Development of biomarkers to predict disease outcome in gut inflammation

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Abstract

Reusability and reliability of published data are fundamental requirements for translating results from animal experiments into reliable clinical biomarkers. Current success rates for biomarker discovery are poor, and we need to develop new tools to collate, integrate and analyse datasets, such as knowledge bases, that would enable more effective translation from mouse to human or across disciplines. However, concerns about the validity, reproducibility and replicability of existing data must be addressed first. Here, I interrogate the quality of methods reporting in experimental models of infection and inflammation. Despite evidence that most of the assessed parameters, such as sex and age, can influence the experimental results, the quality of methods reporting was poor. Inadequate methods reporting means that it is not always possible to confirm whether findings were due to improper experimental conditions that biased the results. Thus, such inaccuracies would have an impact on the construction of knowledge base tools that require appropriate annotation. However, I provide reusable checklists that could improve the quality of methods reporting prior to publication and can be used to verify papers post-publication to enable researchers from different fields to interrogate published data.Another reason that biomarkers may fail is that it can be difficult to determine the causative pathways that will better predict disease outcome in a chronically inflamed tissue where multiple pathways are happening simultaneously. I conducted novel research to identify and verify whether investigating animal models long before onset of colitis would identify potentially causative biomarkers for colitis in an animal model. Previous studies have shown early influx of dendritic cells are associated with resistance to Trichuris muris-induced colitis in mice, suggesting early biomarkers may be detectable that might predict disease outcome in inflammatory diseases, such as inflammatory bowel disease (IBD).In the T. muris colitis model, I identified differences in gene expression of multiple components of the receptor for advanced glycation end-products (RAGE) activation pathway between colitis-resistant and colitis-susceptible mice, occurring just 24 hours post infection; before any observable clinical symptoms were present. RAGE is a receptor that binds products of damage, such as calprotectin, and initiates pro-inflammatory cascades. However, RAGE can be cleaved from the cell membrane to form a soluble receptor (sRAGE) that cannot mediate proinflammatory signals, yet can bind to damage products, effectively rendering them harmless. During a longer infection timecourse, colitis-resistant mice produced significantly more sRAGE during infection, consistent with an increased ability to prevent inflammatory ligands from activating membrane-bound RAGE. These findings were also supported by additional experiments using T. muris infection in Il-10-/- mice.In summary, I have carried out an analysis of methods reporting quality in immunology research that can help improve the reliability of existing data relevant to the further study of IBD and beyond. I have also identified sRAGE as a potential biomarker for the onset of colitis in the T. muris infection model, with implications for diagnosis and treatment of IBD in a clinical setting.

Keyword(s)

Animal models; Biomarkers; Colitis; IBD; Reproducibility

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