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
-
- 6,682
- views
-
- 1,244
- 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
- Evolutionary Biology
A comprehensive census of McrBC systems, among the most common forms of prokaryotic Type IV restriction systems, followed by phylogenetic analysis, reveals their enormous abundance in diverse prokaryotes and a plethora of genomic associations. We focus on a previously uncharacterized branch, which we denote coiled-coil nuclease tandems (CoCoNuTs) for their salient features: the presence of extensive coiled-coil structures and tandem nucleases. The CoCoNuTs alone show extraordinary variety, with three distinct types and multiple subtypes. All CoCoNuTs contain domains predicted to interact with translation system components, such as OB-folds resembling the SmpB protein that binds bacterial transfer-messenger RNA (tmRNA), YTH-like domains that might recognize methylated tmRNA, tRNA, or rRNA, and RNA-binding Hsp70 chaperone homologs, along with RNases, such as HEPN domains, all suggesting that the CoCoNuTs target RNA. Many CoCoNuTs might additionally target DNA, via McrC nuclease homologs. Additional restriction systems, such as Type I RM, BREX, and Druantia Type III, are frequently encoded in the same predicted superoperons. In many of these superoperons, CoCoNuTs are likely regulated by cyclic nucleotides, possibly, RNA fragments with cyclic termini, that bind associated CARF (CRISPR-Associated Rossmann Fold) domains. We hypothesize that the CoCoNuTs, together with the ancillary restriction factors, employ an echeloned defense strategy analogous to that of Type III CRISPR-Cas systems, in which an immune response eliminating virus DNA and/or RNA is launched first, but then, if it fails, an abortive infection response leading to PCD/dormancy via host RNA cleavage takes over.
-
- Computational and Systems Biology
Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canonical approach for imputation of clinical data and widely used algorithms introduce variance in the downstream classification. Here we propose novel imputation methods based on determinantal point processes (DPP) that enhance popular techniques such as the multivariate imputation by chained equations and MissForest. Their advantages are twofold: improving the quality of the imputed data demonstrated by increased accuracy of the downstream classification and providing deterministic and reliable imputations that remove the variance from the classification results. We experimentally demonstrate the advantages of our methods by performing extensive imputations on synthetic and real clinical data. We also perform quantum hardware experiments by applying the quantum circuits for DPP sampling since such quantum algorithms provide a computational advantage with respect to classical ones. We demonstrate competitive results with up to 10 qubits for small-scale imputation tasks on a state-of-the-art IBM quantum processor. Our classical and quantum methods improve the effectiveness and robustness of clinical data prediction modeling by providing better and more reliable data imputations. These improvements can add significant value in settings demanding high precision, such as in pharmaceutical drug trials where our approach can provide higher confidence in the predictions made.
