Characterization of chromatin accessibility in psoriasis

Abstract

The pathological hallmarks of psoriasis involve alterations in T cell genes associated with transcriptional levels, which are determined by chromatin accessibility. However, to what extent these alterations in T cell transcriptional levels recapitulate the epigenetic features of psoriasis remains unknown. Here, we systematically profiled chromatin accessibility on Th1, Th2, Th1-17, Th17, and Treg cells and found that chromatin remodeling contributes significantly to the pathogenesis of the disease. The chromatin remodeling tendency of different subtypes of Th cells were relatively consistent. Next, we profiled chromatin accessibility and transcriptional dynamics on memory Th/Treg cells. In the memory Th cells, 803 increased and 545 decreased chromatin-accessible regions were identified. In the memory Treg cells, 713 increased and 1206 decreased chromatin-accessible regions were identified. A total of 54 and 53 genes were differentially expressed in the peaks associated with the memory Th and Treg cells. FOSL1, SPI1, ATF3, NFKB1, RUNX, ETV4, ERG, FLI1, and ETC1 were identified as regulators in the development of psoriasis. The transcriptional regulatory network showed that NFKB1 and RELA were highly connected and central to the network. NFKB1 regulated the genes of CCL3, CXCL2, and IL1RN. Our results provided candidate transcription factors and a foundational framework of the regulomes of the disease.

Keywords

psoriasis / ATAC-seq / epigenetics / transcription factor

1 Introduction

Psoriasis is a chronic inflammatory disease involving the innate and adaptive immune compartments. Over the last two decades, research has unequivocally demonstrated that psoriasis is a bona fide T cell-mediated disease [1]. Cytokines secreted by T cells, including IL17, IL23, and TNF [2], lead to inflammatory responses in keratinocytes. Tyrosine kinase 2 variants in IL12-stimulated T helper (Th) 1 cells [3] and shRNA-mediated silencing of TRAF3 interacting protein 2 in keratinocytes play important roles in the pathogenesis of psoriasis [4]. Our genome-wide association studies (GWAS) of psoriasis identified 40 psoriasis susceptibility loci in the Chinese population [5]. To date, the study of various cells and their key genes has made great progress in deciphering the pathogenesis of psoriasis. Then, what is the most relevant path for psoriasis research going forward? From our respective, shifting the focus of research from genetics to epigenetics is of key importance.

The transcriptional activity of genes is regulated through interactions among promoters, enhancers, and other regulatory elements, which bind to transcription factors (TFs) [6]. Several TFs play important roles in the development of psoriasis. For instance, STAT TFs, as components of the JAK-STAT pathway, play a central role in disease development, as evidenced by the therapeutic potential of FDA-approved Jakinib tofacitinib. TF retinoid-related orphan receptor γt is a potential target for the treatment of psoriasis. Despite the obvious importance of transcriptional alterations in the pathogenesis of psoriasis, much work is needed in order for us to claim that we have a complete understanding of fundamental genetic and epigenetic changes involved in psoriasis.

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) [7,8] is a recent technology that enables the genome-wide profiling of chromatin accessibility patterns at base pair resolution with a limited number of cells. This technology provides an opportunity for the identification of the interference of psoriasis-associated disruptions to transcriptional regulatory programs, including changes in noncoding cis-acting sequences, such as enhancers and TF activity. To study the regulatory mechanism of psoriasis and further understand the regulatory mechanism of psoriasis gene regulatory networks, we conducted genome-wide analysis of open chromatin in Th1, Th17, Th1-17, naïve T cells, and Treg cells. Finally, we focused on genome-wide analysis of gene expression and open chromatin analysis on memory Th and memory Treg cells.

2 Materials and methods

2.1 ATAC-seq sequencing, library quality control, and data analysis

Blood samples were collected from 12 patients with psoriasis and 13 healthy controls in Huashan Hospital of Fudan University. Diagnosis was confirmed by at least two dermatologists. All participants were required to provide informed consent. The study was based on the principles set out in the Helsinki Declaration. PBMCs were isolated from 20 mL fresh blood by density gradient centrifugation method using Ficoll reagent, and naïve T cells (CD4⁺ CD45RA⁺CD25⁻CD127^hi), regulatory T cells (Treg cells; CD4⁺CD25⁺CD127^low), Th1 cells (CD4⁺CD45RA⁻ CD25⁻CD127^hiCXCR3⁺CCR6⁻CXCR5⁻), Th17 cells (CD4⁺CD45RA⁻CD25⁻CD127^hiCXCR3⁻CCR6⁺CXCR5⁻), Th1-17 cells (CD4⁺CD45RA⁻CD25⁻CD127^hiCXCR3⁺ CCR6⁺CXCR5⁻), Th2 cells (CD4⁺CD45RA⁻CD25⁻ CD127^hiCXCR3⁻CCR6⁻CXCR5⁻), memory Th (CD4⁺ CD25⁺CD127^lowCD45RA⁻) and memory Treg (CD45RA⁻CD4⁺CD25⁻CD127^hi; Fig. 1B, Fig. S1) cells were stained with a fluorescent antibody (BD) and selected by FACS (MoFlo XDP platform,Beckman). For all cell types, approximately 50 000 cells were used for ATAC-seq library preparation according to the original protocol [9]. Each ATAC-seq library was sequenced on the Illumina NovaSeq sequencer for the generation of 80–100 million of 150 bp paired-end reads per sample. Adaptors and low-quality base with Phred quality score of<20 were trimmed with Trimmomatic [10] for the generation of high-quality clean data. Clean data were mapped on human reference genome GRCh38 with Bowtie2 [11] with the following parameters: “–very-sensitive-X 2000.” Picard Tools (v.2.2.4) was used in marking and removing duplicate reads. Reads mapped on mitochondrial chromosome were removed, and only properly paired reads with mapping quality of>30 were kept. Mapped reads were shifted by+4/−5 bp depending on the strand of the read to represent the Tn5 cleavage position. Peak calling was performed with MACS2 [12], and the following parameters were used: -f BAMPE-g hs-q 1e-8. For the ATAC-seq differential peak analysis, we employed DiffBind [13] to merge peaks from subset and obtain the count of reads overlapping with peaks. Differential accessibility analysis was conducted using edgeR [14]. ChIPseeker [15] was used to annotate peaks overlapping known transcription start sites (TSSs), promoters, exons, and introns. Motif discovery at chromatin-accessible regions was performed using the default setting in the HOMER suite [16] function findMotifsGenome.pl tool. Tracks were extracted and visualized using the IGV genome browser [17,18]. K-means cluster and heatmaps showing ATAC-seq signals were built using the R package ComplexHeatmap [19].

2.2 RNA-seq profiling and differential gene expression analysis

Total RNA was extracted from the matched Th or Treg cells used for ATAC-seq with Trizol regent (Thermo Fisher Scientific). RNA quality was accessed with Bioanalyzer (Agilent Technologies), and RIN higher than 7 was used in constructing an mRNA-library. RNA-seq library preparation was performed using VAHTS mRNA-seq V3 library prep kit (Vazyme Biotech) according to the manufacturer’s instruction. Briefly, polyadenylated mRNA was enriched with oligodT magnetic beads and then fragmentation, end repair, adapter ligation, and PCR amplification were performed. Libraries were sequenced with an Illumina NovaSeq platform (150 bp, paired-end). Adaptors and low-quality bases with Phred quality scores of<20 were trimmed with Trimmomatic [10] for the generation of high-quality clean data. After quality control, these reads were aligned to human reference genome GRCh38 with a STAR splice-aware aligner (v.2.5.2b) [20]. RSEM [21] with default settings were used in determining read counts and estimating gene-level fragments per kilobase million (FPKM) reads per sample per gene. Differential gene expression analysis was performed using edgeR by supplying the read count data of genes (5%<FDR and log₂ (fold change)≥1) [14].

2.3 Chromatin accessibility and gene expression correlation

For comparison and visualization, ATAC-seq signal and RNA-seq data (FPKM values) were quantified and plotted. Significantly altered ATAC-seq peaks were extracted with FDR and categorized into gain of accessibility and loss of accessibility groups in patients with psoriasis. If the promoters of genes were intersected with differentially accessible ATAC-seq peaks, these genes and peaks were annotated, and ATAC signals around these genes were plotted using deepTools [22].

2.4 Gene functional enrichment analysis

The Genomic Regions Enrichment of Annotations Tool (GREAT, v.3.0.0) was used to annotate genes [23]. The GREAT annotated genes in different ways, including GO, mouse phenotype, MSigDB, and disease annotations. For differentially expressed genes, the R package clusterProfiler was used in GO enrichment analysis.

2.5 Motif enrichment and TF footprinting analysis

To determine the motifs enriched in regions with gain or loss of chromatin accessibility in Th cells or Treg cells from psoriasis patients, we used the HOMER suite tool findMotifsGenome.pl (v.3.12) [16] with the options “hg38-mask.” TF footprinting analysis was performed using HINT-ATAC (v0.13.0) [24] with the following parameters: rgt-hint function footprinting with the options “–atac-seq –paired-end –organism= hg38”; rgt-motif analysis function matching with the option “–organism= hg38” to match footprints to known TFs in the JASPAR database; and rgt-hint function differential with the options “–organism= hg38 –bc –nc 30.” The purpose was to obtain insights into differences between patients with psoriasis and healthy donors with regard to Th and Treg cells. All accessible regions called using MACS2 as described above were considered for footprinting analysis.

2.6 Construction of transcriptional regulatory network

HOMER was used in finding the enriched motif (P<1e−20) and binding TFs. Then, the TFs were used in constructing a transcriptional regulatory network-based regulation relationship from the TRRUST database [25]. Only significant expressed gene between psoriasis and healthy were included in the network.

2.7 Statistical analyses

Comparison between two groups was conducted with Student’s t-test. A P value of<0.05 was considered statistically significant. Statistical analyses were performed using the GraphPad Prism v7.00 (GraphPad, La Jolla, CA, USA).

3 Results

3.1 Landscape of chromatin accessibility in subtypes of CD4⁺ T cells

To study the epigenetic landscape of T cell subset-specific chromatin landscape benchmarks in psoriasis, we generated ATAC-seq profiles from cell-surface-marker-defined CD4⁺ naïve and memory T cell subtypes. Peripheral blood was obtained from three healthy subjects and two patients with psoriasis (Fig. S1, Table S1). Flow cytometry was used in obtaining naïve T, Treg, Th1, Th17, Th1-17, and Th2 cells, and the cells were subjected to ATAC-seq. In total, we obtained an average of 78.5% mappability and 35.8 million qualified fragments from each sample (Table S1). A total of 54 915 peaks were identified in the naïve T cells, 52 695 peaks were identified in the Th1 cells, 68 337 peaks were identified in the Th17 cells, 53 741 peaks were identified in the Th1-17 cells, 57 880 peaks were identified in the Th2 cells, and 58 109 peaks were identified in the Treg cells. Principal component analysis results showed the unsupervised clustering of distinct chromatin signatures of T cells in cases and controls (Fig. 1C), suggesting further chromatin remodeling in the pathogenesis of disease. In the control samples, Th cell subsets, including Th1, Th17, and Th1-17 cells, closely clustered together, indicating that they have the same degree of chromatin accessibility under rest conditions. Naïve T cell progenitors showed a lower degree of remodeling than other subtypes of T cells. Compared with the Treg cells, the Th cells had consistent chromatin remodeling tendency of subtypes.

3.2 ATAC-seq reveals regulatory signatures in the host immunity of psoriasis

We profiled the chromatin accessibility of sorted memory Th and memory Treg cells from 10 psoriasis patients and 10 normal controls (see the section of “Materials and methods”). A similar number of peaks was captured in the four subsets (peaks of Th cell were 47 136 in the patients and 44 985 in the controls). The peaks of the Treg cells were 53 403 in the patients and 47 160 in the controls, and 63 308 accessible peaks were observed after the peaks of the four data sets were merged (Table S2). Memory Th and memory Treg cells exhibited different changes in their open chromatin sites in psoriasis. A total of 803 regions displayed increased accessibility and 545 regions displayed decreased accessibility in the memory Th cells from patients with psoriasis, and 713 regions displayed increased accessibility and 1206 regions displayed decreased accessibility in the memory Treg cells from patients with psoriasis. Functional state annotation from ChIPseeker [15] revealed that chromatin remodeling in memory Th and Treg cells occurred mostly at promoter distal intergenic regions (Fig. 2A).

3.3 Epigenomic signatures of Th and Treg cells in psoriasis

To better understand the regulatory genome of memory Treg and Th cells in psoriasis, we conducted pairwise comparison of psoriasis and control samples of memory Th and Treg cells with edgeR. Unsupervised K-means clustering of differential peaks revealed four different regulatory element clusters, suggesting the potential normal and psoriasis epigenetic characteristics of memory Th and Treg cells in patients with psoriasis [8]. To better understand the function of differential peaks, we used GREAT to annotate the peaks enriched in each cluster (Fig. 2B, Table S3). Cluster I consisted of 1360 elements that are more accessible in Treg cells from patients with psoriasis. These elements may reflect the epigenomic signature of Treg cells, which is lost in psoriasis. GREAT analysis showed that genes near the elements in this cluster were highly enriched for the positive regulation of immune system processes, defense response, and response to wounding, suggesting that the genes are critical to the regulation of autoimmune response. Several signaling pathways are enriched in cluster I peaks, and one pathway is related to the classical NFKB pathway. Another pathway, the thromboxane A2 receptor signaling pathway, plays an important role in the pathogenesis of psoriasis. Cluster II consists of 659 elements, which are less accessible in the memory Th cells of patients with psoriasis. Compared with cluster I elements, the cluster II elements are more strongly enriched in immune responses, such as abnormal self-tolerance, abnormal immune tolerance, and inflammatory response. Cluster II, including the promoter of TGF-β gene and several distal elements, plays an important role in the pathogenesis of psoriasis. Cluster III consists of 492 elements, which are highly accessible in Treg cells from normal controls. In mice, phenotype terms show that peaks in cluster III are highly enriched during cytokine and interleukin secretion. The example of cluster III includes multiple elements in the FOXP3 locus. A function of FOXP3 is to inhibit the function of NFAT and NFKB and thereby inhibit the expression of some genes, including those of IL-2 and effector T cytokines. FOXP3 acts as a transcriptional activator for many genes inducing CD2S, cytotoxic T-lymphocyte antigen 4, glucocorticoid-induced TNF receptor family gene, and folate receptor 4. Cluster IV consists of 575 elements, and elements in this cluster are highly enriched in immune-related diseases, including lupus erythematosus, chronic leukemia, and virus infections. This finding suggested that gain of accessibilities of peaks may cause general immunodeficiency diseases. The results showed that psoriasis affected the open chromatin of distinct immune cells in different ways.

3.4 DNA accessibility change is correlated with differential mRNA expression in memory Th and Treg cells in psoriasis

From RNA-seq data, we identified 1348 differentially expressed (DE) genes in Th cells (545 up, 803 down) and 1919 DE genes in Treg cells (1206 up, 713 down; FDR<5%; Fig. 3A). DE genes separated significantly between Th and Treg cells, and Th-upregulated genes were enriched in the positive regulation of leukocyte differentiation and α-β T cell differentiation, and Th-downregulated genes were enriched in humoral immune response (Fig. 3B). Meanwhile, Treg-upregulated genes were enriched in neutrophil activation involved in immune response and neutrophil degranulation, and no significant enrichment in Treg-downregulated genes was observed (Fig. 3C). Then, we determined whether or not epigenetic features are associated with gene expression. By comparing the genome-wide RNA-seq and ATAC-seq data, we found that gene loci that gained ATAC-seq signals showed significant increase in gene expression level. Gene loci that lost ATAC-seq signals had decreased expression (Fig. 3D), indicating a high correlation of epigenetic and RNA profiling. According to the finding that the ATAC-seq signal was significantly enriched at –1 kb to 1 kb from TSSs, we selected genes that can be annotated to TSS regions for the subsequent analysis. We detected 54 genes associated with an increased ATAC-seq signal in Th cells, 19 of which showed upregulated mRNA levels and 35 were downregulated. A total of 55 genes were associated with an increased ATAC-seq signal of Treg cells, of which 40 exhibited upregulated mRNA levels and 15 were downregulated.

3.5 Identification of potential TFs involved in the pathogenesis of psoriasis

We used HOMER v4.8 to search TFs enriched at accessible sites in memory Th and Treg cells. As accessible DNA sites are often obligated when TFs bind to their cognate DNA motifs, the integration of TF motifs and DNA accessibility data from ATAC-seq can be used in predicting TF occupancy on chromatin and creating regulatory networks [26–28]. The TFs enriched in Th constitute a group of redox-sensitive TFs [29] (Fig. 4A). For instance, the nuclear factor-κB (NFKB) family consists of a family of transcription factors that play critical roles in immunity cell proliferation, inflammation, and differentiation [30]. Th-downregulated genes were enriched with RUNX1 and the ROR family, which are key regulators for T cell differentiation [31,32]. In memory Treg cells, Treg-upregulated genes were enriched in the Jun family and AP-1, which constitute a group of TFs that regulate cell proliferation, differentiation, transformation, and apoptosis [33]. No psoriasis-related TFs in Treg downregulation peaks have been reported. In summary, the TFs predicted from the ATAC-seq data are highly associated with the pathogenesis of psoriasis, and unknown psoriasis regulators may be present.

Given that one caveat of only using motif analysis for TF prediction is that TFs or TF families can share the same motif, we combined motif enrichment analysis of ATAC-seq data and gene expression profiles in RNA-seq data to predict TF occupancy in memory Th and Treg cells. Here, we plotted the expression value and motif enrichment score on the same figure (Fig. 4B). Some well-known immune-associated TFs were highly expressed, and their combination states were changed during the disease, including FOSL1, SPI1, ATF3, NKFB1, RUNX1, ETV4, ERG, FLI1, and ETS1. SPI1, ETV4, and FLI1 showed similar high expression patterns in cases and controls and have not been associated with psoriasis. To further improve our prediction of potential TF in the involvement psoriasis, we conducted TF footprint analysis, which provided evidence for the direct possession of TF candidate genes on genomic DNA. Here, we illustrated the footprints of the most enriched TFs. We observed the deep footprints and high DNA accessibility flanking motifs of NFKB1, ATF3, FOSL1, and RORC in cases compared with controls (Fig. 4C). The orthogonal footprint results were consistent with the motif enrichment results.

3.6 Prediction of TF regulatory networks in memory Th/Treg cells in psoriasis

To investigate the transcriptional regulatory program in psoriasis patients, ATAC-seq, RNA-seq data, and TF footprinting data were integrated. We restricted the network by considering only motif-enriched TF genes, and their regulated genes that were detected significantly expressed between cases and controls. In brief, enrich motif was used in calling the bindings of TF genes, and TF-regulated genes were selected from TRRUST database. TF regulatory networks were constructed for Th and Treg cells (Fig. 5). As we expected, NFKB1 and RELA were highly connected and central to the network. Recently, the importance of the Fos (FOSL2/FOSL1) and Jun (JUN/JUNB/JUND) gene family members has been demonstrated. Several TFs, SPI1, and ETS family may be novel key psoriasis TFs. Thus, our TF regulatory network identified TFs, which are critical to T cell-mediated pathobiology, as well as several novel TFs not previously associated with psoriasis. To evaluate the central TFs in the involvement in psoriasis development, we studied differentially expressed downstream genes containing the binding motifs of NFKB1 and RELA. TRRUST database showed that NFKB1 can regulate CXCL2, IL1RN, and CCL3. The expression of CXCL2 and IL1RN in Treg cells and the expression of CCL3 in Treg and Th cells increased in patients with psoriasis, and the binding motif of NFKB1 enriched in regions gained accessibility in psoriasis. This result further showed that NFKB1 participates in the regulation of psoriasis (Figs. 2C, 4B, and 5).

4 Discussion

The genome-wide landscape of psoriasis has been studied extensively. To date, more than 40 psoriasis susceptibility loci have been identified [34–37]. Most of these variations were located in the intergenic region, indicating the importance of analyzing gene regulatory regions. Recently, ATAC-seq has rapidly emerged as one of the most powerful approaches for chromatin accessibility profiling [38]. It has been broadly used in the research of numerous disorders, including cancer and metabolic and malaria disorders [26,39–41]. In the present study, we performed ATAC-seq to examine chromatin accessibility in patients with psoriasis compared with normal controls, demonstrating significant alterations in chromatin accessibility, and described transcriptional profiles in psoriasis samples.

We performed an extensive comparative analysis of various subtypes of T cells between patients with psoriasis and normal controls and focused on their epigenetic profiles. The profiles highlighted the central role of chromatin remodeling in the pathogenesis of psoriasis. As expected, the chromatin remodeling tendencies among various subtypes of Th cells were relatively consistent, compared with that of Treg cells. Thus, we focused our analysis on memory Th and Treg cells.

In memory Th and Treg cells, the most accessible regions localized around the TSS and open chromatins at these sites are predictive of active transcription, in agreement with the pattern reported in previous studies [42]. Unsupervised K-means clustering of the differential peaks revealed four distinct clusters of regulatory elements. Recent large-scale association studies found psoriasis risk loci are involved in many different biological pathways [43]. Here, we analyzed different biological processes and pathways in these four clusters, which are associated with immune processes, including immune defense, immune response, interleukin secretion, and immunodeficiency diseases. Among the signaling pathways, only NFKB and TGF-β mediate inflammatory response. With regard to NFKB signaling, TNFAIP3 (TNF-α induced protein 3) and TNIP1 (TNFAIP3 interacting protein 1), whose gene products work downstream of TNF-α for the regulation of NFKB, TNFAIP3 temporally limits immune responses by inhibiting NFKB activation and terminating NFKB-mediated responses that trigger the pathogenesis of psoriasis [44]. Relevant to TGF-β signaling, TGF-β uniquely promotes the differentiation of human naïve CD4⁺ T cells into Th17 cells accompanied by the expression of the transcription factor RORC2 [45]. IL-23 drives the expansion of Th17 cells that produce IL-17A/F, which is another set of cytokines, whose role is pivotal to the pathogenesis of psoriasis. Monoclonal antibodies targeting the common p40 and the specific p19 subunit of IL-23 have high clinical efficacy [46].

In the TF enrichment analysis, we observed many known key TFs for the development of psoriasis, such as NFKB1, FOSL1, SPI1, ATF3, RUNX1, ETV4, ERG, FLI1, and ETS1. Among them, SPI1, ETV4, and FLI1 have not been associated with psoriasis. Thus, the transcriptional regulatory network expands our understanding of molecular regulation in psoriasis. Next, the footprint analysis showed that ATF3, NFKB1, and FOSL1 had deep footprints and high DNA accessibility. Finally, we constructed the network of the significant TFs in psoriasis. As we expected, NFKB, RELA, and FOSL were found to be highly connected and central to the network. The combined analysis of ATAC-seq and RNA-seq showed that NFKB1 can regulate CXCL2, IL1RN, and CCL3. The expression of CXCL2 and IL1RN in Treg cells and that of CCL3 in Treg and Th cells increased in patients with psoriasis, and the binding motif of NFKB1 was enriched in regions that gained accessibility in psoriasis. NFKB/Rel TFs have critical role in the regulation of the expression of many cytokines involved in the pathogenesis of psoriasis. The dysregulation of the NFKB system is central to inflammatory and stress responses [47]. NFKB1 mediates Th activation in psoriasis [48], but the exact mechanism is unclear. CXCL2 is a chemoattractant chemokine with proinflammatory function [49]. The hepatic tissue NFKB1 transcriptionally represses CXCL2 expression [50]. CCL3 is one of the CC chemokines, attracting monocytes expressing CCR1 receptors in the circulatory system and helping them enter injured and inflamed tissues [51]. IL1RN codes a protein that binds to IL-1 receptor, and inhibits the binding of IL-1α and IL-1β, this is the first IL-1 family member described that has an antagonist function [52]. IL1RN is the susceptibility gene of psoriasis [53]. We reported for the first time that NFKB1 can regulate CCL3 and IL1RN in psoriasis.

In summary, our data present an association between chromatin-accessible regions and gene regulation, providing new insights into the molecular mechanisms of psoriasis.

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