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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.
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.