The human cerebrospinal fluid metabolome

Influence of sex, age, ethnicity/race, and body mass index on the cerumen volatilome using two data analysis approaches: binary and semiquantitative

Human cerumen analysis is an innovative and non-invasive trend in diagnosing diseases. Recently, new cerumen volatile-based methods using binary (volatile presence/absence) and semiquantitative (volatile intensity) data approaches have shown great potential in detecting biomarkers for cancer, chronic and rare diseases, and xenobiotic exposures. However, to date, the impacts of demographic factors such as body mass index (BMI), sex, age, and ethnicity/race in cerumen data have not been widely described, which can hamper interpretation in biomarker discovery investigations. This study examined the effects of such factors in cerumen, defining the baseline volatile organic metabolites (VOMs) across different physiological groups. Cerumen samples from seventy volunteers were analyzed using headspace/gas chromatography-mass spectrometry (HS/GC-MS) and multivariate statistical analysis using binary and semiquantitative data approaches. In the binary data approach, several VOMs exhibited patterns of high occurrence in some specific demographic groups. However, no pattern of discrimination that could be attributed to demographic factors was observed. In the semiquantitative approach, the relative abundance of cerumen VOMs was more impacted by sex and BMI than age and ethnicity/race. In summary, we describe how cerumen VOM occurrence and abundance are affected by patient phenotype, which can pave the way for more personalized medicine in future cerumen volatile-based methods.

In-Depth Chemical Analysis of the Brain Extracellular Space Using In Vivo Microdialysis with Liquid Chromatography-Tandem Mass Spectrometry

Metabolomic analysis of samples acquired in vivo from the brain extracellular space by microdialysis sampling can provide insights into chemical underpinnings of a given brain state and how it changes over time. Small sample volumes and low physiological concentrations have limited the identification of compounds from this compartment, so at present, we have scant knowledge of its composition. As a result, most in vivo measurements have limited depth of analysis. Here, we describe an approach to (1) identify hundreds of compounds in brain dialysate and (2) routinely detect many of these compounds in 5 μL microdialysis samples to enable deep monitoring of brain chemistry in time-resolved studies. Dialysate samples collected over 12 h were concentrated 10-fold and then analyzed using liquid chromatography with iterative tandem mass spectrometry (LC-MS/MS). Using this approach on dialysate from the rat striatum with both reversed-phase and hydrophilic interaction liquid chromatography yielded 479 unique compound identifications. 60% of the identified compounds could be detected in 5 μL of dialysate without further concentration using a single 20 min LC-MS analysis, showing that once identified, most compounds can be detected using small sample volumes and shorter analysis times compatible with routine in vivo monitoring. To detect more neurochemicals, LC-MS analysis of dialysate derivatized with light and isotopically labeled benzoyl chloride was employed. 872 nondegenerate benzoylated features were detected with this approach, including most small-molecule neurotransmitters and several metabolites involved in dopamine metabolism. This strategy allows deeper annotation of the brain extracellular space than previously possible and provides a launching point for defining the chemistry underlying brain states.

Profiling metabolome of mouse embryonic cerebrospinal fluid following maternal immune activation

The embryonic cerebrospinal fluid (eCSF) plays an essential role in the development of the central nervous system (CNS), influencing processes from neurogenesis to lifelong cognitive functions. An important process affecting eCSF composition is inflammation. Inflammation during development can be studied using the maternal immune activation (MIA) mouse model, which displays altered cytokine eCSF composition and mimics neurodevelopmental disorders including autism spectrum disorder (ASD). The limited nature of eCSF as a biosample restricts its research and has hindered our understanding of the eCSF's role in brain pathologies. Specifically, investigation of the small molecule composition of the eCSF is lacking, leaving this aspect of eCSF composition under-studied. We report here the eCSF metabolome as a resource for investigating developmental neuropathologies from a metabolic perspective. Our reference metabolome includes comprehensive MS1 and MS2 datasets and evaluates two mouse strains (CD-1 and C57Bl/6) and two developmental time points (E12.5 and E14.5). We illustrate the reference metabolome's utility by using untargeted metabolomics to identify eCSF-specific compositional changes following MIA. We uncover MIA-relevant metabolic pathways as differentially abundant in eCSF and validate changes in glucocorticoid and kynurenine pathways through targeted metabolomics. Our resource can guide future studies into the causes of MIA neuropathology and the impact of eCSF composition on brain development.

Towards a better understanding of idiopathic epilepsy through metabolic fingerprinting of cerebrospinal fluid in dogs

Cerebrospinal fluid metabolomics is a promising research technology in the elucidation of nervous system disorders. Therefore, in this work, a cerebrospinal fluid (CSF) metabolomics method using liquid chromatography coupled to mass spectrometry was optimized and validated to cover a wide range of metabolites. An acceptable coefficient of variance regarding instrumental, within-lab and intra-assay precision was found for 95, 70 and 96 of 102 targeted metabolites, together with 1256, 676 and 976 untargeted compounds, respectively. Moreover, approximately 75% of targeted metabolites and 50% of untargeted compounds displayed good linearity across different dilution ranges. Consequently, metabolic alterations in CSF of dogs with idiopathic epilepsy (IE) were studied by comparing CSF of dogs diagnosed with IE (Tier II) to dogs with non-brain related disease. Targeted metabolome analysis revealed higher levels of cortisol, creatinine, glucose, hippuric acid, mannose, pantothenol, and 2-phenylethylamine (P values < 0.05) in CSF of dogs with IE, whereas CSF of dogs with IE showed lower levels of spermidine (P value = 0.02). Untargeted CSF metabolic fingerprints discriminated dogs with IE from dogs with non-brain related disease using Orthogonal Partial Least Squares Discriminant Analysis (R2(Y) = 0.997, Q2(Y) = 0.828), from which norepinephrine was putatively identified as an important discriminative metabolite.

Human Cerebrospinal Fluid Sample Preparation and Annotation for Integrated Lipidomics and Metabolomics Profiling Studies

Cerebrospinal fluid (CSF) is a metabolically diverse biofluid and a key specimen for exploring biochemical changes in neurodegenerative diseases. Detecting lipid species in CSF using mass spectrometry (MS)-based techniques remains challenging because lipids are highly complex in structure, and their concentrations span over a broad dynamic range. This work aimed to develop a robust lipidomics and metabolomics method based on commonly used two-phase extraction systems from human CSF samples. Prioritizing lipid detection, biphasic extraction methods, Folch, Bligh and Dyer (B&D), Matyash, and acidified Folch and B&D (aFolch and aB&D) were compared using 150 μL of human CSF samples for the simultaneous extraction of lipids and metabolites with a wide range of polarity. Multiple chromatographical separation approaches, including reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), and gas chromatography (GC), were utilized to characterize human CSF metabolome. The aB&D method was found as the most reproducible technique (RSD < 15%) for lipid extraction. The aB&D and B&D yielded the highest peak intensities for targeted lipid internal standards and displayed superior extracting power for major endogenous lipid classes. A total of 674 unique metabolites with a wide polarity range were annotated in CSF using, combining RPLC-MS/MS lipidomics (n = 219), HILIC-MS/MS (n = 304), and GC-quadrupole time of flight (QTOF) MS (n = 151). Overall, our findings show that the aB&D extraction method provided suitable lipid coverage, reproducibility, and extraction efficiency for global lipidomics profiling of human CSF samples. In combination with RPLC-MS/MS lipidomics, complementary screening approaches enabled a comprehensive metabolite signature that can be employed in an array of clinical studies.

Can Small Molecules Provide Clues on Disease Progression in Cerebrospinal Fluid from Mild Cognitive Impairment and Alzheimer's Disease Patients?

Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disease, which is currently diagnosed via clinical symptoms and nonspecific biomarkers (such as Aβ1-42, t-Tau, and p-Tau) measured in cerebrospinal fluid (CSF), which alone do not provide sufficient insights into disease progression. In this pilot study, these biomarkers were complemented with small-molecule analysis using non-target high-resolution mass spectrometry coupled with liquid chromatography (LC) on the CSF of three groups: AD, mild cognitive impairment (MCI) due to AD, and a non-demented (ND) control group. An open-source cheminformatics pipeline based on MS-DIAL and patRoon was enhanced using CSF- and AD-specific suspect lists to assist in data interpretation. Chemical Similarity Enrichment Analysis revealed a significant increase of hydroxybutyrates in AD, including 3-hydroxybutanoic acid, which was found at higher levels in AD compared to MCI and ND. Furthermore, a highly sensitive target LC-MS method was used to quantify 35 bile acids (BAs) in the CSF, revealing several statistically significant differences including higher dehydrolithocholic acid levels and decreased conjugated BA levels in AD. This work provides several promising small-molecule hypotheses that could be used to help track the progression of AD in CSF samples.

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Metabolomics of Mouse Embryonic CSF Following Maternal Immune Activation

The cerebrospinal fluid (CSF) serves various roles in the developing central nervous system (CNS), from neurogenesis to lifelong cognitive functions. Changes in CSF composition due to inflammation can impact brain function. We recently identified an abnormal cytokine signature in embryonic CSF (eCSF) following maternal immune activation (MIA), a mouse model of autism spectrum disorder (ASD). We hypothesized that MIA leads to other alterations in eCSF composition and employed untargeted metabolomics to profile changes in the eCSF metabolome in mice after inducing MIA with polyI:C. We report these data here as a resource, include a comprehensive MS1 and MS2 reference dataset, and present additional datasets comparing two mouse strains (CD-1 and C57Bl/6) and two developmental time points (E12.5 and E14.5). Targeted metabolomics further validated changes upon MIA. We show a significant elevation of glucocorticoids and kynurenine pathway related metabolites. Both pathways are relevant for suppressing inflammation or could be informative as disease biomarkers. Our resource should inform future mechanistic studies regarding the etiology of MIA neuropathology and roles and contributions of eCSF metabolites to brain development.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

CSF metabolomics alterations after aneurysmal subarachnoid hemorrhage: what do we know?

PURPOSE

The purpose of this mini review is to describe metabolomics in cerebrospinal fluid (CSF) and its potential in aneurysmal subarachnoid hemorrhage (aSAH). In brain injury, patients' micro dialysis enables detecting biochemical change in brain tissue. Indicators for ischemia were detected such as lactate, pyruvate, glucose, and glutamate. In aSAH patients, the pathophysiology and the factor for poor outcome are not completely understood yet. Routine use of biomarkers in CSF, particularly in aSAH patients, is still lacking.

METHODS

This mini review was performed on the role of metabolomics alterations after aneurysmal subarachnoid hemorrhage.

RESULTS

We identified five clinical studies that addressed metabolomics in patients with aneurysmal subarachnoid hemorrhage.

CONCLUSION

There is increasing evidence suggesting that biomarkers can give insight in the pathogenesis and can serve as an outcome predictor. In this mini review, we present a brief overview of metabolomics profiling in neuroscience and wish to discuss the predictive and therapeutic value in aSAH patients.

Application of metabolomics in diagnostics and differentiation of meningitis: A narrative review with a critical approach to the literature

Due to its high mortality rate associated with various life-threatening sequelae, meningitis poses a vital problem in contemporary medicine. Numerous algorithms, many of which were derived with the aid of artificial intelligence, were brought up in a strive for perfection in predicting the status of sepsis-related survival or exacerbation. This review aims to provide key insights on the contextual utilization of metabolomics. The aim of this the metabolomic approach set of methods can be used to investigate both bacterial and host metabolite sets from both the host and its microbes in several types of specimens - even in one's breath, mainly with use of two methods - Mass Spectrometry (MS) and Nuclear Magnetic Resonance (NMR). Metabolomics, and has been used to elucidate the mechanisms underlying disease development and metabolic identification changes in a wide range of metabolite contents, leading to improved methods of diagnosis, treatment, and prognosis of meningitis. Mass spectrometry (MS) and Nuclear Magnetic Resonance (NMR) are the main analytical platforms used in metabolomics. Its high sensitivity accounts for the usefulness of metabolomics in studies into meningitis, its sequelae, and concomitant comorbidities. Metabolomics approaches are a double-edged sword, due to not only their flexibility, but also - high complexity, as even minor changes in the multi-step methods can have a massive impact on the results. Information on the differential diagnosis of meningitis act as a background in presenting the merits and drawbacks of the use of metabolomics in context of meningeal infections.

Early life high fructose exposure disrupts microglia function and impedes neurodevelopment

Despite the success of fructose as a low-cost food additive, recent epidemiological evidence suggests that high fructose consumption by pregnant mothers or during adolescence is associated with disrupted neurodevelopment 1-7 . An essential step in appropriate mammalian neurodevelopment is the synaptic pruning and elimination of newly-formed neurons by microglia, the central nervous system's (CNS) resident professional phagocyte 8-10 . Whether early life high fructose consumption affects microglia function and if this directly impacts neurodevelopment remains unknown. Here, we show that both offspring born to dams fed a high fructose diet and neonates exposed to high fructose exhibit decreased microglial density, increased uncleared apoptotic cells, and decreased synaptic pruning in vivo . Importantly, deletion of the high affinity fructose transporter SLC2A5 (GLUT5) in neonates completely reversed microglia dysfunction, suggesting that high fructose directly affects neonatal development. Mechanistically, we found that high fructose treatment of both mouse and human microglia suppresses synaptic pruning and phagocytosis capacity which is fully reversed in GLUT5-deficient microglia. Using a combination of in vivo and in vitro nuclear magnetic resonance- and mass spectrometry-based fructose tracing, we found that high fructose drives significant GLUT5-dependent fructose uptake and catabolism, rewiring microglia metabolism towards a hypo-phagocytic state. Importantly, mice exposed to high fructose as neonates exhibited cognitive defects and developed anxiety-like behavior which were rescued in GLUT5-deficient animals. Our findings provide a mechanistic explanation for the epidemiological observation that early life high fructose exposure is associated with increased prevalence of adolescent anxiety disorders.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Cerebrospinal Fluid Metabolomics: Pilot Study of Using Metabolomics to Assess Diet and Metabolic Interventions in Alzheimer's Disease and Mild Cognitive Impairment

Brain glucose hypometabolism is an early sign of Alzheimer's disease (AD), and interventions which offset this deficit, such as ketogenic diets, show promise as AD therapeutics. Conversely, high-fat feeding may exacerbate AD risk. We analyzed the metabolomic profile of cerebrospinal fluid (CSF) in a pilot study of older adults who underwent saline and triglyceride (TG) infusions. Older adults (12 cognitively normal (CN), age 65.3 ± 8.1, and 9 with cognitive impairment (CI), age 70.9 ± 8.6) underwent a 5 h TG or saline infusion on different days using a random crossover design; CSF was collected at the end of infusion. Aqueous metabolites were measured using a targeted mass spectroscopy (MS) platform focusing on 215 metabolites from over 35 different metabolic pathways. Data were analyzed using MetaboAnalyst 4.0 and SAS. Of the 215 targeted metabolites, 99 were detectable in CSF. Only one metabolite significantly differed by treatment: the ketone body 3-hydroxybutyrate (HBA). Post hoc analyses showed that HBA levels were associated with age and markers of metabolic syndrome and demonstrated different correlation patterns for the two treatments. When analyzed by cognitive diagnosis group, TG-induced increases in HBA were over 3 times higher for those with cognitive impairment (change score CN +9.8 uM ± 8.3, CI +32.4 ± 7.4, p = 0.0191). Interestingly, individuals with cognitive impairment had higher HBA levels after TG infusion than those with normal cognition. These results suggest that interventions that increase plasma ketones may lead to higher brain ketones in groups at risk for AD and should be confirmed in larger intervention studies.

Glutaminolysis and the Control of Neural Progenitors in Neocortical Development and Evolution

Multiple types of neural progenitor cells (NPCs) contribute to the development of the neocortex, a brain region responsible for our higher cognitive abilities. Proliferative capacity of NPCs varies among NPC types, developmental stages, and species. The higher proliferative capacity of NPCs in the developing human neocortex is thought to be a major contributing factor why humans have the most expanded neocortex within primates. Recent studies have shed light on the importance of cell metabolism in the neocortical NPC proliferative capacity. Specifically, glutaminolysis, a metabolic pathway that converts glutamine to glutamate and then to α-ketoglutarate, has been shown to play a critical role in human NPCs, both in apical and basal progenitors. In this review, we summarize our current knowledge of NPC metabolism, focusing especially on glutaminolysis, and discuss the role of NPC metabolism in neocortical development, evolution, and neurodevelopmental disorders, providing a broader perspective on a newly emerging research field.

Pocket-Size Wireless Nanoelectrospray Ionization Mass Spectrometry for Metabolic Analysis of Salty Biofluids and Single Cells

Analysis of volume-limited biological samples such as single cells and biofluids not only benefits clinical purposes but also promotes fundamental research in life sciences. Detection of these samples, however, imposes strict requirements on measurement performance because of the minimal volume and concentrated salts of the samples. Herein, we developed a self-cleaning nanoelectrospray ionization device powered by a pocket-size "MasSpec Pointer" (MSP-nanoESI) for metabolic analysis of salty biological samples with limited volume. The self-cleaning effect induced by Maxwell-Wagner electric stress helps with keeping the borosilicate glass capillary tip free from clogging and thus increasing salt tolerance. This device possesses a high sample economy (about 0.1 μL per test) due to its pulsed high voltage supply, sampling method (dipping the nanoESI tip into analyte solution), and contact-free electrospray ionization (ESI) (the electrode does not touch the analyte solution during ESI). High repeatable results could be acquired by the device with a relative standard deviation (RSD) of 1.02% for voltage output and 12.94% for MS signals of caffeine standard. Single MCF-7 cells were metabolically analyzed directly from phosphate buffered saline, and two types of untreated cerebrospinal fluid from hydrocephalus patients were distinguished with 84% accuracy. MSP-nanoESI gets rid of the bulky apparatus and could be held in hand or put into one's pocket for transportation, and it could operate for more than 4 h without recharge. We believe this device will boost scientific research and clinical usage of volume-limited biological samples with high-concentration salts in a low-cost, convenient, and rapid manner.

Multifaceted Analysis of Cerebrospinal Fluid and Serum from Progressive Multiple Sclerosis Patients: Potential Role of Vitamin C and Metal Ion Imbalance in the Divergence of Primary Progressive Multiple Sclerosis and Secondary Progressive Multiple Sclerosis

The progressive forms of multiple sclerosis (MS) primary progressive MS (PPMS) and secondary progressive MS (SPMS) are clinically distinguished by the rate at which symptoms worsen. Little is however known about the pathological mechanisms underlying the differential rate of accumulation of pathological changes. In this study, ¹H NMR spectroscopy was used to measure low-molecular-weight metabolites in paired cerebrospinal fluid (CSF) and serum of PPMS, SPMS, and control patients, as well as to determine lipoproteins and glycoproteins in serum samples. Additionally, neurodegenerative and inflammatory markers, neurofilament light (NFL) and chitinase-3-like protein 1 (CHI3L1), and the concentration of seven metal elements, Mg, Mn, Cu, Fe, Pb, Zn, and Ca, were also determined in both CSF and serum. The results indicate that the pathological changes associated with progressive MS are mainly localized in the central nervous system (CNS). More so, PPMS and SPMS patients with comparable disability status are pathologically similar in relation to neurodegeneration, neuroinflammation, and some metabolites that distinguish them from controls. However, the rapid progression of PPMS from the onset may be driven by a combination of neurotoxicity induced by heavy metals coupled with diminished CNS antioxidative capacity associated with differential intrathecal ascorbate retention and imbalance of Mg and Cu.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for &gt;40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Cerebrospinal Fluid and Plasma Amine Profiles in Interictal Migraine

OBJECTIVE

Impaired amine metabolism has been associated with the etiology of migraine, that is, why patients continue to get migraine attacks. However, evidence from cerebrospinal fluid (CSF) is lacking. Here, we evaluated individual amine levels, global amine profiles, and amine pathways in CSF and plasma of interictal migraine patients and healthy controls.

METHODS

CSF and plasma were sampled between 8:30 am and 1:00 pm, randomly and interchangeably over the time span to avoid any diurnal and seasonal influences, from healthy volunteers and interictal migraine patients, matched for age, sex, and sampling time. The study was approved by the local medical ethics committee. Individual amines (n = 31), global amine profiles, and specific amine pathways were analyzed using a validated ultraperformance liquid chromatography mass spectrometry platform.

RESULTS

We analyzed n = 99 participants with migraine with aura, n = 98 with migraine without aura, and n = 96 healthy volunteers. Univariate analysis with Bonferroni correction indicated that CSF L-arginine was reduced in migraine with aura (10.4%, p < 0.001) and without aura (5.0%, p = 0.03). False discovery rate-corrected CSF L-phenylalanine was also lower in migraine with aura (6.9%, p = 0.011) and without aura (8.1%, p = 0.001), p = 0.088 after Bonferroni correction. Multivariate analysis revealed that CSF global amine profiles were similar for both types of migraine (p = 0.64), but distinct from controls (p = 0.009). Global profile analyses were similar in plasma. The strongest associated pathways with migraine were related to L-arginine metabolism.

INTERPRETATION

L-Arginine was decreased in the CSF (but not in plasma) of interictal patients with migraine with or without aura, and associated pathways were altered. This suggests that dysfunction of nitric oxide signaling is involved in susceptibility to getting migraine attacks. ANN NEUROL 2023.

Global metabolic profiles in a non-human primate model of maternal immune activation: implications for neurodevelopmental disorders

Epidemiological evidence implicates severe maternal infections as risk factors for neurodevelopmental disorders, such as ASD and schizophrenia. Accordingly, animal models mimicking infection during pregnancy, including the maternal immune activation (MIA) model, result in offspring with neurobiological, behavioral, and metabolic phenotypes relevant to human neurodevelopmental disorders. Most of these studies have been performed in rodents. We sought to better understand the molecular signatures characterizing the MIA model in an organism more closely related to humans, rhesus monkeys (Macaca mulatta), by evaluating changes in global metabolic profiles in MIA-exposed offspring. Herein, we present the global metabolome in six peripheral tissues (plasma, cerebrospinal fluid, three regions of intestinal mucosa scrapings, and feces) from 13 MIA and 10 control offspring that were confirmed to display atypical neurodevelopment, elevated immune profiles, and neuropathology. Differences in lipid, amino acid, and nucleotide metabolism discriminated these MIA and control samples, with correlations of specific metabolites to behavior scores as well as to cytokine levels in plasma, intestinal, and brain tissues. We also observed modest changes in fecal and intestinal microbial profiles, and identify differential metabolomic profiles within males and females. These findings support a connection between maternal immune activation and the metabolism, microbiota, and behavioral traits of offspring, and may further the translational applications of the MIA model and the advancement of biomarkers for neurodevelopmental disorders such as ASD or schizophrenia.

Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics

Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.

Metabolomics as a promising tool for improving understanding of multiple sclerosis: A review of recent advances

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system that usually affects young adults. The development of MS is closely related to the changes in the metabolome. Metabolomics studies have been performed using biofluids or tissue samples from rodent models and human patients to reveal metabolic alterations associated with MS progression. This review aims to provide an overview of the applications of metabolomics that for the investigations of the perturbed metabolic pathways in MS and to reveal the potential of metabolomics in personalizing treatments. In conclusion, informative variations of metabolites can be potential biomarkers in advancing our understanding of MS pathogenesis for MS diagnosis, predicting the progression of the disease, and estimating drug effects. Metabolomics will be a promising technique for improving clinical care in MS.

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.

Metabolomic profiling of cerebrospinal fluid reveals an early diagnostic model for central nervous system involvement in acute lymphoblastic leukaemia

The pathogenesis of central nervous system involvement (CNSI) in patients with acute lymphoblastic leukaemia (ALL) remains unclear and a robust biomarker of early diagnosis is missing. An untargeted cerebrospinal fluid (CSF) metabolomics analysis was performed to identify independent risk biomarkers that could diagnose CNSI at the early stage. Thirty-three significantly altered metabolites between ALL patients with and without CNSI were identified, and a CNSI evaluation score (CES) was constructed to predict the risk of CNSI based on three independent risk factors (8-hydroxyguanosine, l-phenylalanine and hypoxanthine). This predictive model could diagnose CNSI with positive prediction values of 95.9% and 85.6% in the training and validation sets respectively. Moreover, CES score increased with the elevated level of central nervous system (CNSI) involvement. In addition, we validated this model by tracking the changes in CES at different stages of CNSI, including before CNSI and during CNSI, and in remission after CNSI. The CES showed good ability to predict the progress of CNSI. Finally, we constructed a nomogram to predict the risk of CNSI in clinical practice, which performed well compared with observed probability. This unique CSF metabolomics study may help us understand the pathogenesis of CNSI, diagnose CNSI at the early stage, and sequentially achieve personalized precision treatment.

Short chain fatty acids: Microbial metabolites for gut-brain axis signalling

The role of the intestinal microbiota as a regulator of gut-brain axis signalling has risen to prominence in recent years. Understanding the relationship between the gut microbiota, the metabolites it produces, and the brain will be critical for the subsequent development of new therapeutic approaches, including the identification of novel psychobiotics. A key focus in this regard have been the short-chain fatty acids (SCFAs) produced by bacterial fermentation of dietary fibre, which include butyrate, acetate, and propionate. Ongoing research is focused on the entry of SCFAs into systemic circulation from the gut lumen, their migration to cerebral circulation and across the blood brain barrier, and their potential to exert acute and chronic effects on brain structure and function. This review aims to discuss our current mechanistic understanding of the direct and indirect influence that SCFAs have on brain function, behaviour and physiology, which will inform future microbiota-targeted interventions for brain disorders.

Predictive Modeling of Alzheimer's and Parkinson's Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid

In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer's disease (AD), Parkinson's disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.

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

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

Development of a translational inflammation panel for the quantification of cerebrospinal fluid Pterin, Tryptophan-Kynurenine and Nitric oxide pathway metabolites

Background

Neuroinflammatory diseases such as encephalitis, meningitis, multiple sclerosis and other neurological diseases with inflammatory components, have demonstrated a need for diagnostic biomarkers to define treatable and reversible neuroinflammation. The development and clinical validation of a targeted translational inflammation panel using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) could provide early diagnosis, rapid treatment and insights into neuroinflammatory mechanisms.

Methods

An inflammation panel of 13 metabolites (neopterin, tryptophan, kynurenine, kynurenic acid, 3-hydroxykynurenine, xanthurenic acid, anthranilic acid, 3-hydroxyanthranilic acid, quinolinic acid, picolinic acid, arginine, citrulline and methylhistamine) was measured based on a simple precipitation and filtration method using minimal CSF volume. The chromatographic separation was achieved using the Acquity UPLC BEH C18 column in combination with a gradient elution within a 12-min time frame. Acute encephalitis (n=10; myelin oligodendrocyte glycoprotein encephalitis n=3, anti-N-methyl-D-aspartate encephalitis n=2, acute disseminated encephalomyelitis n=2, herpes simplex encephalitis n=1, enteroviral encephalitis n=1) and frequency-matched non-inflammatory neurological disease controls (n=10) were examined.

Findings

The method exhibited good sensitivity as the limits of quantification ranged between 0.75 and 3.00 ng mL⁻¹, good linearity (r² > 0.99), acceptable matrix effects (<± 19.4%) and high recoveries (89.8-109.1 %). There were no interferences observed from common endogenous CSF metabolites, no carryover and concordance with well-established clinical methods. The accuracy and precision for all analytes were within tolerances, at <± 15 mean relative error and < 15 % coefficient of variation respectively. All analytes in matrix-matched pooled human CSF calibrators and human CSF extracts were stable for 24 h after extraction and two freeze-thaw cycles.

Interpretation

The method was successfully applied to a pilot study investigating acute brain inflammation case-control groups. Statistical discrimination between encephalitis (n=10) and control groups (n=10) was achieved using orthogonal partial least squares discriminant analysis and heatmap cluster analysis. Statistical analysis of the measured metabolites identified significant alterations of seven metabolites in the tryptophan-kynurenine pathway (tryptophan, kynurenine, kynurenic acid, 3-hydroxykynurenine, anthranilic acid, 3-hydroxyanthranilic acid, quinolinic acid), arginine and neopterin in presence of acute neuroinflammation. Furthermore, elevated ratios of CSF kynurenine/tryptophan ratio, quinolinic acid/kynurenic acid and anthranilic acid/3-hydroxyanthranilic acid provided strong discriminative power for neuroinflammatory conditions. Studies of large groups of neurological diseases are required to explore the sensitivity and specificity of the inflammation panel.

Funding

Financial support for the study was granted by Dale NHMRC Investigator grant APP1193648, Petre Foundation, Cerebral Palsy Alliance and Department of Biochemistry at the Children's Hospital at Westmead.

Small phenolic and indolic gut-dependent molecules in the primate central nervous system: levels vs. bioactivity

INTRODUCTION

A rapidly growing body of data documents associations between disease of the brain and small molecules generated by gut-microbiota (GMB). While such metabolites can affect brain function through a variety of mechanisms, the most direct action would be on the central nervous system (CNS) itself.

OBJECTIVE

Identify indolic and phenolic GMB-dependent small molecules that reach bioactive concentrations in primate CNS.

METHODS

We conducted a PubMed search for metabolomic studies of the primate CNS [brain tissue or cerebrospinal fluid (CSF)] and then selected for phenolic or indolic metabolites that (i) had been quantified, (ii) were GMB-dependent. For each chemical we then conducted a search for studies of bioactivity conducted in vitro in human cells of any kind or in CNS cells from the mouse or rat.

RESULTS

36 metabolites of interests were identified in primate CNS through targeted metabolomics. Quantification was available for 31/36 and in vitro bioactivity for 23/36. The reported CNS range for 8 metabolites 2-(3-hydroxyphenyl)acetic acid, 2-(4-hydroxyphenyl)acetic acid, 3-(3-hydroxyphenyl)propanoic acid, (E)-3-(3,4-dihydroxyphenyl)prop-2-enoic acid [caffeic acid], 3-hydroxybenzoic acid, 4-hydroxybenzoic acid, 2-acetamido-3-(1H-indol-3-yl)propanoic acid [N-acetyltryptophan], 1H-indol-3-yl hydrogen sulfate [indoxyl-3-sulfate] overlapped with a bioactive concentration. However, the number and quality of relevant studies of CNS neurochemistry as well as of bioactivity were highly limited. Structural isomers, multiple metabolites and potential confounders were inadequately considered.

CONCLUSION

The potential direct bioactivity of GMB-derived indolic and phenolic molecules on primate CNS remains largely unknown. The field requires additional strategies to identify and prioritize screening of the most promising small molecules that enter the CNS.

Metabolomics Work Flow and Analytics in Systems Biology

Metabolomics is an omics approach of systems biology that involves the development and assessment of large-scale, comprehensive biochemical analysis tools for metabolites in biological systems. This review describes the metabolomics workflow and provides an overview of current analytic tools used for the quantification of metabolic profiles. We explain analytic tools such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, ionization techniques, and approaches for data extraction and analysis.

Which Role Plays 2-Hydroxybutyric Acid on Insulin Resistance?

Type 2 Diabetes Mellitus (T2D) is defined as a chronic condition caused by beta cell loss and/or dysfunction and insulin resistance (IR). The discovering of novel biomarkers capable of identifying T2D and other metabolic disorders associated with IR in a timely and accurate way is critical. In this review, 2-hydroxybutyric acid (2HB) is presented as that upheaval biomarker with an unexplored potential ahead. Due to the activation of other metabolic pathways during IR, 2HB is synthesized as a coproduct of protein metabolism, being the progression of IR intrinsically related to the increasing of 2HB levels. Hence, the focus of this review will be on the 2HB metabolite and its involvement in glucose homeostasis. A literature review was conducted, which comprised an examination of publications from different databases that had been published over the previous ten years. A total of 19 articles fulfilled the intended set of criteria. The use of 2HB as an early indicator of IR was separated into subjects based on the number of analytes examined simultaneously. In terms of the association between 2HB and IR, it has been established that increasing 2HB levels can predict the development of IR. Thus, 2HB has demonstrated considerable promise as a clinical monitoring molecule, not only as an IR biomarker, but also for disease follow-up throughout IR treatment.

Metabolomics in Autoimmune Diseases: Focus on Rheumatoid Arthritis, Systemic Lupus Erythematous, and Multiple Sclerosis

The metabolomics approach represents the last downstream phenotype and is widely used in clinical studies and drug discovery. In this paper, we outline recent advances in the metabolomics research of autoimmune diseases (ADs) such as rheumatoid arthritis (RA), multiple sclerosis (MuS), and systemic lupus erythematosus (SLE). The newly discovered biomarkers and the metabolic mechanism studies for these ADs are described here. In addition, studies elucidating the metabolic mechanisms underlying these ADs are presented. Metabolomics has the potential to contribute to pharmacotherapy personalization; thus, we summarize the biomarker studies performed to predict the personalization of medicine and drug response.

The Investigation of Metabonomic Pathways of Serum of Iranian Women with Recurrent Miscarriage Using 1H NMR

Purpose

Recurrent miscarriage applies to pregnancy loss expulsion of the fetus within the first 24 weeks of pregnancy. This study is aimed at comparatively investigating the sera of women with RM with those who have no record of miscarriages to identify if there were any metabolite and metabolic pathway differences using ¹H NMR spectroscopy.

Methods

Serum samples were collected from women with RM (n = 30) and those who had no records of RM (n = 30) to obtain metabolomics information. ¹H NMR spectroscopy was carried out on the samples using Carr Purcell Meiboom Gill spin echo; also, Partial Least Squares Discriminant Analysis was performed in MATLAB software using the ProMetab program to obtain the classifying chemical shifts; the metabolites were identified by using the Human Metabolome Database (HMDB) in both the experimental and control groups. The pathway analysis option of the Metaboanalyst.ca website was used to identify the changed metabolic pathways.

Results

The results of the study revealed that 14 metabolites were different in the patients with RM. Moreover, the pathway analysis showed that taurine and hypotaurine metabolism along with phenylalanine, tyrosine, and tryptophan biosynthesis was significantly different in patients with RM.

Conclusion

The present study proposes that any alteration in the above metabolic pathways might lead to metabolic dysfunctions which may result in a higher probability of RM.

Metabolomic Characterization of Cerebrospinal Fluid from Intracranial Bacterial Infection Pediatric Patients: A Pilot Study

Intracranial bacterial infection remains a major cause of morbidity and mortality in neurosurgical cases. Metabolomic profiling of cerebrospinal fluid (CSF) holds great promise to gain insights into the pathogenesis of central neural system (CNS) bacterial infections. In this pilot study, we analyzed the metabolites in CSF of CNS infection patients and controls in a pseudo-targeted manner, aiming at elucidating the metabolic dysregulation in response to postoperative intracranial bacterial infection of pediatric cases. Untargeted analysis uncovered 597 metabolites, and screened out 206 differential metabolites in case of infection. Targeted verification and pathway analysis filtered out the glycolysis, amino acids metabolism and purine metabolism pathways as potential pathological pathways. These perturbed pathways are involved in the infection-induced oxidative stress and immune response. Characterization of the infection-induced metabolic changes can provide robust biomarkers of CNS bacterial infection for clinical diagnosis, novel pathways for pathological investigation, and new targets for treatment.

A Targeted Serum Metabolomics GC-MS Approach Identifies Predictive Blood Biomarkers for Retained Placenta in Holstein Dairy Cows

The retained placenta is a common pathology of dairy cows. It is associated with a significant drop in the dry matter intake, milk yield, and increased susceptibility of dairy cows to metritis, mastitis, and displaced abomasum. The objective of this study was to identify metabolic alterations that precede and are associated with the disease occurrence. Blood samples were collected from 100 dairy cows at −8 and −4 weeks prior to parturition and on the day of retained placenta, and only 16 healthy cows and 6 cows affected by retained placenta were selected to measure serum polar metabolites by a targeted gas chromatography–mass spectroscopy (GC-MS) metabolomics approach. A total of 27 metabolites were identified and quantified in the serum. There were 10, 18, and 17 metabolites identified as being significantly altered during the three time periods studied. However, only nine metabolites were identified as being shared among the three time periods including five amino acids (Asp, Glu, Ser, Thr, and Tyr), one sugar (myo-inositol), phosphoric acid, and urea. The identified metabolites can be used as predictive biomarkers for the risk of retained placenta in dairy cows and might help explain the metabolic processes that occur prior to the incidence of the disease and throw light on the pathomechanisms of the disease.

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.

A Metabolomic Aging Clock Using Human Cerebrospinal Fluid

Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that, in regression models using "-omic" platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these "omic clocks" provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid (CSF) from healthy human subjects and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimer's and Parkinson's disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. However, evidence is not found to support the hypothesis that our models will consistently overpredict the age of these subjects. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small data set (n = 85) and discuss techniques for drift correction, missing data imputation, and regularized regression, which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of CSF.

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.

Improved Automated Quantification Algorithm (AQuA) and Its Application to NMR-Based Metabolomics of EDTA-Containing Plasma

We have recently presented an Automated Quantification Algorithm (AQuA) and demonstrated its utility for rapid and accurate absolute metabolite quantification in ¹H NMR spectra in which positions and line widths of signals were predicted from a constant metabolite spectral library. The AQuA quantifies based on one preselected signal per metabolite and employs library spectra to model interferences from other metabolite signals. However, for some types of spectra, the interspectral deviations of signal positions and line widths can be pronounced; hence, interferences cannot be modeled using a constant spectral library. We here address this issue and present an improved AQuA that handles interspectral deviations. The improved AQuA monitors and characterizes the appearance of specific signals in each spectrum and automatically adjusts the spectral library to model interferences accordingly. The performance of the improved AQuA was tested on a large data set from plasma samples collected using ethylenediaminetetraacetic acid (EDTA) as an anticoagulant (n = 772). These spectra provided a suitable test system for the improved AQuA since EDTA signals (i) vary in intensity, position, and line width between spectra and (ii) interfere with many signals from plasma metabolites targeted for quantification (n = 54). Without the improvement, ca. 20 out of the 54 metabolites would have been overestimated. This included acetylcarnitine and ornithine, which are considered particularly difficult to quantify with ¹H NMR in EDTA-containing plasma. Furthermore, the improved AQuA performed rapidly (<10 s for all spectra). We believe that the improved AQuA provides a basis for automated quantification in other data sets where specific signals show interspectral deviations.

Metabolic Profiling and Quantitative Analysis of Cerebrospinal Fluid Using Gas Chromatography-Mass Spectrometry: Current Methods and Future Perspectives

Cerebrospinal fluid is a key biological fluid for the investigation of new potential biomarkers of central nervous system diseases. Gas chromatography coupled to mass-selective detectors can be used for this investigation at the stages of metabolic profiling and method development. Different sample preparation conditions, including extraction and derivatization, can be applied for the analysis of the most of low-molecular-weight compounds of the cerebrospinal fluid, including metabolites of tryptophan, arachidonic acid, glucose; amino, polyunsaturated fatty and other organic acids; neuroactive steroids; drugs; and toxic metabolites. The literature data analysis revealed the absence of fully validated methods for cerebrospinal fluid analysis, and it presents opportunities for scientists to develop and validate analytical protocols using modern sample preparation techniques, such as microextraction by packed sorbent, dispersive liquid-liquid microextraction, and other potentially applicable techniques.

Cerebrospinal fluid metabolites in tryptophan-kynurenine and nitric oxide pathways: biomarkers for acute neuroinflammation

AIM

To explore the cerebrospinal fluid (CSF) metabolite features in acute neuroinflammatory diseases and identify potential biomarkers to diagnose and monitor neuroinflammation.

METHOD

A cohort of 14 patients (five females, nine males; mean [median] age 7y 9mo [9y], range 6mo-13y) with acute encephalitis (acute disseminated encephalomyelitis n=6, unknown suspected viral encephalitis n=3, enteroviral encephalitis n=2, seronegative autoimmune encephalitis n=2, herpes simplex encephalitis n=1) and age-matched non-inflammatory neurological disease controls (n=14) were investigated using an untargeted metabolomics approach. CSF metabolites were analyzed with liquid chromatography coupled to high resolution mass spectrometry, followed by subsequent multivariate and univariate statistical methods.

RESULTS

A total of 35 metabolites could be discriminated statistically between the groups using supervised orthogonal partial least squares discriminant analysis and analysis of variance. The tryptophan-kynurenine pathway contributed nine key metabolites. There was a statistical increase of kynurenine, quinolinic acid, and anthranilic acid in patients with encephalitis, whereas tryptophan, 3-hydroxyanthrnailic acid, and kynurenic acid were decreased. The nitric oxide pathway contributed four metabolites, with elevated asymmetric dimethylarginine and argininosuccinic acid, and decreased arginine and citrulline in patients with encephalitis. An increase in the CSF kynurenine/tryptophan ratio (p<0.001), anthranilic acid/3-hydroxyanthranilic acid ratio (p<0.001), asymmetric dimethylarginine/arginine ratio (p<0.001), and neopterin (p<0.001) strongly predicted neuroinflammation.

INTERPRETATION

The combination of alterations in the tryptophan-kynurenine pathway, nitric oxide pathway, and neopterin represent a useful potential panel for neuroinflammation and holds potential for clinical translation practice.

WHAT THIS PAPER ADDS

The kynurenine/tryptophan and anthranilic acid/3-hydroxyanthranilic acid ratios hold great potential as biomarkers of neuroinflammation. Elevation of the asymmetric dimethylarginine/arginine ratio in acute brain inflammation shows dysregulation of the nitric oxide pathway.

Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

NMR-based metabolomics of human cerebrospinal fluid identifies signature of brain death

INTRODUCTION

Brain death (BD) is the irreversible cessation of all functions of the entire brain, including the brainstem. Cerebrospinal fluid (CSF) is a biological liquid that circulates in brain and spine. Metabolomics is able to reveal the response of biological systems to diverse factors in a specific moment or condition. Therefore, the study of this neurological condition through metabolic profiling using high resolution Nuclear Magnetic Resonance (NMR) spectroscopy is important for understanding biochemical events.

OBJECTIVES

The aim of the current study is to identify the metabolomics signature of BD using ¹H-NMR spectroscopy in human CSF.

METHODS

¹H-NMR spectroscopy has been employed for metabolomic untargeted analysis in 46 CSF samples: 22 control and 24 with BD. Spectral data were further subjected to multivariate analysis.

RESULTS

Statistically significant multivariate models separated subject's samples with BD from controls and revealed twenty one discriminatory metabolites. The statistical analysis of control and BD subjects using Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) model resulted in R²X of 0.733 and Q² of 0.635. An elevation in the concentration of statistically discriminant metabolites in BD was observed.

CONCLUSION

This study identifies a metabolic signature associated with BD and the most relevant enriched selected metabolic pathways.

Dengue and metabolomics in humans

BACKGROUND

Dengue virus causes dengue fever (DF)disease, transmitted by the mosquito Aedes aegypti. The symptoms could be severe and disable the affected individuals for weeks. The severe form, dengue hemorrhagic fever (DHF), can lead to death if not adequately attended to. Due to global warming, the vector mosquito will advance over new areas and expose more people to this disease over the next decades. Despite the severity, there are no treatments nor efficient vaccines available. Metabolomic studies have shown a new perspective to understand this disease better at a new molecular level.

AIM OF REVIEW

Many published works rely on samples obtained from animal studies. This review will mainly focus on human samples and cell culture experiments to view how the dengue virus affects the metabolomic profile.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review compiles the sample sources, metabolomic techniques used, the detected compounds, and how they behave in different DF stages. This disease causes a significant change in many metabolites, but some results are still conflicting between studies. The results gathered here show that metabolomic approaches prove to be an excellent and viable way to expand knowledge about DF.

Automatic 1D 1H NMR Metabolite Quantification for Bioreactor Monitoring

High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.

The diversity and coexistence of extracellular mitochondria in circulation: A friend or foe of the immune system

The diversity and coexistence of extracellular mitochondria may have a key role in the maintenance of health and progression of disease. Studies report that active mitochondria can be found physiologically outside of cells and circulating in the blood without inducing an inflammatory response. In addition, inactive or harmed mitochondria have been recognized as activators of immune cells, as they play an essential role in diseases characterized by the metabolic deregulation of these cells, such as sepsis. In this review we analyze key aspects regarding the existence of a diversity of extracellular mitochondria, their coexistence in body fluids and their effects on various immune cells. Additionally, we introduce models of how extracellular mitochondria could be interacting to maintain health and affect disease prognosis. Unwrapped mitochondria (freeMitos) can exist as viable, active, inactive or harmed organelles. Mitochondria can also be found wrapped in a membrane (wrappedMitos) that may differ depending on the cell of origin. Mitochondrial fragments can also be present in various body fluids as DAMPs, as mtDNA enclosed in vesicles or as circulating-cell-free mtDNA (ccf-mtDNA). Interestingly, the great quantity of evidence regarding the levels of ccf-mtDNA and their correlation with aging and disease allows for the identification of the diversity, but not type, of extracellular mitochondria. The existence of a diversity of mitochondria and their effects on immune cells opens a new concept in the biomedical field towards the understanding of health, the progression of disease and the development of mitochondria as therapeutic agents.

Extracellular mitochondria in the cerebrospinal fluid (CSF): Potential types and key roles in central nervous system (CNS) physiology and pathogenesis

The cerebrospinal fluid (CSF) has an important role in the transport of nutrients and signaling molecules to the central nervous and immune systems through its circulation along the brain and spinal cord tissues. The mitochondrial activity in the central nervous system (CNS) is essential in processes such as neuroplasticity, neural differentiation and production of neurotransmitters. Interestingly, extracellular and active mitochondria have been detected in the CSF where they act as a biomarker for the outcome of pathologies such as subarachnoid hemorrhage and delayed cerebral ischemia. Additionally, cell-free-circulating mitochondrial DNA (ccf-mtDNA) has been detected in both the CSF of healthy donors and in that of patients with neurodegenerative diseases. Key questions arise as there is still much debate regarding if ccf-mtDNA detected in CSF is associated with a diversity of active or inactive extracellular mitochondria coexisting in distinct pathologies. Additionally, it is of great scientific and medical importance to identify the role of extracellular mitochondria (active and inactive) in the CSF and the difference between them being damage associated molecular patterns (DAMPs) or factors that promote homeostasis. This review analyzes the different types of extracellular mitochondria, methods for their identification and their presence in CSF. Extracellular mitochondria in the CSF could have an important implication in health and disease, which may lead to the development of medical approaches that utilize mitochondria as therapeutic agents.

Metabolomics of Cerebrospinal Fluid from Healthy Subjects Reveal Metabolites Associated with Ageing

To increase our understanding of age-related diseases affecting the central nervous system (CNS) it is important to understand the molecular processes of biological ageing. Metabolomics of cerebrospinal fluid (CSF) is a promising methodology to increase our understanding of naturally occurring processes of ageing of the brain and CNS that could be reflected in CSF. In the present study the CSF metabolomes of healthy subjects aged 30-74 years (n = 23) were studied using liquid chromatography high-resolution mass spectrometry (LC-HRMS), and investigated in relation to age. Ten metabolites were identified with high confidence as significantly associated with ageing, eight with increasing levels with ageing: isoleucine, acetylcarnitine, pipecolate, methionine, glutarylcarnitine, 5-hydroxytryptophan, ketoleucine, and hippurate; and two decreasing with ageing: methylthioadenosine and 3-methyladenine. To our knowledge, this is the first time the CSF metabolomes of healthy subjects are assessed in relation to ageing. The present study contributes to the field of ageing metabolomics by presenting a number of metabolites present in CSF with potential relevance for ageing and the results motivate further studies.