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Gene Expression Profiling in Asthma

  • Joanne SordilloEmail author
  • Benjamin A. Raby
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 795)

Abstract

Transcriptomics (gene expression profiling) refers to the quantitative and qualitative characterization of the collection of ribose nucleic acid (RNA) elements expressed in a biological system and represents one of the first truly genome-wide hypothesis-free investigative approaches in molecular biology. The advent of synthetic oligonucleotide microarray technologies has enabled large-scale application of gene expression profiling in the study of human disease, particularly malignant and hematological processes. Due to favorable characteristics of these processes, including their involvement of one cellular compartment (and often a specific, monoclonal cell type), the severity of the underlying cellular perturbation under study (malignant vs. benign cells), and the accessibility to large numbers of available banked samples obtained during clinically indicated medical procedures, the study of transcriptomics in oncology has been quite fruitful, with notable translation of these techniques to novel clinical applications with diagnostic, prognostic, and therapeutic implications. Furthermore, the discovery of large populations of noncoding RNA elements, including microRNA and long-intergenic noncoding RNA (LINCC-RNA) has expanded the scope of transciptomic profiling beyond the protein-coding messenger RNAs (mRNA).

In this chapter, we provide a brief survey of prior applications of this approach to the study of asthma, followed by an overview of the primary technical and analytical considerations that should be addressed when conducting such studies. For more detailed review of study protocols and specific analytical platforms, readers are referred to several recent publications (Matson 2009; Yakovlev et al. 2013; Dehmer et al. 2012; Rodriguez-Ezpelete et al. 2012).

Keywords

Gene expression Transcriptomics Differential expression Normalization RNA-Seq Microarray Pathway analysis Systems biology Ribose nucleic acid 

References

  1. Alberts R, Terpstra P, Li Y et al (2007) Sequence polymorphisms cause many false cis eQTLs. PLoS One 2:e622PubMedCrossRefGoogle Scholar
  2. Alonzi T, Maritano D, Gorgoni B et al (2001) Essential role of STAT3 in the control of the acute-phase response as revealed by inducible gene inactivation [correction of activation] in the liver. [erratum appears in Mol Cell Biol 2001 Apr;21(8):2967]. Mol Cell Biol 21:1621–1632PubMedCrossRefGoogle Scholar
  3. Aoki T, Matsumoto Y, Hirata K et al (2009) Expression profiling of genes related to asthma exacerbations. Clin Exp Allergy 39:213–221PubMedCrossRefGoogle Scholar
  4. Baines KJ, Simpson JL, Bowden NA et al (2010) Differential gene expression and cytokine production from neutrophils in asthma phenotypes. Eur Respir J 35:522–531PubMedCrossRefGoogle Scholar
  5. Baines KJ, Simpson JL, Wood LG et al (2011) Transcriptional phenotypes of asthma defined by gene expression profiling of induced sputum samples. J Allergy Clin Immunol 127:153–160, 60.e1-9PubMedCrossRefGoogle Scholar
  6. Barabasi AL (2009) Scale-free networks: a decade and beyond. Science 325:412–413PubMedCrossRefGoogle Scholar
  7. Benito M, Parker J, Du Q et al (2004) Adjustment of systematic microarray data biases. Bioinformatics 20:105–114PubMedCrossRefGoogle Scholar
  8. Bjornsdottir US, Holgate ST, Reddy PS et al (2011) Pathways activated during human asthma exacerbation as revealed by gene expression patterns in blood. PLoS One 6:e21902PubMedCrossRefGoogle Scholar
  9. Bochkov YA, Hanson KM, Keles S et al (2010) Rhinovirus-induced modulation of gene expression in bronchial epithelial cells from subjects with asthma. Mucosal Immunol 3:69–80PubMedCrossRefGoogle Scholar
  10. Bolstad BM, Irizarry RA, Astrand M et al (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185–193PubMedCrossRefGoogle Scholar
  11. Chomczynski P, Sacchi N (1987) Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem 162:156–159PubMedCrossRefGoogle Scholar
  12. Choy DF, Modrek B, Abbas AR et al (2011) Gene expression patterns of Th2 inflammation and intercellular communication in asthmatic airways. J Immunol 186:1861–1869PubMedCrossRefGoogle Scholar
  13. Chu JH, Weiss ST, Carey VJ et al (2009) A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism. BMC Syst Biol 3:55PubMedCrossRefGoogle Scholar
  14. Chu JH, Lazarus R, Carey VJ et al (2011) Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes. BMC Syst Biol 5:89PubMedCrossRefGoogle Scholar
  15. Dehmer M, Basak SC (2012) Statistical and machine learning approaches for network analysis. Hoboken, N.J.: WileyPubMedCrossRefGoogle Scholar
  16. Fare TL, Coffey EM, Dai H et al (2003) Effects of atmospheric ozone on microarray data quality. Anal Chem 75:4672–4675PubMedCrossRefGoogle Scholar
  17. Freishtat RJ, Benton AS, Watson AM et al (2009) Delineation of a gene network underlying the pulmonary response to oxidative stress in asthma. J Investig Med 57:756–764PubMedGoogle Scholar
  18. Gentleman RC, Carey VJ, Bates DM et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80PubMedCrossRefGoogle Scholar
  19. Giardine B, Riemer C, Hardison RC et al (2005) Galaxy: a platform for interactive large-scale genome analysis. Genome Res 15:1451–1455PubMedCrossRefGoogle Scholar
  20. Hakonarson H, Bjornsdottir US, Halapi E et al (2005) Profiling of genes expressed in peripheral blood mononuclear cells predicts glucocorticoid sensitivity in asthma patients. Proc Natl Acad Sci USA 102:14789–14794PubMedCrossRefGoogle Scholar
  21. Hansen KD, Irizarry RA, Wu Z (2012) Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 13:204–216PubMedCrossRefGoogle Scholar
  22. Howe EA, Sinha R, Schlauch D et al (2011) RNA-Seq analysis in MeV. Bioinformatics 27:3209–3210PubMedCrossRefGoogle Scholar
  23. Hunninghake GM, Chu JH, Sharma SS et al (2011) The CD4+ T-cell transcriptome and serum IgE in asthma: IL17RB and the role of sex. BMC Pulm Med 11:17PubMedCrossRefGoogle Scholar
  24. Hwang S, Son SW, Kim SC et al (2008) A protein interaction network associated with asthma. J Theor Biol 252:722–731PubMedCrossRefGoogle Scholar
  25. Hyduke DR, Palsson BO (2010) Towards genome-scale signalling network reconstructions. Nat Rev Genet 11:297–307PubMedCrossRefGoogle Scholar
  26. Inza I, Calvo B, Armananzas R et al (2010) Machine learning: an indispensable tool in bioinformatics. Methods Mol Biol 593:25–48PubMedCrossRefGoogle Scholar
  27. Irizarry RA, Hobbs B, Collin F et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264PubMedCrossRefGoogle Scholar
  28. Irizarry RA, Warren D, Spencer F et al (2005) Multiple-laboratory comparison of microarray platforms. Nat Methods 2:345–350PubMedCrossRefGoogle Scholar
  29. Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–127PubMedCrossRefGoogle Scholar
  30. Kapitein B, Hoekstra MO, Nijhuis EH et al (2008) Gene expression in CD4+ T-cells reflects heterogeneity in infant wheezing phenotypes. Eur Respir J 32:1203–1212PubMedCrossRefGoogle Scholar
  31. Kerr MK (2003) Design considerations for efficient and effective microarray studies. Biometrics 59:822–828PubMedCrossRefGoogle Scholar
  32. Kerr G, Ruskin HJ, Crane M et al (2008) Techniques for clustering gene expression data. Comput Biol Med 38:283–293PubMedCrossRefGoogle Scholar
  33. Kicic A, Hallstrand TS, Sutanto EN et al (2010) Decreased fibronectin production significantly contributes to dysregulated repair of asthmatic epithelium. Am J Respir Crit Care Med 181:889–898PubMedCrossRefGoogle Scholar
  34. Larranaga P, Calvo B, Santana R et al (2006) Machine learning in bioinformatics. Brief Bioinform 7:86–112PubMedCrossRefGoogle Scholar
  35. Leek JT, Storey JD (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3:1724–1735PubMedCrossRefGoogle Scholar
  36. Lim WK, Wang K, Lefebvre C et al (2007) Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks. Bioinformatics 23:i282–i288PubMedCrossRefGoogle Scholar
  37. Luo J, Schumacher M, Scherer A et al (2010) A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data. Pharmacogenomics J 10:278–291PubMedCrossRefGoogle Scholar
  38. Madore AM, Perron S, Turmel V et al (2010) Alveolar macrophages in allergic asthma: an expression signature characterized by heat shock protein pathways. Hum Immunol 71:144–150PubMedCrossRefGoogle Scholar
  39. Marioni JC, Mason CE, Mane SM et al (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 18:1509–1517PubMedCrossRefGoogle Scholar
  40. Matson RS (2009) Microarray methods and protocols. Boca Raton: CRC PressPubMedCrossRefGoogle Scholar
  41. Melen E, Kho AT, Sharma S et al (2011) Expression analysis of asthma candidate genes during human and murine lung development. Respir Res 12:86PubMedCrossRefGoogle Scholar
  42. Metzker ML (2010) Sequencing technologies – the next generation. Nat Rev Genet 11:31–46PubMedCrossRefGoogle Scholar
  43. Mortazavi A, Williams BA, McCue K et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5:621–628PubMedCrossRefGoogle Scholar
  44. Murphy A, Chu JH, Xu M et al (2010) Mapping of numerous disease-associated expression polymorphisms in primary peripheral blood CD4+ lymphocytes. Hum Mol Genet 19:4745–4757PubMedCrossRefGoogle Scholar
  45. Orsmark-Pietras C, James A, Konradsen JR et al (2013) Transcriptome analysis reveals upregulation of bitter taste receptors in severe asthmatics. Eur Respir J 42:65–78CrossRefGoogle Scholar
  46. Pickrell JK, Marioni JC, Pai AA et al (2010) Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464:768–772PubMedCrossRefGoogle Scholar
  47. Rajeevan MS, Dimulescu IM, Vernon SD et al (2003) Global amplification of sense RNA: a novel method to replicate and archive mRNA for gene expression analysis. Genomics 82:491–497PubMedCrossRefGoogle Scholar
  48. Rodríguez-Ezpeleta N, Hackenberg M, Aransay AM (2012) Bioinformatics for high throughput sequencing. New York, NY: SpringerPubMedCrossRefGoogle Scholar
  49. Schafer J, Strimmer K (2005) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21:754–764PubMedCrossRefGoogle Scholar
  50. Schena M, Shalon D, Davis RW et al (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470PubMedCrossRefGoogle Scholar
  51. Scherer A (2009) Batch effects and noise in microarray experiments: sources and solutions. Wiley, Chichester, U.KCrossRefGoogle Scholar
  52. Schuster SC (2008) Next-generation sequencing transforms today’s biology. Nat Methods 5:16–18PubMedCrossRefGoogle Scholar
  53. Shi L, Campbell G, Jones WD et al (2010) The microarray quality control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol 28:827–838PubMedCrossRefGoogle Scholar
  54. Shin SW, Oh TJ, Park SM et al (2011) Asthma-predictive genetic markers in gene expression profiling of peripheral blood mononuclear cells. Allergy Asthma Immunol Res 3:265–272PubMedCrossRefGoogle Scholar
  55. Smyth GK (2005) Limma: linear models for microarray data. In: Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S (eds) Bioinformatics and computational biology solutions using R and bioconductor. Springer, New York, NY, pp 397–420CrossRefGoogle Scholar
  56. Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102:15545–15550PubMedCrossRefGoogle Scholar
  57. Subrata LS, Bizzintino J, Mamessier E et al (2009) Interactions between innate antiviral and atopic immunoinflammatory pathways precipitate and sustain asthma exacerbations in children. J Immunol 183:2793–2800PubMedCrossRefGoogle Scholar
  58. Sutcliffe A, Hollins F, Gomez E et al (2012) Increased nicotinamide adenine dinucleotide phosphate oxidase 4 expression mediates intrinsic airway smooth muscle hypercontractility in asthma. Am J Respir Crit Care Med 185:267–274PubMedCrossRefGoogle Scholar
  59. Tsitsiou E, Williams AE, Moschos SA et al (2012) Transcriptome analysis shows activation of circulating CD8+ T cells in patients with severe asthma. J Allergy Clin Immunol 129:95–103PubMedCrossRefGoogle Scholar
  60. Vidal M, Cusick ME, Barabasi AL (2011) Interactome networks and human disease. Cell 144:986–998PubMedCrossRefGoogle Scholar
  61. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63PubMedCrossRefGoogle Scholar
  62. Wold B, Myers RM (2008) Sequence census methods for functional genomics. Nat Methods 5:19–21PubMedCrossRefGoogle Scholar
  63. Woodruff PG, Boushey HA, Dolganov GM et al (2007) Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids. Proc Natl Acad Sci USA 104:15858–15863PubMedCrossRefGoogle Scholar
  64. Woodruff PG, Modrek B, Choy DF et al (2009) T-helper type 2-driven inflammation defines major subphenotypes of asthma. Am J Respir Crit Care Med 180:388–395PubMedCrossRefGoogle Scholar
  65. Yakovlev AY, Klebanov L, Gaile D (2013) Statistical methods for microarray data analysis: Methods and Protocols. New York, NY: Springer New YorkPubMedCrossRefGoogle Scholar
  66. Yick CY, Zwinderman AH, Kunst PW et al (2013) Transcriptome sequencing (RNA-Seq) of human endobronchial biopsies: asthma versus controls. Eur Respir J, in pressGoogle Scholar
  67. Youssef LA, Schuyler M, Gilmartin L et al (2007) Histamine release from the basophils of control and asthmatic subjects and a comparison of gene expression between “releaser” and “nonreleaser” basophils. J Immunol 178:4584–4594PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Channing Division of Network Medicine, Department of MedicineBrigham and Women’s Hospital, Harvard Medical SchoolBostonUSA

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