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Organismic Interactions

Evolutionary perspectives of Arabidopsis thaliana pan genome

Molecular evolution of organisms is driven by mutations and genetic drift acting at the level of the genome, but constrained by natural selection acting at the level of the phenotype. The key objective of this research is to gain an understanding of how the impact of evolution varies across the genome of Arabidopsis thaliana. Therefore, we use Shannon - Entropy (H), based on information theory, as a measure of diversity of each nucleotide position, across the entire genome, between several accessions. A comparison between metabolic and non-metabolic genes revealed a lower Shannon-Entropy in the genes performing metabolic functions. In contrast, the functional categories hormone metabolism, metal handling, secondary metabolism, stress and cell wall posse a significantly higher Shannon-Entropy than expected. Comparison between different gene categories and ecotypes from different geographical regions revealed that clusters of genes performing particular functions exhibit constant amount of acquired mutations independent of ecotype origin compared to other gene clusters that shows a more dynamical behaviour that relies on the ecotype geographical origin.

Phage – Bacteria Interaction

Phages are viruses that prey on prokaryotes, are the most abundant and diverse inhabitants of the planet. Temperate (or lysogenic) phages multiply via the lysogenic cycle, which is established by an integration of the phage genome into the host chromosomes, creating a prophage within the host genome and maintaining a long-term association. The close relationship between host and virus has significantly shaped microbial evolution. Phages are known vectors of DNA transfer between microbial cells The excision of phage DNA from the host genome and the production of phages may be accompanied by packing of host DNA into the phages, which can then transfer it to the next host in a process that has been termed transduction. Specialized transduction occurs when the phage integrases cleave, in addition to the prophage, bacterial genes that are encoded at the prophage flanking regions. These are packed with the phage DNA into the phages. Generalized transduction occurs when random bacterial DNA is packed into the phages. In our research we focus in the reconstruction of an phage – host association, the impact of transducted metabolic genes on the microbial host and the transcription regulation through H-NS like proteins, called silencer.

Phage-Host Association: To infer the associations between phages to prophages to hosts we used a graph-based approach to reconstruct a network of phage – prophages - bacteria associations. The nodes in the network are phages, prophages and bacterial hosts and the two types of edges correspond either to shared functional similarity between phage and prophage or logical link between the prophage and host. This method allows us to reconstruct more distant relation between the phage and their putative targets with new perspectives on evolutionary mechanisms that influence the phage-bacteria interaction.

Impact of transduction on host metabolism: Bacterial metabolic genes that are encoded on prophages are often the results of a transduction event. These genes have the capability to influence the bacterial metabolism significantly providing additional functional chances that can increase the host fitness. To measure the impact of these genes we apply the “network expansion algorithm” in order to calculate the differences in metabolic SCOPES with and without the prophage encoded gene. As far as we know, this is the first method that allows to systematically quantify the impact of transduction on host metabolism.

Transcription regulation – H-NS like silencer proteins: Foreign genes that enter the host genome can posses negative effects on the host fitness. Some regulatory mechanism like the H-NS proteins targets on binding AT – rich DNA material and silences their transcription. Today we know about several types of silencer proteins that are encoded in a wide range of prokaryotic taxa. In a collaboration with Prof. Frunzke from IBG1 Jülich we focus on the research of LSR2 silencer proteins encoded in different Actinobacteria species. The LSR2 proteins are today the only silencers that were found on phage and prophage genomes of Streptomyces hosts. Using different bioinformatic approaches we were able to identify different groups of LSR2 proteins, which differ in their amino acid properties between the phage and host, encoded ones. Further we pay attention to the Cyanobacteria species in which no transcription silencers were described until today. In a combination of bioinformatic methods and laboratory experiments we aim towards the characterisation of theses protein in cyanobacterial genomes.

Interdisciplinary Statistical Data Analysis

PSINK / SCIEF: Our research includes the statistical data analysis of biological origin. In our collaboration with the medical department from HHU we support the PSINK project with a statistical module SCIEF (Spinal Cord Injury Evaluation Framework). Here we develop and implement specific statistical methods to analyse the data that is generated in PSINK. The aim of this collaboration is to discover main effects of several treatments as well as side effects from a study design to an outcome in a spinal cord injury experiment. Thus we hope to improve the research towards a more promising treatment for patients suffering from spinal cord injury.

Neuropathology: An additional collaboration with the institute of neuropathology HHU (Prof. Korth) is focusing towards understanding the regulation of specific immune-related network of genes for a better clinical diagnosis of schizophrenia. In this project we analysed a gene expression data set of Disrupted-in-Schizophrenia 1 (DISC1) transgenic rat model and built a co-expression network using the WGCNA 1 method. Therefore we were able to identify hub genes that are co-expressed and deregulated in the transgenic rat model

Contact: Dr. Ovidiu Popa, Nima Saadat

Key Publications

  1. Handorf, T., Ebenhöh, O., & Heinrich, R. (2005). Expanding metabolic networks: scopes of compounds, robustness, and evolution. Journal of molecular evolution, 61(4), 498-512.
  2. Popa, O., Landan, G., & Dagan, T. (2017). Phylogenomic networks reveal limited phylogenetic range of lateral gene transfer by transduction. The ISME journal, 11(2), 543-554.
  3. Pfeifer, E., Hünnefeld, M., Popa, O., Polen, T., Kohlheyer, D., Baumgart, M., & Frunzke, J. (2016). Silencing of cryptic prophages in Corynebacterium glutamicum. Nucleic acids research, 44(21), 10117-10131.
  4. Pfeifer, E., Hünnefeld, M., Popa, O., & Frunzke, J. (2019). Impact of xenogeneic silencing on phage–host interactions. Journal of molecular biology, 431(23), 4670-4683.
  5. Langfelder, P., & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC bioinformatics, 9(1), 559.
  6. Trossbach, S. V., Hecher, L., Schafflick, D., Deenen, R., Popa, O., Lautwein, T., ... & Malchow, B. (2019). Dysregulation of a specific immune-related network of genes biologically defines a subset of schizophrenia. Translational psychiatry, 9(1), 1-16.
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