Identification of Southeast Asian Anopheles mosquito species using MALDI-TOF mass spectrometry

Malaria elimination in Southeast Asia remains a challenge, underscoring the importance of accurately identifying malaria mosquitoes to understand transmission dynamics and improve vector control. Traditional methods such as morphological identification require extensive training and cannot distinguish between sibling species, while molecular approaches are costly for extensive screening. Matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as a rapid and cost-effective tool for Anopheles species identification, yet its current use is limited to few specialized laboratories. This study aimed to develop and validate an online reference database for MALDI-TOF MS identification of Southeast Asian Anopheles species. The database, constructed using the in-house data analysis pipeline MSI2 (Sorbonne University), comprised 2046 head mass spectra from 209 specimens collected at the Thailand-Myanmar border. Molecular identification via COI and ITS2 DNA barcodes enabled the identification of 20 sensu stricto species and 5 sibling species complexes. The high quality of the mass spectra was demonstrated by a MSI2 median score (min-max) of 61.62 (15.94–77.55) for correct answers, using the best result of four technical replicates of a test panel. Applying an identification threshold of 45, 93.9% (201/214) of the specimens were identified, with 98.5% (198/201) consistency with the molecular taxonomic assignment. In conclusion, MALDI-TOF MS holds promise for malaria mosquito identification and can be scaled up for entomological surveillance in Southeast Asia. The free online sharing of our database on the MSI2 platform (https://msi.happy-dev.fr/) represents an important step towards the broader use of MALDI-TOF MS in malaria vector surveillance

Author Correction:The temporal dynamics and infectiousness of subpatent Plasmodium falciparum infections in relation to parasite density

Correction to: Nature Communications https://doi.org/10.1038/s41467-019-09441-1; published online 29 March 201

Nouveaux outils protéomiques pour la surveillance des arthropodes vecteurs : caractérisation des espèces et des déterminants de transmission

Vector control programmes are a strategic priority in the fight against malaria and other vector-borne diseases. However, the entomological tools for characterizing the arthropod vectors are limited and difficult to establish in the field. The aim of this thesis was to develop new proteomic tools for monitoring arthropod vectors through the use of matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry. Thus, the use of MALDI-TOF mass spectrometry was validated for the species identification of sand flies from French Guiana and anopheles from Guinea and Mali. For the laboratory-reared Anopheles stephensi specimens, we have shown that artificial neural networks coupled with MALDI-TOF mass spectrometry specifically recognized spectral patterns related to the biology of Anopheles such as age, blood feeding and Plasmodium berghei infection. Future studies will need to validate the new approaches on a larger scale with field-collected specimens. An online application, developed at Sorbonne University for MALDI-TOF mass spectrometry identification in microbiology, will facilitate the access of the tool for vector surveillance by making available mass spectra libraries of arthropods. Finally, computational biology tools offer interesting prospects for improving the performance of MALDI-TOF, providing new applications for vector control.Les programmes de contrôle vectoriel sont une priorité stratégique dans le contrôle du paludisme et des autres maladies à transmission vectorielle. Toutefois, les outils entomologiques de caractérisation des arthropodes vecteurs sont limités et difficiles à mettre en œuvre sur le terrain. Ainsi, l’objectif de cette thèse était la mise au point de nouveaux outils protéomiques de surveillance des arthropodes vecteurs grâce à la spectrométrie de masse MALDI-TOF (désorption et ionisation assistée par une matrice avec détection en temps de vol). Les travaux ont validé l’outil MALDI-TOF pour identifier les espèces de phlébotomes de Guyane et d’anophèles de Guinée et du Mali. Pour les spécimens d’Anopheles stephensi d’élevage, les réseaux de neurones artificiels couplés au MALDI-TOF reconnaissaient des motifs spectraux liés à la biologie des anophèles : l’âge, les antécédents de repas sanguin et l’infection par Plasmodium berghei. Les études futures devront valider les nouvelles approches à plus grande échelle à partir de spécimens collectés sur le terrain. Une application en ligne, développée à Sorbonne Université pour l’identification MALDI-TOF en microbiologie, facilitera l’utilisation pour la surveillance vectorielle en partageant des banques de spectres d’arthropodes. Enfin, les approches bio-informatiques pourront améliorer les performances et fournir de nouvelles applications

New proteomic tools for the surveillance of arthropod vectors : characterization of species and determinants of transmission

Les programmes de contrôle vectoriel sont une priorité stratégique dans le contrôle du paludisme et des autres maladies à transmission vectorielle. Toutefois, les outils entomologiques de caractérisation des arthropodes vecteurs sont limités et difficiles à mettre en œuvre sur le terrain. Ainsi, l’objectif de cette thèse était la mise au point de nouveaux outils protéomiques de surveillance des arthropodes vecteurs grâce à la spectrométrie de masse MALDI-TOF (désorption et ionisation assistée par une matrice avec détection en temps de vol). Les travaux ont validé l’outil MALDI-TOF pour identifier les espèces de phlébotomes de Guyane et d’anophèles de Guinée et du Mali. Pour les spécimens d’Anopheles stephensi d’élevage, les réseaux de neurones artificiels couplés au MALDI-TOF reconnaissaient des motifs spectraux liés à la biologie des anophèles : l’âge, les antécédents de repas sanguin et l’infection par Plasmodium berghei. Les études futures devront valider les nouvelles approches à plus grande échelle à partir de spécimens collectés sur le terrain. Une application en ligne, développée à Sorbonne Université pour l’identification MALDI-TOF en microbiologie, facilitera l’utilisation pour la surveillance vectorielle en partageant des banques de spectres d’arthropodes. Enfin, les approches bio-informatiques pourront améliorer les performances et fournir de nouvelles applications.Vector control programmes are a strategic priority in the fight against malaria and other vector-borne diseases. However, the entomological tools for characterizing the arthropod vectors are limited and difficult to establish in the field. The aim of this thesis was to develop new proteomic tools for monitoring arthropod vectors through the use of matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry. Thus, the use of MALDI-TOF mass spectrometry was validated for the species identification of sand flies from French Guiana and anopheles from Guinea and Mali. For the laboratory-reared Anopheles stephensi specimens, we have shown that artificial neural networks coupled with MALDI-TOF mass spectrometry specifically recognized spectral patterns related to the biology of Anopheles such as age, blood feeding and Plasmodium berghei infection. Future studies will need to validate the new approaches on a larger scale with field-collected specimens. An online application, developed at Sorbonne University for MALDI-TOF mass spectrometry identification in microbiology, will facilitate the access of the tool for vector surveillance by making available mass spectra libraries of arthropods. Finally, computational biology tools offer interesting prospects for improving the performance of MALDI-TOF, providing new applications for vector control

Artificial Intelligence and Malaria

International audienceMalaria disease is due to the infection with Plasmodium parasites transmitted by a mosquito vector belonging to the genus Anopheles. To combat malaria, effective diagnosis and treatment using artemisinin-based combinations are needed, as well as strategies that are aimed at reducing or stopping transmission by mosquito vectors. Even if the conventional microscopic diagnosis is the gold standard for malaria diagnosis, it is time consuming, and the diagnostic performance depends on techniques and human expertise. In addition, tools for characterizing Anopheles vectors are limited and difficult to establish in the field. The advent of computational biology, information technology infrastructures, and mobile computing power offers the opportunity to use artificial intelligence (AI) approaches to address challenges and technical needs specific to malaria-endemic countries. This chapter illustrates the trends, advances, and future challenges linked to the deployment of AI in malaria. Two innovative AI approaches are described. The first is the image-based automatic classification of malaria parasites and vectors, and the second is the proteomics analysis of vectors. The developed applications are aimed at facilitating malaria diagnosis by performing malaria parasite detection, species identification, and estimation of parasitaemia. In the future, they can lead to efficient and accurate diagnostic tools, revolutionizing the urgent diagnosis of malaria. Other applications focus on the characterization of mosquito vectors by performing species identification, behavior, and biology descriptions. If field-validated, these promising approaches will facilitate the epidemiological monitoring of malaria vectors and saving resources by preventing or reducing malaria transmission

Improving the Detection of Epidemic Clones in Candida parapsilosis Outbreaks by Combining MALDI-TOF Mass Spectrometry and Deep Learning Approaches

International audienceIdentifying fungal clones propagated during outbreaks in hospital settings is a problem that increasingly confronts biologists. Current tools based on DNA sequencing or microsatellite analysis require specific manipulations that are difficult to implement in the context of routine diagnosis. Using deep learning to classify the mass spectra obtained during the routine identification of fungi by MALDI-TOF mass spectrometry could be of interest to differentiate isolates belonging to epidemic clones from others. As part of the management of a nosocomial outbreak due to Candida parapsilosis in two Parisian hospitals, we studied the impact of the preparation of the spectra on the performance of a deep neural network. Our purpose was to differentiate 39 otherwise fluconazole-resistant isolates belonging to a clonal subset from 56 other isolates, most of which were fluconazole-susceptible, collected during the same period and not belonging to the clonal subset. Our study carried out on spectra obtained on four different machines from isolates cultured for 24 or 48 h on three different culture media showed that each of these parameters had a significant impact on the performance of the classifier. In particular, using different culture times between learning and testing steps could lead to a collapse in the accuracy of the predictions. On the other hand, including spectra obtained after 24 and 48 h of growth during the learning step restored the good results. Finally, we showed that the deleterious effect of the device variability used for learning and testing could be largely improved by including a spectra alignment step during preprocessing before submitting them to the neural network. Taken together, these experiments show the great potential of deep learning models to identify spectra of specific clones, providing that crucial parameters are controlled during both culture and preparation steps before submitting spectra to a classifier

Identification of French Guiana sand flies using MALDI-TOF mass spectrometry with a new mass spectra library.

Phlebotomine sand flies are insects that are highly relevant in medicine, particularly as the sole proven vectors of leishmaniasis. Accurate identification of sand fly species is an essential prerequisite for eco-epidemiological studies aiming to better understand the disease. Traditional morphological identification is painstaking and time-consuming, and molecular methods for extensive screening remain expensive. Recent studies have shown that matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a promising tool for rapid and cost-effective identification of arthropod vectors, including sand flies. The aim of this study was to validate the use of MALDI-TOF MS for the identification of Northern Amazonian sand flies. We constituted a MALDI-TOF MS reference database comprising 29 species of sand flies that were field-collected in French Guiana, which are expected to cover many of the more common species of the Northern Amazonian region, including known vectors of leishmaniasis. Carrying out a blind test, all the sand flies tested (n = 157) with a log (score) threshold greater than 1.7 were correctly identified at the species level. We confirmed that MALDI-TOF MS protein profiling is a useful tool for the study of sand flies, including neotropical species, known for their great diversity. An application that includes the spectra generated here will be available to the scientific community in the near future via an online platform

Prediction of malaria transmission drivers in Anopheles mosquitoes using artificial intelligence coupled to MALDI-TOF mass spectrometry

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Investigations upon the Improvement of Dermatophyte Identification Using an Online Mass Spectrometry Application

International audienceOnline MALDI-TOF mass spectrometry applications, such as MSI-2, have been shown to help identify dermatophytes, but recurrent errors are still observed between phylogenetically close species. The objective of this study was to assess different approaches to reduce the occurrence of such errors by adding new reference spectra to the MSI-2 application. Nine libraries were set up, comprising an increasing number of spectra obtained from reference strains that were submitted to various culture durations on two distinct culture media: Sabouraud gentamicin chloramphenicol medium and IDFP Conidia medium. The final library included spectra from 111 strains of 20 species obtained from cultures on both media collected every three days after the appearance of the colony. The performance of each library was then analyzed using a cross-validation approach. The spectra acquisitions were carried out using a Microflex Bruker spectrometer. Diversifying the references and adding spectra from various culture media and culture durations improved identification performance. The percentage of correct identification at the species level rose from 63.4 to 91.7% when combining all approaches. Nevertheless, residual confusion between close species, such as Trichophyton rubrum, Trichophyton violaceum and Trichophyton soudanense, remained. To distinguish between these species, mass spectrometry identification should take into account basic morphological and/or clinico-epidemiological features

Pediatric Amazonian Toxoplasmosis Caused by Atypical Strains in French Guiana, 2002–2017

International audienceAmazonian toxoplasmosis is a recently described form of Toxoplasma gondii infection, characterized by severe clinical and biological features and involvement of atypical genetic strains circulating through a forest-based cycle. Though mostly reported in French Guiana since 1998, this disease is probably under-diagnosed in other areas of South America. Few data are available on its specific features in children