¹ Biomedical Informatics Centre, National Institute of Pathology-ICMR, New Delhi, India

²ICMR-AIIMS Computational Genomics Centre, Indian Council of Medical Research, New Delhi, India

³Faculty of Health Sciences, Symbiosis International (Deemed University), Pune, India.

⁴Department of Molecular and Cellular Engineering, Sam Higginbottom University of Agriculture, Prayagraj, India.

Corresponding author email: drakjain@gmail.com

Article Publishing History

INTRODUCTION

Psoriasis is one of the most common chronic multifactorial skin disorder with global prevalence of 0.2-11.4% in adults and 0-2.1% in children (Egeberg and Thyssen, 2019; Zhang et al. 2019).  Psoriasis is a worldwide prevalent disease, diverges within different parts of the world depending on varying geographical areas. Off late psoriasis has expanded its manifestation beyond skin and has been reported as a multisystemic disorder being independent precursor for cardiovascular diseases, psoriatic arthritis, atherosclerosis, diabetes and insulin resistance, hypertension, metabolic syndrome, myocardial infarction and obesity burdening the quality of life (Menter et al. 2018; Huang et al. 2019; Choudhary et al. 2020).

Over the last decade, there has been extensive utilization of omics-based technologies to identify potential therapeutic targets. As a result, at genome, transcriptome, proteome, and metabolome levels, numerous vital biomarkers for psoriasis have been uncovered. These discoveries have provided new insights into the underlying molecular mechanisms and signaling pathways in psoriasis pathogenesis. These findings have offered fresh perspectives on the fundamental molecular mechanisms and signaling pathways involved in psoriasis pathogenesis (Tang et al., 2014; Singh et al., 2019).

The etiology of psoriasis is little known however histopathologically psoriasis lesion is majorly characterized by hyperkeratotic plaques, resulting due to infiltration of effector leukocytes leading to epidermal hyperproliferation accompanied by abnormal keratinocyte differentiation, inflammation, acanthosis and angiogenesis. Th17 cells and their secreted cytokines, interleukin (IL)-17, and IL-22, in synergy with interferon (IFN)-γ and tumour necrosis factor alpha (TNF-α), is believed to form a cluster during the early phase of the disease initiation. Hence, these T-cell infiltrate leads to a cytokine storm and further induces overexpression of inflammatory mediators. Amongst various psoriasis mechanisms, pathways mediated by IL-36 cytokines are believed to be essentially relevant (Lowes, Suárez-Fariñas and Krueger, 2014; Arakawa et al. 2015; Singh et al. 2019).

IL-36 cytokines belong to IL-1 family with IL-36alpha, IL-36beta, and IL-36gamma as three agonist and IL-36 receptor antagonist (known as IL-36Ra or IL-36RN) and IL-38 as two antagonists with opposite functions and competitively binding to IL-36R/IL-1R/AcP complex. Several authors have reported the over expression of IL-36 in psoriasis lesions at both early & late phase of the disease and is found to be localized at epidermis (Towne and Sims, 2012; Boutet et al., 2016; Mercurio et al., 2018). In psoriasis, IL-36 RN is believed to comprehend the pathogenic role of IL-36 axis in psoriasis development. In this backdrop, our study considered GEO profile, GSE75343 from GPL571 to understand the role of cytokine-cytokine and cytokine-chemokine pathways and associated differentially expressed genes in psoriasis. Further the study intends to identify the top priority biomarkers and other functionally relevant biological processes in psoriasis.

MATERIAL AND METHODS

Microarray Data Information: Gene expression profile, GSE75343 was retrieved from NCBI-GEO database, deposited from Rockefeller University, USA. The GSE record was generated on GPL571 platform (Affymetrix Human Genome U133A 2.0 Array) (Ruano et al., 2016).

Data Pre-Processing and Identification of DEGs: The raw data files used for the analysis included .cel files, imported into R, version 4.0.3. Background correction, normalization and log 2 transformation was performed based on robust multiarray average (RMA) method embedded in the Affy package in Bioconductor version 3.8 (https://bioconductor.org/) (Gautier et al., 2004). Bioconductor package of GEOquery and limma was used to identify differentially expressed genes between normal healthy skin, lesional and non-lesional skin of psoriasis patients. Benjamini and Hochberg method was applied to calculate the false discovery rate (FDR) (Chen et al., 2016). Statistically significant DEGs between different sample groupings viz., lesional-non-lesional (LS-NL), lesion-normal (LS-NS) and non-lesional-normal skin (NL-NS) samples were defined with p<0.05 and [log FC] >=2 as the cut off criterion. For any gene with multiple corresponding probes, the mean expression value of all probes was considered as its final expression value (Wang and Zheng, 2014).

Gene Ontology and Pathway Enrichment Analysis: The Candidate DEGs were analyzed in cluster Profiler package in Bioconductor version 3.4.4 (https://bioconductor.org/). This package automates the process of biological term classification including biological process (BP), cellular component (CC) and molecular function (MF) (Yu et al., 2012). It also annotates genes to pathways using Kyoto Encyclopaedia of Genes and Genomes (KEGG) (Kanehisa et al., 2017).

Gene- Disease Network: The DEGs were annotated based on gene-disease association data from knowledge bases viz. genome wide association study catalogue (GWAS), genetics association database (GAD), comparative toxicogenomics database (CTD), ClinVar, and UniProt through online tools DisGeNET and Target Validation. The gene–disease network was inspected by integrating the data into Cytoscape v3.7.2 (Piñero et al., 2017; Carvalho-Silva et al., 2019).

Integration of Protein–Protein Interaction (PPI) Network, Module Analysis, Significant Candidate Genes and Pathway Identification: Protein-protein Interaction was constructed using STRING database in Cytoscape v3.7.2 (Szklarczyk et al., 2019; Shannon et al., 2003). Hub genes were identified using Cytohubba plugin using all the 12 parameters, i.e., MCC, DMNC, MNC, Degree, EPC, Bottleneck, Eccentricity, Closeness, Radiality, Betweenness, Stress, and Clustering Coefficient (Yang et al., 2018). The genes were considered to be high priority hub targets if they were present in more than 6 parameters (Nayak et al., 2019). Molecular complex detection (MCODE) was used to screen the modules of PPIs network with degree cut-off=10, node score cut-off=0.2, k-core=2, and maximum depth=100. The gene ontology analysis of top three modules was performed by STRING Enrichment plugin. FDR<0.05 was set as the cut off criteria for gene ontology (GO) analysis.

RESULTS AND DISCUSSION

Psoriasis has nearly affected 3% of the population worldwide with no permanent therapy. Hence it is important to recognize important molecules with crucial role in pathogenesis with therapeutic potential. In the present microarray study, lesional, non-lesional and normal samples were analysed using limma package in R, 4.0.3.

Data Pre-processing and Identification of DEGs: The retrieved expression profile GSE57343 from GPL571 is based on the data deposited in NCBI-GEO from Rockefeller University, USA. GSE75343 had 45 samples including 10 normal skin (NS) tissue sample, 22 lesional skin (LS) and 13 non-lesional skin (NL). With FDR<0.05 and [log FC] >=2 as cut-off criterion, 174 DEGs (115 upregulated and 79 downregulated); 373 DEGs (201 upregulated and 172 downregulated) and 373 DEGs (201 upregulated and 172 downregulated) were obtained between different sample groupings including LS-NL, LS-NS and NL-NS respectively (Figure 1A, 1B and 1C).

Amongst these differentially expressed genes 131 DEGs from LS-NL group were shared between LS-NS and NS-NL, furthermore all 373 DEGs were shared between LS-NS & NS-NL. Moreover, it is interesting to note that all 373 DEGs obtained amongst LS-NS & NS-NL were identically expressed (Figure 2).

To understand and comprehend psoriasis and its progression, the differentially expressed genes among LS-NL were considered. Top 50 dysregulated expressed genes were visualized as heatmap (Figure 3). The result identified several DEGs and pathways believed to be functionally relevant. In total 173 DEGs were identified between LS-NL, 374 between LS-NS and NS-NL individually. It is interesting to note that both LS-NS and NS-NL shared all 374 DEGs between them. Among 173 LS-NL DEGs, 43 DEGs were unique with respect to other two groups.

DEGs Gene Ontology Analysis in Lesional – Non-lesional Psoriasis: Gene ontology analysis was used to understand and visualize the functional profiles of DEGs (GO and KEGG). The up-regulated and down-regulated DEGs were classified into three categories explaining biological process, molecular function and cellular components. Among upregulated DEGs, defense response to other organism, neutrophil activation involved in immune response, neutrophil degranulation, epidermis development, epidermal cell differentiation and keratinocyte differentiation are primarily enriched biological processes while endopeptidase activity, receptor ligand activity, cytokine activity, RAGE receptor binding, chemokine receptor binding and CCR chemokine receptor binding are vital enriched molecular functions. Further, secretory granule lumen, cytoplasmic vesicle lumen, vesicle lumen, specific granule and specific granule lumen are the top enriched cellular components (Table 1).

Table 1. The top 10 significant enriched analysis of up-regulated genes in lesional psoriasis skin

ID	GO Enriched Term	FDR	Gene Count	Category
GO:0006631	fatty acid metabolic process	5.16E-13	17	BP
GO:0072330	monocarboxylic acid biosynthetic process	8.95E-10	14	BP
GO:0046394	carboxylic acid biosynthetic process	1.16E-08	14	BP
GO:0016053	organic acid biosynthetic process	1.16E-08	14	BP
GO:0016042	lipid catabolic process	5.17E-09	13	BP
GO:1901615	organic hydroxy compound metabolic process	6.43E-06	12	BP
GO:0044242	cellular lipid catabolic process	7.25E-09	11	BP
GO:0030258	lipid modification	7.25E-09	11	BP
GO:0008202	steroid metabolic process	9.46E-07	11	BP
GO:0006633	fatty acid biosynthetic process	7.90E-09	10	BP
GO:0048037	cofactor binding	0.001165963	9	MF
GO:0016614	oxidoreductase activity, acting on CH-OH group of donors	0.000494932	6	MF
GO:0050662	coenzyme binding	0.028034967	5	MF
GO:0016616	oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor	0.019853107	4	MF
GO:0019842	vitamin binding	0.021213538	4	MF
GO:0016747	transferase activity, transferring acyl groups other than amino-acyl groups	0.045693548	4	MF
GO:0033293	monocarboxylic acid binding	0.021213538	3	MF
GO:0003996	acyl-CoA ligase activity	0.019853107	2	MF
GO:0004467	long-chain fatty acid-CoA ligase activity	0.019853107	2	MF
GO:0015645	fatty acid ligase activity	0.021213538	2	MF
GO:0044438	microbody part	3.69E-05	6	CC
GO:0044439	peroxisomal part	3.69E-05	6	CC
GO:0005777	Peroxisome	9.67E-05	6	CC
GO:0042579	Microbody	9.67E-05	6	CC
GO:0005782	peroxisomal matrix	0.000397	4	CC
GO:0031907	microbody lumen	0.000397	4	CC
GO:0001725	stress fiber	0.008641	3	CC
GO:0097517	contractile actin filament bundle	0.008641	3	CC
GO:0005778	peroxisomal membrane	0.008641	3	CC
GO:0031903	microbody membrane	0.008641	3	CC

Similarly, amongst the downregulated DEGs fatty acid metabolic process, monocarboxylic acid biosynthetic process, carboxylic acid biosynthetic process, organic acid biosynthetic process and lipid catabolic process are the top 5 enriched biological processes while cofactor binding, oxidoreductase activity-acting on CH-OH group of donors’ coenzyme binding, vitamin binding and transferase activity, transferring acyl groups other than amino-acyl groups are highly enriched molecular functions. Furthermore, microbody part peroxisomal part, stress fibre, contractile actin filament bundle and lipid droplet are topmost enriched cellular components (Table 2).

Table 2. The top 10 significant enriched analysis of down-regulated genes in lesional psoriasis skin

ID	GO Enriched Term	FDR	Gene Count	Category
GO:0098542	defense response to other organisms	2.81E-12	24	BP
GO:0042119	neutrophil activation	1.11E-09	21	BP
GO:0002446	neutrophil mediated immunity	5.89E-09	20	BP
GO:0002283	neutrophil activation involved in immune response	4.51E-09	20	BP
GO:0043312	neutrophil degranulation	4.51E-09	20	BP
GO:0008544	epidermis development	2.21E-06	16	BP
GO:0043588	skin development	6.89E-07	16	BP
GO:0009615	response to virus	2.53E-07	15	BP
GO:0009913	epidermal cell differentiation	3.16E-06	14	BP
GO:0030216	keratinocyte differentiation	6.75E-07	14	BP
GO:0004175	endopeptidase activity	5.42E-05	22	MF
GO:0004252	serine-type peptidase activity	2.46E-05	22	MF
GO:0030414	peptidase inhibitor activity	0.01166427	20	MF
GO:0061134	peptidase regulator activity	0.023446559	19	MF
GO:0004867	serine-type endopeptidase inhibitor activity	0.004038224	19	MF
GO:0002020	protease binding	0.019596	19	MF
GO:0005125	cytokine activity	0.038523	18	MF
GO:0050786	RAGE receptor binding	3.45E-05	17	MF
GO:0042379	chemokine receptor binding	0.00695	17	MF
GO:0048020	CCR chemokine receptor binding	0.011677	17	MF
GO:0034774	secretory granule lumen	1.31E-09	17	CC
GO:0060205	cytoplasmic vesicle lumen	1.31E-09	17	CC
GO:0031983	vesicle lumen	1.31E-09	17	CC
GO:0042581	specific granule	1.64E-05	9	CC
GO:0035580	specific granule lumen	1.60E-07	8	CC
GO:0005775	vacuolar lumen	0.000240936	8	CC
GO:0001533	cornified envelope	2.79E-07	7	CC
GO:0005766	primary lysosome	0.000708625	7	CC
GO:0042582	azurophil granule	0.000708625	7	CC
GO:0035578	azurophil granule lumen	0.000400823	6	CC

Gene ontology analysis of upregulated DEGs reported the molecular function viz., proteolytic enzymes, cytokine and chemokine receptor activity to be highly enriched in psoriasis identified genes. Henry et al. (2011) and  Chularojanamontri et al. (2019) in their study pointed that proteasome system is engrossed with psoriasis pathogenesis with involvement of cytokine and chemokine mediated genes (TLTF, TMPRSS11D, IL36G, CXCL8, CXCL1, IL36RN, CXCL13, TYMP, NAMPT and PPY) which is in concordance with the finding of the present study. Further, Nickoloff and Nestle. (2004); Mabuchi et al. (2012); Singh et al (2013); Meephansan et al. (2017) in their studies highlighted the importance of chemokine receptors and their corresponding chemokine ligands in psoriasis, role of similar receptor & ligands was also implicated in the present gene ontology analysis. Further, the involvement of IL-17 signalling pathway, NOD-like receptor signalling pathway, Cytokine-cytokine receptor interaction, Epstein-Barr virus infection, RIG-I-like receptor signalling pathway in up-streamed DEGs is indicative of the fact that psoriasis is immune mediated disorder (Ainali et al., 2012; Keermann et al., 2015; Melero et al., 2018; Choudhary et al., 2021).

S100A7, CCL20, IL36RN, CXCL8, IFIH1, MX1, LCN2, ARG1, CXCL1, LEP, STAT1, IRF7, FASN, ISG15 and OAS1 are top 15 hub genes associated with psoriasis. It is crucial to comprehend the role of these hub genes in disease initiation and progression. S100A7, also known as Psoriasin, is a gene implicated in cell differentiation. Its expression in close proximity to the suprabasal differentiating layer within the epidermal region strongly indicates its involvement in keratinocyte differentiation in psoriasis. A similar role of S100A7 was confirmed in the studies conducted by Ekman et al. (2017) and Rangaraj et al. (2017). Numerous hub targets such as, CCL20, IL36RN, CXCL8, IFIH1, MX1, CXCL1, STAT1, IRF7 and ISG15 can be classified as cytokines, its mediators or interferons.

The dysregulation of these genes in psoriasis transcriptome has been reported by several researchers, including Swindell et al (2017); Albanesi et al (2018) and Bai et al (2018). Like our findings, Elnabawi et al. (2020) suggested that CCL20 could serve as pro-inflammatory biomarkers and potentially contribute to enhancing vascular health in inflammatory diseases. Further, the interplay between IL-17 and TNFα is significant to the enhance the mRNA expression of pro-psoriatic genes CXCL8, CXCL1, S100A7 in keratinocytes (Benezeder and Wolf, 2019). Further, Albanesi et al. (2018) in their study highlighted crosstalk’s between cytokines, cytokines mediators and interferons to be responsible for unceasing inflammation and proliferation.

Signalling Pathway Enrichment Analysis: Influenza A, IL-17 signalling pathway, NOD-like receptor signalling pathway, Cytokine-cytokine receptor interaction and Epstein-Barr virus infection pathways are the top 5 enriched pathways amongst upregulated DEGs (Figure 4A) similarly downregulated DEGs were found to high enriched in PPAR signalling pathway, Peroxisome, Fatty acid metabolism, Carbon metabolism and AMPK signalling pathway (Figure 4B).

Genes–Disease Network of DEGs: The identified lesional skin DEGs were mapped to the validated disease genes in DisGeNET and target validation human genetic disorder databases. This analysis confirmed presence of 130 psoriasis associated DEGs (75.14%) in our datasets indicating that the identified DEGs are appropriate to signify the disease. The gene-disease network for 20 highest ranking genes found it to be associated with several other dermal disorder including parakeratosis, psoriasiform eczema, rosacea, atopic dermatitis, with several other respiratory, inflammatory and autoimmune disorders (Figure 5).

Key Candidate Genes and Pathway Identification with DEGs Protein-Protein Interaction (PPI) and Module Analysis: One hundred seventy-three (173) DEGs with 168 nodes and 474 edges were imported from PPI network complex. The nodes denote genes and the edges denote the interaction between genes. Cytohubba plugin was used for hub gene identification, the top 15 hub genes identified are illustrated in Figure 6.

Top 2 clusters with MCODE score greater than 10 were further analysed and pathway enrichment analysis of the modules found them to be functionally related. Influenza A, NOD-like receptor signalling pathway and RIG-I-like receptor signalling pathway are the most enriched pathway among cluster 1 whereas Cytokine-cytokine receptor interaction, Chemokine signalling pathway and IL-17 signalling pathway are the most enriched pathway in cluster 2 indicating the relevance of cytokine and cytokine-chemokine mediated pathways in psoriasis (Table 3).

Table 3. Signalling pathway enrichment analysis of top clusters in lesiona psoriasis skin

Cluster	Description	Number of Genes Enriched	Enriched Gene	FDR
Cluster 1	Measles	7	OAS3, IFIH1, OAS2, STAT1, IRF7, MX1, OAS1	5.04E-12
	Influenza A	7	OAS3, IFIH1, OAS2, STAT1, IRF7, MX1, OAS1	2.45E-11
	Herpes simplex infection	6	OAS3, IFIH1, OAS2, STAT1, IRF7, OAS1	3.26E-09
	Hepatitis C	5	OAS3, OAS2, STAT1, IRF7, OAS1	4.26E-08
	NOD-like receptor signalling pathway	5	OAS3, OAS2, STAT1, IRF7, OAS1	1.34E-07
	RIG-I-like receptor signalling pathway	3	IFIH1, ISG15, IRF7	2.19E-05
	Human papillomavirus infection	4	OASL, STAT1, ISG15, MX1	8.39E-05
	Hepatitis B	3	IFIH1, STAT1, IRF7	1.70E-04
	Toll-like receptor signaling pathway	2	STAT1, IRF7	0.0028
	Kaposi’s sarcoma-associated herpesvirus infection	2	STAT1, IRF7	0.0086
Cluster 2	Cytokine-cytokine receptor interaction	6	CCL27, CXCL13, LEP, CCL20, CXCL1, CXCL2	1.01E-05
	Chemokine signaling pathway	5	CCL27, CXCL13, CCL20, CXCL1, CXCL2	2.30E-05
	IL-17 signaling pathway	4	CCL20, LCN2, CXCL1, CXCL2	3.55E-05
	PPAR signaling pathway	3	FABP4, PLIN1, LPL	6.00E-04
	TNF signaling pathway	3	CCL20, CXCL1, CXCL2	0.0015
	Legionellosis	2	CXCL1, CXCL2	0.0092
	Regulation of lipolysis in adipocytes	2	FABP4, PLIN1	0.0092
	Rheumatoid arthritis	2	CCL20, CXCL1	0.0166
	Amoebiasis	2	ARG1, CXCL1	0.0166
	Salmonella infection	2	CXCL1, CXCL2	0.0166
	AMPK signalling pathway	2	FASN, LEP	0.0238
	NOD-like receptor signalling pathway	2	CXCL1, CXCL2	0.0402
	Kaposi’s sarcoma-associated herpesvirus infection	2	CXCL1, CXCL2	0.0446

The module analysis was done to understand the pathogenesis of the disease. The top 2 clusters identified Influenza A, NOD-like receptor signalling pathway, RIG-I-like receptor signalling pathway, Cytokine-cytokine receptor interaction, Chemokine signalling pathway and IL-17 signalling pathway as crucial pathways in disease pathogenesis. The reported result is in concordance with findings of indicating the involvement of cytokines, chemokines, in disease progression and subsequently alterations in these pathways could help in disease control (Ainali et al., 2012; Keermann et al., 2015; Melero et al., 2018; Choudhary et al., 2020).

CONCLUSION

The present study described the comprehensive gene expression profile of control healthy tissue samples, non-lesional and lesional tissue of psoriasis patients. The study identified 173 LS-NL DEGs of which S100A7, CCL20, IL36RN, CXCL8, IFIH1, MX1, LCN2, ARG1, CXCL1, LEP, STAT1, IRF7, FASN, ISG15 and OAS1 were identified as top 15 genes that could be denoted as hub targets. Involvement of these DEGs in Gene ontology enrichment (Molecular function, biological processes and cellular components) and pathway analysis establishes its essential role as therapeutic drug targets and biomarkers for psoriasis with further experimental validation.

Ethics Approval and Consent to Participate: None Required

Human and Animal Rights: None violated

Conflict of Interest: There is no conflict of interest to declare.

ACKNOWLEDGEMENTS

The authors duly acknowledge facilities provided by Biomedical Informatics Centre, ICMR-National Institute of Pathology, New Delhi.

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