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Modern Applied Statistics With S

Antibiotics have always been considered one of the wonder discoveries of the 20th century. This is true, but the real wonder is the rise of antibiotic resistance in hospitals, communities, and the environment concomitant with their use. The extraordinary genetic capacities of microbes have benefitted from man's overuse of antibiotics to exploit every source of resistance genes and every means of horizontal gene transmission to develop multiple mechanisms of resistance for each and every antibiotic introduced into practice clinically, agriculturally, or otherwise. This review presents the salient aspects of antibiotic resistance development over the past half-century, with the oft-restated conclusion that it is time to act. To achieve complete restitution of therapeutic applications of antibiotics, there is a need for more information on the role of environmental microbiomes in the rise of antibiotic resistance. In particular, creative approaches to the discovery of novel antibiotics and their expedited and controlled introduction to therapy are obligatory.

Origins and Evolution of Antibiotic Resistance

A Common Mechanism of Cellular Death Induced by Bactericidal Antibiotics

Antibiotic drug-target interactions, and their respective direct effects, are generally well characterized. By contrast, the bacterial responses to antibiotic drug treatments that contribute to cell death are not as well understood and have proven to be complex as they involve many genetic and biochemical pathways. In this Review, we discuss the multilayered effects of drug-target interactions, including the essential cellular processes that are inhibited by bactericidal antibiotics and the associated cellular response mechanisms that contribute to killing. We also discuss new insights into these mechanisms that have been revealed through the study of biological networks, and describe how these insights, together with related developments in synthetic biology, could be exploited to create new antibacterial therapies.

How antibiotics kill bacteria: from targets to networks

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.

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.

/pdf/large-scale-mapping-and-validation-of-escherichia-coli-5efq1yb3s5.pdf

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

Modulation of bacterial chromosomal supercoiling is a function of DNA gyrase-catalyzed strand breakage and rejoining. This reaction is exploited by both antibiotic and proteic gyrase inhibitors, which trap the gyrase molecule at the DNA cleavage stage. Owing to this interaction, doublestranded DNA breaks are introduced and replication machinery is arrested at blocked replication forks. This immediately results in bacteriostasis and ultimately induces cell death. Here we demonstrate, through a series of phenotypic and gene expression analyses, that superoxide and hydroxyl radical oxidative species are generated following gyrase poisoning and play an important role in cell killing by gyrase inhibitors. We show that superoxide-mediated oxidation of iron–sulfur clusters promotes a breakdown of iron regulatory dynamics; in turn, iron misregulation drives the generation of highly destructive hydroxyl radicals via the Fenton reaction. Importantly, our data reveal that blockage of hydroxyl radical formation increases the survival of gyrase-poisoned cells. Together, this series of biochemical reactions appears to compose a maladaptive response, that serves to amplify the primary effect of gyrase inhibition by oxidatively damaging DNA, proteins and lipids. Molecular Systems Biology 13 March 2007; doi:10.1038/msb4100135 Subject Categories: cellular metabolism; microbiology & pathogenesis

https://www.embopress.org/doi/pdf/10.1038/msb4100135

Gyrase Inhibitors Induce an Oxidative Damage Cellular Death Pathway in Escherichia Coli

Summary Background Better understanding and prediction of progression of Parkinson's disease could improve disease management and clinical trial design. We aimed to use longitudinal clinical, molecular, and genetic data to develop predictive models, compare potential biomarkers, and identify novel predictors for motor progression in Parkinson's disease. We also sought to assess the use of these models in the design of treatment trials in Parkinson's disease. Methods A Bayesian multivariate predictive inference platform was applied to data from the Parkinson's Progression Markers Initiative (PPMI) study (NCT01141023). We used genetic data and baseline molecular and clinical variables from patients with Parkinson's disease and healthy controls to construct an ensemble of models to predict the annual rate of change in combined scores from the Movement Disorder Society—Unified Parkinson's Disease Rating Scale (MDS-UPDRS) parts II and III. We tested our overall explanatory power, as assessed by the coefficient of determination ( R 2 ), and replicated novel findings in an independent clinical cohort from the Longitudinal and Biomarker Study in Parkinson's disease (LABS-PD; NCT00605163). The potential utility of these models for clinical trial design was quantified by comparing simulated randomised placebo-controlled trials within the out-of-sample LABS-PD cohort. Findings 117 healthy controls and 312 patients with Parkinson's disease from the PPMI study were available for analysis, and 317 patients with Parkinson's disease from LABS-PD were available for validation. Our model ensemble showed strong performance within the PPMI cohort (five-fold cross-validated R 2 41%, 95% CI 35–47) and significant—albeit reduced—performance in the LABS-PD cohort ( R 2 9%, 95% CI 4–16). Individual predictive features identified from PPMI data were confirmed in the LABS-PD cohort. These included significant replication of higher baseline MDS-UPDRS motor score, male sex, and increased age, as well as a novel Parkinson's disease-specific epistatic interaction, all indicative of faster motor progression. Genetic variation was the most useful predictive marker of motor progression (2·9%, 95% CI 1·5–4·3). CSF biomarkers at baseline showed a more modest (0·3%, 95% CI 0·1–0·5) but still significant effect on prediction of motor progression. The simulations (n=5000) showed that incorporating the predicted rates of motor progression (as assessed by the annual change in MDS-UPDRS score) into the final models of treatment effect reduced the variability in the study outcome, allowing significant differences to be detected at sample sizes up to 20% smaller than in naive trials. Interpretation Our model ensemble confirmed established and identified novel predictors of Parkinson's disease motor progression. Improvement of existing prognostic models through machine-learning approaches should benefit trial design and evaluation, as well as clinical disease monitoring and treatment. Funding Michael J Fox Foundation for Parkinson's Research and National Institute of Neurological Disorders and Stroke.

Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation

The Gene Ontology (GO) offers a comprehensive and standardized way to describe a protein's biological role. Proteins are annotated with GO terms based on direct or indirect experimental evidence. Term assignments are also inferred from homology and literature mining. Regardless of the type of evidence used, GO assignments are manually curated or electronic. Unfortunately, manual curation cannot keep pace with the data, available from publications and various large experimental datasets. Automated literature-based annotation methods have been developed in order to speed up the annotation. However, they only apply to proteins that have been experimentally investigated or have close homologs with sufficient and consistent annotation. One of the homology-based electronic methods for GO annotation is provided by the InterPro database. The InterPro2GO/PFAM2GO associates individual protein domains with GO terms and thus can be used to annotate the less studied proteins. However, protein classification via a single functional domain demands stringency to avoid large number of false positives. This work broadens the basic approach. We model proteins via their entire functional domain content and train individual decision tree classifiers for each GO term using known protein assignments. We demonstrate that our approach is sensitive, specific and precise, as well as fairly robust to sparse data. We have found that our method is more sensitive when compared to the InterPro2GO performance and suffers only some precision decrease. In comparison to the InterPro2GO we have improved the sensitivity by 22%, 27% and 50% for Molecular Function, Biological Process and Cellular GO terms respectively.

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Boris Hayete

Papers

A Common Mechanism of Cellular Death Induced by Bactericidal Antibiotics

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

Gyrase Inhibitors Induce an Oxidative Damage Cellular Death Pathway in Escherichia Coli

Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation

Gotrees: predicting go associations from protein domain composition using decision trees.