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.

Comparative analysis of affected and unaffected areas of systemic sclerosis skin biopsies by high-throughput proteomic approaches.

BACKGROUND: Pathogenesis and aetiology of systemic sclerosis (SSc) are currently unclear, thus rendering disease prognosis, diagnosis and treatment challenging. The aim of this study was to use paired skin biopsy samples from affected and unaffected areas of the same patient, in order to compare the proteomes and identify biomarkers and pathways which are associated with SSc pathogenesis. METHODS: Biopsies were obtained from affected and unaffected skin areas of SSc patients. Samples were cryo-pulverised and proteins were extracted and analysed using mass spectrometry (MS) discovery analysis. Differentially expressed proteins were revealed after analysis with the Progenesis QIp software. Pathway analysis was performed using the Enrichr Web server. Using specific criteria, fifteen proteins were selected for further validation with targeted-MS analysis. RESULTS: Proteomic analysis led to the identification and quantification of approximately 2000 non-redundant proteins. Statistical analysis showed that 169 of these proteins were significantly differentially expressed in affected versus unaffected tissues. Pathway analyses showed that these proteins are involved in multiple pathways that are associated with autoimmune diseases (AIDs) and fibrosis. Fifteen of these proteins were further investigated using targeted-MS approaches, and five of them were confirmed to be significantly differentially expressed in SSc affected versus unaffected skin biopsies. CONCLUSION: Using MS-based proteomics analysis of human skin biopsies from patients with SSc, we identified a number of proteins and pathways that might be involved in SSc progression and pathogenesis. Fifteen of these proteins were further validated, and results suggest that five of them may serve as potential biomarkers for SSc.

Genetic Susceptibility to Systemic Sclerosis in the Greek-Cypriot Population: A Pilot Study.

Background: Systemic Sclerosis (SSc), also known as scleroderma, is an autoimmune rheumatic disease, which is clinically subdivided into two major subgroups; limited (lcSSc) and diffuse cutaneous scleroderma (dcSSc). Even though the SSc etiologies remains unclear, some HLA and non-HLA genetic variants have been associated with the disease. Aim: This study was designed to evaluate the associations between several HLA-related genetic variants and SSc in the Greek-Cypriot population. Methods: Forty-one SSc patients and 164 controls were genotyped at 18 selected single nucleotide polymorphisms (SNPs) using restriction fragment length polymorphism analyses, Sanger sequencing, and a multiplex SNaPshot minisequencing assay. Logistic regression analysis under the log-additive model was used to evaluate all possible associations between these SNPs and SSc; nominal statistical significance was assumed at p < 0.05. Results: Associations of SSc with SNPs rs3117230, rs3128930, and rs3128965 within the HLA-DPB1 and HLA-DPB2 regions were observed in the Greek-Cypriot population at the level of p < 0.05. However, none of these associations survived a Bonferroni correction. The direction of the effect is consistent with the direction reported in previous studies. In addition, allele frequencies of the majority of the selected SNPs in the Greek-Cypriot population are similar to those reported in the European population. Conclusion: This study initiates the genetic investigation of SSc in the Greek-Cypriot population, a relatively small newly investigated population. Further investigation with a larger sample size and/or additional SSc susceptibility loci may confirm the association of some of these variants with SSc in the Greek-Cypriot population that could potentially be used for predictive testing.

Genomic and genetic studies of systemic sclerosis: A systematic review.

Systemic sclerosis is an autoimmune rheumatic disease characterised by fibrosis, vasculopathy and inflammation. The exact aetiology of SSc remains unknown but evidences show that various genetic factors may be involved. This review aimed to assess HLA alleles/non-HLA polymorphisms, microsatellites and chromosomal abnormalities that have thus far been associated with SSc. PubMed, Embase and Scopus databases were searched up to July 29, 2015 using a combination of search-terms. Articles retrieved were evaluated based on set exclusion and inclusion criteria. A total of 150 publications passed the filters. HLA and non-HLA studies showed that particular alleles in the HLA-DRB1, HLA-DQB1, HLA-DQA1, HLA-DPB1 genes and variants in STAT4, IRF5 and CD247 are frequently associated with SSc. Non-HLA genes analysis was performed using the PANTHER and STRING10 databases. PANTHER classification revealed that inflammation mediated by chemokine and cytokine, interleukin and integrin signalling pathways are among the common extracted pathways associated with SSc. STRING10 analysis showed that NFKB1, CSF3R, STAT4, IFNG, PRL and ILs are the main "hubs" of interaction network of the non-HLA genes associated with SSc. This study gathers data of valid genetic factors associated with SSc and discusses the possible interactions of implicated molecules.