Multi-omics analyses reveal molecular mechanisms for the antagonistic toxicity of carbon nanotubes and ciprofloxacin to Escherichia coli

The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

Background Several R packages exist for the detection of differentially expressed genes from RNA-Seq data. The analysis process includes three main steps, namely normalization, dispersion estimation and test for differential expression. Quality control steps along this process are recommended but not mandatory, and failing to check the characteristics of the dataset may lead to spurious results. In addition, normalization methods and statistical models are not exchangeable across the packages without adequate transformations the users are often not aware of. Thus, dedicated analysis pipelines are needed to include systematic quality control steps and prevent errors from misusing the proposed methods. Results SARTools is an R pipeline for differential analysis of RNA-Seq count data. It can handle designs involving two or more conditions of a single biological factor with or without a blocking factor (such as a batch effect or a sample pairing). It is based on DESeq2 and edgeR and is composed of an R package and two R script templates (for DESeq2 and edgeR respectively). Tuning a small number of parameters and executing one of the R scripts, users have access to the full results of the analysis, including lists of differentially expressed genes and a HTML report that (i) displays diagnostic plots for quality control and model hypotheses checking and (ii) keeps track of the whole analysis process, parameter values and versions of the R packages used. Conclusions SARTools provides systematic quality controls of the dataset as well as diagnostic plots that help to tune the model parameters. It gives access to the main parameters of DESeq2 and edgeR and prevents untrained users from misusing some functionalities of both packages. By keeping track of all the parameters of the analysis process it fits the requirements of reproducible research.

Quinolones are one of the most commonly prescribed classes of antibacterials in the world and are used to treat a variety of bacterial infections in humans. Because of the wide use (and overuse) of these drugs, the number of quinolone-resistant bacterial strains has been growing steadily since the 1990s. As is the case with other antibacterial agents, the rise in quinolone resistance threatens the clinical utility of this important drug class. Quinolones act by converting their targets, gyrase and topoisomerase IV, into toxic enzymes that fragment the bacterial chromosome. This review describes the development of the quinolones as antibacterials, the structure and function of gyrase and topoisomerase IV, and the mechanistic basis for quinolone action against their enzyme targets. It will then discuss the following three mechanisms that decrease the sensitivity of bacterial cells to quinolones. Target-mediated resistance is the most common and clinically significant form of resistance. It is caused by specific mutations in gyrase and topoisomerase IV that weaken interactions between quinolones and these enzymes. Plasmid-mediated resistance results from extrachromosomal elements that encode proteins that disrupt quinolone–enzyme interactions, alter drug metabolism, or increase quinolone efflux. Chromosome-mediated resistance results from the underexpression of porins or the overexpression of cellular efflux pumps, both of which decrease cellular concentrations of quinolones. Finally, this review will discuss recent advancements in our understanding of how quinolones interact with gyrase and topoisomerase IV and how mutations in these enzymes cause resistance. These last findings suggest approaches to designing new drugs that display improved activity against resistant strains.