Emergence of bacterial resistance is a major issue for all classes of antibiotics; therefore, the identification of new classes is critically needed. Recently we reported the discovery of platensimycin by screening natural product extracts using a target-based whole-cell strategy with antisense silencing technology in concert with cell free biochemical validations. Continued screening efforts led to the discovery of platencin, a novel natural product that is chemically and biologically related but different from platensimycin. Platencin exhibits a broad-spectrum Gram-positive antibacterial activity through inhibition of fatty acid biosynthesis. It does not exhibit cross-resistance to key antibiotic resistant strains tested, including methicillin-resistant Staphylococcus aureus, vancomycin-intermediate S. aureus, and vancomycin-resistant Enterococci. Platencin shows potent in vivo efficacy without any observed toxicity. It targets two essential proteins, -ketoacyl-[acyl carrier protein (ACP)] synthase II (FabF) and III (FabH) with IC 50 values of 1.95 and 3.91 g/ml, respectively, whereas platensimycin targets only FabF (IC 50 ؍ 0.13 g/ml) in S. aureus, emphasizing the fact that more antibiotics with novel structures and new modes of action can be discovered by using this antisense differential sensitivity wholecell screening paradigm.platensimycin ͉ natural product ͉ thiolactomycin ͉ antisense ͉ fatty acid synthesis
Canonical correlations analysis (CCA) is an exploratory statistical method to highlight correlations between two data sets acquired on the same experimental units. The cancor() function in R (R Development Core Team 2007) performs the core of computations but further work was required to provide the user with additional tools to facilitate the interpretation of the results. We implemented an R package, CCA, freely available from the Comprehensive R Archive Network (CRAN, http://CRAN.R-project.org/), to develop numerical and graphical outputs and to enable the user to handle missing values. The CCA package also includes a regularized version of CCA to deal with data sets with more variables than units. Illustrations are given through the analysis of a data set coming from a nutrigenomic study in the mouse.
Motivation: With the availability of many ‘omics’ data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels. However, the development of such an analysis is a major computational and technical challenge as most approaches suffer from high data dimensionality. New methodologies need to be developed and validated.Results: efficiently performs integrative analyses of two types of ‘omics’ variables that are measured on the same samples. It includes a regularized version of canonical correlation analysis to enlighten correlations between two datasets, and a sparse version of partial least squares (PLS) regression that includes simultaneous variable selection in both datasets. The usefulness of both approaches has been demonstrated previously and successfully applied in various integrative studies.Availability: is freely available from http://CRAN.R-project.org/ or from the web site companion (http://math.univ-toulouse.fr/biostat) that provides full documentation and tutorials.Contact: k.lecao@uq.edu.auSupplementary information: Supplementary data are available at Bioinformatics online.
BackgroundEach omics platform is now able to generate a large amount of data. Genomics, proteomics, metabolomics, interactomics are compiled at an ever increasing pace and now form a core part of the fundamental systems biology framework. Recently, several integrative approaches have been proposed to extract meaningful information. However, these approaches lack of visualisation outputs to fully unravel the complex associations between different biological entities.ResultsThe multivariate statistical approaches ‘regularized Canonical Correlation Analysis’ and ‘sparse Partial Least Squares regression’ were recently developed to integrate two types of highly dimensional ‘omics’ data and to select relevant information. Using the results of these methods, we propose to revisit few graphical outputs to better understand the relationships between two ‘omics’ data and to better visualise the correlation structure between the different biological entities. These graphical outputs include Correlation Circle plots, Relevance Networks and Clustered Image Maps. We demonstrate the usefulness of such graphical outputs on several biological data sets and further assess their biological relevance using gene ontology analysis.ConclusionsSuch graphical outputs are undoubtedly useful to aid the interpretation of these promising integrative analysis tools and will certainly help in addressing fundamental biological questions and understanding systems as a whole.AvailabilityThe graphical tools described in this paper are implemented in the freely available package and in its associated web application.
For decades, microbial natural products have been one of the major sources of novel drugs for pharmaceutical companies, and today all evidence suggests that novel molecules with potential therapeutic applications are still waiting to be discovered from these natural sources, especially from actinomycetes. Any appropriate exploitation of the chemical diversity of these microbial sources relies on proper understanding of their biological diversity and other related key factors that maximize the possibility of successful identification of novel molecules. Without doubt, the discovery of platensimycin has shown that microbial natural products can continue to deliver novel scaffolds if appropriate tools are put in place to reveal them in a cost-effective manner. Whereas today innovative technologies involving exploitation of uncultivated environmental diversity, together with chemical biology and in silico approaches, are seeing rapid development in natural products research, maximization of the chances of exploiting chemical diversity from microbial collections is still essential for novel drug discovery. This work provides an overview of the integrated approaches developed at the former Basic Research Center of Merck Sharp and Dohme in Spain to exploit the diversity and biosynthetic potential of actinomycetes, and includes some examples of those that were successfully applied to the discovery of novel antibiotics.
BackgroundComparative genomics studies are central in identifying the coding and non-coding elements associated with complex traits, and the functional annotation of genomes is a critical step to decipher the genotype-to-phenotype relationships in livestock animals. As part of the Functional Annotation of Animal Genomes (FAANG) action, the FR-AgENCODE project aimed to create reference functional maps of domesticated animals by profiling the landscape of transcription (RNA-seq), chromatin accessibility (ATAC-seq) and conformation (Hi-C) in species representing ruminants (cattle, goat), monogastrics (pig) and birds (chicken), using three target samples related to metabolism (liver) and immunity (CD4+ and CD8+ T cells).ResultsRNA-seq assays considerably extended the available catalog of annotated transcripts and identified differentially expressed genes with unknown function, including new syntenic lncRNAs. ATAC-seq highlighted an enrichment for transcription factor binding sites in differentially accessible regions of the chromatin. Comparative analyses revealed a core set of conserved regulatory regions across species. Topologically associating domains (TADs) and epigenetic A/B compartments annotated from Hi-C data were consistent with RNA-seq and ATAC-seq data. Multi-species comparisons showed that conserved TAD boundaries had stronger insulation properties than species-specific ones and that the genomic distribution of orthologous genes in A/B compartments was significantly conserved across species.ConclusionsWe report the first multi-species and multi-assay genome annotation results obtained by a FAANG project. Beyond the generation of reference annotations and the confirmation of previous findings on model animals, the integrative analysis of data from multiple assays and species sheds a new light on the multi-scale selective pressure shaping genome organization from birds to mammals. Overall, these results emphasize the value of FAANG for research on domesticated animals and reinforces the importance of future meta-analyses of the reference datasets being generated by this community on different species.
Two birds with one stone: Platencin (1) is a novel and potent broad‐spectrum Gram‐positive antibiotic. Whereas platensimycin is a selective inhibitor of FabF, platencin exerts its activity by a novel mode of action by dual inhibition of FabH and FabF.
A systematic survey of the endophytic assemblages of Quercus ilex in central Spain has been performed, with the goal of evaluating the importance of geographical and seasonal factors on these fungal communities. Four sampling sites were selected ; one of them was sampled twice, in the spring and the autumn. The collected plant material consisted of bark, twigs and leaves from eight trees per site. Fungal strains were isolated with the use of a surfacesterilization method with sodium hypochlorite. A total of 2921 fungal strains grouped into 149 ' species ' or morphological types were recovered. The 10 dominant species, with isolation frequencies 1.5 %, were Pyrenochaeta sp., Periconiella anamorph of Biscogniauxia mediterranea (De Not.) Kuntze, Pseudonectria sp., Cryptosporiopsis quercina Petrak, Alternaria alternata (Fr :) Keissl., two undetermined coelomycetes, Penicillium funiculosum Thom, Diplodia mutila Fr. apud Mont. and Ascochyta sp. Medians of fungal species per tree were significantly different among the sampled sites. The isolation frequencies of the dominant species, as well as other less frequent species, were significantly dependent on the sampling site. The degree of endophytic infection and the diversity of fungal species were significantly higher in the spring. The frequencies of all dominant species at one of the sites depended significantly on the season, except for C. quercina, Acremonium sclerotigenum (F & V Moreau ex Valenta) Gams. and D. mutila. Cluster analysis of the whole endophytic mycoflora of the sampled trees suggested that the geographical factor affects the endophytic distribution patterns more significantly than the seasonal factor.
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