MALDI-TOF MS: An effective tool for a global surveillance of dengue vector species

MALDI-TOF MS Profiling and Its Contribution to Mosquito-Borne Diseases: A Systematic Review

Mosquito-borne diseases are responsible for hundreds of thousands of deaths per year. The identification and control of the vectors that transmit pathogens to humans are crucial for disease prevention and management. Currently, morphological classification and molecular analyses via DNA barcoding are the standard methods used for vector identification. However, these approaches have several limitations. In the last decade, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) profiling has emerged as an innovative technology in biological sciences and is now considered as a relevant tool for the identification of pathogens and arthropods. Beyond species identification, this tool is also valuable for determining various life traits of arthropod vectors. The purpose of the present systematic review was to highlight the contribution of MALDI-TOF MS to the surveillance and control of mosquito-borne diseases. Published articles from January 2003 to August 2024 were retrieved, focusing on different aspects of mosquito life traits that could be determinants in disease transmission and vector management. The screening of the scientific literature resulted in the selection of 54 published articles that assessed MALDI-TOF MS profiling to study various mosquito biological factors, such species identification, life expectancy, gender, trophic preferences, microbiota, and insecticide resistance. Although a large majority of the selected articles focused on species identification, the present review shows that MALDI-TOF MS profiling is promising for rapidly identifying various mosquito life traits, with high-throughput capacity, reliability, and low cost. The strengths and weaknesses of this proteomic tool for vector control and surveillance are discussed.

Speciation patterns of Aedes mosquitoes in the Scutellaris Group: a mitochondrial perspective

The Scutellaris Group of Aedes comprises 47 mosquito species, including Aedes albopictus. While Ae. albopictus is widely distributed, the other species are mostly found in the Asia-Pacific region. Evolutionary history researches of Aedes species within the Scutellaris Group have mainly focused on Ae. albopictus, a species that raises significant public health concerns, neglecting the other species. In this study, we aimed to assess genetic diversity and estimate speciation times of several species within the Scutellaris Group. Mosquitoes were therefore collected from various Asia-Pacific countries. Their mitochondrial cytochrome c oxidase subunit 1 (cox1) and subunit 3 (cox3) sequences were analyzed alongside those of other Scutellaris Group species available in the GenBank database. To estimate the divergence time, we analyzed 1849 cox1 gene sequences from 21 species, using three species (Aedes aegypti, Aedes notoscriptus and Aedes vigilax) as outgroups. We found that most of the speciation dates occurred during the Paleogene and the Neogene periods. A separation between the Scutellaris Subgroup and the Albopictus Subgroup occurred approximately 64-61 million years ago (MYA). We also identified a split between species found in Asia/Micronesia and those collected in Melanesia/Polynesia approximately 36-35 MYA. Our findings suggest that the speciation of Aedes species within the Scutellaris Group may be driven by diversity in mammalian hosts, climate and environmental changes, and geological dynamics rather than human migration.

A universal tool for marine metazoan species identification: towards best practices in proteomic fingerprinting

Proteomic fingerprinting using MALDI-TOF mass spectrometry is a well-established tool for identifying microorganisms and has shown promising results for identification of animal species, particularly disease vectors and marine organisms. And thus can be a vital tool for biodiversity assessments in ecological studies. However, few studies have tested species identification across different orders and classes. In this study, we collected data from 1246 specimens and 198 species to test species identification in a diverse dataset. We also evaluated different specimen preparation and data processing approaches for machine learning and developed a workflow to optimize classification using random forest. Our results showed high success rates of over 90%, but we also found that the size of the reference library affects classification error. Additionally, we demonstrated the ability of the method to differentiate marine cryptic-species complexes and to distinguish sexes within species.

Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest

Hematophagous insects belonging to the Aedes genus are proven vectors of viral and filarial pathogens of medical interest. Aedes albopictus is an increasingly important vector because of its rapid worldwide expansion. In the context of global climate change and the emergence of zoonotic infectious diseases, identification tools with field application are required to strengthen efforts in the entomological survey of arthropods with medical interest. Large scales and proactive entomological surveys of Aedes mosquitoes need skilled technicians and/or costly technical equipment, further puzzled by the vast amount of named species. In this study, we developed an automatic classification system of Aedes species by taking advantage of the species-specific marker displayed by Wing Interferential Patterns. A database holding 494 photomicrographs of 24 Aedes spp. from which those documented with more than ten pictures have undergone a deep learning methodology to train a convolutional neural network and test its accuracy to classify samples at the genus, subgenus, and species taxonomic levels. We recorded an accuracy of 95% at the genus level and > 85% for two (Ochlerotatus and Stegomyia) out of three subgenera tested. Lastly, eight were accurately classified among the 10 Aedes sp. that have undergone a training process with an overall accuracy of > 70%. Altogether, these results demonstrate the potential of this methodology for Aedes species identification and will represent a tool for the future implementation of large-scale entomological surveys.

Potential of MALDI-TOF MS-based proteomic fingerprinting for species identification of Cnidaria across classes, species, regions and developmental stages

Morphological identification of cnidarian species can be difficult throughout all life stages due to the lack of distinct morphological characters. Moreover, in some cnidarian taxa genetic markers are not fully informative, and in these cases combinations of different markers or additional morphological verifications may be required. Proteomic fingerprinting based on MALDI-TOF mass spectra was previously shown to provide reliable species identification in different metazoans including some cnidarian taxa. For the first time, we tested the method across four cnidarian classes (Staurozoa, Scyphozoa, Anthozoa, Hydrozoa) and included different scyphozoan life-history stages (polyp, ephyra, medusa) in our dataset. Our results revealed reliable species identification based on MALDI-TOF mass spectra across all taxa with species-specific clusters for all 23 analysed species. In addition, proteomic fingerprinting was successful for distinguishing developmental stages, still by retaining a species specific signal. Furthermore, we identified the impact of different salinities in different regions (North Sea and Baltic Sea) on proteomic fingerprints to be negligible. In conclusion, the effects of environmental factors and developmental stages on proteomic fingerprints seem to be low in cnidarians. This would allow using reference libraries built up entirely of adult or cultured cnidarian specimens for the identification of their juvenile stages or specimens from different geographic regions in future biodiversity assessment studies.