Mistranslation of membrane proteins and two-component system activation trigger antibiotic-mediated cell death.

Aminoglycoside antibiotics, such as gentamicin and kanamycin, directly target the ribosome, yet the mechanisms by which these bactericidal drugs induce cell death are not fully understood. Recently, oxidative stress has been implicated as one of the mechanisms whereby bactericidal antibiotics kill bacteria. Here, we use systems-level approaches and phenotypic analyses to provide insight into the pathway whereby aminoglycosides ultimately trigger hydroxyl radical formation. We show, by disabling systems that facilitate membrane protein traffic, that mistranslation and misfolding of membrane proteins are central to aminoglycoside-induced oxidative stress and cell death. Signaling through the envelope stress-response two-component system is found to be a key player in this process, and the redox-responsive two-component system is shown to have an associated role. Additionally, we show that these two-component systems play a general role in bactericidal antibiotic-mediated oxidative stress and cell death, expanding our understanding of the common mechanism of killing induced by bactericidal antibiotics.

Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles.

Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data.

Combination drugs, an emerging option for antibacterial therapy.

The emerging and sustained resistance to antibiotics and the poor pipeline of new antibacterials is creating a major health issue worldwide. Bacterial pathogens are increasingly becoming resistant even to the most recently approved antibiotics. Few antibiotics are being approved by regulatory organizations, which reflects both the difficulty of developing such agents and the fact that antibiotic discovery programs have been terminated at several major pharmaceutical companies in the past decade. As a result, the output of the drug pipelines is simply not well positioned to control the growing army of resistant pathogens, although academic institutions and smaller companies are trying to fill that gap. An emerging option to fight such pathogens is combination therapy. Combinations of two antibiotics or antibiotics with adjuvants are emerging as a promising therapeutic approach. This article provides and discusses clinical and scientific challenges to support the development of combination therapy to treat bacterial infections.

Protein interaction mapping on a functional shotgun sequence of Rickettsia sibirica.

Protein interaction maps can reveal novel pathways and functional complexes, allowing 'guilt by association' annotation of uncharacterized proteins. To address the need for large-scale protein interaction analyses, a bacterial two-hybrid system was coupled with a whole genome shotgun sequencing approach for microbial genome analysis. We report the first large-scale proteomics study using this system, integrating de novo genome sequencing with functional interaction mapping and annotation in a high-throughput format. We apply the approach by shotgun sequencing and annotating the genome of Rickettsia sibirica strain 246, an obligate intracellular human pathogen among the Spotted Fever Group rickettsiae. The bacteria invade endothelial cells and cause lysis after large amounts of progeny have accumulated. Little is known about specific Rickettsial virulence factors and their mode of pathogenicity. Analysis of the combined genomic sequence and protein-protein interaction data for a set of virulence related Type IV secretion system (T4SS) proteins revealed over 250 interactions and will provide insight into the mechanism of Rickettsial pathogenicity.

Discovering drug mode of action using reverse-engineered gene networks.

A major challenge in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets. Here, we present an integrated computational-experimental approach for computing the likelihood that gene products and associated pathways are targets of a compound. This is achieved by filtering the mRNA expression profile of compound-exposed cells using a reverse-engineered model of the cell's gene regulatory network. We apply the method to a set of 6 whole-genome Escherichia coli expression profiles at different time points after treatment with the antibiotic Norfloxacin. We show that the algorithm can correctly identify the known drug targets and associated pathways.