The Model of PPARγ Downregulated Signaling in Psoriasis

Interactions of genes in intersecting signaling pathways, as well as environmental influences, are required for the development of psoriasis. Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor and transcription factor which inhibits the expression of many proinflammatory genes. We tested the hypothesis that low levels of PPARγ expression promote the development of psoriatic lesions. We combined experimental results and network functional analysis to reconstruct the model of PPARγ downregulated signaling in psoriasis. We found that the expression of PPARγ maybe be slightly downregulated in human psoriatic skin and laser treatment may facilitate it. We tested the reconstructed model and found that at least on mRNA level the expression of IL17, STAT3, FOXP3, and RORC and FOSL1 genes in psoriatic skin before and after laser treatment were correlated with the level of PPARγ mRNA expression suggesting that genes belong to the same signaling pathway that may regulate the development of psoriasis lesion.


INTRODUCTION
Psoriasis is an example of chronic inflammatory skin disorder with a complex multifactorial origin. Multiple genes cause heterogeneous heredity of psoriasis (Nickoloff and Nestle, 2004;Peters et al., 2000). Interactions of predisposing genes, as well as environmental influences, are required for the development of the disease.
Family genotyping supports the hypothesis that different phenotypes or manifestations of psoriasis are determined by different genetic loci (Samuelsson et al., 1999). These loci are associated with psoriasis and located at least on 13 different chromosomes and are named PSORS (Psoriasis Susceptibility) PSORS1-PSORS13 (Hız Meliha Merve, 2017). Each PSORS contains a list with several revealed genes-candidates (Barker, 2001).
Peroxisome proliferator-activated receptors (PPARs) do not get on lists of gene-candidates for psoriasis, however, the important role of PPARs in anti-inflammatory and immunomodulatory cellular signaling pathways has been established. Recently association of proline12/alanine gene polymorphism (rs1801282) in peroxisome proliferator-activated receptor gamma (PPARγ, NCBI Gene ID: 5468) was found to be associated with psoriasis and obesity in Egyptian patients (Seleit et al., 2019).
PPARs perform function primarily as ligand-dependent transcription factors which activate genes with PPAR-responsive elements (PPREs) in their promoter. PPARγ is detected mostly in well-differentiated suprabasal keratinocytes within the human epidermis (Icre et al., 2006). Human hair follicle epithelial stem cells also express PPARγ which maintains their survival in normal conditions (Billoni, Bruno Buan, Brigitte Gauti, 2000). Skin adipocytes and sebocytes are the next large PPARγ depositions (Alestas et al., 2006;Inoue et al., 2014) and the protein is vital for their differentiation (Nehrenheim et al., 2013;Paus et al., 2007). The PPARγ expression was reported to be downregulated in the psoriatic skin of mice and DDH1 dose-dependently could restore the gene expression (Kitahata et al., 2018). In vitro experimental models of psoriasis showed the expression of other PPARs (PPARa) was also decreased in the skin, while PPARb and PPARd expression were increased (Friedmann et al., 2005). Mice model of inflammatory skin diseases revealed that the expression of PPARγ and PPARa was decreased in the skin due to the absence of the Dlx3 gene (Hwang et al., 2011). The medical suppression of PPARγ improved the health of the mice model of atopic dermatitis (Jung et al., 2011). Wang X at all. reported that gene PPARγ had high level of expression in the skin of IMQ-induced psoriasis mice, and а PPARγ -selective antagonist GSK3787 was able to decrease the inflammation in the skin (Wang et al., 2016). Finally, another animal model research showed that mutant mice with deleted PPARγ did not have sebaceous glands and normal hair follicles (HF), and developed scarring alopecia and skin inflammation (Sardella et al., 2018). There is no experimental evidence about PPARγ activity level in human skin of patients with psoriasis to our knowledge.
PPARγ signaling in psoriasis has been studied at a good level, but conflicting experimental results do not allow describing a clear picture of protein-protein interactions and pathological changes in cell pathways leading to the development of psoriasis (read below, section "Pathway model of PPARγ signaling in psoriasis").
In this work we tested a hypothesis that low levels of PPARγ may change the activity of cellular signalling pathways in the skin and facilitate the chronic inflammatory and immune response in psoriatic lesion in humans. Based on the literature-based protein-protein interactome (PPI) and pathway analysis we proposed that low PPARγ activity promotes the development of psoriatic lesions due to changes in the inflammatory signaling pathways regulated by STAT3, RORC, FOXP3, FOSL1 and IL17A. To check the hypothesis, we measured the expression of these genes altogether with PPARγ on the mRNA level in the skin of patients with psoriasis before and after low-intensity laser treatment.

Protein-protein interactome (PPI) analysis and pathway model reconstruction
To reconstruct the PPARγ-psoriasis interactome we used the literature-based database PSD (Resnet -2020 ®, Elsevier Pathway Studio database). PSD is a mammal -centered database where relationships between biological terms and molecules extracted from published papers with natural language processing technology (NLP). Data from public databases with experimental types of connections are also present in PSD. Resnet -2020 contains over one million objects and more than 12 million relationships with more than 55 million supporting sentences ( (Nesterova et al., 2019), www.pathwaystudio.com).
For PPI analysis we used SQL language and ran queries to filter PSD connections and found inhibited by PPARγ expression targets that simultaneously have positive relationships with psoriasis (see "PPARγ targets and regulators" file, list 1 in supplemental materials). To find PPARγ regulators we selected genes that negatively regulate expression of PPARγ and simultaneously negatively regulate PPARγ expression targets (see "PPARγ targets and regulators" file, list 2 in supplemental materials). To focus only on gene expression signaling and exclude other molecular types of interaction, we considered only two types of relationships in PSD that indexed sentences about the changing of mRNA or gene expression ("Expression" and "PromoterBinding). Queries and other parameters of network filtering are available by a request.
We used Pathway Studio software to reconstruct the model of PPARγ signaling. Models are interactive networks which describe connections between molecules and related phenotype or biological processes. Models are kept in RNEF format, connected with PSD and include different annotations of molecules and relationships (synonymes, identificators, references, sentences, effects, mechanism of actions and more). All files can be found in supplemental materials (see below).

Pathway functional analysis
List of proteins that we had identified in the PPI analysis was set up with Sub-Network Enrichment Analysis (SNEA, Pathways Studio), Fisher exact test, Enrichr tool (Chen et al., 2013), and KEGG mapping tool (Kanehisa and Sato, 2020). SNEA was used to find cell processes statistically enriched with genes from list 1 and 2. SNEA is the modification of gene set enrichment analysis that accounts for relationships between genes in the network (Kotelnikova et al., 2010). Fisher test was used to find associated Pathway Studio pathways and Gene Ontology (GO) functional gene groups (Ashburner et al., 2000). Associated KEGG pathways we found with the KEGG mapping tool and other associations we found with Enrichr tool.
Cell processes were selected if more than 5 genes from the list 3 (combined genes from list 1 and list 2) were overlapped with total genes associated with the pathway, and if more than 5% genes from the list 1 and 2 were overlapped with a sub-network or GO group. We selected top sub-networks and KEGG pathways filtered by rank, and top PS pathways and GO groups filtered by Jaccard index. For the comparison of methods, we selected top 50 sub-networks, 50 pathways, and 50 GO groups after manual filtering off unrelated diseases (such as cancer), viral and bacterial KEGG pathways. See supplemental materials for results of pathway functional analysis ("PPARG network analysis" file and "PPARG Enrichr analysis" file).

Microarray in-silico analysis
Public microarray data (GEO, GSE13355) was used to verify the reconstructed model of PPARγ signaling in psoriasis. GSE13355 contains data about the expression of the human genome in skin samples of 58 patients with psoriasis (Ding et al., 2010). DE (differentially expressed genes) were identified with a two-class unpaired T-test between samples of lesional skin of each patient (PP samples) and non-lesional skin uninvolved samples (PN samples). Multiple probes were averaged by the best p-value or maximum magnitude. Pathway Studio software was used for calculation of DE and pathway analysis.

Skin samples
We analysed biopsies from 23 patients who were treated in the V G Korolenko Hospital, Moscow Scientific and Practical Centre of Dermatovenerology and Cosmetology. Patients were diagnosed with Psoriasis vulgaris. The diagnoses were confirmed by the pathomorphological examination of skin biopsies. The age of patients varied from 25 to 56 years. There were 10 men and 13 women Common PASI scale for all 23 patients was 22,1±6,25 (PASI evaluates the severity of lesions between 0 and 72 score). See scores for each patient in supplemental materials, "PPARG expression" file). Local anesthesia and dermatological punch (4 mm) were used for the collection of skin samples. Healthy skin samples were taken at a distance of 3 cm from a psoriatic lesion. The research was approved by the Local Ethical Committee at the Center for Theoretical Problems of Physical-Chemical Pharmacology, Russian Academy of Science, and complies with the principles of the Helsinki Declaration. The laser treatment was provided 2-3 times a week. Skin samples were collected before the treatment and one day after the 7th laser seance.

Genes Expression
Qiagen spin column and standard RNeasy Mini Kit® for the skin were used for the RNA isolation. Additional treatment of samples with the DNAase (Qiagen) was used to remove DNA traces. RNA concentration was measured with NanoDrop 1000 (Thermo Scientific, США).

Statistical analysis
To calculate the results, we used numbers from real-time PCR reactions with primer efficiency at least 95%, 0.99 correlation coefficient and the curve (slope) -3.4 ± 0.2. PCR results were analyzed using the 2-ΔΔCT method to compare the levels of expressions detected in affected and unaffected samples [18]. Each ΔCt was calculated as ΔCt = Ct (tested gene) -Ct (GAPDH). ΔΔCt was calculated as ΔΔCt = ΔCt (psoriatic skin sample) -ΔCt (health skin sample). The experiments were repeated three times for each sample. Intergroup differences were calculated using the Mann-Whitney U-test. See results for each genes in supplemental materials, "PPARG expression" file.

Supplemental materials
All supplemental materials are available to download from ResearchGate resource by the link https://www.researchgate.net/publication/340427568_Supplemental_Materials_The_role_ of_PPARg_downregulated_signaling_in_psoriasis (Sobolev, 2020). All pathways models and their annotations are available for browsing and can be downloaded at http://www.transgene.ru/ppar-pathways.

Reconstruction of downregulated PPARγ pathway model associated with psoriasis.
For testing the hypothesis that low levels of PPARγ trigger inflammatory signaling pathways in the skin, we analysed protein-protein interaction literature-based network (PSD, Elsevier Pathway Studio) and several public ontologies and databases (Gene Ontology, Human Protein Atlas, KEGG, Reactome).
First, in the PSD network, we identified PPARγ downstream expression targets and upstream regulators (inhibitors) of PPARγ expression. For researching the downstream targets, we looked for genes and proteins which were reported to be inhibited by PPARγ and simultaneously were positive biomarkers for psoriasis. 146 associated with psoriasis genes and gene families whose expressions are repressed by PPARγ had been found. For researching the upstream of PPARγ signaling we focused on the transcriptional factors which can inhibit both the expression of PPARγ and his direct targets. 99 associated with psoriasis unique negative regulators of PPARγ had been identified. Then we combine regulators with targets to obtain 182 names of unique genes forming the PPARγ downregulated sub-network associated with psoriasis (see supplemental materials, "PPARG regulators and targets" file, list 3). (Figure 1).

Comparative pathway analysis of PPARγ downregulated signaling associated with psoriasis
Several methods of pathway analysis were performed to explore the functional roles of 182 targets and regulators of PPARγ revealed in PPI analysis. Methods of pathway functional analysis are widely used for discovering cellular processes and signalings that are statistically associated with the list of genes or proteins (Nesterova et al., 2020).
We compared results from pathway functional analysis with three resources: Gene Ontology, Elsevier Pathways, and KEGG Pathways. Gene Ontology is the source of groups of proteins or genes manually assigned by their different functional roles. Elsevier Pathways and KEGG Pathways are manually reconstructed schemas or models of interactions between proteins describing molecular mechanisms of one or several biological processes. Gene Set Enrichment Analysis (GSEA) is a well-known method to analyse predefined and manually created collections of gene groups and pathways (Subramanian et al., 2005). Besides GSEA we used SNEA method which allows finding associated cellular processes based on literature -based PPI network. SNEA does not use predefined groups of genes or pathways and is considered less biased (Kotelnikova et al., 2010;Nesterova et al., 2020).
According to the results of comparative pathway analysis, PPARγ downregulated signaling is associated with adipogenesis, activation of myeloid pro-inflammatory cells (with a predominance of mast cells and dendritic cells), and activation of overall immune system response (with a predominance of Th17 cells). Also, fibrogenesis, cell-to-cell contacts, vascular-related processes and universal cell processes, such as cell proliferation or cell death, were identified ( Figure 2). Cellular possesses directly associated with psoriasis were present in results from each source that we compared (Table 1).  Top sub-networks from SNEA were neighbours of adipogenesis and adipocyte differentiation, followed by the immune response, and T-development. The sub-networks "neighbours of monocyte recruitment or differentiation" and "macrophage differentiation" had the most percent (9%) of overlapped genes from PPARγ down-regulated signaling.
GSEA analysis of PS Pathway Collection and KEGG pathways resulted in many cancerrelated processes. The disease taxonomy filtering with PS pathways about skin and immune system identified processes related to adipokines and IL17 signaling ( Table 2). The signaling of aryl hydrocarbon receptor (AHR) in Th17 cells was the pathway with the biggest percent (48%) of overlapped genes from PPARγ down-regulated signaling. Among top KEGG pathways enriched with our gene list, we identified general MAPK and PI3K signaling and cancer-related pathways (for example, "hsa05200 Pathways in cancer -Homo sapiens"). TNF signaling pathway (hsa04668), as well as Th17-cell (hsa04659) and IL17 pathway (hsa04657), were also in the top 10 results. The cytokine-cytokine receptor interaction (hsa05200) and PI3K-Akt signaling (hsa04151) had the highest number of overlapped entities (48 and 34).
The list of revealed in pathway analysis molecular cascades complete the lists of cell processes.
There was no surprise that activation of general cellular flows like ERK/MAPK, RAS/ACT1, and adipose cells related AMPK, mTOR, and cAMP cascades were associated with the list of PPARγ targets and regulators. Also, among the top of associated molecular signalings there were well predictable inflammatory cascades like Toll-like receptors, interleukins and interleukins receptors signaling (IL17, IL1B, IL6, and IL1R1) altogether with all-purpose cytokines and cytokines receptors signaling (CXCR3, CCR1, TNF). Signalings related to transcription factors NFKB and STATs also were significantly associated with the analysed list. GO functional group "GO: glycosaminoglycan binding"; "IL1R1 signaling in Pneumocytes'' from PS Pathway Collection; and "ErbB signaling pathway" (hsa04012) from KEGG had the maximum rank (See complete results with additional statistics in supplemental materials, "PPRAG network analysis" file). Glycosaminoglycans are essential for skin functioning. IL1R1 is a receptor commonly activated in any non-specific inflammatory processes. Finally, the ERbB/EGFR family is involved in cell proliferation and tumor development.
Additional comparison of pathway analysis results with other pathways databases (WikiPathways, Reactome, Biocarta analysed with Enrichr tool) confirmed results obtained with PS Pathway Collection (Figure 3). Pathways from all sources revealed skin inflammatory processes, TLRs and interleukins related cascades. However, the list of molecules was different compared with PS and KEGG results presenting IL10 and IL22R and no IL17 associations. In addition, analysis with DisGeNET (Piñero et al., 2017) confirmed that the PPARγ regulators and targets are connected with psoriasis since top diseases associated with the list 3 were: psoriasis, epithelial hyperplasia of skin, and inflammatory dermatosis. Allergic reaction, neutrophilia, and vascular diseases were also in the top 10 results (see supplemental materials, file "PPARG Enrichr analysis" file).

Pathway model of PPARγ signaling in psoriasis
Considering results of PPI network and functional pathway analysis we build a hypothetical model that describes cellular molecular mechanisms of involvement of PPARγ in the maintenance of chronic inflammatory and immune response in human psoriatic skin. Literature -based network (PSD) were used to build the model. Figure 4 described the adopted for the publication simplified version of the downregulated PPARγ pathway model. See supplemental materials for the completed version of the pathway model. Based on the model, reducing the level of the PPARγ gene expression may be a result of the over-regulation of several cascades. Pattern recognition receptors (TLRs, NOD1, NOD2, CLEC7A) that sensor pathogens and highly expressed in keratinocytes and monocytes during the infection may be one of such cascades. All-purpose cellular cascades like growth factors signaling, G-proteins and MTOR signaling also were reported to be inhibitors of PPARγ expression in literature and revealed in our analysis. Moreover, transcription factors including NF-kBs, JUN-FOS, AHR, GATA3, HIF1A, FOXO1 and FOSL1 can directly inhibit PPARγ expression. All these transcription factors are over-stimulated in the inflammatory and immune response. For example, it is reported that NF-kBs are stimulated in systemic inflammatory processes in general, and in psoriasis as well (Tang et al., 2010;Xu et al., 2015).
In healthy skin PPARγ inhibits mentioned transcriptional factors in a feedback regulation loop. PPARγ may directly bind and suppress transcriptional factors STAT3 and RORC, by thus blocking the synthesis of pro-inflammatory cytokines including IL17. Less quantity of expressed cytokines decreases the Th17 cell proliferation, minimises chemotaxis of neutrophils and monocytes and results in the reduction of inflammation in psoriatic lesions.
IL17 which is produced mostly by TH17 cells plays the central role in the development of psoriasis because it stimulates keratinocytes to secrete pro-inflammatory cytokines and anti-bacterial peptides (Srivastava et al., 2017). IL17 pathway and Th17 cells had a strong association with PPARγ downregulated signaling confirmed by our network and functional analysis.
Th17 cells need robust activity of STAT3 gene for their function and differentiation. Also, STAT3 is described as an important linkage between keratinocytes and immune cells (Chowdhari and Saini, 2014). Previously the expression of STAT3 was shown to be repressed due to PPARγ activation (Hsu et al., 2016). STAT3 may also act as a regulator of PPARγ expression however it is not clear whether with positive or negative effect (Tuna et al., 2014).
As a transcription factor, STAT3 is reported to be a strong activator of RORC (RORγ) and, probably, IL17 gene expression. From the other side, gene RORC is the major inductor of the expression of IL17 cytokines family (Takaishi et al., 2017). PPARγ was shown to bind the RORC promoter and suppress its expression altogether with RORC-mediated Th17 cell differentiation (Hermann-Kleiter et al., 2012).
Transcription factor FOXP3 is closely associated with psoriasis and the diminishing of Treg-cell number (Jorn Bovenschen et al., 2011;Shu et al., 2017). It was shown that activated PPARγ induces the stable FOXP3 expression by strong inhibiting effect on DNAmethyltransferases. The activating effect of PPARγ on FOXP3 results in the proliferation of iTreg-cells (Lei et al., 2010).
FOSL1 is the transcriptional factor which plays important role in many processes related to cell differentiation and tissue remodeling (Sobolev et al., 2011(Sobolev et al., , 2010Young and Colburn, 2006). FOSL1 (FOS-like antigen 1) is expressed in low level in healthy tissues, however its expression rises due to presence of mitogens or toxins. The accumulation of the FOSL1 protein in the skin depends on the stage of the keratinocyte differentiation (Mehic et al., 2005). Markers of stratum corneum differentiation like gene IVL are the main expression targets of FOSL1 (Adhikary et al., 2004).
The degree of the pathogenicity of downregulated PPARγ in psoriatic lesion depends on the cell type. It is known that PPARγ is expressed in Th17 cells as well as in keratinocytes, sebocytes and other cells of the psoriatic lesion (Billoni, Bruno Buan, Brigitte Gauti, 2000;Icre et al., 2006;Inoue et al., 2014;Nehrenheim et al., 2013;Paus et al., 2007). Functional and network analysis supported the association of PPARγ down-regulated signaling with keratinocytes, vascular endothelium, vascular smooth muscle cells, macrophages, fibroblasts and adipocytes, and monocytes lineage (particular with CD33+, CD14+ monocytes) ( Figure 5). However, we did not attempt to separate the PPARγ pathway model by appropriate cell types which is a disadvantage of this work. There is no reliable way to take in account cell specificity in our modeling paradigm. Moreover, we expect that most of the revealed from the literature network analysis cascades will be equal for different human cells due to insufficient experimental studies. For additional evaluation of the reconstructed model, we analysed the public microarray data (GEO:GSE13355). In that experiment biopsies from 58 psoriatic patients were run on Affymetrix microarrays containing more 50 000 gene probes (Nair et al., 2009). We uploaded raw data from GEO and calculated differentially expressed genes (DE) between samples of psoriatic skin and unaltered samples for all patients. Then we used pathway analysis to explore the difference in the expression for genes of the PPARγ model we build (Figure 6).
PPARγ gene was slightly down regulated in psoriatic lesions comparing to non-altered lesions in GSE13355 microarray data ( Figure 6, PPARγ expression diagram).
We assumed that regulators of PPARγ signaling should have higher expression in psoriatic lesion than in normal skin. Only S100A12 (S100 calcium binding protein A12) had a significantly higher level of expression in analyzed microarray data comparing with all regulators of PPARγ that we selected for the model (Figure 6, 7). S100A12 binds to the AGER receptor which belongs to the immunoglobulin superfamily and is involved in many processes of inflammation and immune response. S100A12 is thought to be the most prominent biomarker of psoriasis (Wilsmann-Theis et al., 2016). Also, polymorphisms in AGER receptor were found to be associated with psoriasis (Puig and López-Ferrer, 2017).
The EGFR signaling almost completely was down-expressed in this microarray data including the FOXO1 expression which is one of the direct inhibitors of PPARγ. Therefore, EGFR / FOXO1 signaling probably does not play an important role in the regulation of PPARγ in psoriasis (Figure 7). Figure 6. Evaluation of PPARγ downregulated sub-network (selected PPARγ regulators and targets) associated with psoriasis using microarray data analysis (results of differential expression analysis of psoriatic lesions vs unaltered lesions). The saturation in blue indicates the degree of genes down-expression in psoriatic samples in comparison with unaltered samples. The saturation in red indicates the degree of genes over-expression. The list of targets and regulators see in supplemental materials, "PPARG regulators and targets", list 3. Figure 7. Evaluation of PPARγ downregulated model associated with psoriasis using microarray data analysis (results of differential expression analysis of psoriatic lesions vs unaltered lesions from GEO:GSE13355). The saturation in blue indicates the degree of genes down-expression in psoriatic samples in comparison with unaltered samples. The saturation in red indicates the degree of genes over-expression. The plots of expression pattern in psoriatic lesions compared with healthy skins samples are shown for PPARγ and S100A12 genes.

PPARγ expression is slightly downregulated in psoriatic skin
To test the hypothesis of downregulated PPARγ signaling in psoriasis, we measured gene expression of PPARγ and several key members of the reconstructed model in skin samples from patients with psoriasis.
Several key players in the PPARγ signaling were selected for experimental validation of the hypothesis that low levels of PPARγ may contribute to the development of psoriatic lesions. There were IL17A gene (interleukin 17A), STAT3 gene (signal transducer and activator of transcription 3), RORC gene (retinoid-related orphan receptor-gamma), FOXP3 gene (forkhead box P3) and FOSL1 (FOS-like antigen 1) gene.
For each of 23 patients, we compared levels of expression of PPARγ in the psoriatic skin samples and the skins without visually noticed lesions at the distance of 3 cm from the psoriatic surface. Such comparison helps to exclude the influence of unrelated psoriasis factors on the molecular profile (Yao et al., 2008). Results of real-time PCR showed that in the psoriatic samples the PPARγ gene was expressed below the level of its expression in unaffected skin. The average level of the PPARγ expression in the skin of patients was slightly reduced in 1.3±0.27 times in psoriatic skin compared with the skin without visually noticed lesions. We found a significant increase in the level of the gene IL17 expression in 42.39±16.68 times, gene STAT3 in 4.42±0.90 times, gene RORC in 7.68±1.62, and FOSL1 in 9.72 ±4.98 times. The level of expression of gene FOXP3 was decreased in 1.72±0.14 times, and of gene PPARγ in 1.3±0.18times (Figure 8).

Low laser treatment stabilises PPARγ related signalings in psoriatic skin
For the next step of validation, we studied the expression of PPARγ, STAT3, IL17A, RORC, FOXP3, and FOSL1 in human psoriatic skin samples and visually healthy skin samples before and after laser treatment.
Patients received low-intensity laser treatment with 1.27 microns wavelength (infrared short waves). The molecular mechanism of laser treatment is not well understood. Lowintensity laser waves are absorbed by oxygen, CO2, water molecules switching them into an activated state. Proteins with activated molecules participate in interactions more intensively. There was shown that low laser treatment stimulates Ca2-related signaling pathways including general membrane reparation and cell proliferation. There are expectations that low-level laser treatment will result in the replacement of "old" cells with new ones thus reducing the inflammation in the psoriatic lesion (Avci et al., 2013).
We detected a reliable reduction in the expression of studied PPARγ, STAT3, IL17A, RORC, FOXP3 and FOSL1 genes after low level (1.27 microns) laser treatment. The level of STAT3 expression was decreased in 2.08±0.33 times (   and RORC (F) genes expression in the skin of 23 patients with psoriasis before and after low-level laser therapy. The levels of mRNA concentration for genes in psoriatic skin samples was calculated in relation to the level of the same genes in unaffected skin samples (which was taken as conditional 1, p<0.05). See supplemental materials for detailed statistics ("PPARG expression file).
Therefore, low laser treatment caused significant growth of the PPARγ and FOXP3 expression while reducing the expression of STAT3, IL17A, and RORC.
Similar to previously published results by different groups of medical researchers, the lowlaser treatment had a positive effect on the health of observed patients and reduction of psoriatic skin inflammation was achieved.

CONCLUSION
In our previous work, we reviewed the recent progress in psoriasis pathways and published two pathway models. The first pathway model described the shift to TH17 cell production during the differentiation of psoriatic T cells. The primary cause of the shift of the T-cell differentiation is supposed to be genetic mutations, for example in IL23R receptors. The second model showed how elevated levels of IL17 and IL22 may activate keratinocytes to release different cytokines and chemokines for attracting neutrophils and other inflammatory cells in the psoriatic lesion (Nesterova et al., 2019).
In this work, we tested the hypothesis that PPARγ signaling when downregulated may promote psoriasis. We built the model of PPARγ dependent pathways involved in the development of the psoriatic lesions. However, we used a different approach for reconstructing the pathway model and selected key members with bioinformatic analysis. We included in the pathway model top statistically significant regulators of PPARγ gene expression and PPARγ-depended expression targets. Then we included significant molecular cascades and cell processes from results of the functional analysis (IL17 signaling, TLRs signaling, activation of STAT3 or NFKB transcription factors and others). We tested the model with analysis of published microarray data. Finally, we included in the model data from own experimental analysis of genes expression in human psoriatic skin.
We detected down-regulation of PPARγ gene expression in human psoriatic skin from 23 patients with real-time PCR method. Our results are similar to data from microarray on 58 patients where average PPARγ gene expression also is slightly down-regulated in psoriatic lesions (Nair et al., 2009). Our results do not confirm the work of Westergaard M et al. which described the slightly higher level of the PPARγ expression in human psoriatic skin compared to normal skin. However, the level of PPARγ mRNA was close to the detection limit in their research (Lei et al., 2010). This difference may be due detection of different isoforms of PPARγ which all have different patterns of the expression (Meirhaeghe and Amouyel, 2004). More research on protein level is needed to answer the question about the level of the PPARγ expression in human psoriatic skin. The limitation of current study is that we analysed gene expression only on the mRNA level using only one method of RT-PCR. Additional analysis on protein levels, more samples, and analysis with specific cell types rather than with whole skin are needed to conclude whether PPARγ gene expression is downregulated in psoriatic lesions.
Within the framework of the model validation we supposed that signalling related to repressed PPARγ activity is correlated with the development of psoriasis. IL17A, STATS3, and RORC (RORg) are statistically significant PPARγ negative targets and they have expected higher levels of expression in psoriatic lesion which decrease after laser treatment. Since PPARγ may act as a suppressor of the IL17 gene transcription by inhibiting his direct regulator gene RORC, our preliminary experimental results showing the high levels of expression for IL17 and RORC are aligned well with low activity of PPARγ in psoriatic skin.
While the prominent role of RORC in psoriasis as the major controller of Th17 cell differentiation is well described, however, the evidence of RORC expression in psoriasis is controversial and supported by work where mice T-cells and dendritic cells had increased STAT3/RORC expression (Nadeem et al., 2017) still patients with psoriasis had elevated level of RORC (RORG-t isoform) (Mendoza et al., 1989). In analysed published microarray data, the level of expression of RORC was downregulated in most of 58 patients. We detected the high level of RORC mRNA in the psoriatic skin of patients and this level was reduced after laser treatment. We report the downregulation of FOXP3 which is a direct inhibitor of RORC and positive target of PPARγ. Though, low level of PPARγ as well as high level of RORC is supported by down-regulated FOXP3 expression and validates reconstructed model. FOSL1 and STAT3 were overexpressed in psoriatic lesion on mRNA level in our experiment and may directly inhibit PPARγ expression. The expressions of FOSL1 and STAT3 were reduced significantly after laser treatment that may play role in the stabilization of psoriatic inflammation as well as PPARγ related pathways. The limitation of current work when only one methodology was used to study gene expression, does not allow us to assert the high reliability of biological conclusions. However, the alignment of our results with microarray data and PPI network analysis shows that the model of PPARγ downregulated signaling in psoriasis reconstructed in this work can be useful for further research.