TY  - JOUR
A1  - Tolay, Nazife
A1  - Buchberger, Alexander
T1  - Comparative profiling of stress granule clearance reveals differential contributions of the ubiquitin system
JF  - Life Science Alliance
N2  - Stress granules (SGs) are cytoplasmic condensates containing untranslated mRNP complexes. They are induced by various proteotoxic conditions such as heat, oxidative, and osmotic stress. SGs are believed to protect mRNPs from degradation and to enable cells to rapidly resume translation when stress conditions subside. SG dynamics are controlled by various posttranslationalmodifications, but the role of the ubiquitin system has remained controversial. Here, we present a comparative analysis addressing the involvement of the ubiquitin system in SG clearance. Using high-resolution immuno-fluorescence microscopy, we found that ubiquitin associated to varying extent with SGs induced by heat, arsenite, H2O2, sorbitol, or combined puromycin and Hsp70 inhibitor treatment. SG-associated ubiquitin species included K48- and K63-linked conjugates, whereas free ubiquitin was not significantly enriched. Inhibition of the ubiquitin activating enzyme, deubiquitylating enzymes, the 26S proteasome and p97/VCP impaired the clearance of arsenite- and heat-induced SGs, whereas SGs induced by other stress conditions were little affected. Our data underline the differential involvement of the ubiquitin system in SG clearance, a process important to prevent the formation of disease-linked aberrant SGs.
KW  - phase transition
KW  - quality control
KW  - protein
KW  - inhibition
KW  - complexity
KW  - separation
KW  - diversity
KW  - autophagy
KW  - ALS
KW  - P97
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259810
VL  - 4
IS  - 5
ER  - 
TY  - JOUR
A1  - Kato, Hiroki
A1  - Lu, Qiping
A1  - Rapaport, Doron
A1  - Kozjak-Pavlovic, Vera
T1  - Tom70 Is Essential for PINK1 Import into Mitochondria
JF  - PLoS ONE
N2  - PTEN induced kinase 1 (PINK1) is a serine/threonine kinase in the outer membrane of mitochondria (OMM), and known as a responsible gene of Parkinson's disease (PD). The precursor of PINK1 is synthesized in the cytosol and then imported into the mitochondria via the translocase of the OMM (TOM) complex. However, a large part of PINK1 import mechanism remains unclear. In this study, we examined using cell-free system the mechanism by which PINK1 is targeted to and assembled into mitochondria. Surprisingly, the main component of the import channel, Tom40 was not necessary for PINK1 import. Furthermore, we revealed that the import receptor Tom70 is essential for PINK1 import. In addition, we observed that although PINK1 has predicted mitochondrial targeting signal, it was not processed by the mitochondrial processing peptidase. Thus, our results suggest that PINK1 is imported into mitochondria by a unique pathway that is independent of the TOM core complex but crucially depends on the import receptor Tom70.
KW  - binding
KW  - outer-membrane proteins
KW  - Parkinsons diesease
KW  - intracellular membranes
KW  - quality control
KW  - pathway
KW  - recruitment
KW  - biogenesis
KW  - mechanisms
KW  - complex
Y1  - 2013
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-131061
VL  - 8
IS  - 3
ER  - 
TY  - JOUR
A1  - Griebel, Matthias
A1  - Segebarth, Dennis
A1  - Stein, Nikolai
A1  - Schukraft, Nina
A1  - Tovote, Philip
A1  - Blum, Robert
A1  - Flath, Christoph M.
T1  - Deep learning-enabled segmentation of ambiguous bioimages with deepflash2
JF  - Nature Communications
N2  - Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the training, evaluation, and application of deep learning models on ambiguous data. The tool’s training and evaluation pipeline uses multiple expert annotations and deep model ensembles to achieve accurate results. The application pipeline supports various use-cases for expert annotations and includes a quality assurance mechanism in the form of uncertainty measures. Benchmarked against other tools, deepflash2 offers both high predictive accuracy and efficient computational resource usage. The tool is built upon established deep learning libraries and enables sharing of trained model ensembles with the research community. deepflash2 aims to simplify the integration of deep learning into bioimage analysis projects while improving accuracy and reliability.
KW  - machine learning
KW  - microscopy
KW  - quality control
KW  - software
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-357286
VL  - 14
ER  -