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INFOBIOMED – Pilot applications WP 6.3 Genomics and chronic inflammation

INFOBIOMED – Pilot applications WP 6.3 Genomics and chronic inflammation. Barcelona, January 9-10, 2004. Academic Center for Dentistry Amsterdam. Dept. of Informatics Carol van der Palen. Dept. of Periodontology Bruno G. Loos, ACTIVITY LEADER Ubele van der Velden Section of Microbiology

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INFOBIOMED – Pilot applications WP 6.3 Genomics and chronic inflammation

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  1. INFOBIOMED – Pilot applications WP 6.3 Genomics and chronic inflammation Barcelona, January 9-10, 2004 Academic Center for Dentistry Amsterdam Dept. of Informatics Carol van der Palen Dept. of Periodontology Bruno G. Loos, ACTIVITY LEADER Ubele van der Velden Section of Microbiology Marja Laine Arie Jan van Winkelhoff Dept. of Immunogenetics Salvador Pena Bart Crusius Servaas Morre

  2. Chronic inflammatory diseases are complex diseases Interactions between genetic and environmental factors

  3. Chronic inflammatory diseases are complex diseases Interactions between genetic and environmental factors Infections Smoking Diet Stress ……

  4. Chronic inflammatory diseases are complex diseases Interactions between genetic and environmental factors Infections Smoking Diet Stress ……

  5. Examples complex diseases • heart diseases • rheumatoid arthritis • Crohn’s disease • gastric ulcers • periodontitis

  6. Complex human diseases • vary in severity and age of onset • various biological pathways • caused by numerous genes, • each with a small overall • contribution and relative risk: • POLYGENIC • (Tabor et al. 2002, Nature Reviews Genetics)

  7. We have entered the NoEINFOBIOMED with PERIODONTITIS as model for chronic inflammatory diseases Periodontitis is a chronic inflammatory disease of the supporting tissues of the teeth Prevalence: 10-15%

  8. periodontitis is an excellent model for inflammatory diseases Infectious component Genetic susceptibility Environmental influences Relative high prevalence Easy access to samples Patient cooperation Existence of network: within university university to specialists

  9. normal situation

  10. normal situation periodontitis

  11. Current paradigm for the pathophysiology of periodontitis (Adapted from: Page and Kornman 1997)

  12. Current paradigm for the pathophysiology of periodontitis Perio- dontitis local risk factors microbiota Host (Adapted from: Page and Kornman 1997)

  13. Current paradigm for the pathophysiology of periodontitis Perio- dontitis local risk factors microbiota Host } A. actinomycetemcomitans P. gingivalis P. intermedia T. forsythensus P. micros F. nucleatum anaerobic, gram - (Adapted from: Page and Kornman 1997)

  14. Perio- dontitis local risk factors microbiota Host environmental risk facors: smoking, stress (Adapted from: Page and Kornman 1997)

  15. Susceptibility genetic factors Perio- dontitis local risk factors microbiota Host environmental risk factors smoking, stress (Adapted from: Page and Kornman 1997)

  16. ACTA’s workplan for INFOBIOMED

  17. ACTA’s workplan for INFOBIOMED } 800 patients with chronic periodontitis 200 controls without periodontitis 1000 subjects • Generation of databases • Demographic data • Clinical data • Environmental data • Microbiological data • Genetic data

  18. ACTA’s workplan for INFOBIOMED } 800 patients with chronic periodontitis 200 controls without periodontitis 1000 subjects • Generation of databases • Demographic data • Clinical data • Environmental data • Microbiological data • Genetic data • age • gender • race • educational level • general health

  19. ACTA’s workplan for INFOBIOMED } 800 patients with chronic periodontitis 200 controls without periodontitis 1000 subjects • Generation of databases • Demographic data • Clinical data • Environmental data • Microbiological data • Genetic data • # teeth involved • amount of bone loss • (extent and severity)

  20. ACTA’s workplan for INFOBIOMED } 800 patients with chronic periodontitis 200 controls without periodontitis 1000 subjects • Generation of databases • Demographic data • Clinical data • Environmental data • Microbiological data • Genetic data • smoking • (packyears, history) • stress

  21. ACTA’s workplan for INFOBIOMED } 800 patients with chronic periodontitis 200 controls without periodontitis 1000 subjects • Generation of databases • Demographic data • Clinical data • Environmental data • Microbiological data • Genetic data • total # anaerobic cfu • A. actinomycetemc. • P. gingivalis • P. intermedia • T. forsythensus • P. micros • F. nucleatum

  22. ACTA’s workplan for INFOBIOMED } 800 patients with chronic periodontitis 200 controls without periodontitis 1000 subjects • Generation of databases • Demographic data • Clinical data • Environmental data • Microbiological data • Genetic data candidate gene approach: genetic polymorphisms in a series of genes theme: innate immunity and pro-inflammatory cytokines and chemokines

  23. Why innate immunity and pro-inflammatory cytokines Sensible biological mechanisms Previous studies Chromosomal hotspots

  24. Perspectives in some immune disorders Becker, PNAS 1998

  25. FcgRII, III CD14 TLR-2 TLR-4 Receptors: MBL Cell membrane CARD15 Nucleus Cytoplasm

  26. Ligands: LPS LTA, PGN Ab LPS PGN Lectins FcgRII, III CD14 TLR-2 TLR-4 Receptors: MBL Cell membrane CARD15 Nucleus PGN Cytoplasm

  27. Ligands: LPS LTA, PGN Ab LPS PGN Lectins FcgRII, III CD14 TLR-2 TLR-4 Receptors: MBL Cell membrane CARD15 Nucleus Intracellular signaling PGN Cytoplasm

  28. Ligands: LPS LTA, PGN Ab LPS PGN Lectins FcgRII, III CD14 TLR-2 TLR-4 Receptors: MBL Cell membrane CARD15 Nucleus Intracellular signaling PGN Cytoplasm Inflammatory responses: IL-1, -6, -8, -10, -12, TNF-a, RANTES

  29. disease genes in relation to periodontitis Receptor Cytokine 1. CD14 2. TLR2 3. TLR4 4. MBL 5. CARD15 6. FcgR-Ila 7. FcgR-Illa • 8. IL-1 • 9. IL-6 • 10. IL-8 • 11. IL-10 • 12. IL-12p40 • 13. IL-12p70 • 14. RANTES • 15. TNF-a

  30. ACTA’s workplan for INFOBIOMED

  31. potential impact of WP 6.3 INFOBIOMED Genomics and chronic inflammation • Disease etiology • Disease severity • Disease classification • Pathophysiology • Risk profiling screening • Prognostic factors • Innovations in therapy

  32. INFOBIOMED NoE Activity work plan - first 12 months Activity code and title: WP 6.3, Genomics and chronic inflammation Responsible partner: ACTA Activity objectives: 1. review and re-enter existing dispersed data files 2. start to make operational the SNP analysis for more of the proposed genes Expected results (at least one to be delivered within first 6 months; include contributions to WP deliverables as set on page 61 of DOW): Month 6: 1. Availability of a limited database on 100 patients and 50 controls, based on existing dispersed data files Relationships with other activities (input/output):

  33. INFOBIOMED NoE Activity work plan schedule Activity code and title: Brief description of work to be done during the full 12 months: Details of work to be done in first 6 months:

  34. INFOBIOMED NoE Activity code and title: Indicate the expected involvement of partners in the table below, describing role and estimated effort, expressed in person-months. *: Code as follows: L - Activity Leader. W - Works. I - Provides input. R - Reviews. O - Other (please specify).

  35. END

  36. VU University Medical Center Laboratory of Immunogenetics

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