title: Identification of cryptic Anopheles mosquito species by molecular protein profiling.
abstract: Vector control is the mainstay of malaria control programmes. Successful vector control profoundly relies on accurate information on the target mosquito populations in order to choose the most appropriate intervention for a given mosquito species and to monitor its impact. An impediment to identify mosquito species is the existence of morphologically identical sibling species that play different roles in the transmission of pathogens and parasites. Currently PCR diagnostics are used to distinguish between sibling species. PCR based methods are, however, expensive, time-consuming and their development requires a priori DNA sequence information. Here, we evaluated an inexpensive molecular proteomics approach for Anopheles species: matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). MALDI-TOF MS is a well developed protein profiling tool for the identification of microorganisms but so far has received little attention as a diagnostic tool in entomology. We measured MS spectra from specimens of 32 laboratory colonies and 2 field populations representing 12 Anopheles species including the A. gambiae species complex. An important step in the study was the advancement and implementation of a bioinformatics approach improving the resolution over previously applied cluster analysis. Borrowing tools for linear discriminant analysis from genomics, MALDI-TOF MS accurately identified taxonomically closely related mosquito species, including the separation between the M and S molecular forms of A. gambiae sensu stricto. The approach also classifies specimens from different laboratory colonies; hence proving also very promising for its use in colony authentication as part of quality assurance in laboratory studies. While being exceptionally accurate and robust, MALDI-TOF MS has several advantages over other typing methods, including simple sample preparation and short processing time. As the method does not require DNA sequence information, data can also be reviewed at any later stage for diagnostic or functional patterns without the need for re-designing and re-processing biological material.
authors.affiliation: Department of Medical Services and Diagnostic, Swiss Tropical and Public Health Institute, Basel, Switzerland. pie.mueller@unibas.ch
authors.forename: Pie
authors.initials: P
authors.lastname: Müller
authors.forename: Valentin
authors.initials: V
authors.lastname: Pflüger
authors.forename: Matthias
authors.initials: M
authors.lastname: Wittwer
authors.forename: Dominik
authors.initials: D
authors.lastname: Ziegler
authors.forename: Fabrice
authors.initials: F
authors.lastname: Chandre
authors.forename: Frédéric
authors.initials: F
authors.lastname: Simard
authors.forename: Christian
authors.initials: C
authors.lastname: Lengeler
mesh_terms: D000818:Animals; D000852:Anopheles / Q000145:classification*; D016000:Cluster Analysis; D016002:Discriminant Analysis; D007303:Insect Vectors; D016133:Polymerase Chain Reaction; D040901:Proteomics; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization