Arboviruses and the challenge to establish systemic and persistent infections in competent mosquito vectors : the interaction with the RNAi mechanism

Arboviruses are capable to establish long-term persistent infections in mosquitoes that do not affect significantly the physiology of the insect vectors. Arbovirus infections are controlled by the RNAi machinery via the production of viral siRNAs and the formation of RISC complexes targeting viral genomes and mRNAs. Engineered arboviruses that contain cellular gene sequences can therefore be transformed to "viral silencing vectors" for studies of gene function in reverse genetics approaches. More specifically, "ideal" viral silencing vectors must be competent to induce robust RNAi effects while other interactions with the host immune system should be kept at a minimum to reduce non-specific effects. Because of their inconspicuous nature, arboviruses may approach the "ideal" viral silencing vectors in insects and it is therefore worthwhile to study the mechanisms by which the interactions with the RNAi machinery occur. In this review, an analysis is presented of the antiviral RNAi response in mosquito vectors with respect to the major types of arboviruses (alphaviruses, flaviviruses, bunyaviruses, and others). With respect to antiviral defense, the exo-RNAi pathway constitutes the major mechanism while the contribution of both miRNAs and viral piRNAs remains a contentious issue. However, additional mechanisms exist in mosquitoes that are capable to enhance or restrict the efficiency of viral silencing vectors such as the amplification of RNAi effects by DNA forms, the existence of incorporated viral elements in the genome and the induction of a non-specific systemic response by Dicer-2. Of significance is the observation that no major "viral suppressors of RNAi" (VSRs) seem to be encoded by arboviral genomes, indicating that relatively tight control of the activity of the RNA-dependent RNA polymerase (RdRp) may be sufficient to maintain the persistent character of arbovirus infections. Major strategies for improvement of viral silencing vectors therefore are proposed to involve engineering of VSRs and modifying of the properties of the RdRp. Because of safety issues (pathogen status), however, arbovirus-based silencing vectors are not well suited for practical applications, such as RNAi-based mosquito control. In that case, related mosquito-specific viruses that also establish persistent infections and may cause similar RNAi responses may represent a valuable alternative solution

Defense mechanisms against viral infection in Drosophila : RNAi and non-RNAi

RNAi is considered a major antiviral defense mechanism in insects, but its relative importance as compared to other antiviral pathways has not been evaluated comprehensively. Here, it is attempted to give an overview of the antiviral defense mechanisms in Drosophila that involve both RNAi and non-RNAi. While RNAi is considered important in most viral infections, many other pathways can exist that confer antiviral resistance. It is noted that very few direct recognition mechanisms of virus infections have been identified in Drosophila and that the activation of immune pathways may be accomplished indirectly through cell damage incurred by viral replication. In several cases, protection against viral infection can be obtained in RNAi mutants by non-RNAi mechanisms, confirming the variability of the RNAi defense mechanism according to the type of infection and the physiological status of the host. This analysis is aimed at more systematically investigating the relative contribution of RNAi in the antiviral response and more specifically, to ask whether RNAi efficiency is affected when other defense mechanisms predominate. While Drosophila can function as a useful model, this issue may be more critical for economically important insects that are either controlled (agricultural pests and vectors of diseases) or protected from parasite infection (beneficial insects as bees) by RNAi products

Sparse Bayesian Learning Approach for Discrete Signal Reconstruction

This study addresses the problem of discrete signal reconstruction from the perspective of sparse Bayesian learning (SBL). Generally, it is intractable to perform the Bayesian inference with the ideal discretization prior under the SBL framework. To overcome this challenge, we introduce a novel discretization enforcing prior to exploit the knowledge of the discrete nature of the signal-of-interest. By integrating the discretization enforcing prior into the SBL framework and applying the variational Bayesian inference (VBI) methodology, we devise an alternating update algorithm to jointly characterize the finite alphabet feature and reconstruct the unknown signal. When the measurement matrix is i.i.d. Gaussian per component, we further embed the generalized approximate message passing (GAMP) into the VBI-based method, so as to directly adopt the ideal prior and significantly reduce the computational burden. Simulation results demonstrate substantial performance improvement of the two proposed methods over existing schemes. Moreover, the GAMP-based variant outperforms the VBI-based method with an i.i.d. Gaussian measurement matrix but it fails to work for non i.i.d. Gaussian matrices.Comment: 13 pages, 7 figure