All Publications


  • AP-1 is a temporally regulated dual gatekeeper of reprogramming to pluripotency. Proceedings of the National Academy of Sciences of the United States of America Markov, G. J., Mai, T., Nair, S., Shcherbina, A., Wang, Y. X., Burns, D. M., Kundaje, A., Blau, H. M. 2021; 118 (23)

    Abstract

    Somatic cell transcription factors are critical to maintaining cellular identity and constitute a barrier to human somatic cell reprogramming; yet a comprehensive understanding of the mechanism of action is lacking. To gain insight, we examined epigenome remodeling at the onset of human nuclear reprogramming by profiling human fibroblasts after fusion with murine embryonic stem cells (ESCs). By assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and chromatin immunoprecipitation sequencing we identified enrichment for the activator protein 1 (AP-1) transcription factor c-Jun at regions of early transient accessibility at fibroblast-specific enhancers. Expression of a dominant negative AP-1 mutant (dnAP-1) reduced accessibility and expression of fibroblast genes, overcoming the barrier to reprogramming. Remarkably, efficient reprogramming of human fibroblasts to induced pluripotent stem cells was achieved by transduction with vectors expressing SOX2, KLF4, and inducible dnAP-1, demonstrating that dnAP-1 can substitute for exogenous human OCT4. Mechanistically, we show that the AP-1 component c-Jun has two unexpected temporally distinct functions in human reprogramming: 1) to potentiate fibroblast enhancer accessibility and fibroblast-specific gene expression, and 2) to bind to and repress OCT4 as a complex with MBD3. Our findings highlight AP-1 as a previously unrecognized potent dual gatekeeper of the somatic cell state.

    View details for DOI 10.1073/pnas.2104841118

    View details for PubMedID 34088849

  • Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts Nair, S., Kim, D. S., Perricone, J., Kundaje, A. OXFORD UNIV PRESS. 2019: I108–I116
  • Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays. PloS one Movva, R. n., Greenside, P. n., Marinov, G. K., Nair, S. n., Shrikumar, A. n., Kundaje, A. n. 2019; 14 (6): e0218073

    Abstract

    The relationship between noncoding DNA sequence and gene expression is not well-understood. Massively parallel reporter assays (MPRAs), which quantify the regulatory activity of large libraries of DNA sequences in parallel, are a powerful approach to characterize this relationship. We present MPRA-DragoNN, a convolutional neural network (CNN)-based framework to predict and interpret the regulatory activity of DNA sequences as measured by MPRAs. While our method is generally applicable to a variety of MPRA designs, here we trained our model on the Sharpr-MPRA dataset that measures the activity of ∼500,000 constructs tiling 15,720 regulatory regions in human K562 and HepG2 cell lines. MPRA-DragoNN predictions were moderately correlated (Spearman ρ = 0.28) with measured activity and were within range of replicate concordance of the assay. State-of-the-art model interpretation methods revealed high-resolution predictive regulatory sequence features that overlapped transcription factor (TF) binding motifs. We used the model to investigate the cell type and chromatin state preferences of predictive TF motifs. We explored the ability of our model to predict the allelic effects of regulatory variants in an independent MPRA experiment and fine map putative functional SNPs in loci associated with lipid traits. Our results suggest that interpretable deep learning models trained on MPRA data have the potential to reveal meaningful patterns in regulatory DNA sequences and prioritize regulatory genetic variants, especially as larger, higher-quality datasets are produced.

    View details for DOI 10.1371/journal.pone.0218073

    View details for PubMedID 31206543

  • The Big Win Strategy on Multi-Value Network: An Improvement over AlphaZero Approach for 6x6 Othello Chang, N., Chen, C., Lin, S., Nair, S., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 78–81