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A Brain Tissue Metabolomic Signature Discloses Alzheimer’s Disease Post‐Mortem
Author(s) -
Jasbi Paniz,
Shi Xiaojian,
Turner Cassidy,
Jentarra Garilyn,
Gu Haiwei
Publication year - 2020
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2020.34.s1.05931
Subject(s) - metabolomics , dementia , metabolite , receiver operating characteristic , disease , medicine , pathology , chemistry , chromatography
Alzheimer’s disease (AD) is the most common cause of dementia, accounting for an estimated 60 to 80% of cases, and is the sixth‐leading cause of death in the United States. A major challenge persists in the post‐mortem diagnosis of AD patients from those suffering mild cognitive impairment (MCI) and control patients with high pathology (HPC). Mass spectrometry (MS)‐based metabolomics has shown significant potential in disease diagnosis over the past two decades. In this study, we present a combination of gas chromatography‐MS (GC‐MS) for global metabolic profiling and analysis of long‐ and short‐chain fatty acids, in addition to liquid chromatography‐tandem MS (LC‐MS/MS) for the detection of targeted aqueous metabolites and lipids. Using this approach, 2084 metabolites and features were reliably detected and monitored in 48 tissue samples harvested from the superior frontal gyrus of male and female subjects post‐mortem. Samples were taken from four groups: 12 normal control (NC) patients, 12 subjects characterized as HPC, 12 with sub‐clinical MCI, and 12 diagnosed with AD. Multivariate significance testing informed the construction and cross‐validation (p < 0.05) of partial least squares‐discriminant analysis (PLS‐DA) models defined by a 9‐metabolite panel of potential diagnostic biomarkers. Receiver operating characteristic (ROC) analysis showed high predictive accuracy of the resulting PLS‐DA models for discrimination of NC (96.6%), HPC (91.7%), MCI (93.9%) and AD (95.1%). Pathway analysis revealed significant disturbances in lysine degradation (p = 0.007), fatty acid metabolism (p = 0.009), and the degradation of branched‐chain amino acids (p = 0.025). Over representation analysis of lipid signatures revealed significant alterations in glycerophospholipid (FDR q = 8.3E‐07) and choline metabolism (FDR q = 0.006). Network analysis showed significant enrichment in 11 enzymes, mostly of the mitochondria. The current results allow for improved clinical staging post‐mortem, expand basic knowledge of the metabolome related to AD pathogenesis, and reveal pathways that can be targeted therapeutically in future studies. This study also provides a promising basis for the development of larger multi‐site 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 across population groups. Support or Funding Information Support from the College of Health Solutions at Arizona State University and the National Institutes of Health is gratefully acknowledged.