Exploring the potential of T7 bacteriophage protein Gp2 as a novel inhibitor of mycobacterial RNA polymerase.

Over the past six decades, there has been a decline in novel therapies to treat tuberculosis, while the causative agent of this disease has become increasingly resistant to current treatment regimens. Bacteriophages (phages) are able to kill bacterial cells and understanding this process could lead to novel insights for the treatment of mycobacterial infections. Phages inhibit bacterial gene transcription through phage-encoded proteins which bind to RNA polymerase (RNAP), thereby preventing bacterial transcription. Gp2, a T7 phage protein which binds to the beta prime (ß') subunit of RNAP in Escherichia coli, has been well characterized in this regard. Here, we aimed to determine whether Gp2 is able to inhibit RNAP in Mycobacterium tuberculosis as this may provide new possibilities for inhibiting the growth of this deadly pathogen. Results from an electrophoretic mobility shift assay and in vitro transcription assay revealed that Gp2 binds to mycobacterial RNAP and inhibits transcription; however to a much lesser degree than in E. coli. To further understand the molecular basis of these results, a series of in silico techniques were used to assess the interaction between mycobacterial RNAP and Gp2, providing valuable insight into the characteristics of this protein-protein interaction.

The transcriptional landscape of the mammalian genome.

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

STACK: Sequence Tag Alignment and Consensus Knowledgebase.

STACK is a tool for detection and visualisation of expressed transcript variation in the context of developmental and pathological states. The datasystem organizes and reconstructs human transcripts from available public data in the context of expression state. The expression state of a transcript can include developmental state, pathological association, site of expression and isoform of expressed transcript. STACK consensus transcripts are reconstructed from clusters that capture and reflect the growing evidence of transcript diversity. The comprehensive capture of transcript variants is achieved by the use of a novel clustering approach that is tolerant of sub-sequence diversity and does not rely on pairwise alignment. This is in contrast with other gene indexing projects. STACK is generated at least four times a year and represents the exhaustive processing of all publicly available human EST data extracted from GenBank. This processed information can be explored through 15 tissue-specific categories, a disease-related category and a whole-body index and is accessible via WWW at http://www.sanbi.ac.za/Dbases.html. STACK represents a broadly applicable resource, as it is the only reconstructed transcript database for which the tools for its generation are also broadly available (http://www.sanbi.ac.za/CODES).

A comprehensive approach to clustering of expressed human gene sequence: the sequence tag alignment and consensus knowledge base.

The expressed human genome is being sequenced and analyzed by disparate groups producing disparate data. The majority of the identified coding portion is in the form of expressed sequence tags (ESTs). The need to discover exonic representation and expression forms of full-length cDNAs for each human gene is frustrated by the partial and variable quality nature of this data delivery. A highly redundant human EST data set has been processed into integrated and unified expressed transcript indices that consist of hierarchically organized human transcript consensi reflecting gene expression forms and genetic polymorphism within an index class. The expression index and its intermediate outputs include cleaned transcript sequence, expression, and alignment information and a higher fidelity subset, SANIGENE. The STACK_PACK clustering system has been applied to dbEST release 121598 (GenBank version 110). Sixty-four percent of 1,313, 103 Homo sapiens ESTs are condensed into 143,885 tissue level multiple sequence clusters; linking through clone-ID annotations produces 68,701 total assemblies, such that 81% of the original input set is captured in a STACK multiple sequence or linked cluster. Indexing of alignments by substituent EST accession allows browsing of the data structure and its cross-links to UniGene. STACK metaclusters consolidate a greater number of ESTs by a factor of 1. 86 with respect to the corresponding UniGene build. Fidelity comparison with genome reference sequence AC004106 demonstrates consensus expression clusters that reflect significantly lower spurious repeat sequence content and capture alternate splicing within a whole body index cluster and three STACK v.2.3 tissue-level clusters. Statistics of a staggered release whole body index build of STACK v.2.0 are presented.