Proteomic characterization between males and females with moderate-to-severe psoriasis

doi:10.21203/rs.3.rs-4848017/v1

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Proteomic characterization between males and females with moderate-to-severe psoriasis

https://doi.org/10.21203/rs.3.rs-4848017/v1

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Men often presented with higher severity of psoriasis than women, but the underlying reasons are still unclear. In this study, we evaluate proteomic differences in psoriatic lesions between men and women with moderate-to-severe psoriasis and explore possible protective and risk proteins using data-independent acquisition mass spectrometry (DIA-MS) and verified by 4D-parallel reaction monitoring (4D-PRM). 416 differentially expressed proteins (DEPs) were identified between two groups. Among them, 94 proteins were upregulated, while 322 were down-regulated. Some DEPs were enriched to pathways associated with psoriasis, such as the IL − 17 signalling pathway, T cell receptor signalling pathway, Th17 cell differentiation, Oxidative phosphorylation, PI3K − Akt signalling pathway, and MAPK signalling pathway; meanwhile, numerous pathways associated with infection. Nine DEPs (KRT36, KRT13, KRT15, SHC1, GNAI1, SRC, HSPA6, HSPA1L, and HSP90AB4P) were involved in the estrogen pathway, which was predicted to be activated in males. Through Ingenuity pathway Analysis (IPA), our data also identified three upstream regulators (TNF, KRAS, TGFB1). 4D-PRM suggested that HMGB2 and PML were upregulated, while LAMTOR3 was downregulated in male group compared to female one. Our study suggests that sex may influence protein changes in psoriasis, pathogenesis and disease severity. Targeting these molecules may improve the severity and therapeutic efficacy of psoriasis.

Biological sciences/Immunology

Biological sciences/Molecular biology

Health sciences/Diseases

Health sciences/Medical research

Health sciences/Risk factors

Proteomics

Psoriasis vulgaris

4D-Parallel Reaction Monitoring

Data-Independent Acquisition Mass Spectrometry

Biomarker

Psoriasis vulgaris (PV) is a common, chronic and immune-related scaly skin disease affecting about 0.12–3.2% of the population^1,2. It is estimated that there are about 6 million psoriasis patients in China. Biologics have been widely used in treating this disease in China. However, like the previous study, many patients experience failure after 2–3 years of treatment³. The drug failed or became less effective, forcing patients to switch biologics more frequently. Considering this situation, further exploration of disease pathogenesis is needed to discover new pathogenic molecules and develop new drugs.

Several studies have indicated that women have less severe psoriasis than men^4,5 and a lower treatment response than that in women⁶, which may be caused by differences in protein molecules, such as estradiol hormones⁷. Sevimoglu T et al. identified several gene and protein clusters as systems biomarkers. They presented gender differences in psoriasis through qRT-PCR and ELISA techniques using skin biopsies and blood plasma⁸. These results led to the hypothesis that specific molecules may be differently expressed in men and women and affect the disease severity. To our knowledge, there have been no studies on proteomic differences between men and women in skin biopsies.

Data-independent acquisition (DIA) is a large-scale proteomics method with high quantitative accuracy and reproducibility⁹, used to explore skin disease as described in our previous study¹⁰. Here, we used data-independent acquisition mass spectrometry (DIA-MS) combined 4D-parallel reaction monitoring (4D-PRM) to characterise and verify the proteomic differences between men and women with moderate to severe psoriasis in skin biopsies and identify possible protective and risk protein molecules in psoriasis.

Determination of the proteomic landscape using DIA

Fifteen samples were collected from Male (n = 10) and Female (n = 5) with moderate-to-severe psoriasis for DIA detection. After that, 4D-PRM was conducted for validation in eleven newly collected cases. The characteristics of enrolled patients are shown in Table 1.

Table 1

Baseline Characteristics of Psoriasis Patients
	DIA test		PRM validation
Variables	Male (n = 10)	Female (n = 5)	Male (n = 6)	Female (n = 5)
Severity- no. (%)
Moderate	4(40.0%)	4(80.0%)	5(83.3%)	4(80.0%)
Severe	6(60.0%)	1(20.0%)	1(16.7%)	1(20.0%)
Age, year
Mean ± SD.	50.8 ± 17.5	38.4 ± 10.2	47.0 ± 16.0	36.2 ± 9.1
Median (IQR)	55.0(33.8–64.5)	41.0(29.0-46.5)	43.0(34.8–59.8)	36.0(28.5–44.0)
Range	28.0–76.0	22.0–47.0	31.0–74.0	22.0–46.0
Course, year
Mean ± SD.	21.8 ± 15.2	8.1 ± 8.7	10.0 ± 8.6	6.3 ± 5.4
Median (IQR)	18.0(11.3–30.3)	7.0(0.8–16.0)	6.5(5.5–14.3)	7.0(0.8–11.5)
Range	6.0–50.0	0.5–22.0	4.0–27.0	0.5–12.0
Previous treatment - no. (%)
Topical therapy	9(90.0%)	5(100.0%)	5(83.3%)	4(80.0%)
Systemic therapy	10(100.0%)	3(60.0%)	4(66.7%)	3(60.0%)
Phototherapy	6(60.0%)	1(20.0%)	1(16.7%)	2(40.0%)
Biologic therapy	3(30.0%)	0(0.0%)	0(0.0%)	0(0.0%)
Family history - no. (%)
Yes	4(40.0%)	0(0.0%)	3(50.0%)	1(20.0%)
No	6(60.0%)	5(100.0%)	3(50.0%)	4(80.0%)
Comorbidity- no. (%)
Yes	3(30.0%)	1(20.0%)	0(0.0%)	2(40.0%)
No	7(70.0%)	4(80.0%)	6(100.0%)	3(60.0%)
Psoriasis Area and Severity Index(PASI) (0 weeks), score
Mean ± SD.	20.3 ± 8.0	11.7 ± 5.0	27.6 ± 14.3	12.8 ± 6.8
Median (IQR)	20.1(12.2–27.4)	10.0(8.4–16)	25.3(16.9–43.6)	11.0(8.4–18.2)
Range	11.4–33.3	6.8–20.1	7.1–44.6	6.8–24.6
no. (%), number; SD, standard deviation; IQR, interquartile range.

The DIA test quantified 6418 proteins with a false discovery rate (FDR) of less than 1% at the peptide and protein levels. Data showed a relatively low coefficient variance (CV), as discussed in our previous study (see Supplementary Fig. S2 online).

Function analysis of DEPs in Male and Female

416 proteins were significantly changed in the male group compared to the female group (FC ≥ ± 2.0, P < 0.05). Among them, 94 proteins were upregulated, while 322 were down-regulated (see Supplementary Table S1 online). In the upregulated proteins, the nine proteins with the largest + FC were S100A7L2, ATXN2L, ZSWIM8, KRT6A, ELN, COL4A3, RAB6B, KRT15 and MED23. Among the down-regulated proteins, nine proteins with the smallest -FC were CKM, SUPT5H, TAOK3, CIB2, TSC, RFX7, KRT72, UPF2, and AP4E1 (Fig. 1).

We conducted Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to explore these proteins' functions and the pathways involved and search for essential proteins. The top 10 most changed clusters of GO terms are exhibited in Fig. 2. Specifically, Molecular function contained cyclosporin A binding, chloride channel activity, lipopeptide binding, IkappaB kinase activity, endogenous lipid antigen binding, exogenous lipid antigen binding, ATPase inhibitor activity, structural constituent of cytoskeleton, serine-type endopeptidase inhibitor activity and kinase activator activity. The biological processes included hyaluronan metabolic process, learning, cornification, negative regulation of entry of bacterium into the host cell, receptor-mediated endocytosis, Fc-epsilon receptor signalling pathway, short-chain fatty acid catabolic process, cytolysis in other organisms involved in symbiotic interaction, cellular oxidant detoxification and skeletal muscle contraction.

Proteins and pathways associated with psoriasis pathogenesis and disease severity

Since we were interested in pathways closely associated with the pathogenesis of psoriasis, we analysed the DEPs by KEGG. Some proteins were enriched to crucial pathways, such as the IL − 17 signalling pathway, T cell receptor signalling pathway, Th17 cell differentiation, Oxidative phosphorylation, PI3K − Akt signalling pathway, MAPK signalling pathway, and numerous pathways associated with various infections (Fig. 3, see Supplementary Table S2 online). According to the Z score using ingenuity pathway analysis (IPA), the GP6 signalling pathway, extracellular matrix organisation, integrin cell surface interactions, and keratinisation were predicted to be active. In contrast, Neutrophil extracellular trap formation was predicted to be suppressive (see Supplementary Fig. S3 online). In all, 7 DEPs are involved in the IL − 17 signalling pathway. S100A7L2 was upregulated, whereas IKBKB, CHUK, DEFB4B, CASP3, S100A7A, and HSP90AB4P were down-regulated. 9 DEPs involved in the T cell receptor signalling pathway, in which NCK1 and MALT1 were upregulated. Instead IKBKB, CHUK, NFATC1, PPP3CC, PPP2CB, PPP3R1, and LCP2 were down-regulation.

Seven proteins (IKBKB, CHUK, NFATC1, HLA-DPB1, PPP3CC, PPP3R1 and HSP90AB4P) focused on Th17 cell differentiation were all significantly decreased in the male group compared to the female. Similarly, 12 proteins participating in the MAPK signalling pathway were down-regulated in the Male group. The reason is that some proteins, such as IKBKB and CHUK, overlap between the two pathways. Moreover, all 14 oxidative phosphorylation proteins were down-regulated in the Male group. Interestingly, we identified 9 DEPs involved in the estrogen pathway, which was predicted to be activated in males according to the Z score using IPA. Among them, KRT36, KRT13, KRT15, SHC1 and GNAI1 were up-regulated, while SRC, HSPA6, HSPA1L, and HSP90AB4P were down-regulated.

Then, we performed Pearson correlation analyses to explore the correlation between DEPs and disease severity, represented by the Psoriasis Area and Severity Index (PASI) score. Specifically, CHUK and PIM1 were negatively correlated with PASI in males, while KRT2 was positively correlated with PASI in females (see Supplementary Table S3 online).

CHUK, IKBKB, SRC, TLR2, and CASP3 are associated with expression of IL-17 or TNF

To clearly show the interactions among DEPs, we selected 40 proteins (4 essential psoriasis-related cytokines: IL-17, IL-12, IL-23 and TNF, which were not detected in this study, were added to the network) for protein-protein interaction (PPI) analysis using the STRING and drawn with Cytoscape (Fig. 4a, see Supplementary Table S4 online). It is a concern that CHUK, IKBKB, SRC, TLR2, and CASP3 are associated with IL-17 or TNF, and the correlation has a high degree of confidence (confidence ≥ 0.5). These results suggest that these proteins play an essential role in the changes in IL-17 and TNF. At the same time, we found that these DEPs were less correlated with IL12 and IL-23.

We further analysed these proteins using causal network analysis obtained from IPA to explore their relationships and possible biological effects. IPA identified the top potential upstream down-regulators are TNF (p-value = 3.08e-08, z-score − 0.138), and KRAS (p-value = 1.60e-06, z-score=-0.923), while the top potential up-regulators are TGFB1 (p-value = 1.87e-07, z-score=-1.58). TNF was predicted to connect with 11 regulators to regulate 51 dataset genes downstream in the network. TGFB1 connected with 16 regulators and was predicted to regulate 59 dataset genes downstream (Fig. 4b, 4c, 4d).

HMGB2, PML and LAMTOR3 were validated in 4D-PRM

To validate the results obtained from the DIA, we collected eleven samples for validation. We screened forty proteins for further validation using 4D-PRM in a separate cohort study. Twenty-three proteins were quantitated, and the results demonstrated that HMGB2 and PML were upregulated, while LAMTOR3 were downregulated in the male group compared to the female group (see Supplementary Table S5 online).

In this study, we analysed proteomic differences in skin lesions between males and females with moderate-to-severe psoriasis using DIA-MS, then used the new 4D-PRM technique to simultaneously validate 26 screened proteins (In fact, we simultaneously validated up to 96 proteins selected from three different studies). Our research identified 416 proteins significantly changed in males compared to females. Consistently with the previous study, the KEGG enrichment of these proteins observed some crucial pathways closely related to psoriases, such as the IL − 17 signalling pathway, T cell receptor signalling pathway, Th17 cell differentiation, Oxidative phosphorylation, PI3K − Akt signalling pathway, MAPK signalling pathway, and numerous pathways associated with various infection¹¹.

Our dataset found that CHUK (IKKA), IKBKB (IKKB), MALT1, SRC, TLR2, and CASP3 are associated with IL-17 or TNF, which suggests that these proteins may play a critical role in the maintenance or progression of psoriatic inflammation. Inhibitor of nuclear factor kappa-B kinase subunit alpha (CHUK) is a serine kinase that participates in the core IkB kinase (IKK) complex in the canonical Nuclear factor kappa B (NF-κB) pathway and noncanonical NF-κB pathway, which regulate B cell survival, or coding for chemokines and cytokines¹². Furthermore, CHUK has some unique functions independent of NF-κB^13–15 and can negatively regulate canonical NF-kB signals to limit inflammation¹⁶. In our study, CHUK is down-regulated and negatively correlated with PASI in the male group, suggesting it may play a negative regulatory role in psoriatic inflammation. Interestingly, CHUK was involved in estrogen receptor (ER)-mediated gene transcription¹⁷ and regulates cell cycle progression¹⁸. Therefore, it can act as a multifunctional signalling protein¹⁹.

Inhibitor of nuclear factor kappa-B kinase subunit beta (IKBKB) shares structural and biochemical similarities with CHUK and acts as a part of the IKK complex in NF-kappa-B to activate genes involved in immune response²⁰. Interestingly, IKBKB and CHUK were all down-regulated in our study. The reason may be the regulation of other proteins, such as IKK-related kinases^21,22, prolonged stimulation with TNFα²³, or mutual regulation among them^24,25. One study revealed that the canonical IKKs and IKK-related kinases regulate each other by a complicated network to regulate their activities in innate immunity critically²⁶. Our PPI network also found that TLR2 and TNF are connected and indirectly affect cytokines such as IL-17 and IL-22. The specific relationship among them and their role in psoriasis requires further study.

Another well-known protein closely associated with the NF-kappa-B signalling pathway and TNF is MALT1. It is a protease that is involved in the activation of NF-kB as well as the MAP signalling²⁷ by the formation of CARD14-BCL10-MALT1 (CBM) signalling complex, which is similar to that formed in antigen receptor-stimulated lymphocytes along with the recruitment of TRAF6, MALT1, and BCL10^28,29. Furthermore, a distinct role for MALT1 was found in inflammatory signalling in keratinocytes. For example, stimuli such as Staphylococcus aureus induced MALT1 proteolytic activity in keratinocytes³⁰. Inhibition of MALT1 in keratinocytes can reduce proinflammatory cytokines (TNF, IL-1b, and IL-17C), chemokines, and antimicrobial peptides³⁰. Moreover, A20 and CYLD, associated with psoriasis and as substrates of MALT1^31–33, can inhibit CARD14-mediated signalling³⁰. These findings imply that MALT1 may play a role in the pathogenesis of psoriasis and provide a basis that MALT1 inhibitors can be used as a treatment for psoriasis³⁴.

Notably, the CBM complex activates the NF-kB signalling in two ways. In addition to acting as a scaffold to recruit and activate the IKK complex and the nuclear translocation of NF-kB, it can promote optimal NF-kB activation by MALT1-mediated cleavage of RelB and A20³⁴. In our data, MALT1 is upregulated while CHUK and IKBKB are down-regulated. We speculate that the CBM complex can compete with the IKK complex to activate the NF-kB signalling pathway. These results explain the higher severity in men and the lower treatment response than in women⁶. Remarkably, one study found that estradiol acts as a protective factor against psoriatic inflammation in mice⁷, and our study also found 9 DEPs involved in estrogen signalling; however, this pathway was predicted to be activated in males but not in women. Whether hormonal hormones affect the onset and severity of psoriasis needs more research.

TLR2 is part of the TLR family expressed on various cells, such as DCs, macrophages, and lymphocytes^35–37. Although TLR2 signalling can induce an innate immune response to microbial lipoproteins and other cell components³⁸ through canonical or noncanonical NF-kappaB cascade^39–41, one study contradicts that TLR2 signalling suppress psoriasis-like skin inflammation⁴². Consistent with Nakao M et al., our dataset showed that TLR2 was down-regulated in the male group which means it acts as an immune suppressant. The down-regulation of TLR2 is likely related to the downregulation of receptor-mediated endocytosis. Saturated fatty acids can activate TLR2. However, psoriatic patients are not advised to eat foods rich in saturated fatty acids because they can lead to the expression of proinflammatory genes. Omega-3 polyunsaturated fatty acids and eicosapentaenoic acids, which downregulate the TLR2 signal, are often used to treat psoriasis⁴³. The reason may be that TLR2-induced cytokines are preferred over the induction of an anti-inflammatory cytokine in humans⁴². Furthermore, as our results shown, they may interact with other proteins to increase the inflammatory response.

Our study has several limitations. Firstly, there is a small number of cases. Secondly, DIA-mass did not detect some proteins, such as IL-17 and TNF, as discussed in the previous study. Thirdly, little differences in severity between men and women (Student's t-test, t = 2.21, P = 0.045). These confounding factors may affect the accuracy of the result.

Patients and samples

This study recruited patients with moderate to severe psoriasis (PASI > 5) from April 2020 to March 2024 in the Dermatology Hospital of Zhejiang Province. The inclusion and exclusion criteria of participants and obtaining and storage conditions of skin specimens have been stated in previous studies¹⁰. Participants were assessed and sampled at baseline. 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

Please refer to our previous study for DIA analysis, DIA data analysis, and quality control of proteome data (see Supplementary Fig. S1 online)¹⁰. Part of the proteomic data comes from our two-part, cross-sectional study¹⁰.

4D-PRM

Samples were analysed on a 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 was operated in the GO database. Pearson correlation was used to evaluate the correlation between DEPs and clinical severity (Psoriasis Area and Severity Index, PASI). The KEGG database was used for pathway enrichment analysis. The protein interaction network was performed by STRING database and IPA (Version 24.0.1) (with P < 0.05, Z score > 0 or < 0)⁴⁴.

Statistical analysis

Quantitative data between the two groups were compared using the Student's t-test. The selection criteria of the DEPs for bioinformatic analysis were cut off with P < 0.05 and FC ≥ ± 2.0. Statistical analysis was performed in R software (version 4.0.1).

In conclusion, this study showed a proteomic difference in psoriatic lesions between males and females with moderate-to-severe psoriasis. Our data found that a complicated network connected CHUK, IKBKB, MALT1 and TLR2, meanwhile, indirectly affected cytokines such as TNF, IL-17 and IL-22. These proteins may play a critical role in the maintenance or progression of psoriasis. Our data also suggests that sex may influence protein changes in psoriasis, pathogenesis and disease severity.

Competing interest

The authors declare no competing interests.

Author Contribution

X.Y. and C.C. contributed equally as co-first authors to the study’s investigation and the writing of the original draft. B.B.X., N.N.S. and Y.L. 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.

Acknowledgement

The authors thank Genechem Co., Ltd. (Shanghai, China) for the mass spectrometric analysis and 4D-PRM. 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.

Parisi, R. et al. National, regional, and worldwide epidemiology of psoriasis: systematic analysis and modelling study. Bmj 369, m1590, doi:10.1136/bmj.m1590 (2020).
Rachakonda, T. D., Schupp, C. W. & Armstrong, A. W. Psoriasis prevalence among adults in the United States. J Am Acad Dermatol 70, 512-516, doi:10.1016/j.jaad.2013.11.013 (2014).
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).
Kojanova, M. et al. Demographic data, comorbidities, quality of life, and survival probability of biologic therapy associated with sex-specific differences in psoriasis in the Czech Republic. Dermatol Ther 34, e14849, doi:10.1111/dth.14849 (2021).
Hägg, D., Sundström, A., Eriksson, M. & Schmitt-Egenolf, M. Severity of Psoriasis Differs Between Men and Women: A Study of the Clinical Outcome Measure Psoriasis Area and Severity Index (PASI) in 5438 Swedish Register Patients. Am J Clin Dermatol 18, 583-590, doi:10.1007/s40257-017-0274-0 (2017).
Maul, J. T. et al. Association of sex and systemic therapy treatment outcomes in psoriasis: a two-country, multicentre, prospective, noninterventional registry study. Br J Dermatol 185, 1160-1168, doi:10.1111/bjd.20387 (2021).
Adachi, A. et al. Estradiol suppresses psoriatic inflammation in mice by regulating neutrophil and macrophage functions. J Allergy Clin Immunol 150, 909-919.e908, doi:10.1016/j.jaci.2022.03.028 (2022).
Sevimoglu, T., Turanli, B., Bereketoglu, C., Arga, K. Y. & Karadag, A. S. Systems biomarkers in psoriasis: Integrative evaluation of computational and experimental data at transcript and protein levels. Gene 647, 157-163, doi:10.1016/j.gene.2018.01.033 (2018).
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).
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).
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).
Senftleben, U. et al. Activation by IKKalpha of a second, evolutionary conserved, NF-kappa B signaling pathway. Science 293, 1495-1499, doi:10.1126/science.1062677 (2001).
Chariot, A. The NF-kappaB-independent functions of IKK subunits in immunity and cancer. Trends Cell Biol 19, 404-413, doi:10.1016/j.tcb.2009.05.006 (2009).
Huang, W. C. & Hung, M. C. Beyond NF-κB activation: nuclear functions of IκB kinase α. J Biomed Sci 20, 3, doi:10.1186/1423-0127-20-3 (2013).
Anest, V. et al. A nucleosomal function for IkappaB kinase-alpha in NF-kappaB-dependent gene expression. Nature 423, 659-663, doi:10.1038/nature01648 (2003).
Shembade, N., Pujari, R., Harhaj, N. S., Abbott, D. W. & Harhaj, E. W. The kinase IKKα inhibits activation of the transcription factor NF-κB by phosphorylating the regulatory molecule TAX1BP1. Nat Immunol 12, 834-843, doi:10.1038/ni.2066 (2011).
Park, K. J., Krishnan, V., O'Malley, B. W., Yamamoto, Y. & Gaynor, R. B. Formation of an IKKalpha-dependent transcription complex is required for estrogen receptor-mediated gene activation. Mol Cell 18, 71-82, doi:10.1016/j.molcel.2005.03.006 (2005).
Tu, Z. et al. IKK alpha regulates estrogen-induced cell cycle progression by modulating E2F1 expression. J Biol Chem 281, 6699-6706, doi:10.1074/jbc.M512439200 (2006).
Xu, X. et al. Autophagic feedback-mediated degradation of IKKα requires CHK1- and p300/CBP-dependent acetylation of p53. J Cell Sci 133, doi:10.1242/jcs.246868 (2020).
Mercurio, F. et al. IKK-1 and IKK-2: cytokine-activated IkappaB kinases essential for NF-kappaB activation. Science 278, 860-866, doi:10.1126/science.278.5339.860 (1997).
Bain, J. et al. The selectivity of protein kinase inhibitors: a further update. Biochem J 408, 297-315, doi:10.1042/bj20070797 (2007).
Clark, K., Takeuchi, O., Akira, S. & Cohen, P. The TRAF-associated protein TANK facilitates cross-talk within the IkappaB kinase family during Toll-like receptor signaling. Proc Natl Acad Sci U S A 108, 17093-17098, doi:10.1073/pnas.1114194108 (2011).
Delhase, M., Hayakawa, M., Chen, Y. & Karin, M. Positive and negative regulation of IkappaB kinase activity through IKKbeta subunit phosphorylation. Science 284, 309-313, doi:10.1126/science.284.5412.309 (1999).
Li, Q. et al. Enhanced NF-kappaB activation and cellular function in macrophages lacking IkappaB kinase 1 (IKK1). Proc Natl Acad Sci U S A 102, 12425-12430, doi:10.1073/pnas.0505997102 (2005).
Correa, R. G. et al. Zebrafish IkappaB kinase 1 negatively regulates NF-kappaB activity. Curr Biol 15, 1291-1295, doi:10.1016/j.cub.2005.06.023 (2005).
Clark, K. et al. Novel cross-talk within the IKK family controls innate immunity. Biochem J 434, 93-104, doi:10.1042/bj20101701 (2011).
Afonina, I. S. et al. The paracaspase MALT1 mediates CARD14-induced signaling in keratinocytes. EMBO Rep 17, 914-927, doi:10.15252/embr.201642109 (2016).
Deng, L. et al. Activation of the IkappaB kinase complex by TRAF6 requires a dimeric ubiquitin-conjugating enzyme complex and a unique polyubiquitin chain. Cell 103, 351-361, doi:10.1016/s0092-8674(00)00126-4 (2000).
Oeckinghaus, A. et al. Malt1 ubiquitination triggers NF-kappaB signaling upon T-cell activation. Embo j 26, 4634-4645, doi:10.1038/sj.emboj.7601897 (2007).
Schmitt, A. et al. MALT1 Protease Activity Controls the Expression of Inflammatory Genes in Keratinocytes upon Zymosan Stimulation. J Invest Dermatol 136, 788-797, doi:10.1016/j.jid.2015.12.027 (2016).
Nititham, J. et al. Meta-analysis of the TNFAIP3 region in psoriasis reveals a risk haplotype that is distinct from other autoimmune diseases. Genes Immun 16, 120-126, doi:10.1038/gene.2014.75 (2015).
Oudot, T. et al. An association study of 22 candidate genes in psoriasis families reveals shared genetic factors with other autoimmune and skin disorders. J Invest Dermatol 129, 2637-2645, doi:10.1038/jid.2009.157 (2009).
Tejasvi, T. et al. TNFAIP3 gene polymorphisms are associated with response to TNF blockade in psoriasis. J Invest Dermatol 132, 593-600, doi:10.1038/jid.2011.376 (2012).
Van Nuffel, E. et al. CARD14-Mediated Activation of Paracaspase MALT1 in Keratinocytes: Implications for Psoriasis. J Invest Dermatol 137, 569-575, doi:10.1016/j.jid.2016.09.031 (2017).
Yang, R. B. et al. Toll-like receptor-2 mediates lipopolysaccharide-induced cellular signalling. Nature 395, 284-288, doi:10.1038/26239 (1998).
Triantafilou, M. et al. Membrane sorting of toll-like receptor (TLR)-2/6 and TLR2/1 heterodimers at the cell surface determines heterotypic associations with CD36 and intracellular targeting. J Biol Chem 281, 31002-31011, doi:10.1074/jbc.M602794200 (2006).
Muzio, M. et al. Differential expression and regulation of toll-like receptors (TLR) in human leukocytes: selective expression of TLR3 in dendritic cells. J Immunol 164, 5998-6004, doi:10.4049/jimmunol.164.11.5998 (2000).
Jin, M. S. et al. Crystal structure of the TLR1-TLR2 heterodimer induced by binding of a tri-acylated lipopeptide. Cell 130, 1071-1082, doi:10.1016/j.cell.2007.09.008 (2007).
Shi, H. et al. N4BP1 negatively regulates NF-κB by binding and inhibiting NEMO oligomerization. Nat Commun 12, 1379, doi:10.1038/s41467-021-21711-5 (2021).
Ben-Ali, M. et al. Functional characterization of naturally occurring genetic variants in the human TLR1-2-6 gene family. Hum Mutat 32, 643-652, doi:10.1002/humu.21486 (2011).
Yu, N. et al. Cultured human melanocytes express functional toll-like receptors 2-4, 7 and 9. J Dermatol Sci 56, 113-120, doi:10.1016/j.jdermsci.2009.08.003 (2009).
Nakao, M. et al. TLR2 Deficiency Exacerbates Imiquimod-Induced Psoriasis-Like Skin Inflammation through Decrease in Regulatory T Cells and Impaired IL-10 Production. Int J Mol Sci 21, doi:10.3390/ijms21228560 (2020).
Lee, J. Y., Zhao, L. & Hwang, D. H. Modulation of pattern recognition receptor-mediated inflammation and risk of chronic diseases by dietary fatty acids. Nutr Rev 68, 38-61, doi:10.1111/j.1753-4887.2009.00259.x (2010).
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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Proteomic characterization between males and females with moderate-to-severe psoriasis

Status:

Version 1

Abstract

Figures

Introduction

Results

Determination of the proteomic landscape using DIA

Function analysis of DEPs in Male and Female

Proteins and pathways associated with psoriasis pathogenesis and disease severity

CHUK, IKBKB, SRC, TLR2, and CASP3 are associated with expression of IL-17 or TNF

HMGB2, PML and LAMTOR3 were validated in 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