Characterising a Novel Therapeutic Target for Psoriasis, TYK2, Using Functional Genomics

Abstract

Psoriasis (Ps) is a debilitating immune-mediated chronic skin condition. It affects about 1-3% of the world population, with an 8-11% prevalence in Northern European populations. Tyrosine kinase 2 (TYK2) is a newly identified target for Ps. An independent non-coding genetic association with Ps has been identified ~400 kb upstream of TYK2. The variants making up the credible Ps Single-Nucleotide Polymorphism (SNP) set were identified in their genomic context with the potential to influence TYK2 expression by interacting with regulatory elements involved in gene regulation. Previous evidence from our laboratory has suggested that credible SNP sets in intronic regions can be distal regulators of the genes of interest through long-range chromatin interactions. We hypothesise that SNPs at ILF3 are distal regulators of TYK2 expression via long-range chromatin interactions and Ps risk. The dysregulation of the TYK2 pathway in Ps may be mediated by a combination of GWAS risk SNPs at ILF3 and TYK2 and downstream genes. We investigated this by employing functional genomics and molecular biology methods. We developed a CD4 T cell model system with Jurkat-dCAS9-VP64 and Jurkat-dCAS9-KRAB cells using CRISPR activation and CRISPR inhibition of the risk variants rs892086 and rs7248205, selected from the latest Ps GWAS SNP set for their long-range interaction and light Linkage Disequilibrium (R² > 0.8), respectively. Using CRISPR activation, we demonstrate here that these risk SNPs, although distal to TYK2, do indeed regulate the TYK2 gene. Investigations into annotating the TYK2 pathway using RNA-seq analysis revealed differentially regulated genes, including VEGFA, C1R, ADORA1, GLUD2, NDUFB8, and FCGR2C, which are thought to be implicated in Ps. These genes were observed to be associated with conditions such as psoriatic arthritis, atopic dermatitis, and systemic sclerosis when compared using published databases, which confirms their relevance and importance in inflammatory conditions. With the developed cell model systems using CRISPR technology and differential gene regulation, we demonstrate here that these genes have the potential to define the TYK2/Ps pathway and our understanding of the disease biology.

Keywords: STAT; TYK2; deucravacitinib; protective allele; psoriasis; risk allele.

Figure 1

rs892086 and rs7248205 SNPs in strong LD (R² > 0.8) overlapped with enhancer and promoter marks (a). The SNPs are eQTLs for SMARCA4, SLC44A2, and ILF3 in skin and skeletal muscle (GTEx portal). The SNPs were mapped using LD Proxy for a European population. An independent non-coding genetic association with psoriasis was identified ~400 kb upstream of TYK2, and this is shown in (b,c) for rs892086 and rs7248205 SNPs, respectively, highlighted in blue.

Figure 2

The expression of TYK2 was assessed using the RT qPCR experiment. TYK2 gene expression was measured using quantitative real-time PCR. The results from the inhibition experiment are shown in (a). The results from the CRISPR activation experiment are shown in (b), comparing the SNPs rs892086 and rs7248205 with the scramble negative control. IL1RN and ST3GAL4 were used as positive controls for the CRISPR activation and inhibition experiments, respectively, and are shown in (c,d). The expression of genes can be seen as a fold change in the graph. Triplicates were used for each test. The fold change was measured using the 2^−∆∆Ct method by normalising the geometric mean of the Ct values of the housekeeping genes GAPDH and YWHAZ and the variation between genes. Data were normalised against the background. Experiments were analysed using a t test and non-parametric Mann–Whitney test (* p < 0.05). Data are presented as means +/− SD and are representative of 3 independent experiments.

Figure 3

Differential gene expression comparison of Jurkat-dCAS9-VP64-TYK2 with scramble control assessed using RNA-seq experiment. Jurkat-dCAS9-VP64-TYK2 CD4+ T cells and Jurkat-dCAS9-VP64 scramble control samples were prepared using RNA extraction in triplicate. Two groups were compared to assess gene regulation. The results are shown as differential gene expression. Gene ontology results are shown for highlighted pathways in (a). Differential gene expression using a volcano plot is shown in (b), comparing Jurkat-dCAS9-VP64-TYK2 CD4+ T cells and the scramble control, followed by a heatmap comparing the two groups shown in (c). For both the differential gene expression and pathway analyses, results with padj < 10⁻⁵ are represented.

Figure 3

Differential gene expression comparison of Jurkat-dCAS9-VP64-TYK2 with scramble control assessed using RNA-seq experiment. Jurkat-dCAS9-VP64-TYK2 CD4+ T cells and Jurkat-dCAS9-VP64 scramble control samples were prepared using RNA extraction in triplicate. Two groups were compared to assess gene regulation. The results are shown as differential gene expression. Gene ontology results are shown for highlighted pathways in (a). Differential gene expression using a volcano plot is shown in (b), comparing Jurkat-dCAS9-VP64-TYK2 CD4+ T cells and the scramble control, followed by a heatmap comparing the two groups shown in (c). For both the differential gene expression and pathway analyses, results with padj < 10⁻⁵ are represented.