@article{AudretschGrataniWolzetal.2021,
  author    = {Audretsch, Christof and Gratani, Fabio and Wolz, Christiane and Dandekar, Thomas},
  title     = {Modeling of stringent-response reflects nutrient stress induced growth impairment and essential amino acids in different Staphylococcus aureus mutants},
  series = {Scientific Reports},
  volume    = {11},
  journal   = {Scientific Reports},
  number    = {1},
  doi       = {10.1038/s41598-021-88646-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260313},
  year      = {2021},
  abstract  = {Stapylococcus aureus colonises the nose of healthy individuals but can also cause a wide range of infections. Amino acid (AA) synthesis and their availability is crucial to adapt to conditions encountered in vivo. Most S. aureus genomes comprise all genes required for AA biosynthesis. Nevertheless, different strains require specific sets of AAs for growth. In this study we show that regulation inactivates pathways under certain conditions which result in these observed auxotrophies. We analyzed in vitro and modeled in silico in a Boolean semiquantitative model (195 nodes, 320 edges) the regulatory impact of stringent response (SR) on AA requirement in S. aureus HG001 (wild-type) and in mutant strains lacking the metabolic regulators RSH, CodY and CcpA, respectively. Growth in medium lacking single AAs was analyzed. Results correlated qualitatively to the in silico predictions of the final model in 92\% and quantitatively in 81\%. Remaining gaps in our knowledge are evaluated and discussed. This in silico model is made fully available and explains how integration of different inputs is achieved in SR and AA metabolism of S. aureus. The in vitro data and in silico modeling stress the role of SR and central regulators such as CodY for AA metabolisms in S. aureus.},
  language  = {en}
}
@phdthesis{Audretsch2013,
  author    = {Audretsch, Christof},
  title     = {Analysing Quorum Sensing and Biofilm formation in Staphylococcus aureus},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-92189},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {Staphylococcus aureus (SA) causes nosocomial infections including life threatening sepsis by multi-resistant strains (MRSA). It has the ability to form biofilms to protect it from the host immune system and from anti staphylococcal drugs. Biofilm and planctonic life style is regulated by a complex Quorum-Sensing (QS) system with agr as a central regulator. To study biofilm formation and QS mechanisms in SA a Boolean network was build (94 nodes, 184 edges) including two different component systems such as agr, sae and arl. Important proteins such as Sar, Rot and SigB were included as further nodes in the model. System analysis showed there are only two stable states biofilm forming versus planctonic with clearly different subnetworks turned on. Validation according to gene expression data confirmed this. Network consistency was tested first according to previous knowledge and literature. Furthermore, the predicted node activity of different in silico knock-out strains agreed well with corresponding micro array experiments and data sets. Additional validation included the expression of further nodes (Northern blots) and biofilm production compared in different knock-out strains in biofilm adherence assays. The model faithfully reproduces the behaviour of QS signalling mutants. The integrated model allows also prediction of various other network mutations and is supported by experimental data from different strains. Furthermore, the well connected hub proteins elucidate how integration of different inputs is achieved by the QS network. For in silico as well as in vitro experiments it was found that the sae-locus is also a central modulator of biofilm production. Sae knock-out strains showed stronger biofilms. Wild type phenotype was rescued by sae complementation. To elucidate the way in which sae takes influence on biofilm formation the network was used and Venn-diagrams were made, revealing nodes regulated by sae and changed in biofilms. In these Venn-diagrams nucleases and extracellular proteins were found to be promising nodes. The network revealed DNAse to be of great importance. Therefore qualitatively the DNAse amount, produced by different SA mutants was measured, it was tried to dissolve biofilms with according amounts of DNAse and the concentration of nucleic acids, proteins and polysaccharides were measured in biofilms of different SA mutants. With its thorough validation the network model provides a powerful tool to study QS and biofilm formation in SA, including successful predictions for different knock-out mutant behaviour, QS signalling and biofilm formation. This includes implications for the behaviour of MRSA strains and mutants. Key regulatory mutation combinations (agr-, sae-, sae-/agr-, sigB+, sigB+/sae-) were directly tested in the model but also in experiments. High connectivity was a good guide to identify master regulators, whose detailed behaviour was studied both in vitro and in the model. Together, both lines of evidence support in particular a refined regulatory role for sae and agr with involvement in biofilm repression and/or SA dissemination. With examination of the composition of different mutant biofilms as well as with the examination of the reaction cascade that connects sae to the biofilm forming ability of SA and also by postulating that nucleases might play an important role in that, first steps were taken in proving and explaining regulatory links leading from sae to biofilms. Furthermore differences in biofilms of different mutant SA strains were found leading us in perspective towards a new understanding of biofilms including knowledge how to better regulate, fight and use its different properties.},
  subject      = {Staphylococcus aureus},
  language  = {en}
}