Metabolic Stress-Induced Phosphorylation of KAP1 Ser473 Blocks Mitochondrial Fusion in Breast Cancer Cells

Mitochondrial dynamics during nutrient starvation of cancer cells likely exert profound effects on their capability for metastatic progression. Here, we report that KAP1 (TRIM28), a transcriptional coadaptor protein implicated in metastatic progression in breast cancer, is a pivotal regulator of mitochondrial fusion in glucose-starved cancer cells. Diverse metabolic stresses induced Ser473 phosphorylation of KAP1 (pS473-KAP1) in a ROS- and p38-dependent manner. Results from live-cell imaging and molecular studies revealed that during the first 6 to 8 hours of glucose starvation, mitochondria initially underwent extensive fusion, but then subsequently fragmented in a pS473-KAP1-dependent manner. Mechanistic investigations using phosphorylation-defective mutants revealed that KAP1 Ser473 phosphorylation limited mitochondrial hyperfusion in glucose-starved breast cancer cells, as driven by downregulation of the mitofusin protein MFN2, leading to reduced oxidative phosphorylation and ROS production. In clinical specimens of breast cancer, reduced expression of MFN2 corresponded to poor prognosis in patients. In a mouse xenograft model of human breast cancer, there was an association in the core region of tumors between MFN2 downregulation and the presence of highly fragmented mitochondria. Collectively, our results suggest that KAP1 Ser473 phosphorylation acts through MFN2 reduction to restrict mitochondrial hyperfusion, thereby contributing to cancer cell survival under conditions of sustained metabolic stress

Mindfulness-based cognitive therapy v. group psychoeducation for people with generalised anxiety disorder: randomised controlled trial

Background: Research suggests that an 8-week mindfulness-based cognitive therapy (MBCT) course may be effective for generalised anxiety disorder (GAD). Aims: To compare changes in anxiety levels among participants with GAD randomly assigned to MBCT, cognitive–behavioural therapy-based psychoeducation and usual care. Method: In total, 182 participants with GAD were recruited (trial registration number: CUHK_CCT00267) and assigned to the three groups and followed for 5 months after baseline assessment with the two intervention groups followed for an additional 6 months. Primary outcomes were anxiety and worry levels. Results: Linear mixed models demonstrated significant group × time interaction (F(4,148) = 5.10, P = 0.001) effects for decreased anxiety for both the intervention groups relative to usual care. Significant group × time interaction effects were observed for worry and depressive symptoms and mental health-related quality of life for the psychoeducation group only. Conclusions: These results suggest that both of the interventions appear to be superior to usual care for the reduction of anxiety symptoms

Crystal Structures of a Piscine Betanodavirus: Mechanisms of Capsid Assembly and Viral Infection

Betanodaviruses cause massive mortality in marine fish species with viral nervous necrosis. The structure of a T = 3 Grouper nervous necrosis virus-like particle (GNNV-LP) is determined by the ab initio method with non-crystallographic symmetry averaging at 3.6 Å resolution. Each capsid protein (CP) shows three major domains: (i) the N-terminal arm, an inter-subunit extension at the inner surface; (ii) the shell domain (S-domain), a jelly-roll structure; and (iii) the protrusion domain (P-domain) formed by three-fold trimeric protrusions. In addition, we have determined structures of the T = 1 subviral particles (SVPs) of (i) the delta-P-domain mutant (residues 35−217) at 3.1 Å resolution; and (ii) the N-ARM deletion mutant (residues 35−338) at 7 Å resolution; and (iii) the structure of the individual P-domain (residues 214−338) at 1.2 Å resolution. The P-domain reveals a novel DxD motif asymmetrically coordinating two Ca2+ ions, and seems to play a prominent role in the calcium-mediated trimerization of the GNNV CPs during the initial capsid assembly process. The flexible N-ARM (N-terminal arginine-rich motif) appears to serve as a molecular switch for T = 1 or T = 3 assembly. Finally, we find that polyethylene glycol, which is incorporated into the P-domain during the crystallization process, enhances GNNV infection. The present structural studies together with the biological assays enhance our understanding of the role of the P-domain of GNNV in the capsid assembly and viral infection by this betanodavirus

Polylogarithmic Approximation Algorithm for k-Connected Directed Steiner Tree on Quasi-Bipartite Graphs

In the k-Connected Directed Steiner Tree problem (k-DST), we are given a directed graph G = (V,E) with edge (or vertex) costs, a root vertex r, a set of q terminals T, and a connectivity requirement k > 0; the goal is to find a minimum-cost subgraph H of G such that H has k edge-disjoint paths from the root r to each terminal in T. The k-DST problem is a natural generalization of the classical Directed Steiner Tree problem (DST) in the fault-tolerant setting in which the solution subgraph is required to have an r,t-path, for every terminal t, even after removing k-1 vertices or edges. Despite being a classical problem, there are not many positive results on the problem, especially for the case k ? 3. In this paper, we present an O(log k log q)-approximation algorithm for k-DST when an input graph is quasi-bipartite, i.e., when there is no edge joining two non-terminal vertices. To the best of our knowledge, our algorithm is the only known non-trivial approximation algorithm for k-DST, for k ? 3, that runs in polynomial-time Our algorithm is tight for every constant k, due to the hardness result inherited from the Set Cover problem

Toward Transparent Sequence Models with Model-Based Tree Markov Model

In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care Units (ICU). This model leverages knowledge distilled from Deep Neural Networks (DNN) to enhance predictive performance while offering clear explanations. Our experimental results indicate the improved performance of Model-Based trees (MOB trees) via employing LSTM for learning sequential patterns, which are then transferred to MOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in the MOB-HSMM enables uncovering potential and explainable sequences using available information

Two-Year Clinical and Functional Outcomes of an Asian Cohort at Ultra-High Risk of Psychosis

Background: To determine the 2-year clinical and functional outcomes of an Asian cohort at ultra-high risk (UHR) of psychosis.Method: This was a longitudinal study with a follow-up period of 2 years on 255 help-seeking adolescents and young adults at UHR of psychosis managed by a multi-disciplinary mental health team in Singapore. Clients received case management, psychosocial, and pharmacological treatment as appropriate. Data comprising symptom and functional outcomes were collected over the observation period by trained clinicians and psychiatrists.Results: The 2-year psychosis transition rate was 16.9%, with a median time to transition of 168 days. After 2 years, 14.5% of the subjects had persistent at-risk symptoms while 7.5% developed other non-psychotic psychiatric disorders. 38.4% of the cohort had recovered and was discharged from mental health services. The entire cohort's functioning improved as reflected by an increase in the score of the Social and Occupational Functioning Assessment Scale during the follow-up period. Predictors to psychosis transition included low education level, baseline unemployment, a history of violence, and brief limited intermittent psychotic symptoms, while male gender predicted the persistence of UHR state, or the development of non-psychotic disorders.Conclusion: Use of the current UHR criteria allows us to identify individuals who are at imminent risk of developing not just psychosis, but also those who may develop other mental health disorders. Future research should include identifying the needs of those who do not transition to psychosis, while continuing to refine on ways to improve the UHR prediction algorithm for psychosis