Metabolic Profiling of Neocortical Tissue Discriminates Alzheimer's Disease from Mild Cognitive Impairment, High Pathology Controls, and Normal Controls
| Autor: | Xiaojian Shi, Brandon Chow, Li Liu, Garilyn Jentarra, Paniz Jasbi, Geidy E. Serrano, Natalie Elliott, Haley Hudson, Douglas B. Jones, Haiwei Gu, Ping Chu, Thomas G. Beach |
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| Rok vydání: | 2021 |
| Předmět: |
Male
Pathology medicine.medical_specialty Neocortex Disease Biochemistry Gas Chromatography-Mass Spectrometry chemistry.chemical_compound Metabolomics Alzheimer Disease medicine Metabolome Palmitoleic acid Dementia Humans Cognitive Dysfunction Cause of death Fatty acid metabolism business.industry General Chemistry medicine.disease Superior frontal gyrus chemistry Female business Biomarkers |
| Zdroj: | Journal of proteome research. 20(9) |
| ISSN: | 1535-3907 |
| Popis: | Alzheimer's disease (AD) is the most common cause of dementia, accounting for an estimated 60-80% of cases, and is the sixth-leading cause of death in the United States. While considerable advancements have been made in the clinical care of AD, it remains a complicated disorder that can be difficult to identify definitively in its earliest stages. Recently, mass spectrometry (MS)-based metabolomics has shown significant potential for elucidation of disease mechanisms and identification of therapeutic targets as well diagnostic and prognostic markers that may be useful in resolving some of the difficulties affecting clinical AD studies, such as effective stratification. In this study, complementary gas chromatography- and liquid chromatography-MS platforms were used to detect and monitor 2080 metabolites and features in 48 postmortem tissue samples harvested from the superior frontal gyrus of male and female subjects. Samples were taken from four groups: 12 normal control (NC) patients, 12 cognitively normal subjects characterized as high pathology controls (HPC), 12 subjects with nonspecific mild cognitive impairment (MCI), and 12 subjects with AD. Multivariate statistics informed the construction and cross-validation (p < 0.01) of partial least squares-discriminant analysis (PLS-DA) models defined by a nine-metabolite panel of disease markers (lauric acid, stearic acid, myristic acid, palmitic acid, palmitoleic acid, and four unidentified mass spectral features). Receiver operating characteristic analysis showed high predictive accuracy of the resulting PLS-DA models for discrimination of NC (97%), HPC (92%), MCI (∼96%), and AD (∼96%) groups. Pathway analysis revealed significant disturbances in lysine degradation, fatty acid metabolism, and the degradation of branched-chain amino acids. Network analysis showed significant enrichment of 11 enzymes, predominantly within the mitochondria. The results expand basic knowledge of the metabolome related to AD and reveal pathways that can be targeted therapeutically. This study also provides a promising basis for the development of larger multisite projects to validate these candidate markers in readily available biospecimens such as blood to enable the effective screening, rapid diagnosis, accurate surveillance, and therapeutic monitoring of AD. All raw mass spectrometry data have been deposited to MassIVE (data set identifier MSV000087165). |
| Databáze: | OpenAIRE |
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