We applied whole-genome resequencing of Escherichia coli to monitor the acquisition and fixation of mutations that conveyed a selective growth advantage during adaptation to a glycerol-based growth medium. We identified 13 different de novo mutations in five different E. coli strains and monitored their fixation over a 44-d period of adaptation. We obtained proof that the observed spontaneous mutations were responsible for improved fitness by creating single, double and triple site-directed mutants that had growth rates matching those of the evolved strains. The success of this new genome-scale approach indicates that real-time evolution studies will now be practical in a wide variety of contexts.Comparative genomics has been almost entirely focused on genomic changes over long periods of time, on the order of millions of years. A new microarray-based method of whole-genome resequencing called comparative genome sequencing (CGS) 1 now makes it cost efficient to monitor bacterial evolution comprehensively over short time periods as well. This capability is important because many microbial phenomena, such as the emergence of new pathogens and the acquisition of antibiotic resistance factors, can occur over relatively short time scales. Experimental evolution of bacteria and viruses 2,3 is a facile approach to study these topics. It has been used to test predictions of evolutionary theory 4 and to study parallel changes in populations evolved for 20,000 generations 5 , acquisition of antibiotic resistance 1 and in vitro symbiosis 6 . Our laboratory has used experimental evolution as a tool for metabolic engineering 7 and to study the recovery of strains with gene knockouts in central metabolic genes 8 . Nevertheless, much remains unknown about genome plasticity over short evolutionary timescales.It is a common mistake to think of bacteria as static; that is, to assume that a culture grown overnight is the same as it was the day before. It has been estimated that nearly 10% of the individual bacteria in a Salmonella enterica population carry large-scale genome rearrangements 9 , and in a suboptimal environment, selection can alter a population very rapidly. The 10-20 generations that occur in the process of growing a bacterial culture are sufficient to create a heterogeneous population, depending on the magnitude of the selective advantage of adaptive mutations. This problem is avoided by using strains of bacteria that are adapted to common laboratory media, but there are interesting cases where a seemingly straightforward growth medium poses a great challenge to a bacterium.An example is E. coli K-12 grown in minimal medium supplemented with glycerol as the carbon and energy source. Despite a complete pathway for glycerol catabolism, large variations in the growth rates of various strains have been noted 10 . Growth of the sequenced strain MG1655 has been observed to differ from computational predictions based on flux balance analysis 11 . Upon extended logarithmic growth in glycerol minimal medium, the growth rate...

Genome-scale models of Escherichia coli K-12 MG1655 metabolism have been able to predict growth phenotypes in most, but not all, defined growth environments. Here we introduce the use of an optimization-based algorithm that predicts the missing reactions that are required to reconcile computation and experiment when they disagree. The computer-generated hypotheses for missing reactions were verified experimentally in five cases, leading to the functional assignment of eight ORFs (yjjLMN, yeaTU, dctA, idnT, and putP) with two new enzymatic activities and four transport functions. This study thus demonstrates the use of systems analysis to discover metabolic and transport functions and their genetic basis by a combination of experimental and computational approaches.constraint-based ͉ flux balance analysis ͉ functional genomics ͉ metabolic reconstruction ͉ systems biology C urrent genome annotations include a substantial fraction of ORFs with unknown function (1, 2); methods are needed to provide insight into the possible function of these genes, without the need for screening individual gene products across a multitude of possible activities. Metabolic and regulatory networks are reconstructed from genome annotations and scientific literature to integrate and represent our current knowledge of network components and interactions (3). Dual-perturbation methods have been developed to study regulatory networks (4), which can be used to reconcile model predictions and experimental data, thus leading to possible iterative model refinements and experimentally testable hypotheses (4, 5). Iterative modelbuilding can be systematized through the use of computational algorithms (6). Such an approach is presented here, and it consists of four steps. First, computational analysis identified discrepancies between model predictions and growth phenotyping data by using a reconstructed genome-scale Escherichia coli metabolic network. Second, an algorithm then identified enzymatic and transport reactions that likely were missing from the current metabolic reconstruction that could reconcile model predictions and experimental observations. Third, ORFs that might be responsible for these missing activities then were identified by using literature searches, sequence-homology searches (7), context-based homology methods (8, 9), and in some cases unpublished microarray data. Fourth, experimental verification of the algorithm's predictions then were carried out by evaluating growth phenotypes of single-deletion strains available in the Keio collection (10) and gene-expression measurements. Here we present a comprehensive combined computational and experimental approach to analyze phenotypic data and genome annotation information in a global manner to uncover individual ORF function. ResultsGrowth phenotyping data (11), available from Biolog (Hayward CA; www.biolog.com), were used to identify missing reactions from the reconstructed genome-scale metabolic network of E. coli MG1655 (iJR904) (12). Using a flux balance model of E. coli, we ...

By exploiting a genome-wide collection of bacterial single-gene deletion mutants, we have studied the toxicological pathways of a 60-nm cationic (amino-functionalized) polystyrene nanomaterial (PS-NH(2)) in bacterial cells. The IC(50) of commercially available 60 nm PS-NH(2) was determined to be 158 μg/mL, the IC(5) is 108 μg/mL, and the IC(90) is 190 μg/mL for the parent E. coli strain of the gene deletion library. Over 4000 single nonessential gene deletion mutants of Escherichia coli were screened for the growth phenotype of each strain in the presence and absence of PS-NH(2). This revealed that genes clusters in the lipopolysaccharide biosynthetic pathway, outer membrane transport channels, ubiquinone biosynthetic pathways, flagellar movement, and DNA repair systems are all important to how this organism responds to cationic nanomaterials. These results, coupled with those from confirmatory assays described herein, suggest that the primary mechanisms of toxicity of the 60-nm PS-NH(2) nanomaterial in E. coli are destabilization of the outer membrane and production of reactive oxygen species. The methodology reported herein should prove generally useful for identifying pathways that are involved in how cells respond to a broad range of nanomaterials and for determining the mechanisms of cellular toxicity of different types of nanomaterials.

Macitentan (Opsumit®) is a novel dual endothelin receptor antagonist (ERA) with sustained receptor binding properties developed by Actelion Pharmaceuticals Ltd. In October 2013, oral macitentan 10 mg once daily received its first global approval in the US, followed closely by Canada, for the treatment of pulmonary arterial hypertension (PAH). The drug has also received a positive opinion in the EU from the Committee for Medicinal Products for Human Use for the treatment of PAH, and is under regulatory review in several other countries for the same indication. Endothelin (ET)-1 influences pathological changes via two ET receptor subtypes (ETA and ETB), to which it binds with high affinity. ET-1 is implicated in several forms of vascular disease making it a valid target for the treatment of pulmonary vascular diseases such as PAH. Clinical development is underway for other indications, including Eisenmenger syndrome, ischaemic digital ulcers secondary to systemic sclerosis, and glioblastoma. Macitentan was also evaluated in idiopathic pulmonary fibrosis; however, a phase 2 trial did not meet its primary endpoint and further investigation in this indication was discontinued. Macitentan was developed by modifying the structure of bosentan in the search for an optimal dual ERA with improved efficacy and tolerability compared with other ERAs. This article summarizes the milestones in the development of macitentan leading to this first approval for PAH.

The development of high throughput screening (HTS) assays in the field of nanotoxicology provide new opportunities for the hazard assessment and ranking of engineered nanomaterials (ENMs). It is often necessary to rank lists of materials based on multiple risk assessment parameters, often aggregated across several measures of toxicity and possibly spanning an array of experimental platforms. Bayesian models coupled with the optimization of loss functions have been shown to provide an effective framework for conducting inference on ranks. In this article we present various loss-function-based ranking approaches for comparing ENM within experiments and toxicity parameters. Additionally, we propose a framework for the aggregation of ranks across different sources of evidence while allowing for differential weighting of this evidence based on its reliability and importance in risk ranking. We apply these methods to high throughput toxicity data on two human cell-lines, exposed to eight different nanomaterials, and measured in relation to four cytotoxicity outcomes. This article has supplementary material online.