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Insect saliva induces significant antibody responses associated with the intensity of exposure to bites and the risk of disease in humans. Several salivary biomarkers have been characterized to determine exposure intensity to Old World Anopheles mosquito species. However, new tools are needed to quantify the intensity of human exposure to Anopheles bites and understand the risk of malaria in low-transmission areas in the Americas. To address this need, we conducted proteomic and bioinformatic analyses of immunogenic candidate proteins present in the saliva of uninfected Anopheles albimanus from two separate colonies-one originating from Central America (STECLA strain) and one originating from South America (Cartagena strain). A ~65 kDa band was identified by IgG antibodies in serum samples from healthy volunteers living in a malaria endemic area in Colombia, and a total of five peptides were designed from the sequences of two immunogenic candidate proteins that were shared by both strains. ELISA-based testing of human IgG antibody levels against the peptides revealed that the transferrin-derived peptides, TRANS-P1, TRANS-P2 and a salivary peroxidase peptide (PEROX-P3) were able to distinguish between malaria-infected and uninfected groups. Interestingly, IgG antibody levels against PEROX-P3 were significantly lower in people that have never experienced malaria, suggesting that it may be a good marker for mosquito bite exposure in naïve populations such as travelers and deployed military personnel. In addition, the strength of the differences in the IgG levels against the peptides varied according to location, suggesting that the peptides may able to detect differences in intensities of bite exposure according to the mosquito population density. Thus, the An. albimanus salivary peptides TRANS-P1, TRANS-P2, and PEROX-P3 are promising biomarkers that could be exploited in a quantitative immunoassay for determination of human-vector contact and calculation of disease risk.
Pubmed ID: 31973044
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Web application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
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