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Cytokine Polymorphisms (SNPs) In Silicosis and Other Chronic Inflammatory Diseases

Cytokine Polymorphisms (SNPs) In Silicosis and Other Chronic Inflammatory Diseases Berran Yucesoy, Ph.D. Toxicology and Molecular Biology Branch National Institute for Occupational Safety and Health Morgantown, WV.

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Cytokine Polymorphisms (SNPs) In Silicosis and Other Chronic Inflammatory Diseases

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  1. Cytokine Polymorphisms (SNPs) In Silicosis and Other Chronic Inflammatory Diseases Berran Yucesoy, Ph.D. Toxicology and Molecular Biology Branch National Institute for Occupational Safety and Health Morgantown, WV

  2. When the rate of sequence variation at a specific point in the DNA is more than 1% of a given population, it is referred to as a polymorphism. When the incidence of a variant sequence is less than 1%, it is referred to as a mutation. About 90% of sequence variants in humans are differences in single bases of DNA, called single nucleotide polymorphisms(SNPs)

  3. Classification of SNPs • Coding region SNPs: Synonymous: mutation does not change amino acid. Non-synonymous (replacement): mutation change amino acid sequence • Non-coding region SNPs: 5’ and 3’ UTR’s Introns Intergenic spacer

  4. SNPs- Application • Gene discovery and mapping *physical mapping *genetic linkage mapping • Association-based candidate gene testing • Diagnostics and risk profiling • Pharmacogenetics • Homogeneity testing and epidemiological study design

  5. Toxicological Application of SNPs Family and Linkage Studies Family Studies Case - Control cohorts Association Studies Candidate Regions Positional Cloning Complex Diseases Drug-Target Design Pharmacogenetics Risk Profiling Candidate Genes Functional Studies Single Gene Disease Drug-Target Design Diagnostics

  6. SNPs- Advantages • Very common-high density in genome • Less mutable than other types of polymorphisms • Very applicable to serve as a biological biomarker for genetic susceptibility • Could be used to enhance gene-mapping • Could be used in population-based genetic studies • Advanced technology allows high-throughput genotyping • A large number of human SNPs are available • An important addition to human genome project

  7. Genetic Disorders • Single-gene (monogenic or Mendelian) • Polygenic (complex or multifactorial) • Chromosomal • Mitochondrial

  8. Monogenic Diseases Cystic Fibrosis R Tay-Sachs R Retinoblastoma D ADD R Hypercholesterolemia D Phenylketoniuria R Huntington D Gaucher Disease R Family Studies

  9. Polygenic Diseases • Asthma • Mental Disorders (Alzheimer’s) • Cardiovascular • Cancer (ovarian, breast, endometrial) • Periodontal diseases • Autoimmunity

  10. Multifactorial (complex or polygenic)Diseases - complex interactions among multiple genes as well as environmental and lifestyle factors -often chronic inflammatory involvement

  11. Modifying Factors and Multifactorial Disease Occupational Exposure Mild - Moderate Physiological and Environmental Factors (e.g., smoking diet, stress) SNPs Severe Disease

  12. Common Disease-Common Variant (CD-CV) Hypothesis • Alzheimer’s disease - ApoE4 genotype • Factor V leiden - deep venous thrombosis • PPAR Pro12Ala – type II diabetes • CCR5∆32 – HIV

  13. Example of cytokine SNPs found to effect expression levels and modify disease Polymorphic locus • -889, +4845 • -511, +3953 • VNTR, +2018 • -590, +33 • -174, -572 • -627, -1082, -819,-592 • -1055, -1111 • -308, -238 • -509, codon 10, 25 Cytokine • IL-1 • IL-1 • IL-1RA • IL-4 • IL-6 • IL-10 • IL-13 • TNF- • TGF-1

  14. Acute inflammation • mononuclear cell infiltration • granulation tissue formation • fibrosis, angiogenesis and tissue destruction (new vessel formation) • regeneration Chronic inflammation occurs when acute phase cannot be resolved

  15. Development of pulmonary fibrosis Injury and inflammation Epithelial cells TGF,, IL-1, TNF, PDGF, IFN, IGF-1 PDGF, TGF,, ET-1, FGF fibroblast Inflammatory cells TGF Epithelialcells Fibrosis Fibroblast, myofibroblast accumulation and ECM deposition Dysfunctional parenchyma

  16. Silicosis -is a chronic fibrosing disease of the lungsproduced by prolonged and extensive exposure to free crystalline silica. -presents in two forms , depending on the duration of exposure *simple silicosis *progressive massive fibrosis

  17. Specific Aims • the role of the IL-1 and TNF SNPs in silicosis frequency and severity • Gene-gene interactions • Gene-gene-environment interactions

  18. Animal Studies * Increased expression of inflammatory cytokines in the lung of silicotic rodents (Struhar,1989; Driscoll,1990; Mohr,1991; Orfila,1998; Davis,1998) * Resistance of TNF deficient mice to developing fibrosis from silica (Piguet,1990; Gossart, 1996) * Protection of TNF receptor knock-out mice against the fibrogenic effects of silica (Ortiz, 1999) * Induction of expression of TNF- and its receptors in C57BL/6J mice exposed to silica (Ohtsuka,1995)

  19. Human Studies *Association between the local release of IL-1 and TNFand pathogenesis of silicosis(Schmidt, 1984; Savici, 1994) *Higher levels of spontaneous TNF and IL-1 secretion by AMs in patients with CWP(Lassalle, 1990) * Increased coal mine dust-stimulated release of TNF from PBM in subjects with pneumoconiosis(Borm, 1988; Schins&Borm,1995; Kim, 1995) *Increased TNF release in the lungs of pneumoconiotic patients (Vanhee, 1995) *Up-regulation of cytokines and growth factors in CWP(Vallyathan, 2000)

  20. Age, smoking status and years of exposure by disease status

  21. Distribution of genotypes and allele frequencies for IL-1 *Represents total population studied with silicosis **odds ratio (95% confidence limits) adjusted for exposure with logistic regression a Significantly associated with moderate, severe and overall disease

  22. Distribution of Genotypes and Allele Frequencies for TNF *Represents total population studied with silicosis **odds ratio (95% confidence limits) adjusted for exposure with logistic regression aSignificantly associated with moderate, severe and overall disease

  23. (52) (13) (37) (136) (53) (76) (16) (24) (44) (87) (78) (15) (49) (64) (28) (7) (83) (74) (60) (24) (133) (36) (108) (10) (21) (50) (11) (37) (71) (86) (75) (45)

  24. Gene-gene interactions Periodontitis IL-1 - IL-1 Kornman, 1997 Gore, 1998 EOP IL-1 - IL-1RA Parkhill, 2000 Silicosis IL-1RA - TNF Yucesoy, 2001 Asthma IL-4RA - IL-13 Howard, 2002

  25. Association Studies Population-based • Case-control studies • Cohort studies If a significant association appears, • the polymorphism itself is the locus of interest • the polymorphism is in linkage disequilibrium with the locus • confounding factors are present

  26. Family-based • Parents-affected child trios (TDT) -looking for unequal transmission of SNP alleles to affected and non affected siblings

  27. Issues in case-control studies • Population stratification • Genetic heterogeneity • Linkage disequilibrium (LD) • Candidate genes • Random error • Study design problems

  28. Population stratification Occurs when the cases and controls are unintentionally drawn from two or more ethnic groups . Factors responsible could be: • migration • geographical distribution • ecology • local adaptation *to use family-based studies such as TDT *to study multiple case-control populations from different ethnic groups

  29. Genetic heterogeneity • Different genetic mechanisms in different populations Linkage disequilibrium (LD) • Association between particular alleles due to their proximity on the same chromosome • allows mapping of disease loci in large populations.

  30. Candidate genes • Biological plausibility and functional importance of polymorphisms tested Random error • False positive/false negative results Study design problems • Small sample size • Poor control group • Problem in replication • Poor defined phenotypes

  31. Expectations • Prediction of disease • Disease mechanisms • Targeted therapy-Pharmacogenetic (The correlation between an individual’s genetic make-up and their response to drug treatment, personalized medicine)

  32. SNP Resources • http://www.ncbi.nlm.nih.gov/SNP/ • http://innateimmunity.net • http://snpper.chip.org • http://www.genome.utah.edu/genesnps/ • http://genome.cse.ucsc.edu/ • http://pga.lbl.gov/PGA/PGA_inventory.html • http://snp500cancer.nci.nih.gov/home.cfm • http://www.bris.ac.uk/pathandmicro/services/GAI/cytokine4.htm

  33. Collaborators • Michael I. Luster, Ph.D • Val Vallyathan, Ph.D. • Douglas P. Landsittel, Ph.D. • Michael L. Kashon, Ph.D. • Vic Johnson, Ph.D. • Michael McKinstry • Kara Fluharty

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