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NYU School of Medicine

NYU School of Medicine. METABOLIC ALTERATIONS TO THE MUCOSAL MICROBIOTA OF INFLAMMATORY BOWEL DISEASE PATIENTS ARE ASSOCIATED WITH CD4+ T CELL HOMEOSTASIS

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NYU School of Medicine

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  1. NYU School of Medicine METABOLIC ALTERATIONS TO THE MUCOSAL MICROBIOTA OF INFLAMMATORY BOWEL DISEASE PATIENTS ARE ASSOCIATED WITH CD4+ T CELL HOMEOSTASIS Michael Davenport, Jordan Poles, Jacqueline Leung, Martin J. Wolff, Wasif M. Abidi, Thomas Ullman, Lloyd Mayer, Ilseung Cho, and P’ngLoke. 12/13/13

  2. DISCLOSURES • Nothing to disclose Presentation Title Goes Here

  3. Background • Pathogenesis of IBD is likely related to abnormal interactions between the immune system and gut microbiota • Studies using 16S sequencing have shown that IBD is associated with dysbiosis of the gut microbiota: • Decreased microbial diversity • Alterations in specific bacterial taxa • The mucosal microbiota is distinct from the luminal microbiota • Inflammation may alter nutrient availability to mucosal bacteria and impact their metabolic function. • Microbial metabolites may also regulate intestinal CD4+ T cell homeostasis.

  4. Methods • Paired pinch biopsy samples of known inflammation states were collected and analyzed from patients with UC (n = 23) and CD (n = 21) at Mt Sinai • Active inflammation was defined histopathologically by neutrophil infiltration into the epithelium • Previously, we found that TH22 cells were depleted in actively inflamed tissue only from these UC patients and not CD patients. (Leung et. al. Mucosal Immunology 2013)

  5. Methods • 16S sequencing can be used identify changes to the mucosal microbiota from pinch biopsies of known inflammation states • However, there is not enough bacterial DNA to determine microbial function by shotgun metagenomic sequencing analyses • PICRUSt is a new technique for inferring the metagenomethrough the closest available bacterial whole genome sequences in available databases using the 16S data. (Langille et al. Nature Biotechnology 2013)

  6. Metabolic pathways are relatively stable despite variations in bacterial community structures

  7. Microbial function is significantly altered in inflamed tissue of UC patients B A Normal PC2 PC1 Inflamed

  8. Reduction in carbohydrate and nucleotide metabolism and increased lipid and amino acid metabolism with inflammation

  9. Characterization of CD4+ cells in LPMCs by Flow Cytometry

  10. CD4+IL-22+ cells are associated with lipid, carbohydrate and amino acid metabolism KEGG pathways in UC.

  11. Foxp3+ cells are associated with lipid, carbohydrate and amino acid metabolism KEGG pathways in CD.

  12. Summary • Metabolic pathways are relatively stable despite variations in bacterial community structures • Microbial function is significantly altered in inflamed tissue of UC patients, but not in CD • Reduction in carbohydrate and nucleotide metabolism and increased lipid and amino acid metabolism with inflammation • CD4+IL-22+ cells are associated with lipid, carbohydrate, and amino acid metabolism KEGG pathways in UC patients • Foxp3+ cells are associated with lipid, carbohydrate and amino acid metabolism KEGG pathways in CD

  13. Conclusions Metabolic pathways of the mucosal microbiota in CD do not vary as much as UC with inflammation state, indicating a more systemic perturbation of host-bacteria interactions in CD compared to more localized dysfunction in UC. The alterations in metabolic pathways correlate specifically with frequency of Tregs during CD, but with TH22 cells during UC. Alterations to metabolic pathways of the mucosal microbiota may affect the production of metabolites that can regulate intestinal CD4+ T cell populations and inflammatory responses of the gut.

  14. Acknowledgements: Principal Investigator: P’ngLoke Labmates: Jordan Poles Jacqueline Leung Martin Wolff Collaborators: Ilseung Cho (NYU) Mike Poles (NYU) Marty Blaser (NYU) Lloyd Mayer (Mt. Sinai) Thomas Ullman (Mt. Sinai) WasifAbidi (Mt. Sinai) Funding: Broad Foundation BMRP NIH: NIAID, NCRR NYU CTSI AGA-Eli and Edythe Broad Student Research Fellowship Acknowledgements: Knight Lab (QIIME) Huttenhower Lab (PICRUSt and LEFse)

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