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COPD pp 321-332 | Cite as

Big Data and Network Medicine in COPD

  • Edwin K. Silverman
Chapter
  • 1.1k Downloads

Abstract

Big Data provides substantial technical challenges related to organizing, moving, storing, and analyzing vast amounts of information. Importantly, useful “Big Data” needs to be more than just a lot of data; it needs to have complex, accurate, and relevant information. Key sources of Big Data in COPD research include genetics, other Omics (e.g., transcriptomics, proteomics, metabolomics, and epigenetics), and imaging. Network science provides approaches that can assist in the analysis of Big Data. Based on graph theory, networks provide a useful structure to visualize and analyze relationships—both linear and nonlinear—between variables of interest. Network Medicine involves the application of network science approaches to diseases. Key components of Network Medicine include identifying molecular interactions, defining optimal disease phenotypes, and integrating multiple Omics data. These three approaches are utilized to define disease-related networks, which will be utilized to reclassify diseases like COPD based on their etiology instead of end-stage physiological and pathological manifestations. Finally, new treatments and preventative strategies will be developed using systems pharmacology approaches. Integration of multiple types of Big Data will be essential in Network Medicine, and leveraging both Omics and animal model experimental data can overcome the limitations of an individual method. To realize the potential of these exciting opportunities in Big Data and Network Medicine, we will need to restructure the way that we study the pathogenesis of COPD and the approaches that we utilize to develop new COPD treatments.

Keywords

Network Medicine Big data Genetics Imaging Subtyping 

Notes

Acknowledgements

The author thanks Dawn DeMeo, Craig Hersh, Peter Castaldi, and Michael Cho for helpful comments on this manuscript.

Conflict of Interest Statement In the past 3 years, Edwin K. Silverman received honoraria and consulting fees from Merck, grant support and consulting fees from GlaxoSmithKline, and honoraria from Novartis.

References

  1. 1.
    Agusti A, Anto JM, Auffray C, Barbe F, Barreiro E, Dorca J, et al. Personalized respiratory medicine: exploring the horizon, addressing the issues. Summary of a BRN-AJRCCM workshop held in Barcelona on June 12, 2014. Am J Respir Crit Care Med. 2015;191(4):391–401.PubMedPubMedCentralGoogle Scholar
  2. 2.
    Marx V. Biology: the big challenges of big data. Nature. 2013;498(7453):255–60.PubMedGoogle Scholar
  3. 3.
    Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C, Efron MJ, et al. Big data: Astronomical or Genomical? PLoS Biol. 2015;13(7):e1002195.PubMedPubMedCentralGoogle Scholar
  4. 4.
    Silverman EK, Mosley JD, Palmer LJ, Barth M, Senter JM, Brown A, et al. Genome-wide linkage analysis of severe, early-onset chronic obstructive pulmonary disease: airflow obstruction and chronic bronchitis phenotypes. Hum Mol Genet. 2002;11(6):623–32.PubMedGoogle Scholar
  5. 5.
    Silverman EK, Palmer LJ, Mosley JD, Barth M, Senter JM, Brown A, et al. Genomewide linkage analysis of quantitative spirometric phenotypes in severe early-onset chronic obstructive pulmonary disease. Am J Hum Genet. 2002;70(5):1229–39.PubMedPubMedCentralGoogle Scholar
  6. 6.
    Hardin M, Silverman EK. Chronic obstructive pulmonary disease genetics: a review of the past and a look into the future. J COPD Found. 2014;1(1):33–46.Google Scholar
  7. 7.
    Cho MH, McDonald ML, Zhou X, Mattheisen M, Castaldi PJ, Hersh CP, et al. Risk loci for chronic obstructive pulmonary disease: a genome-wide association study and meta-analysis. Lancet Respir Med. 2014;2(3):214–25.PubMedPubMedCentralGoogle Scholar
  8. 8.
    Cho MH, Boutaoui N, Klanderman BJ, Sylvia JS, Ziniti JP, Hersh CP, et al. Variants in FAM13A are associated with chronic obstructive pulmonary disease. Nat Genet. 2010;42:200–2.PubMedPubMedCentralGoogle Scholar
  9. 9.
    Cho MH, Castaldi PJ, Hersh CP, Hobbs BD, Barr RG, Tal-Singer R, et al. A genome-wide association study of emphysema and airway quantitative imaging phenotypes. Am J Respir Crit Care Med. 2015;192(5):559–69.PubMedPubMedCentralGoogle Scholar
  10. 10.
    Pillai SG, Ge D, Zhu G, Kong X, Shianna KV, Need AC, et al. A genome-wide association study in chronic obstructive pulmonary disease (COPD): identification of two major susceptibility loci. PLoS Genet. 2009;5(3):e1000421.PubMedPubMedCentralGoogle Scholar
  11. 11.
    Wilk JB, Chen TH, Gottlieb DJ, Walter RE, Nagle MW, Brandler BJ, et al. A genome-wide association study of pulmonary function measures in the Framingham Heart Study. PLoS Genet. 2009;5(3):e1000429.PubMedPubMedCentralGoogle Scholar
  12. 12.
    Castaldi PJ, San Jose Estepar R, Mendoza CS, Hersh CP, Laird N, Crapo JD, et al. Distinct quantitative computed tomography emphysema patterns are associated with physiology and function in smokers. Am J Respir Crit Care Med. 2013;188(9):1083–90.PubMedPubMedCentralGoogle Scholar
  13. 13.
    Castaldi PJ, Cho MH, San Jose Estepar R, McDonald ML, Laird N, Beaty TH, et al. Genome-wide association identifies regulatory loci associated with distinct local histogram emphysema patterns. Am J Respir Crit Care Med. 2014;190(4):399–409.PubMedPubMedCentralGoogle Scholar
  14. 14.
    Ryan DM, Vincent TL, Salit J, Walters MS, Agosto-Perez F, Shaykhiev R, et al. Smoking dysregulates the human airway basal cell transcriptome at COPD risk locus 19q13.2. PLoS One. 2014;9(2):e88051.PubMedPubMedCentralGoogle Scholar
  15. 15.
    Cho MH, Castaldi PJ, Wan ES, Siedlinski M, Hersh CP, Demeo DL, et al. A genome-wide association study of COPD identifies a susceptibility locus on chromosome 19q13. Hum Mol Genet. 2012;21(4):947–57.PubMedGoogle Scholar
  16. 16.
    Kim WJ, Lim JH, Lee JS, Lee SD, Kim JH, Oh YM. Comprehensive analysis of transcriptome sequencing data in the lung tissues of COPD subjects. Int J Genomics. 2015;2015:9.Google Scholar
  17. 17.
    Telenga ED, Hoffmann RF, Ruben TK, Hoonhorst SJ, Willemse BW, van Oosterhout AJ, et al. Untargeted lipidomic analysis in chronic obstructive pulmonary disease. Uncovering sphingolipids. Am J Respir Crit Care Med. 2014;190(2):155–64.PubMedGoogle Scholar
  18. 18.
    Bowler RP, Jacobson S, Cruickshank C, Hughes GJ, Siska C, Ory DS, et al. Plasma sphingolipids associated with chronic obstructive pulmonary disease phenotypes. Am J Respir Crit Care Med. 2015;191(3):275–84.PubMedPubMedCentralGoogle Scholar
  19. 19.
    Lomas DA, Silverman EK, Edwards LD, Locantore NW, Miller BE, Horstman DH, et al. Serum surfactant protein D is steroid sensitive and associated with exacerbations of COPD. Eur Respir J. 2009;34(1):95–102.PubMedGoogle Scholar
  20. 20.
    Lomas DA, Silverman EK, Edwards LD, Miller BE, Coxson HO, Tal-Singer R. Evaluation of serum CC-16 as a biomarker for COPD in the ECLIPSE cohort. Thorax. 2008;63(12):1058–63.PubMedGoogle Scholar
  21. 21.
    Sin DD, Miller BE, Duvoix A, Man SF, Zhang X, Silverman EK, et al. Serum PARC/CCL-18 concentrations and health outcomes in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2011;183(9):1187–92.PubMedPubMedCentralGoogle Scholar
  22. 22.
    Duvoix A, Dickens J, Haq I, Mannino D, Miller B, Tal-Singer R, et al. Blood fibrinogen as a biomarker of chronic obstructive pulmonary disease. Thorax. 2013;68(7):670–6.PubMedGoogle Scholar
  23. 23.
    Yonchuk JG, Silverman EK, Bowler R, Agusti A, Lomas DA, Miller BE, et al. Circulating sRAGE as a biomarker of emphysema and the RAGE Axis in the lung. Am J Respir Crit Care Med. 2015;192(7):785–92.PubMedGoogle Scholar
  24. 24.
    Cheng DT, Kim DK, Cockayne DA, Belousov A, Bitter H, Cho MH, et al. Systemic soluble receptor for advanced glycation endproducts is a biomarker of emphysema and associated with AGER genetic variants in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2013;188(8):948–57.Google Scholar
  25. 25.
    Terracciano R, Pelaia G, Preiano M, Savino R. Asthma and COPD proteomics: current approaches and future directions. Proteomics Clin Appl. 2015;9(1–2):203–20.PubMedGoogle Scholar
  26. 26.
    Wan ES, Qiu W, Baccarelli A, Carey VJ, Bacherman H, Rennard SI, et al. Cigarette smoking behaviors and time since quitting are associated with differential DNA methylation across the human genome. Hum Mol Genet. 2012;21(13):3073–82.PubMedPubMedCentralGoogle Scholar
  27. 27.
    Qiu W, Baccarelli A, Carey VJ, Boutaoui N, Bacherman H, Klanderman B, et al. Variable DNA methylation is associated with chronic obstructive pulmonary disease and lung function. Am J Respir Crit Care Med. 2012;185(4):373–81.PubMedPubMedCentralGoogle Scholar
  28. 28.
    Barabasi AL. Network medicine--from obesity to the “diseasome”. N Engl J Med. 2007;357(4):404–7.PubMedGoogle Scholar
  29. 29.
    Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12(1):56–68.PubMedPubMedCentralGoogle Scholar
  30. 30.
    Vidal M, Cusick ME, Barabasi AL. Interactome networks and human disease. Cell. 2011;144(6):986–98.PubMedPubMedCentralGoogle Scholar
  31. 31.
    Jia P, Zheng S, Long J, Zheng W, Zhao Z. dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics. 2011;27(1):95–102.PubMedGoogle Scholar
  32. 32.
    McDonald ML, Mattheisen M, Cho M, Liu Y-Y, Harshfield B, Hersh C, et al. Beyond GWAS in COPD: probing the landscape between gene-set associations, genome-wide associations and protein-protein interaction networks. Hum Hered. 2014;78:131–9.PubMedPubMedCentralGoogle Scholar
  33. 33.
    Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.PubMedPubMedCentralGoogle Scholar
  34. 34.
    Glass K, Huttenhower C, Quackenbush J, Yuan GC. Passing messages between biological networks to refine predicted interactions. PLoS One. 2013;8(5):e64832.PubMedPubMedCentralGoogle Scholar
  35. 35.
    Lao T, Glass K, Qiu W, Polverino F, Gupta K, Morrow J, et al. Haploinsufficiency of hedgehog interacting protein causes increased emphysema induced by cigarette smoke through network rewiring. Genome Med. 2015;7(1):12.PubMedPubMedCentralGoogle Scholar
  36. 36.
    Chu JH, Hersh CP, Castaldi PJ, Cho MH, Raby BA, Laird N, et al. Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD. BMC Syst Biol. 2014;8:78.PubMedPubMedCentralGoogle Scholar
  37. 37.
    Silverman EK, Loscalzo J. Network medicine approaches to the genetics of complex diseases. Discov Med. 2012;14(75):143–52.PubMedPubMedCentralGoogle Scholar
  38. 38.
    Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–60.PubMedGoogle Scholar
  39. 39.
    Castaldi PJ, Dy J, Ross J, Chang Y, Washko GR, Curran-Everett D, et al. Cluster analysis in the COPDGene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema. Thorax. 2014;69(5):415–22.PubMedGoogle Scholar
  40. 40.
    Jiang Z, et al. A chronic obstructive pulmonary disease susceptibility gene, FAM13A, regulates protein stability of b-catenin. Am J Resp Crit Care Med 2016;194:185–97.PubMedPubMedCentralGoogle Scholar
  41. 41.
    Cloonan SM, et al. Mitochondrial iron chelation ameliorates cigarette smoke-induced bronchitis and emphysema in mice. Nature Medicine 2016;22:163–74.PubMedPubMedCentralGoogle Scholar
  42. 42.
    D’Armiento J, Dalal SS, Okada Y, Berg RA, Chada K. Collagenase expression in the lungs of transgenic mice causes pulmonary emphysema. Cell. 1992;71(6):955–61.PubMedPubMedCentralGoogle Scholar
  43. 43.
    Hautamaki RD, Kobayashi DK, Senior RM, Shapiro SD. Requirement for macrophage elastase for cigarette smoke-induced emphysema in mice. Science. 1997;277:2002–4.PubMedGoogle Scholar
  44. 44.
    Hersh CP, Demeo DL, Lange C, Litonjua AA, Reilly JJ, Kwiatkowski D, et al. Attempted replication of reported chronic obstructive pulmonary disease candidate gene associations. Am J Respir Cell Mol Biol. 2005;33(1):71–8.PubMedPubMedCentralGoogle Scholar
  45. 45.
    Ansel J, Bottin H, Rodriguez-Beltran C, Damon C, Nagarajan M, Fehrmann S, et al. Cell-to-cell stochastic variation in gene expression is a complex genetic trait. PLoS Genet. 2008;4(4):e1000049.PubMedPubMedCentralGoogle Scholar
  46. 46.
    Lee MJ, Ye AS, Gardino AK, Heijink AM, Sorger PK, MacBeath G, et al. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell. 2012;149(4):780–94.PubMedPubMedCentralGoogle Scholar
  47. 47.
    Sorger PK, Allerheiligen SRB, Abernethy DR, Altman RB, Brouwer KLR, Califano A, et al. Quantitative and systems pharmacology in the post-genomic era: new approaches to discovering drugs and understanding therapeutic mechanisms. October 2011. Report No.Google Scholar
  48. 48.
    Silverman EK, Loscalzo J. Developing new drug treatments in the era of network medicine. Clin Pharmacol Ther. 2013;93(1):26–8.PubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Edwin K. Silverman
    • 1
  1. 1.Channing Division of Network MedicineBrigham and Women’s Hospital, Harvard Medical SchoolBostonUSA

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