Structure and function of the mycobacterial transcription initiation complex with the essential regulator RbpA

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Abstract

RbpA and CarD are essential transcription regulators in mycobacteria. Mechanistic analyses of promoter open complex (RPo) formation establish that RbpA and CarD cooperatively stimulate formation of an intermediate (RP2) leading to RPo; formation of RP2 is likely a bottleneck step at the majority of mycobacterial promoters. Once RPo forms, CarD also disfavors its isomerization back to RP2. We determined a 2.76 Å-resolution crystal structure of a mycobacterial transcription initiation complex (TIC) with RbpA as well as a CarD/RbpA/TIC model. Both CarD and RbpA bind near the upstream edge of the −10 element where they likely facilitate DNA bending and impede transcription bubble collapse. In vivo studies demonstrate the essential role of RbpA, show the effects of RbpA truncations on transcription and cell physiology, and indicate additional functions for RbpA not evident in vitro. This work provides a framework to understand the control of mycobacterial transcription by RbpA and CarD.

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featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

, , (2013)

Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

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Christopher Sassetti, Dana Boyd, Eric Rubin (2003)

Despite over a century of research, tuberculosis remains a leading cause of infectious death worldwide. Faced with increasing rates of drug resistance, the identification of genes that are required for the growth of this organism should provide new targets for the design of antimycobacterial agents. Here, we describe the use of transposon site hybridization (TraSH) to comprehensively identify the genes required by the causative agent, Mycobacterium tuberculosis, for optimal growth. These genes include those that can be assigned to essential pathways as well as many of unknown function. The genes important for the growth of M. tuberculosis are largely conserved in the degenerate genome of the leprosy bacillus, Mycobacterium leprae, indicating that non-essential functions have been selectively lost since this bacterium diverged from other mycobacteria. In contrast, a surprisingly high proportion of these genes lack identifiable orthologues in other bacteria, suggesting that the minimal gene set required for survival varies greatly between organisms with different evolutionary histories.

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Linking crystallographic model and data quality.

P. Andrew Karplus, Kay Diederichs (2012)

In macromolecular x-ray crystallography, refinement R values measure the agreement between observed and calculated data. Analogously, R(merge) values reporting on the agreement between multiple measurements of a given reflection are used to assess data quality. Here, we show that despite their widespread use, R(merge) values are poorly suited for determining the high-resolution limit and that current standard protocols discard much useful data. We introduce a statistic that estimates the correlation of an observed data set with the underlying (not measurable) true signal; this quantity, CC*, provides a single statistically valid guide for deciding which data are useful. CC* also can be used to assess model and data quality on the same scale, and this reveals when data quality is limiting model improvement.