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Zika virus (ZIKV) is an arthropod-borne flavivirus predominantly transmitted by Aedes aegypti mosquitoes and poses a global human health threat. All flaviviruses, including those that exclusively replicate in mosquitoes, produce a highly abundant, noncoding subgenomic flavivirus RNA (sfRNA) in infected cells, which implies an important function of sfRNA during mosquito infection. Currently, the role of sfRNA in flavivirus transmission by mosquitoes is not well understood. Here, we demonstrate that an sfRNA-deficient ZIKV (ZIKVΔSF1) replicates similar to wild-type ZIKV in mosquito cell culture but is severely attenuated in transmission by Ae. aegypti after an infectious blood meal, with 5% saliva-positive mosquitoes for ZIKVΔSF1 vs. 31% for ZIKV. Furthermore, viral titers in the mosquito saliva were lower for ZIKVΔSF1 as compared to ZIKV. Comparison of mosquito infection via infectious blood meals and intrathoracic injections showed that sfRNA is important for ZIKV to overcome the mosquito midgut barrier and to promote virus accumulation in the saliva. Next-generation sequencing of infected mosquitoes showed that viral small-interfering RNAs were elevated upon ZIKVΔSF1 as compared to ZIKV infection. RNA-affinity purification followed by mass spectrometry analysis uncovered that sfRNA specifically interacts with a specific set of Ae. aegypti proteins that are normally associated with RNA turnover and protein translation. The DEAD/H-box helicase ME31B showed the highest affinity for sfRNA and displayed antiviral activity against ZIKV in Ae. aegypti cells. Based on these results, we present a mechanistic model in which sfRNA sequesters ME31B to promote flavivirus replication and virion production to facilitate transmission by mosquitoes.

The last decade has witnessed a remarkable increase in our ability to measure genetic information. Advancements of sequencing technologies are challenging the existing methods of data storage and analysis. While methods to cope with the data deluge are progressing, many biologists have lagged behind due to the fast pace of computational advancements and tools available to address their scientific questions. Future generations of biologists must be more computationally aware and capable. This means they should be trained to give them the computational skills to keep pace with technological developments. Here, we propose a model that bridges experimental and bioinformatics concepts using the Oxford Nanopore Technologies (ONT) sequencing platform. We provide both a guide to begin to empower the new generation of educators, scientists, and students in performing long-read assembly of bacterial and bacteriophage genomes and a standalone virtual machine containing all the required software and learning materials for the course., Author summary Genomes contain all the information required for an organism to function. Understanding the genome sequence is often the key to answer important biological questions. For example, the sequences of human genomes are used for diagnosis of genetic disorders or for the development of personalized treatments, while the sequences of microbes may inform about their mechanisms of infection and guide the development of novel drugs. Today, our capacity to generate genome sequencing data is tremendous. However, our capacity to process this information is insufficient. This is partially due to limitations of current methods for data analysis but is mostly caused by lack of training for most biologists to leverage high-throughput sequencing data and use their full potential. It is urgent that we train the new generations of biologists to become computationally aware and able to keep pace with technological developments in the field. In this manuscript, we illustrate our efforts in adopting an integrated teaching model that bridges experimental and bioinformatics works. Our course integrates data generation in the lab with bioinformatics work to illustrate the interlinking of lab practices and downstream effects. In our demonstration course, we used nanopore sequencing to train nanobiology students, but the model is easily customizable to suit students of different educational backgrounds or alternative technologies. The tools we provide help not only science educators but also biologists to address many relevant questions in biology.