Dataset Information

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 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.

SUBMITTER: Rakotonirina A

PROVIDER: S-EPMC8560810 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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

Rakotonirina Antsa A Caruzzo Cédric C Ballan Valentine V Kainiu Malia M Marin Marie M Colot Julien J Richard Vincent V Dupont-Rouzeyrol Myrielle M Selmaoui-Folcher Nazha N Pocquet Nicolas N

Scientific reports 20211101 1

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 ensu ...[more]

PMID: 34725401

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Project description:Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited. Here, we used MALDI-TOF MS with artificial intelligence (AI) approaches to successfully predict AMR in Pseudomonas aeruginosa, a priority pathogen with complex AMR mechanisms. The highest performance was achieved for modern β-lactam/β-lactamase inhibitor drugs, namely, ceftazidime/avibactam and ceftolozane/tazobactam. For these drugs, the model demonstrated area under the receiver operating characteristic curve (AUROC) of 0.869 and 0.856, specificity of 0.925 and 0.897, and sensitivity of 0.731 and 0.714, respectively. As part of this work, we developed dynamic binning, a feature engineering technique that effectively reduces the high-dimensional feature set and has wide-ranging applicability to MALDI-TOF MS data. Compared to conventional feature engineering approaches, the dynamic binning method yielded highest performance in 7 of 10 antimicrobials. Moreover, we showcased the efficacy of transfer learning in enhancing the AUROC performance for 8 of 11 antimicrobials. By assessing the contribution of features to the model's prediction, we identified proteins that may contribute to AMR mechanisms. Our findings demonstrate the potential of combining AI with MALDI-TOF MS as a rapid AMR diagnostic tool for Pseudomonas aeruginosa.IMPORTANCEPseudomonas aeruginosa is a key bacterial pathogen that causes significant global morbidity and mortality. Antimicrobial resistance (AMR) emerges rapidly in P. aeruginosa and is driven by complex mechanisms. Drug-resistant P. aeruginosa is a major challenge in clinical settings due to limited treatment options. Early detection of AMR can guide antibiotic choices, improve patient outcomes, and avoid unnecessary antibiotic use. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid species identification in clinical microbiology. In this study, we repurposed mass spectra generated by MALDI-TOF and used them as inputs for artificial intelligence approaches to successfully predict AMR in P. aeruginosa for multiple key antibiotic classes. This work represents an important advance toward using MALDI-TOF as a rapid AMR diagnostic for P. aeruginosa in clinical settings.

Dataset Information

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

Publications

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

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