Relatively little is known about the genomic basis and evolution of wood-feeding in beetles. We undertook genome sequencing and annotation, gene expression assays, studies of plant cell wall degrading enzymes, and other functional and comparative studies of the Asian longhorned beetle, Anoplophora glabripennis, a globally significant invasive species capable of inflicting severe feeding damage on many important tree species. Complementary studies of genes encoding enzymes involved in digestion of woody plant tissues or detoxification of plant allelochemicals were undertaken with the genomes of 14 additional insects, including the newly sequenced emerald ash borer and bull-headed dung beetle. The Asian longhorned beetle genome encodes a uniquely diverse arsenal of enzymes that can degrade the main polysaccharide networks in plant cell walls, detoxify plant allelochemicals, and otherwise facilitate feeding on woody plants. It has the metabolic plasticity needed to feed on diverse plant species, contributing to its highly invasive nature. Large expansions of chemosensory genes involved in the reception of pheromones and plant kairomones are consistent with the complexity of chemical cues it uses to find host plants and mates. Amplification and functional divergence of genes associated with specialized feeding on plants, including genes originally obtained via horizontal gene transfer from fungi and bacteria, contributed to the addition, expansion, and enhancement of the metabolic repertoire of the Asian longhorned beetle, certain other phytophagous beetles, and to a lesser degree, other phytophagous insects. Our results thus begin to establish a genomic basis for the evolutionary success of beetles on plants.

Translocator protein 18 kDa (TSPO) was previously known as the peripheral benzodiazepine receptor (PBR) in eukaryotes, where it is mainly localized to the mitochondrial outer membrane. Considerable evidence indicates that it plays regulatory roles in steroidogenesis and apoptosis and is involved in various human diseases, such as metastatic cancer, Alzheimer’s and Parkinson’s disease, inflammation, and anxiety disorders. Ligands of TSPO are widely used as diagnostic tools and treatment options, despite there being no clear understanding of the function of TSPO. An ortholog in the photosynthetic bacterium Rhodobacter was independently discovered as the tryptophan-rich sensory protein (TspO) and found to play a role in the response to changes in oxygen and light conditions that regulate photosynthesis and respiration. As part of this highly conserved protein family found in all three kingdoms, the rat TSPO is able to rescue the knockout phenotype in Rhodobacter, indicating functional as well as structural conservation. Recently, a major breakthrough in the field was achieved: the determination of atomic-resolution structures of TSPO from different species by several independent groups. This now allows us to reexamine the function of TSPO with a molecular perspective. In this review, we focus on recently determined structures of TSPO and their implications for potential functions of this ubiquitous multifaceted protein. We suggest that TSPO is an ancient bacterial receptor/stress sensor that has developed additional interactions, partners, and roles in its mitochondrial outer membrane environment in eukaryotes.

Hinge motions are important for molecular recognition, and knowledge of their location can guide the sampling of protein conformations for docking. Predicting domains and intervening hinges is also important for identifying structurally self‐determinate units and anticipating the influence of mutations on protein flexibility and stability. Here we present StoneHinge, a novel approach for predicting hinges between domains using input from two complementary analyses of noncovalent bond networks: StoneHingeP, which identifies domain‐hinge‐domain signatures in ProFlex constraint counting results, and StoneHingeD, which does the same for DomDecomp Gaussian network analyses. Predictions for the two methods are compared to hinges defined in the literature and by visual inspection of interpolated motions between conformations in a series of proteins. For StoneHingeP, all the predicted hinges agree with hinge sites reported in the literature or observed visually, although some predictions include extra residues. Furthermore, no hinges are predicted in six hinge‐free proteins. On the other hand, StoneHingeD tends to overpredict the number of hinges, while accurately pinpointing hinge locations. By determining the consensus of their results, StoneHinge improves the specificity, predicting 11 of 13 hinges found both visually and in the literature for nine different open protein structures, and making no false‐positive predictions. By comparison, a popular hinge detection method that requires knowledge of both the open and closed conformations finds 10 of the 13 known hinges, while predicting four additional, false hinges.