@article{BlaettnerDasPaprotkaetal.2016, author = {Bl{\"a}ttner, Sebastian and Das, Sudip and Paprotka, Kerstin and Eilers, Ursula and Krischke, Markus and Kretschmer, Dorothee and Remmele, Christian W. and Dittrich, Marcus and M{\"u}ller, Tobias and Schuelein-Voelk, Christina and Hertlein, Tobias and Mueller, Martin J. and Huettel, Bruno and Reinhardt, Richard and Ohlsen, Knut and Rudel, Thomas and Fraunholz, Martin J.}, title = {Staphylococcus aureus Exploits a Non-ribosomal Cyclic Dipeptide to Modulate Survival within Epithelial Cells and Phagocytes}, series = {PLoS Pathogens}, volume = {12}, journal = {PLoS Pathogens}, number = {9}, doi = {10.1371/journal.ppat.1005857}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180380}, year = {2016}, abstract = {Community-acquired (CA) Staphylococcus aureus cause various diseases even in healthy individuals. Enhanced virulence of CA-strains is partly attributed to increased production of toxins such as phenol-soluble modulins (PSM). The pathogen is internalized efficiently by mammalian host cells and intracellular S. aureus has recently been shown to contribute to disease. Upon internalization, cytotoxic S. aureus strains can disrupt phagosomal membranes and kill host cells in a PSM-dependent manner. However, PSM are not sufficient for these processes. Here we screened for factors required for intracellular S. aureus virulence. We infected escape reporter host cells with strains from an established transposon mutant library and detected phagosomal escape rates using automated microscopy. We thereby, among other factors, identified a non-ribosomal peptide synthetase (NRPS) to be required for efficient phagosomal escape and intracellular survival of S. aureus as well as induction of host cell death. By genetic complementation as well as supplementation with the synthetic NRPS product, the cyclic dipeptide phevalin, wild-type phenotypes were restored. We further demonstrate that the NRPS is contributing to virulence in a mouse pneumonia model. Together, our data illustrate a hitherto unrecognized function of the S. aureus NRPS and its dipeptide product during S. aureus infection.}, language = {en} } @article{RemmeleXianAlbrechtetal.2014, author = {Remmele, Christian W. and Xian, Yibo and Albrecht, Marco and Faulstich, Michaela and Fraunholz, Martin and Heinrichs, Elisabeth and Dittrich, Marcus T. and M{\"u}ller, Tobias and Reinhardt, Richard and Rudel, Thomas}, title = {Transcriptional landscape and essential genes of Neisseria gonorrhoeae}, doi = {10.1093/nar/gku762}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-113676}, year = {2014}, abstract = {The WHO has recently classified Neisseria gonorrhoeae as a super-bacterium due to the rapid spread of antibiotic resistant derivatives and an overall dramatic increase in infection incidences. Genome sequencing has identified potential genes, however, little is known about the transcriptional organization and the presence of non-coding RNAs in gonococci. We performed RNA sequencing to define the transcriptome and the transcriptional start sites of all gonococcal genes and operons. Numerous new transcripts including 253 potentially non-coding RNAs transcribed from intergenic regions or antisense to coding genes were identified. Strikingly, strong antisense transcription was detected for the phase-variable opa genes coding for a family of adhesins and invasins in pathogenic Neisseria, that may have regulatory functions. Based on the defined transcriptional start sites, promoter motifs were identified. We further generated and sequenced a high density Tn5 transposon library to predict a core of 827 gonococcal essential genes, 133 of which have no known function. Our combined RNA-Seq and Tn-Seq approach establishes a detailed map of gonococcal genes and defines the first core set of essential gonococcal genes.}, language = {en} } @article{RemmeleLutherBalkenholetal.2015, author = {Remmele, Christian W. and Luther, Christian H. and Balkenhol, Johannes and Dandekar, Thomas and M{\"u}ller, Tobias and Dittrich, Marcus T.}, title = {Integrated inference and evaluation of host-fungi interaction networks}, series = {Frontiers in Microbiology}, volume = {6}, journal = {Frontiers in Microbiology}, number = {764}, doi = {10.3389/fmicb.2015.00764}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148278}, year = {2015}, abstract = {Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi human and fungi mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host fungi transcriptome and proteome data.}, language = {en} } @article{UrbanRemmeleDittrichetal.2020, author = {Urban, Lara and Remmele, Christian W. and Dittrich, Marcus and Schwarz, Roland F. and M{\"u}ller, Tobias}, title = {covRNA: discovering covariate associations in large-scale gene expression data}, series = {BMC Reserach Notes}, volume = {13}, journal = {BMC Reserach Notes}, doi = {10.1186/s13104-020-04946-1}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229258}, year = {2020}, abstract = {Objective The biological interpretation of gene expression measurements is a challenging task. While ordination methods are routinely used to identify clusters of samples or co-expressed genes, these methods do not take sample or gene annotations into account. We aim to provide a tool that allows users of all backgrounds to assess and visualize the intrinsic correlation structure of complex annotated gene expression data and discover the covariates that jointly affect expression patterns. Results The Bioconductor package covRNA provides a convenient and fast interface for testing and visualizing complex relationships between sample and gene covariates mediated by gene expression data in an entirely unsupervised setting. The relationships between sample and gene covariates are tested by statistical permutation tests and visualized by ordination. The methods are inspired by the fourthcorner and RLQ analyses used in ecological research for the analysis of species abundance data, that we modified to make them suitable for the distributional characteristics of both, RNA-Seq read counts and microarray intensities, and to provide a high-performance parallelized implementation for the analysis of large-scale gene expression data on multi-core computational systems. CovRNA provides additional modules for unsupervised gene filtering and plotting functions to ensure a smooth and coherent analysis workflow.}, language = {en} }