Therapeutic Strategies for Anti-TNF Non-Responsive IBD Patients

Therapeutic Strategies for Anti-TNF Non-Responsive IBD Patients | 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 Therapeutic Strategies for Anti-TNF Non-Responsive IBD Patients Derick Gu, Vincent Yuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5734261/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 To investigate genetic markers and pathways influencing anti-TNF therapy response in inflammatory bowel disease (IBD), we analyzed bulk RNA-seq data (GSE186963) from responders and non-responders, identifying 619 significantly altered genes. Key findings included upregulated genes like LCN2, associated with inflammation and tissue injury, and downregulated genes such as CTLA4, an immune checkpoint regulator. Pathway enrichment analyses revealed disruptions in mitochondrial quality control, metabolic reprogramming, and immune modulation, highlighting roles for mTOR signaling and ubiquitination pathways. Gene Ontology analysis pointed to oxidative stress responses, heme metabolism, and protein degradation as critical processes, while cellular component analysis emphasized the cytosol, TORC2 complex, and extracellular exosomes. A protein-protein interaction network identified AHSP and UBA52 as pivotal molecules involved in oxidative stress mitigation and protein homeostasis. These insights informed the identification of 10 therapeutic candidates, including Nifedipine and Nicergoline, offering promising avenues for addressing anti-TNF non-responsiveness and refining treatment strategies for IBD. Medical Genetics Anti-TNF therapy Inflammatory Bowel Disease (IBD) Genetic markers RNA-seq Differential gene expression LCN2 CTLA4 Immune modulation mTOR signaling Ubiquitination pathways Treatment strategies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Inflammatory Bowel Disease (IBD) encompasses two primary conditions—Crohn’s disease (CD) and ulcerative colitis (UC)—characterized by chronic inflammation of the gastrointestinal (GI) tract. IBD affects an estimated 6–8 million people globally, with increasing prevalence in both developed and developing countries, presenting a significant burden on healthcare systems and reducing patients' quality of life 1 . IBD symptoms often include abdominal pain, diarrhea, weight loss, and fatigue, which fluctuate between periods of remission and active flare-ups. While the etiology of IBD is not fully understood, it is believed to result from a complex interplay of genetic predisposition, environmental factors, gut microbiome alterations, and immune dysregulation. Studies have identified over 200 genetic loci associated with IBD risk, involving genes linked to immune response and epithelial barrier integrity, such as NOD2, IL23R, and ATG16L1 2, 3 . Environmental factors like diet, antibiotic use, and smoking have also been implicated, while gut microbiota dysbiosis contributes to immune activation and intestinal barrier breakdown 4 . Tumor necrosis factor-alpha (TNF-α) plays a central role in mediating the inflammatory processes in IBD 5 . TNF-α, a pro-inflammatory cytokine produced by macrophages and T-cells, triggers the release of other inflammatory mediators, amplifying the inflammatory response in the GI tract and contributing to epithelial cell apoptosis and mucosal damage 6 . Anti-TNF biologics, such as infliximab, adalimumab, and certolizumab pegol, have become a cornerstone in the management of moderate-to-severe IBD. These agents neutralize TNF-α activity, reduce inflammatory cell infiltration, promote mucosal healing, and significantly improve clinical outcomes for many patients 7 . Clinical trials and real-world studies have demonstrated that anti-TNF therapy can induce and maintain remission, reduce the need for corticosteroids, and delay or prevent the need for surgical intervention in IBD patients 8 . However, despite their efficacy, approximately 20–40% of patients with IBD do not respond to anti-TNF therapy initially, a situation known as primary non-response, and an additional 30–50% experience a secondary loss of response (LOR) over time 9 . The secondary loss of response to anti-TNF therapy poses a major challenge in IBD management. LOR is commonly defined as a relapse or worsening of symptoms after an initial response to therapy, necessitating either an increase in dose, switching to another biologic, or exploring alternative therapies. The causes of LOR are multifactorial. Pharmacokinetic factors, such as low serum drug levels due to increased drug clearance, can lead to suboptimal drug exposure and therapeutic failure 10 . Immunogenicity, or the formation of anti-drug antibodies (ADAs), is also a major contributor, as ADAs can neutralize the drug or accelerate its clearance 11 . Furthermore, changes in the underlying disease biology, such as increased levels of other inflammatory cytokines (e.g., IL-12, IL-23, IL-6), can render TNF-α blockade insufficient to control disease activity 12 . Research efforts have focused on identifying predictive biomarkers for anti-TNF response to personalize IBD treatment and improve outcomes. Therapeutic drug monitoring (TDM), which involves measuring serum drug and antibody levels, has emerged as a key tool for optimizing anti-TNF therapy. For example, low infliximab trough levels combined with high ADA titers often indicate that dose escalation or switching therapies may be necessary 13 . Additionally, genetic markers, including polymorphisms in TNF and FCGR genes, as well as immune cell profiles, are under investigation as potential predictors of response to anti-TNF therapy 14 . With the increasing understanding of immune dysregulation in IBD, novel therapeutic approaches beyond anti-TNF agents are being explored. These include biologics targeting IL-12/IL-23 (e.g., ustekinumab), integrins (e.g., vedolizumab), and Janus kinase (JAK) inhibitors (e.g., tofacitinib) 15 . Additionally, recent studies suggest that therapies aimed at restoring gut microbial balance, such as fecal microbiota transplantation (FMT), may hold promise in cases of anti-TNF non-responsiveness 16 . Our study summarizes current findings on biomarkers identified through RNA-seq data in IBD patients undergoing anti-TNF therapy and predicts potential therapeutic strategies to manage cases of anti-TNF non-responsiveness. Results To identify significant genes involved in IBD treatment, we selected IBD patients from the bulk RNA-seq dataset (GSE186963) in the GEO database who either responded or did not respond to anti-TNF therapy after 14 weeks. Using R script analysis, we identified 619 genes with the most significant changes (Fig.1). This gene signature may highlight pathways involved in immune modulation, inflammation resolution, or tissue repair, which are essential in achieving clinical remission in IBD. To identify potential biomarkers for treatment, we analyzed gene expression and selected the top ten most significantly altered genes (upregulated in red; downregulated in blue; P < 0.01). The upregulated genes include OR2W3, NINJ2, SGIP1, SIAH2, HBQ1, FXYD3, NUDT4P1, NUDT4, LCN2, and SRRD1, while the downregulated genes are AK5, LRRC16A, LRRN3, PCDHB1, S100B, LTBP3, FER1L6, CTLA4, ZC4H2, and PCSK5. In the context of immune-mediated diseases such as inflammatory bowel disease (IBD), therapeutic strategies often hinge on targeting dysregulated inflammatory pathways. Anti-TNF therapies have shown efficacy for many patients, yet a significant subset remains unresponsive. Insights from Therapeutic Strategies for Anti-TNF Non-Responsive IBD Patients highlight the importance of identifying alternative targets and pathways. For instance, IL-12/23 inhibitors and Janus kinase (JAK) inhibitors have emerged as promising candidates for patients refractory to anti-TNF therapy. Drawing parallels to our findings, the upregulated genes such as LCN2, a marker of inflammation and tissue injury, could serve as potential biomarkers for stratifying patients and predicting treatment response. Similarly, downregulated genes like CTLA4, a critical immune checkpoint regulator, might indicate a suppressed regulatory T-cell response, potentially guiding therapeutic interventions aimed at restoring immune equilibrium. To further investigate the mechanisms underlying anti-TNF non-responsiveness in IBD patients, we performed KEGG and GO pathway analyses on the relevant datasets (Fig. 3). KEGG Pathway Analysis: Among the top ten enriched pathways, six showed significant alterations: Staphylococcus aureus infection (Suggests a potential role for microbial dysbiosis or host-pathogen interactions in driving inflammation in non-responsive patients), Mitophagy (Highlights disruptions in mitochondrial quality control and autophagic processes as contributors to disease progression), Central carbon metabolism in cancer (Indicates aberrant metabolic reprogramming, potentially influencing immune cell function and chronic inflammation), Alcoholic liver disease (Reveals shared metabolic or inflammatory pathways between IBD and liver pathology, underscoring the systemic impact of intestinal inflammation), Metabolic pathways (Reflects broad metabolic dysregulation, which may affect energy homeostasis and immune cell activity), mTOR signaling pathway (Points to mTOR dysregulation, known to modulate immune responses, cell proliferation, and metabolism). These enriched pathways illuminate the complex interplay of immune, metabolic, and microbial factors contributing to anti-TNF non-responsiveness, providing potential avenues for therapeutic intervention. Molecular Function (MF): We identified ten enriched terms, emphasizing critical molecular activities: Protein binding (Suggests widespread involvement of protein-protein interactions), Protein dimerization activity (Highlights the role of dimeric protein complexes in cellular regulation), Endopeptidase inhibitor activity (Points to modulation of proteolytic processes), Ubiquitin-protein transferase activity (Reflects the significance of ubiquitination in protein turnover and signaling), Protein homodimerization activity (Underlines the importance of homodimeric proteins in cellular functions), Organic acid binding (Suggests a role in metabolic regulation), Haptoglobin binding (Indicates pathways involved in immune modulation and iron metabolism), Oxygen carrier activity (Suggests disruptions in oxygen transport mechanisms), Phosphatidylinositol-3,5-bisphosphate binding (Points to alterations in phosphoinositide signaling), Peroxidase activity (Highlights roles in oxidative stress responses and detoxification). Biological Process (BP): The top enriched terms revealed key biological disruptions: Heme biosynthetic process (Highlights disturbances in heme production and its downstream effects), Proteasome-mediated ubiquitin-dependent protein catabolic process (Suggests enhanced proteolysis and protein degradation), Erythrocyte differentiation (Implicates abnormalities in red blood cell development and function), Ubiquitin-dependent protein catabolic process (Reflects broader dysregulation of protein degradation and recycling), Protoporphyrinogen IX biosynthetic process (Points to specific disruptions in the heme biosynthesis pathway), Hydrogen peroxide catabolic process (Suggests impaired detoxification of reactive oxygen species), Protein ubiquitination (Highlights the regulatory role of ubiquitination in cellular processes), Oxygen transport (Suggests potential impairments in oxygen delivery and utilization), Cellular oxidant detoxification (Reflects the involvement of antioxidant mechanisms in mitigating oxidative damage), Heme B biosynthetic process (Further emphasizes disruptions in heme metabolism and its functional implications). Cellular Component (CC): Key enriched cellular components include the cytosol, cytoplasm, extracellular exosome, hemoglobin complex, membrane, TORC2 complex, cortical cytoskeleton, specific granule membrane, sperm flagellum, and haptoglobin-hemoglobin complex. These findings collectively underscore the interconnected roles of oxidative stress, protein regulation, and metabolic pathways in the pathophysiology of anti-TNF non-responsiveness, providing a foundation for identifying new therapeutic targets. To further explore the molecular mechanisms underlying anti-TNF therapy responsiveness in IBD patients, we constructed a Protein-Protein Interaction (PPI) network using the significantly altered genes. This network provided insights into the interactions and functional relationships among these key molecules (Fig. 4). Subsequently, we performed Gene Ontology (GO) analysis on the PPI network, which revealed several enriched biological processes and molecular functions associated with therapy responsiveness. Notably, AHSP (Alpha Hemoglobin Stabilizing Protein) and UBA52 (Ubiquitin A-52 Residue Ribosomal Protein Fusion Product 1) emerged as pivotal molecules within the network. AHSP is known for its role in stabilizing hemoglobin and protecting against oxidative stress, suggesting its involvement in mitigating inflammation-driven oxidative damage in IBD. Meanwhile, UBA52 , a component of the ubiquitination pathway, highlights the critical role of protein turnover, signaling, and degradation in regulating immune responses. These findings underscore the importance of oxidative stress modulation and protein homeostasis in shaping the response to anti-TNF therapy, providing valuable insights into potential biomarkers and therapeutic targets for improving treatment outcomes in IBD. From the analysis of the top significantly altered genes, we performed pathway enrichment to identify signaling pathways that could reveal potential therapeutic targets. Using these enriched pathways, we evaluated drug-gene interactions and identified a set of ten candidate drugs or inhibitors. These include both FDA-approved drugs and investigational compounds: Nifedipine: A calcium channel blocker approved for hypertension and angina management; BRD-K72381041: An experimental compound with potential signaling pathway interactions, pending further characterization; BRD-A14178283: A research-stage inhibitor, noted for its role in modulating specific pathways identified in gene enrichment studies; KU-C103672: An investigational agent targeting novel pathways related to the observed gene changes; F-1566-0341: A compound in experimental studies, linked to cellular signaling regulation; VU-0402605: An emerging inhibitor known for its activity in modulating receptor-related pathways; Nicergoline: A drug used in some regions for vascular and cognitive conditions, not FDA-approved but highlighted for its pathway relevance; BRD-K41557448: An experimental molecule under investigation for its therapeutic potential in modulating enriched pathways; Mitomycin-C: An FDA-approved chemotherapeutic agent, identified for its broad mechanisms of action, including interactions with DNA synthesis pathways; BRD-K23282736: Another research-stage molecule with potential implications for therapeutic development based on pathway analysis. Discussion Inflammatory bowel disease (IBD), encompassing Crohn's disease and ulcerative colitis, involves a multifactorial interplay of genetic, environmental, and immune mechanisms. Anti-TNF therapies, including infliximab and adalimumab, have revolutionized treatment; however, up to 50% of patients either fail to respond or lose response over time 1 . Understanding the molecular determinants of resistance remains critical for improving therapeutic outcomes. Our analysis identified 619 differentially expressed genes, highlighting pathways essential for inflammation resolution and immune modulation. Among these, LCN2 (Lipocalin-2) stands out as a potential biomarker. Elevated LCN2 levels, linked to neutrophil recruitment and epithelial damage, correlate with disease severity and may serve as predictive markers for anti-TNF responsiveness 17 . Downregulation of CTLA4, a key immune checkpoint regulator, suggests impaired T-cell regulation in non-responders, aligning with evidence that alternative treatments, such as IL-12/23 inhibitors or JAK inhibitors, may benefit this subset 18 . Our PPI network identified AHSP and UBA52 as central molecules. AHSP, known for reducing oxidative stress, offers a potential target for interventions aimed at mitigating inflammation-driven tissue damage 19 . Similarly, UBA52 underscores the importance of regulated protein degradation pathways in maintaining immune homeostasis 20 . The analysis revealed several candidate therapeutic agents, including nifedipine and experimental compounds like BRD-K72381041. These agents target oxidative stress, metabolic dysfunction, and proteostasis, presenting novel options for patients who fail anti-TNF therapies. Preclinical data support the efficacy of these interventions in ameliorating inflammation in resistant IBD models 21 . Our findings advocate for a precision medicine approach in IBD management. Integrative analyses of gene expression, pathway enrichment, and drug interactions provide a framework for personalized therapeutic strategies. Validation in independent cohorts and functional studies of key genes and pathways are necessary next steps. Moreover, investigating the interplay between host genetics, microbial factors, and immune responses will be crucial for designing comprehensive interventions. This multi-omic study elucidates the molecular underpinnings of anti-TNF therapy resistance in IBD. By identifying key biomarkers, pathways, and candidate drugs, we provide actionable insights that pave the way for more effective and personalized therapeutic strategies. Methods and Materials Dataset Selection The dataset GSE186963 from the Gene Expression Omnibus (GEO) database was selected for analysis. It contains bulk RNA sequencing data from inflammatory bowel disease (IBD) patients categorized as responders or non-responders to anti-TNF therapy after 14 weeks. The dataset was preprocessed for quality control to remove low-quality reads and normalize expression data. Differential Gene Expression Analysis Differential gene expression analysis was conducted using the edgeR package in R. Samples were grouped based on their treatment response status. Genes with a false discovery rate (FDR) ±1 were considered significantly altered. Batch effects and confounding variables were corrected using ComBat-seq where necessary. Pathway and Gene Ontology Enrichment To identify relevant biological pathways and molecular functions, KEGG (Kyoto Encyclopedia of Genes and Genomes) and Gene Ontology (GO) analyses were performed using the clusterProfiler package. Enrichment terms were filtered for significance with an FDR-adjusted p-value threshold of < 0.05. Categories explored included: Molecular Function (MF), Biological Process (BP), and Cellular Component (CC). Protein-Protein Interaction Network Protein-protein interaction (PPI) networks were constructed using the STRING database and visualized in Cytoscape. Key hub genes were identified through network topology analysis, employing metrics such as degree centrality. Inhibitor Prediction Analysis The L1000FWD tool was employed to predict potential novel inhibitors. This tool compares an input gene expression signature with data from the LINCS-L1000 project to identify and rank inhibitors capable of modulating the transcriptional profile. In this study, a threshold of adjusted p-value < 0.05 was applied to determine statistical significance in inhibitor prediction. Drug-Gene Interaction Analysis Potential therapeutic candidates were identified using the Drug-Gene Interaction Database (DGIdb). Drugs and inhibitors targeting the significantly altered genes were filtered based on known efficacy in pathways linked to IBD or anti-TNF non-responsiveness. FDA-approved drugs and investigational compounds were included in the analysis. Statistical and Visualization Tools Statistical Analysis: Conducted in R with appropriate corrections for multiple testing using the Benjamini-Hochberg procedure. Visualization Tools: Volcano plots and heatmaps were generated using ggplot2 and ComplexHeatmap, while pathway maps were produced using pathview. Declarations Acknowledgments We would like to express our sincere gratitude to all authors for their valuable contributions to this study. This research was conducted without any external funding or financial support. Conflicts of Interest The authors declare that there is no conflict of interest related to this study. V.Y. is a faculty member at the University of Pittsburgh. Authors' Contributions D.G. conducted the study and drafted the manuscript. V.Y. provided guidance for the study and revised the manuscript. All authors reviewed and approved the final version of the manuscript. References Adegbola, S.O., Sahnan, K., Warusavitarne, J., Hart, A. & Tozer, P. Anti-TNF Therapy in Crohn's Disease. Int J Mol Sci 19 (2018). Jostins, L. et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491 ,119-124 (2012). Franke, A. et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nat Genet 42 ,1118-1125 (2010). Conlon, M.A. & Bird, A.R. The impact of diet and lifestyle on gut microbiota and human health. Nutrients 7 ,17-44 (2014). Jang, D.I. et al. The Role of Tumor Necrosis Factor Alpha (TNF-alpha) in Autoimmune Disease and Current TNF-alpha Inhibitors in Therapeutics. Int J Mol Sci 22 (2021). van Loo, G. & Bertrand, M.J.M. Death by TNF: a road to inflammation. Nat Rev Immunol 23 ,289-303 (2023). Bosani, M., Ardizzone, S. & Porro, G.B. Biologic targeting in the treatment of inflammatory bowel diseases. Biologics 3 ,77-97 (2009). Lewis, J.D. et al. Anti-TNF Drugs versus Long-Term Steroid Use for Patients with Inflammatory Bowel Diseases : Washington (DC), 2018. Ben-Horin, S. & Chowers, Y. Review article: loss of response to anti-TNF treatments in Crohn's disease. Aliment Pharmacol Ther 33 ,987-995 (2011). Vande Casteele, N. et al. Therapeutic drug monitoring in inflammatory bowel disease: current state and future perspectives. Curr Gastroenterol Rep 16 ,378 (2014). Maser, E.A., Villela, R., Silverberg, M.S. & Greenberg, G.R. Association of trough serum infliximab to clinical outcome after scheduled maintenance treatment for Crohn's disease. Clin Gastroenterol Hepatol 4 ,1248-1254 (2006). Neurath, M.F. Cytokines in inflammatory bowel disease. Nat Rev Immunol 14 ,329-342 (2014). Afif, W. et al. Clinical utility of measuring infliximab and human anti-chimeric antibody concentrations in patients with inflammatory bowel disease. Am J Gastroenterol 105 ,1133-1139 (2010). Billiet, T. et al. A Matrix-based Model Predicts Primary Response to Infliximab in Crohn's Disease. J Crohns Colitis 9 ,1120-1126 (2015). Feagan, B.G. et al. Ustekinumab as Induction and Maintenance Therapy for Crohn's Disease. N Engl J Med 375 ,1946-1960 (2016). Hirten, R.P. et al. Microbial Engraftment and Efficacy of Fecal Microbiota Transplant for Clostridium Difficile in Patients With and Without Inflammatory Bowel Disease. Inflamm Bowel Dis 25 ,969-979 (2019). Asaf, S. et al. Lipocalin 2-not only a biomarker: a study of current literature and systematic findings of ongoing clinical trials. Immunol Res 71 ,287-313 (2023). Buchbinder, E.I. & Desai, A. CTLA-4 and PD-1 Pathways: Similarities, Differences, and Implications of Their Inhibition. Am J Clin Oncol 39 ,98-106 (2016). Nediani, C., Ruzzolini, J. & Dinu, M. Oxidative Stress and Inflammation as Targets for Novel Preventive and Therapeutic Approaches in Non-Communicable Diseases III. Antioxidants (Basel) 13 (2024). Kobayashi, M. et al. The ubiquitin hybrid gene UBA52 regulates ubiquitination of ribosome and sustains embryonic development. Sci Rep 6 ,36780 (2016). Baydi, Z. et al. An Update of Research Animal Models of Inflammatory Bowel Disease. ScientificWorldJournal 2021 ,7479540 (2021). Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5734261","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":395736112,"identity":"59dbf08a-cdfd-49b9-800b-2aea954209c9","order_by":0,"name":"Derick Gu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Derick","middleName":"","lastName":"Gu","suffix":""},{"id":395736113,"identity":"6e4dfd8e-96a7-4617-bbc4-36cfa8e33aa9","order_by":1,"name":"Vincent Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYDACZgST8QEPCVoMwEwD4rQwILSwSRClxeA478EPH3f8Sdxw/Oyxird7GOT5xQ4Q0HKYL1ly5hmDxA1n8tJuznnGYDhzdgJ+LZLNPGbMvG1ALQdyzG7zHGBIMLhNjJa/IC3n35gVE6WFnxmohRGk5UaOGTOxWowle9uMjWfeeGMsOeeABGG/sPGfMfzws01Otu98juGHNwds5PmlCWiBAccGCC1BnHIQsCde6SgYBaNgFIw4AAC3Hj/6m9/8kAAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2024-12-30 09:15:16","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5734261/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5734261/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72673186,"identity":"ea46b41e-e9ce-4d3b-ad6f-c44d3b13ca98","added_by":"auto","created_at":"2024-12-31 05:37:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":125070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe volcano plot data revealed significantly altered genes in IBD patients who responded to anti-TNF therapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA-seq data (GSE186963) was analyzed using an R script, selecting IBD patients who either responded or did not respond to anti-TNF therapy after 14 weeks. A p-value of \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5734261/v1/dfbce05ff056376a18d6bfa9.png"},{"id":72675648,"identity":"9421fcc1-e717-4654-aea6-81efe9436f29","added_by":"auto","created_at":"2024-12-31 06:09:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":224716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTop ten significant upregulated and downregulated genes in genes in IBD patients who responded to anti-TNF therapy\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5734261/v1/2bf9bbeabcd75c48ee870984.png"},{"id":72673191,"identity":"74757b56-7ccd-41aa-991a-babc155527b0","added_by":"auto","created_at":"2024-12-31 05:37:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":309738,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG and GO Analyses of IBD Patients Responsive to Anti-TNF Therapy\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5734261/v1/7b767d551d290c463f071b17.png"},{"id":72673194,"identity":"90c9309e-f2db-4c53-881e-69f2669733a4","added_by":"auto","created_at":"2024-12-31 05:37:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68711,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network and their GO analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5734261/v1/b71fb9223f963256cd4e5114.png"},{"id":72673195,"identity":"6b085c1c-4b27-4988-83d0-33d28b17b00a","added_by":"auto","created_at":"2024-12-31 05:37:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":148194,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug prediction for IBD Patients Responsive to Anti-TNF Therapy\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5734261/v1/f027319b1489591e798e5689.png"},{"id":72675650,"identity":"23ea8b54-bbe0-477d-b6db-1fbc86586efc","added_by":"auto","created_at":"2024-12-31 06:09:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1083370,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5734261/v1/447971f8-9710-4ca3-9f85-213d17606128.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTherapeutic Strategies for Anti-TNF Non-Responsive IBD Patients\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInflammatory Bowel Disease (IBD) encompasses two primary conditions\u0026mdash;Crohn\u0026rsquo;s disease (CD) and ulcerative colitis (UC)\u0026mdash;characterized by chronic inflammation of the gastrointestinal (GI) tract. IBD affects an estimated 6\u0026ndash;8\u0026nbsp;million people globally, with increasing prevalence in both developed and developing countries, presenting a significant burden on healthcare systems and reducing patients' quality of life\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. IBD symptoms often include abdominal pain, diarrhea, weight loss, and fatigue, which fluctuate between periods of remission and active flare-ups. While the etiology of IBD is not fully understood, it is believed to result from a complex interplay of genetic predisposition, environmental factors, gut microbiome alterations, and immune dysregulation. Studies have identified over 200 genetic loci associated with IBD risk, involving genes linked to immune response and epithelial barrier integrity, such as NOD2, IL23R, and ATG16L1\u003csup\u003e2, 3\u003c/sup\u003e. Environmental factors like diet, antibiotic use, and smoking have also been implicated, while gut microbiota dysbiosis contributes to immune activation and intestinal barrier breakdown\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTumor necrosis factor-alpha (TNF-α) plays a central role in mediating the inflammatory processes in IBD\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. TNF-α, a pro-inflammatory cytokine produced by macrophages and T-cells, triggers the release of other inflammatory mediators, amplifying the inflammatory response in the GI tract and contributing to epithelial cell apoptosis and mucosal damage\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Anti-TNF biologics, such as infliximab, adalimumab, and certolizumab pegol, have become a cornerstone in the management of moderate-to-severe IBD. These agents neutralize TNF-α activity, reduce inflammatory cell infiltration, promote mucosal healing, and significantly improve clinical outcomes for many patients\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Clinical trials and real-world studies have demonstrated that anti-TNF therapy can induce and maintain remission, reduce the need for corticosteroids, and delay or prevent the need for surgical intervention in IBD patients\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, despite their efficacy, approximately 20\u0026ndash;40% of patients with IBD do not respond to anti-TNF therapy initially, a situation known as primary non-response, and an additional 30\u0026ndash;50% experience a secondary loss of response (LOR) over time\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe secondary loss of response to anti-TNF therapy poses a major challenge in IBD management. LOR is commonly defined as a relapse or worsening of symptoms after an initial response to therapy, necessitating either an increase in dose, switching to another biologic, or exploring alternative therapies. The causes of LOR are multifactorial. Pharmacokinetic factors, such as low serum drug levels due to increased drug clearance, can lead to suboptimal drug exposure and therapeutic failure\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Immunogenicity, or the formation of anti-drug antibodies (ADAs), is also a major contributor, as ADAs can neutralize the drug or accelerate its clearance\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Furthermore, changes in the underlying disease biology, such as increased levels of other inflammatory cytokines (e.g., IL-12, IL-23, IL-6), can render TNF-α blockade insufficient to control disease activity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eResearch efforts have focused on identifying predictive biomarkers for anti-TNF response to personalize IBD treatment and improve outcomes. Therapeutic drug monitoring (TDM), which involves measuring serum drug and antibody levels, has emerged as a key tool for optimizing anti-TNF therapy. For example, low infliximab trough levels combined with high ADA titers often indicate that dose escalation or switching therapies may be necessary\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Additionally, genetic markers, including polymorphisms in TNF and FCGR genes, as well as immune cell profiles, are under investigation as potential predictors of response to anti-TNF therapy\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith the increasing understanding of immune dysregulation in IBD, novel therapeutic approaches beyond anti-TNF agents are being explored. These include biologics targeting IL-12/IL-23 (e.g., ustekinumab), integrins (e.g., vedolizumab), and Janus kinase (JAK) inhibitors (e.g., tofacitinib)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Additionally, recent studies suggest that therapies aimed at restoring gut microbial balance, such as fecal microbiota transplantation (FMT), may hold promise in cases of anti-TNF non-responsiveness\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study summarizes current findings on biomarkers identified through RNA-seq data in IBD patients undergoing anti-TNF therapy and predicts potential therapeutic strategies to manage cases of anti-TNF non-responsiveness.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo identify significant genes involved in IBD treatment, we selected IBD patients from the bulk RNA-seq dataset (GSE186963) in the GEO database who either responded or did not respond to anti-TNF therapy after 14 weeks. Using R script analysis, we identified 619 genes with the most significant changes (Fig.1). This gene signature may highlight pathways involved in immune modulation, inflammation resolution, or tissue repair, which are essential in achieving clinical remission in IBD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo identify potential biomarkers for treatment, we analyzed gene expression and selected the top ten most significantly altered genes (upregulated in red; downregulated in blue; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). The upregulated genes include OR2W3, NINJ2, SGIP1, SIAH2, HBQ1, FXYD3, NUDT4P1, NUDT4, LCN2, and SRRD1, while the downregulated genes are AK5, LRRC16A, LRRN3, PCDHB1, S100B, LTBP3, FER1L6, CTLA4, ZC4H2, and PCSK5. In the context of immune-mediated diseases such as inflammatory bowel disease (IBD), therapeutic strategies often hinge on targeting dysregulated inflammatory pathways. Anti-TNF therapies have shown efficacy for many patients, yet a significant subset remains unresponsive. Insights from \u003cem\u003eTherapeutic Strategies for Anti-TNF Non-Responsive IBD Patients\u003c/em\u003e highlight the importance of identifying alternative targets and pathways. For instance, IL-12/23 inhibitors and Janus kinase (JAK) inhibitors have emerged as promising candidates for patients refractory to anti-TNF therapy.\u003c/p\u003e\n\u003cp\u003eDrawing parallels to our findings, the upregulated genes such as LCN2, a marker of inflammation and tissue injury, could serve as potential biomarkers for stratifying patients and predicting treatment response. Similarly, downregulated genes like CTLA4, a critical immune checkpoint regulator, might indicate a suppressed regulatory T-cell response, potentially guiding therapeutic interventions aimed at restoring immune equilibrium.\u003c/p\u003e\n\u003cp\u003eTo further investigate the mechanisms underlying anti-TNF non-responsiveness in IBD patients, we performed KEGG and GO pathway analyses on the relevant datasets (Fig. 3).\u003c/p\u003e\n\u003cp\u003eKEGG Pathway Analysis: Among the top ten enriched pathways, six showed significant alterations:\u003c/p\u003e\n\u003cp\u003eStaphylococcus aureus infection (Suggests a potential role for microbial dysbiosis or host-pathogen interactions in driving inflammation in non-responsive patients), Mitophagy (Highlights disruptions in mitochondrial quality control and autophagic processes as contributors to disease progression), Central carbon metabolism in cancer (Indicates aberrant metabolic reprogramming, potentially influencing immune cell function and chronic inflammation), Alcoholic liver disease (Reveals shared metabolic or inflammatory pathways between IBD and liver pathology, underscoring the systemic impact of intestinal inflammation), \u0026nbsp;Metabolic pathways (Reflects broad metabolic dysregulation, which may affect energy homeostasis and immune cell activity), mTOR signaling pathway (Points to mTOR dysregulation, known to modulate immune responses, cell proliferation, and metabolism). These enriched pathways illuminate the complex interplay of immune, metabolic, and microbial factors contributing to anti-TNF non-responsiveness, providing potential avenues for therapeutic intervention.\u003c/p\u003e\n\u003cp\u003eMolecular Function (MF): We identified ten enriched terms, emphasizing critical molecular activities: Protein binding (Suggests widespread involvement of protein-protein interactions), Protein dimerization activity (Highlights the role of dimeric protein complexes in cellular regulation), Endopeptidase inhibitor activity (Points to modulation of proteolytic processes), \u0026nbsp;Ubiquitin-protein transferase activity (Reflects the significance of ubiquitination in protein turnover and signaling), Protein homodimerization activity (Underlines the importance of homodimeric proteins in cellular functions), Organic acid binding (Suggests a role in metabolic regulation), Haptoglobin binding (Indicates pathways involved in immune modulation and iron metabolism), Oxygen carrier activity (Suggests disruptions in oxygen transport mechanisms), \u0026nbsp; Phosphatidylinositol-3,5-bisphosphate binding (Points to alterations in phosphoinositide signaling), Peroxidase activity (Highlights roles in oxidative stress responses and detoxification).\u003c/p\u003e\n\u003cp\u003eBiological Process (BP):\u003cbr\u003e\u0026nbsp;The top enriched terms revealed key biological disruptions: Heme biosynthetic process (Highlights disturbances in heme production and its downstream effects), Proteasome-mediated ubiquitin-dependent protein catabolic process (Suggests enhanced proteolysis and protein degradation), Erythrocyte differentiation (Implicates abnormalities in red blood cell development and function),\u003c/p\u003e\n\u003cp\u003eUbiquitin-dependent protein catabolic process (Reflects broader dysregulation of protein degradation and recycling), Protoporphyrinogen IX biosynthetic process (Points to specific disruptions in the heme biosynthesis pathway), Hydrogen peroxide catabolic process (Suggests impaired detoxification of reactive oxygen species), Protein ubiquitination (Highlights the regulatory role of ubiquitination in cellular processes), Oxygen transport (Suggests potential impairments in oxygen delivery and utilization), Cellular oxidant detoxification (Reflects the involvement of antioxidant mechanisms in mitigating oxidative damage), Heme B biosynthetic process (Further emphasizes disruptions in heme metabolism and its functional implications).\u003c/p\u003e\n\u003cp\u003eCellular Component (CC):\u003cbr\u003e\u0026nbsp;Key enriched cellular components include the cytosol, cytoplasm, extracellular exosome, hemoglobin complex, membrane, TORC2 complex, cortical cytoskeleton, specific granule membrane, sperm flagellum, and haptoglobin-hemoglobin complex. These findings collectively underscore the interconnected roles of oxidative stress, protein regulation, and metabolic pathways in the pathophysiology of anti-TNF non-responsiveness, providing a foundation for identifying new therapeutic targets.\u003c/p\u003e\n\u003cp\u003eTo further explore the molecular mechanisms underlying anti-TNF therapy responsiveness in IBD patients, we constructed a Protein-Protein Interaction (PPI) network using the significantly altered genes. This network provided insights into the interactions and functional relationships among these key molecules (Fig. 4). Subsequently, we performed Gene Ontology (GO) analysis on the PPI network, which revealed several enriched biological processes and molecular functions associated with therapy responsiveness. Notably, \u003cem\u003eAHSP\u003c/em\u003e (Alpha Hemoglobin Stabilizing Protein) and \u003cem\u003eUBA52\u003c/em\u003e (Ubiquitin A-52 Residue Ribosomal Protein Fusion Product 1) emerged as pivotal molecules within the network. \u003cem\u003eAHSP\u003c/em\u003e is known for its role in stabilizing hemoglobin and protecting against oxidative stress, suggesting its involvement in mitigating inflammation-driven oxidative damage in IBD. Meanwhile, \u003cem\u003eUBA52\u003c/em\u003e, a component of the ubiquitination pathway, highlights the critical role of protein turnover, signaling, and degradation in regulating immune responses. These findings underscore the importance of oxidative stress modulation and protein homeostasis in shaping the response to anti-TNF therapy, providing valuable insights into potential biomarkers and therapeutic targets for improving treatment outcomes in IBD.\u003c/p\u003e\n\u003cp\u003eFrom the analysis of the top significantly altered genes, we performed pathway enrichment to identify signaling pathways that could reveal potential therapeutic targets. Using these enriched pathways, we evaluated drug-gene interactions and identified a set of ten candidate drugs or inhibitors. These include both FDA-approved drugs and investigational compounds: Nifedipine: A calcium channel blocker approved for hypertension and angina management; BRD-K72381041: An experimental compound with potential signaling pathway interactions, pending further characterization; BRD-A14178283: A research-stage inhibitor, noted for its role in modulating specific pathways identified in gene enrichment studies; KU-C103672: An investigational agent targeting novel pathways related to the observed gene changes; F-1566-0341: A compound in experimental studies, linked to cellular signaling regulation; VU-0402605: An emerging inhibitor known for its activity in modulating receptor-related pathways; Nicergoline: A drug used in some regions for vascular and cognitive conditions, not FDA-approved but highlighted for its pathway relevance; BRD-K41557448: An experimental molecule under investigation for its therapeutic potential in modulating enriched pathways; Mitomycin-C: An FDA-approved chemotherapeutic agent, identified for its broad mechanisms of action, including interactions with DNA synthesis pathways; BRD-K23282736: Another research-stage molecule with potential implications for therapeutic development based on pathway analysis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eInflammatory bowel disease (IBD), encompassing Crohn's disease and ulcerative colitis, involves a multifactorial interplay of genetic, environmental, and immune mechanisms. Anti-TNF therapies, including infliximab and adalimumab, have revolutionized treatment; however, up to 50% of patients either fail to respond or lose response over time\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Understanding the molecular determinants of resistance remains critical for improving therapeutic outcomes.\u003c/p\u003e \u003cp\u003eOur analysis identified 619 differentially expressed genes, highlighting pathways essential for inflammation resolution and immune modulation. Among these, LCN2 (Lipocalin-2) stands out as a potential biomarker. Elevated LCN2 levels, linked to neutrophil recruitment and epithelial damage, correlate with disease severity and may serve as predictive markers for anti-TNF responsiveness\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Downregulation of CTLA4, a key immune checkpoint regulator, suggests impaired T-cell regulation in non-responders, aligning with evidence that alternative treatments, such as IL-12/23 inhibitors or JAK inhibitors, may benefit this subset\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur PPI network identified AHSP and UBA52 as central molecules. AHSP, known for reducing oxidative stress, offers a potential target for interventions aimed at mitigating inflammation-driven tissue damage\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Similarly, UBA52 underscores the importance of regulated protein degradation pathways in maintaining immune homeostasis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe analysis revealed several candidate therapeutic agents, including nifedipine and experimental compounds like BRD-K72381041. These agents target oxidative stress, metabolic dysfunction, and proteostasis, presenting novel options for patients who fail anti-TNF therapies. Preclinical data support the efficacy of these interventions in ameliorating inflammation in resistant IBD models\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Our findings advocate for a precision medicine approach in IBD management. Integrative analyses of gene expression, pathway enrichment, and drug interactions provide a framework for personalized therapeutic strategies. Validation in independent cohorts and functional studies of key genes and pathways are necessary next steps. Moreover, investigating the interplay between host genetics, microbial factors, and immune responses will be crucial for designing comprehensive interventions.\u003c/p\u003e \u003cp\u003eThis multi-omic study elucidates the molecular underpinnings of anti-TNF therapy resistance in IBD. By identifying key biomarkers, pathways, and candidate drugs, we provide actionable insights that pave the way for more effective and personalized therapeutic strategies.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cp\u003eDataset Selection\u003c/p\u003e\n\u003cp\u003eThe dataset GSE186963 from the Gene Expression Omnibus (GEO) database was selected for analysis. It contains bulk RNA sequencing data from inflammatory bowel disease (IBD) patients categorized as responders or non-responders to anti-TNF therapy after 14 weeks. The dataset was preprocessed for quality control to remove low-quality reads and normalize expression data.\u003c/p\u003e\n\u003cp\u003eDifferential Gene Expression Analysis\u003c/p\u003e\n\u003cp\u003eDifferential gene expression analysis was conducted using the edgeR package in R. Samples were grouped based on their treatment response status. Genes with a false discovery rate (FDR) \u0026lt; 0.05 and a log2 fold change \u0026gt; ±1 were considered significantly altered. Batch effects and confounding variables were corrected using ComBat-seq where necessary.\u003c/p\u003e\n\u003cp\u003ePathway and Gene Ontology Enrichment\u003c/p\u003e\n\u003cp\u003eTo identify relevant biological pathways and molecular functions, KEGG (Kyoto Encyclopedia of Genes and Genomes) and Gene Ontology (GO) analyses were performed using the clusterProfiler package. Enrichment terms were filtered for significance with an FDR-adjusted p-value threshold of \u0026lt; 0.05. Categories explored included: Molecular Function (MF), Biological Process (BP), and Cellular Component (CC).\u003c/p\u003e\n\u003cp\u003eProtein-Protein Interaction Network\u003c/p\u003e\n\u003cp\u003eProtein-protein interaction (PPI) networks were constructed using the STRING database and visualized in Cytoscape. Key hub genes were identified through network topology analysis, employing metrics such as degree centrality.\u003c/p\u003e\n\u003cp\u003eInhibitor Prediction Analysis\u003c/p\u003e\n\u003cp\u003eThe L1000FWD tool was employed to predict potential novel inhibitors. This tool compares an input gene expression signature with data from the LINCS-L1000 project to identify and rank inhibitors capable of modulating the transcriptional profile. In this study, a threshold of adjusted p-value \u0026lt; 0.05 was applied to determine statistical significance in inhibitor prediction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDrug-Gene Interaction Analysis\u003c/p\u003e\n\u003cp\u003ePotential therapeutic candidates were identified using the Drug-Gene Interaction Database (DGIdb). Drugs and inhibitors targeting the significantly altered genes were filtered based on known efficacy in pathways linked to IBD or anti-TNF non-responsiveness. FDA-approved drugs and investigational compounds were included in the analysis.\u003c/p\u003e\n\u003cp\u003eStatistical and Visualization Tools\u003c/p\u003e\n\u003cp\u003eStatistical Analysis: Conducted in R with appropriate corrections for multiple testing using the Benjamini-Hochberg procedure. Visualization Tools: Volcano plots and heatmaps were generated using ggplot2 and ComplexHeatmap, while pathway maps were produced using pathview.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all authors for their valuable contributions to this study. This research was conducted without any external funding or financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest related to this study. V.Y. is a faculty member at the University of Pittsburgh.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.G. conducted the study and drafted the manuscript. V.Y. provided guidance for the study and revised the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdegbola, S.O., Sahnan, K., Warusavitarne, J., Hart, A. \u0026amp; Tozer, P. Anti-TNF Therapy in Crohn\u0026apos;s Disease. \u003cem\u003eInt J Mol Sci\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e (2018).\u003c/li\u003e\n\u003cli\u003eJostins, L.\u003cem\u003e et al.\u003c/em\u003e Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e491\u003c/strong\u003e,119-124 (2012).\u003c/li\u003e\n\u003cli\u003eFranke, A.\u003cem\u003e et al.\u003c/em\u003e Genome-wide meta-analysis increases to 71 the number of confirmed Crohn\u0026apos;s disease susceptibility loci. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e,1118-1125 (2010).\u003c/li\u003e\n\u003cli\u003eConlon, M.A. \u0026amp; Bird, A.R. The impact of diet and lifestyle on gut microbiota and human health. \u003cem\u003eNutrients\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e,17-44 (2014).\u003c/li\u003e\n\u003cli\u003eJang, D.I.\u003cem\u003e et al.\u003c/em\u003e The Role of Tumor Necrosis Factor Alpha (TNF-alpha) in Autoimmune Disease and Current TNF-alpha Inhibitors in Therapeutics. \u003cem\u003eInt J Mol Sci\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e (2021).\u003c/li\u003e\n\u003cli\u003evan Loo, G. \u0026amp; Bertrand, M.J.M. Death by TNF: a road to inflammation. \u003cem\u003eNat Rev Immunol\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e,289-303 (2023).\u003c/li\u003e\n\u003cli\u003eBosani, M., Ardizzone, S. \u0026amp; Porro, G.B. Biologic targeting in the treatment of inflammatory bowel diseases. \u003cem\u003eBiologics\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e,77-97 (2009).\u003c/li\u003e\n\u003cli\u003eLewis, J.D.\u003cem\u003e et al.\u003c/em\u003e \u003cem\u003eAnti-TNF Drugs versus Long-Term Steroid Use for Patients with Inflammatory Bowel Diseases\u003c/em\u003e: Washington (DC), 2018.\u003c/li\u003e\n\u003cli\u003eBen-Horin, S. \u0026amp; Chowers, Y. Review article: loss of response to anti-TNF treatments in Crohn\u0026apos;s disease. \u003cem\u003eAliment Pharmacol Ther\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e,987-995 (2011).\u003c/li\u003e\n\u003cli\u003eVande Casteele, N.\u003cem\u003e et al.\u003c/em\u003e Therapeutic drug monitoring in inflammatory bowel disease: current state and future perspectives. \u003cem\u003eCurr Gastroenterol Rep\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e,378 (2014).\u003c/li\u003e\n\u003cli\u003eMaser, E.A., Villela, R., Silverberg, M.S. \u0026amp; Greenberg, G.R. Association of trough serum infliximab to clinical outcome after scheduled maintenance treatment for Crohn\u0026apos;s disease. \u003cem\u003eClin Gastroenterol Hepatol\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e,1248-1254 (2006).\u003c/li\u003e\n\u003cli\u003eNeurath, M.F. Cytokines in inflammatory bowel disease. \u003cem\u003eNat Rev Immunol\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e,329-342 (2014).\u003c/li\u003e\n\u003cli\u003eAfif, W.\u003cem\u003e et al.\u003c/em\u003e Clinical utility of measuring infliximab and human anti-chimeric antibody concentrations in patients with inflammatory bowel disease. \u003cem\u003eAm J Gastroenterol\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e,1133-1139 (2010).\u003c/li\u003e\n\u003cli\u003eBilliet, T.\u003cem\u003e et al.\u003c/em\u003e A Matrix-based Model Predicts Primary Response to Infliximab in Crohn\u0026apos;s Disease. \u003cem\u003eJ Crohns Colitis\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e,1120-1126 (2015).\u003c/li\u003e\n\u003cli\u003eFeagan, B.G.\u003cem\u003e et al.\u003c/em\u003e Ustekinumab as Induction and Maintenance Therapy for Crohn\u0026apos;s Disease. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e375\u003c/strong\u003e,1946-1960 (2016).\u003c/li\u003e\n\u003cli\u003eHirten, R.P.\u003cem\u003e et al.\u003c/em\u003e Microbial Engraftment and Efficacy of Fecal Microbiota Transplant for Clostridium Difficile in Patients With and Without Inflammatory Bowel Disease. \u003cem\u003eInflamm Bowel Dis\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e,969-979 (2019).\u003c/li\u003e\n\u003cli\u003eAsaf, S.\u003cem\u003e et al.\u003c/em\u003e Lipocalin 2-not only a biomarker: a study of current literature and systematic findings of ongoing clinical trials. \u003cem\u003eImmunol Res\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e,287-313 (2023).\u003c/li\u003e\n\u003cli\u003eBuchbinder, E.I. \u0026amp; Desai, A. CTLA-4 and PD-1 Pathways: Similarities, Differences, and Implications of Their Inhibition. \u003cem\u003eAm J Clin Oncol\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e,98-106 (2016).\u003c/li\u003e\n\u003cli\u003eNediani, C., Ruzzolini, J. \u0026amp; Dinu, M. Oxidative Stress and Inflammation as Targets for Novel Preventive and Therapeutic Approaches in Non-Communicable Diseases III. \u003cem\u003eAntioxidants (Basel)\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e (2024).\u003c/li\u003e\n\u003cli\u003eKobayashi, M.\u003cem\u003e et al.\u003c/em\u003e The ubiquitin hybrid gene UBA52 regulates ubiquitination of ribosome and sustains embryonic development. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e,36780 (2016).\u003c/li\u003e\n\u003cli\u003eBaydi, Z.\u003cem\u003e et al.\u003c/em\u003e An Update of Research Animal Models of Inflammatory Bowel Disease. \u003cem\u003eScientificWorldJournal\u003c/em\u003e \u003cstrong\u003e2021\u003c/strong\u003e,7479540 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"VitDek Health Solutions","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":"Anti-TNF therapy, Inflammatory Bowel Disease (IBD), Genetic markers, RNA-seq, Differential gene expression, LCN2, CTLA4, Immune modulation ,mTOR signaling, Ubiquitination pathways, Treatment strategies","lastPublishedDoi":"10.21203/rs.3.rs-5734261/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5734261/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo investigate genetic markers and pathways influencing anti-TNF therapy response in inflammatory bowel disease (IBD), we analyzed bulk RNA-seq data (GSE186963) from responders and non-responders, identifying 619 significantly altered genes. Key findings included upregulated genes like LCN2, associated with inflammation and tissue injury, and downregulated genes such as CTLA4, an immune checkpoint regulator. Pathway enrichment analyses revealed disruptions in mitochondrial quality control, metabolic reprogramming, and immune modulation, highlighting roles for mTOR signaling and ubiquitination pathways. Gene Ontology analysis pointed to oxidative stress responses, heme metabolism, and protein degradation as critical processes, while cellular component analysis emphasized the cytosol, TORC2 complex, and extracellular exosomes. A protein-protein interaction network identified AHSP and UBA52 as pivotal molecules involved in oxidative stress mitigation and protein homeostasis. These insights informed the identification of 10 therapeutic candidates, including Nifedipine and Nicergoline, offering promising avenues for addressing anti-TNF non-responsiveness and refining treatment strategies for IBD.\u003c/p\u003e","manuscriptTitle":"Therapeutic Strategies for Anti-TNF Non-Responsive IBD Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-31 05:36:56","doi":"10.21203/rs.3.rs-5734261/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":"8b3d9f3f-dfaa-4b15-a70d-77a8c5cf51da","owner":[],"postedDate":"December 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42155849,"name":"Medical Genetics"}],"tags":[],"updatedAt":"2024-12-31T05:36:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-31 05:36:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5734261","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5734261","identity":"rs-5734261","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}