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Gaikwad PS, Hole A, Saxena V, Choudhury S, Nath BB, Krishna CM, Mukhopadhyaya R. Vibrational spectroscopic detection of radiation-induced structural changes in Chironomus hemoglobin. Biochem Biophys Rep 2024; 38:101721. [PMID: 38766383 PMCID: PMC11101703 DOI: 10.1016/j.bbrep.2024.101721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 05/22/2024] Open
Abstract
Purpose Chironomus hemoglobin is known to exhibit higher gamma radiation resistance compared to human hemoglobin. In the present study, we have introduced a sensitive method to analyze radiation-induced alterations in Chironomus hemoglobin using Vibrational spectroscopy and further highlighting its potential for monitoring radiotoxicity in aquatic environments. Materials and methods Vibrational spectroscopic methods such as Raman and FT-IR spectroscopy were used to capture the distinctive chemical signature of Chironomus hemoglobin (ChHb) under both in vitro and in vivo conditions. Any radiation dose-dependent shifts could be analyzed Human hemoglobin (HuHb) as standard reference. Results Distinctive Raman peak detected at 930 cm-1 in (ChHb) was attributed to C-N stretching in the heterocyclic ring surrounding the iron atom, preventing heme degradation even after exposure to 2400 Gy dose. In contrast, for (HuHb), the transition from deoxy-hemoglobin to met-hemoglobin at 1210 cm-1 indicated a disruption in oxygen binding after exposure to 1200 Gy dose. Furthermore, while ChHb exhibited a consistent peak at 1652 cm-1 in FT-IR analysis, HuHb on the other hand, suffered damage after gamma irradiation. Conclusion The findings suggest that vibrational spectroscopic methods hold significant potential as a sensitive tool for detecting radiation-induced molecular alterations and damages. Chironomus hemoglobin, with its robust interaction of the pyrrole ring with Fe, serves as a reliable bioindicator molecule to detect radiation damage using vibrational spectroscopic method.
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Affiliation(s)
- Pallavi S. Gaikwad
- Department of Zoology, Savitribai Phule Pune University, Pune, 411007, India
- Gene Technology Section, Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India
| | - Arti Hole
- Advanced Centre for Treatment, Research & Education in Cancer (ACTREC), Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India
| | - Vibha Saxena
- Technical Physics Division, Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India
| | - Sipra Choudhury
- Chemistry Division, Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India
| | - Bimalendu B. Nath
- Department of Zoology, Savitribai Phule Pune University, Pune, 411007, India
- MIE-SPPU Institute of Higher Education, Doha, Qatar, 122104
| | - C. Murali Krishna
- Homi Bhabha National Institute, Anushaktinagar, Mumbai-400094, India
- Advanced Centre for Treatment, Research & Education in Cancer (ACTREC), Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India
| | - Rita Mukhopadhyaya
- Gene Technology Section, Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India
- Homi Bhabha National Institute, Anushaktinagar, Mumbai-400094, India
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Ferreira TN, Santos LMB, Valladares V, Flanley CM, McDowell MA, Garcia GA, Mello-Silva CC, Maciel-de-Freitas R, Genta FA. Age, sex, and mating status discrimination in the sand fly Lutzomyia longipalpis using near infra-red spectroscopy (NIRS). Parasit Vectors 2024; 17:19. [PMID: 38217054 PMCID: PMC10787389 DOI: 10.1186/s13071-023-06097-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 12/13/2023] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND Understanding aspects related to the physiology and capacity of vectors is essential for effectively controlling vector-borne diseases. The sand fly Lutzomyia longipalpis has great importance in medical entomology for disseminating Leishmania parasites, the causative agent of Leishmaniasis, one of the main neglected diseases listed by the World Health Organization (WHO). In this respect, it is necessary to evaluate the transmission potential of this species and the success of vector control interventions. Near-infrared spectroscopy (NIRS) has been used to estimate the age of mosquitoes in different conditions (laboratory, semi-field, and conservation), taxonomic analysis, and infection detection. However, no studies are using NIRS for sand flies. METHODS In this study, we developed analytic models to estimate the age of L. longipalpis adults under laboratory conditions, identify their copulation state, and evaluate their gonotrophic cycle and diet. RESULTS Sand flies were classified with an accuracy of 58-82% in 3 age groups and 82-92% when separating them into young (<8 days) or old (>8 days) insects. The classification between mated and non-mated sandflies was 98-100% accurate, while the percentage of hits of females that had already passed the first gonotrophic cycle was only 59%. CONCLUSIONS We consider the age and copula estimation results very promising, as they provide essential aspects of vector capacity assessment, which can be obtained quickly and at a lower cost with NIRS.
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Affiliation(s)
- Tainá Neves Ferreira
- Laboratório de Bioquímica e Fisiologia de Insetos, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | - Lilha M B Santos
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | - Vanessa Valladares
- Malacology Laboratory, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | - Catherine M Flanley
- Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Mary Ann McDowell
- Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Gabriela A Garcia
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | | | - Rafael Maciel-de-Freitas
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
- Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular, Rio de Janeiro, Brazil
| | - Fernando Ariel Genta
- Laboratório de Bioquímica e Fisiologia de Insetos, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil.
- Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular, Rio de Janeiro, Brazil.
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Mshani IH, Siria DJ, Mwanga EP, Sow BB, Sanou R, Opiyo M, Sikulu-Lord MT, Ferguson HM, Diabate A, Wynne K, González-Jiménez M, Baldini F, Babayan SA, Okumu F. Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis. Malar J 2023; 22:346. [PMID: 37950315 PMCID: PMC10638832 DOI: 10.1186/s12936-023-04780-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
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.
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Affiliation(s)
- Issa H Mshani
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania.
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
| | - Doreen J Siria
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Emmanuel P Mwanga
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Bazoumana Bd Sow
- Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Roger Sanou
- Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Mercy Opiyo
- Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique
- Malaria Elimination Initiative (MEI), Institute for Global Health Sciences, University of California, San Francisco, USA
| | - Maggy T Sikulu-Lord
- Faculty of Science, School of the Environment, The University of Queensland, Brisbane, QLD, Australia
| | - Heather M Ferguson
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Abdoulaye Diabate
- Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Klaas Wynne
- School of Chemistry, The University of Glasgow, Glasgow, G12 8QQ, UK
| | - Mario González-Jiménez
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
- School of Chemistry, The University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Simon A Babayan
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
| | - Fredros Okumu
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania.
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
- School of Life Sciences and Biotechnology, Nelson Mandela African Institution of Science and Technology, Arusha, United Republic of Tanzania.
- School of Public Health, The University of the Witwatersrand, Park Town, South Africa.
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Pazmiño-Betancourth M, Ochoa-Gutiérrez V, Ferguson HM, González-Jiménez M, Wynne K, Baldini F, Childs D. Evaluation of diffuse reflectance spectroscopy for predicting age, species, and cuticular resistance of Anopheles gambiae s.l under laboratory conditions. Sci Rep 2023; 13:18499. [PMID: 37898634 PMCID: PMC10613238 DOI: 10.1038/s41598-023-45696-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 10/23/2023] [Indexed: 10/30/2023] Open
Abstract
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.
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Affiliation(s)
- Mauro Pazmiño-Betancourth
- School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK.
| | - Victor Ochoa-Gutiérrez
- School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
- School of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Heather M Ferguson
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | | | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - David Childs
- School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
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Gao Z, Harrington LC, Zhu W, Barrientos LM, Alfonso-Parra C, Avila FW, Clark JM, He L. Accurate age-grading of field-aged mosquitoes reared under ambient conditions using surface-enhanced Raman spectroscopy and artificial neural networks. JOURNAL OF MEDICAL ENTOMOLOGY 2023; 60:917-923. [PMID: 37364175 DOI: 10.1093/jme/tjad067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/27/2023] [Accepted: 06/08/2023] [Indexed: 06/28/2023]
Abstract
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.
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Affiliation(s)
- Zili Gao
- Department of Food Science, University of Massachusetts, Amherst, MA 01003, USA
- Raman, IR and XRF Core Facility, University of Massachusetts, Amherst, MA 01003, USA
| | - Laura C Harrington
- Department of Entomology, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA
| | - Wei Zhu
- Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003, USA
| | - Luisa M Barrientos
- Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia, Medellin, Colombia
| | - Catalina Alfonso-Parra
- Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia, Medellin, Colombia
- Instituto Colombiano de Medicina Tropical, Universidad CES, Sabaneta, Colombia
| | - Frank W Avila
- Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia, Medellin, Colombia
| | - John M Clark
- Department of Veterinary and Animal Sciences, University of Massachusetts, Amherst, MA 01003, USA
| | - Lili He
- Department of Food Science, University of Massachusetts, Amherst, MA 01003, USA
- Raman, IR and XRF Core Facility, University of Massachusetts, Amherst, MA 01003, USA
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
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6
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Wagner I, Grigoraki L, Enevoldson P, Clarkson M, Jones S, Hurst JL, Beynon RJ, Ranson H. Rapid identification of mosquito species and age by mass spectrometric analysis. BMC Biol 2023; 21:10. [PMID: 36690979 PMCID: PMC9872345 DOI: 10.1186/s12915-022-01508-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 12/21/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND A rapid, accurate method to identify and to age-grade mosquito populations would be a major advance in predicting the risk of pathogen transmission and evaluating the public health impact of vector control interventions. Whilst other spectrometric or transcriptomic methods show promise, current approaches rely on challenging morphological techniques or simple binary classifications that cannot identify the subset of the population old enough to be infectious. In this study, the ability of rapid evaporative ionisation mass spectrometry (REIMS) to identify the species and age of mosquitoes reared in the laboratory and derived from the wild was investigated. RESULTS The accuracy of REIMS in identifying morphologically identical species of the Anopheles gambiae complex exceeded 97% using principal component/linear discriminant analysis (PC-LDA) and 84% based on random forest analysis. Age separation into 3 different age categories (1 day, 5-6 days, 14-15 days) was achieved with 99% (PC-LDA) and 91% (random forest) accuracy. When tested on wild mosquitoes from the UK, REIMS data could determine the species and age of the specimens with accuracies of 91 and 90% respectively. CONCLUSIONS The accuracy of REIMS to resolve the species and age of Anopheles mosquitoes is comparable to that achieved by infrared spectroscopy approaches. The processing time and ease of use represent significant advantages over current, dissection-based methods. Importantly, the accuracy was maintained when using wild mosquitoes reared under differing environmental conditions, and when mosquitoes were stored frozen or desiccated. This high throughput approach thus has potential to conduct rapid, real-time monitoring of vector populations, providing entomological evidence of the impact of alternative interventions.
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Affiliation(s)
- Iris Wagner
- Centre for Proteome Research, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB UK
| | - Linda Grigoraki
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA UK
| | - Peter Enevoldson
- Walton Centre NHS Foundation Trust, Lower Lane, Liverpool, L9 7LJ UK
- Department of Livestock and One Health, University of Liverpool, Institute of Infection, Veterinary and Ecological Sciences, Leahurst Campus, Neston, CH64 7TE UK
| | - Michael Clarkson
- Department of Livestock and One Health, University of Liverpool, Institute of Infection, Veterinary and Ecological Sciences, Leahurst Campus, Neston, CH64 7TE UK
| | - Sam Jones
- International Pheromone Systems Ltd, Evolution House, Long Acres Road, Clayhill Industrial Estate, Neston, CH64 3RL Cheshire UK
| | - Jane L. Hurst
- Mammalian Behaviour and Evolution Group, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, Neston, CH64 7TE UK
| | - Robert J. Beynon
- Centre for Proteome Research, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB UK
| | - Hilary Ranson
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA UK
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Gray L, Asay BC, Hephaestus B, McCabe R, Pugh G, Markle ED, Churcher TS, Foy BD. Back to the Future: Quantifying Wing Wear as a Method to Measure Mosquito Age. Am J Trop Med Hyg 2022; 107:tpmd211173. [PMID: 35895347 PMCID: PMC9490652 DOI: 10.4269/ajtmh.21-1173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 04/15/2022] [Indexed: 11/07/2022] Open
Abstract
Vector biologists have long sought the ability to accurately quantify the age of wild mosquito populations, a metric used to measure vector control efficiency. This has proven difficult due to the difficulties of working in the field and the biological complexities of wild mosquitoes. Ideal age grading techniques must overcome both challenges while also providing epidemiologically relevant age measurements. Given these requirements, the Detinova parity technique, which estimates age from the mosquito ovary and tracheole skein morphology, has been most often used for mosquito age grading despite significant limitations, including being based solely on the physiology of ovarian development. Here, we have developed a modernized version of the original mosquito aging method that evaluated wing wear, expanding it to estimate mosquito chronological age from wing scale loss. We conducted laboratory experiments using adult Anopheles gambiae held in insectary cages or mesocosms, the latter of which also featured ivermectin bloodmeal treatments to change the population age structure. Mosquitoes were age graded by parity assessments and both human- and computational-based wing evaluations. Although the Detinova technique was not able to detect differences in age population structure between treated and control mesocosms, significant differences were apparent using the wing scale technique. Analysis of wing images using averaged left- and right-wing pixel intensity scores predicted mosquito age at high accuracy (overall test accuracy: 83.4%, average training accuracy: 89.7%). This suggests that this technique could be an accurate and practical tool for mosquito age grading though further evaluation in wild mosquito populations is required.
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Affiliation(s)
- Lyndsey Gray
- Center for Vector-Borne Infectious Diseases, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado
| | | | | | - Ruth McCabe
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, London, United Kingdom
| | - Greg Pugh
- Center for Vector-Borne Infectious Diseases, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado
| | - Erin D. Markle
- Center for Vector-Borne Infectious Diseases, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado
| | - Thomas S. Churcher
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, London, United Kingdom
| | - Brian D. Foy
- Center for Vector-Borne Infectious Diseases, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado
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Autofluorescent Biomolecules in Diptera: From Structure to Metabolism and Behavior. Molecules 2022; 27:molecules27144458. [PMID: 35889334 PMCID: PMC9318335 DOI: 10.3390/molecules27144458] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/08/2022] [Accepted: 07/08/2022] [Indexed: 02/04/2023] Open
Abstract
Light-based phenomena in insects have long attracted researchers’ attention. Surface color distribution patterns are commonly used for taxonomical purposes, while optically-active structures from Coleoptera cuticle or Lepidoptera wings have inspired technological applications, such as biosensors and energy accumulation devices. In Diptera, besides optically-based phenomena, biomolecules able to fluoresce can act as markers of bio-metabolic, structural and behavioral features. Resilin or chitinous compounds, with their respective blue or green-to-red autofluorescence (AF), are commonly related to biomechanical and structural properties, helpful to clarify the mechanisms underlying substrate adhesion of ectoparasites’ leg appendages, or the antennal abilities in tuning sound detection. Metarhodopsin, a red fluorescing photoproduct of rhodopsin, allows to investigate visual mechanisms, whereas NAD(P)H and flavins, commonly relatable to energy metabolism, favor the investigation of sperm vitality. Lipofuscins are AF biomarkers of aging, as well as pteridines, which, similarly to kynurenines, are also exploited in metabolic investigations. Beside the knowledge available in Drosophila melanogaster, a widely used model to study also human disorder and disease mechanisms, here we review optically-based studies in other dipteran species, including mosquitoes and fruit flies, discussing future perspectives for targeted studies with various practical applications, including pest and vector control.
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9
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Estimating female malaria mosquito age by quantifying Y-linked genes in stored male spermatozoa. Sci Rep 2022; 12:10570. [PMID: 35732703 PMCID: PMC9217924 DOI: 10.1038/s41598-022-15021-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/16/2022] [Indexed: 11/19/2022] Open
Abstract
Vector control strategies are among the most effective measures to combat mosquito-borne diseases, such as malaria. These strategies work by altering the mosquito age structure through increased mortality of the older female mosquitoes that transmit pathogens. However, methods to monitor changes to mosquito age structure are currently inadequate for programmatic implementation. Female mosquitoes generally mate a single time soon after emergence and draw down spermatozoa reserves with each oviposition cycle. Here, we demonstrate that measuring spermatozoa quantity in female Anopheles mosquitoes is an effective approach to assess mosquito age. Using multiplexed qPCR targeted at male spermatozoa, we show that Y-linked genes in female mosquitoes are exclusively found in the spermatheca, the organ that houses spermatozoa, and the quantity of these gene sequences significantly declines with age. The method can accurately identify mosquitoes more than 10 days old and thus old enough to potentially transmit pathogens harbored in the salivary glands during blood feeding. Furthermore, mosquito populations that differ by 10% in daily survivorship have a high likelihood of being distinguished using modest sample sizes, making this approach scalable for assessing the efficacy of vector intervention control programs.
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Joy T, Chen M, Arnbrister J, Williamson D, Li S, Nair S, Brophy M, Garcia VM, Walker K, Ernst K, Gouge DH, Carrière Y, Riehle MA. Assessing Near-Infrared Spectroscopy (NIRS) for Evaluation of Aedes aegypti Population Age Structure. INSECTS 2022; 13:insects13040360. [PMID: 35447802 PMCID: PMC9029691 DOI: 10.3390/insects13040360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/03/2022] [Accepted: 04/05/2022] [Indexed: 02/04/2023]
Abstract
Given that older Aedes aegypti (L.) mosquitoes typically pose the greatest risk of pathogen transmission, the capacity to age grade wild Ae. aegypti mosquito populations would be a valuable tool in monitoring the potential risk of arboviral transmission. Here, we compared the effectiveness of near-infrared spectroscopy (NIRS) to age grade field-collected Ae. aegypti with two alternative techniques—parity analysis and transcript abundance of the age-associated gene SCP1. Using lab-reared mosquitoes of known ages from three distinct populations maintained as adults under laboratory or semi-field conditions, we developed and validated four NIRS models for predicting the age of field-collected Ae. aegypti. To assess the accuracy of these models, female Ae. aegypti mosquitoes were collected from Maricopa County, AZ, during the 2017 and 2018 monsoon season, and a subset were age graded using the three different age-grading techniques. For both years, each of the four NIRS models consistently graded parous mosquitoes as significantly older than nulliparous mosquitoes. Furthermore, a significant positive linear association occurred between SCP1 and NIRS age predictions in seven of the eight year/model combinations, although considerable variation in the predicted age of individual mosquitoes was observed. Our results suggest that although the NIRS models were not adequate in determining the age of individual field-collected mosquitoes, they have the potential to quickly and cost effectively track changes in the age structure of Ae. aegypti populations across locations and over time.
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Affiliation(s)
- Teresa Joy
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Minhao Chen
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Joshua Arnbrister
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Daniel Williamson
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Shujuan Li
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Shakunthala Nair
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Maureen Brophy
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Valerie Madera Garcia
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85724, USA; (V.M.G.); (K.E.)
| | - Kathleen Walker
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Kacey Ernst
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85724, USA; (V.M.G.); (K.E.)
| | - Dawn H. Gouge
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Yves Carrière
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Michael A. Riehle
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
- Correspondence: ; Tel.: +1-520-626-8500
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11
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Rapid age-grading and species identification of natural mosquitoes for malaria surveillance. Nat Commun 2022; 13:1501. [PMID: 35314683 PMCID: PMC8938457 DOI: 10.1038/s41467-022-28980-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 02/19/2022] [Indexed: 12/03/2022] Open
Abstract
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.
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12
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Weeraratne TC, Karunaratne SHPP, Reimer L, de Silva WAPP, Wondji CS. Use of transcriptional age grading technique to determine the chronological age of Sri Lankan Aedes aegypti and Aedes albopictus females. Parasit Vectors 2021; 14:493. [PMID: 34565445 PMCID: PMC8474866 DOI: 10.1186/s13071-021-04994-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 09/08/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Aedes aegypti and Ae. albopictus are important vectors of human diseases such as dengue, chikungunya, and zika. In Sri Lanka, they have been responsible for transmitting dengue virus. One of the most important parameters influencing the likelihood of arbovirus transmission is the age structure of the mosquito population. However, mosquito age is difficult to measure with accuracy. This study aims to construct multivariate calibration models using the transcriptional abundance of three age-responsive genes: Ae15848 (calcium-binding protein), Ae8505 (structural component of cuticle), and Ae4274 (fizzy cell cycle/cell division cycle 20). METHODS The transcriptional age-grading technique was applied to determine the chronological age of Ae. aegypti and Ae. albopictus female mosquito populations from Sri Lanka using the age-responsive genes Ae15848, Ae8505, and Ae4274. Furthermore, Ae. aegypti samples obtained from colonies reared at two temperatures (23 and 27 °C) were used to investigate the influence of temperature on this age-grading technique. Expression levels of these three genes were quantified using reverse transcription qualitative PCR (qRT-PCR), and results were normalized against the housekeeping gene ribosomal gene S17 (RpS17). RESULTS The expression of Ae15848 and Ae8505 decreased with the age of mosquitoes and showed the most significant and consistent change while expression of Ae4274 increased with age. The multivariate calibration models showed > 80% correlation between expression of these age-responsive genes and the age of female mosquitoes at both temperatures. At 27 °C the accuracy of age predictions using the models was 2.19 (± 1.66) days and 2.58 (± 2.06) days for Ae. aegypti and Ae. albopictus females, respectively. The accuracy of the model for Ae. aegypti at 23 °C was 3.42 (± 2.74) days. CONCLUSIONS An adult rearing temperature difference of 4 °C (23-27 °C) did not significantly affect the age predictions. The calibration models created during this study could be successfully used to estimate the age of wild Ae. aegypti and Ae. albopictus mosquitoes from Sri Lanka.
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Affiliation(s)
| | | | - Lisa Reimer
- Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | | | - Charles S Wondji
- Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK.,Medical Entomology Department, Centre for Research in Infectious Diseases (CRID), Yaoundé, Cameroon
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13
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Faiman R, Krajacich BJ, Graber L, Dao A, Yaro AS, Yossi O, Sanogo ZL, Diallo M, Samaké D, Sylla D, Coulibaly M, Kone S, Goita S, Coulibaly MB, Muratova O, McCormack A, Gonçalves BP, Hume J, Duffy P, Lehmann T. A novel fluorescence and DNA combination for versatile, long-term marking of mosquitoes. Methods Ecol Evol 2021; 12:1008-1016. [PMID: 34249305 PMCID: PMC8252004 DOI: 10.1111/2041-210x.13592] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 02/19/2021] [Indexed: 11/29/2022]
Abstract
Current mark-release-recapture methodologies are limited in their ability to address complex problems in vector biology, such as studying multiple groups overlapping in space and time. Additionally, limited mark retention, reduced post-marking survival and the large effort in marking, collection and recapture all complicate effective insect tracking.We have developed and evaluated a marking method using a fluorescent dye (SmartWater®) combined with synthetic DNA tags to informatively and efficiently mark adult mosquitoes using an airbrush pump and nebulizer. Using a handheld UV flashlight, the fluorescent marking enabled quick and simple initial detection of recaptures in a field-ready and non-destructive approach that when combined with an extraction-free PCR on individual mosquito legs provides potentially unlimited marking information.This marking, first tested in the laboratory with Anopheles gambiae s.l. mosquitoes, did not affect survival (median ages 24-28 days, p-adj > 0.25), oviposition (median eggs/female of 28.8, 32.5, 33.3 for water, green, red dyes, respectively, p-adj > 0.44) or Plasmodium competence (mean oocysts 5.56-10.6, p-adj > 0.95). DNA and fluorescence had 100% retention up to 3 weeks (longest time point tested) with high intensity, indicating marks would persist longer.We describe a novel, simple, no/low-impact and long-lasting marking method that allows separation of multiple insect subpopulations by combining unlimited length and sequence variation in the synthetic DNA tags. This method can be readily deployed in the field for marking multiple groups of mosquitoes or other insects.
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Affiliation(s)
- Roy Faiman
- Laboratory of Malaria and Vector ResearchNIAIDNIHRockvilleMDUSA
| | | | - Leland Graber
- Laboratory of Malaria and Vector ResearchNIAIDNIHRockvilleMDUSA
| | - Adama Dao
- Malaria Research and Training Center (MRTC)/Faculty of Medicine, Pharmacy and Odonto‐stomatologyUniversity of Sciences, Techniques and TechnologiesBamakoMali
| | - Alpha Seydou Yaro
- Malaria Research and Training Center (MRTC)/Faculty of Medicine, Pharmacy and Odonto‐stomatologyUniversity of Sciences, Techniques and TechnologiesBamakoMali
| | - Ousmane Yossi
- Malaria Research and Training Center (MRTC)/Faculty of Medicine, Pharmacy and Odonto‐stomatologyUniversity of Sciences, Techniques and TechnologiesBamakoMali
| | - Zana Lamissa Sanogo
- Malaria Research and Training Center (MRTC)/Faculty of Medicine, Pharmacy and Odonto‐stomatologyUniversity of Sciences, Techniques and TechnologiesBamakoMali
| | - Moussa Diallo
- Malaria Research and Training Center (MRTC)/Faculty of Medicine, Pharmacy and Odonto‐stomatologyUniversity of Sciences, Techniques and TechnologiesBamakoMali
| | - Djibril Samaké
- Malaria Research and Training Center (MRTC)/Faculty of Medicine, Pharmacy and Odonto‐stomatologyUniversity of Sciences, Techniques and TechnologiesBamakoMali
| | - Daman Sylla
- Malaria Research and Training Center (MRTC)/Faculty of Medicine, Pharmacy and Odonto‐stomatologyUniversity of Sciences, Techniques and TechnologiesBamakoMali
| | - Moribo Coulibaly
- Malaria Research and Training Center (MRTC)/Faculty of Medicine, Pharmacy and Odonto‐stomatologyUniversity of Sciences, Techniques and TechnologiesBamakoMali
| | - Salif Kone
- Malaria Research and Training Center (MRTC)/Faculty of Medicine, Pharmacy and Odonto‐stomatologyUniversity of Sciences, Techniques and TechnologiesBamakoMali
| | - Sekou Goita
- Malaria Research and Training Center (MRTC)/Faculty of Medicine, Pharmacy and Odonto‐stomatologyUniversity of Sciences, Techniques and TechnologiesBamakoMali
| | - Mamadou B. Coulibaly
- Malaria Research and Training Center (MRTC)/Faculty of Medicine, Pharmacy and Odonto‐stomatologyUniversity of Sciences, Techniques and TechnologiesBamakoMali
| | - Olga Muratova
- Laboratory of Malaria Immunology and VaccinologyNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthBethesdaMDUSA
| | - Ashley McCormack
- Laboratory of Malaria Immunology and VaccinologyNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthBethesdaMDUSA
| | - Bronner P. Gonçalves
- Laboratory of Malaria Immunology and VaccinologyNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthBethesdaMDUSA
| | - Jennifer Hume
- Laboratory of Malaria Immunology and VaccinologyNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthBethesdaMDUSA
| | - Patrick Duffy
- Laboratory of Malaria Immunology and VaccinologyNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthBethesdaMDUSA
| | - Tovi Lehmann
- Laboratory of Malaria and Vector ResearchNIAIDNIHRockvilleMDUSA
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14
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Goh B, Ching K, Soares Magalhães RJ, Ciocchetta S, Edstein MD, Maciel-de-Freitas R, Sikulu-Lord MT. 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. PLoS Negl Trop Dis 2021; 15:e0009218. [PMID: 33886567 PMCID: PMC8061870 DOI: 10.1371/journal.pntd.0009218] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
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.
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Affiliation(s)
- Brendon Goh
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Koek Ching
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Ricardo J Soares Magalhães
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Brisbane, Australia
- Children's Health Research Centre, Children's Health and Environment Program, The University of Queensland, Brisbane, Australia
| | - Silvia Ciocchetta
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Brisbane, Australia
| | - Michael D Edstein
- Australian Defence Force, Malaria and Infectious Disease Institute, Brisbane, Australia
| | | | - Maggy T Sikulu-Lord
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
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15
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A New Test of a Theory about Old Mosquitoes. Trends Parasitol 2020; 37:185-194. [PMID: 33250441 DOI: 10.1016/j.pt.2020.10.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 10/26/2020] [Accepted: 10/26/2020] [Indexed: 11/24/2022]
Abstract
In vector control, it is widely accepted that killing adult mosquitoes would sharply reduce the proportion of old mosquitoes and cause the greatest changes to malaria transmission. The principle is based on a mathematical model of the sporozoite rate (the proportion of infective mosquitoes) that emphasized changes in mosquito age. Killing adult mosquitoes also reduces mosquito population densities, which are directly proportional to human biting rates (the number of bites, per person, per day). Eect sizes of vector control can be compared using sporozoite rates and human biting rates, which are commonly measured. We argue that human biting rates convey more use- ful information for planning, monitoring and evaluating vector control, and operational research should focus on understanding mosquito ecology.
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16
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An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra. PLoS One 2020; 15:e0234557. [PMID: 32555660 PMCID: PMC7302571 DOI: 10.1371/journal.pone.0234557] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 05/27/2020] [Indexed: 11/19/2022] Open
Abstract
After mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases in humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which are currently used to determine the parity status of mosquitoes, are very tedious and limited to few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. In this study, we train an artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae s.s collected from Muleba, Tanzania (Muleba-GA); An. gambiae s.s collected from Burkina Faso (Burkina-GA); and An.gambiae s.s from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train the ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9±2.8 (N = 274), 68.7±4.8 (N = 43), 80.3±2.0 (N = 48), and 75.7±2.5 (N = 91), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1±2.2% (N = 274), 89.8 ± 1.7% (N = 43), 93.3±1.2% (N = 48), and 92.7±1.8% (N = 91) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively. These results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate the parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections.
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17
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Ong OTW, Kho EA, Esperança PM, Freebairn C, Dowell FE, Devine GJ, Churcher TS. Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions. Parasit Vectors 2020; 13:160. [PMID: 32228670 PMCID: PMC7106667 DOI: 10.1186/s13071-020-04031-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 03/24/2020] [Indexed: 01/04/2023] Open
Abstract
Background Practical, field-ready age-grading tools for mosquito vectors of disease are urgently needed because of the impact that daily survival has on vectorial capacity. Previous studies have shown that near-infrared spectroscopy (NIRS), in combination with chemometrics and predictive modeling, can forecast the age of laboratory-reared mosquitoes with moderate to high accuracy. It remains unclear whether the technique has utility for identifying shifts in the age structure of wild-caught mosquitoes. Here we investigate whether models derived from the laboratory strain of mosquitoes can be used to predict the age of mosquitoes grown from pupae collected in the field. Methods NIRS data from adult female Aedes albopictus mosquitoes reared in the laboratory (2, 5, 8, 12 and 15 days-old) were analysed against spectra from mosquitoes emerging from wild-caught pupae (1, 7 and 14 days-old). Different partial least squares (PLS) regression methods trained on spectra from laboratory mosquitoes were evaluated on their ability to predict the age of mosquitoes from more natural environments. Results Models trained on spectra from laboratory-reared material were able to predict the age of other laboratory-reared mosquitoes with moderate accuracy and successfully differentiated all day 2 and 15 mosquitoes. Models derived with laboratory mosquitoes could not differentiate between field-derived age groups, with age predictions relatively indistinguishable for day 1–14. Pre-processing of spectral data and improving the PLS regression framework to avoid overfitting can increase accuracy, but predictions of mosquitoes reared in different environments remained poor. Principal components analysis confirms substantial spectral variations between laboratory and field-derived mosquitoes despite both originating from the same island population. Conclusions Models trained on laboratory mosquitoes were able to predict ages of laboratory mosquitoes with good sensitivity and specificity though they were unable to predict age of field-derived mosquitoes. This study suggests that laboratory-reared mosquitoes do not capture enough environmental variation to accurately predict the age of the same species reared under different conditions. Further research is needed to explore alternative pre-processing methods and machine learning techniques, and to understand factors that affect absorbance in mosquitoes before field application using NIRS.![]()
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Affiliation(s)
- Oselyne T W Ong
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
| | - Elise A Kho
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Queensland, Australia
| | - Pedro M Esperança
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Chris Freebairn
- Private Contracting Entomologist, Hammond Island, Queensland, Australia
| | - Floyd E Dowell
- USDA, Centre for Grain and Animal Health Research, 1515 College Avenue, Manhattan, KS, 66502, USA
| | - Gregor J Devine
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Thomas S Churcher
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
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18
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Mosquito Age Grading and Vector-Control Programmes. Trends Parasitol 2019; 36:39-51. [PMID: 31836285 DOI: 10.1016/j.pt.2019.10.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 10/24/2019] [Accepted: 10/24/2019] [Indexed: 12/16/2022]
Abstract
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.
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19
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Mwanga EP, Minja EG, Mrimi E, Jiménez MG, Swai JK, Abbasi S, Ngowo HS, Siria DJ, Mapua S, Stica C, Maia MF, Olotu A, Sikulu-Lord MT, Baldini F, Ferguson HM, Wynne K, Selvaraj P, Babayan SA, Okumu FO. Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis. Malar J 2019; 18:341. [PMID: 31590669 PMCID: PMC6781347 DOI: 10.1186/s12936-019-2982-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 09/28/2019] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. METHODS Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm-1 to 500 cm-1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. RESULTS Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. CONCLUSION These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.
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Affiliation(s)
- Emmanuel P Mwanga
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.
| | - Elihaika G Minja
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | - Emmanuel Mrimi
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | | | - Johnson K Swai
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | - Said Abbasi
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | - Halfan S Ngowo
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Doreen J Siria
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | - Salum Mapua
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
- School of Life Sciences, University of Keele, Keele, Staffordshire, ST5 5BG, UK
| | - Caleb Stica
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | - Marta F Maia
- KEMRI Wellcome Trust Research Programme, P.O. Box 230, Kilifi, 80108, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Old Road Campus Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Ally Olotu
- KEMRI Wellcome Trust Research Programme, P.O. Box 230, Kilifi, 80108, Kenya
- Interventions and Clinical Trials Department, Ifakara Health Institute, Bagamoyo, Tanzania
| | - Maggy T Sikulu-Lord
- School of Public Health, University of Queensland, Saint Lucia, Australia
- Department of Mathematics, Statistics and Computer Science, Marquette University, Wisconsin, USA
| | - Francesco Baldini
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Heather M Ferguson
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | | | - Simon A Babayan
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Fredros O Okumu
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK.
- School of Public Health, University of Witwatersrand, Johannesburg, South Africa.
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20
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González Jiménez M, Babayan SA, Khazaeli P, Doyle M, Walton F, Reedy E, Glew T, Viana M, Ranford-Cartwright L, Niang A, Siria DJ, Okumu FO, Diabaté A, Ferguson HM, Baldini F, Wynne K. Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning. Wellcome Open Res 2019; 4:76. [PMID: 31544155 PMCID: PMC6753605 DOI: 10.12688/wellcomeopenres.15201.3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/10/2019] [Indexed: 11/20/2022] Open
Abstract
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.
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Affiliation(s)
| | - Simon A. Babayan
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Pegah Khazaeli
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Margaret Doyle
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Finlay Walton
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Elliott Reedy
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Thomas Glew
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Mafalda Viana
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Lisa Ranford-Cartwright
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Abdoulaye Niang
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Doreen J. Siria
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Fredros O. Okumu
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Abdoulaye Diabaté
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Heather M. Ferguson
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
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Age grading An. gambiae and An. arabiensis using near infrared spectra and artificial neural networks. PLoS One 2019; 14:e0209451. [PMID: 31412028 PMCID: PMC6693756 DOI: 10.1371/journal.pone.0209451] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 07/29/2019] [Indexed: 01/28/2023] Open
Abstract
Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers. Conclusion We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier.
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González Jiménez M, Babayan SA, Khazaeli P, Doyle M, Walton F, Reedy E, Glew T, Viana M, Ranford-Cartwright L, Niang A, Siria DJ, Okumu FO, Diabaté A, Ferguson HM, Baldini F, Wynne K. Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning. Wellcome Open Res 2019; 4:76. [PMID: 31544155 PMCID: PMC6753605 DOI: 10.12688/wellcomeopenres.15201.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/31/2019] [Indexed: 01/14/2023] Open
Abstract
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.
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Affiliation(s)
| | - Simon A. Babayan
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Pegah Khazaeli
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Margaret Doyle
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Finlay Walton
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Elliott Reedy
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Thomas Glew
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Mafalda Viana
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Lisa Ranford-Cartwright
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Abdoulaye Niang
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Doreen J. Siria
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Fredros O. Okumu
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Abdoulaye Diabaté
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Heather M. Ferguson
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
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Mwanga EP, Mapua SA, Siria DJ, Ngowo HS, Nangacha F, Mgando J, Baldini F, González Jiménez M, Ferguson HM, Wynne K, Selvaraj P, Babayan SA, Okumu FO. Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis. Malar J 2019; 18:187. [PMID: 31146762 PMCID: PMC6543689 DOI: 10.1186/s12936-019-2822-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 05/25/2019] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or polymerase chain reactions (PCR). Here, mid-infrared (MIR) spectroscopy and supervised machine learning are used to accurately distinguish between vertebrate blood meals in guts of malaria mosquitoes, without any molecular techniques. METHODS Laboratory-reared Anopheles arabiensis females were fed on humans, chickens, goats or bovines, then held for 6 to 8 h, after which they were killed and preserved in silica. The sample size was 2000 mosquitoes (500 per host species). Five individuals of each host species were enrolled to ensure genotype variability, and 100 mosquitoes fed on each. Dried mosquito abdomens were individually scanned using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra (4000 cm-1 to 400 cm-1). The spectral data were cleaned to compensate atmospheric water and CO2 interference bands using Bruker-OPUS software, then transferred to Python™ for supervised machine-learning to predict host species. Seven classification algorithms were trained using 90% of the spectra through several combinations of 75-25% data splits. The best performing model was used to predict identities of the remaining 10% validation spectra, which had not been used for model training or testing. RESULTS The logistic regression (LR) model achieved the highest accuracy, correctly predicting true vertebrate blood meal sources with overall accuracy of 98.4%. The model correctly identified 96% goat blood meals, 97% of bovine blood meals, 100% of chicken blood meals and 100% of human blood meals. Three percent of bovine blood meals were misclassified as goat, and 2% of goat blood meals misclassified as human. CONCLUSION Mid-infrared spectroscopy coupled with supervised machine learning can accurately identify multiple vertebrate blood meals in malaria vectors, thus potentially enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities. The technique is cost-effective, fast, simple, and requires no reagents other than desiccants. However, scaling it up will require field validation of the findings and boosting relevant technical capacity in affected countries.
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Affiliation(s)
- Emmanuel P Mwanga
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania.
| | - Salum A Mapua
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
| | - Doreen J Siria
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
| | - Halfan S Ngowo
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francis Nangacha
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
| | - Joseph Mgando
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
| | - Francesco Baldini
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | | | - Heather M Ferguson
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | | | - Simon A Babayan
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Fredros O Okumu
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- School of Public Health, University of Witwatersrand, Johannesburg, South Africa
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24
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González Jiménez M, Babayan SA, Khazaeli P, Doyle M, Walton F, Reedy E, Glew T, Viana M, Ranford-Cartwright L, Niang A, Siria DJ, Okumu FO, Diabaté A, Ferguson HM, Baldini F, Wynne K. Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning. Wellcome Open Res 2019; 4:76. [PMID: 31544155 PMCID: PMC6753605 DOI: 10.12688/wellcomeopenres.15201.1] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/24/2019] [Indexed: 01/17/2023] Open
Abstract
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.
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Affiliation(s)
| | - Simon A. Babayan
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Pegah Khazaeli
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Margaret Doyle
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Finlay Walton
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Elliott Reedy
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Thomas Glew
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Mafalda Viana
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Lisa Ranford-Cartwright
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Abdoulaye Niang
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Doreen J. Siria
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Fredros O. Okumu
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Abdoulaye Diabaté
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Heather M. Ferguson
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
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Nguyen C, Gray M, Burton TA, Foy SL, Foster JR, Gendernalik AL, Rückert C, Alout H, Young MC, Boze B, Ebel GD, Clapsaddle B, Foy BD. Evaluation of a novel West Nile virus transmission control strategy that targets Culex tarsalis with endectocide-containing blood meals. PLoS Negl Trop Dis 2019; 13:e0007210. [PMID: 30845250 PMCID: PMC6424467 DOI: 10.1371/journal.pntd.0007210] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 03/19/2019] [Accepted: 02/04/2019] [Indexed: 11/27/2022] Open
Abstract
Control of arbovirus transmission remains focused on vector control through application of insecticides directly to the environment. However, these insecticide applications are often reactive interventions that can be poorly-targeted, inadequate for localized control during outbreaks, and opposed due to environmental and toxicity concerns. In this study, we developed endectocide-treated feed as a systemic endectocide for birds to target blood feeding Culex tarsalis, the primary West Nile virus (WNV) bridge vector in the western United States, and conducted preliminary tests on the effects of deploying this feed in the field. In lab tests, ivermectin (IVM) was the most effective endectocide tested against Cx. tarsalis and WNV-infection did not influence mosquito mortality from IVM. Chickens and wild Eurasian collared doves exhibited no signs of toxicity when fed solely on bird feed treated with concentrations up to 200 mg IVM/kg of diet, and significantly more Cx. tarsalis that blood fed on these birds died (greater than 80% mortality) compared to controls (less than 25% mortality). Mosquito mortality following blood feeding correlated with IVM serum concentrations at the time of blood feeding, which dropped rapidly after the withdrawal of treated feed. Preliminary field testing over one WNV season in Fort Collins, Colorado demonstrated that nearly all birds captured around treated bird feeders had detectable levels of IVM in their blood. However, entomological data showed that WNV transmission was non-significantly reduced around treated bird feeders. With further development, deployment of ivermectin-treated bird feed might be an effective, localized WNV transmission control tool. West Nile virus (WNV) is a mosquito-borne virus that causes significant disease and death every year in humans, domesticated animals, and wildlife. Control of WNV transmission is focused on controlling the mosquito vector through applications of insecticides directly to the environment. In this study, we evaluate a novel control strategy for WNV transmission by targeting the main mosquito bridge vector in the Great Plains region, Culex tarsalis, through its blood feeding behavior. Because Culex tarsalis favor taking blood meals from particular bird species, our strategy aims to target these bird species with endectocide-treated bird feed that will result in lethal blood meals for Cx. tarsalis. In this study, we developed a safe and effective formulation of ivermectin-treated diet that resulted in increased mortality for Cx. tarsalis blood fed on birds consuming this treated diet as compared to mosquitoes feeding on control birds. We also conducted a pilot field trial in Fort Collins, Colorado to test this strategy in a natural transmission cycle, which demonstrated promising results.
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Affiliation(s)
- Chilinh Nguyen
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, United States of America
- * E-mail:
| | - Meg Gray
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, United States of America
| | - Timothy A. Burton
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, United States of America
| | - Soleil L. Foy
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, United States of America
| | - John R. Foster
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, United States of America
| | - Alex Lazr Gendernalik
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, United States of America
| | - Claudia Rückert
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, United States of America
| | | | - Michael C. Young
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, United States of America
| | - Broox Boze
- Vector Disease Control International, Little Rock, AR, United States of America
| | - Gregory D. Ebel
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, United States of America
| | | | - Brian D. Foy
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, United States of America
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Charlwood JD, Tomás EVE, Andegiorgish AK, Mihreteab S, LeClair C. 'We like it wet': a comparison between dissection techniques for the assessment of parity in Anopheles arabiensis and determination of sac stage in mosquitoes alive or dead on collection. PeerJ 2018; 6:e5155. [PMID: 30018854 PMCID: PMC6044268 DOI: 10.7717/peerj.5155] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 06/11/2018] [Indexed: 11/28/2022] Open
Abstract
Background The determination of parous rates in mosquitoes, despite numerous shortcomings, remains a tool to evaluate the effectiveness of control programs and to determine vectorial capacity in malaria vectors. Two dissection techniques are used for this. For one, the tracheoles of dried ovaries are examined with a compound microscope and in the other the follicular stalk of ovaries is examined, wet, with a stereomicroscope. The second method also enables the sac stage of parous insects (which provides information on the duration of the oviposition cycle) and the mated status of insects to be determined. Despite widespread use the two techniques have not previously been compared. Methods We compared the two dissection techniques using Anopheles arabiensis, collected with a tent-trap in Eritrea. The paired ovaries were removed in water and one was examined by each method. From a separate set of dissections from Tanzania, we also determined if the sac stages of Anopheles gambiae s.l. (83% of 183 identified by PCR being Anopheles arabiensis the remainder being A. gambiae) that were alive on collection were different to those that died on collection and what the implications for vectorial capacity estimation might be. Results Seven per cent of the dry ovaries could not be classified due to granulation (yolk) in the ovariole that obscured the tracheoles. The sensitivity of the dry dissection was 88.51% (CI [79.88–94.35%]) and the specificity was 93.55% (CI [87.68–97.17%]) among the 211 ovaries that could be classified by the dry technique and compared to the ovaries dissected wet. 1,823 live and 1,416 dead from Furvela tent-traps, CDC light-trap and window-trap collections were dissected ‘wet’ from Tanzania. In these collections parous insects were more likely to die compared to nulliparous ones. The proportion of parous mosquitoes with ‘a’ sacs (indicative of recent oviposition) was significantly greater in insects that were dead (0.36) on collection in the morning compared to those that were alive (0.12) (Chi square 138.93, p < 0.001). There was a preponderance of newly emerged virgin insects in the outdoor collection (Chi sq = 8.84, p = 0.003). Conclusions In anophelines the examination of mosquito ovaries using transmitted light in a ‘wet’ dissection is a more useful and informative technique than examination of dry ovaries. In order to correctly estimate the duration of the oviposition cycle mosquitoes should be dissected as soon as possible after collection. Younger insects were more likely to attempt to feed outdoors rather than indoors.
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Affiliation(s)
- Jacques D Charlwood
- Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal
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27
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Milali MP, Sikulu-Lord MT, Kiware SS, Dowell FE, Povinelli RJ, Corliss GF. Do NIR spectra collected from laboratory-reared mosquitoes differ from those collected from wild mosquitoes? PLoS One 2018; 13:e0198245. [PMID: 29851994 PMCID: PMC5978888 DOI: 10.1371/journal.pone.0198245] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 05/16/2018] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Near infrared spectroscopy (NIRS) is a high throughput technique that measures absorbance of specific wavelengths of light by biological samples and uses this information to classify the age of lab-reared mosquitoes as younger or older than seven days with an average accuracy greater than 80%. For NIRS to estimate ages of wild mosquitoes, a sample of wild mosquitoes with known age in days would be required to train and test the model. Mark-release-recapture is the most reliable method to produce wild-caught mosquitoes of known age in days. However, it is logistically demanding, time inefficient, subject to low recapture rates, and raises ethical issues due to the release of mosquitoes. Using labels from Detinova dissection results in a mathematical model with poor accuracy. Alternatively, a model trained on spectra from laboratory-reared mosquitoes where age in days is known can be applied to estimate the age of wild mosquitoes, but this would be appropriate only if spectra collected from laboratory-reared and wild mosquitoes are similar. METHODS AND FINDINGS We performed k-means (k = 2) cluster analysis on a mixture of spectra collected from lab-reared and wild Anopheles arabiensis to determine if there is any significant difference between these two groups. While controlling the numbers of mosquitoes included in the model at each age, we found two clusters with no significant difference in distribution of spectra collected from lab-reared and wild mosquitoes (p = 0.25). We repeated the analysis using hierarchical clustering, and similarly, no significant difference was observed (p = 0.13). CONCLUSION We find no difference between spectra collected from laboratory-reared and wild mosquitoes of the same age and species. The results strengthen and support the on-going practice of applying the model trained on spectra collected from laboratory-reared mosquitoes, especially first-generation laboratory-reared mosquitoes.
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Affiliation(s)
- Masabho P. Milali
- Department of Mathematics, Statistics and Computer Science, Marquette University, Wisconsin, United States of America
- Ifakara Health Institute, Environmental Health and Ecological Sciences Thematic Group, Ifakara, Tanzania
| | - Maggy T. Sikulu-Lord
- Queensland Alliance of Agriculture and Food Innovation, The University of Queensland, Brisbane, Australia
| | - Samson S. Kiware
- Department of Mathematics, Statistics and Computer Science, Marquette University, Wisconsin, United States of America
- Ifakara Health Institute, Environmental Health and Ecological Sciences Thematic Group, Ifakara, Tanzania
| | - Floyd E. Dowell
- USDA, Agricultural Research Service, Center for Grain and Animal Health Research, Manhattan, KS, United States of America
| | - Richard J. Povinelli
- Department of Electrical and Computer Engineering, Marquette University, Wisconsin, United States of America
| | - George F. Corliss
- Department of Electrical and Computer Engineering, Marquette University, Wisconsin, United States of America
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