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

Abstract

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.

Keywords: Alzheimer’s disease; Amino acids; Biomarkers; Lipids; Metabolomics; Systems biology.

Figure 1.. The targeted and untargeted workflow for LC/MS-based metabolomics.

Panel a. In the triple quadrupole (QqQ)-based targeted metabolomic workflow, standard compounds for the metabolites of interest are first used to set up selected reaction monitoring methods. Here, optimal instrument voltages are determined and response curves are generated for absolute quantification. After the targeted methods have been established on the basis of standard metabolites, metabolites are extracted from tissues, biofluids or cell cultures and analysed. The data output provides quantification only of those metabolites for which standard methods have been built. Panel b. In the untargeted metabolomic workflow, metabolites are first isolated from biological samples and subsequently analysed by liquid chromatography followed by mass spectrometry (LC/MS). After data acquisition, the results are processed by using bioinformatic software such as XCMS to perform nonlinear retention time alignment and identify peaks that are changing between the groups of samples measured. The m/z values for the peaks of interest are searched in metabolite databases to obtain putative identifications. Putative identifications are then confirmed by comparing tandem mass spectrometry (MS/MS) data and retention time data to that of standard compounds. The untargeted workflow is global in scope and outputs data related to comprehensive cellular metabolism. Abbreviations: LC-MS, liquid chromatography coupled with mass spectrometry; MS/MS, tandem mass spectrometry; m/z, mass-to-charge ratio; QqQ, triple quadrupole mass analyzer. Note: from Patti, G.J., Yanes, O., Siuzdak, G., 2012. Innovation: Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13, 263–269. https://doi.org/10.1038/nrm3314 Copyright © 2012, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. Reproduced with permission from Springer Nature Customer Service Center GmbH.

Figure 2.. Schematic overview of metabolomic data analysis.

Metabolomics data analysis, regardless of the experimental platform, can be divided into data preparation, data mining and result interpretation. Data preparation includes metabolite assignment and quantification as well as data pre-processing (e.g., normalization). Data mining step should generally include both statistical and machine learning analysis followed by result interpretation through enrichment or network analysis. Metabolomics data can be integrated with other types of omics or clinical data for further interpretation.

Figure 3.. Factors that affect brain lipid metabolism and the importance of lipids in healthy aging and AD.

Factors that affect brain lipid metabolism. Demographic factors, genetics, lifestyle, the environment, and trauma can influence lipid metabolism in the brain. Interestingly, these factors that influence lipid metabolism are also recognized risk factors of AD. Abnormalities in lipid metabolism can contribute to dysfunctional brain networks that associate with AD pathology. Importance of lipid metabolism in brain function and AD pathology. In healthy aging, normal transport of lipids through apolipoproteins contribute to the function of the brain. Homeostatic control of the brain lipid environment is responsible for sustaining a normal BBB, providing the right environment for normal APP processing, the right composition for ion channels and receptors, cytosis, vesicle formation, and secretion, signaling, inflammation, oxidation, energy balance, and membrane biosynthesis and remodeling. Dyshomeostasis in lipid delivery into the brain and its metabolism attributes to disturbed BBB, abnormal APP processing, disturbance in cytosis, signaling, energy balance, and enhanced/sustained inflammation and oxidation. Over time, these processes lead to neuronal death that is the hallmark of AD pathology. Abbreviations: Aβ, amyloid-β; AD, Alzheimer’s disease; APOE ε4, ε4 allele of the APOE gene; APP, amyloid precursor protein; BBB, blood-brain barrier. Note: from Chew, H., Solomon, V.A., Fonteh, A.N., 2020. Involvement of Lipids in Alzheimer’s Disease Pathology and Potential Therapies. Front Physiol 11. https://doi.org/10.3389/fphys.2020.00598 Copyright © 2020 Chew, Solomon and Fonteh. Reproduced under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/).

Figure 4.. Schematic representation of relevant metabolic pathways altered in AD.

Metabolomic/lipidomic studies allow exploring significant alterations of numerous metabolites and, consequently, scrutinizing relevant metabolic pathways altered in AD, primarily those related to fatty acid biosynthesis and lipid metabolism, amino acid metabolism, and mitochondrial bioenergetics. Abbreviations: AD, Alzheimer’s disease; ADP, adenosine diphosphate; ATP, adenosine triphosphate; BCAAs, branched-chain amino acids; CoA, coenzyme A; Cyt c, cytochrome c; PRPP, phosphoribosyl diphosphate; TCA, tricarboxylic acid cycle.

Figure 5.. Integration of multi-dimensional data for the holistic depiction of AD pathophysiology.

The integration of unbiased exploratory omics sciences – including genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics – leads to generate diverse patients’ biological data (genetic/epigenetic, RNA, protein/peptide, and metabolic/lipid). Advanced statistical/computational tools will enable the integrative analysis of such biological data with patients’ non-omics data. These can include socio-demographic data as well as information on lifestyle habits and environmental exposure; data on the dimensions (psychological, cognitive, vitality, sensory, locomotion) of the intrinsic capacity concept; data obtained using imaging modalities (sMRI, fMRI, DTI, molecular PET, metabolic PET); data on patients’ genotype. The combined analysis of these multi-dimensional data will allow – under a precision medicine framework – identifying unique biomarker signatures, facilitating cell type discovery and characterization (and accurately defining cell type signatures), establishing disease sub-types and improving the stratification of patients, developing relevant prediction models of the disease, and discovering novel potential therapeutic targets. This approach is expected to facilitate the holistic assessment of AD pathophysiology and to optimize the development of effective individualized therapies. Abbreviations: AD, Alzheimer’s disease; DTI, diffusion tensor imaging; e-health, electronic health; fMRI, functional magnetic resonance imaging; PA, physical activity; PET, positron emission tomography; sMRI, structural magnetic resonance imaging.