CSF metabolites associated with biomarkers of Alzheimer's disease pathology

Decoding aging clocks: New insights from metabolomics

Chronological age is a crucial risk factor for diseases and disabilities among older adults. However, individuals of the same chronological age often exhibit divergent biological aging states, resulting in distinct individual risk profiles. Chronological age estimators based on omics data and machine learning techniques, known as aging clocks, provide a valuable framework for interpreting molecular-level biological aging. Metabolomics is an intriguing and rapidly growing field of study, involving the comprehensive profiling of small molecules within the body and providing the ultimate genome-environment interaction readout. Consequently, leveraging metabolomics to characterize biological aging holds immense potential. The aim of this review was to provide an overview of metabolomics approaches, highlighting the establishment and interpretation of metabolomic aging clocks while emphasizing their strengths, limitations, and applications, and to discuss their underlying biological significance, which has the potential to drive innovation in longevity research and development.

Mendelian randomization study of causal link from Cerebrospinal fluid metabolomics to neurodegenerative diseases

To investigate the causal relationships between cerebrospinal fluid (CSF) metabolites and various neurodegenerative diseases (NDDs), we conducted a two-sample Mendelian randomization (MR) analysis. This study utilized summary statistics from genome-wide association studies (GWAS) of CSF metabolites and four common neurodegenerative diseases: Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS). MR methods were employed to determine causal associations, with the inverse variance weighted method as the primary approach. Additionally, different GWAS summary data for NDDs were used to validate the initial results and perform sensitivity analyses to enhance the robustness of the findings. Finally, reverse MR analyses were conducted to assess the possibility of reverse causation. Combining results from the initial and replication phases of MR analysis, we identified potential causal relationships between various CSF metabolites and different NDDs. Specifically, we found potential causal relationships between five CSF metabolites and AD, six CSF metabolites and MS, and thirteen CSF metabolites and ALS. Further sensitivity analyses confirmed the robustness of these associations. Reverse MR analysis indicated causal effects of AD on glucuronate and ALS on acetylcarnitine (C2). Our study, through genetic means, demonstrates close causal associations between the specific types of CSF metabolites and the risk of NDDS (AD, PD, MS, and ALS), providing useful guidance for future clinical researches.

Genetic architecture of cerebrospinal fluid and brain metabolite levels and the genetic colocalization of metabolites with human traits

Brain metabolism perturbation can contribute to traits and diseases. We conducted a genome-wide association study for cerebrospinal fluid (CSF) and brain metabolite levels, identifying 205 independent associations (47.3% new signals, containing 11 new loci) for 139 CSF metabolites, and 32 independent associations (43.8% new signals, containing 4 new loci) for 31 brain metabolites. Of these, 96.9% (CSF) and 71.4% (brain) of the new signals belonged to previously analyzed metabolites in blood or urine. We integrated the metabolite quantitative trait loci (MQTLs) with 23 neurological, psychiatric and common human traits and diseases through colocalization to identify metabolites and biological processes implicated in these phenotypes. Combining CSF and brain, we identified 71 metabolite-trait associations, such as glycerophosphocholines with Alzheimer's disease, O-sulfo-L-tyrosine with Parkinson's disease, glycine, xanthine with waist-to-hip ratio and ergothioneine with inflammatory bowel disease. Our study expanded the knowledge of MQTLs in the central nervous system, providing insights into human traits.

Multi-omics analyses highlight molecular differences between clinical and neuropathological diagnoses in Alzheimer's disease

Both clinical diagnosis and neuropathological diagnosis are commonly used in literature to categorize individuals as Alzheimer's disease (AD) or non-AD in omics analyses. Whether these diagnostic strategies result in distinct profiles of molecular abnormalities is poorly understood. Here, we analysed one of the most commonly used AD omics datasets in the literature from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort and compared the two diagnosis strategies using brain transcriptome and metabolome by grouping individuals as non-AD and AD according to clinical or neuropathological diagnosis separately. Differentially expressed genes, associated pathways related with AD hallmarks and AD-related genes showed that the categorization based on neuropathological diagnosis more accurately reflects the disease state at the molecular level than the categorization based on clinical diagnosis. We further identified consensus biomarker candidates between the two diagnosis strategies such as 5-hydroxylysine, sphingomyelin and 1-myristoyl-2-palmitoyl-GPC as metabolite biomarkers and sphingolipid metabolism as a pathway biomarker, which could be robust AD biomarkers since they are independent of diagnosis strategies. We also used consensus AD and consensus non-AD individuals between the two diagnostic strategies to train a machine-learning based model, which we used to classify the individuals who were cognitively normal but diagnosed as AD based on neuropathological diagnosis (asymptomatic AD individuals). The majority of these individuals were classified as consensus AD patients for both omics data types. Our study provides a detailed characterization of both diagnostic strategies in terms of the association of the corresponding multi-omics profiles with AD.

Exploring Intrinsic Disorder in Human Synucleins and Associated Proteins

In this work, we explored the intrinsic disorder status of the three members of the synuclein family of proteins-α-, β-, and γ-synucleins-and showed that although all three human synucleins are highly disordered, the highest levels of disorder are observed in γ-synuclein. Our analysis of the peculiarities of the amino acid sequences and modeled 3D structures of the human synuclein family members revealed that the pathological mutations A30P, E46K, H50Q, A53T, and A53E associated with the early onset of Parkinson's disease caused some increase in the local disorder propensity of human α-synuclein. A comparative sequence-based analysis of the synuclein proteins from various evolutionary distant species and evaluation of their levels of intrinsic disorder using a set of commonly used bioinformatics tools revealed that, irrespective of their origin, all members of the synuclein family analyzed in this study were predicted to be highly disordered proteins, indicating that their intrinsically disordered nature represents an evolutionary conserved and therefore functionally important feature. A detailed functional disorder analysis of the proteins in the interactomes of the human synuclein family members utilizing a set of commonly used disorder analysis tools showed that the human α-synuclein interactome has relatively higher levels of intrinsic disorder as compared with the interactomes of human β- and γ- synucleins and revealed that, relative to the β- and γ-synuclein interactomes, α-synuclein interactors are involved in a much broader spectrum of highly diversified functional pathways. Although proteins interacting with three human synucleins were characterized by highly diversified functionalities, this analysis also revealed that the interactors of three human synucleins were involved in three common functional pathways, such as the synaptic vesicle cycle, serotonergic synapse, and retrograde endocannabinoid signaling. Taken together, these observations highlight the critical importance of the intrinsic disorder of human synucleins and their interactors in various neuronal processes.

Decoding sTREM2: its impact on Alzheimer's disease - a comprehensive review of mechanisms and implications

Soluble Triggering Receptor Expressed on Myeloid Cells 2 (sTREM2) plays a crucial role in the pathogenesis of Alzheimer's disease (AD). This review comprehensively examines sTREM2's involvement in AD, focusing on its regulatory functions in microglial responses, neuroinflammation, and interactions with key pathological processes. We discuss the dynamic changes in sTREM2 levels in cerebrospinal fluid and plasma throughout AD progression, highlighting its potential as a therapeutic target. Furthermore, we explore the impact of genetic variants on sTREM2 expression and its interplay with other AD risk genes. The evidence presented in this review suggests that modulating sTREM2 activity could influence AD trajectory, making it a promising avenue for future research and drug development. By providing a holistic understanding of sTREM2's multifaceted role in AD, this review aims to guide future studies and inspire novel therapeutic strategies.

Metabolomic analysis of swainsonine poisoning in renal tubular epithelial cells

Locoweed is a poisonous plant widely present in grasslands around the world. Swainsonine (SW), an indole alkaloid that, is the main toxic component of the locoweed. To understand the mechanism of SW-induced toxicity and to delineate the metabolic profile of locoweed poisoning we performed the LC-MS/MS untargeted metabolomic study to analyze metabolites in SW-treated renal tubular epithelial cells (0.8 mg/mL, 12 h) and in order to identify the SW-induced metabolomic changes. The analysis identified 2,563 metabolites in positive ion mode and 1,990 metabolites in negative ion mode. Our results showed that the metabolites were mainly benzenoids, lipids and lipid-like molecules, nucleosides, nucleotides, and analogs, organic acids, and derivatives. The differential metabolites were primarily enriched in pathways involving bile secretion, primary bile acid biosynthesis, riboflavin metabolism, ferroptosis, drug metabolism-cytochrome P450, and primidine metabolism. We have screened out substances such as swainsonine, 3alpha,7alpha-Dihydroxy-5beta-cholestanate, 2-Hydroxyiminostilbene, and glycochenodeoxycholate, which may have the potential to serve as biomarkers for swainsonine poisoning. This study provides insights into the types of metabolomic alteration in renal tubular epithelial cells induced by swainsonine.

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.