Prediction and validation of protein-protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach.
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.
Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales 2010, Australia Children's Medical Research Institute, University of Sydney, Westmead, New South Wales 2145, Australia awaardenberg@cmri.org.au.
Ashley J
http://orcid.org/0000-0002-9382-7490
AJ
Waardenberg
Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales 2010, Australia.
Bernou
B
Homan
Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales 2010, Australia.
Stephanie
S
Mohamed
Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales 2010, Australia St Vincent's Clinical School, University of Sydney, Westmead, New South Wales 2145, Australia School of Biotechnology and Biomolecular Science, University of New South Wales, Kensington, New South Wales 2052, Australia r.harvey@victorchang.edu.au.
Richard P
RP
Harvey
Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales 2010, Australia St Vincent's Clinical School, University of Sydney, Westmead, New South Wales 2145, Australia.
Romaric
R
Bouveret
gene regulatory networks; machine learning; protein–protein interactions; transcription factors
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