Leveraging Organ-on-Chip Models to Investigate Host-Microbiota Dynamics and Targeted Therapies for Inflammatory Bowel Disease.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 39491534.
- Also identified by DOI 10.1002/adhm.202402756 and PMC identifier 12004439.
- Licence recorded as CC BY-NC-ND.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Inflammatory bowel disease (IBD) is an idiopathic gastrointestinal disease with drastically increasing incidence rates. Due to its multifactorial etiology, a precise investigation of the pathogenesis is extremely difficult. Although reductionist cell culture models and more complex disease models in animals have clarified the understanding of individual disease mechanisms and contributing factors of IBD in the past, it remains challenging to bridge research and clinical practice. Conventional 2D cell culture models cannot replicate complex host-microbiota interactions and stable long-term microbial culture. Further, extrapolating data from animal models to patients remains challenging due to genetic and environmental diversity leading to differences in immune responses. Human intestine organ-on-chip (OoC) models have emerged as an alternative in vitro model approach to investigate IBD. OoC models not only recapitulate the human intestinal microenvironment more accurately than 2D cultures yet may also be advantageous for the identification of important disease-driving factors and pharmacological interventions targets due to the possibility of emulating different complexities. The predispositions and biological hallmarks of IBD focusing on host-microbiota interactions at the intestinal mucosal barrier are elucidated here. Additionally, the potential of OoCs to explore microbiota-related therapies and personalized medicine for IBD treatment is discussed.
Medical subject headings
- Inflammatory Bowel Diseases
- Lab-On-A-Chip Devices
- Gastrointestinal Microbiome
- Models, Biological