RESUMO
Background: Chikungunya is a zoonotic disease caused by the Chikungunya virus (CHIKV), primarily transmitted to humans through infected Aedes mosquitoes. The infection is characterized by symptoms such as high fever, musculoskeletal pain, polyarthritis, and a rash, which can lead to severe complications such as encephalitis, meningitis, and even fatalities. While many disease manifestations resemble those of other viral infections, chronic arthritis caused by CHIKV is unique, and its molecular mechanisms remain ill-defined. Materials and Methods: Proteomics data from both cellular and patient levels of CHIKV infection were curated from PubMed and screened using inclusion and exclusion criteria. Patient serum proteomics data obtained from P RIDE underwent reanalysis using Proteome Discoverer 2.2. Enrichment and protein-protein interaction network analysis were conducted on differentially expressed proteins from both serum and cellular datasets. Metabolite data from CHIKV-infected patients were further retrieved, and their protein binding partners were identified using BindingDB. The protein-metabolite interaction pathway was further developed using MetaboAnalyst. Results: The proteomics data analysis revealed differential expression of proteins involved in critical host mechanisms, such as cholesterol metabolism and mRNA splicing, during CHIKV infection. Consistent upregulation of two actin cytoskeleton proteins, TAGLN2 and PFN1, was noted in both serum and cellular datasets, and their upregulations are associated with arthritis. Furthermore, alterations in purine metabolism were observed in the integrative proteome-metabolome analysis, correlating with cytoskeletal remodelling. Conclusion: Collectively, this integrative view sheds light on the involvement of actin cytoskeleton remodeling proteins and purine metabolic pathways in the development of arthritis during CHIKV infection.
RESUMO
In recent years, we have seen the widespread devastations and serious health complications manifested by COVID-19 globally. Although we have effectively controlled the pandemic, uncertainties persist regarding its potential long-term effects, including prolonged neurological issues. To gain comprehensive insights, we conducted a meta-analysis of mass spectrometry-based proteomics data retrieved from different studies with a total of 538 COVID-19 patients and 523 healthy controls. The meta-analysis revealed that top-enriched pathways were associated with neurological disorders, including Alzheimer's (AD) and Parkinson's disease (PD). Further analysis confirmed a direct correlation in the expression patterns of 24 proteins involved in Alzheimer's and 23 proteins in Parkinson's disease with COVID-19. Protein-protein interaction network and cluster analysis identified SNCA as a hub protein, a known biomarker for Parkinson's disease, in both AD and PD. To the best of our knowledge, this is the first meta-analysis study providing proteomic profiling evidence linking COVID-19 to neurological complications.
