Genomic opportunities for drug repositioning in systemic seropositive rheumatic diseases

Recent advances in elucidating the genetic basis of systemic sclerosis

PURPOSE OF REVIEW

Systemic sclerosis (SSc) is a complex autoimmune disorder that affects the connective tissue and causes severe vascular damage and fibrosis of the skin and internal organs. There are recent advances in the field that apply novel methods to high throughput genotype information of thousands of patients with SSc and provide promising results towards the use of genomic data to help SSc diagnosis and clinical care.

RECENT FINDINGS

This review addresses the development of the first SSc genomic risk score, which can contribute to differentiating SSc patients from healthy controls and other immune-mediated diseases. Moreover, we explore the implementation of data mining strategies on the results of genome-wide association studies to highlight subtype-specific HLA class II associations and a strong association of the HLA class I locus with SSc for the first time. Finally, the combination of genomic data with transcriptomics informed drug repurposing and genetic association studies in well characterized SSc patient cohorts identified markers of severe complications of the disease.

SUMMARY

Early diagnosis and clinical management of SSc and SSc-related complications are still challenging for rheumatologists. The development of predictive models and tools using genotype data may help to finally deliver personalized clinical care and treatment for patients with SSc in the near future.

Expression Quantitative Trait Locus Analysis in Systemic Sclerosis Identifies New Candidate Genes Associated With Multiple Aspects of Disease Pathology

OBJECTIVE

To identify the genetic variants that affect gene expression (expression quantitative trait loci [eQTLs]) in systemic sclerosis (SSc) and to investigate their role in the pathogenesis of the disease.

METHODS

We performed an eQTL analysis using whole-blood sequencing data from 333 SSc patients and 524 controls and integrated them with SSc genome-wide association study (GWAS) data. We integrated our findings from expression modeling, differential expression analysis, and transcription factor binding site enrichment with key clinical features of SSc.

RESULTS

We detected 49,123 validated cis-eQTLs from 4,539 SSc-associated single-nucleotide polymorphisms (SNPs) (P_GWAS < 10^-5 ). A total of 1,436 genes were within 1 Mb of the 4,539 SSc-associated SNPs. Of those 1,436 genes, 565 were detected as having ≥1 eQTL with an SSc-associated SNP. We developed a strategy to prioritize disease-associated genes based on their expression variance explained by SSc eQTLs (r² > 0.05). As a result, 233 candidates were identified, 134 (58%) of them associated with hallmarks of SSc and 105 (45%) of them differentially expressed in the blood cells, skin, or lung tissue of SSc patients. Transcription factor binding site analysis revealed enriched motifs of 24 transcription factors (5%) among SSc eQTLs, 5 of which were found to be differentially regulated in the blood cells (ELF1 and MGA), skin (KLF4 and ID4), and lungs (TBX4) of SSc patients. Ten candidate genes (4%) can be targeted by approved medications for immune-mediated diseases, of which only 3 have been tested in clinical trials in patients with SSc.

CONCLUSION

The findings of the present study indicate a new layer to the molecular complexity of SSc, contributing to a better understanding of the pathogenesis of the disease.