TY  - JOUR
A1  - Hohenauer, Tobias
A1  - Berking, Carola
A1  - Schmidt, Andreas
A1  - Haferkamp, Sebastian
A1  - Senft, Daniela
A1  - Kammerbauer, Claudia
A1  - Fraschka, Sabine
A1  - Graf, Saskia Anna
A1  - Irmler, Martin
A1  - Beckers, Johannes
A1  - Flaig, Michael
A1  - Aigner, Achim
A1  - Höbel, Sabrina
A1  - Hoffmann, Franziska
A1  - Hermeking, Heiko
A1  - Rothenfusser, Simon
A1  - Endres, Stefan
A1  - Ruzicka, Thomas
A1  - Besch, Robert
T1  - The neural crest transcription factor Brn3a is expressed in melanoma and required for cell cycle progression and survival
JF  - EMBO Molecular Medicine
N2  - 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.
KW  - oncogene-induced senescence
KW  - BRN-3A
KW  - DNA
KW  - DNA damage
KW  - tumourigenesis
KW  - P53
KW  - in-vitro
KW  - neural crest factors
KW  - family
KW  - apoptosis
KW  - melanoma
KW  - BRAF mutations
KW  - domain
Y1  - 2013
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-122193
SN  - 1757-4676
VL  - 5
ER  - 
TY  - THES
A1  - Pinkert, Stefan
T1  - The human proteome is shaped by evolution and interactions
T1  - Das menschliche Proteom ist geformt durch Evolution und Interaktion
N2  - Das menschliche Genom ist seit 2001 komplett sequenziert. Ein Großteil der Proteine wurde mittlerweile beschrieben und täglich werden bioinformatische Vorhersagen praktisch bestätigt. Als weiteres Großprojekt wurde kürzlich die Sequenzierung des Genoms von 1000 Menschen gestartet. Trotzdem ist immer noch wenig über die Evolution des gesamten menschlichen Proteoms bekannt. Proteindomänen und ihre Kombinationen sind teilweise sehr detailliert erforscht, aber es wurden noch nicht alle Domänenarchitekturen des Menschen in ihrer Gesamtheit miteinander verglichen. Der verwendete große hochqualitative Datensatz von Protein-Protein-Interaktionen und Komplexen stammt aus dem Jahr 2006 und ermöglicht es erstmals das menschliche Proteom mit einer vorher nicht möglichen Genauigkeit analysieren zu können. Hochentwickelte Cluster Algorithmen und die Verfügbarkeit von großer Rechenkapazität befähigen uns neue Information über 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 Domänenarchitektur 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 Domäne und die Proteinfunktion ergibt sich aus der Summe der Funktionen der einzelnen enthaltenen Domänen. Proteine, die auf der gleichen Domänenarchitektur basieren, das heißt die die gleichen Domänen in derselben Reihenfolge besitzen, sind homolog und daher aus einem gemeinsamen ursprünglichen Protein entstanden. Die Domänenarchitekturen der ursprünglichen Proteine wurden für 750000 Proteine aus 1313 Spezies bestimmt. Die Gruppierung von Spezies und ihrer Proteine ergibt sich aus taxonomischen Daten von NCBI-Taxonomy, welche mit zusätzlichen Informationen basierend auf molekularen Markern ergänzt wurden. Der resultierende Datensatz, bestehend aus 5817 Domänen und 32868 Domänenarchitekturen, war die Grundlage für die Bestimmung des Ursprungs der Proteine aufgrund ihrer Domänenarchitekturen. Es wurde festgestellt, dass nur ein kleiner Teil der neu evolvierten Domänenarchitekturen eines Taxons gleichzeitig auch im selben Taxon neu entstandene Proteindomänen enthält. Ein weiteres Ergebnis war, dass Domänenarchitekturen im Verlauf der Evolution länger 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 Domänenarchitekturen aufweisen. Der zweite Teil beschäftigt sich mit der Frage wie neu entstandene Proteine Bindungen mit dem schon bestehenden Proteinnetzwerk eingehen. In früheren 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 führen. Erstens müssen neue Proteine um auch Teil des Proteinnetzwerkes zu werden mit Proteinen interagieren, die bereits Teil des Netzwerkes sind. Zweitens interagieren die neuen Proteine, gemäß der Theorie der bevorzugten Bindung, mit höherer 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 für menschliche Proteine zur Verfügung. Basierend auf den in Kapitel I gewonnenen Informationen wurden die Proteine mit dem Ursprungstaxon ihrer Domänenarchitekturen versehen. Dadurch wurde gezeigt, dass ein Protein häufiger 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 für alle Taxa deutlich höher 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 können in kleine hoch vernetzte Teile zerlegt werden, die eine spezifische Funktion ausüben. Diese Gruppen können mit hoher statistischer Signifikanz berechnet werden, haben meistens jedoch keine biologische Relevanz. Mit einem neuen Algorithmus, welcher zusätzlich zu Interaktionen auch Nicht-Interaktionen berücksichtigt, 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 enthält eine signifikant erhöhte Anzahl an Transportproteinen und die zwei benachbarten Gruppen haben eine erhöhte Anzahl an einerseits extrazellulären 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 Verständnis des Ursprungs der Spezies entfernt sind, so hat diese Arbeit doch einen Beitrag zum besseren Verständnis der Evolution auf dem Level der Proteine geleistet. Im Speziellen wurden neue Erkenntnisse über die Beziehung von Proteindomänen und Domänenarchitekturen, sowie ihre Präferenzen für Interaktionspartner im Interaktionsnetzwerk gewonnen.
N2  - The human genome has been sequenced since 2001. Most proteins have been characterized now and with everyday more bioinformatical predictions are experimentally verified. A project is underway to sequence thousand humans. But still, little is known about the evolution of the human proteome itself. Domains and their combinations are analysed in detail but not all of the human domain architectures at once. Like no one before, we have large datasets of high quality human protein-protein-protein interactions and complexes available which allow us to characterize the human proteome with unmatched accuracy. Advanced clustering algorithms and computing power enable us to gain new information about protein interactions without touching a pipette. In this work, the human proteome is analysed at three different levels. First, the origin of the different types of proteins was analysed based on their domain architectures. The second part focuses on the protein-protein interactions. Finally, in the third part, proteins are clustered based on their interactions and non-interactions. Most proteins are built of domains and their function is the sum of their domain functions. Proteins that share the same domain architecture, the linear order of domains are homologues and should have originated from one common ancestral protein. This ancestor was calculated for roughly 750 000 proteins from 1313 species. The relations between the species are based on the NCBI Taxonomy and additional molecular data. The resulting data set of 5817 domains and 32868 domain architectures was used to estimate the origin of these proteins based on their architectures. It could be observed, that new domain architectures are only in a small fraction composed of domains arisen at the same taxon. It was also found that domain architectures increase in length and complexity in the course of evolution and that different organisms like worm, and human share nearly the same amount of proteins but differ in their number of distinct domain architectures. The second part of this thesis focuses on protein-protein interactions. This chapter addresses the question how new evolved proteins form connections within the existing network. The network built of protein-protein interactions was shown to be scale free. Scale free networks, like the internet, consist of few hubs with many connections and many nodes with few connections. They are thought to arise by two mechanisms. First, newly emerged proteins interact with proteins of the network. Second, according to the theory of preferential attachment, new proteins have a higher chance to interact with already interaction rich proteins. The Human Protein Reference Database provides an on in-vivo interaction data based network for human. With the data obtained from chapter one, proteins were marked with their taxon of origin based on their domain architectures. The interaction ratio of proteins of the same taxa compared to all interactions was calculated and higher values than the random model showed for nearly every taxa. On the other hand, there was no enrichment of proteins originated at the taxon of cellular organisms for the node degree found. The node degree is the number of links for this node. According to the theorie of preferential attachment the oldest nodes should have the most interactions and newly arisen proteins should be preferably attached to them not together. Both could not be shown in this analysis, preferential attachment could therefore not be the only explanation for the forming of the human protein interaction network. Finally in part three, proteins and all their interactions in the network are analysed. Protein networks can be divided into smaller highly interacting parts carrying out specific functions. This can be done with high statistical significance but still, it does not reflect the biological significance. Proteins were clustered based on their interactions and non-interactions with other proteins. A version with eleven clusters showed high gene ontology based ratings and clusters related to specific cell parts. One cluster consists of proteins having very few interactions together but many to proteins of two other clusters. This first cluster is significantly enriched with transport proteins and the two others are enriched with extracellular and cytoplasm/membrane located proteins. The algorithm seems therefore well suited to reflect the biological importance behind functional modules. Although we are still far from understanding the origin of species, this work has significantly contributed to a better understanding of evolution at the protein level and has, in particular, shown the relation of protein domains and protein architectures and their preferences for binding partners within interaction networks.
KW  - Evolution
KW  - Protein
KW  - Domäne
KW  - Interaktion
KW  - evolution
KW  - protein
KW  - interaction
KW  - domain
Y1  - 2008
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-35566
ER  - 
TY  - JOUR
A1  - Scholz, Nicole
A1  - Gehring, Jennifer
A1  - Guan, Chonglin
A1  - Ljaschenko, Dmitrij
A1  - Fischer, Robin
A1  - Lakshmanan, Vetrivel
A1  - Kittel, Robert J.
A1  - Langenhan, Tobias
T1  - The adhesion GPCR Latrophilin/CIRL shapes mechanosensation
JF  - Cell Reports
N2  - 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.
KW  - \(\alpha\)-latrotoxin
KW  - chordotonal organs
KW  - Johnstons organ
KW  - ligand CD55
KW  - hearing
KW  - binding
KW  - shear stress
KW  - protein-coupled receptors
KW  - drosophila larvae
KW  - domain
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-148626
VL  - 11
ER  - 
TY  - JOUR
A1  - Rakette, Sonja
A1  - Donat, Stefanie
A1  - Ohlsen, Knut
A1  - Stehle, Thilo
T1  - Structural Analysis of Staphylococcus aureus Serine/Threonine Kinase PknB
JF  - PLoS One
N2  - 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.
KW  - SER/THR kinase
KW  - domain
KW  - subunit
KW  - dependent protein-kinase
KW  - mycobacterium-tuberculosis
KW  - activation mechanism
KW  - crystal structure
KW  - antibiotic resistance
KW  - catalytic
KW  - methicillin
KW  - inhibitor
Y1  - 2012
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-135369
VL  - 7
IS  - 6
ER  - 
TY  - JOUR
A1  - Kneissl, Sabrina
A1  - Abel, Tobias
A1  - Rasbach, Anke
A1  - Brynza, Julia
A1  - Schneider-Schaulies, Jürgen
A1  - Buchholz, Christian J.
T1  - Measles Virus Glycoprotein-Based Lentiviral Targeting Vectors That Avoid Neutralizing Antibodies
JF  - PLoS One
N2  - 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.
KW  - vivo
KW  - gene delivery
KW  - hemagglutinin
KW  - cells
KW  - neurovirulence
KW  - encephalitis
KW  - transduction
KW  - domain
Y1  - 2012
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-134993
VL  - 7
IS  - 10
ER  - 
TY  - JOUR
A1  - Piteau, Marianne
A1  - Papatheodorou, Panagiotis
A1  - Schwan, Carsten
A1  - Schlosser, Andreas
A1  - Aktories, Klaus
A1  - Schmidt, Gudula
T1  - Lu/BCAM Adhesion Glycoprotein Is a Receptor for Escherichia coli Cytotoxic Necrotizing Factor 1 (CNF1)
JF  - PLoS Pathogens
N2  - 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.
KW  - laminin receptor
KW  - factor-I
KW  - toxin
KW  - RHO
KW  - cells
KW  - activation
KW  - protein
KW  - domain
KW  - translocation
KW  - membrane
Y1  - 2014
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-117987
SN  - 1553-7374
VL  - 10
IS  - 1
ER  - 
TY  - JOUR
A1  - Whisnant, Adam W.
A1  - Jürges, Christopher S.
A1  - Hennig, Thomas
A1  - Wyler, Emanuel
A1  - Prusty, Bhupesh
A1  - Rutkowski, Andrzej J.
A1  - L'hernault, Anne
A1  - Djakovic, Lara
A1  - Göbel, Margarete
A1  - Döring, Kristina
A1  - Menegatti, Jennifer
A1  - Antrobus, Robin
A1  - Matheson, Nicholas J.
A1  - Künzig, Florian W. H.
A1  - Mastrobuoni, Guido
A1  - Bielow, Chris
A1  - Kempa, Stefan
A1  - Liang, Chunguang
A1  - Dandekar, Thomas
A1  - Zimmer, Ralf
A1  - Landthaler, Markus
A1  - Grässer, Friedrich
A1  - Lehner, Paul J.
A1  - Friedel, Caroline C.
A1  - Erhard, Florian
A1  - Dölken, Lars
T1  - Integrative functional genomics decodes herpes simplex virus 1
JF  - Nature Communications
N2  - 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.
KW  - infected-cell protein
KW  - messenger RNA
KW  - binding protein
KW  - type 1
KW  - identification
KW  - ICP27
KW  - translation
KW  - expression
KW  - sequence
KW  - domain
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-229884
VL  - 11
ER  - 
TY  - JOUR
A1  - Atak, Sinem
A1  - Langlhofer, Georg
A1  - Schaefer, Natascha
A1  - Kessler, Denise
A1  - Meiselbach, Heike
A1  - Delto, Carolyn
A1  - Schindelin, Hermann
A1  - Villmann, Carmen
T1  - 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
JF  - Frontiers in Molecular Neuroscience
N2  - 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.
KW  - mutations
KW  - trafficking
KW  - domain
KW  - hyperekplexia
KW  - loop B
KW  - side chain properties
KW  - ligand potencies
KW  - Cys-loop receptor
KW  - glycine receptor
KW  - site
KW  - activation
KW  - binding
KW  - channel
KW  - mechanisms
KW  - dominant
KW  - startle
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-144818
VL  - 8
IS  - 79
ER  -