ABSTRACT
The quality and magnitude of the immune and inflammatory responses determine the clinical outcome of Leishmania infection, and contribute to the efficacy of antileishmanial treatments. However, the precise immune mechanisms involved in healing or in chronic immunopathology of human cutaneous leishmaniasis (CL) are not completely understood. Through sequential transcriptomic profiling of blood monocytes (Mo), neutrophils (Nφ), and eosinophils (Eφ) over the course of systemic treatment with meglumine antimoniate, we discovered that a heightened and sustained Type I interferon (IFN) response signature is a hallmark of treatment failure (TF) in CL patients. The transcriptomes of pre-treatment, mid-treatment and end-of-treatment samples were interrogated to identify predictive and prognostic biomarkers of TF. A composite score derived from the expression of 9 differentially expressed genes (common between Mo, Nφ and Eφ) was predictive of TF in this patient cohort for biomarker discovery. Similarly, machine learning models constructed using data from pre-treatment as well as post-treatment samples, accurately classified treatment outcome between cure and TF. Results from this study instigate the evaluation of Type-I IFN responses as new immunological targets for host-directed therapies for treatment of CL, and highlight the feasibility of using transcriptional signatures as predictive biomarkers of outcome for therapeutic decision making.
