Targeted metabolomics of CSF in healthy individuals and patients with secondary progressive multiple sclerosis using high-resolution mass spectrometry

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Abstract

Introduction

Standardized commercial kits enable targeted metabolomics analysis and may thus provide an attractive complement to the more explorative approaches. The kits are typically developed for triple quadrupole mass spectrometers using serum and plasma.

Objectives

Here we measure the concentrations of preselected metabolites in cerebrospinal fluid (CSF) using a kit developed for high-resolution mass spectrometry (HRMS). Secondarily, the study aimed to investigate metabolite alterations in patients with secondary progressive multiple sclerosis (SPMS) compared to controls.

Methods

We performed targeted metabolomics in human CSF on twelve SPMS patients and twelve age and sex-matched healthy controls using the Absolute IDQ-p400 kit (Biocrates Life Sciences AG) developed for HRMS. The extracts were analysed using two methods; liquid chromatography-mass spectrometry (LC-HRMS) and flow injection analysis-MS (FIA-HRMS).

Results

Out of 408 targeted metabolites, 196 (48%) were detected above limit of detection and 35 were absolutely quantified. Metabolites analyzed using LC-HRMS had a median coefficient of variation (CV) of 3% and 2.5% between reinjections the same day and after prolonged storage, respectively. The corresponding results for the FIA-HRMS were a median CV of 27% and 21%, respectively. We found significantly (p < 0.05) elevated levels of glycine, asymmetric dimethylarginine (ADMA), glycerophospholipid PC-O (34:0) and sum of hexoses in SPMS patients compared to controls.

Conclusion

The Absolute IDQ-p400 kit could successfully be used for quantifying targeted metabolites in the CSF. Metabolites quantified using LC-HRMS showed superior reproducibility compared to FIA-HRMS.

Electronic supplementary material

The online version of this article (10.1007/s11306-020-1648-5) contains supplementary material, which is available to authorized users.

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Most cited references 20

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Untargeted Metabolomics Strategies—Challenges and Emerging Directions

Stacy D Sherrod, John A. McLean, Alexandra C. Schrimpe-Rutledge … (2016)

Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes, and as such the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating, workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS based untargeted metabolomics studies – specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described.

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Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study

Vijay R Varma, Anup M Oommen, Sudhir Varma … (2018)

Background The metabolic basis of Alzheimer disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis are unclear. Understanding how global perturbations in metabolism are related to severity of AD neuropathology and the eventual expression of AD symptoms in at-risk individuals is critical to developing effective disease-modifying treatments. In this study, we undertook parallel metabolomics analyses in both the brain and blood to identify systemic correlates of neuropathology and their associations with prodromal and preclinical measures of AD progression. Methods and findings Quantitative and targeted metabolomics (Biocrates AbsoluteIDQ [identification and quantification] p180) assays were performed on brain tissue samples from the autopsy cohort of the Baltimore Longitudinal Study of Aging (BLSA) (N = 44, mean age = 81.33, % female = 36.36) from AD (N = 15), control (CN; N = 14), and “asymptomatic Alzheimer’s disease” (ASYMAD, i.e., individuals with significant AD pathology but no cognitive impairment during life; N = 15) participants. Using machine-learning methods, we identified a panel of 26 metabolites from two main classes—sphingolipids and glycerophospholipids—that discriminated AD and CN samples with accuracy, sensitivity, and specificity of 83.33%, 86.67%, and 80%, respectively. We then assayed these 26 metabolites in serum samples from two well-characterized longitudinal cohorts representing prodromal (Alzheimer’s Disease Neuroimaging Initiative [ADNI], N = 767, mean age = 75.19, % female = 42.63) and preclinical (BLSA) (N = 207, mean age = 78.68, % female = 42.63) AD, in which we tested their associations with magnetic resonance imaging (MRI) measures of AD-related brain atrophy, cerebrospinal fluid (CSF) biomarkers of AD pathology, risk of conversion to incident AD, and trajectories of cognitive performance. We developed an integrated blood and brain endophenotype score that summarized the relative importance of each metabolite to severity of AD pathology and disease progression (Endophenotype Association Score in Early Alzheimer’s Disease [EASE-AD]). Finally, we mapped the main metabolite classes emerging from our analyses to key biological pathways implicated in AD pathogenesis. We found that distinct sphingolipid species including sphingomyelin (SM) with acyl residue sums C16:0, C18:1, and C16:1 (SM C16:0, SM C18:1, SM C16:1) and hydroxysphingomyelin with acyl residue sum C14:1 (SM (OH) C14:1) were consistently associated with severity of AD pathology at autopsy and AD progression across prodromal and preclinical stages. Higher log-transformed blood concentrations of all four sphingolipids in cognitively normal individuals were significantly associated with increased risk of future conversion to incident AD: SM C16:0 (hazard ratio [HR] = 4.430, 95% confidence interval [CI] = 1.703–11.520, p = 0.002), SM C16:1 (HR = 3.455, 95% CI = 1.516–7.873, p = 0.003), SM (OH) C14:1 (HR = 3.539, 95% CI = 1.373–9.122, p = 0.009), and SM C18:1 (HR = 2.255, 95% CI = 1.047–4.855, p = 0.038). The sphingolipid species identified map to several biologically relevant pathways implicated in AD, including tau phosphorylation, amyloid-β (Aβ) metabolism, calcium homeostasis, acetylcholine biosynthesis, and apoptosis. Our study has limitations: the relatively small number of brain tissue samples may have limited our power to detect significant associations, control for heterogeneity between groups, and replicate our findings in independent, autopsy-derived brain samples. Conclusions We present a novel framework to identify biologically relevant brain and blood metabolites associated with disease pathology and progression during the prodromal and preclinical stages of AD. Our results show that perturbations in sphingolipid metabolism are consistently associated with endophenotypes across preclinical and prodromal AD, as well as with AD pathology at autopsy. Sphingolipids may be biologically relevant biomarkers for the early detection of AD, and correcting perturbations in sphingolipid metabolism may be a plausible and novel therapeutic strategy in AD.

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Metabolomic markers reveal novel pathways of ageing and early development in human populations

Cristina Menni, Gabriella Kastenmüller, Ann-Kristin Petersen … (2013)

Background Human ageing is a complex, multifactorial process and early developmental factors affect health outcomes in old age. Methods Metabolomic profiling on fasting blood was carried out in 6055 individuals from the UK. Stepwise regression was performed to identify a panel of independent metabolites which could be used as a surrogate for age. We also investigated the association with birthweight overall and within identical discordant twins and with genome-wide methylation levels. Results We identified a panel of 22 metabolites which combined are strongly correlated with age (R2 = 59%) and with age-related clinical traits independently of age. One particular metabolite, C-glycosyl tryptophan (C-glyTrp), correlated strongly with age (beta = 0.03, SE = 0.001, P = 7.0 × 10−157) and lung function (FEV1 beta = −0.04, SE = 0.008, P = 1.8 × 10−8 adjusted for age and confounders) and was replicated in an independent population (n = 887). C-glyTrp was also associated with bone mineral density (beta = −0.01, SE = 0.002, P = 1.9 × 10−6) and birthweight (beta = −0.06, SE = 0.01, P = 2.5 × 10−9). The difference in C-glyTrp levels explained 9.4% of the variance in the difference in birthweight between monozygotic twins. An epigenome-wide association study in 172 individuals identified three CpG-sites, associated with levels of C-glyTrp (P < 2 × 10−6). We replicated one CpG site in the promoter of the WDR85 gene in an independent sample of 350 individuals (beta = −0.20, SE = 0.04, P = 2.9 × 10−8). WDR85 is a regulator of translation elongation factor 2, essential for protein synthesis in eukaryotes. Conclusions Our data illustrate how metabolomic profiling linked with epigenetic studies can identify some key molecular mechanisms potentially determined in early development that produce long-term physiological changes influencing human health and ageing.

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Author and article information

Contributors

Kim Kultima:

ORCID: http://orcid.org/0000-0002-0680-1410

Kim.Kultima@medsci.uu.se

Journal

Journal ID (nlm-ta): Metabolomics

Journal ID (iso-abbrev): Metabolomics

Title: Metabolomics

Publisher: Springer US (New York )

ISSN (Print): 1573-3882

ISSN (Electronic): 1573-3890

Publication date (Electronic): 12 February 2020

Publication date PMC-release: 12 February 2020

Publication date (Print): 2020

Volume: 16

Issue: 2

Electronic Location Identifier: 26

Affiliations

[1 ]Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala University Hospital, Entrance 61, 3rd Floor, Dag Hammarskjölds Väg 18, 751 85 Uppsala, Sweden

[2 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Clinical Sciences, Danderyd Hospital, , Karolinska Institutet, ; Stockholm, Sweden

[3 ]GRID grid.8993.b, ISNI 0000 0004 1936 9457, Department of Neuroscience, , Uppsala University, ; Uppsala, Sweden

Author information

Kim Kultima http://orcid.org/0000-0002-0680-1410

Article

Publisher ID: 1648

DOI: 10.1007/s11306-020-1648-5

PMC ID: 7015966

PubMed ID: 32052189

SO-VID: de47a617-87be-486a-b23b-e685fb307b11

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 27 September 2019

Date accepted : 1 February 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100009230, Landstinget i Uppsala län;

Funded by: FundRef http://dx.doi.org/10.13039/100007435, Åke Wiberg Stiftelse;

Funded by: Neuro Sweden

Custom metadata

ScienceOpen disciplines: Molecular biology

Keywords: cerebrospinal fluid,targeted metabolomics,flow injection analysis,high-resolution mass spectrometry,secondary progressive multiple sclerosis

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ScienceOpen disciplines: Molecular biology

Keywords: cerebrospinal fluid, targeted metabolomics, flow injection analysis, high-resolution mass spectrometry, secondary progressive multiple sclerosis

Targeted metabolomics of CSF in healthy individuals and patients with secondary progressive multiple sclerosis using high-resolution mass spectrometry

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Abstract

Introduction

Objectives

Methods

Results

Conclusion

Electronic supplementary material

Related collections

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Most cited references 20

Untargeted Metabolomics Strategies—Challenges and Emerging Directions

Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study

Metabolomic markers reveal novel pathways of ageing and early development in human populations

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