Rapid Protein Profiling Facilitates Surveillance of Invasive Mosquito Species

Background: Invasive aedine mosquito species have become a major issue in many parts of the world as most of them are recognised vectors or potentially involved in transmission of pathogens. Surveillance of these mosquitoes (e.g. Ae. aegypti, Yellow fever mosquito, Aedes albopictus, Asian tiger mosquito) is mainly done by collecting eggs using ovitraps and by identification of the larvae hatched in the laboratory. In order to replace this challenging and laborious procedure, we have evaluated matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) for easy and rapid species identification.

Methods: Individual protein profiles were generated using five eggs each of nine aedine species (Ae. aegypti, Ae. albopictus, Ae. atropalpus, Ae. cretinus, Ae. geniculatus, Ae. japonicus, Ae. koreicus, Ae. phoeniciae, Ae. triseriatus) from various geographical origins, and species-specific biomarker mass sets could be generated. A blinded validation using our reference data base for automated egg identification was performed. In addition, pools of 10 aedine eggs (132 two-species and 18 three-species pools) in different ratios were evaluated.

Results: Specific biomarker mass sets comprising 18 marker masses could be generated for eggs of nine container-inhabiting aedine species, including all the major invasive and indigenous species of Europe and North America. Two additional masses shared by all investigated aedine species are used as internal calibrators. Identification of single eggs was highly accurate (100% specificity, 98.75% sensitivity), and this method is also of value for the identification of species in pools of ten eggs. When mixing two or three species, all were identified in all pools in at least 2 or 1 of the 4 loaded replicates, respectively, if the "lesser abundant" species in the pool accounted for three or more eggs.

Conclusions: MALDI-TOF MS, which is widely applied for routine identification of microorganisms in clinical microbiology laboratories, is also suited for robust, low-cost and high throughput identification of mosquito vectors in surveillance programmes. This tool can further be developed to include a wide spectrum of arthropods but also other Metazoa for which surveillance is required, and might become the method of choice for their centralised identification via online platforms.