MALDI-ToF detection of Leishmania infantum infection in Lutzomyia longipalpis and Nyssomyia neivai

Background

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) is widely used for sand fly identification, but its potential to detect Leishmania infections in vectors remain underexplored. This pilot study evaluated whether MALDI-ToF MS protein profiles of lab-reared Lutzomyia longipalpis and Nyssomyia neivai can discriminate Leishmania infantum –infected from uninfected females.

Methodology

Colonies were experimentally infected with L. infantum using membrane feeding, and females were collected at different days post-blood meal. Thoraces and legs were processed individually for MALDI-ToF MS, and spectra were analysed using both Bruker software and custom R pipelines.

Principal findings

Unsupervised approaches (MSP dendrograms, PCA) showed limited or inconsistent separation of infection status for Lu. longipalpis . In contrast, supervised machine-learning models built on peak-intensity matrices achieved excellent discrimination between infected and uninfected specimens for both species, with several algorithms reaching near-perfect performance on an external test set not used for training. Variable-importance analysis highlighted sets of m/z peaks, mainly showing decreased intensity in infected sand flies, as putative infection biomarkers.

Conclusion

This proof-of-concept study highlights that L. infantum infection induces reproducible, species-specific alterations in sand-fly MALDI-TOF profiles, supporting further development of high-throughput, MS-based screening of infected vectors.

Author summary

Leishmania infantum is a parasite responsible for visceral leishmaniasis, a severe neglected tropical disease. It is transmitted to humans by sandfly vectors. This study explored whether the MALDI-ToF mass spectrometry technique can detect infection by the L. infantum parasite in the two main sandfly vectors in Brazil: Lutzomyia longipalpis and Nyssomyia neivai . The method has already been tested to identify sandfly species, but its ability to detect infected insects had not been well studied. We infected laboratory-reared sandflies and analyzed their protein profiles to see whether infected and uninfected individuals could be distinguished.

We found that infection changes the molecular fingerprints of both sandfly species. Machine-learning models were able to distinguish infected from uninfected specimens with very high accuracy. A small part of the most informative signal was shared between both species, while most of the peaks were species-specific, suggesting that infection affects each vector in a slightly different way.

These results show that MALDI-ToF has promise as a rapid, low-cost tool for screening sandflies for Leishmania infection. With further validation, this approach could complement existing surveillance methods and help monitor disease transmission in endemic areas.