Unveiling the genetic link and pathogenesis between psoriasis and IgA nephropathy based on Mendelian randomization and transcriptome data analyses.


Journal

Archives of dermatological research
ISSN: 1432-069X
Titre abrégé: Arch Dermatol Res
Pays: Germany
ID NLM: 8000462

Informations de publication

Date de publication:
26 Oct 2024
Historique:
received: 01 09 2024
accepted: 17 10 2024
revised: 07 10 2024
medline: 26 10 2024
pubmed: 26 10 2024
entrez: 26 10 2024
Statut: epublish

Résumé

It has been reported that many people with psoriasis have been diagnosed with secondary IgA nephropathy (IgAN). However, the mechanisms behind the association between psoriasis and IgAN have not been well clarified. The connection between psoriasis and IgAN deserves deeper exploration. Mendelian randomization (MR) analysis would be employed to explore the link of causality between IgAN and psoriasis, psoriasis vulgaris, other and unspecified psoriasis, guttate psoriasis, and arthropathic psoriasis. Transcriptomic analyses were carried out against the Gene Expression Omnibus databases. We identified crosstalk genes through the analysis of Differentially expressed genes and weight gene co-expression network analysis. Functional annotations were enriched for these crosstalk genes. Subsequently, we established a protein-protein interaction network, and candidate genes would be discovered through the utilization of the MCODE and CytoHubba plug-in applications. Lastly, the predictive efficacy of these genes was examined via creating receiver operating characteristic curves. The MR analysis suggested that psoriasis vulgaris patients were at a higher risk for IgAN. [OR = 1.040, 95%CI (1.005,1.076), p = 0.026 < 0.05]. Additionally, arthropathic psoriasis may augment the incidence of IgAN [OR = 1.081, 95%CI (1.040-1.124), p < 0.01] in the European population. Through the analysis of DEGs and WGCNA, we identified 12 significant genes (NETO2, RRM2, SLAMF7, GBP1, KIF20A, CCL4, MMP1, IL1β, NDC80, CXCL9, C15orf48, GSTA3), which may be potential crosstalk genes between the two diseases. Then, the functional annotation results indicated that the crosstalk genes seemed primarily involved in immune and inflammatory responses. By establishing the PPI network, we further discovered that CXCL9, IL1β, CCL4, and MMP1 play a vital part in psoriasis and IgAN, and all have good diagnostic values. Our MR analysis provided evidence that genetic vulnerability to IgAN may be associated with an elevated risk of psoriasis vulgaris and arthropathic psoriasis respectively among Europeans. Doctors should be aware of these associations when patients with psoriasis present with renal dysfunction, especially those with psoriasis vulgaris and arthropathic psoriasis. Chronic inflammation, drug effects, and immunity may contribute to the generation and development of both diseases. IL1β, CXCL9, CCL4, and MMP1 may be core biomarkers for psoriasis and IgAN.

Identifiants

pubmed: 39460798
doi: 10.1007/s00403-024-03465-4
pii: 10.1007/s00403-024-03465-4
doi:

Substances chimiques

Matrix Metalloproteinase 1 EC 3.4.24.7
MMP1 protein, human EC 3.4.24.7
Interleukin-1beta 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

717

Subventions

Organisme : National Natural Science Foundation of China
ID : NO:82074436

Informations de copyright

© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Références

Griffiths CEM, Armstrong AW, Gudjonsson JE et al (2021) Psoriasis Lancet 397:1301–1315
pubmed: 33812489 doi: 10.1016/S0140-6736(20)32549-6
Armstrong AW, Read C, Pathophysiology (2020) Clinical presentation, and treatment of psoriasis: a review. JAMA 323:1945–1960
pubmed: 32427307 doi: 10.1001/jama.2020.4006
Rendon A, Schäkel K (2019) Psoriasis Pathogenesis and Treatment. Int J Mol Sci. 20
Takeshita J, Grewal S, Langan SM et al (2017) Psoriasis and comorbid diseases: Epidemiology. J Am Acad Dermatol 76:377–390
pubmed: 28212759 pmcid: 5731650 doi: 10.1016/j.jaad.2016.07.064
Singh NP, Prakash A, Kubba S et al (2005) Psoriatic nephropathy–does an entity exist? Ren Fail 27:123–127
pubmed: 15717645
Yeung H, Takeshita J, Mehta NN et al (2013) Psoriasis severity and the prevalence of major medical comorbidity: a population-based study. JAMA Dermatol 149:1173–1179
pubmed: 23925466 pmcid: 3800487 doi: 10.1001/jamadermatol.2013.5015
Dolff S, Witzke O, Wilde B (2019) Th17 cells in renal inflammation and autoimmunity. Autoimmun Rev 18:129–136
pubmed: 30572135 doi: 10.1016/j.autrev.2018.08.006
Munera-Campos M, Ferrándiz C, Mateo L et al (2021) Prevalence and stages of chronic kidney disease in psoriasis and psoriatic arthritis: a cross-sectional study. Indian J Dermatol Venereol Leprol 87:321
pubmed: 33769751 doi: 10.25259/IJDVL_372_19
Visconti L, Leonardi G, Buemi M et al (2016) Kidney disease and psoriasis: novel evidences beyond old concepts. Clin Rheumatol 35:297–302
pubmed: 26615613 doi: 10.1007/s10067-015-3126-4
Perše M, Večerić-Haler Ž (2019) The role of IgA in the Pathogenesis of IgA Nephropathy. Int J Mol Sci. 20
Suzuki H, Kiryluk K, Novak J et al (2011) The pathophysiology of IgA nephropathy. J Am Soc Nephrol 22:1795–1803
pubmed: 21949093 pmcid: 3892742 doi: 10.1681/ASN.2011050464
Ochi M, Toyama T, Ando M et al (2019) A case of secondary IgA nephropathy accompanied by psoriasis treated with secukinumab. CEN Case Rep 8:200–204
pubmed: 30941695 pmcid: 6620223 doi: 10.1007/s13730-019-00393-5
Xue H, Ci X, Luo M et al (2022) Tofacitinib combined with leflunomide for treatment of psoriatic arthritis with IgA nephropathy: a case report with literature review. Clin Rheumatol 41:2225–2231
pubmed: 35192086 doi: 10.1007/s10067-022-06113-2
Dattola A, Zangrilli A, Bianchi L (2021) Risankizumab for Plaque and Guttate Psoriasis in a patient with IgA-Related glomerulonephritis. Dermatol Pract Concept 11:e2021100
pubmed: 35024224 pmcid: 8648422 doi: 10.5826/dpc.1104a100
Sekula P, Del Greco MF, Pattaro C et al (2016) Mendelian randomization as an Approach to assess causality using Observational Data. J Am Soc Nephrol 27:3253–3265
pubmed: 27486138 pmcid: 5084898 doi: 10.1681/ASN.2016010098
Kurki MI, Karjalainen J, Palta P et al (2023) FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613:508–518
pubmed: 36653562 pmcid: 9849126 doi: 10.1038/s41586-022-05473-8
Ben E, Matthew L, Tessa A et al (2020) The MRC IEU OpenGWAS data infrastructure. bioRxiv. 2020.08.10.244293.
Sakaue S, Kanai M, Tanigawa Y et al (2021) A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 53:1415–1424
pubmed: 34594039 doi: 10.1038/s41588-021-00931-x
Staley JR, Blackshaw J, Kamat MA et al (2016) PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32:3207–3209
pubmed: 27318201 pmcid: 5048068 doi: 10.1093/bioinformatics/btw373
Kamat MA, Blackshaw JA, Young R et al (2019) PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics 35:4851–4853
pubmed: 31233103 pmcid: 6853652 doi: 10.1093/bioinformatics/btz469
Grewal SK, Wan J, Denburg MR et al (2017) The risk of IgA nephropathy and glomerular disease in patients with psoriasis: a population-based cohort study. Br J Dermatol 176:1366–1369
pubmed: 27518038 pmcid: 5303688 doi: 10.1111/bjd.14961
He B, Lyu Q, Yin L et al (2021) Depression and osteoporosis: a mendelian randomization study. Calcif Tissue Int 109:675–684
pubmed: 34259888 pmcid: 8531056 doi: 10.1007/s00223-021-00886-5
Hemani G, Zheng J, Elsworth B et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife. 7
Larsson SC, Scott RA, Traylor M et al (2017) Type 2 diabetes, glucose, insulin, BMI, and ischemic stroke subtypes: mendelian randomization study. Neurology 89:454–460
pubmed: 28667182 pmcid: 5539736 doi: 10.1212/WNL.0000000000004173
Bowden J, Del Greco MF, Minelli C et al (2016) Assessing the suitability of summary data for two-sample mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol 45:1961–1974
pubmed: 27616674 pmcid: 5446088
Bowden J, Davey Smith G, Haycock PC et al (2016) Consistent estimation in mendelian randomization with some Invalid instruments using a weighted median estimator. Genet Epidemiol 40:304–314
pubmed: 27061298 pmcid: 4849733 doi: 10.1002/gepi.21965
Yavorska OO, Burgess S (2017) MendelianRandomization: an R package for performing mendelian randomization analyses using summarized data. Int J Epidemiol 46:1734–1739
pubmed: 28398548 pmcid: 5510723 doi: 10.1093/ije/dyx034
Burgess S, Small DS, Thompson SG (2017) A review of instrumental variable estimators for mendelian randomization. Stat Methods Med Res 26:2333–2355
pubmed: 26282889 doi: 10.1177/0962280215597579
Bowden J, Del Greco MF, Minelli C et al (2017) A framework for the investigation of pleiotropy in two-sample summary data mendelian randomization. Stat Med 36:1783–1802
pubmed: 28114746 pmcid: 5434863 doi: 10.1002/sim.7221
Burgess S, Thompson SG (2017) Interpreting findings from mendelian randomization using the MR-Egger method. Eur J Epidemiol 32:377–389
pubmed: 28527048 pmcid: 5506233 doi: 10.1007/s10654-017-0255-x
Verbanck M, Chen CY, Neale B et al (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases. Nat Genet 50:693–698
pubmed: 29686387 pmcid: 6083837 doi: 10.1038/s41588-018-0099-7
Gao N, Kong M, Li X et al (2022) Systemic Lupus Erythematosus and Cardiovascular Disease: a mendelian randomization study. Front Immunol 13:908831
pubmed: 35734181 pmcid: 9207262 doi: 10.3389/fimmu.2022.908831
Gautier L, Cope L, Bolstad BM et al (2004) Affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20:307–315
pubmed: 14960456 doi: 10.1093/bioinformatics/btg405
Bolstad BM, Irizarry RA, Astrand M et al (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185–193
pubmed: 12538238 doi: 10.1093/bioinformatics/19.2.185
Ritchie ME, Phipson B, Wu D et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47
pubmed: 25605792 pmcid: 4402510 doi: 10.1093/nar/gkv007
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559
pubmed: 19114008 pmcid: 2631488 doi: 10.1186/1471-2105-9-559
Wu T, Hu E, Xu S et al (2021) clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innov (Camb) 2:100141
Aleksander SA, Balhoff J, Carbon S et al (2023) The Gene Ontology knowledgebase in 2023. Genetics. 224
Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29
pubmed: 10802651 pmcid: 3037419 doi: 10.1038/75556
Kanehisa M, Furumichi M, Sato Y et al (2023) KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res 51:D587–d92
pubmed: 36300620 doi: 10.1093/nar/gkac963
Kanehisa M (2019) Toward understanding the origin and evolution of cellular organisms. Protein Sci 28:1947–1951
pubmed: 31441146 pmcid: 6798127 doi: 10.1002/pro.3715
Szklarczyk D, Kirsch R, Koutrouli M et al (2023) The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51:D638–d46
pubmed: 36370105 doi: 10.1093/nar/gkac1000
Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504
pubmed: 14597658 pmcid: 403769 doi: 10.1101/gr.1239303
Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4:2
pubmed: 12525261 pmcid: 149346 doi: 10.1186/1471-2105-4-2
Chin CH, Chen SH, Wu HH et al (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(Suppl 4):S11
pubmed: 25521941 pmcid: 4290687 doi: 10.1186/1752-0509-8-S4-S11
Robin X, Turck N, Hainard A et al (2011) pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics 12:77
pubmed: 21414208 pmcid: 3068975 doi: 10.1186/1471-2105-12-77
Jabbar-Lopez ZK, Weatherhead SC, Reynolds NJ (2016) Kidney disease in moderate-to-severe psoriasis: a critical appraisal. Br J Dermatol 174:267–270
pubmed: 26871922 doi: 10.1111/bjd.14302
Xu JR, Zheng DX, Ahn AB et al (2023) Psoriasis and chronic kidney disease among the United States adult population. J Am Acad Dermatol 89:834–837
pubmed: 37321485 doi: 10.1016/j.jaad.2023.06.011
Chiu HY, Huang HL, Li CH et al (2015) Increased risk of glomerulonephritis and chronic kidney disease in relation to the severity of psoriasis, concomitant medication, and comorbidity: a nationwide population-based cohort study. Br J Dermatol 173:146–154
pubmed: 25511692 doi: 10.1111/bjd.13599
Ungprasert P, Raksasuk S (2018) Psoriasis and risk of incident chronic kidney disease and end-stage renal disease: a systematic review and meta-analysis. Int Urol Nephrol 50:1277–1283
pubmed: 29644523 doi: 10.1007/s11255-018-1868-z
Vaz AS, Penteado R, Cordinhã C et al (2021) IgA vasculitis (Henoch-Schönlein purpura) nephritis and psoriasis in a child: is there a relationship? J Bras Nefrol 43:603–607
pubmed: 33605313 pmcid: 8940111 doi: 10.1590/2175-8239-jbn-2020-0101
Kluger N, Du-Thanh A, Bessis D et al (2015) Psoriasis-associated IgA nephropathy under infliximab therapy. Int J Dermatol 54:e79–80
pubmed: 25515529 doi: 10.1111/ijd.12622
Ren F, Zhang M, Zhang C et al (2020) Psoriasis-Like Inflammation Induced Renal Dysfunction through the TLR/NF-κB Signal Pathway. Biomed Res Int. 2020: 3535264
Aixue W, Feng W, Huanhuan Z et al (2024) Cosentyx alleviates psoriasis-induced podocyte injury by inhibiting the tlr/nf-κb signaling pathway. Skin Res Technol 30:e13562
pubmed: 38279604 pmcid: 10818124 doi: 10.1111/srt.13562
Kulaklı S, Akagün T (2024) A case of psoriasis with IgA nephropathy successfully treated with secukinumab. Int J Dermatol 63:e35–e7
pubmed: 37950460 doi: 10.1111/ijd.16903
Lopez-Castejon G, Brough D (2011) Understanding the mechanism of IL-1β secretion. Cytokine Growth Factor Rev 22:189–195
pubmed: 22019906 pmcid: 3714593 doi: 10.1016/j.cytogfr.2011.10.001
Cai Y, Xue F, Quan C et al (2019) A critical role of the IL-1β-IL-1R signaling pathway in skin inflammation and Psoriasis Pathogenesis. J Invest Dermatol 139:146–156
pubmed: 30120937 doi: 10.1016/j.jid.2018.07.025
Jiang W, Zhang T, Qiu Y et al (2024) Keratinocyte-to-macrophage communication exacerbate psoriasiform dermatitis via LRG1-enriched extracellular vesicles. Theranostics 14:1049–1064
pubmed: 38250043 pmcid: 10797285 doi: 10.7150/thno.89180
Syrjänen J, Hurme M, Lehtimäki T et al (2002) Polymorphism of the cytokine genes and IgA nephropathy. Kidney Int 61:1079–1085
pubmed: 11849463 doi: 10.1046/j.1523-1755.2002.00193.x
Chronopoulou I, Tziastoudi M, Pissas G et al (2023) Interleukin variants are Associated with the Development and Progression of IgA Nephropathy: a candidate-gene Association Study and Meta-Analysis. Int J Mol Sci. 24
van der Vorst EP, Döring Y, Weber C, Chemokines (2015) Arterioscler Thromb Vasc Biol 35:e52–e56
pubmed: 26490276
Duarte GV, Boeira V, Correia T et al (2015) Osteopontin, CCL5 and CXCL9 are independently associated with psoriasis, regardless of the presence of obesity. Cytokine 74:287–292
pubmed: 25972108 doi: 10.1016/j.cyto.2015.04.015
Pedrosa E, Carretero-Iglesia L, Boada A et al (2011) CCL4L polymorphisms and CCL4/CCL4L serum levels are associated with psoriasis severity. J Invest Dermatol 131:1830–1837
pubmed: 21614014 doi: 10.1038/jid.2011.127
Ekman AK, Sigurdardottir G, Carlström M et al (2013) Systemically elevated Th1-, Th2- and Th17-associated chemokines in psoriasis vulgaris before and after ultraviolet B treatment. Acta Derm Venereol 93:527–531
pubmed: 23571825 doi: 10.2340/00015555-1545
Park S, Yang SH, Jeong CW et al (2020) RNA-Seq profiling of microdissected glomeruli identifies potential biomarkers for human IgA nephropathy. Am J Physiol Ren Physiol 319:F809–f21
doi: 10.1152/ajprenal.00037.2020
Deng S, Zhou F, Wang F et al (2023) C5a enhances Vδ1 T cells recruitment via the CCL2-CCR2 axis in IgA nephropathy. Int Immunopharmacol 125:111065
pubmed: 37862725 doi: 10.1016/j.intimp.2023.111065
Zhou J, Xu M, Tan J et al (2022) MMP1 acts as a potential regulator of tumor progression and dedifferentiation in papillary thyroid cancer. Front Oncol 12:1030590
pubmed: 36479070 pmcid: 9720150 doi: 10.3389/fonc.2022.1030590
Mezentsev A, Nikolaev A, Bruskin S (2014) Matrix metalloproteinases and their role in psoriasis. Gene 540:1–10
pubmed: 24518811 doi: 10.1016/j.gene.2014.01.068
Michalak-Stoma A, Bartosińska J, Raczkiewicz D et al (2021) Assessment of Selected Matrix Metalloproteinases (MMPs) and Correlation with Cytokines in Psoriatic Patients. Mediators Inflamm. 2021: 9913798
Djuric T, Zivkovic M, Milosevic B et al (2014) MMP-1 and – 3 haplotype is associated with congenital anomalies of the kidney and urinary tract. Pediatr Nephrol 29:879–884
pubmed: 24414606 doi: 10.1007/s00467-013-2699-x

Auteurs

Yingwen Chen (Y)

Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.
Beijing University of Chinese Medicine, Beijing, 100029, China.

Min Huang (M)

Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.

Ziqing You (Z)

Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.
Beijing University of Chinese Medicine, Beijing, 100029, China.

Rule Sa (R)

Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.
Beijing University of Chinese Medicine, Beijing, 100029, China.

Lu Zhao (L)

Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.
Beijing University of Chinese Medicine, Beijing, 100029, China.

Congwen Ku (C)

Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.
Beijing University of Chinese Medicine, Beijing, 100029, China.

Wenying Wang (W)

Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China. wly167@163.com.

Xingwu Duan (X)

Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China. xwduan@sina.com.

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