HT-SuperSAGE of the gut tissue of a Vip3Aa-resistant Heliothis virescens (Lepidoptera: Noctuidae) strain provides insights into the basis of resistance.

Multitoxin Bt-crops expressing insecticidal toxins with different modes of action, for example, Cry and Vip, are expected to improve resistance management in target pests. While Cry1A resistance has been relatively well characterized in some insect species, this is not the case for Vip3A, for which no mechanism of resistance has yet been identified. Here we applied HT-SuperSAGE to analyze the transcriptome of the gut tissue of tobacco budworm Heliothis virescens (F.) laboratory-selected for Vip3Aa resistance. From a total of 1 324 252 sequence reads, 5 895 126-bp tags were obtained representing 17 751 nonsingleton unique transcripts (UniTags) from genetically similar Vip3Aa-resistant (Vip-Sel) and susceptible control (Vip-Unsel) strains. Differential expression was significant (≥2.5 fold or ≤0.4; P < 0.05) for 1989 sequences (11.2% of total UniTags), where 420 represented overexpressed (OE) and 1569 underexpressed (UE) genes in Vip-Sel. BLASTN searches mapped 419 UniTags to H. virescens sequence contigs, of which, 416 (106 OE and 310 UE) were unambiguously annotated to proteins in NCBI nonredundant protein databases. Gene Ontology distributed 345 of annotated UniTags in 14 functional categories with metabolism (including serine-type hydrolases) and translation/ribosome biogenesis being the most prevalent. A UniTag homologous to a particular member of the REsponse to PAThogen (REPAT) family was found among most overexpressed, while UniTags related to the putative Vip3Aa-binding ribosomal protein S2 (RpS2) were underexpressed. qRT-PCR of a subset of UniTags validated the HT-SuperSAGE data. This study is the first providing lepidopteran gut transcriptome associated with Vip3Aa resistance and a foundation for future attempts to elucidate the resistance mechanism.

BisoGenet: a new tool for gene network building, visualization and analysis.

BACKGROUND: The increasing availability and diversity of omics data in the post-genomic era offers new perspectives in most areas of biomedical research. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing omics-data. The development of software tools capable of integrating data from different sources and to provide flexible methods to reconstruct, represent and analyze topological networks is an active field of research in bioinformatics. RESULTS: BisoGenet is a multi-tier application for visualization and analysis of biomolecular relationships. The system consists of three tiers. In the data tier, an in-house database stores genomics information, protein-protein interactions, protein-DNA interactions, gene ontology and metabolic pathways. In the middle tier, a global network is created at server startup, representing the whole data on bioentities and their relationships retrieved from the database. The client tier is a Cytoscape plugin, which manages user input, communication with the Web Service, visualization and analysis of the resulting network. CONCLUSION: BisoGenet is able to build and visualize biological networks in a fast and user-friendly manner. A feature of Bisogenet is the possibility to include coding relations to distinguish between genes and their products. This feature could be instrumental to achieve a finer grain representation of the bioentities and their relationships. The client application includes network analysis tools and interactive network expansion capabilities. In addition, an option is provided to allow other networks to be converted to BisoGenet. This feature facilitates the integration of our software with other tools available in the Cytoscape platform. BisoGenet is available at http://bio.cigb.edu.cu/bisogenet-cytoscape/.