Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence

Abstract The mosquito Aedes aegypti is the major vector of arboviruses like dengue, Zika and chikungunya viruses. Attempts to reduce arboviruses emergence focusing on Ae. aegypti control has proven challenging due to the increase of insecticide resistances. An emerging strategy which consists of rel...

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Autores principales:	Antsa Rakotonirina, Cédric Caruzzo, Valentine Ballan, Malia Kainiu, Marie Marin, Julien Colot, Vincent Richard, Myrielle Dupont-Rouzeyrol, Nazha Selmaoui-Folcher, Nicolas Pocquet
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/63f4aafe29c84737be797d2e58e51509
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spelling	oai:doaj.org-article:63f4aafe29c84737be797d2e58e515092021-11-08T10:49:06ZWolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence10.1038/s41598-021-00888-12045-2322https://doaj.org/article/63f4aafe29c84737be797d2e58e515092021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00888-1https://doaj.org/toc/2045-2322Abstract The mosquito Aedes aegypti is the major vector of arboviruses like dengue, Zika and chikungunya viruses. Attempts to reduce arboviruses emergence focusing on Ae. aegypti control has proven challenging due to the increase of insecticide resistances. An emerging strategy which consists of releasing Ae. aegypti artificially infected with Wolbachia in natural mosquito populations is currently being developed. The monitoring of Wolbachia-positive Ae. aegypti in the field is performed in order to ensure the program effectiveness. Here, the reliability of the Matrix‑Assisted Laser Desorption Ionization‑Time Of Flight (MALDI‑TOF) coupled with the machine learning methods like Convolutional Neural Network (CNN) to detect Wolbachia in field Ae. aegypti was assessed for the first time. For this purpose, laboratory reared and field Ae. aegypti were analyzed. The results showed that the CNN recognized Ae. aegypti spectral patterns associated with Wolbachia-infection. The MALDI-TOF coupled with the CNN (sensitivity = 93%, specificity = 99%, accuracy = 97%) was more efficient than the loop-mediated isothermal amplification (LAMP), and as efficient as qPCR for Wolbachia detection. It therefore represents an interesting method to evaluate the prevalence of Wolbachia in field Ae. aegypti mosquitoes.Antsa RakotonirinaCédric CaruzzoValentine BallanMalia KainiuMarie MarinJulien ColotVincent RichardMyrielle Dupont-RouzeyrolNazha Selmaoui-FolcherNicolas PocquetNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Antsa Rakotonirina Cédric Caruzzo Valentine Ballan Malia Kainiu Marie Marin Julien Colot Vincent Richard Myrielle Dupont-Rouzeyrol Nazha Selmaoui-Folcher Nicolas Pocquet Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence
description	Abstract The mosquito Aedes aegypti is the major vector of arboviruses like dengue, Zika and chikungunya viruses. Attempts to reduce arboviruses emergence focusing on Ae. aegypti control has proven challenging due to the increase of insecticide resistances. An emerging strategy which consists of releasing Ae. aegypti artificially infected with Wolbachia in natural mosquito populations is currently being developed. The monitoring of Wolbachia-positive Ae. aegypti in the field is performed in order to ensure the program effectiveness. Here, the reliability of the Matrix‑Assisted Laser Desorption Ionization‑Time Of Flight (MALDI‑TOF) coupled with the machine learning methods like Convolutional Neural Network (CNN) to detect Wolbachia in field Ae. aegypti was assessed for the first time. For this purpose, laboratory reared and field Ae. aegypti were analyzed. The results showed that the CNN recognized Ae. aegypti spectral patterns associated with Wolbachia-infection. The MALDI-TOF coupled with the CNN (sensitivity = 93%, specificity = 99%, accuracy = 97%) was more efficient than the loop-mediated isothermal amplification (LAMP), and as efficient as qPCR for Wolbachia detection. It therefore represents an interesting method to evaluate the prevalence of Wolbachia in field Ae. aegypti mosquitoes.
format	article
author	Antsa Rakotonirina Cédric Caruzzo Valentine Ballan Malia Kainiu Marie Marin Julien Colot Vincent Richard Myrielle Dupont-Rouzeyrol Nazha Selmaoui-Folcher Nicolas Pocquet
author_facet	Antsa Rakotonirina Cédric Caruzzo Valentine Ballan Malia Kainiu Marie Marin Julien Colot Vincent Richard Myrielle Dupont-Rouzeyrol Nazha Selmaoui-Folcher Nicolas Pocquet
author_sort	Antsa Rakotonirina
title	Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence
title_short	Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence
title_full	Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence
title_fullStr	Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence
title_full_unstemmed	Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence
title_sort	wolbachia detection in aedes aegypti using maldi-tof ms coupled to artificial intelligence
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/63f4aafe29c84737be797d2e58e51509
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Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence

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