Matthew A. Kayala

A prospective analysis of the Ab response to Plasmodium falciparum before and after a malaria season by protein microarray

Abs are central to malaria immunity, which is only acquired after years of exposure to Plasmodium falciparum (Pf). Despite the enormous worldwide burden of malaria, the targets of protective Abs and the basis of their inefficient acquisition are unknown. Addressing these knowledge gaps could accelerate malaria vaccine development. To this end, we developed a protein microarray containing ∼23% of the Pf 5,400-protein proteome and used this array to probe plasma from 220 individuals between the ages of 2–10 years and 18–25 years in Mali before and after the 6-month malaria season. Episodes of malaria were detected by passive surveillance over the 8-month study period. Ab reactivity to Pf proteins rose dramatically in children during the malaria season; however, most of this response appeared to be short-lived based on cross-sectional analysis before the malaria season, which revealed only modest incremental increases in Ab reactivity with age. Ab reactivities to 49 Pf proteins measured before the malaria season were significantly higher in 8–10-year-old children who were infected with Pf during the malaria season but did not experience malaria (n = 12) vs. those who experienced malaria (n = 29). This analysis also provided insight into patterns of Ab reactivity against Pf proteins based on the life cycle stage at which proteins are expressed, subcellular location, and other proteomic features. This approach, if validated in larger studies and in other epidemiological settings, could prove to be a useful strategy for better understanding fundamental properties of the human immune response to Pf and for identifying previously undescribed vaccine targets.

The Stability and Complexity of Antibody Responses to the Major Surface Antigen of Plasmodium falciparum Are Associated with Age in a Malaria Endemic Area

Individuals that are exposed to malaria eventually develop immunity to the disease with one possible mechanism being the gradual acquisition of antibodies to the range of parasite variant surface antigens in their local area. Major antibody targets include the large and highly polymorphic Plasmodium falciparum Erythrocyte Membrane Protein 1 (PfEMP1) family of proteins. Here, we use a protein microarray containing 123 recombinant PfEMP1-DBLα domains (VAR) from Papua New Guinea to seroprofile 38 nonimmune children (<4 years) and 29 hyperimmune adults (≥15 years) from the same local area. The overall magnitude, prevalence and breadth of antibody response to VAR was limited at <2 years and 2–2.9 years, peaked at 3–4 years and decreased for adults compared with the oldest children. An increasing proportion of individuals recognized large numbers of VAR proteins (>20) with age, consistent with the breadth of response stabilizing with age. In addition, the antibody response was limited in uninfected children compared with infected children but was similar in adults irrespective of infection status. Analysis of the variant-specific response confirmed that the antibody signature expands with age and infection. This also revealed that the antibody signatures of the youngest children overlapped substantially, suggesting that they are exposed to the same subset of PfEMP1 variants. VAR proteins were either seroprevalent from early in life, (<3 years), from later in childhood (≥3 years) or rarely recognized. Group 2 VAR proteins (Cys2/MFK-REY+) were serodominant in infants (<1-year-old) and all other sequence subgroups became more seroprevalent with age. The results confirm that the anti-PfEMP1-DBLα antibody responses increase in magnitude and prevalence with age and further demonstrate that they increase in stability and complexity. The protein microarray approach provides a unique platform to rapidly profile variant-specific antibodies to malaria and suggests novel insights into the acquisition of immunity to malaria.

A Burkholderia pseudomallei protein microarray reveals serodiagnostic and cross-reactive antigens

Understanding the way in which the immune system responds to infection is central to the development of vaccines and many diagnostics. To provide insight into this area, we fabricated a protein microarray containing 1,205 Burkholderia pseudomallei proteins, probed it with 88 melioidosis patient sera, and identified 170 reactive antigens. This subset of antigens was printed on a smaller array and probed with a collection of 747 individual sera derived from 10 patient groups including melioidosis patients from Northeast Thailand and Singapore, patients with different infections, healthy individuals from the USA, and from endemic and nonendemic regions of Thailand. We identified 49 antigens that are significantly more reactive in melioidosis patients than healthy people and patients with other types of bacterial infections. We also identified 59 cross-reactive antigens that are equally reactive among all groups, including healthy controls from the USA. Using these results we were able to devise a test that can classify melioidosis positive and negative individuals with sensitivity and specificity of 95% and 83%, respectively, a significant improvement over currently available diagnostic assays. Half of the reactive antigens contained a predicted signal peptide sequence and were classified as outer membrane, surface structures or secreted molecules, and an additional 20% were associated with pathogenicity, adaptation or chaperones. These results show that microarrays allow a more comprehensive analysis of the immune response on an antigen-specific, patient-specific, and population-specific basis, can identify serodiagnostic antigens, and contribute to a more detailed understanding of immunogenicity to this pathogen.

A Genome-Wide Proteome Array Reveals a Limited Set of Immunogens in Natural Infections of Humans and White-Footed Mice with Borrelia burgdorferi

ABSTRACT Humans and other animals with Lyme borreliosis produce antibodies to a number of components of the agent Borrelia burgdorferi, but a full accounting of the immunogens during natural infections has not been achieved. Employing a protein array produced in vitro from 1,292 DNA fragments representing ∼80% of the genome, we compared the antibody reactivities of sera from patients with early or later Lyme borreliosis to the antibody reactivities of sera from controls. Overall, ∼15% of the open reading frame (ORF) products (Orfs) of B. burgdorferi in the array detectably elicited an antibody response in humans with natural infections. Among the immunogens, 103 stood out on the basis of statistical criteria. The majority of these Orfs were also immunogenic with sera obtained from naturally infected Peromyscus leucopus mice, a major reservoir. The high-ranking set included several B. burgdorferi proteins hitherto unrecognized as immunogens, as well as several proteins that have been established as antigens. The high-ranking immunogens were more likely than nonreactive Orfs to have the following characteristics: (i) plasmid-encoded rather than chromosome-encoded proteins, (ii) a predicted lipoprotein, and (iii) a member of a paralogous family of proteins, notably the Bdr and Erp proteins. The newly discovered antigens included Orfs encoded by several ORFs of the lp36 linear plasmid, such as BBK07 and BBK19, and proteins of the flagellar apparatus, such as FliL. These results indicate that the majority of deduced proteins of B. burgdorferi do not elicit antibody responses during infection and that the limited sets of immunogens are similar for two different host species.

Sterile protective immunity to malaria is associated with a panel of novel P. falciparum antigens

The development of an effective malaria vaccine remains a global public health priority. Less than 0.5% of the Plasmodium falciparum genome has been assessed as potential vaccine targets and candidate vaccines have been based almost exclusively on single antigens. It is possible that the failure to develop a malaria vaccine despite decades of effort might be attributed to this historic focus. To advance malaria vaccine development, we have fabricated protein microarrays representing 23% of the entire P. falciparum proteome and have probed these arrays with plasma from subjects with sterile protection or no protection after experimental immunization with radiation attenuated P. falciparum sporozoites. A panel of 19 pre-erythrocytic stage antigens was identified as strongly associated with sporozoite-induced protective immunity; 16 of these antigens were novel and 85% have been independently identified in sporozoite and/or liver stage proteomic or transcriptomic data sets. Reactivity to any individual antigen did not correlate with protection but there was a highly significant difference in the cumulative signal intensity between protected and not protected individuals. Functional annotation indicates that most of these signature proteins are involved in cell cycle/DNA processing and protein synthesis. In addition, 21 novel blood-stage specific antigens were identified. Our data provide the first evidence that sterile protective immunity against malaria is directed against a panel of novel P. falciparum antigens rather than one antigen in isolation. These results have important implications for vaccine development, suggesting that an efficacious malaria vaccine should be multivalent and targeted at a select panel of key antigens, many of which have not been previously characterized.

Cyber-T web server: differential analysis of high-throughput data

The Bayesian regularization method for high-throughput differential analysis, described in Baldi and Long (A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 2001: 17: 509-519) and implemented in the Cyber-T web server, is one of the most widely validated. Cyber-T implements a t-test using a Bayesian framework to compute a regularized variance of the measurements associated with each probe under each condition. This regularized estimate is derived by flexibly combining the empirical measurements with a prior, or background, derived from pooling measurements associated with probes in the same neighborhood. This approach flexibly addresses problems associated with low replication levels and technology biases, not only for DNA microarrays, but also for other technologies, such as protein arrays, quantitative mass spectrometry and next-generation sequencing (RNA-seq). Here we present an update to the Cyber-T web server, incorporating several useful new additions and improvements. Several preprocessing data normalization options including logarithmic and (Variance Stabilizing Normalization) VSN transforms are included. To augment two-sample t-tests, a one-way analysis of variance is implemented. Several methods for multiple tests correction, including standard frequentist methods and a probabilistic mixture model treatment, are available. Diagnostic plots allow visual assessment of the results. The web server provides comprehensive documentation and example data sets. The Cyber-T web server, with R source code and data sets, is publicly available at http://cybert.ics.uci.edu/.

Identification of a common immune signature in murine and human systemic Salmonellosis

Despite the importance of Salmonella infections in human and animal health, the target antigens of Salmonella-specific immunity remain poorly defined. We have previously shown evidence for antibody-mediating protection against invasive Salmonellosis in mice and African children. To generate an overview of antibody targeting in systemic Salmonellosis, a Salmonella proteomic array containing over 2,700 proteins was constructed and probed with immune sera from Salmonella-infected mice and humans. Analysis of multiple inbred mouse strains identified 117 antigens recognized by systemic antibody responses in murine Salmonellosis. Importantly, many of these antigens were independently identified as target antigens using sera from Malawian children with Salmonella bacteremia, validating the study of the murine model. Furthermore, vaccination with SseB, the most prominent antigenic target in Malawian children, provided mice with significant protection against Salmonella infection. Together, these data uncover an overlapping immune signature of disseminated Salmonellosis in mice and humans and provide a foundation for the generation of a protective subunit vaccine.

Serological profiling of a Candida albicans protein microarray reveals permanent host-pathogen interplay and stage-specific responses during candidemia.

Candida albicans in the immunocompetent host is a benign member of the human microbiota. Though, when host physiology is disrupted, this commensal-host interaction can degenerate and lead to an opportunistic infection. Relatively little is known regarding the dynamics of C. albicans colonization and pathogenesis. We developed a C. albicans cell surface protein microarray to profile the immunoglobulin G response during commensal colonization and candidemia. The antibody response from the sera of patients with candidemia and our negative control groups indicate that the immunocompetent host exists in permanent host-pathogen interplay with commensal C. albicans. This report also identifies cell surface antigens that are specific to different phases (i.e. acute, early and mid convalescence) of candidemia. We identified a set of thirteen cell surface antigens capable of distinguishing acute candidemia from healthy individuals and uninfected hospital patients with commensal colonization. Interestingly, a large proportion of these cell surface antigens are involved in either oxidative stress or drug resistance. In addition, we identified 33 antigenic proteins that are enriched in convalescent sera of the candidemia patients. Intriguingly, we found within this subset an increase in antigens associated with heme-associated iron acquisition. These findings have important implications for the mechanisms of C. albicans colonization as well as the development of systemic infection.

Learning to Predict Chemical Reactions

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top-ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of nonproductive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal ( http://cdb.ics.uci.edu) under the Toolkits section.

ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning.

Proposing reasonable mechanisms and predicting the course of chemical reactions is important to the practice of organic chemistry. Approaches to reaction prediction have historically used obfuscating representations and manually encoded patterns or rules. Here we present ReactionPredictor, a machine learning approach to reaction prediction that models elementary, mechanistic reactions as interactions between approximate molecular orbitals (MOs). A training data set of productive reactions known to occur at reasonable rates and yields and verified by inclusion in the literature or textbooks is derived from an existing rule-based system and expanded upon with manual curation from graduate level textbooks. Using this training data set of complex polar, hypervalent, radical, and pericyclic reactions, a two-stage machine learning prediction framework is trained and validated. In the first stage, filtering models trained at the level of individual MOs are used to reduce the space of possible reactions to consider. In the second stage, ranking models over the filtered space of possible reactions are used to order the reactions such that the productive reactions are the top ranked. The resulting model, ReactionPredictor, perfectly ranks polar reactions 78.1% of the time and recovers all productive reactions 95.7% of the time when allowing for small numbers of errors. Pericyclic and radical reactions are perfectly ranked 85.8% and 77.0% of the time, respectively, rising to >93% recovery for both reaction types with a small number of allowed errors. Decisions about which of the polar, pericyclic, or radical reaction type ranking models to use can be made with >99% accuracy. Finally, for multistep reaction pathways, we implement the first mechanistic pathway predictor using constrained tree-search to discover a set of reasonable mechanistic steps from given reactants to given products. Webserver implementations of both the single step and pathway versions of ReactionPredictor are available via the chemoinformatics portal http://cdb.ics.uci.edu/.