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

Task difficulty and life changes among stroke family caregivers: relationship to depressive symptoms

OBJECTIVES: To investigate differences in stroke caregiver task difficulty and life changes based on level of caregiver depressive symptoms, and to estimate probabilities among task difficulty and life change items. DESIGN: Descriptive analysis of baseline data from an ongoing stroke caregiver intervention trial. SETTING: Hospitals and rehabilitation facilities. PARTICIPANTS: Caregivers (N=242; 78.6% women; 47.7% spouses; 71.8% white; mean age, 54.2±12.1y) caring for stroke survivors within 8 weeks of discharge to home. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Baseline measures for task difficulty (Oberst Caregiving Burden Scale) and life changes (Bakas Caregiving Outcomes Scale) were compared based on level of depressive symptoms (Patient Health Questionnaire-9 [PHQ-9] scores <5 means no depressive symptoms; n=126; PHQ-9 scores ≥5 means mild to severe depressive symptoms, n=116). Mean scores were analyzed using general linear modeling, with item analyses using logistic regression and the Benjamini-Hochberg method to control type I error inflation. RESULTS: Caregivers with mild to severe depressive symptoms have greater difficulty with tasks and worse life changes than those with no depressive symptoms (P<.001). Odds ratios were highest for the task of arranging care while away and for negative life changes (eg, addressing self-esteem, coping with stress, physical health). CONCLUSIONS: Findings underscore the importance of depressive symptom screening for stroke caregivers during or shortly after discharge. Assisting caregivers with depressive symptoms to arrange for respite care and addressing negative physical and psychological changes may be priority areas for future interventions

Deep Physics-aware Inference of Cloth Deformation for Monocular Human Performance Capture

Recent monocular human performance capture approaches have shown compelling dense tracking results of the full body from a single RGB camera. However, existing methods either do not estimate clothing at all or model cloth deformation with simple geometric priors instead of taking into account the underlying physical principles. This leads to noticeable artifacts in their reconstructions, such as baked-in wrinkles, implausible deformations that seemingly defy gravity, and intersections between cloth and body. To address these problems, we propose a person-specific, learning-based method that integrates a finite element-based simulation layer into the training process to provide for the first time physics supervision in the context of weakly-supervised deep monocular human performance capture. We show how integrating physics into the training process improves the learned cloth deformations, allows modeling clothing as a separate piece of geometry, and largely reduces cloth-body intersections. Relying only on weak 2D multi-view supervision during training, our approach leads to a significant improvement over current state-of-the-art methods and is thus a clear step towards realistic monocular capture of the entire deforming surface of a clothed human