BACKGROUND & AIMS: Chronic colonic inflammation leads to dysplasia and cancer in patients with inflammatory bowel disease. We have described the critical role of innate immune signaling via Toll-like receptor 4 (TLR4) in the pathogenesis of dysplasia and cancer. In the current study, we interrogate the intersection of TLR4 signaling, epithelial redox activity, and the microbiota in colitisassociated neoplasia. METHODS: Inflammatory bowel disease and colorectal cancer data sets were analyzed for expression of TLR4, dual oxidase 2 (DUOX2), and NADPH oxidase 1 (NOX1). Epithelial production of hydrogen peroxide (H 2 O 2 ) was analyzed in murine colonic epithelial cells and colonoid cultures. Colorectal cancer models were carried out in villin-TLR4 mice, carrying a constitutively active form of TLR4, their littermates, and villin-TLR4 mice backcrossed to DUOXA-knockout mice. The role of the TLR4-shaped microbiota in tumor development was tested in wild-type germ-free mice. RESULTS: Activation of epithelial TLR4 was associated with up-regulation of DUOX2 and NOX1 in inflammatory bowel disease and colorectal cancer. DUOX2 was exquisitely dependent on TLR4 signaling and mediated the production of epithelial H 2 O 2 . Epithelial H 2 O 2 was significantly increased in villin-TLR4 mice; TLR4-dependent tumorigenesis required the presence of DUOX2 and a microbiota. Mucosa-associated microbiota transferred from villin-TLR4 mice to wild-type germ-free mice caused increased H 2 O 2 production and tumorigenesis. CONCLUSIONS: Increased TLR4 signaling in colitis drives expression of DUOX2 and epithelial production of H 2 O 2 . The local milieu imprints the mucosal microbiota and imbues it with pathogenic properties demonstrated by enhanced epithelial reactive oxygen species and increased development of colitis-associated tumors. The interrelationship between epithelial reactive oxygen species and tumor-promoting microbiota requires a 2-pronged strategy to reduce the risk of dysplasia in colitis patients.

Background Inflammatory bowel disease (IBD) involves chronic T cell–mediated inflammatory responses. Vedolizumab (VDZ), a monoclonal antibody against α4β7 integrin, inhibits lymphocyte extravasation into intestinal mucosae and is effective in ulcerative colitis (UC) and Crohn’s disease (CD). Aim We sought to identify immune cell phenotypic and gene expression signatures that related to response to VDZ. Methods Peripheral blood (PBMC) and lamina propria mononuclear cells (LPMCs) were analyzed by flow cytometry and Cytofkit. Sorted CD4 + memory (Tmem) or regulatory T (Treg) cells from PBMC and LPMC were analyzed by RNA sequencing (RNA-seq). Clinical response (≥2-point drop in partial Mayo scores [UC] or Harvey-Bradshaw index [CD]) was assessed 14 to 22 weeks after VDZ initiation. Machine-learning models were used to infer combinatorial traits that predicted response to VDZ. Results Seventy-one patients were enrolled: 37 received VDZ and 21 patients remained on VDZ >2 years. Fourteen of 37 patients (38%; 8 UC, 6 CD) responded to VDZ. Immune cell phenotypes and CD4 + Tmem and Treg transcriptional behaviors were most divergent between the ileum and colon, irrespective of IBD subtype or inflammation status. Vedolizumab treatment had the greatest impact on Treg metabolic pathways, and response was associated with increased expression of genes involved in oxidative phosphorylation. The strongest clinical predictor of VDZ efficacy was concurrent use of thiopurines. Mucosal tissues offered the greatest number of response-predictive biomarkers, whereas PBMC Treg-expressed genes were the best predictors in combinatorial models of response. Conclusions Mucosal and peripheral blood immune cell phenotypes and transcriptional profiles can inform VDZ efficacy and inform new opportunities for combination therapies.

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OBJECTIVES/GOALS: Rapid and accurate identification of primary malaria vector species from collected specimens is the most critical aspect of effective vector surveillance and control. This interdisciplinary team of engineers aims to automate identification using a deep learning computer vision algorithm. METHODS/STUDY POPULATION: The team spent August of 2019 observing and participating in control and surveillance activities in Zambia and Uganda. They conducted >65 interviews with key stakeholders across 9 malaria control and surveillance sites, ranging from field and community health workers, to malaria researchers and Ministry of Health employees. Stakeholder feedback validated the need for a more accurate and efficient method of vector identification in order to more effectively deploy targeted malaria interventions. The team set forth in designing and prototyping a portable, automated field tool that could speciate mosquito vectors to the complex level using artificial intelligence. RESULTS/ANTICIPATED RESULTS: The team’s research demonstrated that accuracy, cost effectiveness, and ease of use would be critical to the successful adoption of the tool. Results of initial prototyping, usability studies, and stakeholder surveys were used to determine the tool’s minimal user specifications: 1) the ability to distinguish between Anopheles Gambiae and Anopheles Funestus, the two principal malaria vectors in the countries visited, 2) achieving an identification accuracy of ≥90% to the complex level, and 3) accessibility to the speciation data 3-7 days following vector collection. Next steps include optimizing the tool to deploy a minimal viable product for testing in Kenya by the summer of 2020. DISCUSSION/SIGNIFICANCE OF IMPACT: The accurate, high-quality surveillance enabled by this device would allow malaria control programs to scale surveillance to remote regions where an entomologist may not be available, allowing malaria programs to deploy effective interventions, monitor results, and prevent disease.

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