Fornasiero, Eugenio Francesco

[["dc.bibliographiccitation.firstpage","3333"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Nature Protocols"],["dc.bibliographiccitation.lastpage","3365"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Alevra, Mihai"],["dc.contributor.author","Mandad, Sunit"],["dc.contributor.author","Ischebeck, Till"],["dc.contributor.author","Urlaub, Henning"],["dc.contributor.author","Rizzoli, Silvio O."],["dc.contributor.author","Fornasiero, Eugenio F."],["dc.date.accessioned","2020-12-10T18:10:05Z"],["dc.date.available","2020-12-10T18:10:05Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1038/s41596-019-0222-y"],["dc.identifier.pmid","31685960"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73844"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/110"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/80"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | A03: Dynamische Analyse der Remodellierung der extrazellulären Matrix (ECM) als Mechanismus der Synapsenorganisation und Plastizität"],["dc.relation.workinggroup","RG Rizzoli (Quantitative Synaptology in Space and Time)"],["dc.relation.workinggroup","RG Urlaub (Bioanalytische Massenspektrometrie)"],["dc.title","A mass spectrometry workflow for measuring protein turnover rates in vivo"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","16913"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Mandad, Sunit"],["dc.contributor.author","Rahman, Raza-Ur"],["dc.contributor.author","Centeno, Tonatiuh Pena"],["dc.contributor.author","Vidal, Ramon O."],["dc.contributor.author","Wildhagen, Hanna"],["dc.contributor.author","Rammner, Burkhard"],["dc.contributor.author","Keihani, Sarva"],["dc.contributor.author","Opazo, Felipe"],["dc.contributor.author","Urban, Inga"],["dc.contributor.author","Ischebeck, Till"],["dc.contributor.author","Kirli, Koray"],["dc.contributor.author","Benito, Eva"],["dc.contributor.author","Fischer, André"],["dc.contributor.author","Yousefi, Roya Y."],["dc.contributor.author","Dennerlein, Sven"],["dc.contributor.author","Rehling, Peter"],["dc.contributor.author","Feußner, Ivo"],["dc.contributor.author","Urlaub, Henning"],["dc.contributor.author","Bonn, Stefan"],["dc.contributor.author","Rizzoli, Silvio O."],["dc.contributor.author","Fornasiero, Eugenio F."],["dc.date.accessioned","2019-07-09T11:50:21Z"],["dc.date.available","2019-07-09T11:50:21Z"],["dc.date.issued","2018"],["dc.description.abstract","The homeostasis of the proteome depends on the tight regulation of the mRNA and protein abundances, of the translation rates, and of the protein lifetimes. Results from several studies on prokaryotes or eukaryotic cell cultures have suggested that protein homeostasis is connected to, and perhaps regulated by, the protein and the codon sequences. However, this has been little investigated for mammals in vivo. Moreover, the link between the coding sequences and one critical parameter, the protein lifetime, has remained largely unexplored, both in vivo and in vitro. We tested this in the mouse brain, and found that the percentages of amino acids and codons in the sequences could predict all of the homeostasis parameters with a precision approaching experimental measurements. A key predictive element was the wobble nucleotide. G-/C-ending codons correlated with higher protein lifetimes, protein abundances, mRNA abundances and translation rates than A-/U-ending codons. Modifying the proportions of G-/C-ending codons could tune these parameters in cell cultures, in a proof-of-principle experiment. We suggest that the coding sequences are strongly linked to protein homeostasis in vivo, albeit it still remains to be determined whether this relation is causal in nature."],["dc.identifier.doi","10.1038/s41598-018-35277-8"],["dc.identifier.pmid","30443017"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15918"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59754"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/209"],["dc.identifier.url","https://sfb1190.med.uni-goettingen.de/production/literature/publications/44"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/339580/EU//MITRAC"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/614765/EU//NEUROMOLANATOMY"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation","SFB 1190: Transportmaschinen und Kontaktstellen zellulärer Kompartimente"],["dc.relation","SFB 1190 | P09: Proteinsortierung in der Synapse: Prinzipien und molekulare Organisation"],["dc.relation.issn","2045-2322"],["dc.relation.workinggroup","RG A. Fischer (Epigenetics and Systems Medicine in Neurodegenerative Diseases)"],["dc.relation.workinggroup","RG Rehling (Mitochondrial Protein Biogenesis)"],["dc.relation.workinggroup","RG Rizzoli (Quantitative Synaptology in Space and Time)"],["dc.relation.workinggroup","RG Urlaub (Bioanalytische Massenspektrometrie)"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","The codon sequences predict protein lifetimes and other parameters of the protein life cycle in the mouse brain"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","eabn4437"],["dc.bibliographiccitation.issue","20"],["dc.bibliographiccitation.journal","Science Advances"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Kluever, Verena"],["dc.contributor.author","Russo, Belisa"],["dc.contributor.author","Mandad, Sunit"],["dc.contributor.author","Kumar, Nisha Hemandhar"],["dc.contributor.author","Alevra, Mihai"],["dc.contributor.author","Ori, Alessandro"],["dc.contributor.author","Rizzoli, Silvio O."],["dc.contributor.author","Urlaub, Henning"],["dc.contributor.author","Schneider, Anja"],["dc.contributor.author","Fornasiero, Eugenio F."],["dc.date.accessioned","2022-06-01T09:39:33Z"],["dc.date.available","2022-06-01T09:39:33Z"],["dc.date.issued","2022"],["dc.description.abstract","Aging is a prominent risk factor for neurodegenerative disorders (NDDs); however, the molecular mechanisms rendering the aged brain particularly susceptible to neurodegeneration remain unclear. Here, we aim to determine the link between physiological aging and NDDs by exploring protein turnover using metabolic labeling and quantitative pulse-SILAC proteomics. By comparing protein lifetimes between physiologically aged and young adult mice, we found that in aged brains protein lifetimes are increased by ~20% and that aging affects distinct pathways linked to NDDs. Specifically, a set of neuroprotective proteins are longer-lived in aged brains, while some mitochondrial proteins linked to neurodegeneration are shorter-lived. Strikingly, we observed a previously unknown alteration in proteostasis that correlates to parsimonious turnover of proteins with high biosynthetic costs, revealing an overall metabolic adaptation that preludes neurodegeneration. Our findings suggest that future therapeutic paradigms, aimed at addressing these metabolic adaptations, might be able to delay NDD onset."],["dc.description.abstract","Physiological brain aging affects protein turnover, altering the stability of proteins linked to neurodegenerative diseases."],["dc.identifier.doi","10.1126/sciadv.abn4437"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/108506"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-572"],["dc.relation.eissn","2375-2548"],["dc.title","Protein lifetimes in aged brains reveal a proteostatic adaptation linking physiological aging to neurodegeneration"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2100387"],["dc.bibliographiccitation.journal","PROTEOMICS"],["dc.contributor.author","Li, Wenxue"],["dc.contributor.author","Salovska, Barbora"],["dc.contributor.author","Fornasiero, Eugenio F."],["dc.contributor.author","Liu, Yansheng"],["dc.date.accessioned","2022-12-01T08:31:35Z"],["dc.date.available","2022-12-01T08:31:35Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1002/pmic.202100387"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118208"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-621"],["dc.relation.eissn","1615-9861"],["dc.relation.issn","1615-9853"],["dc.title","Towards a hypothesis‐free understanding of how Phosphorylation dynamically impacts protein turnover"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1747"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Cells"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Yousefi, Roya"],["dc.contributor.author","Jevdokimenko, Kristina"],["dc.contributor.author","Kluever, Verena"],["dc.contributor.author","Pacheu-Grau, David"],["dc.contributor.author","Fornasiero, Eugenio F."],["dc.date.accessioned","2021-09-01T06:43:07Z"],["dc.date.available","2021-09-01T06:43:07Z"],["dc.date.issued","2021"],["dc.description.abstract","Protein homeostasis is an equilibrium of paramount importance that maintains cellular performance by preserving an efficient proteome. This equilibrium avoids the accumulation of potentially toxic proteins, which could lead to cellular stress and death. While the regulators of proteostasis are the machineries controlling protein production, folding and degradation, several other factors can influence this process. Here, we have considered two factors influencing protein turnover: the subcellular localization of a protein and its functional state. For this purpose, we used an imaging approach based on the pulse-labeling of 17 representative SNAP-tag constructs for measuring protein lifetimes. With this approach, we obtained precise measurements of protein turnover rates in several subcellular compartments. We also tested a selection of mutants modulating the function of three extensively studied proteins, the Ca2+ sensor calmodulin, the small GTPase Rab5a and the brain creatine kinase (CKB). Finally, we followed up on the increased lifetime observed for the constitutively active Rab5a (Q79L), and we found that its stabilization correlates with enlarged endosomes and increased interaction with membranes. Overall, our data reveal that both changes in protein localization and functional state are key modulators of protein turnover, and protein lifetime fluctuations can be considered to infer changes in cellular behavior."],["dc.description.abstract","Protein homeostasis is an equilibrium of paramount importance that maintains cellular performance by preserving an efficient proteome. This equilibrium avoids the accumulation of potentially toxic proteins, which could lead to cellular stress and death. While the regulators of proteostasis are the machineries controlling protein production, folding and degradation, several other factors can influence this process. Here, we have considered two factors influencing protein turnover: the subcellular localization of a protein and its functional state. For this purpose, we used an imaging approach based on the pulse-labeling of 17 representative SNAP-tag constructs for measuring protein lifetimes. With this approach, we obtained precise measurements of protein turnover rates in several subcellular compartments. We also tested a selection of mutants modulating the function of three extensively studied proteins, the Ca2+ sensor calmodulin, the small GTPase Rab5a and the brain creatine kinase (CKB). Finally, we followed up on the increased lifetime observed for the constitutively active Rab5a (Q79L), and we found that its stabilization correlates with enlarged endosomes and increased interaction with membranes. Overall, our data reveal that both changes in protein localization and functional state are key modulators of protein turnover, and protein lifetime fluctuations can be considered to infer changes in cellular behavior."],["dc.description.sponsorship","Schram Stiftung"],["dc.description.sponsorship","DFG"],["dc.identifier.doi","10.3390/cells10071747"],["dc.identifier.pii","cells10071747"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89221"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-455"],["dc.publisher","MDPI"],["dc.relation.eissn","2073-4409"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Influence of Subcellular Localization and Functional State on Protein Turnover"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","436"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","BioEssays"],["dc.bibliographiccitation.lastpage","451"],["dc.bibliographiccitation.volume","37"],["dc.contributor.author","Fornasiero, Eugenio F."],["dc.contributor.author","Opazo, Felipe"],["dc.date.accessioned","2018-11-07T09:59:15Z"],["dc.date.available","2018-11-07T09:59:15Z"],["dc.date.issued","2015"],["dc.description.abstract","The recent 2014 Nobel Prize in chemistry honored an era of discoveries and technical advancements in the field of super-resolution microscopy. However, the applications of diffraction-unlimited imaging in biology have a long road ahead and persistently engage scientists with new challenges. Some of the bottlenecks that restrain the dissemination of super-resolution techniques are tangible, and include the limited performance of affinity probes and the yet not capillary diffusion of imaging setups. Likewise, super-resolution microscopy has introduced new paradigms in the design of projects that require imaging with nanometer-resolution and in the interpretation of biological images. Besides structural or morphological characterization, super-resolution imaging is quickly expanding towards interaction mapping, multiple target detection and live imaging. Here we review the recent progress of biologists employing super-resolution imaging, some pitfalls, implications and new trends, with the purpose of animating the field and spurring future developments."],["dc.identifier.doi","10.1002/bies.201400170"],["dc.identifier.isi","000351546000013"],["dc.identifier.pmid","25581819"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/37549"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","1521-1878"],["dc.relation.issn","0265-9247"],["dc.title","Super-resolution imaging for cell biologists Concepts, applications, current challenges and developments"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","4230"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Nature Communications"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Fornasiero, Eugenio F."],["dc.contributor.author","Mandad, Sunit"],["dc.contributor.author","Wildhagen, Hanna"],["dc.contributor.author","Alevra, Mihai"],["dc.contributor.author","Rammner, Burkhard"],["dc.contributor.author","Keihani, Sarva"],["dc.contributor.author","Opazo, Felipe"],["dc.contributor.author","Urban, Inga"],["dc.contributor.author","Ischebeck, Till"],["dc.contributor.author","Sakib, M. Sadman"],["dc.contributor.author","Fard, Maryam K."],["dc.contributor.author","Kirli, Koray"],["dc.contributor.author","Centeno, Tonatiuh Pena"],["dc.contributor.author","Vidal, Ramon O."],["dc.contributor.author","Rahman, Raza-Ur"],["dc.contributor.author","Benito, Eva"],["dc.contributor.author","Fischer, André"],["dc.contributor.author","Dennerlein, Sven"],["dc.contributor.author","Rehling, Peter"],["dc.contributor.author","Feussner, Ivo"],["dc.contributor.author","Bonn, Stefan"],["dc.contributor.author","Simons, Mikael"],["dc.contributor.author","Urlaub, Henning"],["dc.contributor.author","Rizzoli, Silvio O."],["dc.date.accessioned","2019-07-09T11:46:03Z"],["dc.date.available","2019-07-09T11:46:03Z"],["dc.date.issued","2018"],["dc.description.abstract","The turnover of brain proteins is critical for organism survival, and its perturbations are linked to pathology. Nevertheless, protein lifetimes have been difficult to obtain in vivo. They are readily measured in vitro by feeding cells with isotopically labeled amino acids, followed by mass spectrometry analyses. In vivo proteins are generated from at least two sources: labeled amino acids from the diet, and non-labeled amino acids from the degradation of pre-existing proteins. This renders measurements difficult. Here we solved this problem rigorously with a workflow that combines mouse in vivo isotopic labeling, mass spectrometry, and mathematical modeling. We also established several independent approaches to test and validate the results. This enabled us to measure the accurate lifetimes of ~3500 brain proteins. The high precision of our data provided a large set of biologically significant observations, including pathway-, organelle-, organ-, or cell-specific effects, along with a comprehensive catalog of extremely long-lived proteins (ELLPs)."],["dc.identifier.doi","10.1038/s41467-018-06519-0"],["dc.identifier.pmid","30315172"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15388"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59372"],["dc.identifier.url","https://sfb1190.med.uni-goettingen.de/production/literature/publications/42"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/41"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","In goescholar not merged with http://resolver.sub.uni-goettingen.de/purl?gs-1/15611 but duplicate"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/339580/EU//MITRAC"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/614765/EU//NEUROMOLANATOMY"],["dc.relation","SFB 1190: Transportmaschinen und Kontaktstellen zellulärer Kompartimente"],["dc.relation","SFB 1190 | P09: Proteinsortierung in der Synapse: Prinzipien und molekulare Organisation"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | A03: Dynamische Analyse der Remodellierung der extrazellulären Matrix (ECM) als Mechanismus der Synapsenorganisation und Plastizität"],["dc.relation.issn","2041-1723"],["dc.relation.workinggroup","RG Rehling (Mitochondrial Protein Biogenesis)"],["dc.relation.workinggroup","RG Rizzoli (Quantitative Synaptology in Space and Time)"],["dc.relation.workinggroup","RG Urlaub (Bioanalytische Massenspektrometrie)"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","573"],["dc.subject.ddc","612"],["dc.title","Precisely measured protein lifetimes in the mouse brain reveal differences across tissues and subcellular fractions."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","5"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Molecular and Cell Biology"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Bonnin, Elisa A."],["dc.contributor.author","Fornasiero, Eugenio F."],["dc.contributor.author","Lange, Felix"],["dc.contributor.author","Turck, Christoph W."],["dc.contributor.author","Rizzoli, Silvio O."],["dc.date.accessioned","2021-04-14T08:29:56Z"],["dc.date.available","2021-04-14T08:29:56Z"],["dc.date.issued","2021"],["dc.date.updated","2022-07-29T12:07:12Z"],["dc.description.abstract","Abstract\r\n \r\n Background\r\n Most of the cells of the mammalian retina are terminally differentiated, and do not regenerate once fully developed. This implies that these cells have strict controls over their metabolic processes, including protein turnover. We report the use of metabolic labelling procedures and secondary ion mass spectrometry imaging to examine nitrogen turnover in retinal cells, with a focus on the outer nuclear layer, inner nuclear layer, and outer plexiform layer.\r\n \r\n \r\n Results\r\n We find that turnover can be observed in all cells imaged using NanoSIMS. However, the rate of turnover is not constant, but varies between different cellular types and cell regions. In the inner and outer nuclear layers, turnover rate is higher in the cytosol than in the nucleus of each cell. Turnover rates are also higher in the outer plexiform layer. An examination of retinal cells from mice that were isotopically labeled very early in embryonic development shows that proteins produced during this period can be found in all cells and cell regions up to 2 months after birth, even in regions of high turnover.\r\n \r\n \r\n Conclusions\r\n Our results indicate that turnover in retinal cells is a highly regulated process, with strict metabolic controls. We also observe that turnover is several-fold higher in the synaptic layer than in cell layers. Nevertheless, embryonic proteins can still be found in this layer 2 months after birth, suggesting that stable structures persist within the synapses, which remain to be determined."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.citation","BMC Molecular and Cell Biology. 2021 Jan 11;22(1):5"],["dc.identifier.doi","10.1186/s12860-020-00339-1"],["dc.identifier.pmid","33430763"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17722"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83043"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/121"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/90"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | A05: Mitochondriale Heterogenität in Synapsen"],["dc.relation.eissn","2661-8850"],["dc.relation.orgunit","Institut für Neuro- und Sinnesphysiologie"],["dc.relation.workinggroup","RG Rizzoli (Quantitative Synaptology in Space and Time)"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject","Secondary ion mass spectrometry"],["dc.subject","Imaging"],["dc.subject","Metabolic labelling"],["dc.subject","Protein turnover"],["dc.subject","Retina"],["dc.title","NanoSIMS observations of mouse retinal cells reveal strict metabolic controls on nitrogen turnover"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Nature Communications"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Gerdes, Christoph"],["dc.contributor.author","Waal, Natalia"],["dc.contributor.author","Offner, Thomas"],["dc.contributor.author","Fornasiero, Eugenio F."],["dc.contributor.author","Wender, Nora"],["dc.contributor.author","Verbarg, Hannes"],["dc.contributor.author","Manzini, Ivan"],["dc.contributor.author","Trenkwalder, Claudia"],["dc.contributor.author","Mollenhauer, Brit"],["dc.contributor.author","Strohäker, Timo"],["dc.contributor.author","Zweckstetter, Markus"],["dc.contributor.author","Becker, Stefan"],["dc.contributor.author","Rizzoli, Silvio O."],["dc.contributor.author","Basmanav, Fitnat Buket"],["dc.contributor.author","Opazo, Felipe"],["dc.date.accessioned","2021-04-14T08:25:49Z"],["dc.date.available","2021-04-14T08:25:49Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1038/s41467-020-16575-0"],["dc.identifier.pmid","32483166"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17434"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81740"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/112"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/71"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | A03: Dynamische Analyse der Remodellierung der extrazellulären Matrix (ECM) als Mechanismus der Synapsenorganisation und Plastizität"],["dc.relation.eissn","2041-1723"],["dc.relation.workinggroup","RG Rizzoli (Quantitative Synaptology in Space and Time)"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","A nanobody-based fluorescent reporter reveals human α-synuclein in the cell cytosol"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","16"],["dc.bibliographiccitation.journal","The EMBO Journal"],["dc.bibliographiccitation.volume","39"],["dc.contributor.author","Reshetniak, Sofiia"],["dc.contributor.author","Ußling, Jan‐Eike"],["dc.contributor.author","Perego, Eleonora"],["dc.contributor.author","Rammner, Burkhard"],["dc.contributor.author","Schikorski, Thomas"],["dc.contributor.author","Fornasiero, Eugenio F."],["dc.contributor.author","Truckenbrodt, Sven"],["dc.contributor.author","Köster, Sarah"],["dc.contributor.author","Rizzoli, Silvio O."],["dc.date.accessioned","2021-04-14T08:23:53Z"],["dc.date.available","2021-04-14T08:23:53Z"],["dc.date.issued","2020"],["dc.description.abstract","Abstract Many proteins involved in synaptic transmission are well known, and their features, as their abundance or spatial distribution, have been analyzed in systematic studies. This has not been the case, however, for their mobility. To solve this, we analyzed the motion of 45 GFP‐tagged synaptic proteins expressed in cultured hippocampal neurons, using fluorescence recovery after photobleaching, particle tracking, and modeling. We compared synaptic vesicle proteins, endo‐ and exocytosis cofactors, cytoskeleton components, and trafficking proteins. We found that movement was influenced by the protein association with synaptic vesicles, especially for membrane proteins. Surprisingly, protein mobility also correlated significantly with parameters as the protein lifetimes, or the nucleotide composition of their mRNAs. We then analyzed protein movement thoroughly, taking into account the spatial characteristics of the system. This resulted in a first visualization of overall protein motion in the synapse, which should enable future modeling studies of synaptic physiology."],["dc.description.abstract","Synopsis image Live imaging reveals global protein dynamics in the synaptic bouton, providing a first visualization of overall protein motion in the synapse and showing connections between various protein characteristics and mobility in vivo. Membrane proteins display lower mobility rates than soluble proteins. Proteins are less mobile in synaptic boutons than in axons. Protein mobility is strongly influenced by the protein association with synaptic vesicles. Amino acid composition, mRNA nucleotide composition, and protein lifetimes correlate with mobility parameters. Provided diffusion coefficients for 45 synaptic proteins can be used to generate models of synaptic physiology."],["dc.description.abstract","Live imaging reveals global protein dynamics in the synaptic bouton, providing a first visualization of overall protein motion in the synapse and showing connections between protein characteristics and mobility in vivo. image"],["dc.description.sponsorship","European Research Council http://dx.doi.org/10.13039/100010663"],["dc.description.sponsorship","Germany's Excellence Strategy"],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659"],["dc.identifier.doi","10.15252/embj.2020104596"],["dc.identifier.pmid","32627850"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81086"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/54"],["dc.identifier.url","https://sfb1190.med.uni-goettingen.de/production/literature/publications/117"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/55"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation","SFB 1190: Transportmaschinen und Kontaktstellen zellulärer Kompartimente"],["dc.relation","SFB 1190 | P09: Proteinsortierung in der Synapse: Prinzipien und molekulare Organisation"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | B02: Ein in vitro-Verfahren zum Verständnis der struktur-organisierenden Rolle des Vesikel-Clusters"],["dc.relation","SFB 1286 | Z02: Integrative Datenanalyse und -interpretation. Generierung einer synaptisch-integrativen Datenstrategie (SynIDs)"],["dc.relation.eissn","1460-2075"],["dc.relation.issn","0261-4189"],["dc.relation.orgunit","Institut für Röntgenphysik"],["dc.relation.workinggroup","RG Köster (Cellular Biophysics)"],["dc.relation.workinggroup","RG Rizzoli (Quantitative Synaptology in Space and Time)"],["dc.relation.workinggroup","RG Bonn"],["dc.rights","This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited."],["dc.subject.gro","neuro biophysics"],["dc.subject.gro","molecular biophysics"],["dc.subject.gro","cellular biophysics"],["dc.title","A comparative analysis of the mobility of 45 proteins in the synaptic bouton"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]