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      Genomic and transcriptomic analysis of the toluene degrading black yeast Cladophialophora immunda

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          Abstract

          Cladophialophora immunda is an ascomycotal species belonging to the group of the black yeasts. These fungi have a thick and melanized cell wall and other physiological adaptations that allows them to cope with several extreme physical and chemical conditions. Member of the group can colonize some of the most extremophilic environments on Earth. Cladophialophora immunda together with a few other species of the order Chaetothyriales show a special association with hydrocarbon polluted environments. The finding that the fungus is able to completely mineralize toluene makes it an interesting candidate for bioremediation purposes. The present study is the first transcriptomic investigation of a fungus grown in presence of toluene as sole carbon and energy source. We could observe the activation of genes involved in toluene degradatation and several stress response mechanisms which allowed the fungus to survive the toluene exposure. The thorough comparative genomics analysis allowed us to identify several events of horizontal gene transfer between bacteria and Cladophialophora immunda and unveil toluene degradation steps that were previously reported in bacteria. The work presented here aims to give new insights into the ecology of Cladophialophora immunda and its adaptation strategies to hydrocarbon polluted environments.

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          featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

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          Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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            A dynamic and intricate regulatory network determines Pseudomonas aeruginosa virulence

            Pseudomonas aeruginosa is a metabolically versatile bacterium that is found in a wide range of biotic and abiotic habitats. It is a major human opportunistic pathogen causing numerous acute and chronic infections. The critical traits contributing to the pathogenic potential of P. aeruginosa are the production of a myriad of virulence factors, formation of biofilms and antibiotic resistance. Expression of these traits is under stringent regulation, and it responds to largely unidentified environmental signals. This review is focused on providing a global picture of virulence gene regulation in P. aeruginosa. In addition to key regulatory pathways that control the transition from acute to chronic infection phenotypes, some regulators have been identified that modulate multiple virulence mechanisms. Despite of a propensity for chaotic behaviour, no chaotic motifs were readily observed in the P. aeruginosa virulence regulatory network. Having a ‘birds-eye’ view of the regulatory cascades provides the forum opportunities to pose questions, formulate hypotheses and evaluate theories in elucidating P. aeruginosa pathogenesis. Understanding the mechanisms involved in making P. aeruginosa a successful pathogen is essential in helping devise control strategies.
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              SMURF: Genomic mapping of fungal secondary metabolite clusters.

              Fungi produce an impressive array of secondary metabolites (SMs) including mycotoxins, antibiotics and pharmaceuticals. The genes responsible for their biosynthesis, export, and transcriptional regulation are often found in contiguous gene clusters. To facilitate annotation of these clusters in sequenced fungal genomes, we developed the web-based software SMURF (www.jcvi.org/smurf/) to systematically predict clustered SM genes based on their genomic context and domain content. We applied SMURF to catalog putative clusters in 27 publicly available fungal genomes. Comparison with genetically characterized clusters from six fungal species showed that SMURF accurately recovered all clusters and detected additional potential clusters. Subsequent comparative analysis revealed the striking biosynthetic capacity and variability of the fungal SM pathways and the correlation between unicellularity and the absence of SMs. Further genetics studies are needed to experimentally confirm these clusters. 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                barbara.blasi@boku.ac.at
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 September 2017
                12 September 2017
                2017
                : 7
                : 11436
                Affiliations
                ISNI 0000 0001 2298 5320, GRID grid.5173.0, Department of Biotechnology, VIBT-EQ Extremophile Center, University of Natural Resources and Life Sciences, ; 1190 Vienna, Austria
                Author information
                http://orcid.org/0000-0002-3776-6363
                Article
                11807
                10.1038/s41598-017-11807-8
                5595782
                28900256
                44240d11-08db-4284-af75-cb9aef1634ae
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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                : 6 April 2017
                : 30 August 2017
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