Now showing items 101-112 of 112

• #### Sample size and performance estimation for biomarker combinations based on pilot studies with small sample sizes”

High throughput technologies like microarrays, next generation sequencing and mass spectrometry enable the measurement of tens of thousands of biomarker candidates in pilot studies. Biological systems are often too complex to be based on simple single cause-effect associations and from the medical practice point of view, a single biomarker may not possess the desired sensitivity and/or specificity for disease classification and outcome prediction. Therefore, the efforts of researchers currently aims at combining biomarkers. The intention of biomarker pilot studies with small sample sizes is often to explore the possibility of finding good biomarker combinations and not to find and evaluate a final combination of biomarkers with high predictive value. The aim of the pilot study is to answer the question whether it is worthwhile to extend the study to a larger study and to obtain information about the required sample size. In this paper, we propose a method to judge the potential in a small biomarker pilot study without the need to explicitly identifying and confirming a specific subset of biomarkers. In addition, we provide a method for sample size estimation for an extended study when the results of the pilot study look promising.
• #### Still Something to Discover: Novel Insights into Phage Diversity and Taxonomy.

The aim of this study was to gain further insight into the diversity of Escherichia coli phagesfollowed by enhanced work on taxonomic issues in that field. Therefore, we present the genomiccharacterization and taxonomic classification of 50 bacteriophages against E. coli isolated fromvarious sources, such as manure or sewage. All phages were examined for their host range on a setof different E. coli strains, originating, e.g., from human diagnostic laboratories or poultry farms.Transmission electron microscopy revealed a diversity of morphotypes (70% Myo-, 22% Sipho-, and8% Podoviruses), and genome sequencing resulted in genomes sizes from ~44 to ~370 kb.Annotation and comparison with databases showed similarities in particular to T4- and T5-likephages, but also to less-known groups. Though various phages against E. coli are already describedin literature and databases, we still isolated phages that showed no or only few similarities to otherphages, namely phages Goslar, PTXU04, and KWBSE43-6. Genome-based phylogeny andclassification of the newly isolated phages using VICTOR resulted in the proposal of new generaand led to an enhanced taxonomic classification of E. coli phages.
• #### A structural investigation of complex I and I+III2 supercomplex from Zea mays at 11-13 A resolution: assignment of the carbonic anhydrase domain and evidence for structural heterogeneity within complex I.

The projection structures of complex I and the I+III2 supercomplex from the C4 plant Zea mays were determined by electron microscopy and single particle image analysis to a resolution of up to 11 A. Maize complex I has a typical L-shape. Additionally, it has a large hydrophilic extra-domain attached to the centre of the membrane arm on its matrix-exposed side, which previously was described for Arabidopsis and which was reported to include carbonic anhydrase subunits. A comparison with the X-ray structure of homotrimeric gamma-carbonic anhydrase from the archaebacterium Methanosarcina thermophila indicates that this domain is also composed of a trimer. Mass spectrometry analyses allowed to identify two different carbonic anhydrase isoforms, suggesting that the gamma-carbonic anhydrase domain of maize complex I most likely is a heterotrimer. Statistical analysis indicates that the maize complex I structure is heterogeneous: a less-abundant "type II" particle has a 15 A shorter membrane arm and an additional small protrusion on the intermembrane-side of the membrane arm if compared to the more abundant "type I" particle. The I+III2 supercomplex was found to be a rigid structure which did not break down into subcomplexes at the interface between the hydrophilic and the hydrophobic arms of complex I. The complex I moiety of the supercomplex appears to be only of "type I". This would mean that the "type II" particles are not involved in the supercomplex formation and, hence, could have a different physiological role.
• #### The sulfur carrier protein TusA has a pleiotropic role in Escherichia coli that also affects molybdenum cofactor biosynthesis.

The Escherichia coli L-cysteine desulfurase IscS mobilizes sulfur from L-cysteine for the synthesis of several biomolecules such as iron-sulfur (FeS) clusters, molybdopterin, thiamin, lipoic acid, biotin, and the thiolation of tRNAs. The sulfur transfer from IscS to various biomolecules is mediated by different interaction partners (e.g. TusA for thiomodification of tRNAs, IscU for FeS cluster biogenesis, and ThiI for thiamine biosynthesis/tRNA thiolation), which bind at different sites of IscS. Transcriptomic and proteomic studies of a ΔtusA strain showed that the expression of genes of the moaABCDE operon coding for proteins involved in molybdenum cofactor biosynthesis is increased under aerobic and anaerobic conditions. Additionally, under anaerobic conditions the expression of genes encoding hydrogenase 3 and several molybdoenzymes such as nitrate reductase were also increased. On the contrary, the activity of all molydoenzymes analyzed was significantly reduced in the ΔtusA mutant. Characterization of the ΔtusA strain under aerobic conditions showed an overall low molybdopterin content and an accumulation of cyclic pyranopterin monophosphate. Under anaerobic conditions the activity of nitrate reductase was reduced by only 50%, showing that TusA is not essential for molybdenum cofactor biosynthesis. We present a model in which we propose that the direction of sulfur transfer for each sulfur-containing biomolecule is regulated by the availability of the interaction partner of IscS. We propose that in the absence of TusA, more IscS is available for FeS cluster biosynthesis and that the overproduction of FeS clusters leads to a modified expression of several genes.
• #### Sweep Dynamics (SD) plots: Computational identification of selective sweeps to monitor the adaptation of influenza A viruses.

Monitoring changes in influenza A virus genomes is crucial to understand its rapid evolution and adaptation to changing conditions e.g. establishment within novel host species. Selective sweeps represent a rapid mode of adaptation and are typically observed in human influenza A viruses. We describe Sweep Dynamics (SD) plots, a computational method combining phylogenetic algorithms with statistical techniques to characterize the molecular adaptation of rapidly evolving viruses from longitudinal sequence data. SD plots facilitate the identification of selective sweeps, the time periods in which these occurred and associated changes providing a selective advantage to the virus. We studied the past genome-wide adaptation of the 2009 pandemic H1N1 influenza A (pH1N1) and seasonal H3N2 influenza A (sH3N2) viruses. The pH1N1 influenza virus showed simultaneous amino acid changes in various proteins, particularly in seasons of high pH1N1 activity. Partially, these changes resulted in functional alterations facilitating sustained human-to-human transmission. In the evolution of sH3N2 influenza viruses, we detected changes characterizing vaccine strains, which were occasionally revealed in selective sweeps one season prior to the WHO recommendation. Taken together, SD plots allow monitoring and characterizing the adaptive evolution of influenza A viruses by identifying selective sweeps and their associated signatures. - - all data is published on GitHub: https://github.com/hzi-bifo/SDplots/tree/v1.0.0
• #### TCR signalling network organization at the immunological synapses of murine regulatory T cells.

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.

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.

• #### The zlog value as a basis for the standardization of laboratory results

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.

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.

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

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.