The identification of a potential plasma metabolite marker for Alzheimer’s disease by LC-MS untargeted metabolomics

Can Small Molecules Provide Clues on Disease Progression in Cerebrospinal Fluid from Mild Cognitive Impairment and Alzheimer's Disease Patients?

Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disease, which is currently diagnosed via clinical symptoms and nonspecific biomarkers (such as Aβ1-42, t-Tau, and p-Tau) measured in cerebrospinal fluid (CSF), which alone do not provide sufficient insights into disease progression. In this pilot study, these biomarkers were complemented with small-molecule analysis using non-target high-resolution mass spectrometry coupled with liquid chromatography (LC) on the CSF of three groups: AD, mild cognitive impairment (MCI) due to AD, and a non-demented (ND) control group. An open-source cheminformatics pipeline based on MS-DIAL and patRoon was enhanced using CSF- and AD-specific suspect lists to assist in data interpretation. Chemical Similarity Enrichment Analysis revealed a significant increase of hydroxybutyrates in AD, including 3-hydroxybutanoic acid, which was found at higher levels in AD compared to MCI and ND. Furthermore, a highly sensitive target LC-MS method was used to quantify 35 bile acids (BAs) in the CSF, revealing several statistically significant differences including higher dehydrolithocholic acid levels and decreased conjugated BA levels in AD. This work provides several promising small-molecule hypotheses that could be used to help track the progression of AD in CSF samples.

Discovery of antitussive material basis and mechanisms in Citri Sarcodactylis Fructus by coupling UHPLC-Q/Orbitrap HRMS combined spectrum-effect relationship and metabolomics analyses

Citri Sarcodactylis Fructus (CSF) is widely used as food raw material and traditional Chinese medicine. Fingerprints of different fractions of CSF were established for spectrum-effect relationship analysis, and the main compounds were identified by UHPLC Quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q/Orbitrap HRMS). The antitussive effect was evaluated using a classical mouse model of cough induced by ammonia water. One-way ANOVA was used to determine differences in efficacy. The potential active compounds were screened by spectrum-effect relationship with grey relational degree analysis (GRA), Pearson bivariate correlation analysis (Pearson's), and partial least squares analysis (PLS) analyses. Differential metabolites associated with cough in serum were screened and identified using orthogonal partial least squares-discriminant analysis, HMDB database, and UHPLC-Q/Orbitrap HRMS. Metabolic pathway analysis was performed using MetaboAnalyst 5.0. Results indicate that 70 % ethanol elution fraction (70 % EF) is the major active fraction, and 8 components were identified to possess antitussive effects. Metabolomic analysis showed that 19 metabolites are potential biomarkers related to cough, and 70 % EF can remarkable restore 13 of them to normal levels (P < 0.05). These biomarkers are mainly involved in glycerophospholipid metabolism and sphingolipid metabolism. This study aims to reveal the main pharmacodynamic active sites and potential active ingredients of CSF's antitussive effect. In addition, metabolomics was used to preliminarily elucidate the in-vivo regulatory mechanism of the antitussive effect of the 70 % EF of CSF.

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

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.