Spatiotemporal Architecture of the Genome
Investigators: Chris Glass, Clodagh O’Shea, Trey Ideker, Bing Ren
A central challenge of molecular biology is to understand how transcriptional regulatory elements are selected from the genome thereby specifying cellular identity and cell-specific responses. The goal of this project is to use systems biology approaches and the maps-to-model paradigm to gain insights into general mechanisms responsible for the selection and function of cis-regulatory elements necessary for transcriptional responses to pathogens.
|The SDCSB has previously made substantial progress in understanding how stress remodels transcriptional regulatory networks.
The Ren laboratory adapted a high-throughput DNA sequencing method known as Hi-C to investigate how long range chromatin interactions regulate transcription, resulting in the striking finding that functional enhancer-gene interactions occur within megabase-sized topological domains (Jin et al., Nature 2013). The Glass laboratory used both genetic manipulations and natural genetic variations in mice to identify combinations of transcription factors that bind enhancers to affect a gene regulatory response during inflammation stress (Heinz et al., Nature 2013).
In this project, we will map transcription factor binding sites, enhancer-promoter interactions and the 3D higher-order organization of the genome to develop models that predict how cells from different genotypes and tissues respond transcriptionally to pathogenic stimuli. Primary data sets derived from ChIP-Seq, GRO-Seq and conformation capture assays will be used to build context-dependent maps of cis-regulatory elements. We will then develop and apply novel analytical approaches to construct and test models for how enhancers and promoters are initially selected from the genome by combinations of sequence specific transcription factors, how these genomic regions subsequently acquire features of active enhancers, and how active enhancers ultimately communicate with target genes. DNA-DNA interaction networks will also be analyzed for their underlying hierarchical structure.
The Ideker lab recently developed an innovative new approach, called NeXO (Network-extracted ontologies), which identifies the complete hierarchical structure of modules embedded in a biological network and represents this hierarchy as an ontology (Dutkowski et al., Nat Biotechnol 2013). An improved version of this algorithm, called CliXO (Clique-extracted ontologies), can construct an ontology from any set of quantitative data between elements for which a pairwise similarity score can be defined (Kramer et al., Bioinformatics 2014). Hi-C experiments form such a data set, as an increasing number of reads connecting two genomic regions correlates with a closer position in 3D topological space in the nucleus. This in turn may correlate with functional similarity or cooperation, as we hypothesize that DNA elements in a single topological domain in 3D space are more likely to be functionally related. We are also devising methods to explore whether this chromatin-based ontology can be used to predict transcriptional activity.
It is likely that viruses also target and subvert the 3D landscape of the host genome. Human adenovirus serotype Ad5 is a double stranded DNA virus that induces large-scale changes in cellular mRNA expression, epigenetic marks and nuclear morphology. The activation of viral gene promoters is exquisitely timed in concert with the systems-wide subversion of host transcription. We plan to map the host-viral genome interactions and transcriptional programs, which we predict will play a key role in determining viral tropism and replication.
Recent SDCSB Publications by these Investigators:
- Saito, R, Rocanin-Arjo, A, You, YH, Darshi, M, Van Espen, B, Miyamoto, S et al.. Systems biology analysis reveals role of MDM2 in diabetic nephropathy. JCI Insight. 2016;1 (17):e87877. doi: 10.1172/jci.insight.87877. PubMed PMID:27777973 PubMed Central PMC5070958.
- Eichenfield, DZ, Troutman, TD, Link, VM, Lam, MT, Cho, H, Gosselin, D et al.. Tissue damage drives co-localization of NF-κB, Smad3, and Nrf2 to direct Rev-erb sensitive wound repair in mouse macrophages. Elife. 2016;5 :. doi: 10.7554/eLife.13024. PubMed PMID:27462873 PubMed Central PMC4963201.
- Srivas, R, Shen, JP, Yang, CC, Sun, SM, Li, J, Gross, AM et al.. A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy. Mol. Cell. 2016;63 (3):514-25. doi: 10.1016/j.molcel.2016.06.022. PubMed PMID:27453043 PubMed Central PMC5209245.
- Allison, KA, Sajti, E, Collier, JG, Gosselin, D, Troutman, TD, Stone, EL et al.. Affinity and dose of TCR engagement yield proportional enhancer and gene activity in CD4+ T cells. Elife. 2016;5 :. doi: 10.7554/eLife.10134. PubMed PMID:27376549 PubMed Central PMC4931909.
- Yu, MK, Kramer, M, Dutkowski, J, Srivas, R, Licon, K, Kreisberg, J et al.. Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems. Cell Syst. 2016;2 (2):77-88. doi: 10.1016/j.cels.2016.02.003. PubMed PMID:26949740 PubMed Central PMC4772745.
- Diao, Y, Li, B, Meng, Z, Jung, I, Lee, AY, Dixon, J et al.. A new class of temporarily phenotypic enhancers identified by CRISPR/Cas9-mediated genetic screening. Genome Res. 2016;26 (3):397-405. doi: 10.1101/gr.197152.115. PubMed PMID:26813977 PubMed Central PMC4772021.
- Carvunis, AR, Wang, T, Skola, D, Yu, A, Chen, J, Kreisberg, JF et al.. Evidence for a common evolutionary rate in metazoan transcriptional networks. Elife. 2015;4 :. doi: 10.7554/eLife.11615. PubMed PMID:26682651 PubMed Central PMC4764585.
- Ren, B, Yue, F. Transcriptional Enhancers: Bridging the Genome and Phenome. Cold Spring Harb. Symp. Quant. Biol. 2015;80 :17-26. doi: 10.1101/sqb.2015.80.027219. PubMed PMID:26582789 .
- Link, VM, Gosselin, D, Glass, CK. Mechanisms Underlying the Selection and Function of Macrophage-Specific Enhancers. Cold Spring Harb. Symp. Quant. Biol. 2015;80 :213-21. doi: 10.1101/sqb.2015.80.027367. PubMed PMID:26582787 PubMed Central PMC4936825.
- Gross, AM, Kreisberg, JF, Ideker, T. Analysis of Matched Tumor and Normal Profiles Reveals Common Transcriptional and Epigenetic Signals Shared across Cancer Types. PLoS ONE. 2015;10 (11):e0142618. doi: 10.1371/journal.pone.0142618. PubMed PMID:26555223 PubMed Central PMC4640835.