AUTHOR=Gonçalves Leilane Oliveira , Pulido Andrés F. Vallejo , Mathias Fernando Augusto Siqueira , Enes Alexandre Estevão Silvério , Carvalho Maria Gabriela Reis , de Melo Resende Daniela , Polak Marta E. , Ruiz Jeronimo C. TITLE=Expression Profile of Genes Related to the Th17 Pathway in Macrophages Infected by Leishmania major and Leishmania amazonensis: The Use of Gene Regulatory Networks in Modeling This Pathway JOURNAL=Frontiers in Cellular and Infection Microbiology VOLUME=12 YEAR=2022 URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2022.826523 DOI=10.3389/fcimb.2022.826523 ISSN=2235-2988 ABSTRACT=

Leishmania amazonensis and Leishmania major are the causative agents of cutaneous and mucocutaneous diseases. The infections‘ outcome depends on host–parasite interactions and Th1/Th2 response, and in cutaneous form, regulation of Th17 cytokines has been reported to maintain inflammation in lesions. Despite that, the Th17 regulatory scenario remains unclear. With the aim to gain a better understanding of the transcription factors (TFs) and genes involved in Th17 induction, in this study, the role of inducing factors of the Th17 pathway in Leishmania–macrophage infection was addressed through computational modeling of gene regulatory networks (GRNs). The Th17 GRN modeling integrated experimentally validated data available in the literature and gene expression data from a time-series RNA-seq experiment (4, 24, 48, and 72 h post-infection). The generated model comprises a total of 10 TFs, 22 coding genes, and 16 cytokines related to the Th17 immune modulation. Addressing the Th17 induction in infected and uninfected macrophages, an increase of 2- to 3-fold in 4–24 h was observed in the former. However, there was a decrease in basal levels at 48–72 h for both groups. In order to evaluate the possible outcomes triggered by GRN component modulation in the Th17 pathway. The generated GRN models promoted an integrative and dynamic view of Leishmania–macrophage interaction over time that extends beyond the analysis of single-gene expression.