Slx5/Slx8-dependent ubiquitin hotspots on chromatin contribute to stress tolerance

Chromatin is a highly regulated environment, and protein association with chromatin is often controlled by post-translational modifications and the corresponding enzymatic machinery. Specifically, SUMO-targeted ubiquitin ligases (STUbLs) have emerged as key players in nuclear quality control, genome maintenance, and transcription. However, how STUbLs select specific substrates among myriads of SUMOylated proteins on chromatin remains unclear. Here, we reveal a remarkable co-localization of the budding yeast STUbL Slx5/Slx8 and ubiquitin at seven genomic loci that we term "ubiquitin hotspots". Ubiquitylation at these sites depends on Slx5/Slx8 and protein turnover on the Cdc48 segregase. We identify the transcription factor-like Ymr111c/Euc1 to associate with these sites and to be a critical determinant of ubiquitylation. Euc1 specifically targets Slx5/Slx8 to ubiquitin hotspots via bipartite binding of Slx5 that involves the Slx5 SUMO-interacting motifs and an additional, novel substrate recognition domain. Interestingly, the Euc1-ubiquitin hotspot pathway acts redundantly with chromatin modifiers of the H2A.Z and Rpd3L pathways in specific stress responses. Thus, our data suggest that STUbL-dependent ubiquitin hotspots shape chromatin during stress adaptation

Homological Berglund-Hübsch mirror symmetry for curve singularities

Given a two-variable invertible polynomial, we show that its category of maximally-graded matrix factorisations is quasi-equivalent to the Fukaya–Seidel category of its Berglund–Hübsch transpose. This was previously shown for Brieskorn–Pham and D-type singularities by Futaki–Ueda. The proof involves explicit construction of a tilting object on the B‑side, and comparison with a specific basis of Lefschetz thimbles on the A‑side

Reductive chain separation of botulinum A toxin — a prerequisite to its inhibitory action on exocytosis in chromaffin cells

Cleavage of the disulfide bond linking the heavy and the light chains of tetanus toxin is necessary for its inhibitory action on exocytotic release ofcatecholamines from permeabi1ized chromaffin cells [(1989) FEBS Lett. 242, 245-248; (1989) J. Neurochern., in press]. The related botulinum A toxin also consists of a heavy and a light chain linked by a disulfide bond. The actions ofboth neurotoxins on exocytosis were presently compared using streptolysin O-permeabilized bovine adrenal chromaffin cells. Botulinum A toxin inhibited Ca2 +-stimulated catecholamine release from these cells. Addition of dithiothreitollowered the effective doses to values below 5 nM. Under the same conditions, the effective doses of tetanus toxin were decreased by a factor of five. This indicates that the interchain S-S bond of botulinum A toxin must also be split before the neurotoxin can exert its effect on exocytosis

{HiFECap}: {M}onocular High-Fidelity and Expressive Capture of Human Performances

Monocular 3D human performance capture is indispensable for many applicationsin computer graphics and vision for enabling immersive experiences. However,detailed capture of humans requires tracking of multiple aspects, including theskeletal pose, the dynamic surface, which includes clothing, hand gestures aswell as facial expressions. No existing monocular method allows joint trackingof all these components. To this end, we propose HiFECap, a new neural humanperformance capture approach, which simultaneously captures human pose,clothing, facial expression, and hands just from a single RGB video. Wedemonstrate that our proposed network architecture, the carefully designedtraining strategy, and the tight integration of parametric face and hand modelsto a template mesh enable the capture of all these individual aspects.Importantly, our method also captures high-frequency details, such as deformingwrinkles on the clothes, better than the previous works. Furthermore, we showthat HiFECap outperforms the state-of-the-art human performance captureapproaches qualitatively and quantitatively while for the first time capturingall aspects of the human.<br

The cytoplasmic poly(A) polymerases GLD-2 and GLD-4 promote general gene expression via distinct mechanisms

Post-transcriptional gene regulation mechanisms decide on cellular mRNA activities. Essential gatekeepers of post-transcriptional mRNA regulation are broadly conserved mRNA-modifying enzymes, such as cytoplasmic poly(A) polymerases (cytoPAPs). Although these non-canonical nucleotidyltransferases efficiently elongate mRNA poly(A) tails in artificial tethering assays, we still know little about their global impact on poly(A) metabolism and their individual molecular roles in promoting protein production in organisms. Here, we use the animal model Caenorhabditis elegans to investigate the global mechanisms of two germline-enriched cytoPAPs, GLD-2 and GLD-4, by combining polysome profiling with RNA sequencing. Our analyses suggest that GLD-2 activity mediates mRNA stability of many translationally repressed mRNAs. This correlates with a general shortening of long poly(A) tails in gld-2-compromised animals, suggesting that most if not all targets are stabilized via robust GLD-2-mediated polyadenylation. By contrast, only mild polyadenylation defects are found in gld-4-compromised animals and few mRNAs change in abundance. Interestingly, we detect a reduced number of polysomes in gld-4 mutants and GLD-4 protein co-sediments with polysomes, which together suggest that GLD-4 might stimulate or maintain translation directly. Our combined data show that distinct cytoPAPs employ different RNA-regulatory mechanisms to promote gene expression, offering new insights into translational activation of mRNAs

A Deeper Look into DeepCap

Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality. Many previous performance capture approaches either required expensive multi-view setups or did not recover dense space-time coherent geometry with frame-to-frame correspondences. We propose a novel deep learning approach for monocular dense human performance capture. Our method is trained in a weakly supervised manner based on multi-view supervision completely removing the need for training data with 3D ground truth annotations. The network architecture is based on two separate networks that disentangle the task into a pose estimation and a non-rigid surface deformation step. Extensive qualitative and quantitative evaluations show that our approach outperforms the state of the art in terms of quality and robustness. This work is an extended version of DeepCap where we provide more detailed explanations, comparisons and results as well as applications