Systematic Genetic Nomenclature for Type VII Secretion Systems

CITATION: Bitter, W., et al. 2009. Systematic genetic nomenclature for type VII secretion systems. PLoS Pathogens, 5(10): 1-6, doi: 10.1371/journal.ppat.1000507.The original publication is available at http://journals.plos.org/plospathogensMycobacteria, such as the etiological agent of human tuberculosis, Mycobacterium tuberculosis, are protected by an impermeable cell envelope composed of an inner cytoplasmic membrane, a peptidoglycan layer, an arabinogalactan layer, and an outer membrane. This second membrane consists of covalently linked, tightly packed long-chain mycolic acids [1,2] and noncovalently bound shorter lipids involved in pathogenicity [3–5]. To ensure protein transport across this complex cell envelope, mycobacteria use various secretion pathways, such as the SecA1-mediated general secretory pathway [6,7], an alternative SecA2-operated pathway [8], a twin-arginine translocation system [9,10], and a specialized secretion pathway variously named ESAT-6-, SNM-, ESX-, or type VII secretion [11–16]. The latter pathway, hereafter referred to as type VII secretion (T7S), has recently become a large and competitive research topic that is closely linked to studies of host–pathogen interactions of M. tuberculosis [17] and other pathogenic mycobacteria [16]. Molecular details are just beginning to be revealed [18–22] showing that T7S systems are complex machineries with multiple components and multiple substrates. Despite their biological importance, there has been a lack of a clear naming policy for the components and substrates of these systems. As there are multiple paralogous T7S systems within the Mycobacteria and orthologous systems in related bacteria, we are concerned that, without a unified nomenclature system, a multitude of redundant and obscure gene names will be used that will inevitably lead to confusion and hinder future progress. In this opinion piece we will therefore propose and introduce a systematic nomenclature with guidelines for name selection of new components that will greatly facilitate communication and understanding in this rapidly developing field of research.http://journals.plos.org/plospathogens/article?id=10.1371%2Fjournal.ppat.1000507Publisher's versio

Phosphodiesterase-4 Inhibition Alters Gene Expression and Improves Isoniazid – Mediated Clearance of Mycobacterium tuberculosis in Rabbit Lungs

Tuberculosis (TB) treatment is hampered by the long duration of antibiotic therapy required to achieve cure. This indolent response has been partly attributed to the ability of subpopulations of less metabolically active Mycobacterium tuberculosis (Mtb) to withstand killing by current anti-TB drugs. We have used immune modulation with a phosphodiesterase-4 (PDE4) inhibitor, CC-3052, that reduces tumor necrosis factor alpha (TNF-α) production by increasing intracellular cAMP in macrophages, to examine the crosstalk between host and pathogen in rabbits with pulmonary TB during treatment with isoniazid (INH). Based on DNA microarray, changes in host gene expression during CC-3052 treatment of Mtb infected rabbits support a link between PDE4 inhibition and specific down-regulation of the innate immune response. The overall pattern of host gene expression in the lungs of infected rabbits treated with CC-3052, compared to untreated rabbits, was similar to that described in vitro in resting Mtb infected macrophages, suggesting suboptimal macrophage activation. These alterations in host immunity were associated with corresponding down-regulation of a number of Mtb genes that have been associated with a metabolic shift towards dormancy. Moreover, treatment with CC-3052 and INH resulted in reduced expression of those genes associated with the bacterial response to INH. Importantly, CC-3052 treatment of infected rabbits was associated with reduced ability of Mtb to withstand INH killing, shown by improved bacillary clearance, from the lungs of co-treated animals compared to rabbits treated with INH alone. The results of our study suggest that changes in Mtb gene expression, in response to changes in the host immune response, can alter the responsiveness of the bacteria to antimicrobial agents. These findings provide a basis for exploring the potential use of adjunctive immune modulation with PDE4 inhibitors to enhance the efficacy of existing anti-TB treatment

Semi-supervised Verb Class Discovery Using Noisy Features

We cluster verbs into lexical semantic classes, using a general set of noisy features that capture syntactic and semantic properties of the verbs. The feature set was previously shown to work well in a supervised learning setting, using known English verb classes. In moving to a scenario of verb class discovery, using clustering, we face the problem of having a large number of irrelevant features for a particular clustering task. We investigate various approaches to feature selection, using both unsupervised and semi-supervised methods, comparing the results to subsets of features manually chosen according to linguistic properties. We find that the unsupervised method we tried cannot be consistently applied to our data. However, the semisupervised approach (using a seed set of sample verbs) overall outperforms not only the full set of features, but the hand-selected features as well

A General Feature Space for Automatic Verb Classification

We develop a general feature space for automatic classification of verbs into lexical semantic classes. Previous work was limited in scope by the need for manual selection of discriminating features, through a linguistic analysis of the target verb classes (Merlo and Stevenson, 2001). We instead analyze the classification structure at a higher level, using the possible defining characteristics of classes as the basis for our feature space. The general feature space achieves reductions in error rates of 42-- 69%, on a wider range of classes than investigated previously, with comparable performance to feature sets manually selected for the particular classification tasks. Our results show that the approach is generally applicable, and avoids the need for resource-intensive linguistic analysis for each new task

Tightly Packed Tries: How to Fit Large Models into Memory, and Make them Load Fast, Too

We present Tightly Packed Tries (TPTs), a compact implementation of read-only, compressed trie structures with fast on-demand paging and short load times. We demonstrate the benefits of TPTs for storing n-gram back-off language models and phrase tables for statistical machine translation. Encoded as TPTs, these databases require less space than flat text file representations of the same data compressed with the gzip utility. At the same time, they can be mapped into memory quickly and be searched directly in time linear in the length of the key, without the need to decompress the entire file. The overhead for local decompression during search is marginal.

Manageable Phrase-based Statistical Machine Translation Models

Statistical Machine Translation (SMT) is an evolving field where many techniques in Syntactic Pattern Recognition (SPR) are needed and applied. A typical phrase-based SMT system for translating from a T (target) language to an S (source) language contains one or more n-gram language models (LMs) and one or more phrase translation models (TMs). These LMs and TMs have a large memory footprint (up to several gigabytes). This paper describes novel techniques for filtering these models that ensure only relevant patterns in the LMs and TMs are loaded during translation. In experiments on a large Chinese-English task, these techniques yielded significant reductions in the amount of information loaded during translation: up to 58% reduction for LMs, and up to 75% for TMs.Peer reviewed: YesNRC publication: Ye