Matrix-assisted laser desorption/ionization-mass spectrometry of cuticular lipid profiles can differentiate sex, age, and mating status of Anopheles gambiae mosquitoes

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.

Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus

BACKGROUND

Accurately determining the age and survival probabilities of adult mosquitoes is crucial for understanding parasite transmission, evaluating the effectiveness of control interventions and assessing disease risk in communities. This study was aimed at demonstrating the rapid identification of epidemiologically relevant age categories of Anopheles funestus, a major Afro-tropical malaria vector, through the innovative combination of infrared spectroscopy and machine learning, instead of the cumbersome practice of dissecting mosquito ovaries to estimate age based on parity status.

METHODS

Anopheles funestus larvae were collected in rural south-eastern Tanzania and reared in an insectary. Emerging adult females were sorted by age (1-16 days old) and preserved using silica gel. Polymerase chain reaction (PCR) confirmation was conducted using DNA extracted from mosquito legs to verify the presence of An. funestus and to eliminate undesired mosquitoes. Mid-infrared spectra were obtained by scanning the heads and thoraces of the mosquitoes using an attenuated total reflection-Fourier transform infrared (ATR-FT-IR) spectrometer. The spectra (N = 2084) were divided into two epidemiologically relevant age groups: 1-9 days (young, non-infectious) and 10-16 days (old, potentially infectious). The dimensionality of the spectra was reduced using principal component analysis, and then a set of machine learning and multi-layer perceptron (MLP) models were trained using the spectra to predict the mosquito age categories.

RESULTS

The best-performing model, XGBoost, achieved overall accuracy of 87%, with classification accuracy of 89% for young and 84% for old An. funestus. When the most important spectral features influencing the model performance were selected to train a new model, the overall accuracy increased slightly to 89%. The MLP model, utilizing the significant spectral features, achieved higher classification accuracy of 95% and 94% for the young and old An. funestus, respectively. After dimensionality reduction, the MLP achieved 93% accuracy for both age categories.

CONCLUSIONS

This study shows how machine learning can quickly classify epidemiologically relevant age groups of An. funestus based on their mid-infrared spectra. Having been previously applied to An. gambiae, An. arabiensis and An. coluzzii, this demonstration on An. funestus underscores the potential of this low-cost, reagent-free technique for widespread use on all the major Afro-tropical malaria vectors. Future research should demonstrate how such machine-derived age classifications in field-collected mosquitoes correlate with malaria in human populations.

Evaluation of diffuse reflectance spectroscopy for predicting age, species, and cuticular resistance of Anopheles gambiae s.l under laboratory conditions

Mid-infrared spectroscopy (MIRS) combined with machine learning analysis has shown potential for quick and efficient identification of mosquito species and age groups. However, current technology to collect spectra is destructive to the sample and does not allow targeting specific tissues of the mosquito, limiting the identification of other important biological traits such as insecticide resistance. Here, we assessed the use of a non-destructive approach of MIRS for vector surveillance, micro diffuse reflectance spectroscopy (µDRIFT) using mosquito legs to identify species, age and cuticular insecticide resistance within the Anopheles gambiae s.l. complex. These mosquitoes are the major vectors of malaria in Africa and the focus on surveillance in malaria control programs. Legs required significantly less scanning time and showed more spectral consistence compared to other mosquito tissues. Machine learning models were able to identify An. gambiae and An. coluzzii with an accuracy of 0.73, two ages groups (3 and 10 days old) with 0.77 accuracy and we obtained accuracy of 0.75 when identifying cuticular insecticide resistance. Our results highlight the potential of different mosquito tissues and µDRIFT as tools for biological trait identification on mosquitoes that transmit malaria. These results can guide new ways of identifying mosquito traits which can help the creation of innovative surveillance programs by adapting new technology into mosquito surveillance and control tools.

Dietary and Plasmodium challenge effects on the cuticular hydrocarbon profile of Anopheles albimanus

The cuticular hydrocarbon (CHC) profile reflects the insects' physiological states. These include age, sex, reproductive stage, and gravidity. Environmental factors such as diet, relative humidity or exposure to insecticides also affect the CHC composition in mosquitoes. In this work, the CHC profile was analyzed in two Anopheles albimanus phenotypes with different degrees of susceptibility to Plasmodium, the susceptible-White and resistant-Brown phenotypes, in response to the two dietary regimes of mosquitoes: a carbon-rich diet (sugar) and a protein-rich diet (blood) alone or containing Plasmodium ookinetes. The CHCs were analyzed by gas chromatography coupled to mass spectrometry or flame ionization detection, identifying 19 CHCs with chain lengths ranging from 20 to 37 carbons. Qualitative and quantitative changes in CHCs composition were dependent on diet, a parasite challenge, and, to a lesser extent, the phenotype. Blood-feeding caused up to a 40% reduction in the total CHC content compared to sugar-feeding. If blood contained ookinetes, further changes in the CHC profile were observed depending on the Plasmodium susceptibility of the phenotypes. Higher infection prevalence caused greater changes in the CHC profile. These dietary and infection-associated modifications in the CHCs could have multiple effects on mosquito fitness, impacts on disease transmission, and tolerance to insecticides.

Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used for proteomic fingerprinting and taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method to complement other well-established techniques for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens.

Prediction of malaria transmission drivers in Anopheles mosquitoes using artificial intelligence coupled to MALDI-TOF mass spectrometry

Vector control programmes are a strategic priority in the fight against malaria. However, vector control interventions require rigorous monitoring. Entomological tools for characterizing malaria transmission drivers are limited and are difficult to establish in the field. To predict Anopheles drivers of malaria transmission, such as mosquito age, blood feeding and Plasmodium infection, we evaluated artificial neural networks (ANNs) coupled to matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) and analysed the impact on the proteome of laboratory-reared Anopheles stephensi mosquitoes. ANNs were sensitive to Anopheles proteome changes and specifically recognized spectral patterns associated with mosquito age (0–10 days, 11–20 days and 21–28 days), blood feeding and P. berghei infection, with best prediction accuracies of 73%, 89% and 78%, respectively. This study illustrates that MALDI-TOF MS coupled to ANNs can be used to predict entomological drivers of malaria transmission, providing potential new tools for vector control. Future studies must assess the field validity of this new approach in wild-caught adult Anopheles. A similar approach could be envisaged for the identification of blood meal source and the detection of insecticide resistance in Anopheles and to other arthropods and pathogens.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis. mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with other mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Application of matrix-assisted laser desorption/ionization mass spectrometry to identify species of Neotropical Anopheles vectors of malaria

Background

Malaria control in Panama is problematic due to the high diversity of morphologically similar Anopheles mosquito species, which makes identification of vectors of human Plasmodium challenging. Strategies by Panamanian health authorities to bring malaria under control targeting Anopheles vectors could be ineffective if they tackle a misidentified species.

Methods

A rapid mass spectrometry identification procedure was developed to accurately and timely sort out field-collected Neotropical Anopheles mosquitoes into vector and non-vector species. Matrix-assisted laser desorption/ionization (MALDI) mass spectra of highly-abundant proteins were generated from laboratory-reared mosquitoes using different extraction protocols, body parts, and sexes to minimize the amount of material from specimen vouchers needed and optimize the protocol for taxonomic identification. Subsequently, the mass spectra of field-collected Neotropical Anopheles mosquito species were classified using a combination of custom-made unsupervised (i.e., Principal component analysis—PCA) and supervised (i.e., Linear discriminant analysis—LDA) classification algorithms.

Results

Regardless of the protocol used or the mosquito species and sex, the legs contained the least intra-specific variability with enough well-preserved proteins to differentiate among distinct biological species, consistent with previous literature. After minimizing the amount of material needed from the voucher, one leg was enough to produce reliable spectra between specimens. Further, both PCA and LDA were able to classify up to 12 mosquito species, from different subgenera and seven geographically spread localities across Panama using mass spectra from one leg pair. LDA demonstrated high discriminatory power and consistency, with validation and cross-validation positive identification rates above 93% at the species level.

Conclusion

The selected sample processing procedure can be used to identify field-collected Anopheles species, including vectors of Plasmodium, in a short period of time, with a minimal amount of tissue and without the need of an expert mosquito taxonomist. This strategy to analyse protein spectra overcomes the drawbacks of working without a reference library to classify unknown samples. Finally, this MALDI approach can aid ongoing malaria eradication efforts in Panama and other countries with large number of mosquito’s species by improving vector surveillance in epidemic-prone sites such as indigenous Comarcas.

Electronic supplementary material

The online version of this article (10.1186/s12936-019-2723-0) contains supplementary material, which is available to authorized users.

Detection of Plasmodium falciparum infected Anopheles gambiae using near-infrared spectroscopy

BACKGROUND

Large-scale surveillance of mosquito populations is crucial to assess the intensity of vector-borne disease transmission and the impact of control interventions. However, there is a lack of accurate, cost-effective and high-throughput tools for mass-screening of vectors.

METHODS

A total of 750 Anopheles gambiae (Keele strain) mosquitoes were fed Plasmodium falciparum NF54 gametocytes through standard membrane feeding assay (SMFA) and afterwards maintained in insectary conditions to allow for oocyst (8 days) and sporozoite development (14 days). Thereupon, each mosquito was scanned using near infra-red spectroscopy (NIRS) and processed by quantitative polymerase chain reaction (qPCR) to determine the presence of infection and infection load. The spectra collected were randomly assigned to either a training dataset, used to develop calibrations for predicting oocyst- or sporozoite-infection through partial least square regressions (PLS); or to a test dataset, used for validating the calibration's prediction accuracy.

RESULTS

NIRS detected oocyst- and sporozoite-stage P. falciparum infections with 88% and 95% accuracy, respectively. This study demonstrates proof-of-concept that NIRS is capable of rapidly identifying laboratory strains of human malaria infection in African mosquito vectors.

CONCLUSIONS

Accurate, low-cost, reagent-free screening of mosquito populations enabled by NIRS could revolutionize surveillance and elimination strategies for the most important human malaria parasite in its primary African vector species. Further research is needed to evaluate how the method performs in the field following adjustments in the training datasets to include data from wild-caught infected and uninfected mosquitoes.

A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

Phospholipid Topography of Whole-Body Sections of the Anopheles stephensi Mosquito, Characterized by High-Resolution Atmospheric-Pressure Scanning Microprobe Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

High-resolution atmospheric-pressure scanning microprobe matrix-assisted laser desorption/ionization mass spectrometry imaging (AP-SMALDI MSI) has been employed to study the molecular anatomical structure of rodent malaria vector Anopheles stephensi mosquitoes. A dedicated sample preparation method was developed which suits both, the special tissue properties of the sample and the requirements of high-resolution MALDI imaging. Embedding in 5% carboxymethylcellulose (CMC) was used to maintain the tissue integrity of the whole mosquitoes, being very soft, fragile, and difficult to handle. Individual lipid compounds, specifically representing certain cell types, tissue areas, or organs, were detected and imaged in 20 μm-thick whole-body tissue sections at a spatial resolution of 12 μm per image pixel. Mass spectrometric data and information quality were based on a mass resolution of 70,000 (at m/z 200) and a mass accuracy of better than 2 ppm in positive-ion mode on an orbital trapping mass spectrometer. A total of 67 imaged lipids were assigned by database search and, in a number of cases, identified via additional MS/MS fragmentation studies directly from tissue. This is the first MSI study at 12 μm spatial resolution of the malaria vector Anopheles. The study provides insights into the molecular anatomy of Anopheles stephensi and the distribution and localization of major classes of glycerophospholipids and sphingolipids. These data can be a basis for future experiments, investigating, e.g., the metabolism of Plasmodium-infected and -uninfected Anopheles mosquitoes.

Candidate biomarkers for mosquito age-grading identified by label-free quantitative analysis of protein expression in Aedes albopictus females

We applied a "shotgun" approach based on nanoliquid chromatography-high resolution mass spectrometry associated to label free quantification (LFQ) to identify proteins varying with age, independently from the physiological state, in Aedes albopictus, a mosquito species which in the last decades invaded temperate regions in North America and Europe, creating concerns for associated high nuisance and risk of arbovirus transmission. The combined "shotgun" and LFQ approach was shown to be highly suitable to simultaneously compare several biological samples, as needed in a study aimed to analyze different age-groups and physiological states of adult mosquito females. The results obtained represent the first wide-scale analysis of protein expression in Ae. albopictus females: >1000 and 665 proteins were identified from few micrograms of crude protein extracts of mosquito heads and thoraxes, respectively. Six of these proteins were shown to significantly vary from 2- to 16-day-old females, independently from their physiological state (i.e. virgin, mated, host-seeking, blood-fed, and gravid).

BIOLOGICAL SIGNIFICANCE

Mosquito-borne diseases, such as malaria, dengue and other arboviroses, are a persistent cause of global mortality and morbidity, affecting hundreds of thousands of people. Billions of people living in tropical areas are at risk of being bitten every day by an infective mosquito female and the spread of tropical species such as Aedes albopictus to temperate areas is creating alarm in the northern hemisphere. Mosquito longevity is a critical factor affecting mosquito-borne pathogen transmission cycles and the mosquito capacity to transmit pathogens. However, large scale analyses of the age structure of mosquito field populations is hampered by the lack of optimal age-grading approaches. Our findings open new perspectives for the development of reliable, simple and cheap protein-based assays to age-grade Ae. albopictus females and, most likely, other mosquito species of higher medical relevance, such as the main dengue vector, Aedes aegypti, and the major Afrotropical malaria vectors. These assays would greatly contribute to epidemiological studies aimed at defining the actual vectorial capacity of a given mosquito species. Moreover, they would be very valuable in assessing the effectiveness of mosquito control interventions based on the relative ratio between young and old individuals before and after the intervention.

Resolution of three cryptic agricultural pests (Ceratitis fasciventris, C. anonae, C. rosa, Diptera: Tephritidae) using cuticular hydrocarbon profiling

Discrimination of particular species within the species complexes of tephritid fruit flies is a very challenging task. In this fruit-fly family, several complexes of cryptic species have been reported, including the African cryptic species complex (FAR complex). Cuticular hydrocarbons (CHCs) appear to be an excellent tool for chemotaxonomical discrimination of these cryptic species. In the present study, CHC profiles have been used to discriminate among three important agricultural pests from the FAR complex, Ceratitis fasciventris, Ceratitis anonae and Ceratitis rosa. Hexane body surface extracts of mature males and females were analyzed by two-dimensional gas chromatography with mass spectrometric detection and differences in CHC profiles between species and sexes tested through multivariate statistics and compared with species identification by means of microsatellite markers. Quantitative as well as qualitative CHC profile differences between sexes and species are reported. The CHC profiles consisted of a mixture of linear, internally methyl-branched and mono-, di- and tri-unsaturated alkanes. Twelve compounds were pinpointed as potential chemotaxonomical markers. The present study shows that presence or absence of particular CHCs might be used in the chemical diagnosis of the FAR complex. Moreover, our results represent an important first step in the development of a useful chemotaxonomic tool for cryptic species identification of these important agricultural pests.

New MALDI matrices based on lithium salts for the analysis of hydrocarbons and wax esters

Lithium salts of organic aromatic acids (lithium benzoate, lithium salicylate, lithium vanillate, lithium 2,5-dimethoxybenzoate, lithium 2,5-dihydroxyterephthalate, lithium α-cyano-4-hydroxycinnamate and lithium sinapate) were synthesized and tested as potential matrices for the matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry analysis of hydrocarbons and wax esters. The analytes were desorbed using nitrogen laser (337.1 nm) and ionized via the attachment of a lithium cation, yielding [M + Li](+) adducts. The sample preparation and the experimental conditions were optimized for each matrix using stearyl behenate and n-triacontane standards. The performance of the new matrices in terms of signal intensity and reproducibility, the mass range occupied by matrix ions and the laser power threshold were studied and compared with a previously recommended lithium 2,5-dihydroxybenzoate matrix (LiDHB) (Cvačka and Svatoš, Rapid Commun. Mass Spectrom. 2003, 17, 2203). Several of the new matrices performed better than LiDHB. Lithium vanillate offered a 2-3 times and 7-9 times higher signal for wax esters and hydrocarbons, respectively. Also, the signal reproducibility improved substantially, making this matrix a suitable candidate for imaging applications. In addition, the diffuse reflectance spectra and solubility of the synthesized compounds were investigated and discussed with respect to the compound's ability to serve as MALDI matrices. The applicability of selected matrices was tested on natural samples of wax esters and hydrocarbons.

Seasonal variation and resin composition in the Andean tree Austrocedrus chilensis

Little is known about the changes in resin composition in South American gymnosperms associated with the different seasons of the year. The diterpene composition of 44 resin samples from seven Austrocedrus chilensis (Cupressaceae) trees, including male and female individuals, was investigated in three different seasons of the year (February, June and November). Twelve main diterpenes were isolated by chromatographic means and identified by gas chromatography-mass spectrometry and nuclear magnetic resonance (NMR). The diterpene composition was submitted to multivariate analysis to find possible associations between chemical composition and season of the year. The principal component analysis showed a clear relation between diterpene composition and season. The most characteristic compounds in resins collected in summer were Z-communic acid (9) and 12-oxo-labda-8(17),13E-dien-19 oic acid methyl ester (10) for male trees and 8(17),12,14-labdatriene (7) for female trees. For the winter samples, a clear correlation of female trees with torulosic acid (6) was observed. In spring, E-communic acid (8) and Z-communic acid (9) were correlated with female trees and 18-hydroxy isopimar-15-ene (1) with male tree resin. A comparison between percent diterpene composition and collection time showed p < 0.05 for isopimara-8(9),15-diene (2), sandaracopimaric acid (4), compound (7) and ferruginol (11).

Needs for monitoring mosquito transmission of malaria in a pre-elimination world

As global efforts to eliminate malaria intensify, accurate information on vector populations and transmission dynamics is critical for directing control efforts, developing new control tools, and predicting the effects of these interventions under various conditions. Currently available sampling tools for mosquito population monitoring suffer from well-recognized limitations. As reported in this workshop summary, a recent gathering of medical entomologists, modelers, and malaria experts reviewed these issues and agreed that efforts are needed to improve methods to monitor key transmission parameters. Identified needs include standardized methods for sampling of both mosquito adults and larvae, improved tools for mosquito species identification and age-grading, and a better means for determining the entomological inoculation rate.

Matrix-assisted laser desorption ionization--time of flight mass spectrometry: an emerging tool for the rapid identification of mosquito vectors

Background

The identification of mosquito vectors is typically based on morphological characteristics using morphological keys of determination, which requires entomological expertise and training. The use of protein profiling by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), which is increasingly being used for the routine identification of bacteria, has recently emerged for arthropod identification.

Methods

To investigate the usefulness of MALDI-TOF-MS as a mosquito identification tool, we tested protein extracts made from mosquito legs to create a database of reference spectra. The database included a total of 129 laboratory-reared and field-caught mosquito specimens consisting of 20 species, including 4 Aedes spp., 9 Anopheles spp., 4 Culex spp., Lutzia tigripes, Orthopodomyia reunionensis and Mansonia uniformis. For the validation study, blind tests were performed with 76 specimens consisting of 1 to 4 individuals per species. A cluster analysis was carried out using the MALDI-Biotyper and some spectra from all mosquito species tested.

Results

Biomarker mass sets containing 22 and 43 masses have been detected from 100 specimens of the Anopheles, Aedes and Culex species. By carrying out 3 blind tests, we achieved the identification of mosquito vectors at the species level, including the differentiation of An. gambiae complex, which is possible using MALDI-TOF-MS with 1.8 as the cut-off identification score. A cluster analysis performed with all available mosquito species showed that MALDI-Biotyper can distinguish between specimens at the subspecies level, as demonstrated for An gambiae M and S, but this method cannot yet be considered a reliable tool for the phylogenetic study of mosquito species.

Conclusions

We confirmed that even without any specific expertise, MALDI-TOF-MS profiling of mosquito leg protein extracts can be used for the rapid identification of mosquito vectors. Therefore, MALDI-TOF-MS is an alternative, efficient and inexpensive tool that can accurately identify mosquitoes collected in the field during entomological surveys.

A survey of useful salt additives in matrix-assisted laser desorption/ionization mass spectrometry and tandem mass spectrometry of lipids: introducing nitrates for improved analysis

RATIONALE

Matrix-assisted laser desorption/ionization (MALDI) is a powerful technique for the direct analysis of lipids in complex mixtures and thin tissue sections, making it an extremely attractive method for profiling lipids in health and disease. Lipids are readily detected as [M+H](+), [M+Na](+) and [M+K](+) ions in positive ion MALDI mass spectrometry (MS) experiments. This not only decreases sensitivity, but can also lead to overlapping m/z values of the various adducts of different lipids. Additives can be used to promote formation of a particular adduct, improving sensitivity, reducing spectral complexity and enhancing structural characterization in collision-induced dissociation (CID) experiments.

METHODS

Li(+), Na(+), K(+), Cs(+) and NH(4)(+) cations were considered as a range of salt types (acetates, chlorides and nitrates) incorporated into DHB matrix solutions at concentrations between 5 and 80 mM. The study was extended to evaluate the effect of these additives on CID experiments of a lipid standard, after optimization of collision energy parameters. Experiments were performed on a hybrid quadrupole time-of-flight (QqTOF) instrument.

RESULTS

The systematic evaluation of new and existing additives in MALDI-MS and MS/MS of lipids demonstrated the importance of additive cation and anion choice and concentration for tailoring spectral results.

CONCLUSIONS

The recommended choice of additive depends on the desired outcomes of the experiment to be performed (MS or MS/MS). Nitrates are found to be particularly useful additives for lipid analysis.

Transcriptome analysis of the Chinese white wax scale Ericerus pela with focus on genes involved in wax biosynthesis

Background

The Chinese white wax scale, Ericerus pela Chavannes is economically significant for its role in wax production. This insect has been bred in China for over a thousand years. The wax secreted by the male scale insect during the second-instar larval stage has been widespread used in wax candle production, wax printing, engraving, Chinese medicine, and more recently in the chemical, pharmaceutical, food, and cosmetics industries. However, little is known about the mechanisms responsible for white wax biosynthesis. The characterization of its larval transcriptome may promote better understanding of wax biosynthesis.

Methodology/Principal Findings

In this study, characterization of the transcriptome of E. pela during peak wax secretion was performed using Illumina sequencing technology. Illumina sequencing produced 41,839 unigenes. These unigenes were annotated by blastx alignment against the NCBI Non-Redundant (NR), Swiss-Prot, KEGG, and COG databases. A total of 104 unigenes related to white wax biosynthesis were identified, and 15 of them were selected for quantitative real-time PCR analysis. We evaluated the variations in gene expression across different development stages, including egg, first/second instar larvae, male pupae, and male and female adults. Then we identified five genes involved in white wax biosynthesis. These genes were expressed most strongly during the second-instar larval stage of male E. pela.

Conclusion/Significance

The transcriptome analysis of E. pela during peak wax secretion provided an overview of gene expression information at the transcriptional level and a resource for gene mining. Five genes related to white wax biosynthesis were identified.