Complementary transcriptomic, lipidomic, and targeted functional genetic analyses in cultured Drosophila cells highlight the role of glycerophospholipid metabolism in Flock House virus RNA replication

Abstract Background Cellular membranes are crucial host components utilized by positive-strand RNA viruses for replication of their genomes. Published studies have suggested that the synthesis and distribution of membrane lipids are particularly important for the assembly and function of positive-strand RNA virus replication complexes. However, the impact of specific lipid metabolism pathways in this process have not been well defined, nor have potential changes in lipid expression associated with positive-strand RNA virus replication been examined in detail. Results In this study we used parallel and complementary global and targeted approaches to examine the impact of lipid metabolism on the replication of the well-studied model alphanodavirus Flock House virus (FHV). We found that FHV RNA replication in cultured Drosophila S2 cells stimulated the transcriptional upregulation of several lipid metabolism genes, and was also associated with increased phosphatidylcholine accumulation with preferential increases in lipid molecules with longer and unsaturated acyl chains. Furthermore, targeted RNA interference-mediated downregulation of candidate glycerophospholipid metabolism genes revealed a functional role of several genes in virus replication. In particular, we found that downregulation of Cct1 or Cct2, which encode essential enzymes for phosphatidylcholine biosynthesis, suppressed FHV RNA replication. Conclusion These results indicate that glycerophospholipid metabolism, and in particular phosphatidylcholine biosynthesis, plays an important role in FHV RNA replication. Furthermore, they provide a framework in which to further explore the impact of specific steps in lipid metabolism on FHV replication, and potentially identify novel cellular targets for the development of drugs to inhibit positive-strand RNA viruses.http://deepblue.lib.umich.edu/bitstream/2027.42/78268/1/1471-2164-11-183.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78268/2/1471-2164-11-183-S3.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78268/3/1471-2164-11-183-S2.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78268/4/1471-2164-11-183.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78268/5/1471-2164-11-183-S4.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78268/6/1471-2164-11-183-S1.XLSPeer Reviewe

Simulation studies of a phenomenological model for elongated virus capsid formation

We study a phenomenological model in which the simulated packing of hard, attractive spheres on a prolate spheroid surface with convexity constraints produces structures identical to those of prolate virus capsid structures. Our simulation approach combines the traditional Monte Carlo method with a modified method of random sampling on an ellipsoidal surface and a convex hull searching algorithm. Using this approach we identify the minimum physical requirements for non-icosahedral, elongated virus capsids, such as two aberrant flock house virus (FHV) particles and the prolate prohead of bacteriophage $\phi_{29}$, and discuss the implication of our simulation results in the context of recent experimental findings. Our predicted structures may also be experimentally realized by evaporation-driven assembly of colloidal spheres

Wall following to escape local minima for swarms of agents using internal states and emergent behaviour

Natural examples of emergent behaviour, in groups due to interactions among the group's individuals, are numerous. Our aim, in this paper, is to use complex emergent behaviour among agents that interact via pair-wise attractive and repulsive potentials, to solve the local minima problem in the artificial potential based navigation method. We present a modified potential field based path planning algorithm, which uses agent internal states and swarm emergent behaviour to enhance group performance. The algorithm is used successfully to solve a reactive path-planning problem that cannot be solved using conventional static potential fields due to local minima formation. Simulation results demonstrate the ability of a swarm of agents to perform problem solving using the dynamic internal states of the agents along with emergent behaviour of the entire group