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Jessica Kenison-White, PhD


Job Title

Postdoctoral Research Fellow

Academic Rank

Fellow or Postdoc




Jessica E. Kenison, Liliana M. Sanmarco, Chun-Cheih Chao, Yu-Chao Wang, Zhaorong Li, Joseph M. Rone, Francisco J. Quintana

Principal Investigator

Francisco J. Quintana



Machine learning-based identification of environmental factors that promote intestinal inflammation

Scientific Abstract

Genome-wide association studies have identified risk loci linked to inflammatory bowel disease (IBD)—a complex chronic inflammatory disorder of the gastrointestinal tract. The increasing prevalence of IBD in industrialized countries and the augmented disease risk observed in migrants who move into areas of higher disease prevalence suggest that environmental factors are also important determinants of IBD susceptibility and severity. However, the identification of environmental factors relevant to IBD and the mechanisms by which they influence disease has been hampered by the lack of platforms for their systematic investigation. To overcome this limitation, we developed an integrated systems approach which combines publicly available databases, zebrafish chemical screens, machine learning, and mouse preclinical models to identify environmental factors that control intestinal inflammation. Using this approach we established that the herbicide propyzamide increases inflammation in the small and large intestine. Moreover, we found that an AHR–NF-κB–C/EBPβ signalling axis operates in T cells and dendritic cells to promote intestinal inflammation, and is targeted by propyzamide. In conclusion, we developed a pipeline for the identification of environmental factors and mechanisms of pathogenesis in IBD and, potentially, other inflammatory diseases.

Lay Abstract

Inflammatory bowel disease (IBD) is a complex disorder caused by a combination of both genetic and environmental risk factors. While the tools used to identify genetic risk factors have greatly improved recently, there are still challenges associated with identifying environmental risk factors. In an effort to overcome these challenges, we developed a new screening platform which combines machine-learning based analysis of publicly available databases of chemicals that people are regularly exposed to in everyday life with zebrafish and mouse models of IBD. Using this new platform, we identified a commonly used chemical, the herbicide propyzamide (used in certain garden and lawn care products), which worsened disease in our zebrafish and mouse models. We then investigated the mechanism by which this chemical worsened disease and identified a novel AHR–NF-κB–C/EBPβ signaling axis which drives intestinal inflammation. We believe that using this pipeline we can identify additional environmental factors and signaling pathways which drive IBD disease and help to know which chemicals to avoid exposure to, while also potentially identifying new candidates which might be targeted to treat intestinal inflammation.

Clinical Implications

The pipeline we developed can be used to screen environmental factors and identify relevant signaling pathways, which may identify novel risk factors and new clinical targets for the treatment of intestinal inflammatory diseases like IBD.