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1. Cas9-Mediated Gene-Editing in the Malaria Mosquito Anopheles stephensi by ReMOT Control

   https://doi.org/10.1534/g3.120.401133  

   Macias, Vanessa M.; McKeand, Sage; Chaverra-Rodriguez, Duverney; Hughes, Grant L.; Fazekas, Aniko; Pujhari, Sujit; Jasinskiene, Nijole; James, Anthony A.; Rasgon, Jason L. (April 2020, G3: Genes|Genomes|Genetics)

   Innovative tools are essential for advancing malaria control and depend on an understanding of molecular mechanisms governing transmission of malaria parasites by Anopheles mosquitoes. CRISPR/Cas9-based gene disruption is a powerful method to uncover underlying biology of vector-pathogen interactions and can itself form the basis of mosquito control strategies. However, embryo injection methods used to genetically manipulate mosquitoes (especially Anopheles ) are difficult and inefficient, particularly for non-specialist laboratories. Here, we adapted the ReMOT Control ( Re ceptor- m ediated O vary T ransduction of C argo) technique to deliver Cas9 ribonucleoprotein complex to adult mosquito ovaries, generating targeted and heritable mutations in the malaria vector Anopheles stephensi without injecting embryos. In Anopheles , ReMOT Control gene editing was as efficient as standard embryo injections. The application of ReMOT Control to Anopheles opens the power of CRISPR/Cas9 methods to malaria laboratories that lack the equipment or expertise to perform embryo injections and establishes the flexibility of ReMOT Control for diverse mosquito species. 

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2. Citizen Science as an Approach for Responding to the Threat of Anopheles stephensi in Africa

   https://doi.org/10.5334/cstp.616  

   Carney, Ryan M.; Long, Alex; Low, Russanne D.; Zohdy, Sarah; Palmer, John R.; Elias, Peter; Bartumeus, Frederic; Njoroge, Laban; Muniafu, Maina; Uelmen, Johnny A.; et al (October 2023, Citizen Science: Theory and Practice)

   Even as novel technologies emerge and medicines advance, pathogen-transmitting mosquitoes pose a deadly and accelerating public health threat. Detecting and mitigating the spread of Anopheles stephensi in Africa is now critical to the fight against malaria, as this invasive mosquito poses urgent and unprecedented risks to the continent. Unlike typical African vectors of malaria, An. stephensi breeds in both natural and artificial water reservoirs, and flourishes in urban environments. With An. stephensi beginning to take hold in heavily populated settings, citizen science surveillance supported by novel artificial intelligence (AI) technologies may offer impactful opportunities to guide public health decisions and community-based interventions. Coalitions like the Global Mosquito Alert Consortium (GMAC) and our freely available digital products can be incorporated into enhanced surveillance of An. stephensi and other vector-borne public health threats. By connecting local citizen science networks with global databases that are findable, accessible, interoperable, and reusable (FAIR), we are leveraging a powerful suite of tools and infrastructure for the early detection of, and rapid response to, (re)emerging vectors and diseases. 

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3. Classifying stages in the gonotrophic cycle of mosquitoes from images using computer vision techniques

   https://doi.org/10.1038/s41598-023-47266-7  

   Azam, Farhat Binte; Carney, Ryan M; Kariev, Sherzod; Nallan, Krishnamoorthy; Subramanian, Muthukumaravel; Sampath, Gopalakrishnan; Kumar, Ashwani; Chellappan, Sriram (December 2023, Scientific Reports)

   Abstract The ability to distinguish between the abdominal conditions of adult female mosquitoes has important utility for the surveillance and control of mosquito-borne diseases. However, doing so requires entomological training and time-consuming manual effort. Here, we design computer vision techniques to determine stages in the gonotrophic cycle of female mosquitoes from images. Our dataset was collected from 139 adult female mosquitoes across three medically important species—Aedes aegypti,Anopheles stephensi, andCulex quinquefasciatus—and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid, and gravid). From these mosquitoes and stages, a total of 1959 images were captured on a plain background via multiple smartphones. Subsequently, we trained four distinct AI model architectures (ResNet50,MobileNetV2,EfficientNet-B0, andConvNeXtTiny), validated them using unseen data, and compared their overall classification accuracies. Additionally, we analyzed t-SNE plots to visualize the formation of decision boundaries in a lower-dimensional space. Notably,ResNet50andEfficientNet-B0demonstrated outstanding performance with an overall accuracy of 97.44% and 93.59%, respectively.EfficientNet-B0demonstrated the best overall performance considering computational efficiency, model size, training speed, and t-SNE decision boundaries. We also assessed the explainability of thisEfficientNet-B0model, by implementing Grad-CAMs—a technique that highlights pixels in an image that were prioritized for classification. We observed that the highest weight was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. Our work has significant practical impact. First, image datasets for gonotrophic stages of mosquitoes are not yet available. Second, our algorithms can be integrated with existing citizen science platforms that enable the public to record and upload biological observations. With such integration, our algorithms will enable the public to contribute to mosquito surveillance and gonotrophic stage identification. Finally, we are aware of work today that uses computer vision techniques for automated mosquito species identification, and our algorithms in this paper can augment these efforts by enabling the automated detection of gonotrophic stages of mosquitoes as well. 

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4. The influence of oviposition status on measures of transmission potential in malaria-infected mosquitoes depends on sugar availability

   https://doi.org/10.1186/s13071-024-06317-2  

   Shiau, Justine C; Garcia-Diaz, Nathan; Kyle, Dennis E; Pathak, Ashutosh K (December 2024, Parasites & Vectors)

   Abstract BackgroundLike other oviparous organisms, the gonotrophic cycle of mosquitoes is not complete until they have selected a suitable habitat to oviposit. In addition to the evolutionary constraints associated with selective oviposition behavior, the physiological demands relative to an organism’s oviposition status also influence their nutrient requirement from the environment. Yet, studies that measure transmission potential (vectorial capacity or competence) of mosquito-borne parasites rarely consider whether the rates of parasite replication and development could be influenced by these constraints resulting from whether mosquitoes have completed their gonotrophic cycle. MethodsAnopheles stephensimosquitoes were infected withPlasmodium berghei, the rodent analog of human malaria, and maintained on 1% or 10% dextrose and either provided oviposition sites (‘oviposited’ herein) to complete their gonotrophic cycle or forced to retain eggs (‘non-oviposited’). Transmission potential in the four groups was measured up to 27 days post-infection as the rates of (i) sporozoite appearance in the salivary glands (‘extrinsic incubation period' or EIP), (ii) vector survival and (iii) sporozoite densities. ResultsIn the two groups of oviposited mosquitoes, rates of sporozoite appearance and densities in the salivary glands were clearly dependent on sugar availability, with shorter EIP and higher sporozoite densities in mosquitoes fed 10% dextrose. In contrast, rates of appearance and densities in the salivary glands were independent of sugar concentrations in non-oviposited mosquitoes, although both measures were slightly lower than in oviposited mosquitoes fed 10% dextrose. Vector survival was higher in non-oviposited mosquitoes. ConclusionsCosts to parasite fitness and vector survival were buffered against changes in nutritional availability from the environment in non-oviposited but not oviposited mosquitoes. Taken together, these results suggest vectorial capacity for malaria parasites may be dependent on nutrient availability and oviposition/gonotrophic status and, as such, argue for more careful consideration of this interaction when estimating transmission potential. More broadly, the complex patterns resulting from physiological (nutrition) and evolutionary (egg-retention) trade-offs described here, combined with the ubiquity of selective oviposition behavior, implies the fitness of vector-borne pathogens could be shaped by selection for these traits, with implications for disease transmission and management. For instance, while reducing availability of oviposition sites and environmental sources of nutrition are key components of integrated vector management strategies, their abundance and distribution are under strong selection pressure from the patterns associated with climate change. Graphical Abstract 

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5. Temperature dependence of mosquitoes: Comparing mechanistic and machine learning approaches

   https://doi.org/10.1371/journal.pntd.0012488  

   Athni, Tejas S; Childs, Marissa L; Glidden, Caroline K; Mordecai, Erin A (September 2024, PLOS Neglected Tropical Diseases)

   Mireji, Paul O (Ed.)

   Mosquito vectors of pathogens (e.g.,Aedes,Anopheles, andCulexspp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change. 

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