Serum Glycoproteomics and Identification of Potential Mechanisms Underlying Alzheimer’s Disease

Novel Serum Proteomes Expressed from Benzene Exposure Among Gasoline Station Attendants

Background

Research on the proteomes impact of benzene exposure in fuel station employees remains sparse, underscoring the need for detailed health impact assessments focusing on biomarker evaluation.

Objectives

This investigation aimed to analyze the differences in blood parameters and serum proteomes resulting from benzene exposure between gasoline station attendants (B-GSA) and a control group.

Design and methods

A cross-sectional analytical study was conducted with 96 participants, comprising 54 in the B-GSA group and 42 in the control group. The methodology employed included an interview questionnaire alongside urine and blood sample collections. The urine samples were analyzed for trans,trans-muconic acid (t,t-MA) levels, while the blood samples underwent complete blood count analysis and proteome profiling.

Results

Post-shift analysis indicated that the B-GSA group exhibited significantly higher levels of t,t-MA and monocytes compared to the control group (P < .05). Proteome quantification identified 1448 proteins differentially expressed between the B-GSA and control groups. Among these, 20 proteins correlated with the levels of t,t-MA in urine. Notably, 4 proteins demonstrated more than a 2-fold down-regulation in the B-GSA group: HBS1-like, non-structural maintenance of chromosomes element 1 homolog, proprotein convertase subtilisin/kexin type 4, and zinc finger protein 658. The KEGG pathway analysis revealed associations with apoptosis, cancer pathways, p53 signaling, and the TNF signaling pathway.

Conclusion

The changes in these 4 significant proteins may elucidate the molecular mechanisms underlying benzene toxicity and suggest their potential as biomarkers for benzene poisoning in future assessments.

Personalized Protein-Protein Interaction Networks Towards Unraveling the Molecular Mechanisms of Alzheimer's Disease

Alzheimer's disease (AD) is a highly heterogenous neurodegenerative disease, and several omic-based datasets were generated in the last decade from the patients with the disease. However, the vast majority of studies evaluate these datasets in bulk by considering all the patients as a single group, which obscures the molecular differences resulting from the heterogeneous nature of the disease. In this study, we adopted a personalized approach and analyzed the transcriptome data from 403 patients individually by mapping the data on a human protein-protein interaction network. Patient-specific subnetworks were discovered and analyzed in terms of the genes in the subnetworks, enriched functional terms, and known AD genes. We identified several affected pathways that could not be captured by the bulk comparison. We also showed that our personalized findings point to patterns of alterations consistent with the recently suggested AD subtypes.

Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases

The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.