Identification of novel arthropod vector G protein-coupled receptors

Abstract

Background: The control of vector-borne diseases, such as malaria, dengue fever, and typhus fever is often achieved with the use of insecticides. Unfortunately, insecticide resistance is becoming common among different vector species. There are currently no chemical alternatives to these insecticides because new human-safe classes of molecules have yet to be brought to the vector-control market. The identification of novel targets offer opportunities for rational design of new chemistries to control vector populations. One target family, G protein-coupled receptors (GPCRs), has remained relatively under explored in terms of insecticide development.

Methods: A novel classifier, Ensemble*, for vector GPCRs was developed. Ensemble* was validated and compared to existing classifiers using a set of all known GPCRs from Aedes aegypti, Anopheles gambiae, Apis Mellifera, Drosophila melanogaster, Homo sapiens, and Pediculus humanus. Predictions for unidentified sequences from Ae. aegypti, An. gambiae, and Pe. humanus were validated. Quantitative RT-PCR expression analysis was performed on previously-known and newly discovered Ae. aegypti GPCR genes.

Results: We present a new analysis of GPCRs in the genomes of Ae, aegypti, a vector of dengue fever, An. gambiae, a primary vector of Plasmodium falciparum that causes malaria, and Pe. humanus, a vector of epidemic typhus fever, using a novel GPCR classifier, Ensemble*, designed for insect vector species. We identified 30 additional putative GPCRs, 19 of which we validated. Expression of the newly discovered Ae. aegypti GPCR genes was confirmed via quantitative RT-PCR.

Conclusion: A novel GPCR classifier for insect vectors, Ensemble*, was developed and GPCR predictions were validated. Ensemble* and the validation pipeline were applied to the genomes of three insect vectors (Ae. aegypti, An. gambiae, and Pe. humanus), resulting in the identification of 52 GPCRs not previously identified, of which 11 are predicted GPCRs, and 19 are predicted and confirmed GPCRs.

Figure 1

Flowchart describing the Ensemble*, GPCRHMM*, and Pfam* classifiers. The diamonds represent sequences, while the rectangles represent steps of the classifiers. The dashed rectangles describe to which classifiers the components belong. The input sequences are the sequences to be classified, while the training sequences are sequences which have known designations of either GPCR or not GPCR.

Figure 2

ROC Curve comparing classifier performance on combined data set. The dashed (GPCRHMM*), dotted (Pfam*), and solid (Ensemble*) curves represent the relationship between the percentage of training set GPCRs correctly predicted (true positives) plotted against the percentage of sequences miss-classified as GPCRs (false positives) by the classifiers as the likelihood score threshold, was decreased from 1 (accepting nothing) to 0 (accepting everything). GPCRHMM (diamond), Pfam (circle), and PredCouple (square) only produce true/false predictions for each sequence and hence are represented as single points.

Figure 3

ROC Curves comparing classifier performance on individual organisms. For each ROC curve, the classifiers were trained on five of the organisms and then evaluated on the sixth organism (given in the title). The solid (Ensemble*), dashed (GPCRHMM*), and dotted (Pfam*) lines represent the relationship between the percentage of correctly predicted training set GPCRs (true positives) and the percentage of incorrectly predicted non-GPCRS (false positives) as the likelihood score threshold is decreased from 1 (accept nothing) to 0 (accept everything). GPCRHMM (diamond), Pfam (circle), and PredCouple (square) only produce true/false predictions for each sequence and hence are represented as single points.

Figure 4

GPCR Validation pipeline. Diamonds indicate input and output sequences. Rectangles represent the validation programs and filters. Arrows indicate the movement of sequences and are labeled with the number of sequences that passed from one validation step to the next. Numbers for sequences that were identified as something other than GPCRs are not shown. The first filter identifies sequences which are annotated as GPCRs in the database and removes any sequences which were annotated as something other than GPCRs or were identified by ScanPROSITE as having non-GPCR domains. The second filter uses a combination of the BLAST hits, I-TASSER results, and identification of GPCR domains by ScanPROSITE to categorize the final 52 predicted but unconfirmed sequences.

Figure 5

Distribution of the newly-discovered vector GPCR sequences according to their GPCR status for each vector species.