring values for GCT, which deserve to be further investigated. In addition, it also suggested that metabolomics profiling approach is a promising screening tool for the diagnosis and relapse monitoring of GCT patients.

Beneficial Effects of Kaempferol after Developmental Traumatic Brain Injury Is through Protection of Mitochondrial Function, Oxidative Metabolism, and Neural Viability

Oxidative energy metabolism is depressed after mild/moderate traumatic brain injury (TBI) during early development, accompanied by behavioral debilitation and secondary neuronal death. A TBI metabolome analysis revealed broad effects with a striking impact on energy metabolism. Our studies on mitochondrial modulators and their effects on brain function have shown that kaempferol, a stimulator of the mitochondrial Ca2+ uniporter channel (mCU), enhanced neural and neurovascular activity in the normal brain and improved stimulus-induced brain activation and behavior after TBI during early development. Because kaempferol enhances mitochondrial Ca2+ uptake and cycling, with protective effects after TBI, we tested the hypothesis that kaempferol treatment during the acute/subacute stage after TBI (0-72 h) acted on mitochondria in improving TBI outcome. Developmental age rats (P31) underwent TBI and were treated with vehicle or kaempferol (1 mg/kg intraperitoneally) in three doses at 1, 24, and 48 h after TBI. Brains were harvested at 72 h and subjected to liquid chromatography mass spectrometric measurements. Decrease in pyruvate and tricarboxylic acid (TCA) cycle flux were observed in the untreated and vehicle-treated group, consistent with previously established energy metabolic decline after TBI. Kaempferol improved TCA cycle flux, maintained mitochondrial functional integrity as observed by decreased acyl carnitines, improved neural viability as evidenced by higher N-acetyl aspartate levels. The positive outcomes of kaempferol on metabolic profile corresponded with improved sensorimotor behavior.

Chronic Dysregulation of Cortical and Subcortical Metabolism After Experimental Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading cause of death and long-term disability worldwide. Although chronic disability is common after TBI, effective treatments remain elusive and chronic TBI pathophysiology is not well understood. Early after TBI, brain metabolism is disrupted due to unregulated ion release, mitochondrial damage, and interruption of molecular trafficking. This metabolic disruption causes at least part of the TBI pathology. However, it is not clear how persistent or pervasive metabolic injury is at later stages of injury. Using untargeted 1H-NMR metabolomics, we examined ex vivo hippocampus, striatum, thalamus, frontal cortex, and brainstem tissue in a rat lateral fluid percussion model of chronic brain injury. We found altered tissue concentrations of metabolites in the hippocampus and thalamus consistent with dysregulation of energy metabolism and excitatory neurotransmission. Furthermore, differential correlation analysis provided additional evidence of metabolic dysregulation, most notably in brainstem and frontal cortex, suggesting that metabolic consequences of injury are persistent and widespread. Interestingly, the patterns of network changes were region-specific. The individual metabolic signatures after injury in different structures of the brain at rest may reflect different compensatory mechanisms engaged to meet variable metabolic demands across brain regions.

Current trends in biomarker discovery and analysis tools for traumatic brain injury

Traumatic brain injury (TBI) affects 1.7 million people in the United States each year, causing lifelong functional deficits in cognition and behavior. The complex pathophysiology of neural injury is a primary barrier to developing sensitive and specific diagnostic tools, which consequentially has a detrimental effect on treatment regimens. Biomarkers of other diseases (e.g. cancer) have provided critical insight into disease emergence and progression that lend to developing powerful clinical tools for intervention. Therefore, the biomarker discovery field has recently focused on TBI and made substantial advancements to characterize markers with promise of transforming TBI patient diagnostics and care. This review focuses on these key advances in neural injury biomarkers discovery, including novel approaches spanning from omics-based approaches to imaging and machine learning as well as the evolution of established techniques.

Scaled traumatic brain injury results in unique metabolomic signatures between gray matter, white matter, and serum in a piglet model

Traumatic brain injury (TBI) is a leading cause of death and long-term disability in the United States. The heterogeneity of the disease coupled with the lack of comprehensive, standardized scales to adequately characterize multiple types of TBI remain to be major challenges facing effective therapeutic development. A systems level approach to TBI diagnosis through the use of metabolomics could lead to a better understanding of cellular changes post-TBI and potential therapeutic targets. In the current study, we utilize a GC-MS untargeted metabolomics approach to demonstrate altered metabolism in response to TBI in a translational pig model, which possesses many neuroanatomical and pathophysiologic similarities to humans. TBI was produced by controlled cortical impact (CCI) in Landrace piglets with impact velocity and depth of depression set to 2m/s;6mm, 4m/s;6mm, 4m/s;12mm, or 4m/s;15mm resulting in graded neural injury. Serum samples were collected pre-TBI, 24 hours post-TBI, and 7 days post-TBI. Partial least squares discriminant analysis (PLS-DA) revealed that each impact parameter uniquely influenced the metabolomic profile after TBI, and gray and white matter responds differently to TBI on the biochemical level with evidence of white matter displaying greater metabolic change. Furthermore, pathway analysis revealed unique metabolic signatures that were dependent on injury severity and brain tissue type. Metabolomic signatures were also detected in serum samples which potentially captures both time after injury and injury severity. These findings provide a platform for the development of a more accurate TBI classification scale based unique metabolomic signatures.

Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances

In this review, we summarize established and recent bioinformatic and statistical methods for the analysis of NMR-based metabolomics. Data analysis of NMR metabolic fingerprints exhibits several challenges, including unwanted biases, high dimensionality, and typically low sample numbers. Common analysis tasks comprise the identification of differential metabolites and the classification of specimens. However, analysis results strongly depend on the preprocessing of the data, and there is no consensus yet on how to remove unwanted biases and experimental variance prior to statistical analysis. Here, we first review established and new preprocessing protocols and illustrate their pros and cons, including different data normalizations and transformations. Second, we give a brief overview of state-of-the-art statistical analysis in NMR-based metabolomics. Finally, we discuss a recent development in statistical data analysis, where data normalization becomes obsolete. This method, called zero-sum regression, builds metabolite signatures whose estimation as well as predictions are independent of prior normalization.

Early behavioral and metabolomic change after mild to moderate traumatic brain injury in the developing brain

Pathophysiology of developmental traumatic brain injury (TBI) is unique due to intrinsic differences in the developing brain. Energy metabolic studies of the brain during early development (P13 to P30) have indicated acute oxidative energy metabolic decreases below 24 h after TBI, which generally recovered by 48 h. However, marked neurodegeneration and altered neural functional connectivity have been observed at later stages into adolescence. As secondary neurodegeneration is most prominent during the first week after TBI in the rat model, we hypothesized that the subacute TBI-metabolome may contain predictive markers of neurodegeneration. Sham and TBI metabolomes were examined at 72 h after a mild to moderate intensity TBI in male Sprague-Dawley rats aged P31. Sensorimotor behavior was assessed at 24, 48 and 72 h after injury, followed by 72-hour postmortem brain removal for metabolomics using Liquid Chromatography/Mass Spectrometry (LC-MS) measurement. Broad TBI-induced metabolomic shifts occurred with relatively higher intensity in the injury-lateralized (ipsilateral) hemisphere. Intensity of metabolomic perturbation correlated with the extent of sensorimotor behavioral deficit. N-acetyl-aspartate (NAA) levels at 72 h after TBI, predicted the extent of neurodegeneration assessed histochemically 7-days post TBI. Results from the multivariate untargeted approach clearly distinguished metabolomic shifts induced by TBI. Several pathways including amino acid, fatty acid and energy metabolism continued to be affected at 72 h after TBI, whose collective effects may determine the overall pathological response after TBI in early development including neurodegeneration.

Classification of samples from NMR-based metabolomics using principal components analysis and partial least squares with uncertainty estimation

Recent progress in metabolomics has been aided by the development of analysis techniques such as gas and liquid chromatography coupled with mass spectrometry (GC-MS and LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. The vast quantities of data produced by these techniques has resulted in an increase in the use of machine algorithms that can aid in the interpretation of this data, such as principal components analysis (PCA) and partial least squares (PLS). Techniques such as these can be applied to biomarker discovery, interlaboratory comparison, and clinical diagnoses. However, there is a lingering question whether the results of these studies can be applied to broader sets of clinical data, usually taken from different data sources. In this work, we address this question by creating a metabolomics workflow that combines a previously published consensus analysis procedure ( https://doi.org/10.1016/j.chemolab.2016.12.010 ) with PCA and PLS models using uncertainty analysis based on bootstrapping. This workflow is applied to NMR data that come from an interlaboratory comparison study using synthetic and biologically obtained metabolite mixtures. The consensus analysis identifies trusted laboratories, whose data are used to create classification models that are more reliable than without. With uncertainty analysis, the reliability of the classification can be rigorously quantified, both for data from the original set and from new data that the model is analyzing. Graphical abstract ᅟ.

Metabolomics and Precision Medicine in Trauma: The State of the Field

Trauma is a major problem in the United States. Mortality from trauma is the number one cause of death under the age of 45 in the United States and is the third leading cause of death for all age groups. There are approximately 200,000 deaths per year due to trauma in the United States at a cost of over $671 billion in combined healthcare costs and lost productivity. Unsurprisingly, trauma accounts for approximately 30% of all life-years lost in the United States. Due to immense development of trauma systems, a large majority of trauma patients survive the injury, but then go on to die from complications arising from the injury. These complications are marked by early and significant metabolic changes accompanied by inflammatory responses that lead to progressive organ failure and, ultimately, death. Early resuscitative and surgical interventions followed by close monitoring to identify and rescue treatment failures are key to successful outcomes. Currently, the adequacy of resuscitation is measured using vital signs, noninvasive methods such as bedside echocardiography or stroke volume variation, and other laboratory endpoints of resuscitation, such as lactate and base deficit. However, these methods may be too crude to understand cellular and subcellular changes that may be occurring in trauma patients. Better diagnostic and therapeutic markers are needed to assess the adequacy of interventions and monitor responses at a cellular and subcellular level and inform clinical decision-making before complications are clinically apparent. The developing field of metabolomics holds great promise in the identification and application of biochemical markers toward the clinical decision-making process.

Discovery of Lipidome Alterations Following Traumatic Brain Injury via High-Resolution Metabolomics

Traumatic brain injury (TBI) can occur across wide segments of the population, presenting in a heterogeneous manner that makes diagnosis inconsistent and management challenging. Biomarkers offer the potential to objectively identify injury status, severity, and phenotype by measuring the relative concentrations of endogenous molecules in readily accessible biofluids. Through a data-driven, discovery approach, novel biomarker candidates for TBI were identified in the serum lipidome of adult male Sprague-Dawley rats in the first week following moderate controlled cortical impact (CCI). Serum samples were analyzed in positive and negative modes by ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). A predictive panel for the classification of injured and uninjured sera samples, consisting of 26 dysregulated species belonging to a variety of lipid classes, was developed with a cross-validated accuracy of 85.3% using omniClassifier software to optimize feature selection. Polyunsaturated fatty acids (PUFAs) and PUFA-containing diacylglycerols were found to be upregulated in sera from injured rats, while changes in sphingolipids and other membrane phospholipids were also observed, many of which map to known secondary injury pathways. Overall, the identified biomarker panel offers viable molecular candidates representing lipids that may readily cross the blood-brain barrier (BBB) and aid in the understanding of TBI pathophysiology.

Plasma metabolomic biomarkers accurately classify acute mild traumatic brain injury from controls

Past and recent attempts at devising objective biomarkers for traumatic brain injury (TBI) in both blood and cerebrospinal fluid have focused on abundance measures of time-dependent proteins. Similar independent determinants would be most welcome in diagnosing the most common form of TBI, mild TBI (mTBI), which remains difficult to define and confirm based solely on clinical criteria. There are currently no consensus diagnostic measures that objectively define individuals as having sustained an acute mTBI. Plasma metabolomic analyses have recently evolved to offer an alternative to proteomic analyses, offering an orthogonal diagnostic measure to what is currently available. The purpose of this study was to determine whether a developed set of metabolomic biomarkers is able to objectively classify college athletes sustaining mTBI from non-injured teammates, within 6 hours of trauma and whether such a biomarker panel could be effectively applied to an independent cohort of TBI and control subjects.

A 6-metabolite panel was developed from biomarkers that had their identities confirmed using tandem mass spectrometry (MS/MS) in our Athlete cohort. These biomarkers were defined at ≤6 hours following mTBI and objectively classified mTBI athletes from teammate controls, and provided similar classification of these groups at the 2, 3, and 7 days post-mTBI. The same 6-metabolite panel, when applied to a separate, independent cohort provided statistically similar results despite major differences between the two cohorts. Our confirmed plasma biomarker panel objectively classifies acute mTBI cases from controls within 6 hours of injury in our two independent cohorts. While encouraged by our initial results, we expect future studies to expand on these initial observations.

Metabolomics reveals the effect of Xuefu Zhuyu Decoction on plasma metabolism in rats with acute traumatic brain injury

Xuefu Zhuyu Decoction (XFZY), an important traditional Chinese herbal formula, has been reported effective on traumatic brain injury (TBI) in rats. However, its cerebral protection mechanism has not been clarified at the metabolic level. This work aims to explore the global metabolic characteristics of XFZY in rats during the acute phase of TBI on days 1 and 3. A plasma metabolomics method based on gas chromatography-mass spectrometry coupled with univariate analysis and multivariate statistical analysis was performed in three groups (Sham, Vehicle, XFZY). Then, a pathway analysis using MetaboAnalyst 3.0 was performed to illustrate the pathways of therapeutic action of XFZY in TBI. XFZY treatment attenuates neurological dysfunction and cortical lesion volume post-injury on day 3, and reverses the plasma metabolite abnormalities (glutamic acid, lactic acid, 3-hydroxybutyric acid, and ribitol, etc.). These differential metabolites are mainly involved in D-glutamine and D-glutamate metabolism, alanine, aspartate and glutamate metabolism, and inositol phosphate metabolism. Our study reveals potential biomarkers and metabolic networks of acute TBI and neuroprotection effects of XFZY, and shows this metabolomics approach with MetaboAnalyst would be a feasible way to systematically study therapeutic effects of XFZY on TBI.

Altered metabolites of the rat hippocampus after mild and moderate traumatic brain injury - a combined in vivo and in vitro 1 H-MRS study

Traumatic brain injury (TBI) has been shown to affect hippocampus-associated learning, memory and higher cognitive functions, which may be a consequence of metabolic alterations. Hippocampus-associated disorders may vary depending on the severity of injury [mild TBI (miTBI) and moderate TBI (moTBI)] and time since injury. The underlying hippocampal metabolic irregularities may provide an insight into the pathological process following TBI. In this study, in vivo and in vitro proton magnetic resonance spectroscopy (¹ H-MRS) data were acquired from the hippocampus region of controls and TBI groups (miTBI and moTBI) at D0 (pre-injury), 4 h, Day 1 and Day 5 post-injury (PI). In vitro MRS results indicated trauma-induced changes in both miTBI and moTBI; however, in vivo MRS showed metabolic alterations in moTBI only. miTBI and moTBI showed elevated levels of osmolytes indicating injury-induced edema. Altered levels of citric acid cycle intermediates, glutamine/glutamate and amino acid metabolism indicated injury-induced aberrant bioenergetics, excitotoxicity and oxidative stress. An overall similar pattern of pathological process was observed in both miTBI and moTBI, with the distinction of depleted N-acetylaspartate levels (indicating neuronal loss) at 4 h and Day 1 and enhanced lactate production (indicating heightened energy depletion leading to the commencement of the anaerobic pathway) at Day 5 in moTBI. To the best of our knowledge, this is the first study to investigate the hippocampus metabolic profile in miTBI and moTBI simultaneously using in vivo and in vitro MRS.

Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints

Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum. Such scaling of the data, however, may affect the selection of biomarkers and the biological interpretation of results in unforeseen ways. Here, we studied how both the outcome of hypothesis tests for differential metabolite concentration and the screening for multivariate metabolite signatures are affected by the choice of scale. To overcome this problem for metabolite signatures and to establish a scale-invariant biomarker discovery algorithm, we extended linear zero-sum regression to the logistic regression framework and showed in two applications to ¹H NMR-based metabolomics data how this approach overcomes the scaling problem. Logistic zero-sum regression is available as an R package as well as a high-performance computing implementation that can be downloaded at https://github.com/rehbergT/zeroSum .

Metabolomics Profiling As a Diagnostic Tool in Severe Traumatic Brain Injury

Traumatic brain injury (TBI) is a complex disease with a multifaceted pathophysiology. Impairment of energy metabolism is a key component of secondary insults. This phenomenon is a consequence of multiple potential mechanisms including diffusion hypoxia, mitochondrial failure, and increased energy needs due to systemic trauma responses, seizures, or spreading depolarization. The degree of disturbance in brain metabolism is affected by treatment interventions and reflected in clinical patient outcome. Hence, monitoring of these secondary events in peripheral blood will provide a window into the pathophysiological course of severe TBI. New methods for assessing perturbation of brain metabolism are needed in order to monitor on-going pathophysiological processes and thus facilitate targeted interventions and predict outcome. Circulating metabolites in peripheral blood may serve as sensitive markers of pathological processes in TBI. The levels of these small molecules in blood are less dependent on the integrity of the blood–brain barrier as compared to protein biomarkers. We have recently characterized a specific metabolic profile in serum that is associated with both initial severity and patient outcome of TBI. We found that two medium-chain fatty acids, octanoic and decanoic acids, as well as several sugar derivatives are significantly associated with the severity of TBI. The top ranking peripheral blood metabolites were also highly correlated with their levels in cerebral microdialyzates. Based on the metabolite profile upon admission, we have been able to develop a model that accurately predicts patient outcome. Moreover, metabolomics profiling improved the performance of the well-established clinical prognostication model. In this review, we discuss metabolomics profiling in patients with severe TBI. We present arguments in support of the need for further development and validation of circulating biomarkers of cerebral metabolism and for their use in assessing patients with severe TBI.

Characterization of the concurrent metabolic changes in brain and plasma during insulin-induced moderate hypoglycemia using 1H NMR spectroscopy in juvenile rats

Treatment of hypoglycemia in children is currently based on plasma glucose measurements. This approach may not ensure neuroprotection since plasma glucose does not reflect the dynamic state of cerebral energy metabolism. To determine whether cerebral metabolic changes during hypoglycemia could be better characterized using plasma metabolomic analysis, insulin-induced acute hypoglycemia was induced in 4-week-old rats. Brain tissue and concurrent plasma samples were collected from hypoglycemic (N=7) and control (N=7) rats after focused microwave fixation to prevent post-mortem metabolic changes. The concentration of 29 metabolites in brain and 34 metabolites in plasma were determined using ¹H NMR spectroscopy at 700MHz and examined using partial least squares-discriminant analysis. The sensitivity of plasma glucose for detecting cerebral energy failure was assessed by determining its relationship to brain phosphocreatine. The brain and plasma metabolite profiles of the hypoglycemia group were distinct from the control group (brain: R²=0.92, Q²=0.31; plasma: R²=0.95, Q²=0.74). Concentration differences in glucose, ketone bodies and amino acids were responsible for the intergroup separation. There was 45% concordance between the brain and plasma metabolite profiles. Brain phosphocreatine correlated with brain glucose (control group: R²=0.86; hypoglycemia group: R²=0.59; p<0.05), but not with plasma glucose. The results confirm that plasma glucose is an insensitive biomarker of cerebral energy changes during hypoglycemia and suggest that a plasma metabolite profile is superior for monitoring cerebral metabolism.

Application of 1H NMR spectroscopy to the metabolic phenotyping of rodent brain extracts: A metabonomic study of gut microbial influence on host brain metabolism

¹H NMR Spectroscopy has been applied to determine the neurochemical profiles of brain extracts from the frontal cortex and hippocampal regions of germ free and normal mice and rats. The results revealed a number of differences between germ free (GF) and conventional (CV) rats or specific pathogen-free (SPF) mice with microbiome-associated metabolic variation found to be both species- and region-dependent. In the mouse, the GF frontal cortex contained lower amounts of creatine, N-acetyl-aspartate (NAA), glycerophosphocholine and lactate, but greater amounts of choline compared to that of specific pathogen free (SPF) mice. In the hippocampus, the GF mice had greater creatine, NAA, lactate and taurine content compared to those of the SPF animals, but lower relative quantities of succinate and an unidentified lipid-related component. The GF rat frontal cortex contained higher relative quantities of lactate, creatine and NAA compared to the CV animals whilst the GF hippocampus was characterized by higher taurine and phosphocholine concentrations and lower quantities of NAA, N-acetylaspartylglutamate and choline compared to the CV animals. Of note is that, in both rat and mouse brain extracts, concentrations of hippocampal taurine were found to be greater in the absence of an established microbiome. The results provide further evidence that brain biochemistry can be influenced by gut microbial status, specifically metabolites involved in energy metabolism demonstrating biochemical dialogue between the microbiome and brain.

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

BACKGROUND

Fibromyalgia syndrome (FMS) is a chronic pain syndrome. A plausible pathogenesis of the disease is uncertain and the pursuit of measurable biomarkers for objective identification of affected individuals is a continuing endeavour in FMS research. Our objective was to perform an explorative metabolomics study (1) to elucidate the global urinary metabolite profile of patients suffering from FMS, and (2) to explore the potential of this metabolite information to augment existing medical practice in diagnosing the disease.

METHODS

We selected patients with a medical history of persistent FMS (n = 18), who described their recent state of the disease through the Fibromyalgia Impact Questionnaire (FIQR) and an in-house clinical questionnaire (IHCQ). Three control groups were used: first-generation family members of the patients (n = 11), age-related individuals without any indications of FMS or related conditions (n = 10), and healthy young (18-22 years) individuals (n = 20). All subjects were female and the biofluid under investigation was urine. Correlation analysis of the FIQR showed the FMS patients represented a well-defined disease group for this metabolomics study. Spectral analyses of urine were conducted using a 500 MHz ¹H nuclear magnetic resonance (NMR) spectrometer; data processing and analyses were performed using Matlab, R, SPSS and SAS software.

RESULTS AND DISCUSSION

Unsupervised and supervised multivariate analyses distinguished all three control groups and the FMS patients, and significant increases in metabolites related to the gut microbiome (hippuric, succinic and lactic acids) were observed. We have developed an algorithm for the diagnosis of FMS consisting of three metabolites - succinic acid, taurine and creatine - that have a good level of diagnostic accuracy (Receiver Operating Characteristic (ROC) analysis - area under the curve 90%) and on the pain and fatigue symptoms for the selected FMS patient group.

CONCLUSION

Our data and comparative analyses indicated an altered metabolic profile of patients with FMS, analytically detectable within their urine. Validation studies may substantiate urinary metabolites to supplement information from medical assessment, tender-point measurements and FIQR questionnaires for an improved objective diagnosis of FMS.

Blast Scaling Parameters: Transitioning from Lung to Skull Base Metrics

Neurotrauma from blast exposure is one of the single most characteristic injuries of modern warfare. Understanding blast traumatic brain injury is critical for developing new treatment options for warfighters and civilians exposed to improvised explosive devices. Unfortunately, the pre-clinical models that are widely utilized to investigate blast exposure are based on archaic lung based parameters developed in the early 20th century. Improvised explosive devices produce a different type of injury paradigm than the typical mortar explosion. Protective equipment for the chest cavity has also improved over the past 100 years. In order to improve treatments, it is imperative to develop models that are based more on skull-based parameters. In this mini-review, we discuss the important anatomical and biochemical features necessary to develop a skull-based model.

Plasma, urine and ligament tissue metabolite profiling reveals potential biomarkers of ankylosing spondylitis using NMR-based metabolic profiles

Background

Ankylosing spondylitis (AS) is an autoimmune rheumatic disease mostly affecting the axial skeleton. Currently, anti-tumour necrosis factor α (anti-TNF-α) represents an effective treatment for AS that may delay the progression of the disease and alleviate the symptoms if the diagnosis can be made early. Unfortunately, effective diagnostic biomarkers for AS are still lacking; therefore, most patients with AS do not receive timely and effective treatment. The intent of this study was to determine several key metabolites as potential biomarkers of AS using metabolomic methods to facilitate the early diagnosis of AS.

Methods

First, we collected samples of plasma, urine, and ligament tissue around the hip joint from AS and control groups. The samples were examined by nuclear magnetic resonance spectrometry, and multivariate data analysis was performed to find metabolites that differed between the groups. Subsequently, according to the correlation coefficients, variable importance for the projection (VIP) and P values of the metabolites obtained in the multivariate data analysis, the most crucial metabolites were selected as potential biomarkers of AS. Finally, metabolic pathways involving the potential biomarkers were determined using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and the metabolic pathway map was drawn.

Results

Forty-four patients with AS agreed to provide plasma and urine samples, and 30 provided ligament tissue samples. An equal number of volunteers were recruited for the control group. Multidimensional statistical analysis suggested significant differences between the patients with AS and control subjects, and the models exhibited good discrimination and predictive ability. A total of 20 different metabolites ultimately met the requirements for potential biomarkers. According to KEGG analysis, these marker metabolites were primarily related to fat metabolism, intestinal microbial metabolism, glucose metabolism and choline metabolism pathways, and they were also probably associated with immune regulation.

Conclusions

Our work demonstrates that the potential biomarkers that were identified appeared to have diagnostic value for AS and deserve to be further investigated. In addition, this work also suggests that the metabolomic profiling approach is a promising screening tool for the diagnosis of patients with AS.

Role of Metabolomics in Traumatic Brain Injury Research

Metabolomics is an important member of the omics community in that it defines which small molecules may be responsible for disease states. This article reviews the essential principles of metabolomics from specimen preparation, chemical analysis, to advanced statistical methods. Metabolomics in traumatic brain injury has so far been underutilized. Future metabolomics-based studies focused on the diagnoses, prognoses, and treatment effects need to be conducted across all types of traumatic brain injury.

The novelty of phytofurans, isofurans, dihomo-isofurans and neurofurans: Discovery, synthesis and potential application

Polyunsaturated fatty acids (PUFA) are oxidized in vivo under oxidative stress through free radical pathway and release cyclic oxygenated metabolites, which are commonly classified as isoprostanes and isofurans. The discovery of isoprostanes goes back twenty-five years compared to fifteen years for isofurans, and great many are discovered. The biosynthesis, the nomenclature, the chemical synthesis of furanoids from α-linolenic acid (ALA, C18:3 n-3), arachidonic acid (AA, C20:4 n-6), adrenic acid (AdA, 22:4 n-6) and docosahexaenoic acid (DHA, 22:6 n-3) as well as their identification and implication in biological systems are highlighted in this review.

A simple and accurate protocol for absolute polar metabolite quantification in cell cultures using quantitative nuclear magnetic resonance

Absolute analyte quantification by nuclear magnetic resonance (NMR) spectroscopy is rarely pursued in metabolomics, even though this would allow researchers to compare results obtained using different techniques. Here we report on a new protocol that permits, after pH-controlled serum protein removal, the sensitive quantification (limit of detection [LOD] = 5-25 μM) of hydrophilic nutrients and metabolites in the extracellular medium of cells in cultures. The method does not require the use of databases and uses PULCON (pulse length-based concentration determination) quantitative NMR to obtain results that are significantly more accurate and reproducible than those obtained by CPMG (Carr-Purcell-Meiboom-Gill) sequence or post-processing filtering approaches. Three practical applications of the method highlight its flexibility under different cell culture conditions. We identified and quantified (i) metabolic differences between genetically engineered human cell lines, (ii) alterations in cellular metabolism induced by differentiation of mouse myoblasts into myotubes, and (iii) metabolic changes caused by activation of neurotransmitter receptors in mouse myoblasts. Thus, the new protocol offers an easily implementable, efficient, and versatile tool for the investigation of cellular metabolism and signal transduction.

Serum Metabolic Profiling Reveals Altered Metabolic Pathways in Patients with Post-traumatic Cognitive Impairments

Cognitive impairment, the leading cause of traumatic brain injury (TBI)-related disability, adversely affects the quality of life of TBI patients, and exacts a personal and economic cost that is difficult to quantify. The underlying pathophysiological mechanism is currently unknown, and an effective treatment of the disease has not yet been identified. This study aimed to advance our understanding of the mechanism of disease pathogenesis; thus, metabolomics based on gas chromatography/mass spectrometry (GC-MS), coupled with multivariate and univariate statistical methods were used to identify potential biomarkers and the associated metabolic pathways of post-TBI cognitive impairment. A biomarker panel consisting of nine serum metabolites (serine, pyroglutamic acid, phenylalanine, galactose, palmitic acid, arachidonic acid, linoleic acid, citric acid, and 2,3,4-trihydroxybutyrate) was identified to be able to discriminate between TBI patients with cognitive impairment, TBI patients without cognitive impairment and healthy controls. Furthermore, associations between these metabolite markers and the metabolism of amino acids, lipids and carbohydrates were identified. In conclusion, our study is the first to identify several serum metabolite markers and investigate the altered metabolic pathway that is associated with post-TBI cognitive impairment. These markers appear to be suitable for further investigation of the disease mechanisms of post-TBI cognitive impairment.

Sleep disruption and the sequelae associated with traumatic brain injury

Sleep disruption, which includes a loss of sleep as well as poor quality fragmented sleep, frequently follows traumatic brain injury (TBI) impacting a large number of patients each year in the United States. Fragmented and/or disrupted sleep can worsen neuropsychiatric, behavioral, and physical symptoms of TBI. Additionally, sleep disruption impairs recovery and can lead to cognitive decline. The most common sleep disruption following TBI is insomnia, which is difficulty staying asleep. The consequences of disrupted sleep following injury range from deranged metabolomics and blood brain barrier compromise to altered neuroplasticity and degeneration. There are several theories for why sleep is necessary (e.g., glymphatic clearance and metabolic regulation) and these may help explain how sleep disruption contributes to degeneration within the brain. Experimental data indicate disrupted sleep allows hyperphosphorylated tau and amyloid β plaques to accumulate. As sleep disruption may act as a cellular stressor, target areas warranting further scientific investigation include the increase in endoplasmic reticulum and oxidative stress following acute periods of sleep deprivation. Potential treatment options for restoring the normal sleep cycle include melatonin derivatives and cognitive behavioral therapy.

Expert consensus document: Mind the gaps—advancing research into short-term and long-term neuropsychological outcomes of youth sports-related concussions

Sports-related concussions and repetitive subconcussive exposure are increasingly recognized as potential dangers to paediatric populations, but much remains unknown about the short-term and long-term consequences of these events, including potential cognitive impairment and risk of later-life dementia. This Expert Consensus Document is the result of a 1-day meeting convened by Safe Kids Worldwide, the Alzheimer's Drug Discovery Foundation, and the Andrews Institute for Orthopaedics and Sports Medicine. The goal is to highlight knowledge gaps and areas of critically needed research in the areas of concussion science, dementia, genetics, diagnostic and prognostic biomarkers, neuroimaging, sports injury surveillance, and information sharing. For each of these areas, we propose clear and achievable paths to improve the understanding, treatment and prevention of youth sports-related concussions.

Pattern recognition and biomarker validation using quantitative1H-NMR-based metabolomics

The collection of global metabolic data and their interpretation (both spectral and biochemical) using modern spectroscopic techniques and appropriate statistical approaches, are known as 'metabolic profiling', 'metabonomics' or 'metabolomics'. This review addresses 1H-nuclear magnetic resonance (NMR)-based metabolomic principles and their application in biomedical science, with special emphasis on their potential in translational research in transplantation, oncology, and drug toxicity or discovery. Various steps in metabolomics analysis are described in order to illustrate the types of biological samples, their respective handling and preparation for 1H-NMR analysis; provide a rationale for using pattern-recognition techniques (spectral database concept) versus quantitative 1H-NMR-based metabolomics (metabolite database concept); and identify necessary technological and logistical future developments that will allow 1H-NMR-based metabolomics to become an established tool in biomedical research and patient care.

¹H-NMR protocol for exometabolome analysis of cultured mammalian cells

(1)H-Nuclear magnetic resonance ((1)H-NMR) spectroscopy is a powerful technique to analyze the composition of complex mixtures based on the particular proton fingerprint of each molecule. Here we describe a protocol for exometabolome analysis of mammalian cells using this technique, including sample preparation, spectra acquisition, and integration. The potential of this technique is exemplified by application to cultures of a Chinese hamster ovary (CHO) cell line. The average error associated to this method is under 3% and the limit of quantification for all metabolites analyzed is below 180 μM.

(1)H NMR-based metabolomics exploring biomarkers in rat cerebrospinal fluid after cerebral ischemia/reperfusion

In our study, metabolomics was used to investigate biochemical changes in the early stages of rats focal cerebral ischemia/reperfusion (I/R) injury. Cerebrospinal fluid (CSF) samples at 0, 0.5, 1, 3, and 6 h of reperfusion (n = 10), based on (1)H NMR spectroscopy and multivariate data analyses, were tested to analyze the changing of metabolites during the early disease process. Partial least squares-discriminant analysis scores plots of the (1)H NMR data revealed clear differences among the experiment groups. Combining the results of the loading plot and t-test, we found that twenty-seven metabolites were changed significantly (p < 0.05) in the CSF samples among the different groups. Among that, the potential biomarkers in CSF of ischemic rats were: acetic acid, 3-hydroxyisovaleric acid, 3-hydroxybutyric acid, choline, l-alanine, creatine, creatinine, glycine, pyruvic acid, glycerol, glutamic acid, d-fructose, l-lactic acid and acetone. These findings help us understand the biochemical metabolite changes in CSF of I/R rats in early stages. What's more, metabolomics may, therefore, have the potential to be developed into a clinically useful diagnostic tool of ischemic brain injury.

Metabolomic profiling in blood from umbilical cords of low birth weight newborns

BACKGROUND

Low birth weight has been linked to an increased risk to develop obesity, type 2 diabetes, and hypertension in adult life, although the mechanisms underlying the association are not well understood. The objective was to determine whether the metabolomic profile of plasma from umbilical cord differs between low and normal birth weight newborns.

METHODS

Fifty healthy pregnant women and their infants were selected. The eligibility criteria were being born at term and having a normal pregnancy. Pairs were grouped according to their birth weight: low birth weight (LBW, birth weight < 10th percentile, n = 20) and control (control, birth weight between the 75th-90th percentiles, n = 30). Nuclear Magnetic Resonance (NMR) was used to generate metabolic fingerprints of umbilical cord plasma samples. Simultaneously, the metabolomic profiles of the mothers were analysed. The resulting data were subjected to chemometric, principal component and partial least squares discriminant analyses.

RESULTS

Umbilical cord plasma from LBW and control newborns displayed a clearly differentiated metabolic profile. Seven metabolites were identified that discriminate the LBW from the control group. LBW newborns had lower levels of choline, proline, glutamine, alanine and glucose than did the control newborns, while plasma levels of phenylalanine and citrulline were higher in LBW newborns (p < 0.05). No significant differences were found between the two groups of mothers.

CONCLUSIONS

Low birth weight newborns display a differential metabolomic profile than those of normal birth weight, a finding not present in the mothers. The meaning and the potential utility of the findings as biomarkers of risk need to be addressed in future studies.

Do β-defensins and other antimicrobial peptides play a role in neuroimmune function and neurodegeneration?

It is widely accepted that the brain responds to mechanical trauma and development of most neurodegenerative diseases with an inflammatory sequelae that was once thought exclusive to systemic immunity. Mostly cationic peptides, such as the β-defensins, originally assigned an antimicrobial function are now recognized as mediators of both innate and adaptive immunity. Herein supporting evidence is presented for the hypothesis that neuropathological changes associated with chronic disease conditions of the CNS involve abnormal expression and regulatory function of specific antimicrobial peptides. It is also proposed that these alterations exacerbate proinflammatory conditions within the brain that ultimately potentiate the neurodegenerative process.

Syndromics: a bioinformatics approach for neurotrauma research

Substantial scientific progress has been made in the past 50 years in delineating many of the biological mechanisms involved in the primary and secondary injuries following trauma to the spinal cord and brain. These advances have highlighted numerous potential therapeutic approaches that may help restore function after injury. Despite these advances, bench-to-bedside translation has remained elusive. Translational testing of novel therapies requires standardized measures of function for comparison across different laboratories, paradigms, and species. Although numerous functional assessments have been developed in animal models, it remains unclear how to best integrate this information to describe the complete translational "syndrome" produced by neurotrauma. The present paper describes a multivariate statistical framework for integrating diverse neurotrauma data and reviews the few papers to date that have taken an information-intensive approach for basic neurotrauma research. We argue that these papers can be described as the seminal works of a new field that we call "syndromics", which aim to apply informatics tools to disease models to characterize the full set of mechanistic inter-relationships from multi-scale data. In the future, centralized databases of raw neurotrauma data will enable better syndromic approaches and aid future translational research, leading to more efficient testing regimens and more clinically relevant findings.

Clustering Based on Periodicity in High-Throughput Time Course Data

Nuclear magnetic resonance (NMR) spectroscopy, traditionally used in analytical chemistry, has recently been introduced to studies of metabolite composition of biological fluids and tissues. Metabolite levels change over time, and providing a tool for better extraction of NMR peaks exhibiting periodic behavior is of interest. We propose a method in which NMR peaks are clustered based on periodic behavior. Periodic regression is used to obtain estimates of the parameter corresponding to period for individual NMR peaks. A mixture model is then used to develop clusters of peaks, taking into account the variability of the regression parameter estimates. Methods are applied to NMR data collected from human blood plasma over a 24-hour period. Simulation studies show that the extra variance component due to the estimation of the parameter estimate should be accounted for in the clustering procedure.