Unexpected mode of engagement between enterovirus 71 and its receptor SCARB2

Enterovirus 71 (EV71) is a common cause of hand, foot and mouth disease—a disease endemic especially in the Asia-Pacific region1. Scavenger receptor class B member 2 (SCARB2) is the major receptor of EV71, as well as several other enteroviruses responsible for hand, foot and mouth disease, and plays a key role in cell entry2. The isolated structures of EV71 and SCARB2 are known3,4,5,6, but how they interact to initiate infection is not. Here, we report the EV71–SCARB2 complex structure determined at 3.4 Å resolution using cryo-electron microscopy. This reveals that SCARB2 binds EV71 on the southern rim of the canyon, rather than across the canyon, as predicted3,7,8. Helices 152–163 (α5) and 183–193 (α7) of SCARB2 and the viral protein 1 (VP1) GH and VP2 EF loops of EV71 dominate the interaction, suggesting an allosteric mechanism by which receptor binding might facilitate the low-pH uncoating of the virus in the endosome/lysosome. Remarkably, many residues within the binding footprint are not conserved across SCARB2-dependent enteroviruses; however, a conserved proline and glycine seem to be key residues. Thus, although the virus maintains antigenic variability even within the receptor-binding footprint, the identification of binding ‘hot spots’ may facilitate the design of receptor mimic therapeutics less likely to quickly generate resistance

Habitat manipulation to mitigate the impacts of invasive arthropod pests

Exotic invaders are some of the most serious insect pests of agricultural crops around the globe. Increasingly, the structure of landscape and habitat is recognized as having a major influence on both insect pests and their natural enemies. Habitat manipulation that aims at conserving natural enemies can potentially contribute to safer and more effective control of invasive pests. In this paper, we review habitat management experiments, published during the last 10 years, which have aimed to improve biological control of invasive pests. We then discuss during what conditions habitat management to conserve natural enemies is likely to be effective and how the likelihood of success of such methods can be improved. We finally suggest an ecologically driven research agenda for habitat management programmes.We acknowledge the following funding sources: the Tertiary Education Commission, New Zealand, through the Bio-Protection Research Centre, Lincoln University, New Zealand (Mattias Jonsson and Steve Wratten), the New Zealand Foundation for Research, Science and Technology (FRST); project LINX0303 (Steve Wratten, Ross Cullen, Jean Tompkins), Lincoln University, New Zealand, for a Post-graduate Scholarship to Jean Tompkins, USDA CSREES Risk Avoidance and Mitigation Program (2004-51101-02210), USDA NC SARE Project (LCN 04-249), USDA CSREES Arthropod and Nematode Biology (2004-35302-14811), North Central Regional IPM, NSF-LTER at Kellogg Biological Station (NSF DEB 0423627), and the Michigan Agricultural Experiment Station (Doug Landis)

Collection, pre-processing and on-the-fly analysis of data for high-resolution, single-particle cryo-electron microscopy

The dramatic growth in the use of cryo-electron microscopy (cryo-EM) to generate high-resolution structures of macromolecular complexes has changed the landscape of structural biology. The majority of structures deposited in the Electron Microscopy Data Bank (EMDB) at higher than 4-Å resolution were collected on Titan Krios microscopes. Although the pipeline for single-particle data collection is becoming routine, there is much variation in how sessions are set up. Furthermore, when collection is under way, there are a range of approaches for efficiently moving and pre-processing these data. Here, we present a standard operating procedure for single-particle data collection with Thermo Fisher Scientific EPU software, using the two most common direct electron detectors (the Thermo Fisher Scientific Falcon 3 (F3EC) and the Gatan K2), as well as a strategy for structuring these data to enable efficient pre-processing and on-the-fly monitoring of data collection. This protocol takes 3–6 h to set up a typical automated data collection session