University of Tübingen

About: University of Tübingen is a education organization based out in Tübingen, Germany. It is known for research contribution in the topics: Population & Transplantation. The organization has 40555 authors who have published 84108 publications receiving 3015320 citations. The organization is also known as: Eberhard Karls University & Eberhard-Karls-Universität Tübingen.

PINK1/Parkin-mediated mitophagy is dependent on VDAC1 and p62/SQSTM1

Abstract: Parkinson's disease is the most common neurodegenerative movement disorder. Mutations in PINK1 and PARKIN are the most frequent causes of recessive Parkinson's disease. However, their molecular contribution to pathogenesis remains unclear. Here, we reveal important mechanistic steps of a PINK1/Parkin-directed pathway linking mitochondrial damage, ubiquitylation and autophagy in non-neuronal and neuronal cells. PINK1 kinase activity and its mitochondrial localization sequence are prerequisites to induce translocation of the E3 ligase Parkin to depolarized mitochondria. Subsequently, Parkin mediates the formation of two distinct poly-ubiquitin chains, linked through Lys 63 and Lys 27. In addition, the autophagic adaptor p62/SQSTM1 is recruited to mitochondrial clusters and is essential for the clearance of mitochondria. Strikingly, we identified VDAC1 (voltage-dependent anion channel 1) as a target for Parkin-mediated Lys 27 poly-ubiquitylation and mitophagy. Moreover, pathogenic Parkin mutations interfere with distinct steps of mitochondrial translocation, ubiquitylation and/or final clearance through mitophagy. Thus, our data provide functional links between PINK1, Parkin and the selective autophagy of mitochondria, which is implicated in the pathogenesis of Parkinson's disease.

Guidelines for the use and interpretation of assays for monitoring autophagy in higher eukaryotes

Abstract: Research in autophagy continues to accelerate,(1) and as a result many new scientists are entering the field Accordingly, it is important to establish a standard set of criteria for monitoring macroautophagy in different organisms Recent reviews have described the range of assays that have been used for this purpose(2,3) There are many useful and convenient methods that can be used to monitor macroautophagy in yeast, but relatively few in other model systems, and there is much confusion regarding acceptable methods to measure macroautophagy in higher eukaryotes A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers of autophagosomes versus those that measure flux through the autophagy pathway; thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from fully functional autophagy that includes delivery to, and degradation within, lysosomes (in most higher eukaryotes) or the vacuole (in plants and fungi) Here, we present a set of guidelines for the selection and interpretation of the methods that can be used by investigators who are attempting to examine macroautophagy and related processes, as well as by reviewers who need to provide realistic and reasonable critiques of papers that investigate these processes This set of guidelines is not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to verify an autophagic response

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Abstract: Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.

Genome-wide association study identifies novel breast cancer susceptibility loci

Abstract: Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r2.0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P,1027). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P,0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.

Conduction Model of Metal Oxide Gas Sensors

Abstract: Tin dioxide is a widely used sensitive material for gas sensors. Many research and development groups in academia and industry are contributing to the increase of (basic) knowledge/(applied) know-how. However, from a systematic point of view the knowledge gaining process seems not to be coherent. One reason is the lack of a general applicable model which combines the basic principles with measurable sensor parameters. The approach in the presented work is to provide a frame model that deals with all contributions involved in conduction within a real world sensor. For doing so, one starts with identifying the different building blocks of a sensor. Afterwards their main inputs are analyzed in combination with the gas reaction involved in sensing. At the end, the contributions are summarized together with their interactions. The work presented here is one step towards a general applicable model for real world gas sensors.