Abstract
Background
Psoriasis is a chronic and hyperproliferative skin disease featured by hyperkeratosis with parakeratosis, Munro micro-abscess, elongation of rete pegs, granulosa thinning, and lymphocyte infiltration. We previously profiled gene expression and chromatin accessibility of psoriatic skins by transcriptome sequencing and ATAC-seq. However, integrating both of these datasets to unravel gene expression regulation is lacking. Here, we integrated transcriptome and ATAC-seq of the same psoriatic and normal skin tissues, trying to leverage the potential role of chromatin accessibility and their function in histopathology features.
Results
By inducing binding and expression target analysis (BETA) algorithms, we explored the target prediction of transcription factors binding in 15 psoriatic and 19 control skins. BETA identified 408 upregulated genes (rank product < 0.01) and 133 downregulated genes linked with chromatin accessibility. We noticed that cumulative fraction of genes in upregulation group was statistically higher than background, while that of genes in downregulation group was not significant. KEGG pathway analysis showed that the upregulated 408 genes were enriched in TNF, NOD, and IL-17 signaling pathways. In addition, the motif module in BETA suggested the 57 upregulated genes are targeted by transcription factor AP-1, indicating that increased chromatin accessibility facilitated the binding of AP-1 to the target regions and further induced expression of relevant genes. Among these genes, SQLE, STRN, EIF4, and MYO1B expression was increased in patients with hyperkeratosis, parakeratosis, and acanthosis thickening.
Conclusions
In summary, with the advantage of BETA, we identified a series of genes that contribute to the disease pathogenesis, especially in modulating histopathology features, providing us with new clues in treating psoriasis.
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Background
Psoriasis is an immune-mediated multigenic skin disease characterized by symmetrically well-defined erythema, covered with silvery scales involving elbows, knees, torso, and scalp [1]. The histopathological traits of psoriasis are diverse, typically including hyperkeratosis with parakeratosis and immune cell infiltration, Munro micro-abscess, acanthosis thickening, vascular dilatation congestion, elongation of rete pegs, and granulosa thinning. Psoriasis patients are usually accompanied by mental and physical burden because of its high incidence, chronic course, disability, malformation, and comorbidities, such as metabolic syndrome and cardiovascular diseases [2, 3]. To address this burden, scientists have tried to unravel the etiology and pathogenesis of psoriasis with various strategies and have made significant progress, but there are still many mysteries. More than 80 susceptibility genes were recently identified [4], and some cytokines, such as TNF-α, IL-17, and IL-23, have been developed for biological agents [5]. However, the exact pathogenesis of psoriasis was still not fully revealed.
The histopathological features of psoriasis indicate a critical alteration in disease progression, but the molecular mechanism under these features is largely unknown. Few studies aimed to link epigenetic modifications with the histopathological characteristics in psoriasis. For example, Chandra et al. carried out genome-wide DNA methylation to figure out which epigenetic loci are associated with Munro micro-abscess in psoriasis [6]. Nevertheless, chromatin accessibility and its potential regulatory roles in histopathological changes are still missing.
Assay for targeting accessible-chromatin with high-throughput sequencing (ATAC-seq), based on Tn5 transposase hyperactivity, helps us investigate genome chromatin accessibility and reveals multiple aspects of transcriptional regulation [7]. It provides the whole open chromatin across the entire genome at one time, exploring transcription factor binding and gene expression regulation, which has been widely used in various diseases [8], including psoriasis. Utilizing ATAC-seq on 15 psoriatic lesions, 9 non-psoriatic lesions, and 19 normal healthy skin tissues, we previously identified 4,195 differentially accessible regions [9]. Further analysis showed that the sequence of differentially accessible regions was enriched in the FRA1/AP-1 transcription factor binding region [9]. Upregulation of AP-1 family members has been shown in psoriatic skins, but the exact mechanism is not precise [10]. Currently, we tried to perform an integrative analysis of RNA-seq and ATAC-seq data, exploring the potential network of AP-1 regulating psoriasis and aiming to find out whether some genes are implicated in histopathological alterations.
Several methods have been used to integrate transcription and chromatin accessibility datasets by directly overlap** relevant genes, which might underestimate the potential roles of TF binding targets [7, 11,12,13]. The BETA algorithm is an efficient web tool to identify motifs of transcription factors, infer their target genes, and explore these factors' activating or repressive status [14]. To search the direct targets of differentially accessible regions, we attempted to perform BETA-plus (Version 1.0.7) to integrate ATAC-seq accessible peak data with differential expression data. Interestingly, we identified 408 upregulated genes and 133 downregulated genes (rank product < 0.01) and depicted significant binding motifs and putative collaborating factors. These upregulated genes were strongly targeted by AP-1 family transcription factors. Then, we found that their gene expression differences were related to the different pathological manifestations of psoriasis. It provides us with a novel insight into the potential regulatory mechanism and therapy of psoriasis and a future direction for us to deeply explain the mechanism of AP-1 in psoriatic lesions.
Results
The prediction of TFs' function and direct targets
Based on our RNA-seq and ATAC-seq data, we used BETA-plus to integrate differentially expressed genes and open accessible peaks [9, BETA analysis BETA (version 1.0.7) is an exposed source at http://customer.org/ap/ [14]. Utilized the information of binding site and differential expression, BETA predicts the function of activation or inhibition of TFs, infers the target genes, and identifies the motif and its binders of TFs. All BETA calculations are based on the Regulatory Potential Scores of each target gene, which is the likelihood that a gene is regulated by a factor, and each gene estimates its regulatory potential. The regulatory potential is calculated as Sg = \({\sum }_{i=1}^{k}{e}^{-(0.5+4\Delta i)}\) [14, 60]. k is all binding sites near the TSS of gene (g) within the specified range (default 100 kb) of the different peaks. Δ is the exact distance between k and TSS, proportional to 100 KB, Δ = 0.1 means exact distance = 10 kb. The possibility of gene regulation by factors depends on the number of binding sites in the TSS region and the distance between the binding site and TSS. Data input takes a set of peaks as BED (tissue-specific open chromatin regions from ATAC-seq), and the differential gene expression results from RNA-seq. Analysis was worked using default parameters on the Galaxy Cistrome platform, apart from the significance threshold p < 0.05. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, annotation, visualization was carried out using the R (https://www.r-project.org/) package clusterProfiler. The enriched pathway was plotted by package Pathview. The collection of skin tissues came from the Department of Dermatology, the First Affiliated Hospital, Anhui Medical University. The overall histopathological abnormalities, including rete peg elongation, the presence of hyperkeratosis with parakeratosis, parakeratosis, Munro micro-abscess, elongation of rete pegs, focal hypergranulosis, granulosa thinning, acanthosis thickening, vascular dilatation congestion, and lymphocyte infiltration, were evaluated by H&E-stained slides. Finally, all the histopathological changes were collected in the form of presence or absence. GSE80047, GSE53552, GSE41662, GSE30999, and GSE14905 expression data matrix was downloaded from GEO database (https://www.ncbi.nlm.nih.gov/geo/). Limma package (version: 3.40.2) was used to identify the differentially expressed genes. All statistical analyses were performed on the R (version 4.1.1) platform. T test, Pearson correlation coefficient, and chi-square test were used to evaluate the significance via SPSS. P < 0.05 was considered statistically significant.KEGG pathway analysis
Histopathological section
Statistical analysis
Availability of data and materials
All data generated or analyzed during this study are publicly accessed; ATAC-seq (PRJNA597655) and RNA-seq (PRJNA686863).
Abbreviations
- ATAC-seq:
-
Assay for targeting accessible-chromatin with high-throughput sequencing
- BETA:
-
Binding and expression target analysis
- TF:
-
Transcription factor
- KEGG:
-
Kyoto Encyclopedia of Genes and Genomes
- TNF:
-
Tumor necrosis factor
- NOD:
-
Nucleotide-binding oligomerization domain containing
- IL-17:
-
Interleukin 17
- AP-1:
-
Activator Protein 1
- SQLE:
-
Squalene epoxidase
- STRN:
-
Striatin
- EIF4E:
-
Eukaryotic translation initiation factor 4E
- MYO1B:
-
Myosin IB
- IL-23:
-
Interleukin 23
- FRA1:
-
FOSL1, Fos-related antigen 1, FOS Like 1
- OSMR:
-
Oncostatin M receptor
- CDCP1:
-
CUB domain-containing protein 1
- SHB:
-
SH2 domain-containing adaptor protein B
- JDP2:
-
Jun dimerization protein 2
- NFE2L2:
-
Nuclear factor erythroid-derived 2-like 2
- IRF4:
-
Interferon regulatory factor 4
- BATF:
-
Basic leucine zipper ATF-like transcription factor
- MLX:
-
MAX dimerization protein
- SMAD3:
-
SMAD family member 3
- BCL3:
-
B cell CLL/lymphoma 3
- SOCS3:
-
Suppressor of cytokine signaling 3
- CASP8:
-
Caspase 8
- RPS6KA4:
-
Ribosomal protein S6 kinase A4
- CCL20:
-
C-C motif chemokine ligand 20
- TNFAIP3:
-
TNF alpha-induced protein 3
- MLKL:
-
Mixed lineage kinase domain-like pseudokinase
- MMP9:
-
Matrix metallopeptidase 9
- PTGS2:
-
Prostaglandin-endoperoxide synthase 2
- CASP7:
-
Caspase 7
- CEBPB:
-
CCAAT enhancer-binding protein beta
- S100A7A:
-
S100 calcium-binding protein A7A
- LCN2:
-
Lipocalin 2
- TBK1:
-
TANK-binding kinase 1
- MAPK6:
-
Mitogen-activated protein kinase 6
- IFI16:
-
Interferon gamma-inducible protein 16
- OAS2:
-
2'-5'-Oligoadenylate synthetase 2
- NLRX1:
-
NLR family member X1
- OAS3:
-
2′-5′-Oligoadenylate synthetase 3
- GBP5:
-
Guanylate-binding protein 5
- MYD88:
-
MYD88 innate immune signal transduction adaptor
- NAMPT:
-
Nicotinamide phosphoribosyltransferase
- PANX1:
-
Pannexin 1
- OAS1:
-
2′-5′-Oligoadenylate synthetase 1
- PYDC1:
-
Pyrin domain containing 1
- ATP11B:
-
ATPase phospholipid transporting 11B
- RAP2B:
-
RAP2B, member of RAS oncogene family
- TTC9:
-
Tetratricopeptide repeat domain 9
- HECTD1:
-
HECT domain E3 ubiquitin protein ligase 1
- SDR9C7:
-
Short chain dehydrogenase/reductase family 9C, member 7
- RAB7A:
-
RAB7A, member RAS oncogene family
- LIMK2:
-
LIM domain kinase 2
- FRMD6:
-
FERM domain containing 6
- CLPX:
-
Caseinolytic mitochondrial matrix peptidase chaperone subunit
- PPARD:
-
Peroxisome proliferator-activated receptor delta
- MID1IP1:
-
MID1 interacting protein 1; LDLR: low-density lipoprotein receptor
- ID1:
-
Inhibitor of DNA binding 1, HLH protein
- NUP210:
-
Nucleoporin 210
- CEBPG:
-
CCAAT/enhancer-binding protein gamma
- GEO:
-
Gene Expression Omnibus
- TSS:
-
Transcription start site
- EMT:
-
Epithelial-to-mesenchymal transition. PRS: Potential regulatory score
- DDX3X:
-
DEAD-box helicase 3 X-linked
- NLRP3:
-
NACHT, LRR, and PYD domains-containing protein 3
- SESN2:
-
Sestrin-2
- PD-L1:
-
Programmed death-1
- TGF-β:
-
Transforming growth factor-β
- PTGS2:
-
Prostaglandin G/H synthase 2
- Th17:
-
T helper cell 17
- PASI:
-
Psoriasis area and severity index
- BMI:
-
Body mass index
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Acknowledgements
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Funding
This work was supported by Fund Project of National Natural Science Foundation of China (31671307); Natural Science Foundation of Anhui Medical University (2020xkj150); and the Youth Fund Project of National Natural Science Foundation of China (82103723).
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X.X, X.T, and F.Z conceived and designed the study design and carried out all the data analysis in the article. H.C made a substantial contribution to the study design. X.X drafted the manuscript. Y.Z, Z.P, Q.W, L.T, and C.Z collected the histopathological data. F.Z and H.C were reviewing and editing this article. All authors read and approved the final manuscript.
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Supplementary Information
Additional file 1. Figure S1:
Accessible peaks and their correspondingepigenetic changes. The yellow band at the yellow arrow is the accessiblepeaks annotated to OSMR (A), CDCP1 (B), and SHB (C); the methylationstatus of parallel peak regions decreased significantly.
Additional file 2. Figure S2:
KEGG pathway analysis with upregulatedtarget genes. (A): IL-17 signaling pathway diagram. (B): NON-like receptorsignaling pathway diagram. Red represents upregulated genes, and thedarker the color, the more significant it is.
Additional file 3. Figure S3:
BETA and KEGG pathway analysis withdownregulated target genes. (A): Motifs in downregulated target genes(DOWN) yield by BETA. Because of their high similarity scores, JUND, IRF4,BATF, and six other Leucine Zipper family members are categorized intoone group. (B): KEGG pathway analysis with downregulated target genes,and shows downregulated target genes enriched in “Adipocytokinesignaling pathway,” “FoxO signaling pathway,” “AMPK signaling pathway,”and some others (P < 0.05).
Additional file 4. Figure S4:
Altered expression of AP-1 targets in psoriaticlesions. Altered expression of AP-1 targets in lymphocytes infiltration(A), Elongation of rete pegs (B), Granulosa thinning (C), and vasculardilatation congestion (D).
Additional file 5. Figure S5:
Alteration expression of AP-1 targets inpsoriatic lesions. Altered expression of AP-1 targets in hyperkeratosis withparakeratosis (A) and acanthosis thickening (B).
Additional file 6: Table S1.
BETA finding for 408 upregulated genes and expression profiles from our RNA-seq and GSE30999.
Additional file 7: Table S2.
Methylation levels for loci located within the accessible peaks.
Additional file 8: Table S3.
KEGG pathway analysis shows upregulated target genes enriched in TNF, NON-like receptor, and IL-17 signaling pathway.
Additional file 9: Table S4.
Correlation between confounders and expressions of 46 AP-1 targeted genes. Between Age, PASI, BMI, and gene expression are assessed using the Pearson correlation coefficient, and the P values of gender-related and smoking-related gene expression are calculated by t test, and P < 0.05 means statistical significance.
Additional file 10: Table S5.
The main demographic and clinical characteristics of samples. Pearson correlation coefficient was used to evaluate the continuous variables (age, BMI), and a chi-square test was used to assess the dichotomous variable (Gender). P < 0.05 means statistical significance.
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Xu, X., Tang, X., Zhang, Y. et al. Chromatin accessibility and transcriptome integrative analysis revealed AP-1-mediated genes potentially modulate histopathology features in psoriasis. Clin Epigenet 14, 38 (2022). https://doi.org/10.1186/s13148-022-01250-6
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DOI: https://doi.org/10.1186/s13148-022-01250-6