3 research outputs found
Adhesion and virulence properties of native Metarhizium fungal strains from Burkina Faso for the control of malaria vectors
Get PDFBackground: Local strains of the entomopathogenic fungus Metarhizium pingshaense in Burkina Faso have demonstrated remarkable virulence against malaria vectors, positioning them as promising candidates for inclusion in the future arsenal of malaria control strategies. However, the underlying mechanisms responsible for this virulence remain
unknown. To comprehend the fungal infection process, it is crucial to investigate the attachment mechanisms of fungal spores to the mosquito cuticle and explore the relationship between virulence and attachment kinetics. This study aims to assess the adhesion and virulence properties of native Metarhizium fungal strains from Burkina Faso for controlling malaria vectors.
Methods: Fungal strains were isolated from 201 insects and 1399 rhizosphere samples, and four strains of Metarhizium fungi were selected. Fungal suspensions were used to infect 3-day-old female Anopheles coluzzii mosquitoes at three different concentrations (106, 107, 108 conidia/ml). The survival of the mosquitoes was measured over 14 days, and fungal growth was quantified after 1 and 24 h to assess adhesion of the fungal strains onto the mosquito cuticle.
Results: All four fungi strains increased mosquito mortality compared to control (P<2.2–16). Adhesion of the fungal strains was observed on the mosquito cuticle after 24 h at high concentrations (1× 108 conidia/ml), with one strain, having the highest virulent, showing adhesion after just 1 h.
Conclusion: The native strains of Metarhizium spp. fungi found in Burkina Faso have the potential to be effective biocontrol agents against malaria vectors, with some strains showing high levels of both virulence and adhesion to the mosquito cuticle
Entomopathogenic fungi Metarhizium pingshaense increases susceptibility to insecticides in highly resistant malaria mosquitoes Anopheles coluzzii
Get PDFBackground: Metarhizium spp. based mosquito control products are among the most investigated and could potentially serve as promising complements to chemical insecticides. However, limited knowledge exists on the implementation of this biocontrol tool in conjunction with primary insecticide-based strategies to achieve synergy.
Methods: In laboratory bioassays, we combined 107 conidia/ml natives Metarhizium pingshaense strains with deltamethrin standard dose in three ways, before, after or simultaneously. These combinations were tested on laboratory insecticide resistant Anopheles coluzzii.
Results: Therefore, we found that Metarhizium pingshaense and deltamethrin could be combined to achieve greater mortality against a highly insecticide resistance colony of Anopheles coluzzii. When mosquitoes were exposed to both simultaneously, no effect was observed, as expected for an insecticide resistant colony. However, when fungi were applied earlier than deltamethrin, mosquitoes became more sensitive to insecticide with a minimum Lethal Time to kill at least 50% of mosquito population (LT50) less than 8 days. In addition, when deltamethrin exposure was followed by Metarhizium infection, mosquito survival was similar to Metarhizium alone LT50 (LT50 ~11 days).
Conclusions: These findings suggest that early mosquito infection to Metarhizium pingshaense followed by chemical insecticide exposure synergically improve mosquito control in the laboratory
Rapid age-grading and species identification of natural mosquitoes for malaria surveillance
Get PDFThe 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
