@phdthesis{Sharan2017,
  author    = {Sharan, Malvika},
  title     = {Bio-computational identification and characterization of RNA-binding proteins in bacteria},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-153573},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2017},
  abstract  = {RNA-binding proteins (RBPs) have been extensively studied in eukaryotes, where they post-transcriptionally regulate many cellular events including RNA transport, translation, and stability. Experimental techniques, such as cross-linking and co-purification followed by either mass spectrometry or RNA sequencing has enabled the identification and characterization of RBPs, their conserved RNA-binding domains (RBDs), and the regulatory roles of these proteins on a genome-wide scale. These developments in quantitative, high-resolution, and high-throughput screening techniques have greatly expanded our understanding of RBPs in human and yeast cells. In contrast, our knowledge of number and potential diversity of RBPs in bacteria is comparatively poor, in part due to the technical challenges associated with existing global screening approaches developed in eukaryotes. Genome- and proteome-wide screening approaches performed in silico may circumvent these technical issues to obtain a broad picture of the RNA interactome of bacteria and identify strong RBP candidates for more detailed experimental study. Here, I report APRICOT ("Analyzing Protein RNA Interaction by Combined Output Technique"), a computational pipeline for the sequence-based identification and characterization of candidate RNA-binding proteins encoded in the genomes of all domains of life using RBDs known from experimental studies. The pipeline identifies functional motifs in protein sequences of an input proteome using position-specific scoring matrices and hidden Markov models of all conserved domains available in the databases and then statistically score them based on a series of sequence-based features. Subsequently, APRICOT identifies putative RBPs and characterizes them according to functionally relevant structural properties. APRICOT performed better than other existing tools for the sequence-based prediction on the known RBP data sets. The applications and adaptability of the software was demonstrated on several large bacterial RBP data sets including the complete proteome of Salmonella Typhimurium strain SL1344. APRICOT reported 1068 Salmonella proteins as RBP candidates, which were subsequently categorized using the RBDs that have been reported in both eukaryotic and bacterial proteins. A set of 131 strong RBP candidates was selected for experimental confirmation and characterization of RNA-binding activity using RNA co-immunoprecipitation followed by high-throughput sequencing (RIP-Seq) experiments. Based on the relative abundance of transcripts across the RIP-Seq libraries, a catalogue of enriched genes was established for each candidate, which shows the RNA-binding potential of 90\% of these proteins. Furthermore, the direct targets of few of these putative RBPs were validated by means of cross-linking and co-immunoprecipitation (CLIP) experiments. This thesis presents the computational pipeline APRICOT for the global screening of protein primary sequences for potential RBPs in bacteria using RBD information from all kingdoms of life. Furthermore, it provides the first bio-computational resource of putative RBPs in Salmonella, which could now be further studied for their biological and regulatory roles. The command line tool and its documentation are available at https://malvikasharan.github.io/APRICOT/.},
  language  = {en}
}
@phdthesis{Horn2017,
  author    = {Horn, Hannes},
  title     = {Analysis and interpretation of (meta-)genomic data from host-associated microorganisms},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-152035},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2017},
  abstract  = {Host-microbe interactions are the key to understand why and how microbes inhabit specific environments. With the scientific fields of microbial genomics and metagenomics, evolving on an unprecedented scale, one is able to gain insights in these interactions on a molecular and ecological level. The goal of this PhD thesis was to make (meta-)genomic data accessible, integrate it in a comparative manner and to gain comprehensive taxonomic and functional insights into bacterial strains and communities derived from two different environments: the phyllosphere of Arabidopsis thaliana and the mesohyl interior of marine sponges. This thesis focused first on the de novo assembly of bacterial genomes. A 5-step protocol was developed, each step including a quality control. The examination of different assembly software in a comparative way identified SPAdes as most suitable. The protocol enables the user to chose the best tailored assembly. Contamination issues were solved by an initial filtering of the data and methods normally used for the binning of metagenomic datasets. This step is missed in many published assembly pipelines. The described protocol offers assemblies of high quality ready for downstream analysis. Subsequently, assemblies generated with the developed protocol were annotated and explored in terms of their function. In a first study, the genome of a phyllosphere bacterium, Williamsia sp. ARP1, was analyzed, offering many adaptions to the leaf habitat: it can deal with temperature shifts, react to oxygen species, produces mycosporins as protection against UV-light, and is able to uptake photosynthates. Further, its taxonomic position within the Actinomycetales was infered from 16S rRNA and comparative genomics showing the close relation between the genera Williamsia and Gordonia. In a second study, six sponge-derived actinomycete genomes were investigated for secondary metabolism. By use of state-of-the-art software, these strains exhibited numerous gene clusters, mostly linked to polykethide synthases, non-ribosomal peptide synthesis, terpenes, fatty acids and saccharides. Subsequent predictions on these clusters offered a great variety of possible produced compounds with antibiotic, antifungal or anti-cancer activity. These analysis highlight the potential for the synthesis of natural products and the use of genomic data as screening toolkit. In a last study, three sponge-derived and one seawater metagenomes were functionally compared. Different signatures regarding the microbial composition and GC-distribution were observed between the two environments. With a focus on bacerial defense systems, the data indicates a pronounced repertoire of sponge associated bacteria for bacterial defense systems, in particular, Clustered Regularly Interspaced Short Palindromic Repeats, restriction modification system, DNA phosphorothioation and phage growth limitation. In addition, characterizing genes for secondary metabolite cluster differed between sponge and seawater microbiomes. Moreover, a variety of Type I polyketide synthases were only found within the sponge microbiomes. With that, metagenomics are shown to be a useful tool for the screening of secondary metabolite genes. Furthermore, enriched defense systems are highlighted as feature of sponge-associated microbes and marks them as a selective trait.},
  subject      = {Bakterien},
  language  = {en}
}