RESUMEN
Zebrafish (Danio rerio) is an emerging toxicity screening model for both human health and ecology. As part of the Computational Toxicology Research Program of the U.S. EPA, the toxicity of the 309 ToxCast™ Phase I chemicals was assessed using a zebrafish screen for developmental toxicity. All exposures were by immersion from 6-8 h post fertilization (hpf) to 5 days post fertilization (dpf); nominal concentration range of 1 nM-80 µM. On 6 dpf larvae were assessed for death and overt structural defects. Results revealed that the majority (62%) of chemicals were toxic to the developing zebrafish; both toxicity incidence and potency was correlated with chemical class and hydrophobicity (logP); and inter-and intra-plate replicates showed good agreement. The zebrafish embryo screen, by providing an integrated model of the developing vertebrate, compliments the ToxCast assay portfolio and has the potential to provide information relative to overt and organismal toxicity.
Asunto(s)
Embrión no Mamífero/efectos de los fármacos , Contaminantes Ambientales/toxicidad , Plaguicidas/toxicidad , Teratógenos/toxicidad , Pez Cebra , Animales , Modelos Animales , Bibliotecas de Moléculas Pequeñas , Pruebas de Toxicidad/métodosRESUMEN
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 the development of adverse events (AEs) in patients after smallpox vaccination. As 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 this study, we examined 1442 genetic variables (single nucleotide polymorphisms) 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 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 the 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 intercellular adhesion molecule-1 (ICAM-1 or CD54), interleukin-10 (IL-10), and colony stimulating factor-3 (CSF-3 or G-CSF) and a genetic polymorphism in the cytokine gene interleukin-4 (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 shows the utility of RF for such analytical tasks, while both enhancing and reinforcing our working model of AE development after smallpox vaccination.
Asunto(s)
Citocinas/sangre , Citocinas/genética , Molécula 1 de Adhesión Intercelular/sangre , Molécula 1 de Adhesión Intercelular/genética , Modelos Biológicos , Polimorfismo de Nucleótido Simple , Vacuna contra Viruela/efectos adversos , Biomarcadores/sangre , Toma de Decisiones Asistida por Computador , Femenino , Humanos , Inflamación/sangre , Inflamación/inducido químicamente , Inflamación/genética , Masculino , Proteómica/métodos , Vacuna contra Viruela/administración & dosificación , VacunaciónRESUMEN
MOTIVATION: The development of genome-wide capabilities for genotyping has led to the practical problem of identifying the minimum subset of genetic variants relevant to the classification of a phenotype. This challenge is especially difficult in the presence of attribute interactions, noise and small sample size. METHODS: Analogous to the physical mechanism of evaporation, we introduce an evaporative cooling (EC) feature selection algorithm that seeks to obtain a subset of attributes with the optimum information temperature (i.e. the least noise). EC uses an attribute quality measure analogous to thermodynamic free energy that combines Relief-F and mutual information to evaporate (i.e. remove) noise features, leaving behind a subset of attributes that contain DNA sequence variations associated with a given phenotype. RESULTS: EC is able to identify functional sequence variations that involve interactions (epistasis) between other sequence variations that influence their association with the phenotype. This ability is demonstrated on simulated genotypic data with attribute interactions and on real genotypic data from individuals who experienced adverse events following smallpox vaccination. The EC formalism allows us to combine information entropy, energy and temperature into a single information free energy attribute quality measure that balances interaction and main effects. AVAILABILITY: Open source software, written in Java, is freely available upon request.