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

Maia, M. F., Kapulu, M., Muthui, M., Wagah, M. G., Ferguson, H. M. , Dowell, F. E., Baldini, F. and Ranford-Cartwright, L. (2019) Detection of Plasmodium falciparum infected Anopheles gambiae using near-infrared spectroscopy. Malaria Journal, 18, 85. (doi: 10.1186/s12936-019-2719-9) (PMID:30890179) (PMCID:PMC6423776)

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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.

Item Type:Articles
Additional Information:The authors acknowledge the Swiss National Foundation of Science for the funding provided to MFM through the Marie-Heim Voegtlin fellowship scheme (PMPDP3-164444) and AXA RF fellowship (14-AXA-PDOC-130) and an EMBO LT fellowship (43-2014) for funding to FB. This work also supported MM and MGW through the DELTAS Africa Initiative [DEL-15-003]. The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)'s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa's Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust [107769/Z/10/Z] and the UK government.
Glasgow Author(s) Enlighten ID:Ranford-Cartwright, Dr Lisa and Baldini, Dr Francesco and Maia, Dr Marta and Ferguson, Professor Heather
Authors: Maia, M. F., Kapulu, M., Muthui, M., Wagah, M. G., Ferguson, H. M., Dowell, F. E., Baldini, F., and Ranford-Cartwright, L.
College/School:College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
Journal Name:Malaria Journal
ISSN (Online):1475-2875
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Malaria Journal 18:85
Publisher Policy:Reproduced under a Creative Commons License

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