Cross-sectional study of proteomic differences between moderate and severe psoriasis

doi:10.21203/rs.3.rs-4710909/v1

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Cross-sectional study of proteomic differences between moderate and severe psoriasis

https://doi.org/10.21203/rs.3.rs-4710909/v1

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Although an ongoing understanding of psoriasis vulgaris (PV) pathogenesis, little is known about the proteomic differences between moderate and severe psoriasis. In this cross-sectional study, we evaluated the proteomic differences between moderate and severe psoriasis using data-independent acquisition mass spectrometry (DIA-MS). 173 differentially expressed proteins (DEPs) were significantly differentially expressed between the two groups. Among them, 85 proteins were upregulated, while 88 were downregulated (FC ≥ ± 1.5, P < 0.05). Eighteen DEPs were mainly enriched in the IL − 17 signalling pathway, Neutrophil extracellular trap formation, Neutrophil degranulation and NF − kappa B signalling pathway, which were associated with psoriasis pathogenesis. Ingenuity pathway Analysis (IPA) identified TNF and TDP53 as the top upstream up-regulators, while Lipopolysaccharide and YAP1 were the top potential down-regulators. The main active pathways were antimicrobial peptides and PTEN signalling, while the inhibitory pathways were the neutrophil extracellular trap pathway, neutrophil degranulation, and IL-8 signalling. 4D-parallel reaction monitoring (4D-PRM) suggested that KRT6A were downregulated in severe psoriasis. Our data identify Eighteen DEPs as biomarkers of disease severity, and are associated with IL − 17 signalling pathway, Neutrophil extracellular trap formation, NF − kappa B signalling pathway, and defence response to the bacterium. Targeting these molecules and measures to manage infection may improve psoriasis's severity and therapeutic efficacy.

Biological sciences/Immunology

Biological sciences/Molecular biology

Health sciences/Biomarkers

Health sciences/Medical research

Proteomics

Psoriasis Vulgaris

4D-Parallel Reaction Monitoring

Data-Independent Acquisition Mass Spectrometry

Ingenuity Pathway Analysis

Biomarker

Psoriasis vulgaris (PV) is a common, chronic and immune-related skin disease which can also cause multisystem damage like depression (especially in young people) ^1,2and psoriatic arthritis³. Recent research has focused on the crucial roles of immune molecules in PV, such as IL-17A/F, IL-23, and TNF-α⁴. Biological agents have significantly improved psoriasis efficacy, but a loss of effectiveness⁵ often occurs in clinical practice. Therefore, it is necessary to explore this disease further to discover more critical immune molecules involved in pathogenesis and develop new drugs.

In the last decade, proteomic approaches have been used to identify and characterize proteins that participated in the pathogenesis of psoriasis or other skin diseases. For instance, recent studies have shown the circulating proteomic landscape of psoriasis and hidradenitis suppurativa (HS) using a smaller Olink proteomic platform^6,7. In a large-scale proteomic study using a novel extended Olink platform assessing 1536 biomarkers, Navrazhina K et al. found that HS presents a more extraordinary serum inflammatory burden⁸. The above results suggest that detecting more biomarkers could help us discover critical immune molecules and deepen our understanding of disease pathogenesis.

Several studies have identified proteins associated with the severity of psoriasis, such as some Antimicrobial peptides (AMPs) (S100A7, S100A8, S100A9 and PI3) ^9–11. Still, they mainly evaluate blood samples or the stratum corneum (SC) cells, or the platforms used in these studies have limited ability to detect more proteins.

Data-independent acquisition mass spectrometry (DIA-MS) is a proteomic technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy ¹², which has been successfully used for the investigation of skin disease^13,14, as described in our previous study¹⁵. Until now, no studies have directly compared proteomic differences between severe and moderate psoriasis in psoriatic lesions. Here, we used DIA-MS to characterize the proteomic differences between severe PV (sPV) and moderate PV (mPV) in psoriatic lesions and explore biomarkers associated with disease severity. Finally, we validated selected differentially expressed proteins (DEPs) with 4D-parallel reaction monitoring (4D-PRM) technology¹⁶.

Quantitative analysis of proteins in psoriatic lesions using DIA

For DIA detection, fifteen samples were collected from patients with sPV(n = 7) and mPV(n = 8). Then, 4D-PRM was performed for validation in twelve newly collected cases. The characteristics of included patients are shown in Table 1.

Table 1

Baseline Characteristics of Psoriasis Patients
	DIA test		PRM validation
Variables	sPV groups(n = 7)	mPV groups(n = 8)	sPV groups(n = 6)	mPV groups(n = 6)
Sex- no. (%)
Male	6(85.7%)	4(50.0%)	5(83.3%)	2(33.3%)
Female	1(14.3%)	4(50.0%)	1(16.7%)	4(66.7%)
Age, year
Mean ± SD.	51.6 ± 16.8	42.4 ± 15.5	48.8 ± 14.3	33.5 ± 7.9
Median (IQR)	47.0(34.0–66.0)	41.0(29.3–59.0)	45.5(36.8–59.8)	33.0(28.8–38.5)
Range	34.0–76.0	22.0–64.0	36.0–74.0	22.0–46.0
Course, year
Mean ± SD.	24.3 ± 16.0	13.8 ± 14.9	11.3 ± 8.0	4.6 ± 3.9
Median (IQR)	21.5(11.0-40.8)	10.0(2.3–20.3)	8.5(6.0-15.8)	4.0(0.9-8.0)
Range	6.0–50.0	0.5–46.0	6.0–27.0	0.5–11.0
Previous treatment - no. (%)
Topical therapy	6(85.7%)	8(100.0%)	5(83.3%)	5(83.3%)
Systemic therapy	7(100.0%)	6(75.0%)	4(66.7%)	4(66.7%)
Phototherapy	4(57.1%)	3(37.5%)	1(16.7%)	2(33.3%)
Biologic therapy	2(28.6%)	1(12.5%)	0(0.0%)	0(0.0%)
Family history - no. (%)
Yes	3(42.9%)	1(12.5%)	3(50.0%)	1(16.7%)
No	4(57.1%)	7(87.5%)	3(50.0%)	5(83.3%)
Comorbidity- no. (%)
Yes	4(57.1%)	0(0.0%)	1(16.7%)	1(16.7%)
No	3(42.9%)	8(100.0%)	5(83.3%)	5(83.3%)
PASI (0 weeks), score
Mean ± SD.	24.6 ± 5.8	11.2 ± 2.4	30.6 ± 10.6	9.0 ± 2.2
Median (IQR)	21.0(20.1–31.5)	11.6(10.0-12.2)	25.8(22.8–43.6)	8.8(7.0-11.2)
Range	19.1–33.3	6.8–15.0	20.2–44.6	6.8–11.8
no. (%), number; SD, standard deviation; IQR, interquartile range.

A total of 6418 proteins were quantified in the proteomic experiment, with a false discovery rate (FDR) of less than 1% at both the peptide and protein levels. Protein identified in technical replicates and control samples showed a relatively low coefficient variance (CV) (see Supplementary Fig. S2 online).

Bioinformatics analysis and functional description of DEPs in moderate and severe psoriasis

173 DEPs were significantly changed in the sPV group compared to the mPV group (FC ≥ ± 1.5, P < 0.05). Among them, 85 proteins were upregulated, while 88 were down-regulated (see Supplementary Table S1 online). Nine proteins with the highest FC were OSBPL6, CYFIP2, MYOCD, AGO3, DCD, PCBP4, LRIF1, DLG5 and KRT15 in the upregulated proteins. Among the down-regulated proteins, nine proteins with the highest -FC were MTMR10, CCNDBP1, STK3, LAMP1, UBE2D1, APBB3, BPI, SULT1B1 and IGHM (Fig. 1).

To further investigate the molecular functions of these DEPs, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis in the sPV and mPV groups. The top 10 significant difference clusters of GO terms were displayed in Fig. 2a. Biological process enrichment included defence response to Gram-negative bacterium, antimicrobial humoral immune response mediated by antimicrobial peptide, antibacterial humoral response, positive regulation of stem cell proliferation, nucleotide-excision repair, DNA incision, regulation of protein tyrosine kinase activity, positive regulation of catalytic activity, response to Lipopolysaccharide, regulation of phagocytosis and sequestering of metal ion.

The top 20 RichFactor of enriched pathways were illustrated in Fig. 2b. The top 10 of KEGG enrichment included Caffeine metabolism, RNA polymerase, Cytosolic DNA-sensing pathway, Hematopoietic cell lineage, IL-17 signalling pathway, Neutrophil extracellular trap formation, Insulin resistance, Natural killer cell mediated cytotoxicity, B cell receptor signalling pathway and NF-kappa B signalling pathway.

Identification of proteins and pathways associated with psoriasis pathogenesis and disease severity

As shown in Supplementary Table S2, DEPs in the sPV group, compared to the mPV group, participated in the IL − 17 signalling pathway, Neutrophil extracellular trap formation and NF − kappa B signalling pathway. Neutrophil extracellular trap formation was predicted to be down-regulated according to the Z score using ingenuity pathway analysis (IPA) (see Supplementary Fig. S3 online). In detail, 4 DEPs are involved in the IL − 17 signaling pathway. Among them, HSP90AA1, S100A7, S100A7L2 were upregulated, while CASP3 was down-regulation. 9 DEPs involved in Neutrophil extracellular trap formation, in which ITGB3 and H2BC20P were upregulated, while IGHM, ELANE, RAC2, AZU1, SYK, IGHV3-49 and NCF1B was down-regulation. In all, five proteins focused on the NF-kappa B signalling pathway. MALT1 significantly increased, whereas IGHM, CD14, SYK, and IGHV3-49 decreased in sPV.

We then performed Pearson correlation analyses to explore the associations between DEPs (FC ≥ ± 2.0, P < 0.05) and Psoriasis Area and Severity Index (PASI). The positive and negative correlation data are plotted in Fig. 3. Specifically, in the sPV group, CFAP47 and OSBPL6 have the highest positive Pearson correlation coefficient with PASI. Other proteins, such as CD36 and CD14, showed no statistical difference despite appearing to be associated with PASI. For the mPV group, the ANO7 and S100A7L2 have the highest p-PCC, while CCNDBP1, GFPT1, YTHDF2, TELO2 and BPI have the highest negative Pearson correlation coefficient (see Supplementary Table S3 online).

Protein interaction network showed two sets of molecules interacting with chaperone proteins HSP90AA1

To clearly show the interactions among these DEPs, we constructed an interaction network among molecules using the STRING database (see Supplementary Table S4 online). We found two molecular clusters interacting with chaperone proteins (mainly HSP90AA1). One cluster included CD36 and CD14, which are associated with HSP90AA1 through their effects on LGALS3 or ITGB3. The other contained STK3 and PANX1, which linked with HSP90AA1 through their effects on CASP3 (Fig. 4).

We further analyzed these proteins using IPA. It identified the top potential upstream up-regulators were TNF (p-value = 6.84e-10, z-score 0.266) and TDP53 (p-value = 6.26e-8, z-score = 0.286), while the top potential down-regulators were Lipopolysaccharide (p-value = 1.12e-6, z-score=-1.494) and YAP1 (p-value = 2.52e-6, z-score=-0.814). TNF was predicted to connect with nine regulators to regulate a set containing 77 dataset molecules downstream (Fig. 5a). TP53 was connected with nine regulators and predicted to regulate 69 dataset molecules downstream (Fig. 5b). In contrast, Lipopolysaccharide connected with nine regulators and to regulate 77 dataset protein downstream (Fig. 5c). YAP1 was predicted to regulate 25 dataset genes downstream contained two regulators (Fig. 5d).

Enriched interaction analysis contains 13 networks; the top was selected based on the score. The highest score network (score 46) was centred on 22 proteins (Fig. 5e). Pathway analysis showed that the main active pathways were Antimicrobial peptides and PTEN signalling. In contrast, the main inhibitory pathways were the Neutrophil extracellular trap pathway, Neutrophil degranulation, and IL-8 signalling (see Supplementary Fig. S3 online).

The expressions of KRT6A were downregulated in sPV by 4D-PRM

To validate the results obtained from the DIA, we collected nine new samples and three original samples for validation. In a separate cohort study, we screened thirty-two proteins for further validation using 4D-PRM. Seventeen proteins were quantitated, and the results demonstrated that KRT6A were downregulated in sPV (see Supplementary Table S5 online).

Here, we use DIA-MS to evaluate proteomic differences in psoriatic lesions between sPV and mPV and simultaneously determine some biomarkers associated with disease severity. We quantified 6418 proteins in the DIA-MS test, which was significantly higher than that in a previous proteomic study using LC-MS/MS^17,18 or the Tandem Mass Tags (TMT) approach¹⁹. These results show that DIA-MS has significantly advanced global protein quantification across multiple samples. Our research identified 173 DEPs in the sPV group compared to the mPV group, which likely plays a crucial role in psoriasis and were associated with disease severity. The main active pathways were antimicrobial peptides and PTEN signalling, while the inhibitory pathways were the neutrophil extracellular trap pathway, neutrophil degranulation, and IL-8 signalling.

BP enrichment included several proteins that participate in the defence response to the bacterium, such as defence response to Gram-negative bacterium, antimicrobial humoral immune response mediated by AMP, antibacterial humoral response and response to Lipopolysaccharide. These proteins include AMPs, serine protease, chaperone protein, co-receptor and other molecules. Our work confirms the initial role of bacterial origin elements in psoriasis and the excessive innate immune responses induced by them⁴. Given the initial role of trauma and infection in the onset of psoriasis, it is essential to strengthen skin care, such as enhanced emollients, and to prevent local infections, especially in the progression of psoriasis. Patients should be educated to avoid washing skin lesions with salt or hot water (Especially among the Chinese) to avoid aggravating them.

Consistent with previous studies, KEGG enrichment and IPA analysis found that few DEPs predominantly participated in the IL − 17 signalling pathway and NF − kappa B signalling pathway, which plays a vital role in the pathogenesis of psoriasis²⁰. In addition to being regulated by upstream regulators, interactions network by STRING found these molecules interacting with each other or chaperone proteins (mainly HSP90AA1) and then regulating specific target proteins involved in cell cycle control and signal transduction or the transcription machinery. All of this indicated the diversity and complexity of the functions of these molecules.

S100A7 (Psoriasin) is one of the AMPs belonging to the S100 family, produced by keratinocytes and leukocytes stimulated with IL-17, IL-22 and TNF; therefore, it plays an essential role in innate immunity and angiogenesis^9,21. In our study, it was upregulated in sPV compared to mPV, which agrees with previous work showing higher serum levels of psoriasin in patients with severe psoriasis⁹ and a reduction after treatment with biological agents¹⁵. KRT6A is one of the stress keratins that regulate keratinocyte differentiation and correlation to proteins that participate in EGFR and retinoic acid signalling^22,23. Our results demonstrated that it was upregulated in DIA but downregulated in the PRM test, which the conclusions of the previous study can explain that individual stress keratin genes are associated with partially distinct gene networks ²⁴.

Neutrophil elastase (NE) is a primary proteinase in neutrophils that participates in microbicidal activity²⁵. Previous research has found that the levels and activity of NE reflect disease state and severity²⁶ and augmented staining in the low-density granulocytes (LDGs) of psoriasis²⁷. However, our results show that NE is down-regulated in sPV. Moreover, SLPI is a reversible NE inhibitor that dynamically controls NE activity. Although SLPI expression decreased in sPV, there was no statistical difference between the two groups. These results indicate that NE has a pleiotropic feature; that is, it has not only antibacterial effects and promotes proinflammatory but also impairs innate immunity ²⁶.

HSP90AA1 is a molecular chaperone which plays an essential role in cell survival, cytokine signalling, and immune response. It can bind bacterial Lipopolysaccharide (LPS) and mediates LPS-induced inflammatory response, including monocyte TNF secretion²⁸. Furthermore, HSP90AA1 released by stressed keratinocytes activate DCs to secrete proinflammatory cytokines and AMPs such as S100A7²⁹. Despite previous research finding a significantly decreased expression of HSP90AA1 in both keratinocytes and lymphocytes from psoriatic skin³⁰, this study shows that it is upregulated in sPV. Our data is consistent with another study showing that it is significantly upregulated in epidermal keratinocytes and mast cells of psoriatic lesions and down-regulated after ustekinumab treatment²⁹ .

CD14 act as a co-receptor for toll-like receptors (TLRs) to activate multiple signal pathways of innate immunity responses to pathogens or tissue injury in diverse cells. This function can be achieved by LBP-dependent combination of the CD14-LPS complex or independently of TLRs³¹. Studies have shown that CD14 + DC3s increased in psoriatic lesions and co-produced IL1B and IL23A³²; in contradiction to this, IL-17A blockade induced higher expression of CD1C and CD14, which are markers for CD1c + CD14 + dendritic cell (DC) that suppress antigen-specific T-cell responses, in post-treatment regulatory semimature DCs³³. Accordingly, CD14 are down-regulated, along with CD1c, in sPV according to our proteomic dataset. These findings indicate that, although the CD14-mediated immune response to pathogenic microorganisms is weakened, the reduction of CD14 on specific dendritic cell subpopulations attenuates the inhibitory effect on T cells, which leads to the persistence and aggravation of inflammation in psoriasis. This study found CD36, another co-receptor associated with the TLR2/TLR6, was upregulated in sPV. Meanwhile, CD36 has a more vigorous activity for modulating TLR4-TLR6 signal transduction than CD14. So, we speculate that the reduction of CD14 may be related to the competition of the CD36 receptor pathway and the regulation of other proteins.

IL-33 is a cytokine that signals through the IL1RL1/ST2 receptor, which can activate NF-kappa-B and MAPK signalling pathways³⁴ and is a proinflammatory molecule and modulator in psoriasis^35–37. However, other studies found that IL-33 exert anti-inflammatory and protective activities in psoriatic skin^38,39, which only manifests when the amount of IL-33 is excessive. Considering that IL33 was significantly elevated in sPV, our results agree it is a risk factor for psoriasis. Meanwhile, our results are consistent with previous research that IL-33 can function both as a cytokine and as a nuclear transcriptional regulator³⁹ since it can interact with chaperone proteins to regulate the transcription machinery.

Limitations of the study: (1) The sample size was relatively small. (2) DIA did not detect some low-abundance proteins, as we discussed in our previous study. (3) There are sex differences in patients between the two groups, which may affect the results (Table 1). These confounding factors might affect the results.

Patients and samples

This study enrolled patients with moderate (5 < PASI ≤ 15) to severe psoriasis (PASI > 15) from April 2020 to February 2024 in the Dermatology Hospital of Zhejiang Province. Patients were not receiving treatment at the time of enrollment or receiving treatment other than systematic immunosuppressants or combined with phototherapy. Participants were assessed and sampled at baseline. Other inclusion and exclusion criteria of the patients, obtaining methods and storage conditions of skin specimens, and strategies for evaluating disease severity have been formulated in our previous studies¹⁵. The study was approved by ethical committees at the Dermatology Hospital of Zhejiang Province (LL-2020-15) and performed according to the Declaration of Helsinki. Patients provided written informed consent before sampling.

Proteomic data acquisition

For DIA analysis, DIA data analysis, and quality control of proteome data (see Supplementary Fig. S1 online), please refer to the previous study¹⁵. Part of the proteomic data comes from a two-part prospective cohort study.

4D-PRM

Samples were analyzed on nanoElute (Bruker, Bremen, Germany) coupled to a timsTOF Pro (Bruker, Bremen, Germany) equipped with a CaptiveSpray source. Peptides were separated on a 25cm X 75µm analytical column, 1.6µm C18 beads with a packed emitter tip (IonOpticks, Australia). The timsTOF Pro (Bruker, Bremen, Germany) was operated in PRM-PASEF mode. The detection ion mode is positive, the scanning range of the parent ion is 100–1700 m/z, the range of ion mobility 1/K0 is 0.6-1.6V·s/cm2, the Accu time and Ramp time is 50ms, the Lock Duty Cycle is 100%, the Capillary Voltage is 1500V, the Dry Gas speed is 3L/min, the Dry Temp is 180°C. The charge range is 0–5, and the CID collision energy is 10 eV.

Bioinformatic analysis

GO analysis of the DEPs was analyzed in the GO database and displayed by Cytoscape. Pearson correlation was used to show the correlation between DEPs and clinical severity (PASI). Circus plot was generated using the circle package in R language (version 4.0.1)⁴⁰. The KEGG database was used for pathway analysis. The STRING database performed the interaction network among proteins. Upstream regulator analysis (URA) and interaction analysis were implemented by IPA (Version 24.0.1) (with P < 0.05, Z score > 0 or < 0)⁴¹.

Statistical analysis

Quantitative data between the two groups were calculated using the Student's t-test. The selection criteria of the DEPs for bioinformatic analysis were cut off with P < 0.05 and FC ≥ 1.5. Statistical analysis was performed in R software (version 4.0.1).

In conclusion, this study presents a proteomic difference between sPV and mPV in psoriatic lesions. These proteins comprise AMPs, serine protease, chaperone protein, co-receptor and other molecules. At the same time, they were associated with the IL − 17 signalling pathway, Neutrophil extracellular trap formation, Neutrophil degranulation, NF − kappa B signalling pathway, and defence response to a bacterium, which is closely associated with psoriasis pathogenesis. Our data identified eighteen DEPs as biomarkers of disease severity, and it suggested that targeting these molecules and preventing infection may improve the severity and therapeutic efficacy of psoriasis.

Competing interest

The authors declare no competing interests.

Author Contribution

L.L.W. and C.C. contributed equally as co-first authors to the study’s investigation and the writing of the original draft. B.B.X. and N.N.S. conducted the investigation. L.H.H and J.H. were responsible for resource acquisition. Q.D. was pivotal in conceptualization, funding acquisition, methodology development, and project administration. Q.D. and C.C. were instrumental in reviewing and editing the manuscript.All authors reviewed the manuscript.

Acknowledgement

The authors thank Genechem Co., Ltd. (Shanghai, China) for the mass spectrometric analysis. We thank Dr Sun. for the data computation.

Data Availability

The data are available from the corresponding author on reasonable request. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium(https://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD052886.

Takeshita, J. et al. Psoriasis and comorbid diseases. Journal of the American Academy of Dermatology 76, 377–390, doi:10.1016/j.jaad.2016.07.064 (2017).
Kimball, A. B. et al. Risks of developing psychiatric disorders in pediatric patients with psoriasis. J Am Acad Dermatol 67, 651–657.e651-652, doi:10.1016/j.jaad.2011.11.948 (2012).
Van den Bosch, F. & Coates, L. Clinical management of psoriatic arthritis. Lancet 391, 2285–2294, doi:10.1016/s0140-6736(18)30949-8 (2018).
Griffiths, C. E. M., Armstrong, A. W., Gudjonsson, J. E. & Barker, J. Psoriasis. Lancet 397, 1301–1315, doi:10.1016/s0140-6736(20)32549-6 (2021).
Sutaria, N. & Au, S. C. Failure rates and survival times of systemic and biologic therapies in treating psoriasis: a retrospective study. J Dermatolog Treat 32, 617–620, doi:10.1080/09546634.2019.1688756 (2021).
Elnabawi, Y. A. et al. CCL20 in psoriasis: A potential biomarker of disease severity, inflammation, and impaired vascular health. J Am Acad Dermatol 84, 913–920, doi:10.1016/j.jaad.2020.10.094 (2021).
Glickman, J. W. et al. Cross-sectional study of blood biomarkers of patients with moderate to severe alopecia areata reveals systemic immune and cardiovascular biomarker dysregulation. J Am Acad Dermatol 84, 370–380, doi:10.1016/j.jaad.2020.04.138 (2021).
Navrazhina, K. et al. Large-scale serum analysis identifies unique systemic biomarkers in psoriasis and hidradenitis suppurativa. Br J Dermatol 186, 684–693, doi:10.1111/bjd.20642 (2022).
Maurelli, M. et al. Psoriasin (S100A7) is increased in the serum of patients with moderate-to-severe psoriasis. Br J Dermatol 182, 1502–1503, doi:10.1111/bjd.18807 (2020).
Matsunaga, Y., Hashimoto, Y. & Ishiko, A. Stratum corneum levels of calprotectin proteins S100A8/A9 correlate with disease activity in psoriasis patients. J Dermatol 48, 1518–1525, doi:10.1111/1346-8138.16032 (2021).
Deng, J. et al. Multi-omics approach identifies PI3 as a biomarker for disease severity and hyper-keratinization in psoriasis. J Dermatol Sci 111, 101–108, doi:10.1016/j.jdermsci.2023.07.005 (2023).
Ludwig, C. et al. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 14, e8126, doi:10.15252/msb.20178126 (2018).
Xu, M. et al. In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional Chinese medicine. Theranostics 9, 2475–2488, doi:10.7150/thno.31144 (2019).
Foulkes, A. C. et al. A Framework for Multi-Omic Prediction of Treatment Response to Biologic Therapy for Psoriasis. J Invest Dermatol 139, 100–107, doi:10.1016/j.jid.2018.04.041 (2019).
Dong, Q. et al. IL-17A and TNF-α inhibitors induce multiple molecular changes in psoriasis. Front Immunol 13, 1015182, doi:10.3389/fimmu.2022.1015182 (2022).
Zhou, H. et al. Analysis of the mechanism of Buyang Huanwu Decoction against cerebral ischemia-reperfusion by multi-omics. J Ethnopharmacol 305, 116112, doi:10.1016/j.jep.2022.116112 (2023).
Swindell, W. R. et al. Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance. Genome Med 7, 86, doi:10.1186/s13073-015-0208-5 (2015).
Sobolev, V. V. et al. LC-MS/MS analysis of lesional and normally looking psoriatic skin reveals significant changes in protein metabolism and RNA processing. PLoS One 16, e0240956, doi:10.1371/journal.pone.0240956 (2021).
Wang, W. et al. Proteomic analysis of psoriatic skin lesions in a Chinese population. J Proteomics 240, 104207, doi:10.1016/j.jprot.2021.104207 (2021).
Yan, K. X. et al. iTRAQ-based quantitative proteomics reveals biomarkers/pathways in psoriasis that can predict the efficacy of methotrexate. J Eur Acad Dermatol Venereol 36, 1784–1795, doi:10.1111/jdv.18292 (2022).
Vegfors, J., Ekman, A. K., Stoll, S. W., Bivik Eding, C. & Enerbäck, C. Psoriasin (S100A7) promotes stress-induced angiogenesis. Br J Dermatol 175, 1263–1273, doi:10.1111/bjd.14718 (2016).
Chiang, C. Y. et al. SH3BGRL3 Protein as a Potential Prognostic Biomarker for Urothelial Carcinoma: A Novel Binding Partner of Epidermal Growth Factor Receptor. Clin Cancer Res 21, 5601–5611, doi:10.1158/1078-0432.Ccr-14-3308 (2015).
Zhang, S. et al. Differential CRABP-II and FABP5 expression patterns and implications for medulloblastoma retinoic acid sensitivity. RSC Adv 8, 14048–14055, doi:10.1039/c8ra00744f (2018).
Cohen, E. et al. Significance of stress keratin expression in normal and diseased epithelia. iScience 27, 108805, doi:10.1016/j.isci.2024.108805 (2024).
Zeng, W., Song, Y., Wang, R., He, R. & Wang, T. Neutrophil elastase: From mechanisms to therapeutic potential. J Pharm Anal 13, 355–366, doi:10.1016/j.jpha.2022.12.003 (2023).
Voynow, J. A. & Shinbashi, M. Neutrophil Elastase and Chronic Lung Disease. Biomolecules 11, doi:10.3390/biom11081065 (2021).
Skrzeczynska-Moncznik, J. et al. Differences in Staining for Neutrophil Elastase and its Controlling Inhibitor SLPI Reveal Heterogeneity among Neutrophils in Psoriasis. J Invest Dermatol 140, 1371–1378.e1373, doi:10.1016/j.jid.2019.12.015 (2020).
Triantafilou, K., Triantafilou, M. & Dedrick, R. L. A CD14-independent LPS receptor cluster. Nat Immunol 2, 338–345, doi:10.1038/86342 (2001).
Kakeda, M., Arock, M., Schlapbach, C. & Yawalkar, N. Increased expression of heat shock protein 90 in keratinocytes and mast cells in patients with psoriasis. J Am Acad Dermatol 70, 683–690.e681, doi:10.1016/j.jaad.2013.12.002 (2014).
Gęgotek, A., Domingues, P., Wroński, A., Ambrożewicz, E. & Skrzydlewska, E. The Proteomic Profile of Keratinocytes and Lymphocytes in Psoriatic Patients. Proteomics Clin Appl 13, e1800119, doi:10.1002/prca.201800119 (2019).
Sharygin, D., Koniaris, L. G., Wells, C., Zimmers, T. A. & Hamidi, T. Role of CD14 in human disease. Immunology 169, 260–270, doi:10.1111/imm.13634 (2023).
Nakamizo, S. et al. Single-cell analysis of human skin identifies CD14 + type 3 dendritic cells co-producing IL1B and IL23A in psoriasis. J Exp Med 218, doi:10.1084/jem.20202345 (2021).
Kim, J. et al. Multi-omics segregate different transcriptomic impacts of anti-IL-17A blockade on type 17 T-cells and regulatory immune cells in psoriasis skin. Front Immunol 14, 1250504, doi:10.3389/fimmu.2023.1250504 (2023).
Schmitz, J. et al. IL-33, an interleukin-1-like cytokine that signals via the IL-1 receptor-related protein ST2 and induces T helper type 2-associated cytokines. Immunity 23, 479–490, doi:10.1016/j.immuni.2005.09.015 (2005).
Balato, A. et al. IL-33 is secreted by psoriatic keratinocytes and induces pro-inflammatory cytokines via keratinocyte and mast cell activation. Exp Dermatol 21, 892–894, doi:10.1111/exd.12027 (2012).
Griesenauer, B. & Paczesny, S. The ST2/IL-33 Axis in Immune Cells during Inflammatory Diseases. Front Immunol 8, 475, doi:10.3389/fimmu.2017.00475 (2017).
Zeng, F. et al. An Autocrine Circuit of IL-33 in Keratinocytes Is Involved in the Progression of Psoriasis. J Invest Dermatol 141, 596–606.e597, doi:10.1016/j.jid.2020.07.027 (2021).
Chen, Z. et al. Interleukin-33 alleviates psoriatic inflammation by suppressing the T helper type 17 immune response. Immunology 160, 382–392, doi:10.1111/imm.13203 (2020).
Dragan, M. et al. Epidermis-Intrinsic Transcription Factor Ovol1 Coordinately Regulates Barrier Maintenance and Neutrophil Accumulation in Psoriasis-Like Inflammation. J Invest Dermatol 142, 583–593.e585, doi:10.1016/j.jid.2021.08.397 (2022).
Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res 19, 1639–1645, doi:10.1101/gr.092759.109 (2009).
Krämer, A., Green, J., Pollard, J., Jr. & Tugendreich, S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30, 523–530, doi:10.1093/bioinformatics/btt703 (2014).

No competing interests reported.

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Cross-sectional study of proteomic differences between moderate and severe psoriasis

Status:

Version 1

Abstract

Figures

Introduction

Results

Quantitative analysis of proteins in psoriatic lesions using DIA

Bioinformatics analysis and functional description of DEPs in moderate and severe psoriasis

Identification of proteins and pathways associated with psoriasis pathogenesis and disease severity

Protein interaction network showed two sets of molecules interacting with chaperone proteins HSP90AA1

The expressions of KRT6A were downregulated in sPV by 4D-PRM

Discussion

Materials and Methods

Patients and samples

Proteomic data acquisition

4D-PRM

Bioinformatic analysis

Statistical analysis

Conclusions

Declarations

Competing interest

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1