• TCR signalling network organization at the immunological synapses of murine regulatory T cells.

      van Ham, Marco; Teich, René; Philipsen, Lars; Niemz, Jana; Amsberg, Nicole; Wissing, Josef; Nimtz, Manfred; Gröbe, Lothar; Kliche, Stefanie; Thiel, Nadine; et al. (2017-08-17)
      Regulatory T (Treg) cells require T-cell receptor (TCR) signalling to exert their immunosuppressive activity, but the precise organization of the TCR signalling network compared to conventional T (Tconv) cells remains elusive. By using accurate mass spectrometry and multi-epitope ligand cartography (MELC) we characterized TCR signalling and recruitment of TCR signalling components to the immunological synapse (IS) in Treg cells and Tconv cells. With the exception of Themis which we detected in lower amounts in Treg cells, other major TCR signalling components were found equally abundant, however, their phosphorylation-status notably discriminates Treg cells from Tconv cells. Overall, this study identified 121 Treg cell-specific phosphorylations. Short-term triggering of T cell subsets via CD3 and CD28 widely harmonized these variations with the exception of eleven TCR signalling components that mainly regulate cytoskeleton dynamics and molecular transport. Accordingly, conjugation with B cells indeed caused variant cellular morphology and revealed a Treg cell-specific recruitment of TCR signalling components such as PKCθ, PLCγ1 and ZAP70 as well as B cell-derived CD86 into the IS. Together, results from this study support the existence of a Treg cell-specific IS and suggest Treg cell-specific cytoskeleton dynamics as a novel determinant for the unique functional properties of Treg cells.
    • Terahertz electromagnetic fields (0.106 THz) do not induce manifest genomic damage in vitro.

      Hintzsche, Henning; Jastrow, Christian; Kleine-Ostmann, Thomas; Kärst, Uwe; Schrader, Thorsten; Stopper, Helga; Institut für Pharmakologie und Toxikologie, Universität Würzburg, Würzburg, Germany. (2012)
      Terahertz electromagnetic fields are non-ionizing electromagnetic fields in the frequency range from 0.1 to 10 THz. Potential applications of these electromagnetic fields include the whole body scanners, which currently apply millimeter waves just below the terahertz range, but future scanners will use higher frequencies in the terahertz range. These and other applications will bring along human exposure to these fields. Up to now, only a limited number of investigations on biological effects of terahertz electromagnetic fields have been performed. Therefore, research is strongly needed to enable reliable risk assessment.Cells were exposed for 2 h, 8 h, and 24 h with different power intensities ranging from 0.04 mW/cm(2) to 2 mW/cm(2), representing levels below, at, and above current safety limits. Genomic damage on the chromosomal level was measured as micronucleus formation. DNA strand breaks and alkali-labile sites were quantified with the comet assay. No DNA strand breaks or alkali-labile sites were observed as a consequence of exposure to terahertz electromagnetic fields in the comet assay. The fields did not cause chromosomal damage in the form of micronucleus induction.
    • Testing noisy numerical data for monotonic association

      Bodenhofer, Ulrich; Krone, Martin; Klawonn, Frank (2013-08-21)
    • The zlog value as a basis for the standardization of laboratory results

      Hoffmann, Georg; Klawonn, Frank; Lichtinghagen, Ralf; Orth, Matthias; Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7, 38124 Braunschweig, Germany. (2017-01-26)
      Abstract Background: With regard to the German E-Health Law of 2016, the German Society for Clinical Chemistry and Laboratory Medicine (DGKL) has been invited to develop a standard procedure for the storage and transmission of laboratory results. We suggest the commonly used z-transformation. Methods: This method evaluates by how many standard deviations (SDs) a given result deviates from the mean of the respective reference population. We confirm with real data that laboratory results of healthy individuals can be adjusted to a normal distribution by logarithmic transformation. Results: Thus, knowing the lower and upper reference limits LL and UL, one can transform any result x into a zlog value using the following equation: $\eqalign{ {\rm{zlog}} = & {\rm{(log(x)}}-{\rm{(log(LL)}} + {\rm{log(UL))/2)\cdot3}}{\rm{.92/(log(UL)}} \cr -{\bf{ }}{\rm{log(LL))}} \cr} $ The result can easily be interpreted, as its reference interval (RI) is –1.96 to +1.96 by default, and very low or high results yield zlog values around –5 and +5, respectively. For intuitive data presentation, the zlog values may be transformed into a continuous color scale, e.g. from blue via white to orange. Using the inverse function, any zlog value can then be translated into the theoretical result of an analytical method with another RI: (1) $${\rm{x}} = {\rm{L}}{{\rm{L}}^{0.5 - {\rm{zlog}}/3.92}} \cdot {\rm{U}}{{\rm{L}}^{0.5 + {\rm{zlog}}/3.92}}$$ Conclusions: Our standardization proposal can easily be put into practice and may effectively contribute to data quality and patient safety in the frame of the German E-health law. We suggest for the future that laboratories should provide the zlog value in addition to the original result, and that the data transmission protocols (e.g. HL7, LDT) should contain a special field for this additional value.
    • Towards the characterization of the hidden world of small proteins in Staphylococcus aureus, a proteogenomics approach.

      Fuchs, Stephan; Kucklick, Martin; Lehmann, Erik; Beckmann, Alexander; Wilkens, Maya; Kolte, Baban; Mustafayeva, Ayten; Ludwig, Tobias; Diwo, Maurice; Wissing, Josef; et al. (PLOS, 2021-06-01)
      Small proteins play essential roles in bacterial physiology and virulence, however, automated algorithms for genome annotation are often not yet able to accurately predict the corresponding genes. The accuracy and reliability of genome annotations, particularly for small open reading frames (sORFs), can be significantly improved by integrating protein evidence from experimental approaches. Here we present a highly optimized and flexible bioinformatics workflow for bacterial proteogenomics covering all steps from (i) generation of protein databases, (ii) database searches and (iii) peptide-to-genome mapping to (iv) visualization of results. We used the workflow to identify high quality peptide spectrum matches (PSMs) for small proteins (≤ 100 aa, SP100) in Staphylococcus aureus Newman. Protein extracts from S. aureus were subjected to different experimental workflows for protein digestion and prefractionation and measured with highly sensitive mass spectrometers. In total, 175 proteins with up to 100 aa (SP100) were identified. Out of these 24 (ranging from 9 to 99 aa) were novel and not contained in the used genome annotation.144 SP100 are highly conserved and were found in at least 50% of the publicly available S. aureus genomes, while 127 are additionally conserved in other staphylococci. Almost half of the identified SP100 were basic, suggesting a role in binding to more acidic molecules such as nucleic acids or phospholipids.
    • Two FtsH Proteases Contribute to Fitness and Adaptation of Clone C Strains.

      Kamal, Shady Mansour; Rybtke, Morten Levin; NIMTZ, MANFRED; Sperlein, Stefanie; Giske, Christian; Trček, Janja; Deschamps, Julien; Briandet, Romain; Dini, Luciana; Jänsch, Lothar; et al. (Frontiers, 2019-01-01)
      Pseudomonas aeruginosa is an environmental bacterium and a nosocomial pathogen with clone C one of the most prevalent clonal groups. The P. aeruginosa clone C specific genomic island PACGI-1 harbors a xenolog of ftsH encoding a functionally diverse membrane-spanning ATP-dependent metalloprotease on the core genome. In the aquatic isolate P. aeruginosa SG17M, the core genome copy ftsH1 significantly affects growth and dominantly mediates a broad range of phenotypes, such as secretion of secondary metabolites, swimming and twitching motility and resistance to aminoglycosides, while the PACGI-1 xenolog ftsH2 backs up the phenotypes in the ftsH1 mutant background. The two proteins, with conserved motifs for disaggregase and protease activity present in FtsH1 and FtsH2, have the ability to form homo- and hetero-oligomers with ftsH2 distinctively expressed in the late stationary phase of growth. However, mainly FtsH1 degrades a major substrate, the heat shock transcription factor RpoH. Pull-down experiments with substrate trap-variants inactive in proteolytic activity indicate both FtsH1 and FtsH2 to interact with the inhibitory protein HflC, while the phenazine biosynthesis protein PhzC was identified as a substrate of FtsH1. In summary, as an exception in P. aeruginosa, clone C harbors two copies of the ftsH metallo-protease, which cumulatively are required for the expression of a diversity of phenotypes.
    • xCELLanalyzer: A Framework for the Analysis of Cellular Impedance Measurements for Mode of Action Discovery

      Franke, Raimo; Hinkelmann, Bettina; Fetz, Verena; Stradal, Theresia; Sasse, Florenz; Klawonn, Frank; Brönstrup, Mark; HZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany. (Sage, 2019-01-25)
      Mode of action (MoA) identification of bioactive compounds is very often a challenging and time-consuming task. We used a label-free kinetic profiling method based on an impedance readout to monitor the time-dependent cellular response profiles for the interaction of bioactive natural products and other small molecules with mammalian cells. Such approaches have been rarely used so far due to the lack of data mining tools to properly capture the characteristics of the impedance curves. We developed a data analysis pipeline for the xCELLigence Real-Time Cell Analysis detection platform to process the data, assess and score their reproducibility, and provide rank-based MoA predictions for a reference set of 60 bioactive compounds. The method can reveal additional, previously unknown targets, as exemplified by the identification of tubulin-destabilizing activities of the RNA synthesis inhibitor actinomycin D and the effects on DNA replication of vioprolide A. The data analysis pipeline is based on the statistical programming language R and is available to the scientific community through a GitHub repository.