Identification of gene expression signature for cigarette smoke exposure response - From man to mouse

Human and Experimental Toxicology

Volumn 34, Issue 12, 2015, Pages 1200-1211

  Martin, F ^a   Talikka, M ^a   Hoeng, J ^a   Peitsch, M C ^a  

^a PHILIP MORRIS PRODUCTS S A   (Switzerland)

Author keywords

blood; Gene expression; signature; smoking; species; surrogate

Indexed keywords

B CELL MATURATION ANTIGEN; CIGARETTE SMOKE; CYCLIN DEPENDENT KINASE INHIBITOR 1C; FUCA1 PROTEIN; IGJ PROTEIN; INHIBITOR OF DIFFERENTIATION 3; LLRN3 PROTEIN; PALLD PROTEIN; PROTEIN; RGL1 PROTEIN; RNA; RRM2 PROTEIN; SASH1 PROTEIN; SERPING1 PROTEIN; UNCLASSIFIED DRUG;

ADULT; AGED; ANIMAL EXPERIMENT; ANIMAL MODEL; ARTICLE; CHRONIC OBSTRUCTIVE LUNG DISEASE; CONTROLLED STUDY; ENVIRONMENTAL EXPOSURE; FEMALE; GENE EXPRESSION; GENE IDENTIFICATION; HUMAN; MAJOR CLINICAL STUDY; MALE; MOUSE; NONHUMAN; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SMOKING; ANIMAL; BIOASSAY; BLOOD; C57BL MOUSE; CASE CONTROL STUDY; EPIDEMIOLOGY; GENE EXPRESSION PROFILING; GENETICS; GREAT BRITAIN; MIDDLE AGED;

ADULT; AGED; ANIMALS; BIOLOGICAL ASSAY; CASE-CONTROL STUDIES; FEMALE; GENE EXPRESSION PROFILING; GREAT BRITAIN; HUMANS; MALE; MICE, INBRED C57BL; MIDDLE AGED; PULMONARY DISEASE, CHRONIC OBSTRUCTIVE; SMOKING;

EID: 84948698347     PISSN: 09603271     EISSN: 14770903     Source Type: Journal    
DOI: 10.1177/0960327115600364     Document Type: Article

References (72)

1. US Department of Health. The health consequences of smoking: a report of the Surgeon General. Atlanta: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. 2004, p. 62.
2. Hecht SS, Yuan JM, Hatsukami D,. Applying tobacco carcinogen and toxicant biomarkers in product regulation and cancer prevention. Chem Res Toxicol 2010; 23: 1001-1008.
3. Carmella SG, Akerkar SA, Richie J, et al. Intraindividual and interindividual differences in metabolites of the tobacco-specific lung carcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) in smokers' urine. Canc Epidemiol Biomarkers Prev 1995; 4: 635-642.
4. Acosta MC, Buchhalter AR, Breland AB, et al. Urine cotinine as an index of smoking status in smokers during 96-hr abstinence: comparison between gas chromatography/mass spectrometry and immunoassay test strips. Nicotine Tob Res 2004; 6: 615-620.
5. Jacobson GA, Ferguson SG,. Relationship between cotinine and trans-3′-hydroxycotinine glucuronidation and the nicotine metabolite ratio in Caucasian smokers. Biomarkers 2014; 19 (8): 679-683.
6. Al-Delaimy W,. Hair as a biomarker for exposure to tobacco smoke. Tob Control 2002; 11: 176-182.
7. Al-Delaimy W, Crane J, Woodward A,. Is the hair nicotine level a more accurate biomarker of environmental tobacco smoke exposure than urine cotinine? J Epidemiol Community Health 2002; 56: 66-71.
8. Tzatzarakis M, Vardavas C, Terzi I, et al. Hair nicotine/cotinine concentrations as a method of monitoring exposure to tobacco smoke among infants and adults. Hum Exp Toxicol 2012; 31: 258-265.
9. Phillips K, Bentley MC, Abrar M, et al. Low level saliva cotinine determination and its application as a biomarker for environmental tobacco smoke exposure. Hum Exp Toxicol 1999; 18: 291-296.
10. Krewski D, Acosta D Jr, Andersen M, et al. Toxicity testing in the 21st century: a vision and a strategy. J Toxicol Environ Health, B 2010; 13: 51-138.
11. Thomas RS, Philbert MA, Auerbach SS, et al. Incorporating new technologies into toxicity testing and risk assessment: moving from 21st century vision to a data-driven framework. Toxicol Sci 2013; 136 (1): 4-18.
12. Hamadeh HK, Bushel PR, Jayadev S, et al. Prediction of compound signature using high density gene expression profiling. Toxicol Sci 2002; 67: 232-240.
13. Morgan KT,. Gene expression analysis reveals chemical-specific profiles. Toxicol Sci 2002; 67: 155-156.
14. Waring JF, Jolly RA, Ciurlionis R, et al. Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Toxicol Appl Pharmacol 2001; 175: 28-42.
15. Brody JS, Steiling K,. Interaction of cigarette exposure and airway epithelial cell gene expression. Annu Rev Physiol 2011; 73: 437-456.
16. Gower AC, Steiling K, Brothers JF 2nd, et al. Transcriptomic studies of the airway field of injury associated with smoking-related lung disease. Proc Am Thorac Soc 2011; 8: 173-179.
17. Gustafson AM, Soldi R, Anderlind C, et al. Airway PI3 K pathway activation is an early and reversible event in lung cancer development. Sci Transl Med 2010; 2: 26ra5.
18. Spira A, Beane JE, Shah V, et al. Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer. Nat Med 2007; 13: 361-366.
19. Steiling K, Ryan J, Brody JS, et al. The field of tissue injury in the lung and airway. Canc Prev Res (Phila) 2008; 1: 396-403.
20. Liu J, Liang Q, Frost-Pineda K, et al. Relationship between biomarkers of cigarette smoke exposure and biomarkers of inflammation, oxidative stress, and platelet activation in adult cigarette smokers. Canc Epidemiol Biomarkers Prev 2011; 20: 1760-1769.
21. Forrest MS, Lan Q, Hubbard AE, et al. Discovery of novel biomarkers by microarray analysis of peripheral blood mononuclear cell gene expression in benzene-exposed workers. Environ Health Perspect 2005; 113: 801-807.
22. Bushel PR, Heinloth AN, Li J, et al. Blood gene expression signatures predict exposure levels. Proc Natl Acad Sci U S A 2007; 104: 18211-18216.
23. Chauhan V, Howland M, Wilkins R,. Identification of gene-based responses in human blood cells exposed to alpha particle radiation. BMC Med Genomics 2014; 7: 43.
24. Rotunno M, Hu N, Su H, et al. A gene expression signature from peripheral whole blood for stage I lung adenocarcinoma. Canc Prev Res (Phila) 2011; 4: 1599-1608.
25. Showe MK, Vachani A, Kossenkov AV, et al. Gene expression profiles in peripheral blood mononuclear cells can distinguish patients with non-small cell lung cancer from patients with nonmalignant lung disease. Canc Res 2009; 69: 9202-9210.
26. Poliska S, Csanky E, Szanto A, et al. Chronic obstructive pulmonary disease-specific gene expression signatures of alveolar macrophages as well as peripheral blood monocytes overlap and correlate with lung function. Respiration 2011; 81: 499-510.
27. Büttner P, Mosig S, Funke H,. Gene expression profiles of T lymphocytes are sensitive to the influence of heavy smoking: a pilot study. Immunogenetics 2007; 59: 37-43.
28. Charlesworth JC, Curran JE, Johnson MP, et al. Transcriptomic epidemiology of smoking: the effect of smoking on gene expression in lymphocytes. BMC Med Genomics 2010; 3: 29.
29. Dumeaux V, Olsen KS, Nuel G, et al. Deciphering normal blood gene expression variation-The NOWAC postgenome study. PLoS Genet 2010; 6: e1000873.
30. Lampe JW, Stepaniants SB, Mao M, et al. Signatures of environmental exposures using peripheral leukocyte gene expression: tobacco smoke. Canc Epidemiol Biomarkers Prev 2004; 13: 445-453.
31. Lodovici M, Luceri C, De Filippo C, et al. Smokers and passive smokers gene expression profiles: correlation with the DNA oxidation damage. Free Rad Biol Med 2007; 43: 415-422.
32. Van Leeuwen DM, Van Agen E, Gottschalk RW, et al. Cigarette smoke-induced differential gene expression in blood cells from monozygotic twin pairs. Carcinogenesis 2007; 28: 691-697.
33. Votavova H, Dostalova Merkerova M, Fejglova K, et al. Transcriptome alterations in maternal and fetal cells induced by tobacco smoke. Placenta 2011; 32: 763-770.
34. Barbash S, Soreq H,. Statistically invalid classification of high throughput gene expression data. Sci report 2013; 3: 1102.
35. Zeller T, Blankenberg S,. Blood-based gene expression tests promises and limitations. Cir: Cardiovasc Genet 2013; 6: 139-140.
36. Meyer P, Alexopoulos LG, Bonk T, et al. Verification of systems biology research in the age of collaborative competition. Nat Biotechnol 2011; 29: 811-815.
37. Meyer P, Hoeng J, Rice JJ, et al. Industrial methodology for process verification in research (IMPROVER): toward systems biology verification. Bioinformatics 2012; 28: 1193-1201.
38. Tarca AL, Lauria M, Unger M, et al. Strengths and limitations of microarray-based phenotype prediction: lessons learned from the improver diagnostic signature challenge. Bioinformatics 2013; 29 (22): 2892-2899.
39. Tarca AL, Than NG, Romero R,. Methodological approach from the best overall team in the improver diagnostic signature challenge. Syst Biomed 2013; 1: 4, 1-11.
40. Gautier L, Cope L, Bolstad BM, et al. affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004; 20: 307-315.
41. Gentleman RC, Carey VJ, Bates DM, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004; 5: R80.
42. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Development Core Team, 2007.
43. Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003; 4: 249-264.
44. Affymetrix I,. Statistical algorithms description document. Technical paper, 2002.
45. Dumeaux V, Olsen KS, Nuel G, et al. Deciphering normal blood gene expression variation-The NOWAC postgenome study. PLoS Genet 2010; 6: e1000873.
46. Bahr TM, Hughes GJ, Armstrong M, et al. Peripheral blood mononuclear cell gene expression in chronic obstructive pulmonary disease. Am J Respir Cell Mol Biol 2013; 49 (2): 316-323.
47. McLachlan G,. Discriminant analysis and statistical pattern recognition. Hoboken: Wiley Interscience, 2004.
48. Taniguchi H, Tohyama M, Takagi T,. Cloning and expression of a novel gene for a protein with leucine-rich repeats in the developing mouse nervous system. Mol Brain Res 1996; 36: 45-52.
49. Ito K, Masuko K, Ito K,. Possible function of neuronal leucine-rich repeat protein 3 (NLRR3) in primary immune response. Hirosaki Med J 2010; 61: 46-57.
50. Otey CA, Carpen O,. α-Actinin revisited: a fresh look at an old player. Cell Motil Cytoskeleton 2004; 58: 104-111.
51. Parast MM, Otey CA,. Characterization of palladin, a novel protein localized to stress fibers and cell adhesions. J Cell Biol 2000; 150: 643-656.
52. Äijö T, Edelman SM, Lönnberg T, et al. An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation. BMC Genomics 2012; 13: 572.
53. Dauphinee SM, Clayton A, Hussainkhel A, et al. SASH1 is a scaffold molecule in endothelial TLR4 signaling. J Immunol 2013; 191: 892-901.
54. Sood R, Makalowska I, Carpten JD, et al. The human RGL (RalGDS-like) gene: cloning, expression analysis and genomic organization. Biochim Biophys Acta (BBA)-Gene Struct Expression 2000; 1491: 285-288.
55. Yang M, Hase H, Legarda-Addison D, et al. B cell maturation antigen, the receptor for a proliferation-inducing ligand and B cell-activating factor of the TNF family, induces antigen presentation in B cells. J Immunol 2005; 175: 2814-2824.
56. Hystad ME, Myklebust JH, Bø TH, et al. Characterization of early stages of human B cell development by gene expression profiling. J Immunol 2007; 179: 3662-3671.
57. Ulrich S, Taraseviciene-Stewart L, Huber LC, et al. Peripheral blood B lymphocytes derived from patients with idiopathic pulmonary arterial hypertension express a different RNA pattern compared with healthy controls: a cross sectional study. Respir Res 2008; 9: 20.
58. Hidalgo L, Einecke G, Allanach K, et al. The transcriptome of human cytotoxic T cells: similarities and disparities among allostimulated CD4+ CTL, CD8+ CTL and NK cells. Am J Transplant 2008; 8: 627-636.
59. Petersen SV, Thiel S, Jensen L, et al. Control of the classical and the MBL pathway of complement activation. Mol Immunol 2000; 37: 803-811.
60. Intra J, Perotti M-E, Pavesi G, et al. Comparative and phylogenetic analysis of α-l-fucosidase genes. Gene 2007; 392: 34-46.
61. Jones-Mason ME, Zhao X, Kappes D, et al. E protein transcription factors are required for the development of CD4(+) lineage T cells. Immunity 2012; 36: 348-361.
62. Beineke P, Fitch K, Tao H, et al. A whole blood gene expression-based signature for smoking status. BMC Med Genomics 2012; 5: 58.
63. Guerassimov A, Hoshino Y, Takubo Y, et al. The development of emphysema in cigarette smoke-exposed mice is strain dependent. Am J Respir Cell Mol Biol 2004; 170: 974-980.
64. Takubo Y, Guerassimov A, Ghezzo H, et al. α1-Antitrypsin determines the pattern of emphysema and function in tobacco smoke-exposed mice: parallels with human disease. Am J Respir Cell Mol Biol 2002; 166: 1596-1603.
65. Phillips B, Veljkovic E, Peck MJ, et al. A 7-month cigarette smoke inhalation study in C57BL/6 mice demonstrates reduced lung inflammation and emphysema following smoking cessation or aerosol exposure from a prototypic modified risk tobacco product. Food Chem Toxicol 2015; 80: 328-345.
66. Kittleson MM, Shui QY, Irizarry RA, et al. Identification of a gene expression profile that differentiates between ischemic and nonischemic cardiomyopathy. Circulation 2004; 110: 3444-3451.
67. Wan ES, Qiu W, Baccarelli A, et al. Cigarette smoking behaviors and time since quitting are associated with differential DNA methylation across the human genome. Hum Mol Genet 2012; 21: 3073-3082.
68. Verdugo RA, Zeller T, Rotival M, et al. Graphical modeling of gene expression in monocytes suggests molecular mechanisms explaining increased atherosclerosis in smokers. PloS one 2013; 8: e50888.
69. Fernández JAF, Prats JM, Artero JVM, et al. Systemic inflammation in 222.841 healthy employed smokers and nonsmokers: white blood cell count and relationship to spirometry. Tob Induc Dis 2012; 10: 1-8.
70. Watanabe N, Fukushima M, Taniguchi A, et al. Smoking, white blood cell counts, and TNF system activity in Japanese male subjects with normal glucose tolerance. Tob Induc Dis 2011; 9: 12.
71. Chou JP, Ramirez CM, Wu JE, et al. Accelerated aging in HIV/AIDS: novel biomarkers of senescent human CD8+ T cells. PloS one 2013; 8: e64702.
72. Miller LG, Goldstein G, Murphy M, et al. Reversible alterations in immunoregulatory T cells in smoking. Analysis by monoclonal antibodies and flow cytometry. Chest 1982; 82: 526-529.