Huguenin, A. | Search bioRxiv

bioRxiv2026

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

BackgroundMatrix-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. MethodologyColonies 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 findingsUnsupervised 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. ConclusionThis 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 summaryLeishmania 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.

microbiology↗

bioRxiv2022

MALDI-TOF : A new tool for the identification of Schistosoma cercariae and detection of hybrids

Schistosomiasis is a neglected water-born parasitic disease caused by Schistosoma affecting more than 200 million people. Introgressive hybridization is common among these parasites and raises issues concerning their zoonotic transmission. Morphological identification of Schistosoma cercariae is difficult and does not permit hybrids detection. Our objective was to assess the performance of MALDI-TOF for the specific identification of cercariae in human and non-human Schistosoma and for the detection of hybridization between S. bovis and S. haematobium Spectra were collected from laboratory reared molluscs infested with strains of S. haematobium, S. mansoni, S. bovis, S. rodhaini and S. bovis x S. haematobium natural (Corsican hybrid) and artificial hybrids. Cluster analysis showed a clear separation between S. haematobium, S. bovis, S. mansoni and S. rodhaini. Corsican hybrids are classified with those of the parental strain of S. haematobium whereas other hybrids formed a distinct cluster. In blind test analysis the developed MALDI-TOF spectral database permits identification of Schistosoma cercariae with high accuracy (94%) and good specificity (S. bovis: 99.59%, S. haematobium 99.56%, S. mansoni and S. rodhaini: 100%). Most misidentifications were between S. haematobium and the Corsican hybrids. The use of machine learning permits to improve the discrimination between these last two taxa, with accuracy, F1 score and Sensitivity/Specificity > 97%. In multivariate analysis the factors associated with obtaining a valid identification score (> 1.7) were absence of ethanol preservation (p < 0.001) and a number of 2-3 cercariae deposited per well (p < 0.001). Also spectra acquired from S. mansoni cercariae are more likely to obtain a valid identification score than those acquired from S. haematobium (p<0.001). MALDI-TOF is a reliable technique for high-throughput identification of Schistosoma cercariae of medical and veterinary importance and could be useful for field survey in endemic areas. Author SummarySchistosomoses are neglected tropical diseases, affecting approximately 200 million people worldwide. They are transmitted during contact with water contaminated with the infesting stage of the parasite (the cercaria stage). Species-level recognition of cercariae present in water has important implications for field campaigns aimed at eradicating schistosomiasis. In addition, Schistosomes are able to hybridize between different species. Identification of Schistosomes cercariae on microscopy is difficult because of their similarity, and it does not allow hybrids to be distinguished. Molecular biology techniques allow a reliable diagnosis but are expensive. MALDI-TOF is a recent technique that permits an inexpensive identification of micro-organisms in a few minutes. In this paper, we evaluate MALDI-TOF identification of Schistosomes cercariae. We have implemented a database of MALDI-TOF cercariae spectra obtained from parental strains and hybrids of species of medical or veterinary interest, allowing reliable identification with an accuracy of 94%. The identification errors mainly come from confusion between the natural Corsican hybrid (S. haematobium x S. bovis) and S. haematobium. The use of machine learning algorithms permits to obtain an accuracy of more than 97% in the recognition of these two parasites. In conclusion, MALDI-TOF is a promising tool for the identification of Schistosome cercariae.

microbiology↗