Screening of Wolbachia Endosymbiont Infection in Aedes aegypti Mosquitoes Using Attenuated Total Reflection Mid-Infrared Spectroscopy

Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis

Studies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements-and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.

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.

Accurate age-grading of field-aged mosquitoes reared under ambient conditions using surface-enhanced Raman spectroscopy and artificial neural networks

Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enhanced Raman spectroscopy and artificial neural networks to accurately age-grade field-aged low-generation (F2) female Aedes aegypti mosquitoes held under ambient conditions (error was 1.9 chronological days, in the range 0-22 days). When degree days were used for model calibration, the accuracy was further improved to 20.8 degree days (approximately equal to 1.4 chronological days), which indicates the impact of temperature fluctuation on prediction accuracy. This performance is a significant advancement over binary classification. The great accuracy of this method outperforms traditional age-grading methods and will facilitate effective epidemiological studies, risk assessment, vector intervention monitoring, and evaluation.

Infrared spectroscopy (NIRS and ATR-FTIR) together with multivariate classification for non-destructive differentiation between female mosquitoes of Aedes aegypti recently infected with dengue vs. uninfected females

One of the most important steps in preventing arboviruses is entomological surveillance. The main entomological surveillance action is to detect vector foci in the shortest possible stages. In this work, near and medium infrared spectra collected from female Aedes aegypti mosquitoes recently infected and not infected with dengue were used in order to build chemometric models capable of differentiating the spectra of each class. For this, computational algorithms such as Successive Projection Algorithm (SPA) and Genetic Algorithm (GA) were used together with Linear Discriminant Analysis (LDA). The constructed models were evaluated with sensitivity and specificity calculations. It was observed that models based on near infrared (NIR) spectra have better classification results when compared to mid infrared (MIR) spectra, as well as models based on GA present better results when compared to those based on SPA. Thus, NIR-GA-LDA obtained the best results, reaching 100.00 % for sensitivity and specificity. NIR spectroscopy is 18 times faster and 116 times cheaper than RT-qPCR. The findings reported in this study may have important applications in the field of entomological surveillance, prevention and control of dengue vectors. In the future, mosquito traps equipped with portable NIR instruments capable of detecting infected mosquitoes may be used, in order to enable an action plan to prevent future outbreaks of the disease.

Rapid age-grading and species identification of natural mosquitoes for malaria surveillance

The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We present a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquito cuticle that simultaneously identifies the species and age class of three main malaria vectors in natural populations. Using spectra from over 40, 000 ecologically and genetically diverse An. gambiae, An. arabiensis, and An. coluzzii females, we develop a deep transfer learning model that learns and predicts the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model is able to detect the impact of simulated control interventions on mosquito populations, measured as a shift in their age structures. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.

Knowing the age of malaria-transmitting mosquitoes is important to understand transmission risk as only old mosquitoes can transmit the disease. Here, the authors develop a method based on mid-infrared spectra of mosquito cuticle that can rapidly identify the species and age class of main malaria vectors.

Detection and Identification of Wolbachia pipientis Strains in Mosquito Eggs Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy

The global fight against mosquito-borne viral diseases has in recent years been bolstered by the introduction of the endosymbiotic bacteria Wolbachia to vector populations, which in host mosquitoes suppresses the transmissibility of several viruses. Researchers engaged on this front of the battle often need to know the Wolbachia infection status of individual mosquitoes, as the intervention progresses and the mosquitoes become established in the target population. Previously, we successfully applied attenuated total reflection Fourier transform infrared spectroscopy to the detection of Wolbachia in adult Aedes aegypti mosquitoes; here we apply the same principles to Aedes eggs, with sensitivity and selectivity > 0.95. Further, we successfully distinguish between infections in eggs of the wMel and wMelPop strains of Wolbachia pipientis, with a classification error of 3%. The disruption of host lipid profile by Wolbachia is found to be a key driver in spectral differences between these sample classes.

The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence

CONCLUSIONS/SIGNIFICANCE

The potential of RS as a surveillance tool for malaria and arbovirus vectors and MIRS for the diagnosis and surveillance of arboviruses is yet to be assessed. NIRS capacity as a surveillance tool for malaria and arbovirus vectors should be validated under field conditions, and its potential as a diagnostic tool for malaria and arboviruses needs to be evaluated. It is recommended that all 3 techniques evaluated simultaneously using multiple machine learning techniques in multiple epidemiological settings to determine the most accurate technique for each application. Prior to their field application, a standardised protocol for spectra collection and data analysis should be developed. This will harmonise their application in multiple field settings allowing easy and faster integration into existing disease control platforms. Ultimately, development of rapid and cost-effective point-of-care diagnostic tools for malaria and arboviruses based on spectroscopy techniques may help combat current and future outbreaks of these infectious diseases.

Time Flies-Age Grading of Adult Flies for the Estimation of the Post-Mortem Interval

The estimation of the minimum time since death is one of the main applications of forensic entomology. This can be done by calculating the age of the immature stage of necrophagous flies developing on the corpse, which is confined to approximately 2–4 weeks, depending on temperature and species of the first colonizing wave of flies. Adding the age of the adult flies developed on the dead body could extend this time frame up to several weeks when the body is in a building or closed premise. However, the techniques for accurately estimating the age of adult flies are still in their beginning stages or not sufficiently validated. Here we review the current state of the art of analysing the aging of flies by evaluating the ovarian development, the amount of pteridine in the eyes, the degree of wing damage, the modification of their cuticular hydrocarbon patterns, and the increasing number of growth layers in the cuticula. New approaches, including the use of age specific molecular profiles based on the levels of gene and protein expression and the application of near infrared spectroscopy, are introduced, and the forensic relevance of these methods is discussed.

Dsi-RNA knockdown of genes regulated by Foxo reduces glycogen and lipid accumulations in diapausing Culex pipiens

Culex pipiens is a major carrier of the West Nile Virus, the leading cause of mosquito-borne disease in the continental United States. Cx. pipiens survive overwinter through diapause which is an important survival strategy that is under the control of insulin signaling and Foxo by regulating energy metabolism. Three homologous candidate genes, glycogen synthase (glys), atp-binding cassette transporter (atp), and low-density lipoprotein receptor chaperone (ldlr), that are under the regulation of Foxo transcription factor were identified in Cx. pipiens. To validate the gene functions, each candidate gene was silenced by injecting the target dsi-RNA to female Cx. pipiens during the early phase of diapause. The dsi-RNA injected diapause-destined female post-adult eclosion were fed for 7 days with 10% glucose containing 1% D-[¹³C₆]glucose. The effects of dsi-RNA knockdown on glucose metabolism in intact mosquitoes were monitored using ¹³C solid-state NMR and ATR-FTIR. Our finding shows that the dsi-RNA knockdown of all three candidate genes suppressed glycogen and lipid biosyntheses resulting in inhibition of long-term carbon energy storage in diapausing females.

Classification of Mosquitoes with Infrared Spectroscopy and Partial Least Squares-Discriminant Analysis

Mosquito-borne diseases are responsible for considerable morbidity and mortality globally. Given the absence of effective vaccines for most arthropod-borne viruses, mosquito control efforts remain the dominant method of disease prevention. Ideal control efforts begin with entomologic surveillance in order to determine the abundance, identity, and infection status of pathogen-vectoring mosquito populations. Traditionally, much of the surveillance work involves morphological species identification by trained entomologists. Limited operational funding and lack of specialized training is a known barrier to surveillance and effective control efforts for many operational mosquito control personnel. Therefore, there is a need for surveillance workflow improvements and rapid mosquito identification methods. Herein, is presented a proof of concept study in which infrared spectroscopy coupled with partial least squares-discriminant analysis was explored as a means of automatically classifying mosquitoes at the species level. The developed method resulted in greater than 94% accuracy for four mosquitoes of public health relevance: Aedes aegypti, Aedes albopictus, Aedes japonicus, and Aedes triseriatus.

Mosquito Age Grading and Vector-Control Programmes

An ability to characterize the age of mosquito populations could provide cost-effective and compelling entomological evidence for the potential epidemiological impacts of vector control. The average age of a mosquito population is the most important determinant of vectorial capacity and the likelihood of disease transmission. Yet, despite decades of research, defining the age of a wild-caught mosquito remains a challenging, impractical, and unreliable process. Emerging chemometric and existing transcriptional approaches may overcome many of the limitations of current morphological techniques, but their utility in terms of field-based monitoring programmes remains largely untested. Herein, we review the potential advantages and disadvantages of new and existing age-grading tools in an operational context.

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.

Whole-Organism Analysis by Vibrational Spectroscopy

Vibrational spectroscopy has contributed to the understanding of biological materials for many years. As the technology has advanced, the technique has been brought to bear on the analysis of whole organisms. Here, we discuss advanced and recently developed infrared and Raman spectroscopic instrumentation to whole-organism analysis. We highlight many of the recent contributions made in this relatively new area of spectroscopy, particularly addressing organisms associated with disease with emphasis on diagnosis and treatment. The application of vibrational spectroscopic techniques to entire organisms is still in its infancy, but new developments in imaging and chemometric processing will likely expand in the field in the near future.

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.

Direct nucleic acid analysis of mosquitoes for high fidelity species identification and detection of Wolbachia using a cellphone

Manipulation of natural mosquito populations using the endosymbiotic bacteria Wolbachia is being investigated as a novel strategy to reduce the burden of mosquito-borne viruses. To evaluate the efficacy of these interventions, it will be critical to determine Wolbachia infection frequencies in Aedes aegypti mosquito populations. However, current diagnostic tools are not well-suited to fit this need. Morphological methods cannot identify Wolbachia, immunoassays often suffer from low sensitivity and poor throughput, while PCR and spectroscopy require complex instruments and technical expertise, which restrict their use to centralized laboratories. To address this unmet need, we have used loop-mediated isothermal amplification (LAMP) and oligonucleotide strand displacement (OSD) probes to create a one-pot sample-to-answer nucleic acid diagnostic platform for vector and symbiont surveillance. LAMP-OSD assays can directly amplify target nucleic acids from macerated mosquitoes without requiring nucleic acid purification and yield specific single endpoint yes/no fluorescence signals that are observable to eye or by cellphone camera. We demonstrate cellphone-imaged LAMP-OSD tests for two targets, the Aedes aegypti cytochrome oxidase I (coi) gene and the Wolbachia surface protein (wsp) gene, and show a limit of detection of 4 and 40 target DNA copies, respectively. In a blinded test of 90 field-caught mosquitoes, the coi LAMP-OSD assay demonstrated 98% specificity and 97% sensitivity in identifying Ae. aegypti mosquitoes even after 3 weeks of storage without desiccant at 37°C. Similarly, the wsp LAMP-OSD assay readily identified the wAlbB Wolbachia strain in field-collected Aedes albopictus mosquitoes without generating any false positive signals. Modest technology requirements, minimal execution steps, simple binary readout, and robust accuracy make the LAMP-OSD-to-cellphone assay platform well suited for field vector surveillance in austere or resource-limited conditions.

Mosquitoes spread many human pathogens and novel approaches are required to reduce the burden of mosquito-borne disease. One promising approach is transferring Wolbachia into Aedes aegypti mosquitoes where it blocks transmission of arboviruses like dengue, Zika and Yellow fever viruses and spreads through mosquito populations. For effective evaluation of this approach, regular surveillance of Wolbachia infections in Ae. aegypti is required. However, current diagnostic tools, such as real time polymerase chain reaction, are not well suited to support these critical surveillance needs in resource poor settings due to their dependence on expensive instruments and technical expertise. To fill this need we developed a simple, robust and inexpensive assay to identify Ae. aegypti mosquitoes and Wolbachia using our unique one-pot assay platform, LAMP-OSD, which uses loop-mediated isothermal amplification to amplify nucleic acid targets at a single temperature. Unlike other LAMP-based tests, our assays assure accuracy by coupling amplification with novel nucleic acid strand displacement (OSD) probes that hybridize to specific sequences in LAMP amplification products and thereby generate simple yes/no readout of fluorescence readable by human eye and by off-the-shelf cellphones. To facilitate field use, we developed our assays so they are compatible with crushed mosquito homogenate as the template, meaning no nucleic acid extraction is required. In blinded tests using field collected mosquitoes, LAMP-OSD-cellphone tests performed robustly to identify 29 of 30 Ae. aegypti even after 3 weeks of storage at 37°C while producing only one false positive out of 60 non-specific mosquitoes. Similarly, our assay could identify Wolbachia in field-caught Aedes albopictus without producing any false positives. Our easy to use and easy to interpret assays should facilitate widespread field mosquito surveillance with minimal instrumentation and high accuracy.

Mission Accomplished? We Need a Guide to the 'Post Release' World of Wolbachia for Aedes-borne Disease Control

Historically, sustained control of Aedes aegypti, the vector of dengue, chikungunya, yellow fever, and Zika viruses, has been largely ineffective. Subsequently, two novel 'rear and release' control strategies utilizing mosquitoes infected with Wolbachia are currently being developed and deployed widely. In the incompatible insect technique, male Aedes mosquitoes, infected with Wolbachia, suppress populations through unproductive mating. In the transinfection strategy, both male and female Wolbachia-infected Ae. aegypti mosquitoes rapidly infect the wild population with Wolbachia, blocking virus transmission. It is critical to monitor the long-term stability of Wolbachia in host populations, and also the ability of this bacterium to continually inhibit virus transmission. Ongoing release and monitoring programs must be future-proofed should political support weaken when these vectors are successfully controlled.

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

This review highlights and discusses critically various knowledge gaps in classification modelling using PLS-DA for high dimensional data.