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Mario (Mago) Clerici, M.D. Chair of Immunology

Role of pathogen-driven selection in shaping the predisposition to IBD: identification of disease susceptibility alleles. Mario (Mago) Clerici, M.D. Chair of Immunology Head, PhD School in Molecular Medicine, Universita' di Milano Scientific Director , IRCCS SM Nascente,

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Mario (Mago) Clerici, M.D. Chair of Immunology

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  1. Role of pathogen-driven selection in shaping the predisposition to IBD:identification of disease susceptibility alleles Mario (Mago) Clerici, M.D. Chair of Immunology Head, PhD School in MolecularMedicine, Universita' di Milano ScientificDirector, IRCCS SM Nascente, Fondazione Don C Gnocchi, Milano Bardolino, January 16th, 2013

  2. The evolutionary perspective for Inflammatory Diseases • Inflammatory/autoimmune diseases can have early onset (i.e. before reproductive age) • Inflammatory/autoimmune diseases have a strong genetic component • Inflammatory/autoimmune diseases have a relatively high prevalence in human populations • Why has evolution failed to eliminate the risk alleles? 1) Susceptibility alleles increased in frequency by genetic drift 2) Susceptibility alleles increased in frequency as a result of natural selection (they confer a selective advantage to the carriers; e.g. protection from infection) [hygiene hypothesis] 3) Risk alleles were neutral under different environmental conditions (e.g. high prevalence of infections/worms) [hygiene hypothesis]

  3. Aims Application of population genetic approaches to study the evolutionary history of inflammatory disease risk alleles in human populations Study the role of past infections in shaping the present-day distribution of inflammatory disease risk alleles Use evolutionary information to identify novel risk variants for Crohn’s disease

  4. An innovative approach We developed a strategy to detect pathogen-driven selection Pathogen-driven selection implies that allele frequencies at a locus are shaped by selective pressure imposed by one or more infectious agents Strengths: We test a specific hypothesis on the underlying selective pressure (can distinguish among different pathogen groups) 2) High power to detect selection on standing variation Weaknesses: 1) Use of a relatively low-density SNP panel 2) Use of a population panel with uneven geographic representation

  5. neutral variant selected variant pathogen-driven selective pressure pathogen-driven selective pressure Pathogen-driven selection Identifies correlations between genetic variability and pathogen-driven selective pressure. Allele frequency Allele frequency We need a measure of selective-pressure that reflects historical pressures (evolution acts over long time periods).

  6. Pathogen diversity can be used as a measure of the selective pressure exerted by infectious agents on human populations. Pathogen diversity more closely reflects historical pressures than other estimates such as the prevalence of specific infections

  7. HLAA HLAB

  8. pathogen diversity 99th percentile tau OUR APPROACH Over 650 000 SNPs genotyped in 52 populations (HGDP-CEPH panel). Pathogen diversity: number of different pathogen species/families present in different geographical areas of the world from the Gideon database. We calculate Kendall's rank correlation coefficient (tau) between allele frequencies in HGDP-CEPH populations and pathogen diversity. A SNP was considered to be significantly associated with pathogen diversity if it displayed a significant correlation and a rank higher than 0.99 Allele frequency frequency

  9. Pathogen diversity: Micro-pathogens: viruses, bacteria, fungi and protozoa Macro-pathogens: insects, arthropods and helminths • Among variants subjected to pathogen driven selection we identified an IBD-associated SNP located in IL18RAP. • The risk allele for IBD correlates significantly with pathogen richness rs917997

  10. Six out of 9 risk variants for CeD or IDB/Crohn's disease (CD) were associated with micro- and macro-pathogen richness

  11. A specific measure

  12. Quantifying selection Estimate pathogen-driven selection (virus, protozoa, helminth, bacteria) for single SNPs in the HGDP-CEPH panel Retrieve all GWAS SNPs associated to any trait or disease from the NHGRI Catalog of Published Genome-Wide Association Studies Collapse SNPs in tight LD (r2 >0.8) into single loci and retain only variants genotyped in the HGDP-CEPH panel (n=2773) Total SNPs for CD: 43, UC: 42 Count SNPs that significantly correlate withthediversityofeachpathogengroup (expected 5%; observed 18%) Apply are-samplingapproachonthe 2773 GWASSNPs to assess significance and calculate the empirical probability (on MAF-matched variants)

  13. GWAS SNPs for CD, UC and CeD that correlate with the diversity of different pathogens

  14. Exploiting selection signatures to identify novel risk variants that are not picked up by GWAS Extract all SNPs with 0.05<p value <5x10-5 from CD meta-analysis (Bartett, 2008) Identify those selected by protozoa Rank them based on p value Select genic SNPs Discard SNPs close (less than 2 Mb) to previously associated CD loci Analyse the top 5 SNPs [rs2364403 (ARHGEF2), rs3782567 (HEBP1), rs9636320 (ARID3B), rs199533 (NSF), rs1011312 (TPST2)] in an Italian cohort Combine results with the partially independent 6-study meta-analysis (Franke, 2010)

  15. Association analysis ARHGEF2: a central component of pathogen recognition by NOD1 NSF: involved in autophagy HEBP1: promotes calcium mobilization and chemotaxis in monocytes and dendritic cells

  16. known CD susceptibility gene Increased activation in mucosa of CD patients; pharamacological inhibition of RhoA patway reduces colonic inflammation in rats Disregulated in CD and IBD mouse models; increased in IBD-associated neoplastic transformation; underexpressed in Treg; regulator of FOXP3 expression Interaction (Ingenuity model) network

  17. Conclusions Adaptation to pathogen exposure results in the selection for alleles that confer increased protection against infections, but predispose to CD This information can be exploited to identify novel risk variants for CD These data suggest that infections (e.g. T. gondii) might interact with genotype to determine CD susceptibility These observations help building an evolutionary framework for the development of novel treatment strategies

  18. Lab 1 Chair of Immunology University of Milano MANUELA SIRONI Rachele Cagliani Uberto Pozzoli Diego Forni Stefania Riva Matteo Fumagalli Lab Lab 2 Don C Gnocchi Foundation Milano

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