Human respiratory syncytial virus (RSV) and human metapneumovirus (MPV) are two of the most common causes of serious viral lower respiratory tract illness in humans. CD8+ T cells have been shown to be important in animal models and human clinical studies for the clearance of viral infection, and they may contribute in part to protection against severe disease during reinfections. Precise enumeration and accurate phenotyping of RSV- or MPV-specific CD8+ T cells in humans is currently limited by the relatively small number of T cell epitopes that have been mapped with accompanying identification of MHC restriction patterns. We sought to expand the number of potential RSV and MPV epitopes for use in clinical and translational studies by identifying an expanded set of MHC-binding peptides based on RSV and MPV wild-type virus strain protein sequences. We interrogated the full protein sequences of all 9 or 11 proteins of MPV or RSV respectively using four established epitope prediction algorithms for human HLA A*0101, A*0201, or B*0702 binding and attempted to synthesize the top-scoring 150-152 peptides for each of the two viruses. Synthesis resulted in 442 synthesized and soluble peptides of the 452 predicted epitopes for MPV or RSV. We then determined the binding of the synthetic peptides to recombinant human HLA A*0101, A*0201 or B*0702 molecules with the predicted restriction using a commercially available plate-based assay, iTopia. A total of 230 of the 442 peptides tested exhibited binding to the appropriate MHC molecule. The binding results suggested that existing algorithms for prediction of MHC A*0201 binding are particularly robust. The binding results also provided a large benchmarking data collection for comparison of new prediction algorithms.

Host genetic variation, particularly within the human leukocyte antigen (HLA) loci, reportedly mediates heterogeneity in immune response to certain vaccines; however, no large study of genetic determinants of anthrax vaccine response has been described. We searched for associations between the IgG antibody to protective antigen (AbPA) response to Anthrax Vaccine Adsorbed (AVA) in humans and polymorphisms at HLA class I (HLA-A, -B, and -C) and class II (HLA-DRB1, -DQA1, -DQB1, -DPB1) loci. The study included 794 European-Americans and 200 African-Americans participating in a 43-month, double-blind, placebo-controlled, clinical trial of AVA (clinicaltrials.gov identifier NCT00119067). Among European-Americans, genes from tightly linked HLA-DRB1-DQA1-DQB1 haplotypes displayed significant overall associations with longitudinal variation in AbPA levels at 4, 8, 26, and 30 weeks from baseline in response to vaccination with 3 or 4 doses of AVA (global p=6.53×10−4). In particular, carriage of the DRB1-DQA1-DQB1 haplotypes *1501-*0102-*0602 (p=1.17×10−5), *0101-*0101-*0501 (p=0.009), and *0102-*0101-*0501 (p=0.006) was associated with significantlylower AbPA levels. In carriers of two copies of these haplotypes, lower AbPA levels persisted following subsequent vaccinations. No significant associations were observed amongst African-Americans or for any HLA class I allele/haplotype. Further studies will be required to replicate these findings and to explore the role of host genetic variation outside of the HLA region.

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are ‘the norm’ and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.

rDEN4Δ30-4995 is a live attenuated dengue virus type 4 (DENV4) vaccine candidate specifically designed as a further attenuated derivative of the rDEN4Δ30 parent virus. In a previous study, 5 of 20 vaccinees who received 105 plaque-forming units (PFU) of rDEN4Δ30 developed a transient elevation of the serum alanine aminotransferase (ALT) level and an asymptomatic maculopapular rash developed in 10 of 20. In the current study, 28 healthy adult volunteers were randomized to receive 105 PFU of rDEN4Δ30-4995 (20) or placebo (8) as a single subcutaneous injection. The vaccine was safe, well-tolerated, and immunogenic. An asymptomatic generalized maculopapular rash and elevations in ALT levels were observed in 10% of the rDEN4Δ30-4995 vaccinees. None of the rDEN4Δ30-4995 vaccinees became viremic, yet 95% developed a four-fold or greater increase in neutralizing antibody titers. Thus, rDEN4Δ30-4995 was demonstrated to be safe, highly attenuated, and immunogenic. However, an asymptomatic localized erythematous rash at the injection site was seen in 17/20 rDEN4Δ30-4995 vaccinees. Therefore, alternative DENV4 vaccine strains were selected for further clinical development.

Complex clinical outcomes, such as adverse reaction to vaccination, arise from the concerted interactions among the myriad components of a biological system. Therefore, comprehensive etiological models can be developed only through the integrated study of multiple types of experimental data. In this study, we apply this paradigm to high-dimensional genetic and proteomic data collected to elucidate the mechanisms underlying development of adverse events (AEs) in patients following smallpox vaccination. Since vaccination was successful in all of the patients under study, the AE outcomes reported likely represent the result of interactions among immune system components that result in excessive or prolonged immune stimulation. In the current study, we examined 1442 genetic variables (SNPs) and 108 proteomic variables (serum cytokine concentrations) to model AE risk. To accomplish this daunting analytical task, we employed the Random Forests™ (RF) method to filter out the most important attributes, then we used the selected attributes to build a final decision tree model. This strategy is well-suited to integrated analysis, as relevant attributes may be selected from categorical or continuous data. Importantly, RF is a natural approach for studying the type of gene-gene, gene-protein, and protein-protein interactions we hypothesize to be involved in development of clinical AEs. RF importance scores for particular attributes take interactions into account, and there may be interactions across data types. Combining information from previous studies on AEs related to smallpox vaccination with the genetic and proteomic attributes identified by RF, we built a comprehensive model of AE development that includes the cytokines ICAM-1 (CD54), IL-10, and CSF-3 (G-CSF), and a genetic polymorphism in the cyokine gene IL4. The biological factors included in the model support our hypothesized mechanism for the development of AEs involving prolonged stimulation of inflammatory pathways and an imbalance of normal tissue damage repair pathways. This study demonstrates the utility of RF for such analytical tasks, and both enhances and reinforces our working model of AE development following smallpox vaccination.

Background

Although vaccinia immunization is highly effective in preventing smallpox, post-vaccination reactions are common. Identifying genetic factors associated with AEs might allow screening before vaccinia administration and provide a rational basis for the development of improved vaccine candidates.

Methods

Two independent clinical trials in healthy, vaccinia-naïve adult volunteers were conducted with the Aventis Pasteur smallpox vaccine (APSV). Volunteers were assessed repeatedly for local and systemic AEs to vaccine and were genotyped using the same panel of 1442 single-nucleotide polymorphisms (SNPs).

Results

In the first study, thirty-six SNPs in 26 genes were associated with systemic AEs (p-value ≤ 0.05). In the second study, only those SNPs associated with AEs in the first sample were tested. In the final analysis, three SNPs were associated consistently with AEs in both studies. A nonsynonymous SNP in methylenetetrahydrofolate reductase (MTHFR) was associated with AE risk in both trials (odds ratio [OR]; 95% confidence interval [CI]); p-value [p]): (OR=2.3; CI=1.1–5.2; p=0.04) and (OR=4.1; CI=1.4–11.4; p<0.01). Two SNPs in the interferon regulatory factor 1 (IRF1) gene were associated with AE risk in both sample sets: (OR=3.2; CI=1.1–9.8; p=0.03) and (OR=3.0; CI=1.1–8.3; p=0.03).

Conclusions

Genetic polymorphisms in an enzyme previously associated with adverse reactions to a variety of pharmacologic agents (MTHFR) and an immunological transcription factor (IRF1) were associated with AEs after smallpox vaccination in two independent study samples. These findings highlight common genetic variants with promising clinical significance that merit further investigation.

While human immunodeficiency virus type 1 (HIV-1) infection is associated with hyperimmune activation and systemic depletion of CD4+ T cells, simian immunodeficiency virus (SIV) infection in sooty mangabeys or chimpanzees does not exhibit these hallmarks. Control of immune activation is thought to be one of the major components that govern species-dependent differences in the disease pathogenesis. A previous study introduced the idea that the resistance of chimpanzees to SIVcpz infection-induced hyperimmune activation could be the result of the expression of select sialic acid-recognizing immunoglobulin (Ig)-like lectin (Siglec) superfamily members by chimpanzee T cells. Siglecs, which are absent on human T cells, were thought to control levels of T-cell activation in chimpanzees and were thus suggested as a cause for the pathogenic differences in the course of SIVcpz or HIV-1 infection. As in human models of T-cell activation, stimulation had been attempted using an anti-CD3 monoclonal antibody (MAb) (UCHT1; isotype IgG1), but despite efficient binding, UCHT1 failed to activate chimpanzee T cells, an activation block that could be partially overcome by MAb-induced Siglec-5 internalization. We herein demonstrate that anti-CD3 MAb-mediated chimpanzee T-cell activation is a function of the anti-CD3 MAb isotype and is not governed by Siglec expression. While IgG1 anti-CD3 MAbs fail to stimulate chimpanzee T cells, IgG2a anti-CD3 MAbs activate chimpanzee T cells in the absence of Siglec manipulations. Our results thus imply that prior to studying possible differences between human and chimpanzee T-cell activation, a relevant model of chimpanzee T cell activation needs to be established.

Evidence from human genetic studies of several disorders suggests that interactions between alleles at multiple genes play an important role in influencing phenotypic expression. Analytical methods for identifying Mendelian disease genes are not appropriate when applied to common multigenic diseases, because such methods investigate association with the phenotype only one genetic locus at a time. New strategies are needed that can capture the spectrum of genetic effects, from Mendelian to multifactorial epistasis. Random Forests (RF) and Relief-F are two powerful machine-learning methods that have been studied as filters for genetic case-control data due to their ability to account for the context of alleles at multiple genes when scoring the relevance of individual genetic variants to the phenotype. However, when variants interact strongly, the independence assumption of RF in the tree node-splitting criterion leads to diminished importance scores for relevant variants. Relief-F, on the other hand, was designed to detect strong interactions but is sensitive to large backgrounds of variants that are irrelevant to classification of the phenotype, which is an acute problem in genome-wide association studies. To overcome the weaknesses of these data mining approaches, we develop Evaporative Cooling (EC) feature selection, a flexible machine learning method that can integrate multiple importance scores while removing irrelevant genetic variants. To characterize detailed interactions, we construct a genetic-association interaction network (GAIN), whose edges quantify the synergy between variants with respect to the phenotype. We use simulation analysis to show that EC is able to identify a wide range of interaction effects in genetic association data. We apply the EC filter to a smallpox vaccine cohort study of single nucleotide polymorphisms (SNPs) and infer a GAIN for a collection of SNPs associated with adverse events. Our results suggest an important role for hubs in SNP disease susceptibility networks. The software is available at http://sites.google.com/site/McKinneyLab/software.

Author Summary

Susceptibility to many diseases and disorders is caused by breakdown at multiple points in the genetic network. Each of these points of breakdown by itself may have a very modest effect on disease risk but the points may have a much stronger effect through statistical interactions with each other. Genome-wide association studies provide the opportunity to identify alleles at multiple loci that interact to influence phenotypic variation in common diseases and disorders. However, if each SNP is tested for association as though it were independent of the rest of the genome, then the full advantage of the variation from markers across the genome will be unfulfilled. In this study, we illustrate the utility of a new approach to high-dimensional genetic association analysis that treats the collection of SNPs as interacting on a system level. This approach uses a machine-learning filter followed by an information theoretic and graph theoretic approach to infer a phenotype-specific network of interacting SNPs.

Background

Multifactor Dimensionality Reduction (MDR) has been introduced previously as a non-parametric statistical method for detecting gene-gene interactions. MDR performs a dimensional reduction by assigning multi-locus genotypes to either high- or low-risk groups and measuring the percentage of cases and controls incorrectly labelled by this classification – the classification error. The combination of variables that produces the lowest classification error is selected as the best or most fit model. The correctly and incorrectly labelled cases and controls can be expressed as a two-way contingency table. We sought to improve the ability of MDR to detect gene-gene interactions by replacing classification error with a different measure to score model quality.

Results

In this study, we compare the detection and power of MDR using a variety of measures for two-way contingency table analysis. We simulated 40 genetic models, varying the number of disease loci in the model (2 – 5), allele frequencies of the disease loci (.2/.8 or .4/.6) and the broad-sense heritability of the model (.05 – .3). Overall, detection using NMI was 65.36% across all models, and specific detection was 59.4% versus detection using classification error at 62% and specific detection was 52.2%.

Conclusion

Of the 10 measures evaluated, the likelihood ratio and normalized mutual information (NMI) are measures that consistently improve the detection and power of MDR in simulated data over using classification error. These measures also reduce the inclusion of spurious variables in a multi-locus model. Thus, MDR, which has already been demonstrated as a powerful tool for detecting gene-gene interactions, can be improved with the use of alternative fitness functions.

Chromatin immunoprecipitation (ChIP) analysis is widely used to identify the locations in genomes occupied by transcription factors (TFs). The approach involves chemical cross-linking of DNA with associated proteins, fragmentation of chromatin by sonication or enzymatic digestion, immunoprecipitation of the fragments containing the protein of interest, and then PCR or hybridization analysis to characterize and quantify the genomic sequences enriched. We developed a computational model of quantitative ChIP analysis to elucidate the factors contributing to the method’s resolution. The most important variables identified by the model were, in order of importance, the spacing of the PCR primers, the mean length of the chromatin fragments, and, unexpectedly, the type of fragment width distribution, with very small DNA fragments and smaller amplicons providing the best resolution of TF binding. One of the major predictions of the model was also validated experimentally.

Chromatin immunoprecipitation (ChIP) analysis is widely used to identify the locations in genomes occupied by transcription factors (TFs). The approach involves chemical cross-linking of DNA with associated proteins, fragmentation of chromatin by sonication or enzymatic digestion, immunoprecipitation of the fragments containing the protein of interest, and then PCR or hybridization analysis to characterize and quantify the genomic sequences enriched. We developed a computational model of quantitative ChIP analysis to elucidate the factors contributing to the method’s resolution. The most important variables identified by the model were, in order of importance, the spacing of the PCR primers, the mean length of the chromatin fragments, and, unexpectedly, the type of fragment width distribution, with very small DNA fragments and smaller amplicons providing the best resolution of TF binding. One of the major predictions of the model was also validated experimentally.

Vaccinia virus is reactogenic in a significant number of vaccinees, with the most common adverse events being fever, lymphadenopathy, and rash. Although the inoculation is given in the skin, these adverse events suggest a robust systemic inflammatory response. To elucidate the cytokine response signature of systemic adverse events, we used a protein microarray technique to precisely quantitate 108 serum cytokines and chemokines in vaccine recipients before and 1 week after primary immunization with Aventis Pasteur smallpox vaccine. We studied 74 individuals after vaccination, of whom 22 experienced a systemic adverse event and 52 did not. The soluble factors most associated with adverse events were selected on the basis of voting among a committee of machine-learning methods and statistical procedures, and the selected cytokines were used to build a final decision-tree model. On the basis of changes in protein expression, we identified 6 cytokines that accurately discriminate between individuals on the basis of adverse event status: granulocyte colony–stimulating factor, stem cell factor, monokine induced by interferon-γ (CXCL9), intercellular adhesion molecule–1, eotaxin, and tissue inhibitor of metalloproteinases–2. This cytokine signature is characteristic of particular inflammatory response pathways and suggests that the secretion of cytokines by fibroblasts plays a central role in systemic adverse events.

Background

Understanding the interaction between viral proteins and neutralizing antibodies at atomic resolution is hindered by a lack of experimentally solved complexes. Progress in computational docking has led to the prediction of increasingly high-quality model antibody-antigen complexes. The accuracy of atomic-level docking predictions is improved when integrated with experimental information and expert knowledge.

Methods

Binding affinity data associated with somatic mutations of a rotavirus-specific human adult antibody (RV6-26) are used to filter potential docking orientations of an antibody homology model with respect to the rotavirus VP6 crystal structure. The antibody structure is used to probe the VP6 trimer for candidate interface residues.

Results

Three conformational epitopes are proposed. These epitopes are candidate antigenic regions for site-directed mutagenesis of VP6, which will help further elucidate antigenic function. A pseudo-atomic resolution RV6-26 antibody-VP6 complex is proposed consistent with current experimental information.

Conclusion

The use of mutagenesis constraints in docking calculations allows for the identification of a small number of alternative arrangements of the antigen-antibody interface. The mutagenesis information from the natural evolution of a neutralizing antibody can be used to discriminate between residue-scale models and create distance constraints for atomic-resolution docking. The integration of binding affinity data or other information with computation may be an advantageous approach to assist peptide engineering or therapeutic antibody design.