The paeonol target gene autophagy-related 5 has a potential therapeutic value in psoriasis treatment

Background Paeonol is a potent therapy for psoriasis. This study aimed to screen out paeonol-targeted genes in psoriasis and validate the potential of using paeonol for the management of psoriasis. Methods Microarray datasets were obtained from the Gene Expression Omnibus. The differentially expressed genes (DEGs) in the lesional skin samples and the overlapping genes between DEGs and paeonol- and psoriasis-related genes were defined as potential targets for psoriasis. After being treated with si-ATG5 and pc-ATG5, human HaCaT cells were treated with 100 ng/ml IL-22 and 10 ng/ml TNF-α with and without paeonol. Cell proliferation, apoptosis, and expression of interleukin (IL)-6, IL-1β, Beclin 1, ATG5, and p62 in HaCaT cells were determined using ESLIA, PCR, and Western blot analysis. Results A total of 779 DEGs were identified in the lesional skin samples compared with the non-lesional tissues. The autophagy-related 5 (ATG5) gene was the only gene that overlapped between the DEGs and genes related to paeonol and psoriasis. Cell proliferation, inflammatory cytokines (IL-6 and IL-1β), and ATG5 expression were increased in IL-22/TNF-α-stimulated HaCaT (model) cells compared with control. Paeonol treatment rescued all changes. si-ATG5 transfection increased inflammation and apoptosis in model cells compared with controls. pc-ATG5 prevented IL-22/TNF-α-induced changes in HaCaT cells. Also, si-ATG5 decreased p62 and Beclin 1 proteins, while pc-ATG5 increased them both. Conclusions ATG5-dependent autophagy plays a crucial role in psoriasis. The ATG5 gene might be a therapeutic target for the management of in vitro psoriasis.


INTRODUCTION
Psoriasis is a common skin lesion condition characterized by aberrant proliferation of keratinocytes and chronic inflammation (Boehncke & Schön, 2015). This disorder influences 2-4% of the general population and almost 70% of patients diagnosed with

DEGs identification by meta-analysis
We employed the MetaQC package (https://cran.r-project.org/web/packages/MetaQC/ index.html) in R 3.4.1 to implement quality control (QC). The principal component analysis and standardized mean rank score were also used to evaluate and screen data information. The DEGs were extracted using MetaDE.ES in MetaDE (Chang, Sibille & Tseng, 2013) package (https://cran.r-project.org/web/packages/MetaDE). The cutoffs of consistently DEGs identification were set as tau 2 = 0 and Q pval > 0.05 of heterogeneity test, false discovery rate (FDR) < 0.05 and |log 2 fold change (FC)| > 0.263. Functional analyses of these DEGs, including the Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, were performed by DAVID (Huangda, Sherman & Lempicki, 2009) (version 6.8; https://david.ncifcrf.gov/). P value < 0.05 was used as the threshold of significant enrichment.

Screening of module DEGs related to psoriasis
Weighted gene co-expression network analysis (WGCNA) (Chen et al., 2012) has widely applied to identify the gene module associated with diseases. GSE30999 was set as the training dataset while other thee datasets (GSE13355, GSE14905, and GSE41662) served as the validation datasets. The stable gene module linked with disease features was screened using WGCNA software (version 1.61; https://cran.r-project.org/web/packages/WGCNA/ index.html) with the thresholds of gene number ≥ 25, cutHeight = 0.995, preservation z score >5, and correlation >0.6.

Selecting in database
The genes and pathways related to psoriasis and paeonol were selected from the Comparative Toxicogenomics Database (CTD, 2020 update; http://ctd.mdibl.org/). The immunity-related genes were selected from the AmiGO 2 (http://amigo.geneontology.org/ amigo). The overlapping genes between the DEGs and the genes in the related pathways were selected and regarded as the target of further experiments. The MCODE plugin (http://apps.cytoscape.org/apps/mcode) was used to identify modules in the PPI network.

Plasmid construction and cell transfection
The full-length coding sequence (CDS) of the human autophagy-related 5 (ATG5) gene was multiplied by PCR using the ATG5 specific primers with Bam HI/Xho I restriction enzyme sites (Table 1). The plasmid pc-ATG5 was constructed by cloning the PCR products into the pcDNA3.1 vectors (Genechem Co. Ltd, Shanghai, China). The short interfering RNAs (siRNA) targeting ATG5 (si-ATG5) and scramble sequences were purchased from

Proliferation assay
Psoriatic HaCaT cells were treated with trypsin at 12 h and 24 h post-treatment. Cell Counting Kit-8 (CCK8; Dojindo, Japan) solution was added into cell culture and incubated for 2 h. Cell viability at 450 nm absorbance was analyzed using a microplate reader (Thermo Labsystems, Helsinki, Finland).

Cell apoptosis assay
Cell apoptosis was analyzed using flow cytometry and an annexin V-Cy5-labeled Apoptosis Detection Kit (Beyotime Institute of Biotechnology, Nanjing, China). Briefly, HaCaT cells were harvested and were then suspended in 5 µl of Annexin V-Cy5 and 5 µl of PI. A FACS Calibur flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA) was used for apoptosis analysis.

Measurement of cytokines
The profiles of IL-1β and IL-6 in cell culture were detected using the enzyme-linked immunosorbent assay (ELISA) and commercial ELISA kits (Cusabio Biotech Corporation, USA). A microplate reader (Thermo Labsystems) was used for data analysis.

RNA isolation and quantitative real-time PCR
Total RNA was extracted from psoriatic HaCaT cells using TRIzol Reagent (Invitrogen) at 24 h after stimulation or transfection. The RNA was reversely transcribed to cDNA using a PrimeScript RT-polymerase kit (TaKaRa, Dalian, China) to the manufacturer's protocol.
The expression levels of genes were detected using an SYBR ExScript qRT-PCR Kit (TaKaRa). GAPDH was used as the internal control. Gene-specific PCR primer pairs were synthesized by Sangon (Shanghai, China; Table 1). PCR amplification was conducted according to the following reaction conditions: 95 • C for 5 min; 38 cycles of 95 • C for 30 s, 60 • C for 40 s, and 72 • C for 45 s. The relative expression levels of genes were analyzed using the 2-Ct methods.

Statistical analysis
Each experiment was performed in triplicate in this study. Statistical analysis was performed using the GraphPad Prism 8.0 software (GraphPad Prism Inc., La Jolla, CA, USA). All data were expressed as mean ± standard deviation. Data differences across groups were analyzed using the ordinary one-way analysis of variance (ANOVA) test followed by the Tukey test as post hoc. P values < 0.05 were considered statistically significant.

Identification of consistently DEGs and functional analyses
Data analysis showed that four datasets exhibited even distribution and good quality ( Fig. 2A). A total of 779 DEGs were identified (Table S1). The heatmap clustering of the DEGs in samples is shown in Fig. 2B. Functional enrichment analysis indicated that these genes were enriched with GO biological processes associated with regulation of phosphorus metabolic process (GO:0051174), negative regulation of cell proliferation (GO:0008285), and lipid biosynthetic process (GO:0008610; Fig. 2C), and KEGG pathways including PPAR signaling pathway (hsa03320), complement and coagulation cascades (hsa04610), and ECM-receptor interaction pathway (hsa04512; Fig. 2C).

WGCNA extraction of gene modules related to psoriasis
Before WGCNA, we found that the gene expression profiles in four datasets had high and positive correlations and connectivity (Figs. S1A and S1B). The soft-thresholding power = 10 when the square of the correlation coefficient = 0.9 and mean connectivity =1 (Figs. 3A-3B). Ten WGCNA modules were identified in the training dataset (Fig. 3C) and the validation datasets (GSE13355, GSE14905, and GSE41662; Figs. 3D-3F). Module preservation analysis showed that eight modules related to psoriasis traits. Four modules, including the black, white, red, and turquoise, had negative correlations with the psoriatic phenotype (preservation z score >5, correlation <−0.6, and p < 0.05, Fig. 4) and two modules, including blue (98 upregulated DEGs) and brown (79 upregulated DEGs) modules, had positive correlations with psoriasis (preservation z score >5 and correlation >0.6, Fig. 4).

PPI network construction
The PPI network was constructed using the 177 upregulated DEGs in the blue and brown modules. Accordingly, the PPI network consisted of 236 edges (interaction pairs) and 133 nodes (upregulated gene products; Fig. S2). These genes enriched in 31 biological processes, including cell cycle (GO:0007049) and apoptosis (GO:0006915; Table S2), and 12 KEGG pathways including Cell cycle (hsa04110), Natural killer cell mediated cytotoxicity (hsa04650), Cytokine-cytokine receptor interaction (hsa04060), and T cell receptor signaling pathway (hsa04660; Table 2). The 133 upregulated genes are listed in Table S3.

Selection of psoriasis-related and paeonol-targeted genes
To select the genes that have an important role in psoriasis, genes associated with immunity and psoriasis were identified from the 133 DEGs. A total of 119 DEGs (Table S3) were included in the lists of immunity-related genes (n = 3,324) and psoriasis-related genes (17,246). Also, nine common pathways related to psoriasis, paeonol, and DEGs were  identified from the CTD, including the Jak-STAT signaling pathway (hsa04630), Cytokinecytokine receptor interaction (hsa04060), and RIG-I-like receptor signaling pathway (hsa04622; Table 3). Only two genes (ATG5 and IL12B) were overlapped between the DEGs and genes in the CTD database. Also, a total of 35 paeonol-targeted genes were identified from the CTD database and the network of paeonol-targeted genes is shown in Fig. 5A. ATG5 was the only overlapped gene between paeonol-targeted genes and the DEGs (Fig. 5A). ATG5 interplayed with IL-6, CASP3, TNF, Akt1, and mTOR (Fig. 5A). Besides, we identified an MCODE module (score=15.73) consisting of 19 genes including IL-6, CASP3, TNF, Akt1, and mTOR. This module was related to 15 pathways including Apoptosis (hsa04210), Toll-like receptor signaling pathway (hsa04620), MAPK signaling pathway (hsa04010), and RIG-I-like receptor signaling pathway (hsa04622; Fig. 5B). These results indicated that the ATG5 gene might have an important role in psoriasis and might be a target of paeonol.  Fig. 6G and 6H). These results might indicate that paeonol has an inhibitory effect on psoriasis.

Si-ATG5 increases IL-6 and IL-1β and promotes apoptosis
We transfected si-ATG5 and pc-ATG5 into HaCaT cells and detected the expression of IL-6 and IL-1β to investigate whether the ATG5 gene could be used as a therapeutic target for the management of psoriasis. PCR analysis confirmed that si-ATG5 and pc-ATG5 transfection significantly decreased and increased the expression level of ATG5 in model cells, respectively (p < 0.0001; Fig. 7A). Also, si-ATG5 dramatically increased the contents of IL-6 and IL-1 β in model cells compared with negative controls (NC; p = 0.0011 for IL-6, and p = 0.0393 for IL-1β; Figs. 7B and 7C). However, we observed that pc-ATG5 transfection significantly decreased the contents of IL-6 and IL-1β in model cells compared with NC ( Fig. 7B and 7C). Besides, we found si-ATG5 significantly decreased HaCaT cell viability (p < 0.0001; Fig. 8A) and increased the apoptotic percentage of model cells (from 3.84 ± 0.15% to Data are expressed as mean ± standard deviation. The differences were analyzed using the ordinary oneway ANOVA test followed by Tukey test. Full-size DOI: 10.7717/peerj.11278/ fig-6 14.45 ± 1.07%, p < 0.0001; Fig. 8B). However, pc-ATG5 transfection prevented the effects of IL-22/TNF-α stimuli on HaCaT cell viability and apoptosis (Figs. 8A and 8B). These results showed that ATG5 expression played crucial roles in controlling inflammation and cell viability in HaCaT cell apoptosis.

Figure 7 The effect of ATG5 expression and paeonol on inflammation in HaCaT cell apoptosis. (A)
The expression level of ATG5 mRNA by PCR assay. (B and C) The production of cellular IL-6 and IL-1β by ELISA assay. Data are expressed as mean ± standard deviation. The differences were analyzed using the ordinary one-way ANOVA test followed by Tukey test. Full-size DOI: 10.7717/peerj.11278/ fig-7

DISCUSSION
Studies focusing on illuminating psoriasis pathogenesis using bioinformatics methods have been performed over the past few years (Mei, 2017;Zhang et al., 2019). Zhang et al. (2019) identified that INF-α-inducible genes are the characteristic genes in scalp psoriasis. Also, autophagy-related factors play important roles in psoriasis (Wu & Adamopoulos, 2017).
Our study identified that the ATG5 gene had crucial roles in the pathogenesis and might be a therapeutic target in psoriasis. Also, ATG5 might be a paeonol-targeted gene. Aberrant cell growth, differentiation, and inflammation are the major physiologic characteristics of psoriasis lesions (Victor & Gottlieb, 2002;Wang et al., 2020;Zhang et al., 2015). Autophagy in keratinocytes correlates with disease severity in psoriasis patients . Wang et al., (2020) showed that the high mobility group box 1 (HMGB1)-associated autosecretion is effective in regulating cutaneous inflammation in autophagy-efficient (ATG5 f /f ) keratinocytes. Another study by Peng et al. (2019) showed that the knockout of ATG5 in proximal tubular epithelial cells impaired inflammation through activating the NF-κB pathway. Our study demonstrated that a cluster of psoriasisand immunity-related genes, including the ATG5 gene, were upregulated in psoriatic lesions. These results were in line with the fact that the activation of inflammation and autophagy are the major physiologic features of psoriasis lesions.
Full-size DOI: 10.7717/peerj.11278/ fig-9 Gottlieb (2002) showed that TNF-α overexpression triggered the apoptotic progression and initiated the development of psoriatic lesions. Our in vitro experiments showed that paeonol suppressed IL-22/TNF-α-induced proliferation, ATG5 expression, and inflammation in HaCaT cells. These findings showed that the ATG5 gene might have a crucial role in psoriasis onset and might be a target for psoriasis management.
ATG5-mediated autophagy is crucial for a wide range of biological processes, including DNA damage, cell proliferation, apoptosis, inflammation, differentiation, and drug resistance (Han et al., 2020;Kim et al., 2020b;Peng et al., 2019;Yue et al., 2019). The inhibition of autophagy might induce an increase in cell apoptosis and inflammation and a reduction in cell proliferation in hepatocytes (Wu & Adamopoulos, 2017;Zhang et al., 2020). Also, autophagy inhibition results in exacerbating skin inflammation and increased disease severity (Wu & Adamopoulos, 2017). Zhang et al. (2020) revealed that acetaminophen-induced inflammation, apoptosis, and proliferation inhibition in the hepatocyte L-02 cell line could be enhanced by inhibiting autophagy and rescued by activating autophagy, respectively. They showed that the inhibition of autophagy in hepatocytes increased inflammation cytokines including IL-18 and IL-1β. However, Yue et al. (2019) andFeng et al. (2019) showed that the induction of autophagy ameliorates in vitro psoriasis. Our research showed that the ATG5 gene was upregulated in the in vitro cellular psoriatic model. The inhibition of ATG5 showed a supportive effect on paeonol-induced cell proliferation inhibition, Beclin 1 reduction, and cell apoptosis in HaCaT cells. The increased IL-6 and IL-1β contents by si-ATG5 transfection showed that autophagy inhibition enhanced inflammation in IL-22/TNF-α-treated HaCaT cells. Also, the decreased IL-6 and IL-1β contents by pc-ATG5 transfection showed that ATG5mediated autophagy might be crucial for preventing psoriasis. These findings suggested that the ATG5 gene might have an important role in psoriasis.
Autophagy deficiency in keratinocytes increased the production of inflammatory cytokines (Lee et al., 2011). However, previous studies showed that the mixture of five proinflammatory cytokines (IL-17A, IL-22, Oncostatin-M, TNF-α, and IL-1 α) (Kim et al., 2020a), IL-17A (Varshney & Saini, 2018), and TNF-α (Yue et al., 2019) treatments significantly decreased autophagy and ATG5 expression in HaCaT cells. However, our present study showed that the ATG5 gene was upregulated in HaCaT cells stimulated by IL-22/TNF-α cytokines. Also, it was increased in the lesional skins compared with non-lesional skins from patients with psoriasis. This difference might due to the distinct stimulation strategies (Kim et al., 2020a;Varshney & Saini, 2018;Yue et al., 2019). Besides, our present study showed that paeonol was able to reduce the expression levels of ATG5, IL-6, and IL-1β upon IL-22/TNF-α treatment. However, prior inhibition or overexpression of ATG5 blocked the influence of IL-22/TNF-α treatment on cell inflammation, proliferation, and apoptosis. These findings indicated that the effect of paeonol on HaCaT cells was ATG5-dependent. However, the exact mechanism of autophagy in psoriasis should be further validated.

CONCLUSIONS
In conclusion, we confirmed that ATG5-dependent autophagy was of great value in psoriasis. ATG5 was increased in lesional skin tissues compared with non-lesional tissues. The inhibition of ATG5 promoted inflammation in HaCaT cells and the overexpression of ATG5 prevented IL-22/TNF-α-induced inflammation in HaCaT cells. Also, the effect of paeonol on in vitro psoriatic model was ATG5-dependent. The inhibition of ATG5 might be a targeting management strategy for in vitro psoriasis. However, more experiments should be performed to validate the association of autophagy with psoriasis and the probability of targeting autophagy for the management of psoriasis.

ADDITIONAL INFORMATION AND DECLARATIONS Funding
The authors received no funding for this work.