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1.  Differential co-expression network centrality and machine learning feature selection for identifying susceptibility hubs in networks with scale-free structure 
BioData Mining  2015;8:5.
Biological insights into group differences, such as disease status, have been achieved through differential co-expression analysis of microarray data. Additional understanding of group differences may be achieved by integrating the connectivity structure of the differential co-expression network and per-gene differential expression between phenotypic groups. Such a global differential co-expression network strategy may increase sensitivity to detect gene-gene interactions (or expression epistasis) that may act as candidates for rewiring susceptibility co-expression networks.
We test two methods for inferring Genetic Association Interaction Networks (GAIN) incorporating both differential co-expression effects and differential expression effects: a generalized linear model (GLM) regression method with interaction effects (reGAIN) and a Fisher test method for correlation differences (dcGAIN). We rank the importance of each gene with complete interaction network centrality (CINC), which integrates each gene’s differential co-expression effects in the GAIN model along with each gene’s individual differential expression measure. We compare these methods with statistical learning methods Relief-F, Random Forests and Lasso. We also develop a mixture model and permutation approach for determining significant importance score thresholds for network centralities, Relief-F and Random Forest. We introduce a novel simulation strategy that generates microarray case–control data with embedded differential co-expression networks and underlying correlation structure based on scale-free or Erdos-Renyi (ER) random networks.
Using the network simulation strategy, we find that Relief-F and reGAIN provide the best balance between detecting interactions and main effects, plus reGAIN has the ability to adjust for covariates and model quantitative traits. The dcGAIN approach performs best at finding differential co-expression effects by design but worst for main effects, and it does not adjust for covariates and is limited to dichotomous outcomes. When the underlying network is scale free instead of ER, all interaction network methods have greater power to find differential co-expression effects. We apply these methods to a public microarray study of the differential immune response to influenza vaccine, and we identify effects that suggest a role in influenza vaccine immune response for genes from the PI3K family, which includes genes with known immunodeficiency function, and KLRG1, which is a known marker of senescence.
Electronic supplementary material
The online version of this article (doi:10.1186/s13040-015-0040-x) contains supplementary material, which is available to authorized users.
PMCID: PMC4326454  PMID: 25685197
Lab on a chip  2008;8(10):1700-1712.
Deciphering the signaling pathways that govern stimulation of naïve CD4+ T helper cells by antigen-presenting cells via formation of the immunological synapse is key to a fundamental understanding of the progression of successful adaptive immune response. The study of T cell – APC interactions in vitro is challenging, however, due to the difficulty of tracking individual, nonadherent cell pairs over time. Studying single cell dynamics over time reveals rare, but critical, signaling events that might be averaged out in bulk experiments, but these less common events are undoubtedly important for an integrated understanding of a cellular response to its microenvironment. We describe a novel application of microfluidic technology that overcomes many limitations of conventional cell culture and enables the study of hundreds of passively sequestered hematopoietic cells for extended periods of time. This microfluidic cell trap device consists of 440 18 μm×18 μm×10 μm PDMS, bucket-like structures opposing the direction of flow which serve as corrals for cells as they pass through the cell trap region. Cell viability analysis revealed that more than 70% of naïve CD4+ T cells (TN), held in place using only hydrodynamic forces, subsequently remain viable for 24 hours. Cytosolic calcium transients were successfully induced in TN cells following introduction of chemical, antibody, or cellular forms of stimulation. Statistical analysis of TN cells from a single stimulation experiment reveals the power of this platform to distinguish different calcium response patterns, an ability that might be utilized to characterize T cell signaling states in a given population. Finally, we investigate in real-time contact and non-contact-based interactions between primary T cells and dendritic cells, two main participants in the formation of the immunological synapse. Utilizing the microfluidic traps in a daisy-chain configuration allowed us to observe calcium transients in TN cells exposed only to media conditioned by secretions of lipopolysaccharide-matured dendritic cells, an event which is easily missed in conventional cell culture where large media-to-cell ratios dilute cellular products. Further investigation into this intercellular signaling event indicated that LPS-matured dendritic cells, in the absence of antigenic stimulation, secrete chemical signals that induce calcium transients in TN cells. While the stimulating factor(s) produced by the mature dendritic cells remains to be identified, this report illustrates the utility of these microfluidic cell traps for analyzing arrays of individual suspension cells over time and probing both contact-based and inter-cellular signaling events between one or more cell populations.
PMCID: PMC4160168  PMID: 18813394
3.  Inflammation and Neurological Disease-Related Genes are Differentially Expressed in Depressed Patients with Mood Disorders and Correlate with Morphometric and Functional Imaging Abnormalities 
Brain, behavior, and immunity  2012;31:161-171.
Depressed patients show evidence of both proinflammatory changes and neurophysiological abnormalities such as increased amygdala reactivity and volumetric decreases of the hippocampus and ventromedial prefrontal cortex (vmPFC). However, very little is known about the relationship between inflammation and neuroimaging abnormalities in mood disorders. A whole genome expression analysis of peripheral blood mononuclear cells yielded 12 protein-coding genes (ADM, APBB3, CD160, CFD, CITED2, CTSZ, IER5, NFKBIZ, NR4A2, NUCKS1, SERTAD1, TNF) that were differentially expressed between 29 unmedicated depressed patients with a mood disorder (8 bipolar disorder, 21 major depressive disorder) and 24 healthy controls (HCs). Several of these genes have been implicated in neurological disorders and/or apoptosis. Ingenuity Pathway Analysis yielded two genes networks, one centered around TNF with NFKβ, TGFβ, and ERK as connecting hubs, and the second network indicating cell cycle and/or kinase signaling anomalies. fMRI scanning was conducted using a backward-masking task in which subjects were presented with emotionally-valenced faces. Compared with HCs, the depressed subjects displayed a greater hemodynamic response in the right amygdala, left hippocampus, and the ventromedial prefrontal cortex to masked sad versus happy faces. The mRNA levels of several genes were significantly correlated with the hemodynamic response of the amygdala, vmPFC and hippocampus to masked sad versus happy faces. Differentially-expressed transcripts were significantly correlated with thickness of the left subgenual ACC, and volume of the hippocampus and caudate. Our results raise the possibility that molecular-level immune dysfunction can be mapped onto macro-level neuroimaging abnormalities, potentially elucidating a mechanism by which inflammation leads to depression.
PMCID: PMC3577998  PMID: 23064081
4.  Tissue-Specific Expressed Antibody Variable Gene Repertoires 
PLoS ONE  2014;9(6):e100839.
Recent developments in genetic technologies allow deep analysis of the sequence diversity of immune repertoires, but little work has been reported on the architecture of immune repertoires in mucosal tissues. Antibodies are the key to prevention of infections at the mucosal surface, but it is currently unclear whether the B cell repertoire at mucosal surfaces reflects the dominant antibodies found in the systemic compartment or whether mucosal tissues harbor unique repertoires. We examined the expressed antibody variable gene repertoires from 10 different human tissues using RNA samples derived from a large number of individuals. The results revealed that mucosal tissues such as stomach, intestine and lung possess unique antibody gene repertoires that differed substantially from those found in lymphoid tissues or peripheral blood. Mutation frequency analysis of mucosal tissue repertoires revealed that they were highly mutated, with little evidence for the presence of naïve B cells, in contrast to blood. Mucosal tissue repertoires possessed longer heavy chain complementarity determining region 3 loops than lymphoid tissue repertoires. We also noted a large increase in frequency of both insertions and deletions in the small intestine antibody repertoire. These data suggest that mucosal immune repertoires are distinct in many ways from the systemic compartment.
PMCID: PMC4067404  PMID: 24956460
5.  Vaccinomics, adversomics, and the immune response network theory: Individualized vaccinology in the 21st century 
Seminars in immunology  2013;25(2):89-103.
Vaccines, like drugs and medical procedures, are increasingly amenable to individualization or personalization, often based on novel data resulting from high throughput “omics” technologies. As a result of these technologies, 21st century vaccinology will increasingly see the abandonment of a “one size fits all” approach to vaccine dosing and delivery, as well as the abandonment of the empiric “isolate–inactivate–inject” paradigm for vaccine development. In this review, we discuss the immune response network theory and its application to the new field of vaccinomics and adversomics, and illustrate how vaccinomics can lead to new vaccine candidates, new understandings of how vaccines stimulate immune responses, new biomarkers for vaccine response, and facilitate the understanding of what genetic and other factors might be responsible for rare side effects due to vaccines. Perhaps most exciting will be the ability, at a systems biology level, to integrate increasingly complex high throughput data into descriptive and predictive equations for immune responses to vaccines. Herein, we discuss the above with a view toward the future of vaccinology.
PMCID: PMC3752773  PMID: 23755893
Adaptive immunity; Biotechnology; Computational biology; Genomics; Immunogenetics; Individualized medicine; Proteomics; Systems biology; Vaccination; Vaccines; Modeling; Vaccinomics; Adversomics; Predictive equation; Immune response network theory; Individualized vaccinology
6.  ReliefSeq: A Gene-Wise Adaptive-K Nearest-Neighbor Feature Selection Tool for Finding Gene-Gene Interactions and Main Effects in mRNA-Seq Gene Expression Data 
PLoS ONE  2013;8(12):e81527.
Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k) for each gene to optimize the Relief-F test statistics (importance scores) for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak) Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to detect both main effects and interaction effects. Software Availability:
PMCID: PMC3858248  PMID: 24339943
7.  A Genome-wide Association Study of Host Genetic Determinants of the Antibody Response to Anthrax Vaccine Adsorbed 
Vaccine  2012;30(32):4778-4784.
Several lines of evidence have supported a host genetic contribution to vaccine response, but genome-wide assessments for specific determinants have been sparse. Here we describe a genome-wide association study (GWAS) of protective antigen-specific antibody (AbPA) responses among 726 European-Americans who received Anthrax Vaccine Adsorbed (AVA) as part of a clinical trial. After quality control, 736,996 SNPs were tested for association with the AbPA response to 3 or 4 AVA vaccinations given over a 6-month period. No SNP achieved the threshold of genome-wide significance (p=5x10−8), but suggestive associations (p<1x10−5) were observed for SNPs in or near the class II region of the major histocompatibility complex (MHC), in the promoter region of SPSB1, and adjacent to MEX3C. Multivariable regression modeling suggested that much of the association signal within the MHC corresponded to previously identified HLA DR-DQ haplotypes involving component HLA-DRB1 alleles of *15:01, *01:01, or *01:02. We estimated the proportion of additive genetic variance explained by common SNP variation for the AbPA response after the 6 month vaccination. This analysis indicated a significant, albeit imprecisely estimated, contribution of variation tagged by common polymorphisms (p=0.032). Future studies will be required to replicate these findings in European Americans and to further elucidate the host genetic factors underlying variable immune response to AVA.
PMCID: PMC3387748  PMID: 22658931
Anthrax vaccines; Bacillus anthracis; bacterial vaccines; vaccination; Genome-wide association study
8.  A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis 
Genome Medicine  2012;4(12):103.
The overall influence of gene interaction in human disease is unknown. In cystic fibrosis (CF) a single allele of the cystic fibrosis transmembrane conductance regulator (CFTR-ΔF508) accounts for most of the disease. In cell models, CFTR-ΔF508 exhibits defective protein biogenesis and degradation rather than proper trafficking to the plasma membrane where CFTR normally functions. Numerous genes function in the biogenesis of CFTR and influence the fate of CFTR-ΔF508. However it is not known whether genetic variation in such genes contributes to disease severity in patients. Nor is there an easy way to study how numerous gene interactions involving CFTR-ΔF would manifest phenotypically.
To gain insight into the function and evolutionary conservation of a gene interaction network that regulates biogenesis of a misfolded ABC transporter, we employed yeast genetics to develop a 'phenomic' model, in which the CFTR-ΔF508-equivalent residue of a yeast homolog is mutated (Yor1-ΔF670), and where the genome is scanned quantitatively for interaction. We first confirmed that Yor1-ΔF undergoes protein misfolding and has reduced half-life, analogous to CFTR-ΔF. Gene interaction was then assessed quantitatively by growth curves for approximately 5,000 double mutants, based on alteration in the dose response to growth inhibition by oligomycin, a toxin extruded from the cell at the plasma membrane by Yor1.
From a comparative genomic perspective, yeast gene interactions influencing Yor1-ΔF biogenesis were representative of human homologs previously found to modulate processing of CFTR-ΔF in mammalian cells. Additional evolutionarily conserved pathways were implicated by the study, and a ΔF-specific pro-biogenesis function of the recently discovered ER membrane complex (EMC) was evident from the yeast screen. This novel function was validated biochemically by siRNA of an EMC ortholog in a human cell line expressing CFTR-ΔF508. The precision and accuracy of quantitative high throughput cell array phenotyping (Q-HTCP), which captures tens of thousands of growth curves simultaneously, provided powerful resolution to measure gene interaction on a phenomic scale, based on discrete cell proliferation parameters.
We propose phenomic analysis of Yor1-ΔF as a model for investigating gene interaction networks that can modulate cystic fibrosis disease severity. Although the clinical relevance of the Yor1-ΔF gene interaction network for cystic fibrosis remains to be defined, the model appears to be informative with respect to human cell models of CFTR-ΔF. Moreover, the general strategy of yeast phenomics can be employed in a systematic manner to model gene interaction for other diseases relating to pathologies that result from protein misfolding or potentially any disease involving evolutionarily conserved genetic pathways.
PMCID: PMC3906889  PMID: 23270647
Gene interaction; Genetic buffering; Genotype-phenotype complexity; Phenomics; Quantitative high throughput cell array phenotyping (Q-HTCP); Cystic fibrosis transmembrane conductance regulator (CFTR); ER membrane complex (EMC); ATP binding cassette (ABC) transporter; Membrane protein biogenesis; Yeast model of human disease; Comparative functional genomics
9.  Identification of potential human respiratory syncytial virus and metapneumovirus T cell epitopes using computational prediction and MHC binding assays 
Journal of immunological methods  2011;374(1-2):13-17.
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.
PMCID: PMC3220792  PMID: 21854782
T-Lymphocytes; Immunologic techniques; Epitopes; T-Lymphocyte; Computational Biology; MHC binding peptide
11.  The Role of HLA DR-DQ Haplotypes in Variable Antibody Responses to Anthrax Vaccine Adsorbed 
Genes and immunity  2011;12(6):457-465.
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 ( 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.
PMCID: PMC3165112  PMID: 21368772
Anthrax vaccines; Bacillus anthracis; Bacterial vaccines; Vaccination; HLA Antigens
12.  Machine Learning for Detecting Gene-Gene Interactions 
Applied bioinformatics  2006;5(2):77-88.
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.
PMCID: PMC3244050  PMID: 16722772
13.  Phase 1 Trial of the Dengue Virus Type 4 Vaccine Candidate rDEN4Δ30-4995 in Healthy Adult Volunteers 
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.
PMCID: PMC2829759  PMID: 19861619
14.  Integrated Analysis of Genetic and Proteomic Data Identifies Biomarkers Associated with Adverse Events Following Smallpox Vaccination 
Genes and immunity  2008;10(2):112-119.
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.
PMCID: PMC2692715  PMID: 18923431
15.  Genetic Basis for Adverse Events Following Smallpox Vaccination 
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.
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).
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).
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.
PMCID: PMC2746083  PMID: 18454680
adverse events; vaccination; smallpox; genetics; epidemiology
16.  The Quality of Chimpanzee T-Cell Activation and Simian Immunodeficiency Virus/Human Immunodeficiency Virus Susceptibility Achieved via Antibody-Mediated T-Cell Receptor/CD3 Stimulation Is a Function of the Anti-CD3 Antibody Isotype▿  
Journal of Virology  2008;82(20):10271-10278.
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.
PMCID: PMC2566284  PMID: 18667496
17.  Capturing the Spectrum of Interaction Effects in Genetic Association Studies by Simulated Evaporative Cooling Network Analysis 
PLoS Genetics  2009;5(3):e1000432.
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
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.
PMCID: PMC2653647  PMID: 19300503
18.  Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction 
BMC Bioinformatics  2008;9:238.
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.
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%.
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.
PMCID: PMC2412877  PMID: 18485205
19.  A Computational Model of Quantitative Chromatin Immunoprecipitation (ChIP) Analysis 
Cancer informatics  2008;4:137-145.
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.
PMCID: PMC2367313  PMID: 18458756
chromatin immunoprecipitation analysis; computer modelling; transcription factors
20.  A Computational Model of Quantitative Chromatin Immunoprecipitation (ChIP) Analysis 
Cancer Informatics  2008;6:138-146.
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.
PMCID: PMC2367313  PMID: 18458756
chromatin immunoprecipitation analysis; computer modelling; transcription factors
21.  Cytokine Expression Patterns Associated with Systemic Adverse Events following Smallpox Immunization 
The Journal of infectious diseases  2006;194(4):444-453.
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.
PMCID: PMC1620015  PMID: 16845627
22.  Using the natural evolution of a rotavirus-specific human monoclonal antibody to predict the complex topography of a viral antigenic site 
Immunome Research  2007;3:8.
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.
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.
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.
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.
PMCID: PMC2042970  PMID: 17877819

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