BackgroundThe oriental fruit fly, Bactrocera dorsalis (Hendel), is one of the most economically important pests in the world, causing serious damage to fruit production. However, lack of genetic information on this organism is an obstacle to understanding the mechanisms behind its development and its ability to resist insecticides. Analysis of the B. dorsalis transcriptome and its expression profile data is essential to extending the genetic information resources on this species, providing a shortcut that will support studies on B. dorsalis.Methodology/Principal FindingsWe performed de novo assembly of a transcriptome using short read sequencing technology (Illumina). The results generated 484,628 contigs, 70,640 scaffolds, and 49,804 unigenes. Of those unigenes, 27,455 (55.13%) matched known proteins in the NCBI database, as determined by BLAST search. Clusters of orthologous groups (COG), gene orthology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations were performed to better understand the functions of these unigenes. Genes related to insecticide resistance were analyzed in additional detail. Digital gene expression (DGE) libraries showed differences in gene expression profiles at different developmental stages (eggs, third-instar larvae, pupae, and adults). To confirm the DGE results, the expression profiles of six randomly selected genes were analyzed.Conclusion/SignificanceThis transcriptome greatly improves our genetic understanding of B. dorsalis and makes a huge number of gene sequences available for further study, including both genes of known importance and genes of unknown function. The DGE data provide comprehensive insight into gene expression profiles at different developmental stages. This facilitates the study of the role of each gene in the developmental process and in insecticide resistance.

BackgroundThe rapid accumulation of whole-genome data has renewed interest in the study of using gene-order data for phylogenetic analyses and ancestral reconstruction. Current software and web servers typically do not support duplication and loss events along with rearrangements.ResultsMLGO (Maximum Likelihood for Gene-Order Analysis) is a web tool for the reconstruction of phylogeny and/or ancestral genomes from gene-order data. MLGO is based on likelihood computation and shows advantages over existing methods in terms of accuracy, scalability and flexibility.ConclusionsTo the best of our knowledge, it is the first web tool for analysis of large-scale genomic changes including not only rearrangements but also gene insertions, deletions and duplications. The web tool is available from http://www.geneorder.org/server.php.

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Though essential oils exhibit antibacterial activity against food pathogens, their underlying mechanism is understudied. We extracted ginger essential oil (GEO) using supercritical CO2 and steam distillation. A chemical composition comparison by GC-MS showed that the main components of the extracted GEOs were zingiberene and α-curcumene. Their antibacterial activity and associated mechanism against Staphylococcus aureus and Escherichia coli were investigated. The diameter of inhibition zone (DIZ) of GEO against S. aureus was 17.1 mm, with a minimum inhibition concentration (MIC) of 1.0 mg/mL, and minimum bactericide concentration (MBC) of 2.0 mg/mL. For E. coli, the DIZ was 12.3 mm with MIC and MBC values of 2.0 mg/mL and 4.0 mg/mL, respectively. The SDS-PAGE analysis revealed that some of the electrophoretic bacterial cell proteins bands disappeared with the increase in GEO concentration. Consequently, the nucleic acids content of bacterial suspension was raised significantly and the metabolic activity of bacteria was markedly decreased. GEO could thus inhibit the expression of some genes linked to bacterial energy metabolism, tricarboxylic acid cycle, cell membrane-related proteins, and DNA metabolism. Our findings speculate the bactericidal effects of GEO primarily through disruption of the bacterial cell membrane indicating its suitability in food perseveration.

The increases in GST gene expression levels after malathion and β-cypermethrin exposure in B. dorsalis might increase the ability of this species to detoxify other insecticides and xenobiotics.

The rapid accumulation of whole-genome data has renewed interest in the study of the evolution of genomic architecture, under such events as rearrangements, duplications, losses. Comparative genomics, evolutionary biology, and cancer research all require tools to elucidate the mechanisms, history, and consequences of those evolutionary events, while phylogenetics could use whole-genome data to enhance its picture of the Tree of Life. Current approaches in the area of phylogenetic analysis are limited to very small collections of closely related genomes using low-resolution data (typically a few hundred syntenic blocks); moreover, these approaches typically do not include duplication and loss events. We describe a maximum likelihood (ML) approach for phylogenetic analysis that takes into account genome rearrangements as well as duplications, insertions, and losses. Our approach can handle high-resolution genomes (with 40,000 or more markers) and can use in the same analysis genomes with very different numbers of markers. Because our approach uses a standard ML reconstruction program (RAxML), it scales up to large trees. We present the results of extensive testing on both simulated and real data showing that our approach returns very accurate results very quickly. In particular, we analyze a dataset of 68 high-resolution eukaryotic genomes, with from 3,000 to 42,000 genes, from the eGOB database; the analysis, including bootstrapping, takes just 3 hours on a desktop system and returns a tree in agreement with all well supported branches, while also suggesting resolutions for some disputed placements.

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