TY - JOUR A1 - Krohn-Molt, Ines A1 - Alawi, Malik A1 - Förstner, Konrad U. A1 - Wiegandt, Alena A1 - Burkhardt, Lia A1 - Indenbirken, Daniela A1 - Thieß, Melanie A1 - Grundhoff, Adam A1 - Kehr, Julia A1 - Tholey, Andreas A1 - Streit, Wolfgang R. T1 - Insights into microalga and bacteria interactions of selected phycosphere biofilms using metagenomic, transcriptomic, and proteomic approaches JF - Frontiers in Microbiology N2 - Microalga are of high relevance for the global carbon cycling and it is well-known that they are associated with a microbiota. However, it remains unclear, if the associated microbiota, often found in phycosphere biofilms, is specific for the microalga strains and which role individual bacterial taxa play. Here we provide experimental evidence that \(Chlorella\) \(saccharophila\), \(Scenedesmus\) \(quadricauda\), and \(Micrasterias\) \(crux-melitensis\), maintained in strain collections, are associated with unique and specific microbial populations. Deep metagenome sequencing, binning approaches, secretome analyses in combination with RNA-Seq data implied fundamental differences in the gene expression profiles of the microbiota associated with the different microalga. Our metatranscriptome analyses indicates that the transcriptionally most active bacteria with respect to key genes commonly involved in plant–microbe interactions in the Chlorella (Trebouxiophyceae) and Scenedesmus (Chlorophyceae) strains belong to the phylum of the α-Proteobacteria. In contrast, in the Micrasterias (Zygnematophyceae) phycosphere biofilm bacteria affiliated with the phylum of the Bacteroidetes showed the highest gene expression rates. We furthermore show that effector molecules known from plant-microbe interactions as inducers for the innate immunity are already of relevance at this evolutionary early plant-microbiome level. KW - microbiology KW - microalga-bacteria interaction KW - phycosphere biofilm KW - metagenomics KW - metatranscriptomics KW - metaproteomics Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-173701 VL - 2017 IS - 8 ER - TY - JOUR A1 - Sharan, Malvika A1 - Förstner, Konrad U. A1 - Eulalio, Ana A1 - Vogel, Jörg T1 - APRICOT: an integrated computational pipeline for the sequence-based identification and characterization of RNA-binding proteins JF - Nucleic Acids Research N2 - RNA-binding proteins (RBPs) have been established as core components of several post-transcriptional gene regulation mechanisms. Experimental techniques such as cross-linking and co-immunoprecipitation have enabled the identification of RBPs, RNA-binding domains (RBDs) and their regulatory roles in the eukaryotic species such as human and yeast in large-scale. In contrast, our knowledge of the number and potential diversity of RBPs in bacteria is poorer due to the technical challenges associated with the existing global screening approaches. We introduce APRICOT, a computational pipeline for the sequence-based identification and characterization of proteins using RBDs known from experimental studies. The pipeline identifies functional motifs in protein sequences using position-specific scoring matrices and Hidden Markov Models of the functional domains and statistically scores them based on a series of sequence-based features. Subsequently, APRICOT identifies putative RBPs and characterizes them by several biological properties. Here we demonstrate the application and adaptability of the pipeline on large-scale protein sets, including the bacterial proteome of Escherichia coli. APRICOT showed better performance on various datasets compared to other existing tools for the sequence-based prediction of RBPs by achieving an average sensitivity and specificity of 0.90 and 0.91 respectively. The command-line tool and its documentation are available at https://pypi.python.org/pypi/bio-apricot. KW - RNA-binding proteins KW - identification KW - characterization Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-157963 VL - 45 IS - 11 ER - TY - JOUR A1 - Lavysh, Daria A1 - Sokolova, Maria A1 - Slashcheva, Marina A1 - Förstner, Konrad U. A1 - Severinov, Konstantin T1 - Transcription profiling of "bacillus subtilis" cells infected with AR9, a giant phage encoding two multisubunit RNA polymerases JF - mBio N2 - Bacteriophage AR9 is a recently sequenced jumbo phage that encodes two multisubunit RNA polymerases. Here we investigated the AR9 transcription strategy and the effect of AR9 infection on the transcription of its host, Bacillus subtilis. Analysis of whole-genome transcription revealed early, late, and continuously expressed AR9 genes. Alignment of sequences upstream of the 5′ ends of AR9 transcripts revealed consensus sequences that define early and late phage promoters. Continuously expressed AR9 genes have both early and late promoters in front of them. Early AR9 transcription is independent of protein synthesis and must be determined by virion RNA polymerase injected together with viral DNA. During infection, the overall amount of host mRNAs is significantly decreased. Analysis of relative amounts of host transcripts revealed notable differences in the levels of some mRNAs. The physiological significance of up- or downregulation of host genes for AR9 phage infection remains to be established. AR9 infection is significantly affected by rifampin, an inhibitor of host RNA polymerase transcription. The effect is likely caused by the antibiotic-induced killing of host cells, while phage genome transcription is solely performed by viral RNA polymerases. KW - Bacteriaophage AR9 KW - Transcription profiling Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-181810 VL - 8 IS - 1 ER -