ORCID Profile
0000-0002-6298-4514
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Publisher: Elsevier BV
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 09-2021
Publisher: Wiley
Date: 16-09-2020
DOI: 10.1111/ALL.14552
Publisher: Public Library of Science (PLoS)
Date: 14-03-2018
Publisher: BMJ
Date: 10-2020
DOI: 10.1136/ANNRHEUMDIS-2020-218558
Abstract: Genomic Risk Scores (GRS) successfully demonstrated the ability of genetics to identify those in iduals at high risk for complex traits including immune-mediated inflammatory diseases (IMIDs). We aimed to test the performance of GRS in the prediction of risk for systemic sclerosis (SSc) for the first time. Allelic effects were obtained from the largest SSc Genome-Wide Association Study (GWAS) to date (9 095 SSc and 17 584 healthy controls with European ancestry). The best-fitting GRS was identified under the additive model in an independent cohort that comprised 400 patients with SSc and 571 controls. Additionally, GRS for clinical subtypes (limited cutaneous SSc and diffuse cutaneous SSc) and serological subtypes (anti-topoisomerase positive (ATA+) and anti-centromere positive (ACA+)) were generated. We combined the estimated GRS with demographic and immunological parameters in a multivariate generalised linear model. The best-fitting SSc GRS included 33 single nucleotide polymorphisms (SNPs) and discriminated between patients with SSc and controls (area under the receiver operating characteristic (ROC) curve (AUC)=0.673). Moreover, the GRS differentiated between SSc and other IMIDs, such as rheumatoid arthritis and Sjögren’s syndrome. Finally, the combination of GRS with age and immune cell counts significantly increased the performance of the model (AUC=0.787). While the SSc GRS was not able to discriminate between ATA+ and ACA+ patients (AUC .5), the serological subtype GRS, which was based on the allelic effects observed for the comparison between ACA+ and ATA+ patients, reached an AUC=0.693. GRS was successfully implemented in SSc. The model discriminated between patients with SSc and controls or other IMIDs, confirming the potential of GRS to support early and differential diagnosis for SSc.
Publisher: BMJ
Date: 04-2021
DOI: 10.1136/ANNRHEUMDIS-2021-219884
Abstract: The greatest genetic effect reported for systemic sclerosis (SSc) lies in the major histocompatibility complex (MHC) locus. Leveraging the largest SSc genome-wide association study, we aimed to fine-map this region to identify novel human leucocyte antigen (HLA) genetic variants associated with SSc susceptibility and its main clinical and serological subtypes. 9095 patients with SSc and 17 584 controls genome-wide genotyped were used to impute and test single-nucleotide polymorphisms (SNPs) across the MHC, classical HLA alleles and their composite amino acid residues. Additionally, patients were stratified according to their clinical and serological status, namely, limited cutaneous systemic sclerosis (lcSSc), diffuse cutaneous systemic sclerosis (dcSSc), anticentromere (ACA), antitopoisomerase (ATA) and anti-RNApolIII autoantibodies (ARA). Sequential conditional analyses showed nine SNPs, nine classical alleles and seven amino acids that modelled the observed associations with SSc. This confirmed previously reported associations with HLA-DRB1*11:04 and HLA-DPB1*13:01 , and revealed a novel association of HLA-B*08:01 . Stratified analyses showed specific associations of HLA-DQA1*02:01 with lcSSc, and an exclusive association of HLA-DQA1*05:01 with dcSSc. Similarly, private associations were detected in HLA-DRB1*08:01 and confirmed the previously reported association of HLA-DRB1*07:01 with ACA-positive patients, as opposed to the HLA-DPA1*02:01 and HLA-DQB1*03:01 alleles associated with ATA presentation. This study confirms the contribution of HLA class II and reveals a novel association of HLA class I with SSc, suggesting novel pathways of disease pathogenesis. Furthermore, we describe specific HLA associations with SSc clinical and serological subtypes that could serve as biomarkers of disease severity and progression.
Publisher: BMJ
Date: 19-06-2020
DOI: 10.1136/ANNRHEUMDIS-2020-217116
Abstract: The analysis of annotated transcripts from genome-wide expression studies may help to understand the pathogenesis of complex diseases, such as systemic sclerosis (SSc). We performed a whole blood (WB) transcriptome analysis on RNA collected in the context of the European PRECISESADS project, aiming at characterising the pathways that differentiate SSc from controls and that are reproducible in geographically erse populations. S les from 162 patients and 252 controls were collected in RNA stabilisers. Cases and controls were ided into a discovery (n=79+163 Southern Europe) and validation cohort (n=83+89 Central-Western Europe). RNA sequencing was performed by an Illumina assay. Functional annotations of Reactome pathways were performed with the Functional Analysis of In idual Microarray Expression (FAIME) algorithm. In parallel, immunophenotyping of 28 circulating cell populations was performed. We tested the presence of differentially expressed genes athways and the correlation between absolute cell counts and RNA transcripts/FAIME scores in regression models. Results significant in both populations were considered as replicated. Overall, 15 224 genes and 1277 functional pathways were available of these, 99 and 225 were significant in both sets. Among replicated pathways, we found a deregulation in type-I interferon, Toll-like receptor cascade, tumour suppressor p53 protein function, platelet degranulation and activation. RNA transcripts or FAIME scores were jointly correlated with cell subtypes with strong geographical differences neutrophils were the major determinant of gene expression in SSc-WB s les. We discovered a set of differentially expressed genes athways validated in two independent sets of patients with SSc, highlighting a number of deregulated processes that have relevance for the pathogenesis of autoimmunity and SSc.
Publisher: Springer Science and Business Media LLC
Date: 31-10-2019
DOI: 10.1038/S41467-019-12760-Y
Abstract: Systemic sclerosis (SSc) is an autoimmune disease that shows one of the highest mortality rates among rheumatic diseases. We perform a large genome-wide association study (GWAS), and meta-analysis with previous GWASs, in 26,679 in iduals and identify 27 independent genome-wide associated signals, including 13 new risk loci. The novel associations nearly double the number of genome-wide hits reported for SSc thus far. We define 95% credible sets of less than 5 likely causal variants in 12 loci. Additionally, we identify specific SSc subtype-associated signals. Functional analysis of high-priority variants shows the potential function of SSc signals, with the identification of 43 robust target genes through HiChIP. Our results point towards molecular pathways potentially involved in vasculopathy and fibrosis, two main hallmarks in SSc, and highlight the spectrum of critical cell types for the disease. This work supports a better understanding of the genetic basis of SSc and provides directions for future functional experiments.
Publisher: Wiley
Date: 26-04-2021
DOI: 10.1002/ART.41610
Abstract: Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis. Unsupervised clustering of integrated whole blood transcriptome and methylome cross‐sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time. Four clusters were identified and validated. Three were pathologic, representing “inflammatory,” “lymphoid,” and “interferon” patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse–remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each in idual patient. Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases.
Location: Switzerland
No related grants have been discovered for Martin Kerick.