Combinatorial bZIP dimers define complex DNA-binding specificity landscapes
- University of Wisconsin-Madison, United States
- Massachusetts Institute of Technology, United States
Abstract
How transcription factor dimerization impacts DNA binding specificity is poorly understood. Guided by protein dimerization properties, we examined DNA binding specificities of 270 human bZIP pairs. DNA interactomes of 80 heterodimers and 22 homodimers revealed that 72% of heterodimer motifs correspond to conjoined half-sites preferred by partnering monomers. Remarkably, the remaining motifs are composed of variably-spaced half-sites (12%) or 'emergent' sites (16%) that cannot be readily inferred from half-site preferences of partnering monomers. These binding sites were biochemically validated by EMSA-FRET analysis and validated in vivo by ChIP-seq data from human cell lines. Focusing on ATF3, we observed distinct cognate site preferences conferred by different bZIP partners, and demonstrated that genome-wide binding of ATF3 is best explained by considering many dimers in which it participates. Importantly, our compendium of bZIP-DNA interactomes predicted bZIP binding to 156 disease associated SNPs, of which only 20 were previously annotated with known bZIP motifs.
Data availability
-
Data from: Combinatorial dimerization of human bZIP factors confers preferences for different classes of DNA binding sitesPublicly available on the Ansari Lab (https://ansarilab.biochem.wisc.edu/).
-
Functional Identification of Critical Bmi1 target genes in Neural Progenitor and Malignant Glioma cellsPublicly available at the NCBI Gene Expression Omnibus (accession no: GSE33912).
-
DNA-binding specificities of human transcription factorsPublicly available at the EMBL European Nucleotide Archive (accession no: PRJEB3289).
Article and author information
Author details
Jose A Rodriguez-Martinez
Funding
National Institutes of Health (R01GM096466)
- Amy E Keating
W. M. Keck Foundation
- Aseem Z Ansari
National Institutes of Health (U01HL099773)
- Aseem Z Ansari
National Institutes of Health (R01CA133508)
- Aseem Z Ansari
National Institutes of Health (T32HG002760)
- Jose A Rodriguez-Martinez
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Michael R Green, Howard Hughes Medical Institute, University of Massachusetts Medical School, United States
Version history
- Received: June 30, 2016
- Accepted: February 6, 2017
- Accepted Manuscript published: February 10, 2017 (version 1)
- Version of Record published: March 14, 2017 (version 2)
Copyright
© 2017, Rodriguez-Martinez et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 6,590
- views
-
- 1,231
- downloads
-
- 107
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Combinatorial bZIP dimers define complex DNA-binding specificity landscapes
eLife 6:e19272.
https://doi.org/10.7554/eLife.19272
Further reading
-
- Computational and Systems Biology
- Developmental Biology
Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.
-
- Computational and Systems Biology
- Genetics and Genomics
We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.
