Enrichr in silico analysis of MS-based extracted candidate proteomic biomarkers highlights pathogenic pathways in systemic sclerosis.

Systemic sclerosis (SSc) is a rheumatic disease characterised by vasculopathy, inflammation and fibrosis. Its aetiopathogenesis is still unknown, and the pathways/mechanisms of the disease are not clarified. This study aimed to perform in silico analysis of the already Mass Spectrometry (MS)-based discovered biomarkers of SSc to extract possible pathways/mechanisms implicated in the disease. We recorded all published candidate MS-based found biomarkers related to SSc. We then selected a number of the candidate biomarkers using specific criteria and performed pathway and cellular component analyses using Enrichr. We used PANTHER and STRING to assess the biological processes and the interactions of the recorded proteins, respectively. Pathway analysis extracted several pathways that are associated with the three different stages of SSc pathogenesis. Some of these pathways are also related to other diseases, including autoimmune diseases. We observe that these biomarkers are located in several cellular components and implicated in many biological processes. STRING analysis showed that some proteins interact, creating significant clusters, while others do not display any evidence of an interaction. All these data highlight the complexity of SSc, and further investigation of the extracted pathways/biological processes and interactions may help study the disease from a different angle.

Genetic Study of Early Onset Parkinson's Disease in Cyprus.

Parkinson's Disease (PD) is a multifactorial neurodegenerative disease characterized by motor and non-motor symptoms. The etiology of PD remains unclear. However, several studies have demonstrated the interplay of genetic, epigenetic, and environmental factors in PD. Early-onset PD (EOPD) is a subgroup of PD diagnosed between the ages of 21 and 50. Population genetic studies have demonstrated great genetic variability amongst EOPD patients. Hence, this study aimed to obtain a genetic landscape of EOPD in the Cypriot population. Greek-Cypriot EOPD patients (n = 48) were screened for variants in the six most common EOPD-associated genes (PINK1, PRKN, FBXO7, SNCA, PLA2G6, and DJ-1). This included DNA sequencing and Multiplex ligation-dependent probe amplification (MLPA). One previously described frameshift variant in PINK1 (NM_032409.3:c.889del) was detected in five patients (10.4%)-the largest number to be detected to date. Copy number variations in the PRKN gene were identified in one homozygous and 3 compound heterozygous patients (8.3%). To date, the pathogenic variants identified in this study have explained the PD phenotype for 18.8% of the EOPD cases. The results of this study may contribute to the genetic screening of EOPD in Cyprus.

Prediction of Parkinson's Disease Risk Based on Genetic Profile and Established Risk Factors.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder, and literature suggests that genetics and lifestyle/environmental factors may play a key role in the triggering of the disease. This study aimed to evaluate the predictive performance of a 12-Single Nucleotide Polymorphisms (SNPs) polygenic risk score (PRS) in combination with already established PD-environmental/lifestyle factors. METHODS: Genotypic and lifestyle/environmental data on 235 PD-patients and 464 controls were obtained from a previous study carried out in the Cypriot population. A PRS was calculated for each individual. Univariate logistic-regression analysis was used to assess the association of PRS and each risk factor with PD-status. Stepwise-regression analysis was used to select the best predictive model for PD combining genetic and lifestyle/environmental factors. RESULTS: The 12-SNPs PRS was significantly increased in PD-cases compared to controls. Furthermore, univariate analyses showed that age, head injury, family history, depression, and Body Mass Index (BMI) were significantly associated with PD-status. Stepwise-regression suggested that a model which includes PRS and seven other independent lifestyle/environmental factors is the most predictive of PD in our population. CONCLUSIONS: These results suggest an association between both genetic and environmental factors and PD, and highlight the potential for the use of PRS in combination with the classical risk factors for risk prediction of PD.