CRKL-Mediated Regulation of Immune Cell Recruitment Pathways in Rheumatoid Arthritis: An Integrative Network-Centric Bioinformatics Study

CRKL-Mediated Regulation of Immune Cell Recruitment Pathways in Rheumatoid Arthritis: An Integrative Network-Centric Bioinformatics Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CRKL-Mediated Regulation of Immune Cell Recruitment Pathways in Rheumatoid Arthritis: An Integrative Network-Centric Bioinformatics Study Syed Nayeem, Daniel Alex Anand This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9607329/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by synovial inflammation, leukocyte infiltration, and progressive joint destruction. While cytokine-targeted therapies improve outcomes, incomplete remission rates suggest additional regulatory layers in immune cell trafficking remain unaddressed. CRKL (Crk-like) is an adaptor protein that coordinates signaling complexes governing adhesion, migration, and cytoskeletal dynamics, but its network-level role in RA is not fully defined. Methods We constructed CRKL-centered protein–protein interaction (PPI) networks by integrating STRING (v11.5; confidence ≥ 0.7) and BioGRID interactions and analyzed them in Cytoscape (v3.9.1). Network topology (degree, betweenness, clustering) was quantified using NetworkAnalyzer. Functional enrichment was performed using KEGG, Reactome, and GO-BP with Benjamini–Hochberg correction (FDR < 0.05). Differential expression of CRKL was evaluated across ten GEO datasets (synovium, PBMCs, whole blood) using GEO2R/limma. We further performed in silico perturbation by constraining CRKL activity and quantifying propagated changes in first- and second-order neighbors. Results CRKL emerged as a high-centrality hub (degree = 23; top 5% betweenness) connecting adhesion/migration modules (PXN, BCAR1), tyrosine kinase signaling (ABL1, EGFR), and immune transcriptional programs (STAT4, GRB2, SHC1). Enrichment analyses converged on MAPK, JAK/STAT, Rap1 signaling, chemokine signaling, and integrin-mediated adhesion, alongside Reactome pathways for receptor tyrosine kinase signaling and RHO GTPase cycles. CRKL was not differentially expressed across datasets (adjusted P > 0.05; mean |log2FC|<0.3), consistent with adaptor function. However, perturbation revealed directional control of downstream biology: simulated downregulation decreased FN1, ABL1, PXN, IRAK3, and RHOQ (reduced cytoskeletal remodeling and adhesion), whereas upregulation increased VEGFC, TLR4, and SH2D1A (enhanced inflammation and angiogenesis). Conclusions CRKL acts as a non-transcriptional, high-impact regulator of immune cell recruitment in RA, coordinating integrin activation and cytoskeletal dynamics via the CRKL–C3G–Rap1–LFA-1 axis while interfacing with MAPK and JAK/STAT signaling. Targeting CRKL-mediated scaffolding interactions (e.g., SH2/SH3 domains) may complement cytokine-directed therapies by selectively modulating leukocyte trafficking. Rheumatoid arthritis CRKL protein–protein interaction network biology immune cell migration pathway enrichment GEO Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Rheumatoid arthritis (RA) is a systemic autoimmune disease affecting ~ 1% of the global population and is marked by persistent synovial inflammation, pannus formation, and progressive cartilage and bone damage (Smolen et al., 2018 ). Central to RA pathogenesis is the aberrant recruitment and retention of immune cells—including T cells, B cells, macrophages, and neutrophils—within synovial tissue. These cells orchestrate a sustained inflammatory milieu through cytokine production, antigen presentation, and matrix-degrading enzyme release. Molecularly, RA reflects dysregulation across several signaling axes: the JAK/STAT pathway governs cytokine-driven transcription; the MAPK cascade integrates stress and mitogenic cues; PI3K/AKT signaling modulates survival and metabolism; and NF-κB controls inflammatory gene expression. Complementing these are adhesion and migration pathways—particularly integrin-mediated signaling and RHO GTPase-regulated cytoskeletal remodeling—which determine how immune cells traverse endothelium and navigate inflamed tissue. Despite the success of TNF inhibitors, IL-6 blockade, and JAK inhibitors, a substantial subset of patients fails to achieve durable remission, indicating that upstream coordination of immune trafficking remains insufficiently targeted. Adaptor proteins—non-enzymatic scaffolds that assemble signaling complexes—offer a mechanistic layer capable of integrating multiple pathways without requiring transcriptional changes. Among these, CRKL (Crk-like) is a 39-kDa adaptor with SH2 and SH3 domains that bridges phosphorylated receptors and downstream effectors. CRKL is implicated in integrin activation, cytoskeletal organization, and cell motility. The CRKL–C3G (RAPGEF1)–Rap1–LFA-1 axis is particularly important for “inside-out” integrin activation, enhancing leukocyte adhesion to endothelial cells and facilitating transmigration. Beyond adhesion, CRKL interfaces with receptor tyrosine kinases (e.g., EGFR), non-receptor kinases (ABL1), and adaptors (GRB2, SHC1), thereby influencing MAPK and JAK/STAT pathways. Network analyses have previously identified CRKL as a high-degree node in RA-associated interactomes (Wu et al., 2010 ), yet its role as a coordinator of immune recruitment at a systems level remains underexplored. A key conceptual gap is that many studies prioritize differential gene expression, whereas adaptor proteins like CRKL may exert substantial functional impact without large transcriptional shifts. This necessitates network-centric approaches that integrate topology, pathway enrichment, and perturbation modeling. Objectives (i) construct and analyze a CRKL-centered PPI network; (ii) quantify CRKL’s topological importance; (iii) identify enriched pathways relevant to immune recruitment; (iv) assess CRKL expression across diverse RA cohorts; and (v) evaluate the functional consequences of CRKL perturbation. We hypothesize that CRKL serves as a central, non-transcriptional regulator of leukocyte trafficking in RA. 2. Materials and Methods 2.1 Data sources and preprocessing Protein interaction data for CRKL were obtained from STRING (v11.5) with a minimum combined score of 0.7 (high confidence), including evidence channels for experiments, curated databases, and co-expression. Complementary interactions were retrieved from BioGRID (January 2024 release). Redundant edges were merged, and identifiers were standardized to official gene symbols (HGNC). Self-loops and duplicate edges were removed. For expression analysis, ten GEO datasets were selected to capture heterogeneity across tissue types and disease stages: GSE44446, GSE14248, GSE19188, GSE26252, GSE33432, GSE42458, GSE43402, GSE44234, GSE51304, and GSE20441. These include synovial tissue, PBMCs, and whole blood from RA patients and matched controls. Where necessary, probe IDs were mapped to gene symbols using platform annotation files; when multiple probes mapped to the same gene, the probe with the highest average expression was retained. 2.2 Network construction in Cytoscape Integrated PPI data were imported into Cytoscape (v3.9.1). A CRKL-centered network was constructed by extracting first-order neighbors (“Select → First neighbors of selected nodes”). The resulting sub-network comprised CRKL and its high-confidence interactors. Edge attributes included interaction confidence and evidence types. 2.3 Topological analysis NetworkAnalyzer was used to compute: Node degree (k) : number of direct interactions Betweenness centrality (BC) : fraction of shortest paths passing through a node Clustering coefficient (C) : density of local neighborhoods CRKL’s metrics were compared to network distributions to determine hub status. Nodes within the top 5% of BC were considered critical for information flow. 2.4 Functional enrichment analysis Gene sets from the CRKL network were analyzed using KEGG and Reactome pathway databases and Gene Ontology (Biological Process). Over-representation analysis employed a hypergeometric test with Benjamini–Hochberg correction (FDR < 0.05). A minimum gene count of 3 per term was required. Terms with high overlap were grouped using functional fusion (≥ 50%) to reduce redundancy. Enrichment outputs were visualized as network maps and ranked lists. 2.5 Differential expression analysis (GEO2R/limma) Each GEO dataset was analyzed using GEO2R with the limma package. Data were log2-transformed where required and quantile-normalized. Linear models with empirical Bayes moderation were fitted to compare RA vs. control groups. Significance thresholds were set at adjusted P 1. For CRKL, summary statistics (mean log2FC, adjusted P) were aggregated across datasets. 2.6 In silico perturbation modeling To estimate CRKL’s regulatory influence independent of expression, we implemented a network-based perturbation: Assign baseline weights to edges proportional to STRING confidence. Simulate CRKL downregulation and upregulation by scaling its outgoing influence (e.g., − 1 and + 1 perturbation states). Propagate effects to first- and second-order neighbors using a diffusion-like update rule (iterative until convergence or fixed steps). Compute Δexpression proxies for each node (relative change from baseline). Assess significance via paired comparisons across nodes (two-tailed t-test, P 10% change and significant P-values were considered meaningfully perturbed. 2.7 Reproducibility and software Analyses were conducted using Cytoscape (v3.9.1), R (limma), and standard enrichment tools. Parameter settings (confidence thresholds, FDR cutoffs) are reported above to enable reproducibility. All gene lists and parameters can be provided as supplementary material. 3. Results 3.1 CRKL occupies a high-centrality position in RA networks The CRKL-centered network comprised CRKL and 23 direct interactors with high-confidence edges. CRKL exhibited degree = 23 , placing it among the highest-degree nodes. Betweenness centrality ranked within the top 5% , indicating that CRKL lies on a large fraction of shortest paths and likely governs information flow between modules. The clustering coefficient (C = 0.31) suggests moderate local connectivity, consistent with a node bridging multiple functional clusters rather than residing within a single dense module. Functionally, CRKL connected three major modules: Adhesion/cytoskeleton : PXN (paxillin), BCAR1, DOCK2, RAC1 Tyrosine kinase signaling : ABL1, EGFR Adaptor/transcriptional signaling : GRB2, SHC1, STAT4 This architecture positions CRKL as an integrator of migration, adhesion, and inflammatory signaling. 3.2 Protein–protein interaction landscape highlights migration and signaling crosstalk Fourteen high-confidence first-order interactors were consistently observed, including ABL1, RAPGEF1 (C3G), PXN, STAT4, GRB2, SHC1, BCAR1, ELMO1/2, and BCR. Edge strengths (STRING scores) were highest for GRB2 and ABL1, reflecting robust experimental support. A distinct adhesion–migration subcluster centered on RAPGEF1–PXN–ABL1 was evident, aligning with the CRKL–C3G–Rap1 axis that regulates integrin activation (LFA-1) and leukocyte adhesion. Parallel connections to GRB2/SHC1 link CRKL to receptor tyrosine kinase signaling, enabling crosstalk with MAPK pathways. STAT4 connectivity indicates integration with cytokine-driven transcription (JAK/STAT). 3.3 Enrichment analysis converges on immune recruitment pathways KEGG enrichment identified: MAPK signaling (hsa04010) ErbB signaling (hsa04012) Rap1 signaling (hsa04014) Chemokine signaling (hsa04062) Focal adhesion (hsa04510) Reactome enrichment highlighted: Signaling by receptor tyrosine kinases (highly significant) Downstream signal transduction IL-3/IL-4/GM-CSF signaling RHO GTPase cycle / integrin-mediated adhesion Collectively, these pathways map onto three functional axes central to RA: Integrin activation and firm adhesion (Rap1–LFA-1; focal adhesion) Directional migration (chemokine signaling; RHO GTPases) Inflammatory amplification (MAPK; JAK/STAT; cytokine signaling) 3.4 CRKL is not differentially expressed across RA datasets Across ten GEO datasets spanning synovial tissue, PBMCs, and whole blood, CRKL did not meet differential expression criteria (adjusted P > 0.05; mean |log2FC| < 0.3). This consistency across heterogeneous cohorts supports the interpretation of CRKL as a signal-propagating adaptor whose functional impact is not contingent on transcriptional upregulation. 3.5 Perturbation analysis reveals bidirectional control of adhesion and inflammation Simulated CRKL downregulation led to significant decreases in: FN1 (Δ ≈ −0.38), ABL1 (Δ ≈ −0.34), PXN (Δ ≈ −0.31), IRAK3 (Δ ≈ −0.29), RHOQ (Δ ≈ −0.26), indicating diminished cytoskeletal remodeling and integrin-mediated adhesion. Conversely, CRKL upregulation increased: VEGFC (Δ ≈ +0.44), TLR4 (Δ ≈ +0.37), SH2D1A (Δ ≈ +0.28), suggesting enhanced pro-inflammatory signaling and angiogenesis. These results demonstrate that CRKL exerts directional, network-level control over key processes driving leukocyte recruitment and synovial pathology. 4. Discussion This study positions CRKL as a central, non-transcriptional regulator of immune cell recruitment in RA. By integrating network topology, pathway enrichment, and perturbation modeling, we show that CRKL coordinates adhesion, migration, and inflammatory signaling despite lacking differential expression. 4.1 CRKL as an integrator of adhesion and signaling The CRKL–C3G–Rap1–LFA-1 axis provides a mechanistic basis for our findings. Rap1 activation increases integrin affinity (“inside-out” signaling), enabling T cells to adhere firmly to endothelial ICAMs and undergo transendothelial migration. The observed enrichment of focal adhesion and RHO GTPase pathways aligns with cytoskeletal rearrangements required for cell motility. Concurrently, CRKL’s links to GRB2/SHC1 and EGFR/ABL1 connect adhesion events to MAPK signaling, allowing environmental cues to modulate migration dynamics. 4.2 Network control without differential expression A key insight is that functional centrality can supersede transcriptional change . Adaptor proteins like CRKL act as scaffolds; modest or stable expression can still produce large phenotypic effects by re-wiring interaction patterns or phosphorylation states. This explains why CRKL is not differentially expressed across GEO datasets yet exerts strong downstream influence in perturbation analyses. 4.3 Therapeutic implications Current RA therapies predominantly target cytokines or their receptors. While effective, they may not directly address leukocyte trafficking . CRKL represents a complementary target class: modulating its SH2/SH3 domain interactions could selectively disrupt assembly of pro-migratory complexes without globally suppressing cytokine signaling. This approach could reduce synovial infiltration while potentially limiting systemic immunosuppression. However, CRKL also participates in normal hematopoiesis and immune responses, raising safety considerations. Selective inhibition —for example, targeting interaction interfaces specific to inflamed tissue contexts—may mitigate off-target effects. Structure-guided drug design and peptide inhibitors of SH2/SH3 interactions are plausible avenues. 4.4 Limitations In silico design : While integrative, the study lacks experimental validation (e.g., CRKL knockdown in RA-derived cells). Dataset heterogeneity : GEO cohorts vary in tissue type, disease stage, and platform, introducing potential batch effects. Model assumptions : Perturbation relies on network propagation approximations rather than kinetic parameters. 4.5 Future directions Experimental validation : CRISPR/Cas9-mediated CRKL knockdown/overexpression in synovial fibroblasts and T cells; adhesion and migration assays. Single-cell omics : Define cell-type-specific roles of CRKL across synovial microenvironments. Drug discovery : Molecular docking and dynamics of SH2/SH3 inhibitors; assessment of selectivity and toxicity. Multi-omics integration : Combine transcriptomics, phosphoproteomics, and interactomics to refine causal pathways. 5. Conclusions CRKL functions as a hub adaptor orchestrating immune cell adhesion, migration, and inflammatory signaling in RA. Despite stable expression, its high centrality enables substantial control over downstream pathways, particularly via the Rap1–integrin axis and MAPK/JAK–STAT crosstalk. Targeting CRKL-mediated scaffolding interactions offers a novel therapeutic angle to complement existing cytokine-directed treatments by directly modulating leukocyte trafficking. Abbreviations RA Rheumatoid arthritis PPI Protein–protein interaction GEO Gene Expression Omnibus KEGG Kyoto Encyclopedia of Genes and Genomes GO Gene Ontology FDR False discovery rate PBMCs Peripheral blood mononuclear cells. Declarations Ethics approval and consent to participate: Not applicable (secondary analysis of public datasets). Consent for publication: Not applicable. Availability of data and materials: GEO datasets listed (GSE44446, GSE14248, GSE19188, GSE26252, GSE33432, GSE42458, GSE43402, GSE44234, GSE51304, GSE20441). Competing interests: The authors declare no competing interests. Funding: None. Authors’ contributions: Conceptualization, analysis, and writing performed by the authors. Acknowledgements: We thank mentors and institutional support for guidance. References Birge, R. B., Kalodimos, C., Inagaki, F., & Tanaka, S. (2009). Crk and CrkL adaptor proteins: Networks for physiological and pathological signaling. Cell Communication and Signaling , 7, 13. https://doi.org/10.1186/1478-811X-7-13 Braiman, A., & Isakov, N. (2015). The role of Crk adaptor proteins in T-cell adhesion and migration. Frontiers in Immunology , 6, 509. https://doi.org/10.3389/fimmu.2015.00509 Ciobanu, D. A., Poenariu, I. S., Crînguș, L. I., et al. (2020). JAK/STAT pathway in pathology of rheumatoid arthritis. Experimental and Therapeutic Medicine , 20(4), 3498–3503. https://doi.org/10.3892/etm.2020.8982 Dent, J. E., & Nardini, C. (2013). From desk to bed: Computational simulations provide indication for rheumatoid arthritis clinical trials. BMC Systems Biology , 7, 10. https://doi.org/10.1186/1752-0509-7-10 Ding, Q., Hu, W., Wang, R., et al. (2023). Signaling pathways in rheumatoid arthritis: Implications for targeted therapy. Signal Transduction and Targeted Therapy , 8, 68. https://doi.org/10.1038/s41392-023-01331-9 Huang, J., Fu, X., Chen, X., et al. (2021). Promising therapeutic targets for treatment of rheumatoid arthritis. Frontiers in Immunology , 12, 686155. https://doi.org/10.3389/fimmu.2021.686155 Huang, Y., Clarke, F., Karimi, M., et al. (2015). CRK proteins selectively regulate T cell migration into inflamed tissues. Journal of Clinical Investigation , 125(3), 1019–1032. https://doi.org/10.1172/JCI77278 Roy, N. H., MacKay, J. L., Robertson, T. F., et al. (2018). Crk adaptor proteins mediate actin-dependent T cell migration and mechanosensing induced by integrin LFA-1. Science Signaling , 11(560), eaat3178. https://doi.org/10.1126/scisignal.aat3178 Smolen, J. S., Aletaha, D., Barton, A., et al. (2018). Rheumatoid arthritis. Nature Reviews Disease Primers , 4, 18001. https://doi.org/10.1038/nrdp.2018.1 Wu, G., Zhu, L., Dent, J. E., & Nardini, C. (2010). A comprehensive molecular interaction map for rheumatoid arthritis. PLoS ONE , 5(4), e10137. https://doi.org/10.1371/journal.pone.0010137 Xie, J., Sun, S., Li, Q., et al. (2025). MAPK/ERK signaling pathway in rheumatoid arthritis: Mechanisms and therapeutic potential. PeerJ , 13, e19708. https://doi.org/10.7717/peerj.19708 Ba, X., Huang, Y., Shen, P., et al. (2021). WTD attenuates rheumatoid arthritis via suppressing angiogenesis and modulating the PI3K/AKT pathway. Frontiers in Pharmacology , 12, 696802. https://doi.org/10.3389/fphar.2021.696802 Tables Table 1 Topological properties of CRKL in the RA interaction network Parameter Value Interpretation Node Degree 23 Indicates high connectivity with multiple proteins Betweenness Centrality Top 5% Suggests CRKL acts as a key regulatory hub Clustering Coefficient 0.31 Reflects moderate interaction clustering Network Role Hub Protein Central in signaling and immune pathways Table 2 Key CRKL-interacting proteins and their functional roles Protein Function Biological Significance ABL1 Tyrosine kinase Regulates cytoskeletal remodeling and signaling PXN (Paxillin) Scaffold protein Controls focal adhesion and cell migration RAPGEF1 (C3G) Exchange factor Activates Rap1 signaling pathway STAT4 Transcription factor Involved in immune response regulation GRB2 Adaptor protein Links receptor signaling to downstream pathways SHC1 Signaling adaptor Activates MAPK signaling BCAR1 Scaffold protein Integrin-mediated signaling ELMO1 Motility regulator Controls cell migration BCR GTPase regulator Involved in immune signaling Table 3 Enriched pathways associated with CRKL network Pathway Database FDR Value Functional Role MAPK signaling pathway KEGG 0.002 Controls inflammation and cell proliferation Rap1 signaling pathway KEGG 0.014 Regulates cell adhesion Chemokine signaling pathway KEGG 0.018 Directs immune cell migration Focal adhesion KEGG 0.021 Maintains cell structure and movement Receptor tyrosine kinase signaling Reactome 1.2 × 10⁻⁶ Major signaling hub IL-3/IL-4/GM-CSF signaling Reactome 0.004 Immune activation RHO GTPase cycle Reactome 0.011 Cytoskeletal dynamics Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9607329","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634063678,"identity":"a4bf713a-d1e1-4f61-b323-f782e123c520","order_by":0,"name":"Syed Nayeem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYJACCSidYPChwgZIMzYeIFbLg8IZZ9JAWhqI1cL44DNny2EwE68W+RnJB28X1ByW4xc7nLiZseG83dr2w0BbamyicWkxuJGWbD3j2GFjydlpycaFO24nbzuTCNRyLC23AZcWiRwzaR62w4kbbuekGc88czvZ7ABQC2PDYZxa5Gfkf5Pm+Xe4fv/t/O+/edvOJZudf4hfC8ONHDZp3rbDCQbSCQnGvG0H7MxuELDF4MwzY2vevnTDGbcTEgxnnElOMLsBtCUBj1/k25Mf3ub5Zi3PPzsBFJV29mbn0x8++FBjg9thAgkgshnOTwSrTMClHAT4D4DIOjjfHp/iUTAKRsEoGJkAAFsBa3qGkIRJAAAAAElFTkSuQmCC","orcid":"","institution":"Sathyabama Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Syed","middleName":"","lastName":"Nayeem","suffix":""},{"id":634063679,"identity":"502862a7-5d05-4c93-8fcd-96bc71b0f59e","order_by":1,"name":"Daniel Alex Anand","email":"","orcid":"","institution":"Sathyabama Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Alex","lastName":"Anand","suffix":""}],"badges":[],"createdAt":"2026-05-04 10:39:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9607329/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9607329/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108471369,"identity":"91e5b90e-be50-414e-a9c4-aa687a1a0ac4","added_by":"auto","created_at":"2026-05-05 05:40:16","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":318582,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRA molecular interaction map (adapted from Wu et al., 2010). CRKL (highlighted) is positioned as a central hub in Module 4, connected to proteins involved in immune signaling, cytoskeletal regulation, and inflammation.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9607329/v1/bce0200ae3f03003c43d8222.jpeg"},{"id":108471362,"identity":"47101a6e-63dc-4913-83f7-a9ddebcb16d8","added_by":"auto","created_at":"2026-05-05 05:40:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":806557,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRKL PPI network from STRING (confidence ≥0.7). CRKL (central node) interacts with 14 high-confidence partners involved in cell adhesion, migration, and tyrosine kinase signaling. Edge thickness represents interaction confidence score\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9607329/v1/4cfcc8e03ab2b920d3ef1496.png"},{"id":108471292,"identity":"b81d95d8-84a2-4e64-b0b3-d45579bed782","added_by":"auto","created_at":"2026-05-05 05:40:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":193872,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCytoscape sub-network of CRKL direct interactors. The cluster formed by RAPGEF1/C3G, PXN, and ABL1 (highlighted) represents the core adhesion-migration module relevant to T-cell trafficking in RA.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9607329/v1/51ecaee82ad713a0bfb22465.png"},{"id":108471297,"identity":"6af3ad6e-59e4-427f-8c43-6f04b236c838","added_by":"auto","created_at":"2026-05-05 05:40:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":892533,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClueGO pathway enrichment map of CRKL-associated proteins. Significantly enriched terms (FDR \u0026lt; 0.05) are shown, including ErbB signaling, MAPK cascade, Rap1 signaling, and chemokine receptor binding. Node size reflects gene count; colour denotes functional group.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9607329/v1/68affbdf72beff560248d9d4.png"},{"id":108471300,"identity":"942e6d11-a549-4f20-a384-fce6ab2ecd0b","added_by":"auto","created_at":"2026-05-05 05:40:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":268194,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReactome enrichment results for the CRKL interaction network. Top enriched pathways (FDR \u0026lt; 0.05) are shown, including receptor tyrosine kinase signaling (FDR = 1.2×10⁻⁶), IL-3/IL-4/GM-CSF signaling (FDR = 0.004), and RHO GTPase/integrin adhesion (FDR = 0.011).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9607329/v1/8230eaa5d7ea6627a8d1584c.png"},{"id":108471368,"identity":"67dff050-1e05-4bac-8e6b-c07c03be8d7b","added_by":"auto","created_at":"2026-05-05 05:40:16","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":118396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimulated effect of CRKL down-regulation on network molecule expression. Bars show mean Δexpression for significantly changed molecules (P \u0026lt; 0.05). Key decreases: FN1 (−0.38), ABL1 (−0.34), PXN (−0.31), IRAK3 (−0.29), and RHOQ (−0.26), indicating reduced cytoskeletal and adhesion activity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9607329/v1/1dc8dbb8c56414f69b710155.jpeg"},{"id":108471365,"identity":"ddbd7267-655c-4749-b67b-c87c3c49fdaf","added_by":"auto","created_at":"2026-05-05 05:40:16","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":112414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimulated effect of CRKL up-regulation on network molecule expression. Bars show mean Δexpression for significantly changed molecules (P \u0026lt; 0.05). Key increases: VEGFC (+0.44), TLR4 (+0.37), and SH2D1A (+0.28), indicating enhanced pro-inflammatory signaling and angiogenesis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9607329/v1/18c3822d9a24661a8547e164.jpeg"},{"id":109296381,"identity":"7cd63f3d-4cae-4967-9918-f02519d1ca7b","added_by":"auto","created_at":"2026-05-15 08:46:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2507218,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9607329/v1/907dde17-d782-49a4-b7c5-7e654e7c62b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CRKL-Mediated Regulation of Immune Cell Recruitment Pathways in Rheumatoid Arthritis: An Integrative Network-Centric Bioinformatics Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a systemic autoimmune disease affecting\u0026thinsp;~\u0026thinsp;1% of the global population and is marked by persistent synovial inflammation, pannus formation, and progressive cartilage and bone damage (Smolen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Central to RA pathogenesis is the aberrant recruitment and retention of immune cells\u0026mdash;including T cells, B cells, macrophages, and neutrophils\u0026mdash;within synovial tissue. These cells orchestrate a sustained inflammatory milieu through cytokine production, antigen presentation, and matrix-degrading enzyme release.\u003c/p\u003e \u003cp\u003eMolecularly, RA reflects dysregulation across several signaling axes: the JAK/STAT pathway governs cytokine-driven transcription; the MAPK cascade integrates stress and mitogenic cues; PI3K/AKT signaling modulates survival and metabolism; and NF-κB controls inflammatory gene expression. Complementing these are adhesion and migration pathways\u0026mdash;particularly integrin-mediated signaling and RHO GTPase-regulated cytoskeletal remodeling\u0026mdash;which determine how immune cells traverse endothelium and navigate inflamed tissue.\u003c/p\u003e \u003cp\u003eDespite the success of TNF inhibitors, IL-6 blockade, and JAK inhibitors, a substantial subset of patients fails to achieve durable remission, indicating that upstream coordination of immune trafficking remains insufficiently targeted. Adaptor proteins\u0026mdash;non-enzymatic scaffolds that assemble signaling complexes\u0026mdash;offer a mechanistic layer capable of integrating multiple pathways without requiring transcriptional changes. Among these, CRKL (Crk-like) is a 39-kDa adaptor with SH2 and SH3 domains that bridges phosphorylated receptors and downstream effectors.\u003c/p\u003e \u003cp\u003eCRKL is implicated in integrin activation, cytoskeletal organization, and cell motility. The CRKL\u0026ndash;C3G (RAPGEF1)\u0026ndash;Rap1\u0026ndash;LFA-1 axis is particularly important for \u0026ldquo;inside-out\u0026rdquo; integrin activation, enhancing leukocyte adhesion to endothelial cells and facilitating transmigration. Beyond adhesion, CRKL interfaces with receptor tyrosine kinases (e.g., EGFR), non-receptor kinases (ABL1), and adaptors (GRB2, SHC1), thereby influencing MAPK and JAK/STAT pathways. Network analyses have previously identified CRKL as a high-degree node in RA-associated interactomes (Wu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), yet its role as a coordinator of immune recruitment at a systems level remains underexplored.\u003c/p\u003e \u003cp\u003eA key conceptual gap is that many studies prioritize differential gene expression, whereas adaptor proteins like CRKL may exert substantial functional impact without large transcriptional shifts. This necessitates network-centric approaches that integrate topology, pathway enrichment, and perturbation modeling.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eObjectives\u003c/strong\u003e \u003cp\u003e(i) construct and analyze a CRKL-centered PPI network; (ii) quantify CRKL\u0026rsquo;s topological importance; (iii) identify enriched pathways relevant to immune recruitment; (iv) assess CRKL expression across diverse RA cohorts; and (v) evaluate the functional consequences of CRKL perturbation. We hypothesize that CRKL serves as a central, non-transcriptional regulator of leukocyte trafficking in RA.\u003c/p\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data sources and preprocessing\u003c/h2\u003e \u003cp\u003eProtein interaction data for CRKL were obtained from STRING (v11.5) with a minimum combined score of 0.7 (high confidence), including evidence channels for experiments, curated databases, and co-expression. Complementary interactions were retrieved from BioGRID (January 2024 release). Redundant edges were merged, and identifiers were standardized to official gene symbols (HGNC). Self-loops and duplicate edges were removed.\u003c/p\u003e \u003cp\u003eFor expression analysis, ten GEO datasets were selected to capture heterogeneity across tissue types and disease stages: GSE44446, GSE14248, GSE19188, GSE26252, GSE33432, GSE42458, GSE43402, GSE44234, GSE51304, and GSE20441. These include synovial tissue, PBMCs, and whole blood from RA patients and matched controls. Where necessary, probe IDs were mapped to gene symbols using platform annotation files; when multiple probes mapped to the same gene, the probe with the highest average expression was retained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Network construction in Cytoscape\u003c/h2\u003e \u003cp\u003eIntegrated PPI data were imported into Cytoscape (v3.9.1). A CRKL-centered network was constructed by extracting first-order neighbors (\u0026ldquo;Select \u0026rarr; First neighbors of selected nodes\u0026rdquo;). The resulting sub-network comprised CRKL and its high-confidence interactors. Edge attributes included interaction confidence and evidence types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Topological analysis\u003c/h2\u003e \u003cp\u003eNetworkAnalyzer was used to compute:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNode degree (k)\u003c/b\u003e: number of direct interactions\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBetweenness centrality (BC)\u003c/b\u003e: fraction of shortest paths passing through a node\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eClustering coefficient (C)\u003c/b\u003e: density of local neighborhoods\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCRKL\u0026rsquo;s metrics were compared to network distributions to determine hub status. Nodes within the top 5% of BC were considered critical for information flow.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eGene sets from the CRKL network were analyzed using KEGG and Reactome pathway databases and Gene Ontology (Biological Process). Over-representation analysis employed a hypergeometric test with Benjamini\u0026ndash;Hochberg correction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A minimum gene count of 3 per term was required. Terms with high overlap were grouped using functional fusion (\u0026ge;\u0026thinsp;50%) to reduce redundancy. Enrichment outputs were visualized as network maps and ranked lists.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Differential expression analysis (GEO2R/limma)\u003c/h2\u003e \u003cp\u003eEach GEO dataset was analyzed using GEO2R with the limma package. Data were log2-transformed where required and quantile-normalized. Linear models with empirical Bayes moderation were fitted to compare RA vs. control groups. Significance thresholds were set at adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Benjamini\u0026ndash;Hochberg) and |log2FC| \u0026gt; 1. For CRKL, summary statistics (mean log2FC, adjusted P) were aggregated across datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 In silico perturbation modeling\u003c/h2\u003e \u003cp\u003eTo estimate CRKL\u0026rsquo;s regulatory influence independent of expression, we implemented a network-based perturbation:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAssign baseline weights to edges proportional to STRING confidence.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSimulate CRKL \u003cb\u003edownregulation\u003c/b\u003e and \u003cb\u003eupregulation\u003c/b\u003e by scaling its outgoing influence (e.g., \u0026minus;\u0026thinsp;1 and +\u0026thinsp;1 perturbation states).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePropagate effects to first- and second-order neighbors using a diffusion-like update rule (iterative until convergence or fixed steps).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCompute Δexpression proxies for each node (relative change from baseline).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAssess significance via paired comparisons across nodes (two-tailed t-test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eNodes with \u0026gt;\u0026thinsp;10% change and significant P-values were considered meaningfully perturbed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Reproducibility and software\u003c/h2\u003e \u003cp\u003eAnalyses were conducted using Cytoscape (v3.9.1), R (limma), and standard enrichment tools. Parameter settings (confidence thresholds, FDR cutoffs) are reported above to enable reproducibility. All gene lists and parameters can be provided as supplementary material.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 CRKL occupies a high-centrality position in RA networks\u003c/h2\u003e \u003cp\u003eThe CRKL-centered network comprised CRKL and 23 direct interactors with high-confidence edges. CRKL exhibited \u003cb\u003edegree\u0026thinsp;=\u0026thinsp;23\u003c/b\u003e, placing it among the highest-degree nodes. \u003cb\u003eBetweenness centrality\u003c/b\u003e ranked within the \u003cb\u003etop 5%\u003c/b\u003e, indicating that CRKL lies on a large fraction of shortest paths and likely governs information flow between modules. The \u003cb\u003eclustering coefficient (C\u0026thinsp;=\u0026thinsp;0.31)\u003c/b\u003e suggests moderate local connectivity, consistent with a node bridging multiple functional clusters rather than residing within a single dense module.\u003c/p\u003e \u003cp\u003eFunctionally, CRKL connected three major modules:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAdhesion/cytoskeleton\u003c/b\u003e: PXN (paxillin), BCAR1, DOCK2, RAC1\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTyrosine kinase signaling\u003c/b\u003e: ABL1, EGFR\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAdaptor/transcriptional signaling\u003c/b\u003e: GRB2, SHC1, STAT4\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis architecture positions CRKL as an integrator of migration, adhesion, and inflammatory signaling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Protein\u0026ndash;protein interaction landscape highlights migration and signaling crosstalk\u003c/h2\u003e \u003cp\u003eFourteen high-confidence first-order interactors were consistently observed, including ABL1, RAPGEF1 (C3G), PXN, STAT4, GRB2, SHC1, BCAR1, ELMO1/2, and BCR. Edge strengths (STRING scores) were highest for GRB2 and ABL1, reflecting robust experimental support.\u003c/p\u003e \u003cp\u003eA distinct \u003cb\u003eadhesion\u0026ndash;migration subcluster\u003c/b\u003e centered on RAPGEF1\u0026ndash;PXN\u0026ndash;ABL1 was evident, aligning with the CRKL\u0026ndash;C3G\u0026ndash;Rap1 axis that regulates integrin activation (LFA-1) and leukocyte adhesion. Parallel connections to GRB2/SHC1 link CRKL to receptor tyrosine kinase signaling, enabling crosstalk with MAPK pathways. STAT4 connectivity indicates integration with cytokine-driven transcription (JAK/STAT).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Enrichment analysis converges on immune recruitment pathways\u003c/h2\u003e \u003cp\u003eKEGG enrichment identified:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMAPK signaling (hsa04010)\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eErbB signaling (hsa04012)\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRap1 signaling (hsa04014)\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eChemokine signaling (hsa04062)\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFocal adhesion (hsa04510)\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eReactome enrichment highlighted:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSignaling by receptor tyrosine kinases\u003c/b\u003e (highly significant)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDownstream signal transduction\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIL-3/IL-4/GM-CSF signaling\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRHO GTPase cycle / integrin-mediated adhesion\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCollectively, these pathways map onto three functional axes central to RA:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntegrin activation and firm adhesion\u003c/b\u003e (Rap1\u0026ndash;LFA-1; focal adhesion)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDirectional migration\u003c/b\u003e (chemokine signaling; RHO GTPases)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInflammatory amplification\u003c/b\u003e (MAPK; JAK/STAT; cytokine signaling)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 CRKL is not differentially expressed across RA datasets\u003c/h2\u003e \u003cp\u003eAcross ten GEO datasets spanning synovial tissue, PBMCs, and whole blood, CRKL did not meet differential expression criteria (adjusted P\u0026thinsp;\u0026gt;\u0026thinsp;0.05; mean |log2FC| \u0026lt; 0.3). This consistency across heterogeneous cohorts supports the interpretation of CRKL as a \u003cb\u003esignal-propagating adaptor\u003c/b\u003e whose functional impact is not contingent on transcriptional upregulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Perturbation analysis reveals bidirectional control of adhesion and inflammation\u003c/h2\u003e \u003cp\u003eSimulated \u003cb\u003eCRKL downregulation\u003c/b\u003e led to significant decreases in:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFN1\u003c/b\u003e (Δ \u0026asymp; \u0026minus;0.38),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eABL1\u003c/b\u003e (Δ \u0026asymp; \u0026minus;0.34),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePXN\u003c/b\u003e (Δ \u0026asymp; \u0026minus;0.31),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIRAK3\u003c/b\u003e (Δ \u0026asymp; \u0026minus;0.29),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRHOQ\u003c/b\u003e (Δ \u0026asymp; \u0026minus;0.26),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eindicating diminished cytoskeletal remodeling and integrin-mediated adhesion.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eConversely, \u003cb\u003eCRKL upregulation\u003c/b\u003e increased:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eVEGFC\u003c/b\u003e (Δ \u0026asymp; +0.44),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTLR4\u003c/b\u003e (Δ \u0026asymp; +0.37),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSH2D1A\u003c/b\u003e (Δ \u0026asymp; +0.28),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003esuggesting enhanced pro-inflammatory signaling and angiogenesis.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese results demonstrate that CRKL exerts \u003cb\u003edirectional, network-level control\u003c/b\u003e over key processes driving leukocyte recruitment and synovial pathology.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study positions CRKL as a \u003cb\u003ecentral, non-transcriptional regulator\u003c/b\u003e of immune cell recruitment in RA. By integrating network topology, pathway enrichment, and perturbation modeling, we show that CRKL coordinates adhesion, migration, and inflammatory signaling despite lacking differential expression.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 CRKL as an integrator of adhesion and signaling\u003c/h2\u003e \u003cp\u003eThe CRKL\u0026ndash;C3G\u0026ndash;Rap1\u0026ndash;LFA-1 axis provides a mechanistic basis for our findings. Rap1 activation increases integrin affinity (\u0026ldquo;inside-out\u0026rdquo; signaling), enabling T cells to adhere firmly to endothelial ICAMs and undergo transendothelial migration. The observed enrichment of focal adhesion and RHO GTPase pathways aligns with cytoskeletal rearrangements required for cell motility. Concurrently, CRKL\u0026rsquo;s links to GRB2/SHC1 and EGFR/ABL1 connect adhesion events to MAPK signaling, allowing environmental cues to modulate migration dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Network control without differential expression\u003c/h2\u003e \u003cp\u003eA key insight is that \u003cb\u003efunctional centrality can supersede transcriptional change\u003c/b\u003e. Adaptor proteins like CRKL act as scaffolds; modest or stable expression can still produce large phenotypic effects by re-wiring interaction patterns or phosphorylation states. This explains why CRKL is not differentially expressed across GEO datasets yet exerts strong downstream influence in perturbation analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Therapeutic implications\u003c/h2\u003e \u003cp\u003eCurrent RA therapies predominantly target cytokines or their receptors. While effective, they may not directly address \u003cb\u003eleukocyte trafficking\u003c/b\u003e. CRKL represents a complementary target class: modulating its SH2/SH3 domain interactions could selectively disrupt assembly of pro-migratory complexes without globally suppressing cytokine signaling. This approach could reduce synovial infiltration while potentially limiting systemic immunosuppression.\u003c/p\u003e \u003cp\u003eHowever, CRKL also participates in normal hematopoiesis and immune responses, raising safety considerations. \u003cb\u003eSelective inhibition\u003c/b\u003e\u0026mdash;for example, targeting interaction interfaces specific to inflamed tissue contexts\u0026mdash;may mitigate off-target effects. Structure-guided drug design and peptide inhibitors of SH2/SH3 interactions are plausible avenues.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.4 Limitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIn silico design\u003c/b\u003e: While integrative, the study lacks experimental validation (e.g., CRKL knockdown in RA-derived cells).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDataset heterogeneity\u003c/b\u003e: GEO cohorts vary in tissue type, disease stage, and platform, introducing potential batch effects.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eModel assumptions\u003c/b\u003e: Perturbation relies on network propagation approximations rather than kinetic parameters.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e4.5 Future directions\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExperimental validation\u003c/b\u003e: CRISPR/Cas9-mediated CRKL knockdown/overexpression in synovial fibroblasts and T cells; adhesion and migration assays.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSingle-cell omics\u003c/b\u003e: Define cell-type-specific roles of CRKL across synovial microenvironments.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDrug discovery\u003c/b\u003e: Molecular docking and dynamics of SH2/SH3 inhibitors; assessment of selectivity and toxicity.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMulti-omics integration\u003c/b\u003e: Combine transcriptomics, phosphoproteomics, and interactomics to refine causal pathways.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eCRKL functions as a \u003cb\u003ehub adaptor\u003c/b\u003e orchestrating immune cell adhesion, migration, and inflammatory signaling in RA. Despite stable expression, its high centrality enables substantial control over downstream pathways, particularly via the Rap1\u0026ndash;integrin axis and MAPK/JAK\u0026ndash;STAT crosstalk. Targeting CRKL-mediated scaffolding interactions offers a \u003cb\u003enovel therapeutic angle\u003c/b\u003e to complement existing cytokine-directed treatments by directly modulating leukocyte trafficking.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRheumatoid arthritis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein\u0026ndash;protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBMCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeripheral blood mononuclear cells.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable (secondary analysis of public datasets).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e GEO datasets listed (GSE44446, GSE14248, GSE19188, GSE26252, GSE33432, GSE42458, GSE43402, GSE44234, GSE51304, GSE20441).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e Conceptualization, analysis, and writing performed by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We thank mentors and institutional support for guidance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBirge, R. B., Kalodimos, C., Inagaki, F., \u0026amp; Tanaka, S. (2009). Crk and CrkL adaptor proteins: Networks for physiological and pathological signaling. \u003cem\u003eCell Communication and Signaling\u003c/em\u003e, 7, 13. https://doi.org/10.1186/1478-811X-7-13\u003c/li\u003e\n\u003cli\u003eBraiman, A., \u0026amp; Isakov, N. (2015). The role of Crk adaptor proteins in T-cell adhesion and migration. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e, 6, 509. https://doi.org/10.3389/fimmu.2015.00509\u003c/li\u003e\n\u003cli\u003eCiobanu, D. A., Poenariu, I. S., Cr\u0026icirc;nguș, L. I., et al. (2020). JAK/STAT pathway in pathology of rheumatoid arthritis. \u003cem\u003eExperimental and Therapeutic Medicine\u003c/em\u003e, 20(4), 3498\u0026ndash;3503. https://doi.org/10.3892/etm.2020.8982\u003c/li\u003e\n\u003cli\u003eDent, J. E., \u0026amp; Nardini, C. (2013). From desk to bed: Computational simulations provide indication for rheumatoid arthritis clinical trials. \u003cem\u003eBMC Systems Biology\u003c/em\u003e, 7, 10. https://doi.org/10.1186/1752-0509-7-10\u003c/li\u003e\n\u003cli\u003eDing, Q., Hu, W., Wang, R., et al. (2023). Signaling pathways in rheumatoid arthritis: Implications for targeted therapy. \u003cem\u003eSignal Transduction and Targeted Therapy\u003c/em\u003e, 8, 68. https://doi.org/10.1038/s41392-023-01331-9\u003c/li\u003e\n\u003cli\u003eHuang, J., Fu, X., Chen, X., et al. (2021). Promising therapeutic targets for treatment of rheumatoid arthritis. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e, 12, 686155. https://doi.org/10.3389/fimmu.2021.686155\u003c/li\u003e\n\u003cli\u003eHuang, Y., Clarke, F., Karimi, M., et al. (2015). CRK proteins selectively regulate T cell migration into inflamed tissues. \u003cem\u003eJournal of Clinical Investigation\u003c/em\u003e, 125(3), 1019\u0026ndash;1032. https://doi.org/10.1172/JCI77278\u003c/li\u003e\n\u003cli\u003eRoy, N. H., MacKay, J. L., Robertson, T. F., et al. (2018). Crk adaptor proteins mediate actin-dependent T cell migration and mechanosensing induced by integrin LFA-1. \u003cem\u003eScience Signaling\u003c/em\u003e, 11(560), eaat3178. https://doi.org/10.1126/scisignal.aat3178\u003c/li\u003e\n\u003cli\u003eSmolen, J. S., Aletaha, D., Barton, A., et al. (2018). Rheumatoid arthritis. \u003cem\u003eNature Reviews Disease Primers\u003c/em\u003e, 4, 18001. https://doi.org/10.1038/nrdp.2018.1\u003c/li\u003e\n\u003cli\u003eWu, G., Zhu, L., Dent, J. E., \u0026amp; Nardini, C. (2010). A comprehensive molecular interaction map for rheumatoid arthritis. \u003cem\u003ePLoS ONE\u003c/em\u003e, 5(4), e10137. https://doi.org/10.1371/journal.pone.0010137\u003c/li\u003e\n\u003cli\u003eXie, J., Sun, S., Li, Q., et al. (2025). MAPK/ERK signaling pathway in rheumatoid arthritis: Mechanisms and therapeutic potential. \u003cem\u003ePeerJ\u003c/em\u003e, 13, e19708. https://doi.org/10.7717/peerj.19708\u003c/li\u003e\n\u003cli\u003eBa, X., Huang, Y., Shen, P., et al. (2021). WTD attenuates rheumatoid arthritis via suppressing angiogenesis and modulating the PI3K/AKT pathway. \u003cem\u003eFrontiers in Pharmacology\u003c/em\u003e, 12, 696802. https://doi.org/10.3389/fphar.2021.696802\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eTopological properties of CRKL in the RA interaction network\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eParameter\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eValue\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eInterpretation\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNode Degree\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e23\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eIndicates high connectivity with multiple proteins\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBetweenness Centrality\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTop 5%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eSuggests CRKL acts as a key regulatory hub\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eClustering Coefficient\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.31\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eReflects moderate interaction clustering\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNetwork Role\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHub Protein\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eCentral in signaling and immune pathways\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eKey CRKL-interacting proteins and their functional roles\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eProtein\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eFunction\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eBiological Significance\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eABL1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTyrosine kinase\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eRegulates cytoskeletal remodeling and signaling\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePXN (Paxillin)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eScaffold protein\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eControls focal adhesion and cell migration\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eRAPGEF1 (C3G)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eExchange factor\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eActivates Rap1 signaling pathway\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSTAT4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTranscription factor\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eInvolved in immune response regulation\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eGRB2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdaptor protein\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eLinks receptor signaling to downstream pathways\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSHC1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSignaling adaptor\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eActivates MAPK signaling\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBCAR1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eScaffold protein\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eIntegrin-mediated signaling\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eELMO1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMotility regulator\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eControls cell migration\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBCR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eGTPase regulator\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eInvolved in immune signaling\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eEnriched pathways associated with CRKL network\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePathway\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDatabase\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eFDR Value\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eFunctional Role\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMAPK signaling pathway\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eKEGG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.002\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eControls inflammation and cell proliferation\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eRap1 signaling pathway\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eKEGG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.014\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eRegulates cell adhesion\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eChemokine signaling pathway\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eKEGG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.018\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eDirects immune cell migration\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFocal adhesion\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eKEGG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.021\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eMaintains cell structure and movement\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eReceptor tyrosine kinase signaling\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eReactome\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.2 \u0026times; 10⁻⁶\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eMajor signaling hub\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIL-3/IL-4/GM-CSF signaling\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eReactome\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.004\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eImmune activation\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eRHO GTPase cycle\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eReactome\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.011\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eCytoskeletal dynamics\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rheumatoid arthritis, CRKL, protein–protein interaction, network biology, immune cell migration, pathway enrichment, GEO","lastPublishedDoi":"10.21203/rs.3.rs-9607329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9607329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by synovial inflammation, leukocyte infiltration, and progressive joint destruction. While cytokine-targeted therapies improve outcomes, incomplete remission rates suggest additional regulatory layers in immune cell trafficking remain unaddressed. CRKL (Crk-like) is an adaptor protein that coordinates signaling complexes governing adhesion, migration, and cytoskeletal dynamics, but its network-level role in RA is not fully defined.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe constructed CRKL-centered protein\u0026ndash;protein interaction (PPI) networks by integrating STRING (v11.5; confidence\u0026thinsp;\u0026ge;\u0026thinsp;0.7) and BioGRID interactions and analyzed them in Cytoscape (v3.9.1). Network topology (degree, betweenness, clustering) was quantified using NetworkAnalyzer. Functional enrichment was performed using KEGG, Reactome, and GO-BP with Benjamini\u0026ndash;Hochberg correction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Differential expression of CRKL was evaluated across ten GEO datasets (synovium, PBMCs, whole blood) using GEO2R/limma. We further performed in silico perturbation by constraining CRKL activity and quantifying propagated changes in first- and second-order neighbors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCRKL emerged as a high-centrality hub (degree\u0026thinsp;=\u0026thinsp;23; top 5% betweenness) connecting adhesion/migration modules (PXN, BCAR1), tyrosine kinase signaling (ABL1, EGFR), and immune transcriptional programs (STAT4, GRB2, SHC1). Enrichment analyses converged on MAPK, JAK/STAT, Rap1 signaling, chemokine signaling, and integrin-mediated adhesion, alongside Reactome pathways for receptor tyrosine kinase signaling and RHO GTPase cycles. CRKL was not differentially expressed across datasets (adjusted P\u0026thinsp;\u0026gt;\u0026thinsp;0.05; mean |log2FC|\u0026lt;0.3), consistent with adaptor function. However, perturbation revealed directional control of downstream biology: simulated downregulation decreased FN1, ABL1, PXN, IRAK3, and RHOQ (reduced cytoskeletal remodeling and adhesion), whereas upregulation increased VEGFC, TLR4, and SH2D1A (enhanced inflammation and angiogenesis).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCRKL acts as a non-transcriptional, high-impact regulator of immune cell recruitment in RA, coordinating integrin activation and cytoskeletal dynamics via the CRKL\u0026ndash;C3G\u0026ndash;Rap1\u0026ndash;LFA-1 axis while interfacing with MAPK and JAK/STAT signaling. Targeting CRKL-mediated scaffolding interactions (e.g., SH2/SH3 domains) may complement cytokine-directed therapies by selectively modulating leukocyte trafficking.\u003c/p\u003e","manuscriptTitle":"CRKL-Mediated Regulation of Immune Cell Recruitment Pathways in Rheumatoid Arthritis: An Integrative Network-Centric Bioinformatics Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 05:39:00","doi":"10.21203/rs.3.rs-9607329/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23659792-9cd4-4ccf-9d0c-2c381abefda4","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-15T07:17:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Bioinformatics","date":"2026-05-04T10:29:03+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T07:25:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 05:39:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9607329","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9607329","identity":"rs-9607329","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}