RESUMO
During RNA virus replication, there is the potential to incorporate mutations that affect virulence or pathogenesis. For live-attenuated vaccines, this has implications for stability, as replication may result in mutations that either restore the wild-type phenotype via reversion or compensate for the attenuating mutations by increasing virulence (pseudoreversion). Recent studies have demonstrated that altering the mutation rate of an RNA virus is an effective attenuation tool. To validate the safety of low-fidelity mutations to increase vaccine attenuation, several mutations in the RNA-dependent RNA-polymerase (RdRp) were tested in the live-attenuated Venezuelan equine encephalitis virus vaccine strain, TC-83. Next generation sequencing after passage in the presence of mutagens revealed a mutant containing three mutations in the RdRp, TC-83 3x, to have decreased replication fidelity, while a second mutant, TC-83 4x displayed no change in fidelity, but shared many phenotypic characteristics with TC-83 3x. Both mutants exhibited increased, albeit inconsistent attenuation in an infant mouse model, as well as increased immunogenicity and complete protection against lethal challenge of an adult murine model compared with the parent TC-83. During serial passaging in a highly permissive model, the mutants increased in virulence but remained less virulent than the parent TC-83. These results suggest that the incorporation of low-fidelity mutations into the RdRp of live-attenuated vaccines for RNA viruses can confer increased immunogenicity whilst showing some evidence of increased attenuation. However, while in theory such constructs may result in more effective vaccines, the instability of the vaccine phenotype decreases the likelihood of this being an effective vaccine strategy.
RESUMO
The emergence and rapid spread of the 2009 H1N1 pandemic influenza virus showed that many diagnostic tests were unsuitable for detecting the novel virus isolates. In most countries the probe-based TaqMan assay developed by the U.S. Centers for Disease Control and Prevention was used for diagnostic purposes. The substantial sequence data that became available during the course of the pandemic created the opportunity to utilize bioinformatics tools to evaluate the unique sequence properties of this virus for the development of diagnostic tests. We used a comprehensive computational approach to examine conserved 2009 H1N1 sequence signatures that are at least 20 nucleotides long and contain at least two mismatches compared to any other known H1N1 genome. We found that the hemagglutinin (HA) and neuraminidase (NA) genes contained sequence signatures that are highly conserved among 2009 H1N1 isolates. Based on the NA gene signatures, we used Visual-OMP to design primers with optimal hybridization affinity and we used ThermoBLAST to minimize amplification artifacts. This procedure resulted in a highly sensitive and discriminatory 2009 H1N1 detection assay. Importantly, we found that the primer set can be used reliably in both a conventional TaqMan and a SYBR green reverse transcriptase (RT)-PCR assay with no loss of specificity or sensitivity. We validated the diagnostic accuracy of the NA SYBR green assay with 125 clinical specimens obtained between May and August 2009 in Chile, and we showed diagnostic efficacy comparable to the CDC assay. Our approach highlights the use of systematic computational approaches to develop robust diagnostic tests during a viral pandemic.
Assuntos
Vírus da Influenza A Subtipo H1N1/isolamento & purificação , Influenza Humana/diagnóstico , Compostos Orgânicos/metabolismo , Reação em Cadeia da Polimerase Via Transcriptase Reversa/métodos , Virologia/métodos , Benzotiazóis , Chile , Primers do DNA/genética , Diaminas , Glicoproteínas de Hemaglutininação de Vírus da Influenza/genética , Humanos , Vírus da Influenza A Subtipo H1N1/genética , Neuraminidase/genética , Quinolinas , Sensibilidade e Especificidade , Coloração e Rotulagem/métodos , Estados Unidos , Proteínas Virais/genéticaRESUMO
Finding relations among gene expressions involves the definition of the similarity between experimental data. A simplest similarity measure is the Correlation Coefficient. It is able to identify linear dependences only; moreover, is sensitive to experimental errors. An alternative measure, the Shannon Mutual Information (MI), is free from the above mentioned weaknesses. However, the calculation of MI for continuous variables from the finite number of experimental points, N, involves an ambiguity arising when one divides the range of values of the continuous variable into boxes. Then the distribution of experimental points among the boxes (and, therefore, MI) depends on the box size. An algorithm for the calculation of MI for continuous variables is proposed. We find the optimum box sizes for a given N from the condition of minimum entropy variation with respect to the change of the box sizes. We have applied this technique to the gene expression dataset from Stanford, containing microarray data at 18 time points from yeast Saccharomyces cerevisiae cultures (Spellman et al.,[3]). We calculated MI for all of the pairs of time points. The MI analysis allowed us to identify time patterns related to different biological processes in the cell.