Skip to main content
SpringerLink
Log in

Predicting Functional Interactions Among DNA-Binding Proteins

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11305))

Included in the following conference series:

Abstract

Perturbation of the binding pattern of one or more DNA-binding proteins, called transcription factors, plays a role in many diseases including, but not limited to, cancer. This has prompted efforts to characterise transcription cofactors i.e., transcription factors that work together to regulate gene expression. The Overlap Correlation Value (OCV), ranging from 0 (no correlation) to 1 (highly correlated), has been previously reported as a measure of the statistical significance in the overlap of binding sites of two transcription factors and thus a measure of the extent to which they may act as cofactors. In this study, we examined the variation in the OCV due to the peak caller employed to identify transcription factor binding sites. We identified that the significance of correlation between two transcription factors was unaffected by the peak-caller employed to identify transcription factor binding sites (Spearman R = 0.98). Furthermore, we used OCV measurements to develop a novel network map to study the correlation between twelve breast cancer cell-line datasets. Our proposed novel map revealed that transcription factor FOXA1 influenced the binding of six other transcription factors: JUND, P300, estrogen receptor alpha (ERα), GATA3, progesterone receptor (PR), and XBP1. Our model identified that binding sites that were targeted by PR were different under progesterone agonist (R5020 or ORG2058) or antagonist (RU486) treatment. Interestingly ERα had a significant OCV with PR when stimulated by anti-progestin, while it showed no significant overlap with PR when simulated with progestin. Our proposed network map drawn using OCV measurements is feature rich, more meaningful, and is better interpretable then using Venn diagram. The network map can be used in all scientific domains.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wang, J., et al.: Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res. 22(9), 1798–1812 (2012)

    Article  Google Scholar 

  2. Hu, Z., Hu, B., Collins, J.F.: Prediction of synergistic transcription factors by function conservation. Genome Biol. 8(12), R257 (2007)

    Article  Google Scholar 

  3. Hannenhalli, S., Levy, S.: Predicting transcription factor synergism. Nucleic Acids Res. 30(19), 4278–4284 (2002)

    Article  Google Scholar 

  4. Vassilev, L.T., et al.: In Vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science 303(5659), 844–848 (2004)

    Article  Google Scholar 

  5. Motallebipour, M., et al.: Differential binding and co-binding pattern of FOXA1 and FOXA3 and their relation to H3K4me3 in HepG2 cells revealed by ChIP-seq. Genome Biol. 10(11), R129 (2009)

    Article  Google Scholar 

  6. Park, P.J.: ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet. 10(10), 669–680 (2009)

    Article  Google Scholar 

  7. Simovski, B., et al.: Coloc-stats: a unified web interface to perform colocalization analysis of genomic features. Nucleic Acids Res. 46(W1), W186–W193 (2018)

    Article  Google Scholar 

  8. Stavrovskaya, E.D., et al.: StereoGene: rapid estimation of genome-wide correlation of continuous or interval feature data. Bioinformatics 33(20), 3158–3165 (2017)

    Article  Google Scholar 

  9. Thomas, R., et al.: Features that define the best ChIP-seq peak calling algorithms. Brief. Bioinform. 18, 441–450 (2016)

    Google Scholar 

  10. Heinz, S., et al.: Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38(4), 576–589 (2010)

    Article  Google Scholar 

  11. Zhang, Y., et al.: Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9(9), R137 (2008)

    Article  Google Scholar 

  12. Khushi, M., et al.: Binding sites analyser (BiSA): software for genomic binding sites archiving and overlap analysis. PLoS One 9(2), e87301 (2014)

    Article  Google Scholar 

  13. Khushi, M.: Benchmarking database performance for genomic data. J. Cell. Biochem. 116(6), 877–883 (2015)

    Article  Google Scholar 

  14. Chikina, M.D., Troyanskaya, O.G.: An effective statistical evaluation of ChIPseq dataset similarity. Bioinformatics 28(5), 607–613 (2012)

    Article  Google Scholar 

  15. Landt, S.G., et al.: ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22(9), 1813–1831 (2012)

    Article  Google Scholar 

  16. Martin, M.: Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17(1), 10–12 (2011)

    Article  Google Scholar 

  17. Langmead, B., et al.: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10(3), R25 (2009)

    Article  Google Scholar 

  18. Jemal, A., et al.: Global cancer statistics. CA Cancer J. Clin. 61(2), 69–90 (2011)

    Article  Google Scholar 

  19. Yin, P., et al.: Genome-wide progesterone receptor binding: cell type-specific and shared mechanisms in T47D breast cancer cells and primary leiomyoma cells. PLoS One 7(1), e29021 (2012)

    Article  Google Scholar 

  20. Ballare, C., et al.: Nucleosome-driven transcription factor binding and gene regulation. Mol. Cell 49(1), 67–79 (2013)

    Article  Google Scholar 

  21. Clarke, C.L., Graham, J.D.: Non-overlapping progesterone receptor cistromes contribute to cell-specific transcriptional outcomes. PLoS One 7(4), e35859 (2012)

    Article  Google Scholar 

  22. Joseph, R., et al.: Integrative model of genomic factors for determining binding site selection by estrogen receptor-alpha. Mol. Syst. Biol. 6, 456 (2010)

    Article  Google Scholar 

  23. Gertz, J., et al.: Genistein and bisphenol A exposure cause estrogen receptor 1 to bind thousands of sites in a cell type-specific manner. Genome Res. 22(11), 2153–2162 (2012)

    Article  Google Scholar 

  24. Gertz, J., et al.: Distinct properties of cell-type-specific and shared transcription factor binding sites. Mol. Cell 52(1), 25–36 (2013)

    Article  Google Scholar 

  25. Adomas, A.B., et al.: Breast tumor specific mutation in GATA3 affects physiological mechanisms regulating transcription factor turnover. BMC Cancer 14, 278 (2014)

    Article  Google Scholar 

  26. Yamamoto, S., et al.: JARID1B is a luminal lineage-driving oncogene in breast cancer. Cancer Cell 25(6), 762–777 (2014)

    Article  Google Scholar 

  27. Chen, X., et al.: XBP1 promotes triple-negative breast cancer by controlling the HIF1 alpha pathway. Nature 508(7494), 103–107 (2014)

    Article  Google Scholar 

  28. Ghosh, A.K., Varga, J.: The transcriptional coactivator and acetyltransferase p300 in fibroblast biology and fibrosis. J. Cell. Physiol. 213(3), 663–671 (2007)

    Article  Google Scholar 

  29. Jin, H.J., et al.: Cooperativity and equilibrium with FOXA1 define the androgen receptor transcriptional program. Nat. Commun. 5, 3972 (2014)

    Article  Google Scholar 

  30. Lee, B.K., Iyer, V.R.: Genome-wide studies of CCCTC-binding factor (CTCF) and cohesin provide insight into chromatin structure and regulation. J. Biol. Chem. 287(37), 30906–30913 (2012)

    Article  Google Scholar 

  31. Yusufzai, T.M., et al.: CTCF tethers an insulator to subnuclear sites, suggesting shared insulator mechanisms across species. Mol. Cell. 13(2), 291–298 (2004)

    Article  Google Scholar 

  32. Holwerda, S.J., de Laat, W.: CTCF: the protein, the binding partners, the binding sites and their chromatin loops. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368(1620), 20120369 (2013)

    Article  Google Scholar 

  33. Yamane, K., et al.: PLU-1 is an H3K4 demethylase involved in transcriptional repression and breast cancer cell proliferation. Mol. Cell 25(6), 801–812 (2007)

    Article  Google Scholar 

  34. Li, H., et al.: Functional annotation of HOT regions in the human genome: implications for human disease and cancer. Sci. Rep. 5, 11633 (2015)

    Article  Google Scholar 

  35. Benagiano, G., Bastianelli, C., Farris, M.: Selective progesterone receptor modulators 2: use in reproductive medicine. Expert Opin. Pharmacother. 9(14), 2473–2485 (2008)

    Article  Google Scholar 

  36. Khushi, M., Clarke, C.L., Graham, J.D.: Bioinformatic analysis of cis-regulatory interactions between progesterone and estrogen receptors in breast cancer. PeerJ 2, e654 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technology, The University of Sydney, Sydney, NSW, 2006, Australia

    Matloob Khushi

  2. Children’s Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia

    Matloob Khushi & Jonathan W. Arthur

  3. Faculty of Engineering and IT, The University of Sydney, Sydney, NSW, 2006, Australia

    Matloob Khushi & Nazim Choudhury

  4. Centre for Cancer Research, The Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW, 2145, Australia

    Matloob Khushi, Christine L. Clarke & J. Dinny Graham

  5. Westmead Breast Cancer Institute, Westmead Hospital, Westmead, NSW, 2145, Australia

    J. Dinny Graham

Authors
  1. Matloob Khushi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nazim Choudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonathan W. Arthur
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christine L. Clarke
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. J. Dinny Graham
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matloob Khushi .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Khushi, M., Choudhury, N., Arthur, J.W., Clarke, C.L., Graham, J.D. (2018). Predicting Functional Interactions Among DNA-Binding Proteins. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04221-9_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04220-2

  • Online ISBN: 978-3-030-04221-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation