Back

Acriflavine treatment attenuates inflammatory pathology in cutaneous leishmaniasis independently of parasite control

Fowler, E. A.; Novais, F. O.

2026-06-09 immunology
10.64898/2026.06.05.730355 bioRxiv
Show abstract

Cutaneous leishmaniasis is a parasitic skin disease for which current treatments often fail, highlighting the need for new therapeutic approaches. While high parasite burdens are associated with delayed healing, excessive protective immune responses, including elevated IFN-{gamma} production, have also been linked to worse clinical outcomes, indicating that immunopathology contributes significantly to disease progression. Acriflavine is an antimicrobial compound with reported anti-leishmanial activity and the ability to inhibit hypoxia-driven responses. Because Leishmania-infected skin is hypoxic in both mice and humans, and hypoxia has been implicated in disease pathogenesis, we investigated whether acriflavine alters the course of cutaneous leishmaniasis by affecting parasite control and/or host immune responses. We found that acriflavine treatment significantly reduced lesion size in Leishmania major-infected mice. Unexpectedly, this improvement occurred without changes in parasite burden. Instead, acriflavine treatment reduced the frequency of dendritic cells within lesions and decreased their expression of MHC class II, which correlated with fewer IFN-{gamma}-producing CD4 T cells at the site of infection. These findings indicate that acriflavine ameliorates disease by limiting dendritic cell activation and subsequent IFN-{gamma}-driven immunopathology rather than enhancing parasite clearance. Together, our results identify acriflavine as a potential host-directed therapeutic strategy for cutaneous leishmaniasis and support targeting hypoxia-associated pathways to reduce tissue damage driven by excessive inflammatory responses.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.