Molecular Approaches for the Validation of the Baboon as a Nonhuman Primate Model for the Study of Zika Virus Infection

Nonhuman primates (NHP) are particularly important for modeling infections with viruses that do not naturally replicate in rodent cells. Zika virus (ZIKV) has been responsible for sporadic epidemics, but in 2015 a disseminated outbreak of ZIKV resulted in the World Health Organization declaring it a global health emergency. Since the advent of this last epidemic, several NHP species, including the baboon, have been utilized for modeling and understanding the complications of ZIKV infection in humans; several health issues related to the outcome of infection have not been resolved yet and require further investigation. This study was designed to validate, in baboons, the molecular signatures that have previously been identified in ZIKV-infected humans and macaque models. We performed a comprehensive molecular analysis of baboons during acute ZIKV infection, including flow cytometry, cytokine, immunological, and transcriptomic analyses. We show here that, similar to most human cases, ZIKV infection of male baboons tends to be subclinical, but is associated with a rapid and transient antiviral interferon-based response signature that induces a detectable humoral and cell-mediated immune response. This immunity against the virus protects animals from challenge with a divergent ZIKV strain, as evidenced by undetectable viremia but clear anamnestic responses. These results provide additional support for the use of baboons as an alternative animal model to macaques and validate omic techniques that could help identify the molecular basis of complications associated with ZIKV infections in humans

Influence of RNA-Seq Data Analysis Methods on the Outcome of Transcribing Analyses of Zika Virus Infection in a Baboon Model

RNA-Seq is a high-throughput technique that uses next-generation sequencing of mRNA to quantify expression of genes or gene transcripts. Currently, there is not a clear consensus regarding which data analysis methods are most appropriate for RNA-Seq experiments. Previous studies have shown that different methods, when applied to the same raw data, can produce different conclusions, including different fold-change and false discovery rate (FDR) values. However, these studies did not proceed to the downstream applications of their data, such as Ingenuity Pathway Analysis (IPA) or gene ontology (GO) analysis, that are often the end-goal of many transcriptomics studies to assess the biological relevance of the differentially expressed genes (DEGs). We performed RNA-Seq analyses of mRNA isolated from PBMCs of baboons infected with Zika virus (ZIKV) at days 0, 3, and 15 post-infection, using two currently popular R packages for RNA-seq analyses: edgeR and DESeq2. Within these packages, we compared results obtained from different combinations of statistical methods. We found that these variations in analysis methods led to fairly large changes in the number of DEGs detected, which conforms to the results of previous studies. Interestingly, in most cases, these differences did not appear to drastically change results of Biological Process Gene Ontology analyses performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Functional Annotation tool. The results of our GO analyses also indicated significant up- and down- regulation of GO terms related to inflammation and antiviral immune responses, as we expected based on plasma viral load data