The experiment will be conducted in two phases; find the optimal statistical detection method and apply to collected data.

Transcription

1 SPECIFIC AIMS Plasmodium falciparum is the most lethal causative agent of malaria, affecting over 500 million annually with 1 to 3 million of those cases resulting in death 1. Widespread drug use has been the favored control mechanism but has lead to the evolution of drug and multi-drug resistance. Candidate targets for new drugs and vaccines are the proteins interacting between the host and pathogen. Examples include var genes that encode a family of antigens called pfemp1 2 and host Band3 binding merozoite surface protein 1 3 which have been identified on a case by case basis. This narrow approach to detecting such interactions is time consuming. An alternative, genome wide approach is to use the natural variation in the host and pathogen genes encoding the interacting proteins and investigate correlations between the observed genetic variation and parasitemia using methods similar to the epistatic association methods. The hypothesis behind the proposal is polymorphisms in genes encoding the interacting proteins can affect severity of infection, be identified using a statistical model, and suggest targets for vaccinations and new treatment. The hypothesis is based on the success of association studies to detect associations with HIV-1 viral load 4, type 2 diabetes 5-8, Crohn s disease, rheumatoid arthritis, type 1 diabetes, bipolar disorder, and coronary artery disease 8 when sampling is sufficient, and the emergence of computationally tractable epistatic detection methods. The experiment will be conducted in two phases; find the optimal statistical detection method and apply to collected data. 1. Construct and test methods to detect interacting genes. Human, pathogen, and resulting infection phenotypes will be simulated to provide data to test the interacting gene detection methods. Methods for detecting epistatic interactions will be modified to utilize the host and pathogen genotypes, and tested on the simulated data. 2. Detect interacting genes in P. falciparum and H. sapiens. Africans infected with P. falciparum will be enrolled, host and pathogen genotyped, and parasitemia measured. Additional information will be collected about the subjects for cofactor analysis. The resulting data will be evaluated using the model from aim 1. The specific aims will help in designing a useful tool to detect host pathogen interactions, gain insight to the host pathogen interactions seen in malaria infections, and extend to other hostpathogen combinations including but not limited to Human Immunodeficiency Virus (HIV), Hepatitis, and Mycobacterium tuberculosis. BACKGROUND AND SIGNIFICANCE P. falciparum as a human pathogen. P. falciparum has a multistage, two host life cycle; humans and mosquitoes (Figure A). Severity of the infection or parasitemia is measured as the percentage of infected red blood cells and the number of parasites per µl. Treatment of P. falciparum infection include a wide range of drugs; chloroquine, quinine, Fansidar, and antifolates to name a few. However, the evolution of drug resistance and multiple drug Page 1 of 11

2 resistance, coupled with no available vaccination 9 has made wide scale treatment a global health issue. I believe the proposal has relevance with respect to development of vaccinations and new treatments. Identifying genes that are interacting between the host and the pathogen and validation of the interactions using yeast two hybrid assays can lead to identifying means of disrupting the interactions using small molecule or pharmacogenomic approaches, and subsequently inhibit the pathogen. Figure A Plasmodium falciparum life cycle. A) Mosquitoes feed on a human blood and inject sporozoites into the human. The parasite migrates to the liver, goes through several phases of development, and emerges as a merozoite. Merozoites invade red blood cells, multiply as trophozoites, or develop into gametocytes. B) Mosquitoes biting an infected human take up gametocytes, which develop into male and female gametes. The gametes fuse to form a zygote in the mosquito gut, develop into an ookinete. The ookinete crosses the gut wall, turns into a sporozoite filled oocyst, bursts, and sporozoites migrate to the salivary glands 10. P. falciparum and human interactions. The intricate relationship of the parasite and human host, and ability of the parasite to escape immune responses 11, 12 suggest there is an evolution of interactions. These interactions have been identified by investigation of increased polymorphism in the pathogen, however this does not give indication to the host target. The experiment proposed would utilize the variability in both the host and pathogen to identify the interacting sites genome wide (Specific Aim 2). Genetics of P. falciparum. The 14 P. falciparum chromosomes have been fully sequenced in and genome wide microsatellite and single nucleotide polymorphisms (SNPs) identified 9, 16, 17. This variation has been used to quantify recombination rates 16, 18, reject demographic models[unpublished results], identify genes subject to selection 19, 20, and genes associated with drug resistance The genome is unique in base pair composition; 80% AT 13-15, and the majority (~65%) of the SNPs are nonsynonymous 9. The surplus of nssnps may be due to the lack of possible synonymous sites given the constraints caused by the codon bias 24, 25. Since the majority of the polymorphism are coding, the effects can be observed as variability in phenotypes such as drug resistance and parasitemia. Utilizing natural genomic variation. Genetic variation has been intensely investigated and mapped in humans, with the start of the HapMap 26 project to copy number variation 27. A common use of this information is association studies, where correlations between the genotypes and phenotypes are tested. Several large scale association studies have been reported, detecting susceptibility loci for a wide range of diseases; HIV, type 1 and 2 diabetes, coronary artery disease among others. An extension of these methods would be to use both the P. falciparum Page 2 of 11

3 genome and the human genome to detect variances contributing to infection; using natural variation in a host and pathogen to detect interacting genes associated with severity of infection, and suggest targets for vaccinations and new treatment approaches. Epistatic association methods. Association methods test for correlations between genotypes and phenotypes using a number of methods: linear regression, variants of the χ 2 test like genomic control 28 and Armitage χ 2 29, and logistic regression. Multilocus and epistatic models have been developed to detect genes interacting epistatically. The basic method of testing these effects is an exhaustive 2 locus model which has been shown to have maximum power of detection 30, 31 yet remains computationally intensive because there are L choose 2 combinations, where L is the number of markers sampled. In the case of a typical human genome study with 500,000 markers, this is approximately 1.25 x 11. This also imposes a multiple testing problem where several independent and correlated hypotheses are being tested. Two main approaches to addressing the combinatorial and multiple testing issues are 2 stage designs 31 and Bayesian methods 32, 33. The two-stage approach tests for marginal effects with a liberal effect size cut off in the first stage. In the second stage epistatic interactions are tested either a) within the stage one marker set, or b) stage one marker set x all markers 31. These significantly reduce the number of tests however both (a) or one (b) loci must have a marginal effect to be tested for epistatic effects; this case is not always biologically true. The Bayesian methods use Markov chain Monte Carlo (MCMC) to address the large parameter space. Using the Metropolis-Hastings algorithm parameters from a proposal distribution are proposed and updated based on an acceptance function. After a sufficient number of iterations, parameter estimates approach the true values. A natural extension of these methods is to incorporate a second genome and evaluate epistasis between genomes to detect interacting genes (Specific Aim 1). PRELIMINARY STUDIES <NONE> RESEARCH DESIGN AND METHODS Specific Aim #1. Construct and test methods to detect interacting genes. Rationale. Association studies have been successful at identifying susceptibility loci for several diseases, and epistatic methods are becoming more powerful. The next step is to consider epistasis between genomes as in the case of infecting pathogens and hosts. Using natural variation in the same way association studies do, interacting sites that contribute to severity of Page 3 of 11

4 infection can be identified. In this aim, a tool will be developed and tested to detect these interactions. Experimental design. Association methods are greatly varied in methods and ability to detect effects and are tested on simulated data. Simulating data provides a controlled and cost effective method to evaluate method performance, however it is difficult to simulate interactions when the under lying model is unknown. To circumvent the issue, all unique models will be simulated and used to evaluate model performance. Next, two main statistical methods: goodness of fit, and ANOVA will be used and evaluated in all combinations of host and pathogen genotype for ability to detect the interaction. A problem with evaluating all combinations of host and pathogen genotypes is many independent hypotheses are being tested, making the over all probability of rejecting the null inflated. Applying multiple testing corrections like the Bonferroni correction tend to be conservative, and reduce the ability to detect interactions. The main solution is to decrease the number of tests made. In this aim, two methods of reducing the number of tests; limiting the number of loci tested in the interactive method, and Bayesian methods. Li and Riech 34 addressed this issue of unknown biological epistatic model by enumerating and classifying all two-locus models in bialleic, diploid, case control framework. Similarly, in a bialleic, diploid- haploid, 2 means (0 or 1) phenotype system, there are 64 possible interaction models. Of these models, half are equivalent due to symmetry, 14 of the 32 models are unique, and 10 describe interactive effects (Table 1). Simulating phenotypes based on the 14 models and an additive model will provide an extensive data set of all possible interactive models. M1* M2 M3 M4 M5 M6 M7 M8* M9 M10 M11 M12 M13 M14 M15 M16 M17* M18 M19 M20 M21 M22 M23 M24 M25 M26 M27 M28* M29 M30 M31 M32 Table 1. Two-loci models. Rows index host genotype, columns index pathogen genotype, and entries correspond to phenotype mean. Shown are 32 of the phenotype models, the remaining models can be obtained by substituting 0 with 1 and vice versa. Of these 32 models several models are the same: M2, M3, M6 and M7; M4 and M5; M8 and Page 4 of 11

5 M22; M9, M14, M18 and M21; M10, M13, M19 and M20; M11 and M16; M12 and M15; M23 and M24; M25, M26, and M32; M29 and M31, yielding 14 unique models (designated in red). 4 models (denoted by *) lack interactive effects. Two methods for detecting interactions will be considered: likelihood goodness of fit comparison between full interaction (Table 2), host effect (Table 3), pathogen effect (Table 4), and null modelsp; and the two-way analysis of variance (ANOVA). While these methods are very similar, the emphasis of goodness of fit is on the linear regression fit for 4 models and assumes a normally distributed phenotype while the ANOVA is used to test the means based on the variance components. The number of tests for each method is L Host x L Pathogen where L is the number of loci. To address this high number of tests issue, two approaches to reducing the number of tests will be used. First, a two-stage design will be implemented, requiring host main effects to meet a minimum significance in order to be considered for the full model test. Since number of pathogen sites is relatively small (~5,000), all pathogen loci will be used in the second stage. Second, a Bayesian approach will be used to search the entire sample genotype combination space and refine the model to only the genes interacting. Host Genotype at locus A Pathogen Genotype at locus B B b mean AA µ AAB µ AAb µ AA. p 2 A p B p 2 A p b Aa µ AaB µ Aab µ Aa. 2p A p a p B 2p A p a p b aa µ aab µ aab µ aa. p 2 a p B p 2 a p b mean µ..b µ..b Table 2. Full interactive model. Genotypic means and frequencies. Host Geneotype at locus A AA Aa aa 2 p A 2p A p a 2 p a µ AA µ Aa µ aa Table 3. Host effects model. Genotypic means and frequencies. Pathogen Genotype at locus B B b P B p b µ B µ B Table 4. Pathogen effects model. Genotypic means and frequencies. A. Simulation of interacting data. Methods. A locus in a diploid host population, and a locus in a haploid pathogen population will be simulated hosts will be sampled without replacement, for each host a pathogen will be sampled with replacement to mimic infection. 15 phenotypes will be modeled according to the 14 unique models (Table 1) and an additive model distributed normally with variance = replicates will be generated for each phenotypic model. Additionally, data sets with realistic marker numbers, recombination, and linkage disequilibrium will be simulated and phenotypes assigned using a subset of the models in Table 1. B. Method design. Page 5 of 11

6 Methods. Two approaches will be tested: i) Fit full interactive (Table 2), host effect (Table 3), pathogen effect (Table 4), and null models and evaluate goodness of fit. For each loci combination the likelihoods of the all models will be evaluated using equation 1.! Equation 1 Sample log-likelihood 31. l i = "ln# " (x i " µ i )2 2# 2 l i is host pathogen pair i, x i is the phenotypic value, µ i is the mean of the interaction class (µ AAB, µ AaB, etc), and σ is the maximum likelihood estimate of residual standard error. Evans et al 31 comment that parameters are its sample values when phenotypes are normally distributed. Goodness of fit p values will be obtained by assuming G (equation 2) is approximated by a χ 2 distribution with degrees of freedom equal to the difference in the number of parameters estimated. Equation 2 Goodness of fit test statistic G = 2(lnL alternative " lnl null ) ii) Two-way ANOVA using genotypes as factors. Both methods will be evaluated using a full 2 dimensional scan to evaluate maximum power and use a! Bonferroni multiple testing correction based on 900,000 host markers and 5000 pathogen markers. The two-stage and Bayesian approaches will be applied to each method to reduce the number of tests and better search the parameter space. i) Two stage design. Main host effects will be tested and a liberal cut off ( p <.1, p < 0.05, p < 0.01) will be applied, and the reduced set of loci will then be evaluated in the two dimensional method as described above. ii) A Bayesian approach utilizing the Monte Carlo Markov chain (MCMC) will be constructed. The parameter space of all genotype effects will be searched using the Metropolis-Hastings algorithm 35 and refines the models to only the genes affecting the phenotype. C. Model evaluation. Methods. The models will be evaluated for power to detect the interactive sites and the false positive rates. The power of detection is the number of times the method detects the Page 6 of 11

7 interactive effect at a significance level.05/l Host x L Pathogen out of 1000 replicates. The false positive rate will calculated as the number of interactive effects detected when no interactions are modeled, or in data sets with multiple markers and linkage disequilibrium. Additionally, the time to complete the analysis will be measured, since time is often sacrificed for good approximations. Specific Aim #2: Detect interacting genes in P. falciparum and H. sapiens. Rationale. Once an effective host pathogen model of has been developed, it can be applied to data to detect interacting genes. Experimental design. Study enrollment will include Africans from Kenya to minimize the effect of population structure and phenotypic variability due to genetic backgrounds. Information about the patients for cofactor analysis will be recorded. Blood samples will be collected from the enrolled subjects, the extent of parasitemia measured, and both P. falciparum and human DNA extracted. The samples will be genotyped using microarray technology, which allows high throughput genotyping based on sample DNA hybridizing to probes on the array. The Affymetrix Genome-Wide Human SNP Array 6.0 will be used since it has a higher coverage in African populations compared to the Illumina alternatives 36. A. Sample and phenotype collection. Methods African subjects admitted to hospitals in Kenya for treatment malaria infections will be enrolled in the study via informed consent. State, gender, age, language group, previous infection status (yes/no), and days since onset of symptoms will be recorded for each sample for future population structure and cofactor investigations. Finger pricked blood will be collected and tested for infection using the stevor gene PCR 37 assay. Confirming infection, 1 unit of venus blood will be collected, portioned into 2 aliquots, one to be used for phenotyping, and the other for DNA extraction. Parasitemia will be evaluated within an hour of sampling using the protocol outlined by the Medical Chemical Corporation 38 using Giemsa-stained blood films, reporting the percentage of infected red blood cells per 100 red blood cells counted and the number of parasites per µl of blood. Record of treatment type will be taken for each individual, and follow up finger prick blood samples taken 2 and 6 days post treatment. Parasitemia will be measured using the same protocol. B. Sample DNA extraction and genotyping. Methods. 30 ml of the genotyping aliquot will be centrifuged in the ACCUSPIN System-HISTOPAQUE (Sigma-Aldrich, St. Louis, MO) to separate red and white blood cells. Red blood cells will be collected and used for the extraction of P. falciparum DNA using the method described by Jeffares, DC et al 17. Page 7 of 11

8 The white blood cell portion of the HISTOPAQUE tube and human DNA extracted using the DNAQuik TM Large Scale Blood kit (BioServe, Laurel, MD) and recommended protocol. P. falciparum genome variation will be assayed using (pending release and availability) the Affymetrix P. falciparum genotyping array. Alternatively, the Affymetrix Targeted genotyping array (Affymetrix, Santa Clara, CA) will be customized based on annotated SNPs by Mu, J et al 9 and Jeffares, DC et al 17, and used for the genotyping of P. falciparum. Human DNA will be processed for genotyping using the Affymetrix Genome-Wide Human SNP Nsp/Sty Assay Kit 5.0/6.0 and hybridized to the Affymetrix Genome-Wide SNP Array 6.0. C. Detection of host pathogen interacting genes. Methods. The best performing model from Specific Aim #1 will be applied to the collected data set, using parasitemia as the phenotype. The number of days between symptom onset and blood collection, age, gender, previous infection status, and sampling location can be used as cofactors in the model if they appear to have an effect on the phenotype. To address multiple testing issues, to reduce the number of tests, only coding SNPs will be used in the analysis as it is unlikely that noncoding SNPs are interacting and altering phenotype. Alternatively, rates of parasite clearance can be used based on the follow up data or resistance types 38 scored based on the follow up parasitemia measures can be used as the phenotype, using treatment as a cofactor. Expected Outcomes. The results of this experiment will include a novel statistical tool to detect host pathogen interactions that can be applied to a wide range of host pathogen and host parasite combinations. Additionally, known and novel candidate interactions in P. falciparum and humans will be detected. Pitfalls and limitations. Host pathogen interactions may be detectable in simulated data however the biological effects may not be strong enough to be detected. In this event, the collected data will be used for genome wide associations between the host and phenotype, and parasite and phenotype separately. Additionally the data set will be used to study localized coevolution of host and pathogen; investigating genetic combinations that are invariant in the host pathogen combination but otherwise variable. Simulations are an artificial representation of what is inferred to happen biologically. To obtain an improved testing data set, known P. falciparum and human interacting genes can be investigated in vitro and growth rates or parasitemia quantified in cell cultures with known Page 8 of 11

9 genetic background. Alternatively, HIV and host genotypes with known interactions will be compiled and used for method performance testing. The methods proposed assume a normal distribution of phenotypes, however this may not be the case biologically. Method 1 can be modified to use other distributions that may better represent the data. Alternatively, the ANOVA method will be robust to deviations from the assumed distributions. Page 9 of 11

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