Characterising Alzheimer's disease through integrative NMR- and LC-MS-based metabolomics

Jonas Ellegaard Nielsen; Raluca Georgiana Maltesen; Jesper F Havelund; Nils J Færgeman; Charlotte Held Gotfredsen; Karsten Vestergård; Søren Risom Kristensen; Shona Pedersen

doi:10.1016/j.metop.2021.100125

Characterising Alzheimer's disease through integrative NMR- and LC-MS-based metabolomics

Metabol Open. 2021 Sep 22:12:100125. doi: 10.1016/j.metop.2021.100125. eCollection 2021 Dec.

Authors

Jonas Ellegaard Nielsen^{1

2}, Raluca Georgiana Maltesen^{3

4}, Jesper F Havelund⁵, Nils J Færgeman⁵, Charlotte Held Gotfredsen⁶, Karsten Vestergård⁷, Søren Risom Kristensen^{1

2}, Shona Pedersen⁸

Affiliations

¹ Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.
² Department of Clinical Biochemistry, Aalborg University Hospital, Aalborg, Denmark.
³ Translational Radiation Biology and Oncology Laboratory, Centre for Cancer Research, Westmead Institute of Medical Research, Westmead, Australia.
⁴ Department of Anaesthesia and Intensive Care, Aalborg University Hospital, Aalborg, Denmark.
⁵ Department of Biochemistry and Molecular Biology, Villum Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark.
⁶ Department of Chemistry, Technical University of Denmark, Kgs. Lyngby, Denmark.
⁷ Department of Neurology, Aalborg University Hospital, Aalborg, Denmark.
⁸ Department of Basic Medical Sciences, College of Medicine, Qatar University, Qatar Health, Doha, Qatar.

Abstract

Background: Alzheimer's Disease (AD) is a complex and multifactorial disease and novel approaches are needed to illuminate the underlying pathology. Metabolites comprise the end-product of genes, transcripts, and protein regulations and might reflect disease pathogenesis. Blood is a common biofluid used in metabolomics; however, since extracellular vesicles (EVs) hold cell-specific biological material and can cross the blood-brain barrier, their utilization as biological material warrants further investigation. We aimed to investigate blood- and EV-derived metabolites to add insigts to the pathological mechanisms of AD.

Methods: Blood samples were collected from 10 AD and 10 Mild Cognitive Impairment (MCI) patients, and 10 healthy controls. EVs were enriched from plasma using 100,000×g, 1 h, 4 °C with a wash. Metabolites from serum and EVs were measured using liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. Multivariate and univariate analyses were employed to identify altered metabolites in cognitively impaired individuals.

Results: While no significant EV-derived metabolites were found differentiating patients from healthy individuals, six serum metabolites were found important; valine (p = 0.001, fold change, FC = 0.8), histidine (p = 0.001, FC = 0.9), allopurinol riboside (p = 0.002, FC = 0.2), inosine (p = 0.002, FC = 0.3), 4-pyridoxic acid (p = 0.006, FC = 1.6), and guanosine (p = 0.004, FC = 0.3). Pathway analysis revealed branched-chain amino acids, purine and histidine metabolisms to be downregulated, and vitamin B6 metabolism upregulated in patients compared to controls.

Conclusion: Using a combination of LC-MS and NMR methodologies we identified several altered mechanisms possibly related to AD pathology. EVs require additional optimization prior to their possible utilization as a biological material for AD-related metabolomics studies.

Keywords: ACE, Addenbrooke's cognitive examination; AD, Alzheimer's Disease; AUC, Area under the curve; Alzheimer; Aβ, Amyloid-β; BBB, Blood-brain barrier; BCAA, Branched-chain amino acid; Blood; CNS, Central nervous system; CSF, Cerebrospinal fluid; CV, Cross-validation; EVs, Extracellular vesicles; Extracellular vesicles; FAQ, Functional activities questionnaire; FDR, False discovery rate; MCI, Mild cognitive impairment; MMSE, Mini-mental state examination; Mass spectrometry; Metabolites; Nuclear magnetic resonance; PCA, Principal component analysis; ROC, Receiver operating characteristics; p-tau, Phospho-tau; sPLS-DA, Sparse partial least squared discriminant analysis; t-tau, Total-tau.