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Tumor Necrosis Factor (TNF), a proinflammatory cytokine, contributes significantly to the inflammation seen in Crohn's disease. It exists as soluble TNF and membrane-bound TNF, with actions mediated through TNF receptors (TNFR). TNFR activation leads to the release of proinflammatory cytokines, T-cell activation, collagen production, and leukocyte migration, all contributing to inflammation in Crohn's disease. Anti-TNF monoclonal antibodies, namely infliximab (Remicade), adalimumab...
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Various diagnostic tests are employed in the diagnostic process for Inflammatory Bowel Disease (IBD), particularly to differentiate between Crohn's disease and ulcerative colitis.
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Upon diagnosis, managing Inflammatory Bowel Disease (IBD) involves addressing several crucial aspects. The primary goals include resting the bowel, correcting malnutrition, and providing symptomatic relief. Resting the bowel may consist of medications to reduce inflammation and promote healing. Correcting malnutrition is essential, often requiring dietary adjustments and nutritional supplements. Symptomatic relief aims to ease pain, diarrhea, and other discomforts in IBD.
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Identifying anti-TNF response biomarkers in ulcerative colitis using a diffusion-based signalling model.

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Identifying patients resistant to anti-tumor necrosis factor (anti-TNF) therapy is crucial for ulcerative colitis (UC) treatment. A new computational model quantifies receptor-to-transcription factor network connectivity to predict treatment response, aiding personalized medicine.

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Area of Science:

  • Computational biology
  • Immunology
  • Genomics

Background:

  • Anti-tumor necrosis factor (anti-TNF) therapy resistance is a significant challenge in ulcerative colitis (UC) management, leading to increased healthcare costs.
  • Predicting patient response to anti-TNF therapy is clinically vital, yet current quantitative computational frameworks for complex biomarker development are lacking.
  • Personalized treatment strategies can be advanced by modeling patient-specific receptor-to-transcription factor (TF) network connectivity.

Purpose of the Study:

  • To develop a quantitative computational framework for analyzing receptor-TF network diffusion.
  • To create a complex biomarker for predicting anti-TNF therapy response in UC patients.
  • To validate the model's efficacy in independent datasets and other autoimmune conditions.

Main Methods:

  • Utilized gene expression data for quantitative diffusion analysis between receptors and TFs.
  • Employed the pandaR package to identify key transcription factors (TFs).
  • Quantified network connectivities of immune-specific receptor-TF pairs using network diffusion in UC patients and controls.

Main Results:

  • Developed a patient-specific network model that effectively distinguishes between anti-TNF treatment-resistant and responder UC patients.
  • Validated the model's predictive capability on independent test datasets.
  • Successfully applied the model to discriminate between resistant and responder patients for tocilizumab treatment in rheumatoid arthritis.

Conclusions:

  • The developed network diffusion model serves as a potential complex biomarker for predicting anti-TNF therapy outcomes in UC.
  • This approach may facilitate the identification of patient subpopulations with varying treatment responses.
  • The model's successful validation in rheumatoid arthritis suggests broader applicability in predicting immunomodulatory drug response.