Metabolomics studies in brain tissue: A review

Targeted Chiral Metabolomics of D-Amino Acids: Their Emerging Role as Potential Biomarkers in Neurological Diseases with a Focus on Their Liquid Chromatography-Mass Spectrometry Analysis upon Chiral Derivatization

In neuroscience research, chiral metabolomics is an emerging field, in which D-amino acids play an important role as potential biomarkers for neurological diseases. The targeted chiral analysis of the brain metabolome, employing liquid chromatography (LC) coupled to mass spectrometry (MS), is a pivotal approach for the identification of biomarkers for neurological diseases. This review provides an overview of D-amino acids in neurological diseases and of the state-of-the-art strategies for the enantioselective analysis of chiral amino acids (AAs) in biological samples to investigate their putative role as biomarkers for neurological diseases. Fluctuations in D-amino acids (D-AAs) levels can be related to the pathology of neurological diseases, for example, through their role in the modulation of N-methyl-D-aspartate receptors and neurotransmission. Because of the trace presence of these biomolecules in mammals and the complex nature of biological matrices, highly sensitive and selective analytical methods are essential. Derivatization strategies with chiral reagents are highlighted as critical tools for enhancing detection capabilities. The latest advances in chiral derivatization reactions, coupled to LC-MS/MS analysis, have improved the enantioselective quantification of these AAs and allow the separation of several chiral metabolites in a single analytical run. The enhanced performances of these methods can provide an accurate correlation between specific D-AA profiles and disease states, allowing for a better understanding of neurological diseases and drug effects on the brain.

Extraction Methods for Brain Biopsy NMR Metabolomics: Balancing Metabolite Stability and Protein Precipitation

Background/Objectives: Metabolic profiling of tissue samples via liquid-state nuclear magnetic resonance (NMR) requires the extraction of polar metabolites in a suitable deuterated solvent. Such methods often prioritise metabolite recovery over protein removal due to the relatively low sensitivity of NMR metabolomics and the routine use of methods able to supress residual protein signals. However, residual protein may impact metabolite integrity and the metabolite stability after NMR sample preparation is often overlooked. This study aimed to investigate the effect of residual protein contamination in rodent brain extracts and identify a reproducible extraction method that optimises metabolite recovery while ensuring sample stability. Methods: The performance of acetonitrile/water (50-100% MeCN), methanol/water (50-100% MeOH), and methanol/water/chloroform (MeOH/H2O/CHCl3) were assessed for extraction efficiency, reproducibility, residual protein contamination, and metabolite stability up to eight hours post NMR sample preparation. Results: Aspartate and glutamate deuteration were observed in 50% MeCN, 50% MeOH, and 67% MeOH extractions along with the conversion of N-acetyl aspartate to aspartate and acetate in 50% MeCN and 50% MeOH extractions. Both observations correlated with residual protein contamination and, thus, are a result of inadequate protein precipitation, as confirmed by ultrafiltration. MeOH/H2O/CHCl3 extraction preserved the stability of these metabolites while maintaining good extraction efficiency and reproducibility. Conclusions: Thus, we recommend MeOH/H2O/CHCl3 extraction for untargeted brain NMR metabolic profiling due to its effective protein precipitation and reliable performance. Nonetheless, the performance of detecting metabolites prone to oxidation such as ascorbate and glutathione is not improved by this method.

GREMI: An Explainable Multi-Omics Integration Framework for Enhanced Disease Prediction and Module Identification

Multi-omics integration has demonstrated promising performance in complex disease prediction. However, existing research typically focuses on maximizing prediction accuracy, while often neglecting the essential task of discovering meaningful biomarkers. This issue is particularly important in biomedicine, as molecules often interact rather than function individually to influence disease outcomes. To this end, we propose a two-phase framework named GREMI to assist multi-omics classification and explanation. In the prediction phase, we propose to improve prediction performance by employing a graph attention architecture on sample-wise co-functional networks to incorporate biomolecular interaction information for enhanced feature representation, followed by the integration of a joint-late mixed strategy and the true-class-probability block to adaptively evaluate classification confidence at both feature and omics levels. In the interpretation phase, we propose a multi-view approach to explain disease outcomes from the interaction module perspective, providing a more intuitive understanding and biomedical rationale. We incorporate Monte Carlo tree search (MCTS) to explore local-view subgraphs and pinpoint modules that highly contribute to disease characterization from the global-view. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods in seven different classification tasks, and our model effectively addresses data mutual interference when the number of omics types increases. We further illustrate the functional- and disease-relevance of the identified modules, as well as validate the classification performance of discovered modules using an independent cohort.

Consciousness and Energy Processing in Neural Systems

BACKGROUND

Our understanding of the relationship between neural activity and psychological states has advanced greatly in recent decades. But we are still unable to explain conscious experience in terms of physical processes occurring in our brains.

METHODS

This paper introduces a conceptual framework that may contribute to an explanation. All physical processes entail the transfer, transduction, and transformation of energy between portions of matter as work is performed in material systems. If the production of consciousness in nervous systems is a physical process, then it must entail the same. Here the nervous system, and the brain in particular, is considered as a material system that transfers, transduces, and transforms energy as it performs biophysical work.

CONCLUSIONS

Evidence from neuroscience suggests that conscious experience is produced in the organic matter of nervous systems when they perform biophysical work at classical and quantum scales with a certain level of dynamic complexity or organization. An empirically grounded, falsifiable, and testable hypothesis is offered to explain how energy processing in nervous systems may produce conscious experience at a fundamental physical level.

A single dose of glycogen phosphorylase inhibitor improves cognitive functions of aged mice and affects the concentrations of metabolites in the brain

Inhibition of glycogen phosphorylase (Pyg) - a regulatory enzyme of glycogen phosphorolysis - influences memory formation in rodents. We have previously shown that 2-week intraperitoneal administration of a Pyg inhibitor BAY U6751 stimulated the "rejuvenation" of the hippocampal proteome and dendritic spines morphology and improved cognitive skills of old mice. Given the tedious nature of daily intraperitoneal drug administration, in this study we investigated whether a single dose of BAY U6751 could induce enduring behavioral effects. Obtained results support the efficacy of such treatment in significantly improving the cognitive performance of 20-22-month-old mice. Metabolomic analysis of alterations observed in the hippocampus, cerebellum, and cortex reveal that the inhibition of glycogen phosphorolysis impacts not only glucose metabolism but also various other metabolic processes.

Low-Dose Radiation Induces Alterations in Fatty Acid and Tyrosine Metabolism in the Mouse Hippocampus: Insights from Integrated Multiomics

In recent years, there has been a drastic surge in neurological disorders with sporadic cases contributing more than ever to their cause. Radiation exposure through diagnostic or therapeutic routes often results in neurological injuries that may lead to neurodegenerative pathogenesis. However, the underlying mechanisms regulating the neurological impact of exposure to near-low doses of ionizing radiation are not known. In particular, the neurological changes caused by metabolomic reprogramming have not yet been elucidated. Hence, in the present study, C57BL/6 mice were exposed to a single whole-body X-ray dose of 0.5 Gy, and 14 days post-treatment, the hippocampus was subjected to metabolomic analysis. The hippocampus of the irradiated animals showed significant alterations in 15 metabolites, which aligned with altered tyrosine, phenylalanine, and alpha-linolenic acid metabolism and the biosynthesis of unsaturated fatty acids. Furthermore, a multiomics interaction network comprising metabolomics and RNA sequencing data analysis provided insights into gene-metabolite interactions. Tyrosine metabolism was revealed to be the most altered, which was demonstrated by the interaction of several crucial genes and metabolites. The present study revealed the regulation of low-dose radiation-induced neurotoxicity at the metabolomic level and its implications for the pathogenesis of neurological disorders. The present study also provides novel insights into metabolomic pathways altered following near-low-dose IR exposure and its link with neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease.

Multiscale metabolomics techniques: Insights into neuroscience research

The field of metabolomics examines the overall composition and dynamic patterns of metabolites in living organisms. The primary methods used in metabolomics include liquid chromatography (LC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) analysis. These methods enable the identification and examination of metabolite types and contents within organisms, as well as modifications to metabolic pathways and their connection to the emergence of diseases. Research in metabolomics has extensive value in basic and applied sciences. The field of metabolomics is growing quickly, with the majority of studies concentrating on biomedicine, particularly early disease diagnosis, therapeutic management of human diseases, and mechanistic knowledge of biochemical processes. Multiscale metabolomics is an approach that integrates metabolomics techniques at various scales, including the holistic, tissue, cellular, and organelle scales, to enable more thorough and in-depth studies of metabolic processes in organisms. Multiscale metabolomics can be combined with methods from systems biology and bioinformatics. In recent years, multiscale metabolomics approaches have become increasingly important in neuroscience research due to the nervous system's high metabolic demands. Multiscale metabolomics can offer novel concepts and approaches for the diagnosis, treatment, and development of medication for neurological illnesses in addition to a more thorough understanding of brain metabolism and nervous system function. In this review, we summarize the use of multiscale metabolomics techniques in neuroscience, address the promise and constraints of these techniques, and provide an overview of the metabolome and its applications in neuroscience.

Dysregulation of Lipid Metabolism Serves as A Link Between Alzheimer's and Cardiovascular Disease, As Witnessed in A Cross-Sectional Study

Cardiovascular risk factors and established cardiovascular disease (CVD) increase the risk of suffering dementia of the Alzheimer's type (DAT). Here, we set out to define specific molecular profiles of CVD in patients with DAT to better understand its relationship, to unravel the mechanisms underlying the high risk of developing DAT in CVD patients and to define new markers of early disease. Plasma samples from patients with DAT, with and without CVD, were analyzed through a multiomics approach, with integration of metabolomics and proteomics datasets using the OmicsNet web-based tool. Metabolomics results showed an enrichment in lipids and lipid-like molecules. Similarly, the most significant cluster identified through proteomics was formed by 5 proteins related to lipoprotein and cholesterol metabolism. After integration and functional enrichment, glycerolipid metabolism, fatty acid degradation and sphingolipid metabolism were among the most significant functions. Finally, differential expression of ABCA1 and APOH proteins was verified, in an independent cohort also including controls and patients with CVD alone. Both proteins positively correlated with phospho-Tau (181), a classical hallmark of DAT. Different molecular profiles exist in patients with DAT, with and without CVD, with exacerbated alterations in patients in which DAT and CVD co-exist. This information may help to define biomarkers like ABCA1 and APOH that identify patients with cardiovascular dysfunction that are at high risk of developing DAT. Such markers will allow more personalized interventions to be selected, a further step towards precision medicine for individuals whose molecular profiles indicate a distinct response to the same management strategies.

A new approach towards highly sensitive detection of endogenous N-acetylaspartic acid, N-acetylglutamic acid, and N-acetylaspartylglutamic acid in brain tissues based on strong anion exchange monolith microextraction coupled with UHPLC-MS/MS

A novel in-tube solid-phase microextraction coupled with an ultra-high performance liquid chromatography-mass spectrometry method has been established for simultaneous quantification of three crucial brain biomarkers N-acetylaspartic acid (NAA), N-acetylglutamic acid (NAG), and N-acetylaspartylglutamic acid (NAAG). A polymer monolith with quaternary ammonium as the functional group was designed and exhibited efficient enrichment of target analytes through strong anion exchange interaction. Under the optimized conditions, the proposed method displayed wide linear ranges (0.1-80 nM for NAA and NAG, 0.2-160 nM for NAAG) with good precision (RSDs were lower than 15%) and low limits of detection (0.019-0.052 nM), which is by far the most sensitive approach for NAA, NAG, and NAAG determination. Furthermore, this approach has been applied to measure the target analytes in mouse brain samples, and endogenous NAA, NAG, and NAAG were successfully detected and quantified from only around 5 mg of cerebral cortex, cerebellum, and hippocampus. Compared with existing methods, the newly developed method in the current study provides highest sensitivity and lowest sample consumption for NAA, NAG, and NAAG measurements, which would potentially be utilized in determining and tracking these meaningful brain biomarkers in diseases or treatment processes, benefiting the investigations of pathophysiology and treatment of brain disorders.

Brain areas lipidomics in female transgenic mouse model of Alzheimer's disease

Lipids are the major component of the brain with important structural and functional properties. Lipid disruption could play a relevant role in Alzheimer's disease (AD). Some brain lipidomic studies showed significant differences compared to controls, but few studies have focused on different brain areas related to AD. Furthermore, AD is more prevalent in females, but there is a lack of studies focusing on this sex. This work aims to perform a lipidomic study in selected brain areas (cerebellum, amygdala, hippocampus, entire cortex) from wild-type (WT, n = 10) and APPswe/PS1dE9 transgenic (TG, n = 10) female mice of 5 months of age, as a model of early AD, to identify alterations in lipid composition. A lipidomic mass spectrometry-based method was optimized and applied to brain tissue. As result, some lipids showed statistically significant differences between mice groups in cerebellum (n = 68), amygdala (n = 49), hippocampus (n = 48), and the cortex (n = 22). In addition, some lipids (n = 15) from the glycerolipid, phospholipid, and sphingolipid families were statistically significant in several brain areas simultaneously between WT and TG. A selection of lipid variables was made to develop a multivariate approach to assess their discriminant potential, showing high diagnostic indexes, especially in cerebellum and amygdala (sensitivity 70-100%, sensibility 80-100%).

Quantitative Metabolomic Analysis of the Rat Hippocampus: Effects of Age and of the Development of Alzheimer's Disease-Like Pathology

Background

Alzheimer's disease (AD) is the most common type of dementia in the elderly. Incomplete knowledge about the pathogenesis of this disease determines the absence of medications for the treatment of AD today. Animal models can provide the necessary knowledge to understand the mechanisms of biochemical processes occurring in the body in health and disease.

Objective

To identify the most promising metabolomic predictors and biomarkers reflecting metabolic disorders in the development of AD signs.

Methods

High resolution 1H NMR spectroscopy was used for quantitative metabolomic profiling of the hippocampus of OXYS rats, an animal model of sporadic AD, which demonstrates key characteristics of this disease. Animals were examined during several key periods: 20 days group corresponds to the "preclinical" period preceding the development of AD signs, during their manifestation (3 months), and active progression (18 months). Wistar rats of the same age were used as control.

Results

Ranges of variation and mean concentrations were established for 59 brain metabolites. The main metabolic patterns during aging, which are involved in energy metabolism pathways and metabolic shifts of neurotransmitters, have been established. Of particular note is the significant increase of scyllo-inositol and decrease of hypotaurine in the hippocampus of OXYS rats as compared to Wistars for all studied age groups.

Conclusions

We suggest that the accumulation of scyllo-inositol and the reduction of hypotaurine in the brain, even at an early age, can be considered as predictors and potential biomarkers of the development of AD signs in OXYS rats and, probably, in humans.

Unravelling the brain metabolome: A review of liquid chromatography - mass spectrometry strategies for extracellular brain metabolomics

The analysis of the brain extracellular metabolome is of interest for numerous subdomains within neuroscience. Not only does it provide information about normal physiological functions, it is even more of interest for biomarker discovery and target discovery in disease. The extracellular analysis of the brain is particularly interesting as it provides information about the release of mediators in the brain extracellular fluid to look at cellular signaling and metabolic pathways through the release, diffusion and re-uptake of neurochemicals. In vivo samples are obtained through microdialysis, cerebral open-flow microperfusion or solid-phase microextraction. The analytes of potential interest are typically low in concentration and can have a wide range of physicochemical properties. Liquid chromatography coupled to mass spectrometry has proven its usefulness in brain metabolomics. It allows sensitive and specific analysis of low sample volumes, obtained through different approaches. Several strategies for the analysis of the extracellular fluid have been proposed. The most widely used approaches apply sample derivatization, specific stationary phases and/or hydrophilic interaction liquid chromatography. Miniaturization of these methods allows an even higher sensitivity. The development of chiral metabolomics is indispensable, as it allows to compare the enantiomeric ratio of compounds and provides even more challenges. Some limitations continue to exist for the previously developed methods and the development of new, more sensitive methods remains needed. This review provides an overview of the methods developed for sampling and liquid chromatography-mass spectrometry analysis of the extracellular metabolome.

Iron and Targeted Iron Therapy in Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disease worldwide. β-amyloid plaque (Aβ) deposition and hyperphosphorylated tau, as well as dysregulated energy metabolism in the brain, are key factors in the progression of AD. Many studies have observed abnormal iron accumulation in different regions of the AD brain, which is closely correlated with the clinical symptoms of AD; therefore, understanding the role of brain iron accumulation in the major pathological aspects of AD is critical for its treatment. This review discusses the main mechanisms and recent advances in the involvement of iron in the above pathological processes, including in iron-induced oxidative stress-dependent and non-dependent directions, summarizes the hypothesis that the iron-induced dysregulation of energy metabolism may be an initiating factor for AD, based on the available evidence, and further discusses the therapeutic perspectives of targeting iron.

Lipidomic profiling study on neurobehavior toxicity in zebrafish treated with aflatoxin B1

Mycotoxin aflatoxin B1 (AFB1) has been proven to cause neurotoxicity, but its potential interference with the normal function of brain tissue is not fully defined. As the indispensable role of lipids in maintaining the normal function of brain tissue, the aim of this study is to clarify the effect of AFB1 short-term (7 days) exposure on brain tissue from the perspective of lipid metabolism. In this study, zebrafish were exposed to two concentrations (5, 20 μg/L). Through quantitative analysis of AFB1, the detection of AFB1 in zebrafish brain tissue was discovered for the first time, combined with the changes in zebrafish neurobehavior, the occurrence of brain injury was deduced. Subsequently, 1734 lipids in zebrafish brain tissue were mapped using ion mobility time-of-flight mass spectrometry (UPLC-QTOF-IMS-MS), which has great advantages in lipid detection. Comparative analysis of the abnormal lipid metabolism in zebrafish brain revealed 114 significantly changed lipids, mainly involving two pathways of sphingolipid metabolism and fatty acid degradation. This study discovered the detection of AFB1 in the brain and revealed a potential link between AFB1-induced behavioral abnormalities and lipid metabolism disorders in brain tissue, providing reliable evidence for elucidating the neurotoxicity of AFB1.

Glyphosate-based herbicide (GBH) challenged thermoregulation in lizards (Eremias argus), compensatory warming could mitigate this effect

Chemical pollution and global warming are two major threats to reptiles, and these two factors can interact with each other. Glyphosate have attracted worldwide attention due to their ubiquitous occurrence, yet their impact on reptiles remains unknown. We designed a crossover experiment with different external GBH exposures (control/GBH) x different environmental temperatures (current climate treatment/warmer climate treatment) over 60 days to simulate environmental exposure in the Mongolian Racerunner lizard (Eremias argus). Preferred body temperature and active body temperature data were collected to calculate the accuracy of thermoregulation, while liver detoxification metabolic enzymes, oxidative stress system function, and the non-targeted metabolome of the brain tissue were assessed. Warmer-treated lizards adjusted their physiological levels and behavioral strategies in response to increased ambient temperatures and maintained body temperature homeostasis at moderate thermal perturbations. GBH-treated lizards suffered from oxidative damage to the brain tissue and abnormal histidine metabolism, thus their thermoregulatory accuracy reduced. Interestingly, at elevated ambient temperatures, GBH treatment did not affect on their thermoregulatory, possibly through several temperature-dependent detoxification mechanisms. Importantly, this data suggested that the subtle toxicological effects of GBH may threaten increasingly thermoregulation behavior of E. argus with species-wide repercussions, as climate change and exposure time extension.

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

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

Research progress of ferroptosis in Alzheimer disease: A review

Ferroptosis is an emerging form of programmed cell death triggered by iron-dependent lipid peroxidation and reactive oxygen species (ROS). Alzheimer disease (AD), a neurodegenerative disorder, is characterized by the degeneration of nerve cells. Recent research has indicated a significant association between ferroptosis and AD; however, the precise underlying mechanism remains elusive. It is postulated that ferroptosis may impact the accumulation of iron ions within the body by influencing iron metabolism, amino acid metabolism, and lipid metabolism, ultimately leading to the induction of ferroptosis in nerve cells. This article centers on the attributes and regulatory mechanism of ferroptosis, the correlation between ferroptosis and AD, and the recent advancements in the therapeutic approach of targeting ferroptosis for the treatment of AD. These results suggest that ferroptosis could potentially serve as a pivotal focus in future research on AD.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Toxicological Analysis in Tissues Following Exhumation More Than Two Years after Death (948 Days): A Forensic Perspective in a Fatal Case

Exhumations are performed in accordance with a court order and are crucial instruments in the investigation of death allegations. When a death is thought to be the result of drug misuse, pharmaceutical overdose, or pesticide poisoning, this process may be used on human remains. However, after a protracted postmortem interval (PMI), it might be difficult to detect the cause of death by looking at an exhumed corpse. The following case report reveals problems associated with postmortem drug concentration changes following exhumation more than two years after death. A 31-year-old man was found dead in a prison cell. Onan inspection of the place, two blister packs, one with a tablet and the other empty, were taken and kept by the police officers. The evening before, the deceased would have taken cetirizine and food supplements consisting of carnitine-creatine tablets. No relevant autopsy findings have been observed. The toxicological analysis was performed by gas chromatography coupled to mass spectrometry and was negative for substances of abuse. Proteomic analysis was positive for creatine detection and negative for other drugs (clarithromycin, fenofibrate, and cetirizine). The presented case shows the methods, the findings, and the limitations of toxicological analysis in an exhumation case with a long postmortem interval (PMI).

Systematic review of brain and blood lipidomics in Alzheimer's disease mouse models

Alzheimer's disease (AD) diagnosis is based on invasive and expensive biomarkers. Regarding AD pathophysiological mechanisms, there is evidence of a link between AD and aberrant lipid homeostasis. Alterations in lipid composition have been observed in blood and brain samples, and transgenic mouse models represent a promising approach. Nevertheless, there is great variability among studies in mice for the determination of different types of lipids in targeted and untargeted methods. It could be explained by the different variables (model, age, sex, analytical technique), and experimental conditions used. The aim of this work is to review the studies on lipid alteration in brain tissue and blood samples from AD mouse models, focusing on different experimental parameters. As result, great disparity has been observed among the reviewed studies. Brain studies showed an increase in gangliosides, sphingomyelins, lysophospholipids and monounsaturated fatty acids and a decrease in sulfatides. In contrast, blood studies showed an increase in phosphoglycerides, sterols, diacylglycerols, triacylglycerols and polyunsaturated fatty acids, and a decrease in phospholipids, lysophospholipids and monounsaturated fatty acids. Thus, lipids are closely related to AD, and a consensus on lipidomics studies could be used as a diagnostic tool and providing insight into the mechanisms involved in AD.

Seaweed metabolomics: A review on its nutrients, bioactive compounds and changes in climate change

Seaweed, an important food resource in several Asian countries, contains various metabolites, including sugars, organic acids, and amino acids; however, their content is affected by prevailing environmental conditions. This review discusses seaweed metabolomics, especially the distribution of primary and functional secondary metabolites (e.g., carotenoids, polyphenols) in seaweed. Additionally, the effects of global warming on seaweed metabolite profile changes are discussed. For example, high temperatures can increase amino acid levels in seaweeds. Overall, understanding the effects of global warming on seaweed metabolite profiles can be useful for evaluating the nutritional composition of seaweeds as food. This review provides an overview of recent applications of metabolomics in seaweed research as well as a perspective on the nutrient content and cultivation of seaweeds under climate change scenarios.

Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study

INTRODUCTION

The clinical application of lung cancer detection based on breath test is still challenging due to lack of predictive molecular markers in exhaled breath. This study explored potential lung cancer biomarkers and their related pathways using a typical process for metabolomics investigation.

MATERIAL AND METHODS

Breath samples from 60 lung cancer patients and 176 healthy people were analyzed by GC-MS. The original data were GC-MS peak intensity removing background signal. Differential metabolites were selected after univariate statistical analysis and multivariate statistical analysis based on OPLS-DA and Spearman rank correlation analysis. A multivariate PLS-DA model was established based on differential metabolites for pattern recognition. Subsequently, pathway enrichment analysis was performed on differential metabolites.

RESULTS

The discriminant capability was assessed by ROC curve of whom the average AUC and average accuracy in 100-fold cross validations were 0.871 and 0.787, respectively. Eight potential biomarkers were involved in a total of 18 metabolic pathways. Among them, 11 metabolic pathways have p-value smaller than .1.

DISCUSSION

Some pathways among them are related to risk factors or therapies of lung cancer. However, more of them are dysregulated pathways of lung cancer reported in studies based on genome or transcriptome data.

CONCLUSION

We believe that it opens the possibility of using metabolomics methods to analyze data of exhaled breath and promotes involvement of knowledge dataset to cover more volatile metabolites.

CLINICAL SIGNIFICANCE

Although a series of related research reported diagnostic models with highly sensitive and specific prediction, the clinical application of lung cancer detection based on breath test is still challenging due to disease heterogeneity and lack of predictive molecular markers in exhaled breath. This study may promote the clinical application of this technique which is suitable for large-scale screening thanks to its low-cost and non-invasiveness. As a result, the mortality of lung cancer may be decreased in future.Key messagesIn the present study, 11 pathways involving 8 potential biomarkers were discovered to be dysregulated pathways of lung cancer.We found that it is possible to apply metabolomics methods in analysis of data from breath test, which is meaningful to discover convinced volatile markers with definite pathological and histological significance.

Impact of preanalytical freezing delay time on the stability of metabolites in oral squamous cell carcinoma tissue samples

INTRODUCTION

Metabolite stability is critical for tissue metabolomics. However, changes in metabolites in tissues over time from the operating room to the laboratory remain underexplored.

OBJECTIVES

In this study, we evaluated the effect of postoperative freezing delay time on the stability of metabolites in normal and oral squamous cell carcinoma (OSCC) tissues.

METHODS

Tumor and paired normal tissues from five OSCC patients were collected after surgical resection, and samples was sequentially quenched in liquid nitrogen at 30, 40, 50, 60, 70, 80, 90 and 120 min (80 samples). Untargeted metabolic analysis by liquid chromatography-mass spectrometry/mass spectrometry in positive and negative ion modes was used to identify metabolic changes associated with delayed freezing time. The trends of metabolite changes at 30-120 and 30-60 min of delayed freezing were analyzed.

RESULTS

190 metabolites in 36 chemical classes were detected. After delayed freezing for 120 min, approximately 20% of the metabolites changed significantly in normal and tumor tissues, and differences in the metabolites were found in normal and tumor tissues. After a delay of 60 min, 29 metabolites had changed significantly in normal tissues, and 84 metabolites had changed significantly in tumor tissues. In addition, we constructed three tissue freezing schemes based on the observed variation trends in the metabolites.

CONCLUSION

Delayed freezing of tissue samples has a certain impact on the stability of metabolites. For metabolites with significant changes, we suggest that the freezing time of tissues be reasonably selected according to the freezing schemes and the actual clinical situation.

The gut microbiota affects the social network of honeybees

The gut microbiota influences animal neurodevelopment and behaviour but has not previously been documented to affect group-level properties of social organisms. Here, we use honeybees to probe the effect of the gut microbiota on host social behaviour. We found that the microbiota increased the rate and specialization of head-to-head interactions between bees. Microbiota colonization was associated with higher abundances of one-third of the metabolites detected in the brain, including amino acids with roles in synaptic transmission and brain energetic function. Some of these metabolites were significant predictors of the number of social interactions. Microbiota colonization also affected brain transcriptional processes related to amino acid metabolism and epigenetic modifications in a brain region involved in sensory perception. These results demonstrate that the gut microbiota modulates the emergent colony social network of honeybees and suggest changes in chromatin accessibility and amino acid biosynthesis as underlying processes.

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.

Metabolic and metabolomics insights into dilated cardiomyopathy (DCM)

BACKGROUND

Dilated cardiomyopathy (DCM) is the most common form of heart muscle disease characterized by progressive dilatation and ventricular dysfunction. Metabolomics is an emerging and powerful discipline that provides a global information on the phenotype of mammalian systems via the study of endogenous and exogenous metabolites in cells, tissues and biofluids. These studies aid in the identification of biomarkers to prevent diseases in later life or help to early detect onset of diseases as well as aiding in the elucidation of disease mechanisms.

SUMMARY

Metabolomics provides a unique opportunity to discover biomarkers for DCM. This review demonstrates evidence of metabolite-based biomarkers useful for predicting, diagnosing and monitoring therapeutic interventions of DCM. Key metabolites identified as potential biomarkers for diagnosing DCM include acyl-carnitines, succinic acid, malate, methylhistidine, aspartate, methionine, phenylalanine. In terms of differentiating DCM from ICM, potential biomarkers including 1-pyrroline-2-carboxylate, norvaline, lysophosphatidylinositol (16:0/0:0), phosphatidylglycerol, fatty acid esters of hydroxy fatty acid, and phosphatidylcholine were identified. Acyl-carnitines, isoleucine and linoleic acid and tryptophan were the main biomarkers to monitor treatment response to DCM. Mapping metabolites to metabolic pathways revealed dysregulation of BCAA, glycolysis, tricarboxylic acid cycle and triacylglycerol and pentose phosphate metabolism which have therapeutic potential for DCM. This review shows several limitations including the use of small sample sizes, lack of interpretation of age and sex differences in most studies and the fact that studies have so far been limited to case-control study designs.

KEY MESSAGES

Metabolites have close proximity to disease phenotype. With recent advancements in metabolomics field, potential biomarkers for DCM have been identified based on studies using different biological and metabolomics technologies. However, multi-center studies with larger populations that will lead to validation of these identified biomarkers to enable their clinical translation and utilization are still needed.

Non-Targeted Dried Blood Spot-Based Metabolomics Analysis Showed Rice Bran Supplementation Effects Multiple Metabolic Pathways during Infant Weaning and Growth in Mali

Rice bran contains essential nutrients, antioxidants, and bioactives with anti-inflammatory and diarrheal protective properties important for infants. This 6-month randomized controlled trial investigated the effects of heat-stabilized rice bran supplementation during Malian infant weaning. Fifty healthy 6-month-old infants were randomized to a rice bran intervention (N = 25) or non-intervention control group (N = 25). Intervention infants received dose-escalating rice bran supplementation for 6 months (1–5 g/day). Monthly infant dried blood spot and anthropometric measurements were collected. Dried blood spot metabolite abundances were compared monthly according to diet for six months. Supplementation resulted in favorable weight-for-age and weight-for-length z-score changes. Non-targeted dried blood spot-based metabolomics identified 796 metabolites, of which 33% had significant fold differences between groups (7–12 months). Lipids and amino acids represented 70.6% of the metabolites identified. Rice bran supplementation during infant weaning significantly modulated the metabolites involved in antioxidant defenses and with neuroactive properties including reduced glutathione, glycine, glutamate, cysteinylglycine, tryptophan betaine, and choline. These findings support rice bran as a weaning ingredient to meet infant nutritional requirements and with the potential to reduce oxidative stress and improve cognitive outcomes. This study provides evidence for dried blood spots as a cost-effective tool to detect infant biomarkers of nutritional and metabolic status.

Analysis of the therapeutic effect of Dimu Ningshen (TCM formula) on attention deficit hyperactivity disorder based on gut microbiota and serum metabolomics

Background

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder diagnosed during adolescence and adulthood. Assessment of the long-term risks of the current drugs for ADHD treatment has been insufficient, and little is known concerning the long-term therapeutic effects of psychostimulants. Commercially available traditional Chinese medicine compound oral preparations [e.g., Dimu Ningshen (DMNS)] have been widely used in the clinical treatment of ADHD, but their influence on the interaction between gut microbes and potential metabolomes remains inconclusive.

Methods

We used a series of behavioral experiments to evaluate the behavioral effects of DMNS on adolescent and adult ADHD rats and used 16S rDNA sequencing of gut microbes and nontarget metabolomics to evaluate the potential pathogenesis of ADHD and explore the biological mechanism of DMNS in ADHD treatment.

Results

For the first time, DMNS was shown to reduce the excessive activity of adult and adolescent ADHD rats and improve the attention deficit of adult ADHD rats. DMNS improved the structural composition of the ADHD gut microbiota and reduced the abundance of Ruminococcaceae_NK4A214_group, Ruminococcus_2, and Eubacterium_nodatum_group. Simultaneously, DMNS increased the circulating levels of peripheral monoamine neurotransmitter precursors (e.g., phenylalanine) and reduced the circulating levels of peripheral fatty acid amides (e.g., oleamide). Finally, the changes in the ADHD serum metabolites were strongly correlated with the gut microbiota.

Conclusion

DMNS has a good effect in treating ADHD, and it may exert this effect by regulating the gut microbiota and affecting metabolites in the peripheral circulation.

Metabolic Impact of Anticancer Drugs Pd2Spermine and Cisplatin on the Brain of Healthy Mice

The new palladium agent Pd2Spermine (Spm) has been reported to exhibit promising cytotoxic properties, while potentially circumventing the known disadvantages associated to cisplatin therapeutics, namely acquired resistance and high toxicity. This work presents a nuclear magnetic resonance (NMR) metabolomics study of brain extracts obtained from healthy mice, to assess the metabolic impacts of the new Pd2Spm complex in comparison to that of cisplatin. The proton NMR spectra of both polar and nonpolar brain extracts were analyzed by multivariate and univariate statistics, unveiling several metabolite variations during the time course of exposition to each drug (1-48 h). The distinct time-course dependence of such changes revealed useful information on the drug-induced dynamics of metabolic disturbances and recovery periods, namely regarding amino acids, nucleotides, fatty acids, and membrane precursors and phospholipids. Putative biochemical explanations were proposed, based on existing pharmacokinetics data and previously reported metabolic responses elicited by the same metal complexes in the liver of the same animals. Generally, results suggest a more effective response of brain metabolism towards the possible detrimental effects of Pd2Spm, with more rapid recovery back to metabolites' control levels and, thus, indicating that the palladium drug may exert a more beneficial role than cDDP in relation to brain toxicity.

Metabolic responses in the cortex and hippocampus induced by Il-15rα mutation

Metabolomics showed distinct metabolic phenotypes of the different brain regions related to the IL-15 system, enhancing our understanding of the IL-15 system and its interactions with neuropsychiatric disorders.

The Application of Metabolomics in Forensic Science with Focus on Forensic Toxicology and Time-of-Death Estimation

Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords “forensic” and “metabolomics” were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics’ applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics’ assays.

Changes in metabolomics and lipidomics in brain tissue and their correlations with the gut microbiome after chronic food-derived arsenic exposure in mice

Arsenic can cause neurodegenerative diseases of the brain, but the definite mechanism is still unknown. In this study, to discuss the disturbances on brain metabolome and lipidome under subchronic arsenic exposure, we treated mice with the arsenic-containing feed (concentration of total arsenic = 30 mg/kg) prepared in accordance with the proportion of rice arsenicals for 16 weeks and performed metabolomics and lipidomics studies respectively using UHPLC-Triple-TOF-MS/MS and UHPLC-Q Exactive Focus MS/MS on mice brain. In addition, the distributions of arsenical metabolites along the feed-gut-blood-brain chain were analyzed by ICP-MS and HPLC-ICP-MS, and fecal microbial variations were investigated by 16 s sequencing. The data showed that although only a tiny amount of arsenic (DMA=0.101 mg/kg, uAs=0.071 mg/kg) enters the brain through the blood-brain barrier, there were significant changes in brain metabolism, including 118 metabolites and 17 lipids. These different metabolites were involved in 30 distinct pathways, including glycometabolism, and metabolisms of lipid, nucleic acid, and amino acid were previously reported to be correlated with neurodegenerative diseases. Additionally, these different metabolites were significantly correlated with 12 gut bacterial OTUs, among which Lachnospiraceae, Muribaculaceae, Ruminococcaceae, and Erysipelotrichaceae were also previously reported to be related to the distortion of metabolism, indicating that the disturbance of metabolism in the brain may be associated with the disturbance of gut microbes induced by arsenic. Thus, the current study demonstrated that the brain metabolome and lipidome were significantly disturbed under subchronic arsenic exposure, and the disturbances also significantly correlated with some gut microbiome and may be associated with neurodegenerative diseases. Although preliminary, the results shed some light on the pathophysiology of arsenic-caused neurodegenerative diseases.

Alterations in Glycerolipid and Fatty Acid Metabolic Pathways in Alzheimer's Disease Identified by Urinary Metabolic Profiling: A Pilot Study

An easily accessible and non-invasive biomarker for the early detection of Alzheimer's disease (AD) is needed. Evidence suggests that metabolic dysfunction underlies the pathophysiology of AD. While urine is a non-invasively collectable biofluid and a good source for metabolomics analysis, it is not yet widely used for this purpose. This small-scale pilot study aimed to examine whether the metabolic profile of urine from AD patients reflects the metabolic dysfunction reported to underlie AD pathology, and to identify metabolites that could distinguish AD patients from cognitively healthy controls. Spot urine of 18 AD patients (AD group) and 18 age- and sex-matched, cognitively normal controls (control group) were analyzed by mass spectrometry (MS). Capillary electrophoresis time-of-flight MS and liquid chromatography–Fourier transform MS were used to cover a larger range of molecules with ionic as well as lipid characteristics. A total of 304 ionic molecules and 81 lipid compounds of 12 lipid classes were identified. Of these, 26 molecules showed significantly different relative concentrations between the AD and control groups (Wilcoxon's rank-sum test). Moreover, orthogonal partial least-squares discriminant analysis revealed significant discrimination between the two groups. Pathway searches using the KEGG database, and pathway enrichment and topology analysis using Metaboanalyst software, suggested alterations in molecules relevant to pathways of glycerolipid and glycerophospholipid metabolism, thermogenesis, and caffeine metabolism in AD patients. Further studies of urinary metabolites will contribute to the early detection of AD and understanding of its pathogenesis.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

New Advances in Tissue Metabolomics: A Review

Metabolomics offers a hypothesis-generating approach for biomarker discovery in clinical medicine while also providing better understanding of the underlying mechanisms of chronic diseases. Clinical metabolomic studies largely rely on human biofluids (e.g., plasma, urine) as a more convenient specimen type for investigation. However, biofluids are non-organ specific reflecting complex biochemical processes throughout the body, which may complicate biochemical interpretations. For these reasons, tissue metabolomic studies enable deeper insights into aberrant metabolism occurring at the direct site of disease pathogenesis. This review highlights new advances in metabolomics for ex vivo analysis, as well as in situ imaging of tissue specimens, including diverse tissue types from animal models and human participants. Moreover, we discuss key pre-analytical and post-analytical challenges in tissue metabolomics for robust biomarker discovery with a focus on new methodological advances introduced over the past six years, including innovative clinical applications for improved screening, diagnostic testing, and therapeutic interventions for cancer.

Metabolomic Approaches to Investigate the Effect of Metformin: An Overview

Metformin is the first-line antidiabetic drug that is widely used in the treatment of type 2 diabetes mellitus (T2DM). Even though the various therapeutic potential of metformin treatment has been reported, as well as the improvement of insulin sensitivity and glucose homeostasis, the mechanisms underlying those benefits are still not fully understood. In order to explain the beneficial effects on metformin treatment, various metabolomics analyses have been applied to investigate the metabolic alterations in response to metformin treatment, and significant systemic metabolome changes were observed in biofluid, tissues, and cells. In this review, we compare the latest metabolomic research including clinical trials, animal models, and in vitro studies comprehensively to understand the overall changes of metabolome on metformin treatment.

Fast determination of 16 circulating neurotransmitters and their metabolites in plasma samples of spontaneously hypertensive rats intervened with five different Uncaria

This study aimed to establish a sensitive, reproducible, and rapid liquid chromatography method with tandem mass spectrometry detection to perform simultaneous quantitative analysis of 16 neurotransmitters and their metabolites in rat plasma, including levodopa, dopamine, norepinephrine, epinephrine, L-tryptophan, kynurenic acid, serotonin, melatonin, choline, acetylcholine, histamine, phenylethylamine, as well as excitatory (L-glutamic acid and L-aspartic acid) and inhibitory (γ-aminobutyric acid and L-glycine) neurotransmitters. These analytes were measured by ultra-high performance chromatography coupled with triple quadrupole mass spectrometry using a hydrophilic interaction chromatographic column (ethylene-bridged hybrid amide column). The internal standards of stable isotope labeling were used to improve the reliability of the results. Our method provided high linearity for all neurotransmitters (for all coefficients measured > 0.99), with inter- and intra-day accuracy from -14.82% to 17.49% and precision was between 0.89% and 17.70%. The method was subsequently verified in an animal study, where the intervention of five different Uncarias, the traditional Chinese medicine with hypotensive effects, was applied to the spontaneously hypertensive rats (SHRs). SHRs showed dysregulated plasma kynurenic acid, acetylcholine, and norepinephrine levels, and these neuroactive analytes were significantly restored by Uncaria treatment compared with the model group (SHR group). Compared with captopril, included as a positive control for its hypotensive effect, Uncaria had more effects on perturbing the levels of plasma neurotransmitters, which might indicate Uncaria's potential in treating symptoms related to the nervous system. These results suggested that the changes in the neurotransmitters and their metabolites in plasma may be related to the pathogenesis of hypertension. It also provided valuable information about the action mechanisms of Uncaria on its hypotensive effects.

Glucose metabolic crosstalk and regulation in brain function and diseases

Brain glucose metabolism, including glycolysis, the pentose phosphate pathway, and glycogen turnover, produces ATP for energetic support and provides the precursors for the synthesis of biological macromolecules. Although glucose metabolism in neurons and astrocytes has been extensively studied, the glucose metabolism of microglia and oligodendrocytes, and their interactions with neurons and astrocytes, remain critical to understand brain function. Brain regions with heterogeneous cell composition and cell-type-specific profiles of glucose metabolism suggest that metabolic networks within the brain are complex. Signal transduction proteins including those in the Wnt, GSK-3β, PI3K-AKT, and AMPK pathways are involved in regulating these networks. Additionally, glycolytic enzymes and metabolites, such as hexokinase 2, acetyl-CoA, and enolase 2, are implicated in the modulation of cellular function, microglial activation, glycation, and acetylation of biomolecules. Given these extensive networks, glucose metabolism dysfunction in the whole brain or specific cell types is strongly associated with neurologic pathology including ischemic brain injury and neurodegenerative disorders. This review characterizes the glucose metabolism networks of the brain based on molecular signaling and cellular and regional interactions, and elucidates glucose metabolism-based mechanisms of neurological diseases and therapeutic approaches that may ameliorate metabolic abnormalities in those diseases.

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

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

Applying metabolomics to veterinary pharmacology and therapeutics

Metabolomics is the large-scale study of low-molecular-weight substances in a biological system in a given physiological state at a given time point. Metabolomics can be applied to identify predictors of inter-individual variability in drug response, provide clinicians with data useful for decision-making processes in drug selection, and inform about the pharmacokinetics and pharmacodynamics of a drug. It is, therefore, an exceptional approach for gaining new understanding effects in the field of comparative veterinary pharmacology. However, the incorporation of metabolomics into veterinary pharmacology and toxicology is not yet widespread, and this is probably, at least in part, a result of its highly multidisciplinary nature. This article reviews the potential applications of metabolomics in veterinary pharmacology and therapeutics. It integrates key concepts for designing metabolomics studies and analyzing and interpreting metabolomics data, providing solid foundations for applying metabolomics to the study of drugs in all veterinary species.

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

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

Sampling Method Affects HR-MAS NMR Spectra of Healthy Caprine Brain Biopsies

The metabolic profiling of tissue biopsies using high-resolution–magic angle spinning (HR-MAS) ¹H nuclear magnetic resonance (NMR) spectroscopy may be influenced by experimental factors such as the sampling method. Therefore, we compared the effects of two different sampling methods on the metabolome of brain tissue obtained from the brainstem and thalamus of healthy goats by ¹H HR-MAS NMR spectroscopy—in vivo-harvested biopsy by a minimally invasive stereotactic approach compared with postmortem-harvested sample by dissection with a scalpel. Lactate and creatine were elevated, and choline-containing compounds were altered in the postmortem compared to the in vivo-harvested samples, demonstrating rapid changes most likely due to sample ischemia. In addition, in the brainstem samples acetate and inositols, and in the thalamus samples ƴ-aminobutyric acid, were relatively increased postmortem, demonstrating regional differences in tissue degradation. In conclusion, in vivo-harvested brain biopsies show different metabolic alterations compared to postmortem-harvested samples, reflecting less tissue degradation. Sampling method and brain region should be taken into account in the analysis of metabolic profiles. To be as close as possible to the actual situation in the living individual, it is desirable to use brain samples obtained by stereotactic biopsy whenever possible.

Assessment of solid phase microextraction as a sample preparation tool for untargeted analysis of brain tissue using liquid chromatography-mass spectrometry

This work presents an evaluation of solid-phase microextraction (SPME) SPME in combination with liquid chromatography-high resolution mass spectrometry (LC-HRMS) as an analytical approach for untargeted brain analysis. The study included a characterization of the metabolite coverage provided by C18, mixed-mode (MM, with benzene sulfonic acid and C18 functionalities), and hydrophilic lipophilic balanced (HLB) particles as sorbents in SPME coatings after extraction from cow brain homogenate at static conditions. The effects of desorption solvent, extraction time, and chromatographic modes on the metabolite features detected were investigated. Method precision and absolute matrix effects were also assessed. Among the main findings of this work, it was observed that all three tested coating chemistries were able to provide comparable brain tissue information. HLB provided higher responses for polar metabolites; however, as these fibers were prepared in-house, higher inter-fiber relative standard deviations were also observed. C18 and HLB coatings offered similar responses with respect to lipid-related features, whereas MM and C18 provided the best results in terms of method precision. Our results also showed that the use of methanol is essential for effective desorption of non-polar metabolites. Using a reversed-phase chromatographic method, an average of 800 and 1200 brain metabolite features detected in positive and negative modes, respectively, met inter-fibre RSD values below 30% (n=4) after removal of fibre and solvent artefacts from the associated datasets. For features detected using a lipidomics method, a total of 900 and 1800 features detected using C18 fibers in positive and negative mode, respectively, met the same criteria. In terms of absolute matrix effects, the majority of the model metabolites tested showed values between 80 and 120%, which are within the acceptable range. Overall, the findings of this work lay the foundation for further optimization of parameters for SPME-LC-HRMS methods suitable for in vivo and ex vivo brain (and other tissue) untargeted studies, and support the applicability of this approach for non-destructive tissue metabolomics.

Novel biomarkers in Alzheimer's disease using high resolution proteomics and metabolomics: miRNAS, proteins and metabolites

Alzheimer's disease (AD) is the most common form of dementia. It affects approximately 6% of people over the age of 65 years. It is a clinicopathological, degenerative, chronical and progressive disease that exhibits a deterioration of memory, orientation, speech and other functions. Factors contributing to the pathogenesis of the disease are the presence of extracellular amyloid deposits, called neuritic senile plaques, and fibrillary protein deposits inside neurons, known as neurofibrillary bundles, that appear mainly in the frontal and temporal lobes. AD has a long preclinical latency and is difficult to diagnose and prevent at early stages. Despite the advent of novel high-throughput technologies, it is a great challenge to identify precise biomarkers to understand the progression of the disease and the development of new treatments. In this sense, important knowledge is emerging regarding novel molecular and biological candidates with diagnostic potential, including microRNAs that have a key role in gene repression. On the other hand, proteomic approaches offer a platform for the comprehensive analysis of the whole proteome in a certain physiological time. Proteomic technology investigates protein expression directly and reveals post-translational modifications known to be determinant for many human diseases. Clinically, there is growing evidence for the role of proteomic and metabolomic technologies in AD biomarker discovery. This review discusses the role of several miRNAs identified using genomic technologies, and the importance of novel proteomic and metabolomic approaches to identify new proteins and metabolites that may be useful as biomarkers for monitoring the progression and treatment of AD.

Isoflurane anesthesia disrupts the cortical metabolome

Identifying similarities and differences in the brain metabolome during different states of consciousness has broad relevance for neuroscience and state-dependent autonomic function. This study focused on the prefrontal cortex (PFC) as a brain region known to modulate states of consciousness. Anesthesia was used as a tool to eliminate wakefulness. Untargeted metabolomic analyses were performed on microdialysis samples obtained from mouse PFC during wakefulness and during isoflurane anesthesia. Analyses detected 2,153 molecules, 91 of which could be identified. Analytes were grouped as detected during both wakefulness and anesthesia (n = 61) and as unique to wakefulness (n = 23) or anesthesia (n = 7). Data were analyzed using univariate and multivariate approaches. Relative to wakefulness, during anesthesia there was a significant (q < 0.0001) fourfold change in 21 metabolites. During anesthesia 11 of these 21 molecules decreased and 10 increased. The Kyoto Encyclopedia of Genes and Genomes database was used to relate behavioral state-specific changes in the metabolome to metabolic pathways. Relative to wakefulness, most of the amino acids and analogs measured were significantly decreased during isoflurane anesthesia. Nucleosides and analogs were significantly increased during anesthesia. Molecules associated with carbohydrate metabolism, maintenance of lipid membranes, and normal cell functions were significantly decreased during anesthesia. Significant state-specific changes were also discovered among molecules comprising lipids and fatty acids, monosaccharides, and organic acids. Considered together, these molecules regulate point-to-point transmission, volume conduction, and cellular metabolism. The results identify a novel ensemble of candidate molecules in PFC as putative modulators of wakefulness and the loss of wakefulness.

NEW & NOTEWORTHY The loss of wakefulness caused by a single concentration of isoflurane significantly altered levels of interrelated metabolites in the prefrontal cortex. The results support the interpretation that states of consciousness reflect dynamic interactions among cortical neuronal networks involving a humbling number of molecules that comprise the brain metabolome.

NMR-based metabolomics characterizes metabolic changes in different brain regions of streptozotocin-induced diabetic mice with cognitive decline

Diabetes at advanced age increases rise of cognitive impairment, but its potential mechanisms are still far from being fully understood. In this study, we analyzed the metabolic alterations in six different brain regions between streptozotocin (STZ)-induced diabetic mice with cognitive decline (DM) and age-matched controls (CON) using a ¹H NMR-based metabolomics approach, to explore potential metabolic mechanisms underlying diabetes-induced cognitive decline. The results show that DM mice had a peculiar metabolic phenotype in all brain regions, mainly involving increased lactate level, decreased choline and energy metabolism as well as disrupted astrocyte-neuron metabolism. Furthermore, these metabolic changes exhibited a brain region-specific pattern. Collectively, our results suggest that brain region-specific metabolic disorders may be responsible for diabetes-induced cognitive dysfunction.

Parallel metabolomics and lipidomics enables the comprehensive study of mouse brain regional metabolite and lipid patterns

Global profiling of the metabolome and lipidome of specific brain regions is essential to understanding the cellular and molecular mechanisms regulating brain activity. Given the limited amount of starting material, conventional mouse studies comparing brain regions have mainly targeted a set of known metabolites in large brain regions (e.g., cerebrum, cortex). In this work, we developed a multimodal analytical pipeline enabling parallel analyses of metabolomic and lipidomic profiles from anatomically distinct mouse brain regions starting with less than 0.2 mg of protein content. This analytical pipeline is composed of (1) sonication-based tissue homogenization, (2) parallel metabolite and lipid extraction, (3) BCA-based sample normalization, (4) ultrahigh performance liquid chromatography-mass spectrometry-based multimodal metabolome and lipidome profiling, (5) streamlined data processing, and (6) chord plot-based data visualization. We applied this pipeline to the study of four brain regions in males including the amygdala, dorsal hippocampus, nucleus accumbens and ventral tegmental area. With this novel approach, we detected over 5000 metabolic and 6000 lipid features, among which 134 metabolites and 479 lipids were directly confirmed via automated MS² spectral matching. Interestingly, our analysis identified unique metabolic and lipid profiles in each brain regions. Furthermore, we identified functional relationships amongst metabolic and lipid subclasses, potentially underlying cellular and functional differences across all four brain regions. Overall, our novel workflow generates comprehensive region-specific metabolomic and lipidomic profiles using very low amount of brain sub-regional tissue sample, which could be readily integrated with region-specific genomic, transcriptomic, and proteomic data to reveal novel insights into the molecular mechanisms underlying the activity of distinct brain regions.

Mass spectrometry-based metabolomics for an in-depth questioning of human health

Today, metabolomics is becoming an indispensable tool to get a more comprehensive analysis of complex living systems, providing insights on multiple aspects of physiology. Although its application in large scale population-based studies is very challenging due to the processing of large sample sets as well as the complexity of data information, its potential to characterize human health is well recognized. Technological advances in metabolomics pave the way for the efficient biomarker discovery of disease etiology, diagnosis and prognosis. Here, different steps of the metabolomics workflow, particularly mass spectrometry-based approaches, are discussed to demonstrate the potential of metabolomics to address biological questioning in human health. First an overview of metabolomics is provided with its interest in human health studies. Analytical development and advances in mass spectrometry instrumentation and computational tools are discussed regarding their application limits. Advancing metabolomics for applicability in human health and large-scale studies is presented and discussed in conclusion.