A family-based genome-wide association study reveals an association of spondyloarthritis with<i>MAPK14</i>

Costantino, Félicie; Talpin, Alice; Said‐Nahal, Roula; Zinovieva, Elena; Zélénika, Diana; Gut, Marta; Charon, Céline; Izac, Brigitte; Weissman, M. M.; Chiocchia, Gilles; Reveille, John D.; Bréban, Maxime; Garchon, Henri Jean

doi:10.1136/annrheumdis-2016-209449

Cited by 12 publications

(4 citation statements)

References 21 publications

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“…MAPK14 (mitogen-activated protein kinase 14), also known as p38 MAPK, plays a significant role in inflammation and immune response, linked to AS through gene polymorphisms near the MAPK14 locus ( 55 , 56 ). Elevated gene expression levels of Myd88 (myeloid differentiation primary response 88), NF-kB (nuclear factor-kappa B) and MAPK14 in AS patients, compared to controls, were reported by Roozbehkia et al.…”

Section: Discussionmentioning

confidence: 99%

Genetic associations in ankylosing spondylitis: circulating proteins as drug targets and biomarkers

Zhang,

Liu,

Lai

et al. 2024

Front. Immunol.

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BackgroundAnkylosing spondylitis (AS) is a complex condition with a significant genetic component. This study explored circulating proteins as potential genetic drug targets or biomarkers to prevent AS, addressing the need for innovative and safe treatments.MethodsWe analyzed extensive data from protein quantitative trait loci (pQTLs) with up to 1,949 instrumental variables (IVs) and selected the top single-nucleotide polymorphism (SNP) associated with AS risk. Utilizing a two-sample Mendelian randomization (MR) approach, we assessed the causal relationships between identified proteins and AS risk. Colocalization analysis, functional enrichment, and construction of protein-protein interaction networks further supported these findings. We utilized phenome-wide MR (phenMR) analysis for broader validation and repurposing of drugs targeting these proteins. The Drug-Gene Interaction database (DGIdb) was employed to corroborate drug associations with potential therapeutic targets. Additionally, molecular docking (MD) techniques were applied to evaluate the interaction between target protein and four potential AS drugs identified from the DGIdb.ResultsOur analysis identified 1,654 plasma proteins linked to AS, with 868 up-regulated and 786 down-regulated. 18 proteins (AGER, AIF1, ATF6B, C4A, CFB, CLIC1, COL11A2, ERAP1, HLA-DQA2, HSPA1L, IL23R, LILRB3, MAPK14, MICA, MICB, MPIG6B, TNXB, and VARS1) that show promise as therapeutic targets for AS or biomarkers, especially MAPK14, supported by evidence of colocalization. PhenMR analysis linked these proteins to AS and other diseases, while DGIdb analysis identified potential drugs related to MAPK14. MD analysis indicated strong binding affinities between MAPK14 and four potential AS drugs, suggesting effective target-drug interactions.ConclusionThis study underscores the utility of MR analysis in AS research for identifying biomarkers and therapeutic drug targets. The involvement of Th17 cell differentiation-related proteins in AS pathogenesis is particularly notable. Clinical validation and further investigation are essential for future applications.

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Section: Discussionmentioning

confidence: 99%

Genetic associations in ankylosing spondylitis: circulating proteins as drug targets and biomarkers

Zhang,

Liu,

Lai

et al. 2024

Front. Immunol.

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“…Previous research found that MMP9 [347], CBS (cystathionine beta-synthase) [348], IGF2 [349], IGFBP2 [350], HP (haptoglobin) [351], MAPK14 [352], LRG1 [353], LCN2 [354], SBNO2 [355], ITGA1 [356], IL1R2 [357], IL1RN [358], TLR4 [359], IRS2 [360], CORIN (corin, serine peptidase) [361], WNT16 [362], CD80 [363], FASLG (Fas ligand) [364], CXXC5 [365], CNR2 [366], TCF4 [367], PTCH1 [368], TXNIP (thioredoxin interacting protein) [369], TRAF3IP2 [370], ETS1 [371] and ARHGEF3 [372] are strongly associated with osteoporosis. MMP9 [373], S100A12 [374], IGF2 [375], GPR84 [376], SOCS3 [377], CEBPA (CCAAT enhancer binding protein alpha) [378], PGLYRP1 [379], HP (haptoglobin) [380], MMP8 [381], OSM (oncostatin M) [382], TLR5 [383], S100A8 [384], ARG1 [385], S100A9 [386], BCL2A1 [387], IRAK3 [388], ST3GAL4 [389], IL1R1 [390], MAPK14 [391], IL4R [392], PADI4 [393], LCN2 [394], CEBPB (CCAAT enhancer binding protein beta) [395], TSPO (translocator protein) [396], IL18RAP [397], PLAU (plasminogen activator, urokinase) [398], CDA (cytidinedeaminase) [399], SLC11A1 [400], RETN (resistin) [401], GADD45A [402], BCL6 [403], CD55 [404], THBS1 [405], IL1R2 [406], TGFA (transforming growth factor alpha) [407], IL1RN [408], ALDH1A2 [409], SLC8A3 [410], HMGB2 [411], NFIL3 [412], MSRA (methionine sulfoxidereductase A) [413], ITGAM (integrin subunit alpha M) [414], TLR4 [415], SIGLEC9 [416], GRB10 [417]...…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Cystic fibrosis (CF) is one of the inherited autosomal recessive disorders with very complicated pathogenesis. Identifying the molecular signatures and specific biomarkers of CF might provide novel clues for CF and and CF associated complications prognosis and targeted therapy. Based on the nexgeneration sequencing (NGS) dataset GSE136371 downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between CF samples and normal controls were identified by using DESeq2 bioconductor package of R software. Gene ontology (GO) and REACTOME pathway enrichment analyses were applied for the DEGs. Then protein-protein interaction (PPI) network of these DEGs was visualized by Cytoscape with IMEx interactome database. The most significant module from the PPI network was selected for GO and pathway enrichment analysis. Subsequently, a miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed to identify hub genes, miRNAs and TFs. Finally, receiver operating characteristic curve (ROC) analysis was established to validate these hub genes. Total of 917 DEGs were identified between CF and normal control samples in GSE136371 dataset, including 479 up regulated and 438 down regulated genes. The most enriched DEGs in GO and pathway enrichment analysis were mainly associated with response to stimulus, regulation of cellular process, Neutrophil degranulation and rRNA processing. PPI network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network predicted ten hub genes (FN1, UBE2D1, SRPK1, MAPK14, CEBPB, HSP90AB1, HSPA8, XRCC6, NCL and PARP1). In conclusion, the DEGs, relative pathways and hub genes identified in the present study might aid in understanding of the molecular mechanisms underlying CF and CF associated complications progression and provide potential molecular targets and biomarkers for CF and CF associated complications.

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“…Familial approach can also be applied to genetic association study. To date, only one family-based association analysis has been published in SpA ( Costantino et al, 2017 ). None of the tested SNPs reached genome-wide significance.…”

Section: Family-based Approaches For Identification Of Genes Of Susceptibility To Spamentioning

confidence: 99%

What Have We Learned From Family-Based Studies About Spondyloarthritis?

et al. 2021

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Spondyloarthritis (SpA) is a chronic inflammatory disorder with a high familial aggregation, emphasizing the existence of genetic susceptibility factors. In the last decades, family-based studies have contributed to better understand the genetic background of SpA, in particular by showing that the most likely model of transmission is oligogenic with multiplicative effects. Coexistence of different SpA subtypes within families also highlighted the complex interplay between all subtypes. Several whole-genome linkage analyses using sib-pairs or multiplex families were performed in the 1990s to try to identify genetic susceptibility factors besides HLA-B27. Unfortunately, no consistent results were obtained and family-based studies have been progressively set aside in favor of case-control designs. In particular, case-control genome-wide association studies allowed the identification of more than 40 susceptibility regions. However, all these loci explain only a small fraction of disease predisposition. Several hypotheses have been advanced to account for this unexplained heritability, including rare variants involvement, leading to a renewed interest in family-based designs, which are probably more powerful in the detection of such variants. In this review, our purpose is to summarize what has been learned to date regarding SpA genetics from family-based studies, with a special focus on recent identification of rare associated variants through next-generation sequencing studies.

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A family-based genome-wide association study reveals an association of spondyloarthritis withMAPK14

Abstract: We report here for the first time a family-based GWAS study on SpA and identified an associated polymorphism near MAPK14. Further analyses are needed to better understand the functional basis of this genetic association.

Cited by 12 publications

References 21 publications

Genetic associations in ankylosing spondylitis: circulating proteins as drug targets and biomarkers

Genetic associations in ankylosing spondylitis: circulating proteins as drug targets and biomarkers

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

What Have We Learned From Family-Based Studies About Spondyloarthritis?

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