Recent Advances and Applications of Metabolomics to Investigate Neurodegenerative Diseases

Hippocampal metabolic profile during epileptogenesis in the pilocarpine model of epilepsy

Temporal lobe epilepsy (TLE) is a common form of refractory epilepsy in adulthood. The metabolic profile of epileptogenesis is still poorly investigated. Elucidation of such a metabolic profile using animal models of epilepsy could help identify new metabolites and pathways involved in the mechanisms of epileptogenesis process. In this study, we evaluated the metabolic profile during the epileptogenesis periods. Using a pilocarpine model of epilepsy, we analyzed the global metabolic profile of hippocampal extracts by untargeted metabolomics based on ultra-performance liquid chromatography-high-resolution mass spectrometry, at three time points (3 h, 1 week, and 2 weeks) after status epilepticus (SE) induction. We demonstrated that epileptogenesis periods presented different hippocampal metabolic profiles, including alterations of metabolic pathways of amino acids and lipid metabolism. Six putative metabolites (tryptophan, N-acetylornithine, N-acetyl-L-aspartate, glutamine, adenosine, and cholesterol) showed significant different levels during epileptogenesis compared to their respective controls. These putative metabolites could be associated with the imbalance of neurotransmitters, mitochondrial dysfunction, and cell loss observed during both epileptogenesis and epilepsy. With these findings, we provided an overview of hippocampal metabolic profiles during different stages of epileptogenesis that could help investigate pathways and respective metabolites as predictive tools in epilepsy.

Circulating metabolites and risk of incident dementia: A prospective cohort study

Identifying circulating metabolites associated with dementia, cognition, and brain volume may improve the understanding of dementia pathogenesis and provide novel insights for preventive and therapeutic interventions. This cohort study included a total of 87 885 participants (median follow-up of 9.1 years, 54% female) without dementia at baseline from the UK Biobank. A total of 249 plasma metabolites were measured using nuclear magnetic resonance spectroscopy at baseline. Cox proportional regression was used to examine the associations of each metabolite with incident dementia (cases = 1134), Alzheimer's disease (AD; cases = 488), and vascular dementia (VD; cases = 257) during follow-up. Dementia-associated metabolites were further analyzed for association with cognitive deficits (N = 87 885) and brain volume (N = 7756) using logistic regression and linear regression. We identified 26 metabolites associated with incident dementia, of which 6 were associated with incident AD and 5 were associated with incident VD. These 26 dementia-related metabolites were subfractions of intermediate-density lipoprotein, large low-density lipoprotein (L-LDL), small high-density lipoprotein (S-HDL), very-low-density lipoprotein, fatty acids, ketone bodies, citrate, glucose, and valine. Among them, the cholesterol percentage in L-LDL (L-LDL-C%) was associated with lower risk of AD (HR [95% CI] = 0.92 [0.87-0.97], p = 0.002), higher brain cortical (β = 0.047, p = 3.91 × 10-6 ), and hippocampal (β = 0.043, p = 1.93 × 10-4 ) volume. Cholesteryl ester-to-total lipid ratio in L-LDL (L-LDL-CE%) was associated with lower risk of AD (HR [95% CI] = 0.93 [0.90-0.96], p = 1.48 × 10-4 ), cognitive deficits (odds ratio = 0.98, p = 0.009), and higher hippocampal volume (β = 0.027, p = 0.009). Cholesteryl esters in S-HDL (S-HDL-CE) were associated with lower risk of VD (HR [95% CI] = 0.81 [0.71-0.93], p = 0.002), but not AD. Taken together, circulating levels of L-LDL-CE% and L-LDL-C% were robustly associated with risk of AD and AD phenotypes, but not with VD. S-HDL-CE was associated with lower risk of VD, but not with AD or AD phenotypes. These metabolites may play a role in the advancement of future intervention trials. Additional research is necessary to gain a complete comprehension of the molecular mechanisms behind these associations.

Association Between the Early Serum Lipid Metabolism Profile and Delayed Neurocognitive Recovery After Cardiopulmonary Bypass in Cardiac Surgical Patients: a Pilot Study

Cardiac surgery with extracorporeal circulation is considered to be one of the surgical types with the highest incidence of delayed neurocognitive recovery (DNR), but the mechanism is unclear. Metabolomics technology can be used to understand the early postoperative metabolic profile and find the relationship between serum metabolites and disease. We performed untargeted analyses of postoperative serum metabolites in all surgical groups, as well as serum metabolites in healthy nonsurgical adults, by using liquid chromatography‒mass spectrometry (LC‒MS). DNR after cardiopulmonary bypass surgery occurred in 35% of surgical patients. Sixty-nine metabolites were found to be associated with DNR. Lipids and lipid-like molecules occupy a total of 55 positions. Lipid metabolism occupies an important position in the serum metabolic profile of DNR patients in the early postoperative period. Phosphatidylinositol (PI), sphingomyelin (SM), and phosphatidylglycerol (PG) appear at the highest frequency. Correlation analysis and receiver operator characteristic curve analysis confirmed PI and SM as potential biomarkers for an increased risk of DNR.

Investigating Causal Relations Between Circulating Metabolites and Alzheimer's Disease: A Mendelian Randomization Study

BACKGROUND

Metabolomics is a promising approach that can be used to understand pathophysiological pathways of Alzheimer's disease (AD). However, the causal relationships between metabolism and AD are poorly understood.

OBJECTIVE

We aimed to investigate the causal association between circulating metabolites and risk of AD through two-sample Mendelian randomization (MR) approach.

METHODS

Genetic associations with 123 circulating metabolic traits were utilized as exposures. Summary statistics data from International Genomics of Alzheimer's Project was used in primary analysis, including 21,982 AD cases and 41,944 controls. Validation was performed using family history of AD data from UK Biobank (27,696 cases of maternal AD, 14,338 cases of paternal AD, and 272,244 controls). We utilized inverse-variance weighted method as primary method.

RESULTS

We found significantly increased risks of developing AD per standard deviation increase in the levels of circulating ApoB (odd ratio[OR] = 3.18; 95% confidence interval[CI]: 1.52-6.66, p = 0.0022), glycoprotein acetyls (OR = 1.21; 95% CI: 1.05-1.39, p = 0.0093), total cholesterol (OR = 2.73; 95% CI: 1.41-5.30, p = 0.0030), and low-density lipoprotein (LDL) cholesterol (OR = 2.34; 95% CI: 1.53-3.57, p = 0.0001). Whereas glutamine (OR = 0.81; 95% CI: 0.71-0.92, p = 0.0011) were significantly associated with lower risk of AD. We also detected causal effects of several different composition of LDL fractions on increased AD risk, which has been verified in validation. However, we found no association between circulating high-density lipoprotein cholesterol and AD.

CONCLUSION

Our findings suggest causal effects of circulating glycoprotein acetyls, ApoB, LDL cholesterol, and serum total cholesterol on higher risk of AD, whereas glutamine showed the protective effect.

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.

Cell-Type-Specific Metabolic Profiling Achieved by Combining Desorption Electrospray Ionization Mass Spectrometry Imaging and Immunofluorescence Staining

Cell-type-specific metabolic profiling in tissue with heterogeneous composition has been of great interest across all mass spectrometry imaging (MSI) technologies. We report here a powerful new chemical imaging capability in desorption electrospray ionization (DESI) MSI, which enables cell-type-specific and in situ metabolic profiling in complex tissue samples. We accomplish this by combining DESI-MSI with immunofluorescence staining using specific cell-type markers. We take advantage of the variable frequency of each distinct cell type in the lateral septal nucleus (LSN) region of mouse forebrain. This allows computational deconvolution of the cell-type-specific metabolic profile in neurons and astrocytes by convex optimization-a machine learning method. Based on our approach, we observed 107 metabolites that show different distributions and intensities between astrocytes and neurons. We subsequently identified 23 metabolites using high-resolution mass spectrometry (MS) and tandem MS, which include small metabolites such as adenosine and N-acetylaspartate previously associated with astrocytes and neurons, respectively, as well as accumulation of several phospholipid species in neurons which have not been studied before. Overall, this method overcomes the relatively low spatial resolution of DESI-MSI and provides a new platform for in situ metabolic investigation at the cell-type level in complex tissue samples with heterogeneous cell-type composition.

Untargeted Metabolomics Relative Quantification by SWATH Mass Spectrometry Applied to Cerebrospinal Fluid

Cerebrospinal fluid (CSF) has been considered the key source for the search of biomarkers, in particular for neurological diseases, such as Alzheimer's and Parkinson's disease, since it reflects the state of the central nervous system (CNS). Finding biomarkers in the earliest stages of neurodegenerative diseases has become imperative, since, at the moment, there are no drugs that can reverse these pathological processes. Untargeted metabolomics analysis by liquid chromatography combined with SWATH-MS relative quantification is an emerging approach to search for potential biomarkers. In this chapter, we describe a method for untargeted metabolomics analysis of CSF samples that can also be used in parallel to a proteomics approach. The analysis is focused on the SWATH acquisition mode, where beyond precursor's relative quantification, the information of the MS/MS relative quantification is also used to help in the search of new potential biomarkers.

An Exploratory Analysis of Potential New Biomarkers of Cognitive Function

We examined the relationship between serially measured, novel serum biomarkers and a measure of cognitive functioning in older adults. We assayed stored serum samples from two Fels Longitudinal Study visits in N = 100 adult participants (visit 1 ages 59.3 ± 8.5 years; 53% female), and Montreal Cognitive Assessment (MoCA) scores also assessed at the second visit. Assays included acylcarnitines, amino acids, and 2-hydroxybutyric acid (b-HBA). Cross-sectional correlations between acylcarnitines and amino acids and MoCA were identified. Serial change in short-chain acylcarnitines and visit 2 MoCA were also correlated. Participants with MoCA scores <26 were more likely to have an increase in short-chain acylcarnitines between visits 1 and 2 [adjusted odds ratio (OR) = 5.24; 95% confidence interval (CI) 1.07-25.9]. b-HBA was also correlated with acylcarnitines. Several cross-sectional and serial associations between novel serum biomarkers and cognitive functioning were identified. b-HBA may also be a cost-effective marker of dysregulation associated with cognitive decline.

Different Circulating Trace Amine Profiles in De Novo and Treated Parkinson's Disease Patients

Early diagnosis of Parkinson’s disease (PD) remains a challenge to date. New evidence highlights the potential clinical value of circulating trace amines (TAs) in early-stage PD and their involvement in disease progression. A new ultra performance chromatography mass spectrometry (UPLC-MS/MS) method was developed to quantify plasmatic TAs, and the catecholamines and indolamines pertaining to the same biochemical pathways. Three groups of subjects were recruited: 21 de novo, drug untreated, PD patients, 27 in treatment PD patients and 10 healthy subjects as controls. Multivariate and univariate data analyses were applied to reveal metabolic changes among the groups in attempt to discover new putative markers for early PD detection and disease progression. Different circulating levels of tyrosine (p = 0.002), tyramine (p < 0.001), synephrine (p = 0.015), norepinephrine (p = 0.012), metanephrine (p = 0.001), β-phenylethylamine (p = 0.001) and serotonin (p = 0.006) were found among the three groups. While tyramine behaves as a putative biomarker for early-stage PD (AUC = 0.90) tyramine, norepinephrine, and tyrosine appear to act as biomarkers of disease progression (AUC > 0.75). The findings of this pilot cross-sectional study suggest that biochemical anomalies of the aminergic and indolic neurotransmitters occur in PD patients. Compounds within the TAs family may constitute putative markers for early stage detection and progression of PD.

Mass spectrometry-based metabolomics: Targeting the crosstalk between gut microbiota and brain in neurodegenerative disorders

Metabolomics seeks to take a "snapshot" in a time of the levels, activities, regulation and interactions of all small molecule metabolites in response to a biological system with genetic or environmental changes. The emerging development in mass spectrometry technologies has shown promise in the discovery and quantitation of neuroactive small molecule metabolites associated with gut microbiota and brain. Significant progress has been made recently in the characterization of intermediate role of small molecule metabolites linked to neural development and neurodegenerative disorder, showing its potential in understanding the crosstalk between gut microbiota and the host brain. More evidence reveals that small molecule metabolites may play a critical role in mediating microbial effects on neurotransmission and disease development. Mass spectrometry-based metabolomics is uniquely suitable for obtaining the metabolic signals in bidirectional communication between gut microbiota and brain. In this review, we summarized major mass spectrometry technologies including liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and imaging mass spectrometry for metabolomics studies of neurodegenerative disorders. We also reviewed the recent advances in the identification of new metabolites by mass spectrometry and metabolic pathways involved in the connection of intestinal microbiota and brain. These metabolic pathways allowed the microbiota to impact the regular function of the brain, which can in turn affect the composition of microbiota via the neurotransmitter substances. The dysfunctional interaction of this crosstalk connects neurodegenerative diseases, including Parkinson's disease, Alzheimer's disease and Huntington's disease. The mass spectrometry-based metabolomics analysis provides information for targeting dysfunctional pathways of small molecule metabolites in the development of the neurodegenerative diseases, which may be valuable for the investigation of underlying mechanism of therapeutic strategies.

The metabolome identity: basis for discovery of biomarkers in neurodegeneration

Neurodegenerative disorders are often associated with cellular dysfunction caused by underlying protein-misfolding signalling. Numerous neuropathologies are diagnosed at late stage symptomatic changes which occur in response to these molecular malfunctions and treatment is often too late or restricted only to the slowing of further cell death. Important new strategies to identify early biomarkers with predictive value to intervene with disease progression at stages where cell dysfunction has not progressed irreversibly is of paramount importance. Thus, the identification of these markers presents an essential opportunity to identify and target disease pathways. This review highlights some important metabolic alterations detected in neurodegeneration caused by misfolded prion protein and discusses common toxicity pathways identified across different neurodegenerative diseases. Thus, having established some commonalities between various degenerative conditions, detectable metabolic changes may be of extreme value as an early diagnostic biomarker in disease.

White paper by the Society for CSF Analysis and Clinical Neurochemistry: Overcoming barriers in biomarker development and clinical translation

Body fluid biomarkers have great potential for different clinical purposes, including diagnosis, prognosis, patient stratification and treatment effect monitoring. This is exemplified by current use of several excellent biomarkers, such as the Alzheimer’s disease cerebrospinal fluid (CSF) biomarkers, anti-neuromyelitis optica antibodies and blood neurofilament light. We still, however, have a strong need for additional biomarkers and several gaps in their development and implementation should be filled. Examples of such gaps are i) limited knowledge of the causes of neurological diseases, and thus hypotheses about the best biomarkers to detect subclinical stages of these diseases; ii) the limited success translating discoveries obtained by e.g. initial mass spectrometry proteomic low-throughput studies into immunoassays for widespread clinical implementation; iii) lack of interaction among all stakeholders to optimise and adapt study designs throughout the biomarker development process to medical needs, which may change during the long period needed for biomarker development.

The Society for CSF Analysis and Clinical Neurochemistry (established in 2015) has been founded as a concerted follow-up of large standardisation projects, including BIOMARKAPD and SOPHIA, and the BioMS-consortium.

The main aims of the CSF society are to exchange high level international scientific experience, to facilitate the incorporation of CSF diagnostics into clinical practice and to give advice on inclusion of CSF analysis into clinical guidelines. The society has a broad scope, as its vision is that the gaps in development and implementation of biomarkers are shared among almost all neurological diseases and thus they can benefit from the activities of the society.

Biomarker Research in Parkinson's Disease Using Metabolite Profiling

Biomarker research in Parkinson's disease (PD) has long been dominated by measuring dopamine metabolites or alpha-synuclein in cerebrospinal fluid. However, these markers do not allow early detection, precise prognosis or monitoring of disease progression. Moreover, PD is now considered a multifactorial disease, which requires a more precise diagnosis and personalized medication to obtain optimal outcome. In recent years, advanced metabolite profiling of body fluids like serum/plasma, CSF or urine, known as "metabolomics", has become a powerful and promising tool to identify novel biomarkers or "metabolic fingerprints" characteristic for PD at various stages of disease. In this review, we discuss metabolite profiling in clinical and experimental PD. We briefly review the use of different analytical platforms and methodologies and discuss the obtained results, the involved metabolic pathways, the potential as a biomarker and the significance of understanding the pathophysiology of PD. Many of the studies report alterations in alanine, branched-chain amino acids and fatty acid metabolism, all pointing to mitochondrial dysfunction in PD. Aromatic amino acids (phenylalanine, tyrosine, tryptophan) and purine metabolism (uric acid) are also altered in most metabolite profiling studies in PD.