ArticleNature communications2025
Innate biosignature of treatment failure in human cutaneous leishmaniasis.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Article
- Deciphering the Molecular Basis of Systemic Manifestations in Cutaneous Leishmaniasis Using Integrated Bioinformatics Analysis.Acta parasitologica · 2026Article
- Response and therapeutic failure to meglumine antimoniate, the first-line drug for cutaneous leishmaniasis infections in Colombian soldiers.Parasitology research · 2025Article
- How Common Is Imported Cutaneous Leishmaniasis in Romania? Two Case Reports.Microorganisms · 2025Article
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Authors and funding
8 authors.
Funding
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 the chronic immunopathology of human cutaneous leishmaniasis (CL) are not well understood. Through sequential transcriptomic profiling of blood monocytes, neutrophils, and eosinophils over the course of systemic treatment with meglumine antimoniate, we revealed that a heightened and sustained Type-I interferon response signature is a hallmark of treatment failure (TF) in CL patients infected with Leishmania (Viannia) panamensis and L.V. braziliensis. 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 11 differentially expressed genes (common between monocytes, neutrophils and eosinophils) is predictive of TF. Similarly, machine learning models constructed using data from pre-treatment as well as post-treatment samples, accurately classify treatment outcome into cure and TF. Results from this study instigate the evaluation of Type-I interferon responses as immunological targets for host-directed therapies for the treatment of CL, and highlight the feasibility of using transcriptional signatures as predictive biomarkers of outcome for therapeutic decision making.
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Registered trials
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