Background
Malaria is holding back development in endemic countries and remains one of the leading causes of death in children under 5 years-old in sub-Saharan Africa [
1‐
3]. During the past decade, the large-scale roll-out of long-lasting insecticide-treated nets and indoor residual spraying across Africa has resulted in a substantial reduction in malaria cases [
4]. The Global Technical Strategy for Malaria 2016–2030 of the World Health Organization (WHO) seeks to reduce malaria incidence and related mortality by at least 90% and to eliminate the disease in a minimum of 35 countries [
1]. These bold goals will require new interventions that can address residual malaria transmission as well as new tools to better monitor their impact on vector-borne disease transmission. Mosquito surveillance is a cornerstone of the control of malaria and other vector-borne diseases [
5].
However, presently, there is no high-throughput, cost-efficient method to identify
Plasmodium infection and infectiousness in mosquitoes. Molecular methods such as ELISA and PCR are used to determine parasite infection, but these are expensive and laborious [
6‐
8], challenging resource-poor countries with few funds and limited access to reagents and equipment, and thus are unsuitable for large-scale surveillance. A further complication is that typically only 1–2% of mosquitoes may be infected with transmission stage parasites (sporozoites), meaning that very large sample sizes must be tested to accurately quantify site and time-specific estimates of mosquito infection rates as will be required to assess progress towards malaria elimination [
9].
Recent advances indicate several mosquito traits can be accurately identified through analysis of their tissues with near infrared spectroscopy (NIRS) [
10‐
13]. This method involves the passing of visible and NIR light (wavelength 400–2500 nanometres) through the whole or part of a mosquito specimen and the collection of an absorbance spectrum instantly, without destroying the sample. Changes in spectral peaks at different wavelengths represent how intensely different molecules absorb light, and thus NIR spectra of mosquitoes are determined by the biochemical composition of their tissues, which are known to differ according to age [
14,
15], species [
16,
17], microbiome [
18], physiological stage [
19,
20], and pathogen infection status [
20,
21]. Differences in NIR spectra have been used to distinguish young (e.g. < 7 days old) from older (7 + days old) malaria vectors, to identify morphologically identical
Anopheles sibling species, and to detect the presence of the endosymbiont
Wolbachia in
Aedes aegypti mosquitoes [
10‐
12]. Most recently, NIRS has been used to detect rodent malaria infections in laboratory-reared
Anopheles stephensi [
22] and Zika virus in
Aedes aegypti [
23]. The use of NIRS has not previously been investigated on human malaria infected mosquitoes. The presence of the parasite-specific proteins and other biochemical changes induced by malaria infection in the vector may permit these to be distinguished from uninfected mosquitoes using spectral tools such as NIRS [
24,
25].
Parasite infection in the mosquito can be found in two main forms defined by their parasite development stages: midgut oocyst infections occurring around 2–8 days after feeding on infectious blood; and sporozoite infections occurring 9–14 days after infection, characterized by the release of sporozoites from oocysts into the mosquito’s haemocoel and salivary glands, enabling the mosquito to infect the next human host. Given the different nature of the two infection stages the NIRS profile of an oocyst-infected mosquito may not be the same as a sporozoite-infected one. For this reason, this study aimed to test whether NIRS could successfully identify oocyst and sporozoite infections in Anopheles vectors, and estimate if the method’s prediction accuracy is dependent on the intensity of infection in the mosquito.
This paper presents the successful application of NIRS to differentiate Plasmodium falciparum-infected mosquitoes from uninfected mosquitoes, providing the first evidence of detection of human malaria infections in the An. gambiae mosquito vector by this cost-effective, fast and reagent-free method. The development of a tool such as NIRS to measure malaria infection rates in mosquito populations would be of great service to malaria pre-elimination efforts as it would allow the processing of large numbers of mosquitoes increasing the accuracy of the estimates of human exposure to malaria infection across different regions, and advancing malaria vector surveillance in Africa.
Discussion
This is the first study to show that NIRS can be used to accurately detect human malaria in
An. gambiae mosquitoes. NIRS was able to predict oocyst infection with 87.7% accuracy (79.9–93.3%) and sporozoite infection with 94.5% accuracy (87.6–98.2%). The NIRS predictive accuracy for sporozoite infection of > 90% in this study concurs with previous work done using the rodent malaria in
An. stephensi, which found that NIRS could detect the presence of sporozoites in infected mosquitoes with 77% accuracy [
22]. Unlike the previous study, the present calibration model was also capable of identifying oocyst-infected mosquitoes. The PLS calibration of the present study was based on a narrower interval of the electromagnetic spectrum, 500 to 2400 nm, compared to 350 to 2500 nm previously used. This narrower range excludes noise present in the extremities of the spectra due to light source and sensor limitations and therewith improved the prediction accuracy of the calibration model. Furthermore, the previous study used spectra from mosquitoes that had been saturated with chloroform which was used to knock them down. This contamination led to clear chloroform peaks in the NIR spectra which may have added to the noise and reduced prediction accuracy of the calibration. Differences between the vector species and parasite species may also have played a role in the small discrepancy of predictive accuracy between studies. In addition, the experimental approach used in the present study, allowed to account for the potentially confounding effects of the infected blood meal, given that control group had been fed the same blood but with inactivated gametocytes.
Near infrared light is absorbed differently by diverse biochemical compounds which, in the mosquito, may consistently vary with between species, age and in this case infection status. It is hypothesized that biochemical changes occurring in the mosquito, as a consequence of
P. falciparum infection, made it possible to distinguish between infected and uninfected mosquitoes using NIRS. Consistent differences between the NIR absorbance spectra of infected and uninfected mosquitoes may be related to the presence of parasite-specific molecules in the infected mosquitoes [
31‐
33]. Also, it is possible that tissue changes may occur in the mosquitoes due to their immune response to the parasite which could have an effect on the biochemical composition of the mosquito [
31]. Additionally, it is known that
Plasmodium infection alters metabolic pathways in mosquitoes and leads to higher energy resource storage [
34], which may lead to differences in NIRS spectra. More research is needed to better understand the underlying biochemical features that enable NIRS to distinguish between
Plasmodium-infected and uninfected mosquitoes.
The prediction accuracy of the NIRS calibration to detect sporozoite infection was influenced not only by the presence of
P. falciparum sporozoites but also the parasite load (number of parasite genomes). This was not the case of the calibration to detect oocysts, which was only significantly influenced by the presence of infection in the midgut. It is possible that slight differences in DNA extraction efficiency between samples may have affected the estimate number of parasite genomes in each insect sample and, therefore, it is imprudent to make conclusions on how strongly infection load may be influencing the PLS output scores. The performance accuracy of NIRS was similar to qPCR (sporozoite detection: Cohens kappa = 0.86; oocyst detection: Cohens’s kappa = 0.75). The strong inter-rate agreement between the two methods, suggests that NIRS may have similar sensitivity and specificity to qPCR at detecting malaria sporozoites in the mosquito host. ELISA is less specific than PCR [
35], however due to its low-cost and ease, it is routinely the assay chosen by surveillance programs to measure the proportion of mosquitoes that carry sporozoites and the entomological inoculation rate (EIR). It is possible that EIR estimates could be improved by using a more accurate diagnostic test. ELISA commonly uses pooled samples to reduce costs and time, given that infection rates are usually below 2%. In case of an ELISA well positive for infection, it is assumed that it arises from one mosquito in the respective pool. In contrast, NIRS could be used on all samples since sample processing is less-costly and faster; it takes approximately 20 s to position and collect NIR spectra from one mosquito, allowing around 100 mosquitoes to be analyzed in 30 min. In addition, the method is completely non-destructive which permits using the sample for further tests if needed. However, a direct comparison of NIRS and ELISA was not the objective of this study, as the latter method does not allow quantification of parasite infection which was needed to evaluate if NIRS prediction was affected by
P. falciparum infection load. Presently NIRS still requires further optimization and validation in the field before being considered as a possible replacement for ELISA in surveillance programmes. Furthermore, the experiment described used fresh mosquitoes. Analyzing mosquitoes directly after sorting and morphological identification may be feasible for research programmes, but less so for control or surveillance programmes, which would benefit from evaluating different preservation methods.
It is noteworthy that the NIRS instrument is a rugged piece of equipment, which does not require special installation or frequent maintenance, and does not necessarily need to be installed in a laboratory. It was originally designed to be carried to the field to collect plant and soil spectra, can be transported in a Pelican case (55 × 42 × 32 cm), and requires only a power supply, which can be supplied by battery packs that are included with the instrument or from a 12 V vehicle power outlet. It can be assembled or packed in minutes, and if used frequently, left on the bench simply protected with a vinyl cover as is done with a compound microscope. Generating calibrations requires expert knowledge and technical skills, however, if a calibration file is readily available, predicting a sample’s classification group is simple, requiring only brief training. The current field-deployable NIR spectrometer costs around 55,000 USD but given that analysis requires no consumables or reagents and is high-throughput, the investment could be quickly paid off, particularly given the potential of the same technology for age-grading and species identification.
While the results presented in this paper are promising, NIRS calibrations generated using lab-reared mosquitoes do not necessarily represent the diversity of vectors in the field, providing no guarantee of the robustness of the method when tested on wild-caught mosquitoes. Calibrations must be based on training datasets that capture the diversity of field-mosquitoes reducing confounders that may affect the classification accuracy, including, different mosquito species, age, infection, size, insecticide resistance status, microbiome, and origin. Scale-up will require the assembly of training datasets to generate calibrations that capture this variability, by including a comprehensive range of mosquitoes characterized by diverse geographical, ecological, and epidemiological backgrounds. This approach is likely to narrow the factors needed for prediction by explaining sources of noise and variability in the model that are not directly related to infection and therewith increase prediction accuracy. Depending on how well the signal caused by infection presence is conserved, this could lead to location-, country- or region-specific calibrations for infection detection.
Authors’ contributions
MFM designed the experiment, cultured the parasites, assisted with the SMFAs, scanned the mosquitoes, analysed the data and drafted the manuscript. MK provided the DNA standards, provided training and commented on the final draft of the manuscript. MM optimized the qPCR method and trained MW. MW performed qPCRs. HF provided mentorship to MFM, was involved in the experimental design and commented on the final manuscript draft. FD provided mentorship to MFM, contributed to the experimental design and data analysis. FB and LR-C contributed to the experimental design, setup the parasite culture, led the SMFAs, provided training to MFM in asexual and sexual culture of P. falciparum NF54 as well as contributed to the final manuscript. All authors commented on drafts of the manuscript. All authors read and approved the final manuscript.