@article{WhisnantJuergesHennigetal.2020, author = {Whisnant, Adam W. and J{\"u}rges, Christopher S. and Hennig, Thomas and Wyler, Emanuel and Prusty, Bhupesh and Rutkowski, Andrzej J. and L'hernault, Anne and Djakovic, Lara and G{\"o}bel, Margarete and D{\"o}ring, Kristina and Menegatti, Jennifer and Antrobus, Robin and Matheson, Nicholas J. and K{\"u}nzig, Florian W. H. and Mastrobuoni, Guido and Bielow, Chris and Kempa, Stefan and Liang, Chunguang and Dandekar, Thomas and Zimmer, Ralf and Landthaler, Markus and Gr{\"a}sser, Friedrich and Lehner, Paul J. and Friedel, Caroline C. and Erhard, Florian and D{\"o}lken, Lars}, title = {Integrative functional genomics decodes herpes simplex virus 1}, series = {Nature Communications}, volume = {11}, journal = {Nature Communications}, doi = {10.1038/s41467-020-15992-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229884}, year = {2020}, abstract = {The predicted 80 open reading frames (ORFs) of herpes simplex virus 1 (HSV-1) have been intensively studied for decades. Here, we unravel the complete viral transcriptome and translatome during lytic infection with base-pair resolution by computational integration of multi-omics data. We identify a total of 201 transcripts and 284 ORFs including all known and 46 novel large ORFs. This includes a so far unknown ORF in the locus deleted in the FDA-approved oncolytic virus Imlygic. Multiple transcript isoforms expressed from individual gene loci explain translation of the vast majority of ORFs as well as N-terminal extensions (NTEs) and truncations. We show that NTEs with non-canonical start codons govern the subcellular protein localization and packaging of key viral regulators and structural proteins. We extend the current nomenclature to include all viral gene products and provide a genome browser that visualizes all the obtained data from whole genome to single-nucleotide resolution. Here, using computational integration of multi-omics data, the authors provide a detailed transcriptome and translatome of herpes simplex virus 1 (HSV-1), including previously unidentified ORFs and N-terminal extensions. The study also provides a HSV-1 genome browser and should be a valuable resource for further research.}, language = {en} } @article{ScholzGehringGuanetal.2015, author = {Scholz, Nicole and Gehring, Jennifer and Guan, Chonglin and Ljaschenko, Dmitrij and Fischer, Robin and Lakshmanan, Vetrivel and Kittel, Robert J. and Langenhan, Tobias}, title = {The adhesion GPCR Latrophilin/CIRL shapes mechanosensation}, series = {Cell Reports}, volume = {11}, journal = {Cell Reports}, doi = {10.1016/j.celrep.2015.04.008}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148626}, pages = {866-874}, year = {2015}, abstract = {G-protein-coupled receptors (GPCRs) are typically regarded as chemosensors that control cellular states in response to soluble extracellular cues. However, the modality of stimuli recognized through adhesion GPCR (aGPCR), the second largest class of the GPCR superfamily, is unresolved. Our study characterizes the Drosophila aGPCR Latrophilin/dCirl, a prototype member of this enigmatic receptor class. We show that dCirl shapes the perception of tactile, proprioceptive, and auditory stimuli through chordotonal neurons, the principal mechanosensors of Drosophila. dCirl sensitizes these neurons for the detection of mechanical stimulation by amplifying their input-output function. Our results indicate that aGPCR may generally process and modulate the perception of mechanical signals, linking these important stimuli to the sensory canon of the GPCR superfamily.}, language = {en} } @article{AtakLanglhoferSchaeferetal.2015, author = {Atak, Sinem and Langlhofer, Georg and Schaefer, Natascha and Kessler, Denise and Meiselbach, Heike and Delto, Carolyn and Schindelin, Hermann and Villmann, Carmen}, title = {Disturbances of ligand potency and enhanced degradation of the human glycine receptor at affected positions G160 and T162 originally identified in patients suffering from hyperekplexia}, series = {Frontiers in Molecular Neuroscience}, volume = {8}, journal = {Frontiers in Molecular Neuroscience}, number = {79}, doi = {10.3389/fnmol.2015.00079}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144818}, year = {2015}, abstract = {Ligand-binding of Cys-loop receptors is determined by N-terminal extracellular loop structures from the plus as well as from the minus side of two adjacent subunits in the pentameric receptor complex. An aromatic residue in loop B of the glycine receptor (GIyR) undergoes direct interaction with the incoming ligand via a cation-π interaction. Recently, we showed that mutated residues in loop B identified from human patients suffering from hyperekplexia disturb ligand-binding. Here, we exchanged the affected human residues by amino acids found in related members of the Cys-loop receptor family to determine the effects of side chain volume for ion channel properties. GIyR variants were characterized in vitro following transfection into cell lines in order to analyze protein expression, trafficking, degradation and ion channel function. GIyR α1 G160 mutations significantly decrease glycine potency arguing for a positional effect on neighboring aromatic residues and consequently glycine-binding within the ligand-binding pocket. Disturbed glycinergic inhibition due to T162 α1 mutations is an additive effect of affected biogenesis and structural changes within the ligand-binding site. Protein trafficking from the ER toward the ER-Golgi intermediate compartment, the secretory Golgi pathways and finally the cell surface is largely diminished, but still sufficient to deliver ion channels that are functional at least at high glycine concentrations. The majority of T162 mutant protein accumulates in the ER and is delivered to ER-associated proteasomal degradation. Hence, G160 is an important determinant during glycine binding. In contrast, 1162 affects primarily receptor biogenesis whereas exchanges in functionality are secondary effects thereof.}, language = {en} } @article{RaketteDonatOhlsenetal.2012, author = {Rakette, Sonja and Donat, Stefanie and Ohlsen, Knut and Stehle, Thilo}, title = {Structural Analysis of Staphylococcus aureus Serine/Threonine Kinase PknB}, series = {PLoS One}, volume = {7}, journal = {PLoS One}, number = {6}, doi = {10.1371/journal.pone.0039136}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-135369}, pages = {e39136}, year = {2012}, abstract = {Effective treatment of infections caused by the bacterium Staphylococcus aureus remains a worldwide challenge, in part due to the constant emergence of new strains that are resistant to antibiotics. The serine/threonine kinase PknB is of particular relevance to the life cycle of S. aureus as it is involved in the regulation of purine biosynthesis, autolysis, and other central metabolic processes of the bacterium. We have determined the crystal structure of the kinase domain of PknB in complex with a non-hydrolyzable analog of the substrate ATP at 3.0 angstrom resolution. Although the purified PknB kinase is active in solution, it crystallized in an inactive, autoinhibited state. Comparison with other bacterial kinases provides insights into the determinants of catalysis, interactions of PknB with ligands, and the pathway of activation.}, language = {en} } @article{KneisslAbelRasbachetal.2012, author = {Kneissl, Sabrina and Abel, Tobias and Rasbach, Anke and Brynza, Julia and Schneider-Schaulies, J{\"u}rgen and Buchholz, Christian J.}, title = {Measles Virus Glycoprotein-Based Lentiviral Targeting Vectors That Avoid Neutralizing Antibodies}, series = {PLoS One}, volume = {7}, journal = {PLoS One}, number = {10}, doi = {10.1371/journal.pone.0046667}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-134993}, pages = {e46667}, year = {2012}, abstract = {Lentiviral vectors (LVs) are potent gene transfer vehicles frequently applied in research and recently also in clinical trials. Retargeting LV entry to cell types of interest is a key issue to improve gene transfer safety and efficacy. Recently, we have developed a targeting method for LVs by incorporating engineered measles virus (MV) glycoproteins, the hemagglutinin (H), responsible for receptor recognition, and the fusion protein into their envelope. The H protein displays a single-chain antibody (scFv) specific for the target receptor and is ablated for recognition of the MV receptors CD46 and SLAM by point mutations in its ectodomain. A potential hindrance to systemic administration in humans is pre-existing MV-specific immunity due to vaccination or natural infection. We compared transduction of targeting vectors and non-targeting vectors pseudotyped with MV glycoproteins unmodified in their ectodomains (MV-LV) in presence of \(\alpha\)-MV antibody-positive human plasma. At plasma dilution 1: 160 MV-LV was almost completely neutralized, whereas targeting vectors showed relative transduction efficiencies from 60\% to 90\%. Furthermore, at plasma dilution 1: 80 an at least 4-times higher multiplicity of infection (MOI) of MV-LV had to be applied to obtain similar transduction efficiencies as with targeting vectors. Also when the vectors were normalized to their p24 values, targeting vectors showed partial protection against \(\alpha\)-MV antibodies in human plasma. Furthermore, the monoclonal neutralizing antibody K71 with a putative epitope close to the receptor binding sites of H, did not neutralize the targeting vectors, but did neutralize MV-LV. The observed escape from neutralization may be due to the point mutations in the H ectodomain that might have destroyed antibody binding sites. Furthermore, scFv mediated cell entry via the target receptor may proceed in presence of a-MV antibodies interfering with entry via the natural MV receptors. These results are promising for in vivo applications of targeting vectors in humans.}, language = {en} } @article{HohenauerBerkingSchmidtetal.2013, author = {Hohenauer, Tobias and Berking, Carola and Schmidt, Andreas and Haferkamp, Sebastian and Senft, Daniela and Kammerbauer, Claudia and Fraschka, Sabine and Graf, Saskia Anna and Irmler, Martin and Beckers, Johannes and Flaig, Michael and Aigner, Achim and H{\"o}bel, Sabrina and Hoffmann, Franziska and Hermeking, Heiko and Rothenfusser, Simon and Endres, Stefan and Ruzicka, Thomas and Besch, Robert}, title = {The neural crest transcription factor Brn3a is expressed in melanoma and required for cell cycle progression and survival}, series = {EMBO Molecular Medicine}, volume = {5}, journal = {EMBO Molecular Medicine}, issn = {1757-4676}, doi = {10.1002/emmm.201201862}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-122193}, pages = {919-934}, year = {2013}, abstract = {Pigment cells and neuronal cells both are derived from the neural crest. Here, we describe the Pit-Oct-Unc (POU) domain transcription factor Brn3a, normally involved in neuronal development, to be frequently expressed in melanoma, but not in melanocytes and nevi. RNAi-mediated silencing of Brn3a strongly reduced the viability of melanoma cell lines and decreased tumour growth in vivo. In melanoma cell lines, inhibition of Brn3a caused DNA double-strand breaks as evidenced by Mre11/Rad50-containing nuclear foci. Activated DNA damage signalling caused stabilization of the tumour suppressor p53, which resulted in cell cycle arrest and apoptosis. When Brn3a was ectopically expressed in primary melanocytes and fibroblasts, anchorage-independent growth was increased. In tumourigenic melanocytes and fibroblasts, Brn3a accelerated tumour growth in vivo. Furthermore, Brn3a cooperated with proliferation pathways such as oncogenic BRAF, by reducing oncogene-induced senescence in non-malignant melanocytes. Together, these results identify Brn3a as a new factor in melanoma that is essential for melanoma cell survival and that promotes melanocytic transformation and tumourigenesis.}, language = {en} } @article{PiteauPapatheodorouSchwanetal.2014, author = {Piteau, Marianne and Papatheodorou, Panagiotis and Schwan, Carsten and Schlosser, Andreas and Aktories, Klaus and Schmidt, Gudula}, title = {Lu/BCAM Adhesion Glycoprotein Is a Receptor for Escherichia coli Cytotoxic Necrotizing Factor 1 (CNF1)}, series = {PLoS Pathogens}, volume = {10}, journal = {PLoS Pathogens}, number = {1}, issn = {1553-7374}, doi = {10.1371/journal.ppat.1003884}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-117987}, pages = {e1003884}, year = {2014}, abstract = {The Cytotoxic Necrotizing Factor 1 (CNF1) is a protein toxin which is a major virulence factor of pathogenic Escherichia coli strains. Here, we identified the Lutheran (Lu) adhesion glycoprotein/basal cell adhesion molecule (BCAM) as cellular receptor for CNF1 by co-precipitation of cell surface molecules with tagged toxin. The CNF1-Lu/BCAM interaction was verified by direct protein-protein interaction analysis and competition studies. These studies revealed amino acids 720 to 1014 of CNF1 as the binding site for Lu/BCAM. We suggest two cell interaction sites in CNF1: first the N-terminus, which binds to p37LRP as postulated before. Binding of CNF1 to p37LRP seems to be crucial for the toxin's action. However, it is not sufficient for the binding of CNF1 to the cell surface. A region directly adjacent to the catalytic domain is a high affinity interaction site for Lu/BCAM. We found Lu/BCAM to be essential for the binding of CNF1 to cells. Cells deficient in Lu/BCAM but expressing p37LRP could not bind labeled CNF1. Therefore, we conclude that LRP and Lu/BCAM are both required for toxin action but with different functions. Author Summary We study a crucial virulence factor produced by pathogenic Escherichia coli strains, the Cytotoxic Necrotizing Factor 1 (CNF1). More than 80\% of urinary tract infections (UTIs), which are counted among the most common bacterial infections of humans, are caused by Uropathogenic Escherichia coli (UPEC) strains. We and others elucidated the molecular mechanism of the E. coli toxin CNF1. It constitutively activates Rho GTPases by a direct covalent modification. The toxin enters mammalian cells by receptor-mediated endocytosis. Here, we identified the protein receptor for CNF1 by co-precipitation of cell surface molecules with the tagged toxin and subsequent Maldi-TOF analysis. We identified the Lutheran (Lu) adhesion glycoprotein/basal cell adhesion molecule (BCAM) as receptor for CNF1 and located its interaction site to the C-terminal part of the toxin. We performed direct protein-protein interaction analysis and competition studies. Moreover, cells deficient in Lu/BCAM could not bind labeled CNF1. The identification of a toxin's cellular receptor and receptor binding region is an important task for understanding the pathogenic function of the toxin and, moreover, to make the toxin accessible for its use as a cellbiological and pharmacological tool, for example for the generation of immunotoxins.}, language = {en} } @phdthesis{Pinkert2008, author = {Pinkert, Stefan}, title = {The human proteome is shaped by evolution and interactions}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-35566}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2008}, abstract = {Das menschliche Genom ist seit 2001 komplett sequenziert. Ein Großteil der Proteine wurde mittlerweile beschrieben und t{\"a}glich werden bioinformatische Vorhersagen praktisch best{\"a}tigt. Als weiteres Großprojekt wurde k{\"u}rzlich die Sequenzierung des Genoms von 1000 Menschen gestartet. Trotzdem ist immer noch wenig {\"u}ber die Evolution des gesamten menschlichen Proteoms bekannt. Proteindom{\"a}nen und ihre Kombinationen sind teilweise sehr detailliert erforscht, aber es wurden noch nicht alle Dom{\"a}nenarchitekturen des Menschen in ihrer Gesamtheit miteinander verglichen. Der verwendete große hochqualitative Datensatz von Protein-Protein-Interaktionen und Komplexen stammt aus dem Jahr 2006 und erm{\"o}glicht es erstmals das menschliche Proteom mit einer vorher nicht m{\"o}glichen Genauigkeit analysieren zu k{\"o}nnen. Hochentwickelte Cluster Algorithmen und die Verf{\"u}gbarkeit von großer Rechenkapazit{\"a}t bef{\"a}higen uns neue Information {\"u}ber Proteinnetzwerke ohne weitere Laborarbeit zu gewinnen. Die vorliegende Arbeit analysiert das menschliche Proteom auf drei verschiedenen Ebenen. Zuerst wurde der Ursprung von Proteinen basierend auf ihrer Dom{\"a}nenarchitektur analysiert, danach wurden Protein-Protein-Interaktionen untersucht und schließlich erfolgte Einteilung der Proteine nach ihren vorhandenen und fehlenden Interaktionen. Die meisten bekannten Proteine enthalten mindestens eine Dom{\"a}ne und die Proteinfunktion ergibt sich aus der Summe der Funktionen der einzelnen enthaltenen Dom{\"a}nen. Proteine, die auf der gleichen Dom{\"a}nenarchitektur basieren, das heißt die die gleichen Dom{\"a}nen in derselben Reihenfolge besitzen, sind homolog und daher aus einem gemeinsamen urspr{\"u}nglichen Protein entstanden. Die Dom{\"a}nenarchitekturen der urspr{\"u}nglichen Proteine wurden f{\"u}r 750000 Proteine aus 1313 Spezies bestimmt. Die Gruppierung von Spezies und ihrer Proteine ergibt sich aus taxonomischen Daten von NCBI-Taxonomy, welche mit zus{\"a}tzlichen Informationen basierend auf molekularen Markern erg{\"a}nzt wurden. Der resultierende Datensatz, bestehend aus 5817 Dom{\"a}nen und 32868 Dom{\"a}nenarchitekturen, war die Grundlage f{\"u}r die Bestimmung des Ursprungs der Proteine aufgrund ihrer Dom{\"a}nenarchitekturen. Es wurde festgestellt, dass nur ein kleiner Teil der neu evolvierten Dom{\"a}nenarchitekturen eines Taxons gleichzeitig auch im selben Taxon neu entstandene Proteindom{\"a}nen enth{\"a}lt. Ein weiteres Ergebnis war, dass Dom{\"a}nenarchitekturen im Verlauf der Evolution l{\"a}nger und komplexer werden, und dass so verschiedene Organismen wie der Fadenwurm, die Fruchtfliege und der Mensch die gleiche Menge an unterschiedlichen Proteinen haben, aber deutliche Unterschiede in der Anzahl ihrer Dom{\"a}nenarchitekturen aufweisen. Der zweite Teil besch{\"a}ftigt sich mit der Frage wie neu entstandene Proteine Bindungen mit dem schon bestehenden Proteinnetzwerk eingehen. In fr{\"u}heren Arbeiten wurde gezeigt, dass das Protein-Interaktions-Netzwerk ein skalenfreies Netz ist. Skalenfreie Netze, wie zum Beispiel das Internet, bestehen aus wenigen Knoten mit vielen Interaktionen, genannt Hubs, und andererseits aus vielen Knoten mit wenigen Interaktionen. Man vermutet, dass zwei Mechanismen zur Entstehung solcher Netzwerke f{\"u}hren. Erstens m{\"u}ssen neue Proteine um auch Teil des Proteinnetzwerkes zu werden mit Proteinen interagieren, die bereits Teil des Netzwerkes sind. Zweitens interagieren die neuen Proteine, gem{\"a}ß der Theorie der bevorzugten Bindung, mit h{\"o}herer Wahrscheinlichkeit mit solchen Proteinen im Netzwerk, die schon an zahlreichen weiteren Protein-Interaktionen beteiligt sind. Die Human Protein Reference Database stellt ein auf Informationen aus in-vivo Experimenten beruhendes Proteinnetzwerk f{\"u}r menschliche Proteine zur Verf{\"u}gung. Basierend auf den in Kapitel I gewonnenen Informationen wurden die Proteine mit dem Ursprungstaxon ihrer Dom{\"a}nenarchitekturen versehen. Dadurch wurde gezeigt, dass ein Protein h{\"a}ufiger mit Proteinen, die im selben Taxon entstanden sind, interagiert, als mit Proteinen, die in anderen Taxa neu aufgetreten sind. Es stellte sich heraus, dass diese Interaktionsraten f{\"u}r alle Taxa deutlich h{\"o}her waren, als durch das Zufallsmodel vorhergesagt wurden. Alle Taxa enthalten den gleichen Anteil an Proteinen mit vielen Interaktionen. Diese zwei Ergebnisse sprechen dagegen, dass die bevorzugte Bindung der alleinige Mechanismus ist, der zum heutigen Aufbau des menschlichen Proteininteraktion-Netzwerks beigetragen hat. Im dritten Teil wurden Proteine basierend auf dem Vorhandensein und der Abwesenheit von Interaktionen in Gruppen eingeteilt. Proteinnetzwerke k{\"o}nnen in kleine hoch vernetzte Teile zerlegt werden, die eine spezifische Funktion aus{\"u}ben. Diese Gruppen k{\"o}nnen mit hoher statistischer Signifikanz berechnet werden, haben meistens jedoch keine biologische Relevanz. Mit einem neuen Algorithmus, welcher zus{\"a}tzlich zu Interaktionen auch Nicht-Interaktionen ber{\"u}cksichtigt, wurde ein Datensatz bestehend aus 8,756 Proteinen und 32,331 Interaktionen neu unterteilt. Eine Einteilung in elf Gruppen zeigte hohe auf Gene Ontology basierte Werte und die Gruppen konnten signifikant einzelnen Zellteilen zugeordnet werden. Eine Gruppe besteht aus Proteinen, welche wenige Interaktionen miteinander aber viele Interaktionen zu zwei benachbarten Gruppen besitzen. Diese Gruppe enth{\"a}lt eine signifikant erh{\"o}hte Anzahl an Transportproteinen und die zwei benachbarten Gruppen haben eine erh{\"o}hte Anzahl an einerseits extrazellul{\"a}ren und andererseits im Zytoplasma und an der Membran lokalisierten Proteinen. Der Algorithmus hat damit unter Beweis gestellt das die Ergebnisse nicht bloß statistisch sondern auch biologisch relevant sind. Wenn wir auch noch weit vom Verst{\"a}ndnis des Ursprungs der Spezies entfernt sind, so hat diese Arbeit doch einen Beitrag zum besseren Verst{\"a}ndnis der Evolution auf dem Level der Proteine geleistet. Im Speziellen wurden neue Erkenntnisse {\"u}ber die Beziehung von Proteindom{\"a}nen und Dom{\"a}nenarchitekturen, sowie ihre Pr{\"a}ferenzen f{\"u}r Interaktionspartner im Interaktionsnetzwerk gewonnen.}, subject = {Evolution}, language = {en} }