Integrated multivariate analysis of transcriptomic data reveals immunological mechanisms in mice afterLeishmania infantuminfection

Abstract

Transcriptional analysis of complex biological scenarios has been extensively used, even though sometimes results may prove imprecise or difficult-to interpret due to an overwhelming amount of information. In this study, a large-scale Real-time qPCR experiment was coupled to multivariate statistical analysis to describe the main immunological events underlying the early L. infantum infection in livers of BALB/c mice. High-throughput qPCR was used to evaluate the expression of 223 genes related to immunometabolism 1-, 3-, 5- and 10-days post infection. This integrative analysis showed strikingly different gene signatures at 1- and 10-days post infection, revealing progression of infection in the experimental model based on the upregulation of particular immunological response patterns and mediators. This approach addresses the challenge of integrating large collections of transcriptional data for the identification of candidate biomarkers in experimental models.