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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,
Fondazione Don C Gnocchi, Milano
Bardolino, January 16th, 2013
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]
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
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: 1) 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
Pathogen-driven selection Identifies correlations between genetic variability and pathogen-driven selective pressure.
We need a measure of selective-pressure that reflects historical pressures (evolution acts over long time periods).
pathogen-driven selective pressure pathogen-driven selective pressure
neutral variant selected variantselected variant
Alle
le fr
eque
ncy
Alle
le fr
eque
ncy
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
HLAA
HLAB
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
OUR APPROACH
99th percentile
tau
pathogen diversity
Alle
le fr
eque
ncy
freq
uenc
y
Pathogen diversity:
Micro-pathogens: viruses, bacteria, fungi and protozoaMacro-pathogens: insects, arthropods and helminths
rs917997
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
Six out of 9 risk variants for CeD or IDB/Crohn's disease (CD) were associated with micro- and macro-pathogen richness
A specific measure
Quantifying selectionEstimate 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 with the diversity of each pathogen group (expected 5%; observed 18%)
Apply a re-sampling approach on the 2773 GWAS SNPs to assess significance and calculate the empirical probability (on MAF-matched variants)
GWAS SNPs for CD, UC and CeD that correlate with the diversity of different
pathogens
Exploiting selection signatures to identify novel risk variants that are not picked up by
GWASExtract 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)
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
Interaction (Ingenuity model) network
known CD susceptibility gene
Disregulated in CD and IBD mouse models; increased in IBD-associated neoplastic transformation; underexpressed in Treg; regulator of FOXP3 expression
Increased activation in mucosa of CD patients; pharamacological inhibition of RhoA patway reduces colonic inflammation in rats
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
Lab 1Chair of ImmunologyUniversity of Milano
Lab Lab 2Don C Gnocchi Foundation
Milano
MANUELA SIRONI
Rachele CaglianiUberto PozzoliDiego ForniStefania RivaMatteo Fumagalli