Waardenberg, Ashley J and Homan, Bernou and Mohamed, Stephanie and Harvey, Richard P and Bouveret, Romaric (2016) Prediction and validation of protein-protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach. Open Biology, 6 (9). ISSN 2046-2441 (OA)
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Abstract
The ability to accurately predict the DNA targets and interacting cofactors of transcriptional regulators from genome-wide data can significantly advance our understanding of gene regulatory networks. NKX2-5 is a homeodomain transcription factor that sits high in the cardiac gene regulatory network and is essential for normal heart development. We previously identified genomic targets for NKX2-5 in mouse HL-1 atrial cardiomyocytes using DNA-adenine methyltransferase identification (DamID). Here, we apply machine learning algorithms and propose a knowledge-based feature selection method for predicting NKX2-5 protein : protein interactions based on motif grammar in genome-wide DNA-binding data. We assessed model performance using leave-one-out cross-validation and a completely independent DamID experiment performed with replicates. In addition to identifying previously described NKX2-5-interacting proteins, including GATA, HAND and TBX family members, a number of novel interactors were identified, with direct protein : protein interactions between NKX2-5 and retinoid X receptor (RXR), paired-related homeobox (PRRX) and Ikaros zinc fingers (IKZF) validated using the yeast two-hybrid assay. We also found that the interaction of RXRα with NKX2-5 mutations found in congenital heart disease (Q187H, R189G and R190H) was altered. These findings highlight an intuitive approach to accessing protein-protein interaction information of transcription factors in DNA-binding experiments.
| Item Type: | Article |
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| Additional Information: | COPYRIGHT: Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
| Subjects: | R Medicine > R Medicine (General) |
| Depositing User: | Repository Administrator |
| Date Deposited: | 04 Oct 2016 00:29 |
| Last Modified: | 04 Oct 2016 00:30 |
| URI: | https://eprints.victorchang.edu.au/id/eprint/495 |
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