This application addresses the broad Thematic Area 1, Applying Genomics and Other High Throughput Technologies, to study the transcriptional regulatory network that governs the differentiation of Th17 cells. Following infection with different microbes, CD4+ T lymphocytes differentiate into T helper (Th) cells with diverse effector functions to contain the offending agent. Th17 cells, which produce the cytokines IL-17, IL-17F, and IL-22, are important for clearing mucosal pathogens, but are also major contributors to inflammation and autoimmune disease. Multiple autoimmunity models in mice require Th17 cells, and there is accumulating evidence that these cells have key roles in human diseases such as Crohn's disease, psoriasis, and rheumatoid arthritis. We demonstrated that the orphan nuclear receptor RORyt is required for the differentiation of Th17 cells and is also sufficient to induce activated T cells to acquire a Th17 phenotype, highlighting it as a critical lineage-defining transcription factor (TF). Loss of RORyt or its inhibition with small molecules abrogates autoimmune disease in mouse models, suggesting that it is a good candidate therapeutic target for human inflammatory diseases. RORyt is embedded in a complex network that includes several other TFs that are essential for, or contribute to, differentiation and function of Th17 cells. These include Stat-3, IRF-4, Ahr, BATF, Runx1/CBFK , c-Maf, and RORa. RORyt is also often co-expressed with the regulatory T (Treg) cell- specifying factor Foxp3, which restrains Th17 cell differentiation. We propose to perform a comprehensive genomic analysis of the Th17 transcriptional program in order to learn the regulatory network controlling Th17 cell differentiation. We will do this by using next-generation sequencing technologies to identify target gene occupancy by these relevant transcription factors (measured by ChIP-seq), associated epigenetic changes, and corresponding expression of coding and non-coding RNAs associated with Th17 cell differentiation. Our computational methods, designed specifically for analysis of time series data, will be applied to samples at multiple time points from wild type and TF-deficient T cells and will provide information for characterizing functional cis-regulatory modules, assessing TF cooperatively, and building iterative network models that will identify new critical nodes involved in functional differentiation of these inflammatory cells. Analysis of Th17 cells isolated directly from mice undergoing diverse inflammatory processes and from human blood will be incorporated to test network predictions. We have assembled a team whose members have the complementary skills needed for the success of this project: a group that has made fundamental contributions to the biology of Th17 cells;a genome center with extensive experience in high throughput sequencing and data analysis;and a computational group that has developed advanced algorithms for inferring transcriptional networks and predicting functional nodes. We anticipate that the proposed studies will help identify new targets for therapeutic modulation of Th17 cells in humans to either boost mucosal immunity or attenuate inflammation.

Public Health Relevance

We will use a combination of whole-genome methodologies, including ChIP-seq and RNA-seq, to characterize the transcriptional network of Th17 cells that have key roles in mucosal immunity and in autoimmune diseases. Functional studies based on predictions of the network analysis will uncover novel targets for therapeutic approaches to selectively enhance or attenuate Th17 cell function.

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New York University
Schools of Medicine
New York
United States
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Hafemeister, Christoph; Romero, Roberto; Bilal, Erhan et al. (2015) Inter-species pathway perturbation prediction via data-driven detection of functional homology. Bioinformatics 31:501-8
Lao, Brooke Bullock; Drew, Kevin; Guarracino, Danielle A et al. (2014) Rational design of topographical helix mimics as potent inhibitors of protein-protein interactions. J Am Chem Soc 136:7877-88
Youngs, Noah; Penfold-Brown, Duncan; Bonneau, Richard et al. (2014) Negative example selection for protein function prediction: the NoGO database. PLoS Comput Biol 10:e1003644
Renfrew, P Douglas; Craven, Timothy W; Butterfoss, Glenn L et al. (2014) A rotamer library to enable modeling and design of peptoid foldamers. J Am Chem Soc 136:8772-82
Youngs, Noah; Penfold-Brown, Duncan; Drew, Kevin et al. (2013) Parametric Bayesian priors and better choice of negative examples improve protein function prediction. Bioinformatics 29:1190-8
Greenfield, Alex; Hafemeister, Christoph; Bonneau, Richard (2013) Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks. Bioinformatics 29:1060-7
Ciofani, Maria; Madar, Aviv; Galan, Carolina et al. (2012) A validated regulatory network for Th17 cell specification. Cell 151:289-303
Renfrew, P Douglas; Choi, Eun Jung; Bonneau, Richard et al. (2012) Incorporation of noncanonical amino acids into Rosetta and use in computational protein-peptide interface design. PLoS One 7:e32637
Glasmacher, Elke; Agrawal, Smita; Chang, Abraham B et al. (2012) A genomic regulatory element that directs assembly and function of immune-specific AP-1-IRF complexes. Science 338:975-80
Kirigin, Francis F; Lindstedt, Kenneth; Sellars, Maclean et al. (2012) Dynamic microRNA gene transcription and processing during T cell development. J Immunol 188:3257-67

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