Understanding tissue-specificity with human tissue-specific regulatory networks

Science China Information Sciences volume 59, Article number: 070105 (2016) Cite this article

Abstract

Tissue-specificity is important for the function of human body. However, it is still not clear how the functional diversity of different tissues is achieved. Here we construct gene regulatory networks in 13 human tissues by integrating large-scale transcription factor (TF)-gene regulations with gene and protein expression data. By comparing these regulatory networks, we find many tissue-specific regulations that are important for tissue identity. In particular, the tissue-specific TFs are found to regulate more genes than those expressed in multiple tissues, and the processes regulated by these tissue-specific TFs are closely related to tissue functions. Moreover, the regulations that are present in certain tissue are found to be enriched in the tissue associated disease genes, and these networks provide the molecular context of disease genes. Therefore, recognizing tissuespecific regulatory networks can help better understand the molecular mechanisms underlying diseases and identify new disease genes.

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References

  1. 1

    Greene C S, Krishnan A, Wong A K, et al. Understanding multicellular function and disease with human tissue-specific networks. Nat Genet, 2015, 47: 569–576

    Article  Google Scholar 

  2. 2

    Pierson E, Koller D, Battle A, et al. Sharing and specificity of co-expression networks across 35 human tissues. Plos Comput Biol, 2015, 11: e1004220

    Article  Google Scholar 

  3. 3

    Zhao X M, Chen L. Network-based biomarkers for complex diseases. J Theor Biol, 2014, 362: 1–2

    Article  Google Scholar 

  4. 4

    Lage K, Hansen N T, Karlberg E O, et al. A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc Nat Acad Sci USA, 2008, 105: 20870–20875

    Article  Google Scholar 

  5. 5

    Zheng C H, Huang D S, Zhang L, et al. Tumor clustering using non-negative matrix factorization with gene selection. IEEE Trans Inf Technol Biomed, 2009, 13: 599–607

    Article  Google Scholar 

  6. 6

    Deng S P, Zhu L, Huang D S. Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks. BMC Genom, 2015, 16: S4

    Article  Google Scholar 

  7. 7

    Gerstein M B, Kundaje A, Hariharan M, et al. Architecture of the human regulatory network derived from ENCODE data. Nature, 2012, 489: 91–100

    Article  Google Scholar 

  8. 8

    Ji Z W, Wu D, Zhao W, et al. Systemic modeling myeloma-osteoclast interactions under normoxic/hypoxic condition using a novel computational approach. Sci Rep, 2015, 5: 13291

    Article  Google Scholar 

  9. 9

    Deng S P, Zhu L, Huang D S. Predicting hub genes associated with cervical cancer through gene co-expression networks. IEEE/ACM Trans Comput Biol Bioinform, 2016, 13: 27–35

    Article  Google Scholar 

  10. 10

    Mathelier A, Zhao X, Zhang A W, et al. JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucl Acid Res, 2013, gkt997

    Google Scholar 

  11. 11

    Matys V, Fricke E, Geffers R, et al. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucl Acid Res, 2003, 31: 374–378

    Article  Google Scholar 

  12. 12

    Jiang C, Xuan Z, Zhao F, et al. TRED: a transcriptional regulatory element database, new entries and other development. Nucl Acid Res, 2007, 35: D137–D140

    Article  Google Scholar 

  13. 13

    Griffith O L, Montgomery S B, Bernier B, et al. ORegAnno: an open-access community-driven resource for regulatory annotation. Nucl Acid Res, 2008, 36: D107–D113

    Article  Google Scholar 

  14. 14

    Han H, Shim H, Shin D, et al. TRRUST: a reference database of human transcriptional regulatory interactions. Sci Rep, 2015, 5: 11432

    Article  Google Scholar 

  15. 15

    Zhang X, Liu K, Liu Z P, et al. NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics, 2013, 29: 106–113

    Article  Google Scholar 

  16. 16

    Li J, Hua X, Haubrock M, et al. The architecture of the gene regulatory networks of different tissues. Bioinformatics, 2012, 28: i509–i514

    Article  Google Scholar 

  17. 17

    Consortium E P. An integrated encyclopedia of DNA elements in the human genome. Nature, 2012, 489: 57–74

    Article  Google Scholar 

  18. 18

    Cheng C, Min R, Gerstein M. TIP: a probabilistic method for identifying transcription factor target genes from ChIPseq binding profiles. Bioinformatics, 2011, 27: 3221–3227

    Article  Google Scholar 

  19. 19

    Flicek P, Amode M R, Barrell D, et al. Ensembl 2014. Nucl Acid Res, 2013, gkt1196

    Google Scholar 

  20. 20

    Lefebvre C, Lim W K, Basso K, et al. A context-specific network of protein-DNA and protein-protein interactions reveals new regulatory motifs in human B cells. Syst Biol Comput Proteom, 2007, 4532: 42–56

    Article  Google Scholar 

  21. 21

    Portales-Casamar E, Arenillas D, Lim J, et al. The PAZAR database of gene regulatory information coupled to the ORCA toolkit for the study of regulatory sequences. Nucl Acid Res, 2009, 37: D54–D60

    Article  Google Scholar 

  22. 22

    Essaghir A, Toffalini F, Knoops L, et al. Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data. Nucl Acid Res, 2010, 38: e120

    Article  Google Scholar 

  23. 23

    Severin J, Waterhouse A M, Kawaji H, et al. FANTOM4 EdgeExpressDB: an integrated database of promoters, genes, microRNAs, expression dynamics and regulatory interactions. Genome Biol, 2009, 10: R39

    Article  Google Scholar 

  24. 24

    Kim M S, Pinto S M, Getnet D, et al. A draft map of the human proteome. Nature, 2014, 509: 575–581

    Article  Google Scholar 

  25. 25

    Ge X, Yamamoto S, Tsutsumi S, et al. Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics, 2005, 86: 127–141

    Article  Google Scholar 

  26. 26

    Chang C W, Cheng W C, Chen C R, et al. Identification of human housekeeping genes and tissue-selective genes by microarray meta-analysis. PLoS ONE, 2011, 6: e22859

    Article  Google Scholar 

  27. 27

    Hamosh A, Scott A F, Amberger J S, et al. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucl Acid Res, 2005, 33: D514–D517

    Article  Google Scholar 

  28. 28

    Su A I, Wiltshire T, Batalov S, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Nat Acad Sci Usa, 2004, 101: 6062–6067

    Article  Google Scholar 

  29. 29

    Dennis Jr G, Sherman B T, Hosack D A, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol, 2003, 4: P3

    Article  Google Scholar 

  30. 30

    Santhosh D, Huang Z. Regulation of the nascent brain vascular network by neural progenitors. Mech Develop, 2015, 138: 37–42

    Article  Google Scholar 

  31. 31

    Zlokovic B V. The blood-brain barrier in health and chronic neurodegenerative disorders. Neuron, 2008, 57: 178–201

    Article  Google Scholar 

  32. 32

    Posokhova E, Shukla A, Seaman S, et al. GPR124 functions as a WNT7-specific coactivator of canonical ß-catenin signaling. Cell Rep, 2015, 10: 123–130

    Article  Google Scholar 

  33. 33

    Lynch J K. Epidemiology and classification of perinatal stroke. Semin Fetal Neonatal Med, 2009, 14: 245–249

    Article  Google Scholar 

  34. 34

    Liu X, Liu Z P, Zhao X M, et al. Identifying disease genes and module biomarkers by differential interactions. J Amer Med Inform Assoc, 2012, 19: 241–248

    Article  Google Scholar 

  35. 35

    Qin G, Zhao X M. A survey on computational approaches to identifying disease biomarkers based on molecular networks. J Theor Biol, 2014, 362: 9–16

    Article  Google Scholar 

  36. 36

    Brookes E, Laurent B, Ounap K, et al. Mutations in the intellectual disability gene KDM5C reduce protein stability and demethylase activity. Hum Mol Genet, 2015, ddv046

    Google Scholar 

  37. 37

    Piard J, Roze V, Gzorny A, et al. TCF12 microdeletion in a 72-year-old woman with intellectual disability. Amer J Med Genet Part A, 2015, 167: 1897–1901

    Article  Google Scholar 

  38. 38

    Kuechler A, Willemsen M H, Albrecht B, et al. De novo mutations in beta-catenin (CTNNB1) appear to be a frequent cause of intellectual disability: expanding the mutational and clinical spectrum. Hum Genet, 2015, 134: 97–109

    Article  Google Scholar 

  39. 39

    Li C, Ito H, Fujita K, et al. Sox2 transcriptionally regulates PQBP1, an intellectual disability-microcephaly causative gene, in neural stem progenitor cells. PLoS ONE, 2013, 8: e68627

    Article  Google Scholar 

  40. 40

    Zhang S C, Cui W. Sox2, a key factor in the regulation of pluripotency and neural differentiation. World J Stem Cells, 2014, 6: 305–311

    Article  Google Scholar 

  41. 41

    Tohyama J, Kato M, Kawasaki S, et al. Dandy-Walker malformation associated with heterozygous ZIC1 and ZIC4 deletion: report of a new patient. Amer J Med Genet Part A, 2011, 155: 130–133

    Article  Google Scholar 

  42. 42

    Twigg S R, Forecki J, Goos J A, et al. Gain-of-function mutations in ZIC1 are associated with coronal craniosynostosis and learning disability. Amer J Hum Genet, 2015, 97: 378–388

    Article  Google Scholar 

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Affiliations

  1. College of Electromics and Information Engineering, Tongji University, Shanghai, 201804, China

    Weili Guo, Lin Zhu, Suping Deng, Xingming Zhao & Deshuang Huang

Authors
  1. Weili Guo
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  2. Lin Zhu
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  3. Suping Deng
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  4. Xingming Zhao
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  5. Deshuang Huang
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Corresponding authors

Correspondence to Xingming Zhao or Deshuang Huang.

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Cite this article

Guo, W., Zhu, L., Deng, S. et al. Understanding tissue-specificity with human tissue-specific regulatory networks. Sci. China Inf. Sci. 59, 070105 (2016). https://doi.org/10.1007/s11432-016-5582-0

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Keywords

  • tissue-specificity
  • gene regulatory network
  • transcription factor
  • tissue-specific regulation
  • disease gene