Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks.

Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.

Multiplexed digital mRNA profiling of the inflammatory response in the West Nile Swiss Webster mouse model.

BACKGROUND AND PURPOSE: The ability to track changes in gene expression following viral infection is paramount to understanding viral pathogenesis. This study was undertaken to evaluate the nCounter, a high throughput digital gene expression system, as a means to better understand West Nile virus (WNV) dissemination and the inflammatory response against WNV in the outbred Swiss Webster (SW) mouse model over the course of infection. METHODOLOGY: The nCounter Mouse Inflammation gene expression kit containing 179 inflammation related genes was used to analyze gene expression changes in multiple tissues over a nine day course of infection in SW mice following intraperitoneal injection with WNV. Protein expression levels for a subset of these cytokine/chemokine genes were determined using a multiplex protein detection system (BioPlex) and comparisons of protein/RNA expression levels made. RESULTS: Expression analysis of spleen, lung, liver, kidney and brain of SW mice infected with WNV revealed that Cxcl10 and Il12b are differentially expressed in all tissues tested except kidney. Data stratification of positively confirmed infected (WNV (+)) versus non-infected (WNV (-) tissues allowed differentiation of the systemic inflammatory gene response from tissue-specific responses arising from WNV infection. Significant (p<0.05) decrease in C3ar1 was found in WNV (-) spleen. Il23a was significantly upregulated, while Il10rb was down-regulated in WNV (-) lung. Il3 and Mbl2 were down-regulated in WNV (-) liver. In WNV (+) livers, Stat1, Tlr2, chemokines Cxcl1, Cxcl3, Cxcl9, Cxcl10, cytokines Il6, Il18, cytokine-related gene Il1r and cytokine agonist Ilrn were significantly upregulated. In WNV (-) brain tissues, Csf2 and Cxcl10 were significantly upregulated. Similar gene and protein expression kinetics were found for Ccl2, Ccl3, Ccl4 and Ccl5 and correlated with the presence of infectious virus. In summary, the utility of the nCounter platform for rapid identification of gene expression changes in SW mice associated with WNV infection was demonstrated.

Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.

Disease properties, geography, and mitigation strategies in a simulation spread of rinderpest across the United States.

For the past decade, the Food and Agriculture Organization of the United Nations has been working toward eradicating rinderpest through vaccination and intense surveillance by 2012. Because of the potential severity of a rinderpest epidemic, it is prudent to prepare for an unexpected outbreak in animal populations. There is no immunity to the disease among the livestock or wildlife in the United States (US). If rinderpest were to emerge in the US, the loss in livestock could be devastating. We predict the potential spread of rinderpest using a two-stage model for the spread of a multi-host infectious disease among agricultural animals in the US. The model incorporates large-scale interactions among US counties and the small-scale dynamics of disease spread within a county. The model epidemic was seeded in 16 locations and there was a strong dependence of the overall epidemic size on the starting location. The epidemics were classified according to overall size into small epidemics of 100 to 300 animals (failed epidemics), epidemics infecting 3,000 to 30,000 animals (medium epidemics), and the large epidemics infecting around one million beef cattle. The size of the rinderpest epidemics were directly related to the origin of the disease and whether or not the disease moved into certain key counties in high-livestock-density areas of the US. The epidemic size also depended upon response time and effectiveness of movement controls.

Escape from the dominant HLA-B27-restricted cytotoxic T-lymphocyte response in Gag is associated with a dramatic reduction in human immunodeficiency virus type 1 replication.

Human leukocyte antigen (HLA)-B27-positive subjects are uncommon in their ability to control infection with human immunodeficiency virus type 1 (HIV-1). However, late viral escape from a narrowly directed immunodominant Gag-specific CD8(+) T-lymphocyte (CTL) response has been linked to AIDS progression in these individuals. Identifying the mechanism of the immune-mediated control may provide critical insights into HIV-1 vaccine development. Here, we illustrate that the CTL escape mutation R(264)K in the HLA-B27-restricted KK10 epitope in the capsid resulted in a significant defect in viral replication in vitro. The R(264)K variant was impaired in generating late reverse transcription products, indicating that replication was blocked at a postentry step. Notably, the R(264)K mutation was associated in vivo with the development of a rare secondary mutation, S(173)A, which restored viral replication in vitro. Furthermore, infectivity of the R(264)K variant was rescued by the addition of cyclosporine A or infection of a cyclophilin A-deficient cell line. These data demonstrate a severe functional defect imposed by the R(264)K mutation during an early step in viral replication that is likely due to the inability of this variant to replicate efficiently in the presence of normal levels of cyclophilin A. We conclude that the impact of the R(264)K substitution on capsid structure constrains viral escape and enables long-term maintenance of the dominant CTL response against B27-KK10, providing an explanation for the protective effect of HLA-B27 during HIV infection.

Mutually exclusive T-cell receptor induction and differential susceptibility to human immunodeficiency virus type 1 mutational escape associated with a two-amino-acid difference between HLA class I subtypes.

The relative contributions of HLA alleles and T-cell receptors (TCRs) to the prevention of mutational viral escape are unclear. Here, we examined human immunodeficiency virus type 1 (HIV-1)-specific CD8(+) T-cell responses restricted by two closely related HLA class I alleles, B*5701 and B*5703, that differ by two amino acids but are both associated with a dominant response to the same HIV-1 Gag epitope KF11 (KAFSPEVIPMF). When this epitope is presented by HLA-B*5701, it induces a TCR repertoire that is highly conserved among individuals, cross-recognizes viral epitope variants, and is rarely associated with mutational escape. In contrast, KF11 presented by HLA-B*5703 induces an entirely different, more heterogeneous TCR beta-chain repertoire that fails to recognize specific KF11 escape variants which frequently arise in clade C-infected HLA-B*5703(+) individuals. These data show the influence of HLA allele subtypes on TCR selection and indicate that extensive TCR diversity is not a prerequisite to prevention of allowable viral mutations.

Immunoinformatics comes of age.

With the burgeoning immunological data in the scientific literature, scientists must increasingly rely on Internet resources to inform and enhance their work. Here we provide a brief overview of the adaptive immune response and summaries of immunoinformatics resources, emphasizing those with Web interfaces. These resources include searchable databases of epitopes and immune-related molecules, and analysis tools for T cell and B cell epitope prediction, vaccine design, and protein structure comparisons. There is an agreeable synergy between the growing collections in immune-related databases and the growing sophistication of analysis software; the databases provide the foundation for developing predictive computational tools, which in turn enable more rapid identification of immune responses to populate the databases. Collectively, these resources contribute to improved understanding of immune responses and escape, and evolution of pathogens under immune pressure. The public health implications are vast, including designing vaccines, understanding autoimmune diseases, and defining the correlates of immune protection.

Conformational dependence of a protein kinase phosphate transfer reaction.

Atomic motions and energetics for a phosphate transfer reaction catalyzed by the cAMP-dependent protein kinase are calculated by plane-wave density functional theory, starting from structures of proteins crystallized in both the reactant conformation (RC) and the transition-state conformation (TC). In TC, we calculate that the reactants and products are nearly isoenergetic with a 20-kJ/mol barrier, whereas phosphate transfer is unfavorable by 120 kJ/mol in the RC, with an even higher barrier. With the protein in TC, the motions involved in reaction are small, with only P(gamma) and the catalytic proton moving >0.5 A. Examination of the structures reveals that in the RC the active site cleft is not completely closed and there is insufficient space for the phosphorylated serine residue in the product state. Together, these observations imply that the phosphate transfer reaction occurs rapidly and reversibly in a particular conformation of the protein, and that the reaction can be gated by changes of a few tenths of an angstrom in the catalytic site.