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Rpn11, the only essential deubiquitinase (DUB) of the 26S proteasome, sits at the top of the substrate entry pathway and facilitates substrate degradation through cotranslocational deubiquitination. The structure of the Rpn11–Rpn8 complex, together with functional assays, offers insight into Rpn11's promiscuous DUB activity during proteasomal degradation. Polyubiquitin chains target protein substrates to the 26S proteasome, where they are removed by the deubiquitinase Rpn11 to allow efficient substrate degradation. Despite Rpn11's essential function during substrate processing, its detailed structural and biochemical characterization has been hindered by difficulties in purifying the isolated enzyme. Here we report the 2.0-Å crystal structures of Zn 2+ -free and Zn 2+ -bound Saccharomyces cerevisiae Rpn11 in an MPN-domain heterodimer with Rpn8. The Rpn11-Rpn8 interaction occurs via two distinct interfaces that may be conserved in related MPN-domain complexes. Our structural and mutational studies reveal that Rpn11 lacks a conserved surface to bind the ubiquitin Ile44 patch, does not interact with the moiety on the proximal side of the scissile isopeptide bond and exhibits no linkage specificity for ubiquitin cleavage. These findings explain how Rpn11 functions as a promiscuous deubiquitinase for cotranslocational substrate deubiquitination during proteasomal degradation.

Protein–protein interactions (PPIs) governing the recognition of substrates by E3 ubiquitin ligases are critical to cellular function. There is significant therapeutic potential in the development of small molecules that modulate these interactions; however, rational design of small molecule enhancers of PPIs remains elusive. Herein, we report the prospective identification and rational design of potent small molecules that enhance the interaction between an oncogenic transcription factor, β-Catenin, and its cognate E3 ligase, SCF β-TrCP . These enhancers potentiate the ubiquitylation of mutant β-Catenin by β-TrCP in vitro and induce the degradation of an engineered mutant β-Catenin in a cellular system. Distinct from PROTACs, these drug-like small molecules insert into a naturally occurring PPI interface, with contacts optimized for both the substrate and ligase within the same small molecule entity. The prospective discovery of ‘molecular glue’ presented here provides a paradigm for the development of small molecule degraders targeting hard-to-drug proteins. Directed protein degradation to target hard-to-drug proteins is a promising therapeutic approach. Here, the authors report the prospective discovery of small molecule “molecular glue” that inserts into a naturally occurring E3 ligase-substrate interface leading to degradation of substrate protein.

Muscle development requires that cells adopt specific identities at the right time and place within an embryo. Central to the success of this process are posttranslational modifications, such as ubiquitylation, that change activity, stability or localization of crucial transducers of differentiation signals. While many studies highlighted the importance of ubiquitylation during myogenesis, only few E3 ligases have been ascribed functions in this process. Here, we report that CUL3BTBD9, an E3 ligase associated with restless leg syndrome, is essential for myogenesis in vitro. CUL3BTBD9 binds and ubiquitylates CAV1, the central component of caveolae that modulate insulin signaling during muscle formation. CUL3BTBD9 and CAV1 are both required for insulin-dependent activation of the AKT kinase in myoblasts, thereby safeguarding the ability of muscle precursors to respond to insulin signals. Together, this work identifies CUL3BTBD9 as a regulator of myogenesis that acts by modulating plasma-membrane localized events critical for cell fate specification.

Metazoan development requires that cells adopt specific identities at the right time and place with-in an embryo. Central to the success of this process are posttranslational modifications that con-trol the activity, stability or localization of crucial transducers of differentiation signals. During muscle development, modification of proteins with ubiquitin is known to play an important role, but the enzymatic machinery of ubiquitylation that drives myogenesis remains incompletely under-stood. Here, we identify CUL3BTBD9 as an E3 ubiquitin ligase that is essential for myogenesis in vitro. CUL3BTBD9 binds and ubiquitylates CAV1, the central component of caveolae that modulate insulin signaling during muscle formation. CUL3BTBD9 and CAV1 are required for insulin-dependent activation of the AKT kinase in myoblasts, thereby safeguarding the ability of muscle precursors to respond to insulin signals. Together, this work identifies CUL3BTBD9 as a regulator of myogenesis that acts by modulating plasma-membrane localized events critical for cell fate specification.

In the midst of this construction activity, we'll be reviewing the Geelong Station Precinct Master Plan and finalising the Art and Cultural Precinct Master Plan and the CBD car parking strategy, which may include building new multi-storey car parking.

BackgroundDementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, yet it remains under-recognised and misdiagnosed, which delays treatment, causes inaccurate prognosis and limits research opportunities. Imaging with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive DLB biomarker. We evaluated a multivariate, quantifiable metabolic network biomarker, termed DLB-related pattern (DLBRP), for its further clinical translation across centres and disease stages.MethodsWe analysed demographic, clinical and FDG PET imaging data of 1180 participants from 14 tertiary centres and two multicentre datasets. We included 379 DLB, 28 mild cognitive impairment-LB (MCI-LB), 195 dementia due to Alzheimer’s disease (ADD), 172 MCI-AD without α-synuclein co-pathology (MCI-AD-S–), and 73 MCI-AD with α-synuclein co-pathology (S+) patients, along with a comparative group of 333 normal controls (NCs). From the scans, we calculated the expression of DLBRP, AD-related pattern (ADRP) and Parkinson’s disease-related pattern (PDRP) and compared them across groups. DLBRP scores were correlated with clinical measurements.ResultsAcross independent cohorts, DLBRP robustly distinguished DLB from NCs (sensitivity >89%, specificity >90%), and scores correlated with Unified Parkinson’s Disease Rating Scale Part III and independently predicted Mini–Mental State Examination. DLBRP was elevated already in MCI-LB. In a small longitudinal dataset, we observed steady increases in DLBRP expression with scores exceeding the diagnostic threshold prior to dementia onset. DLBRP and PDRP discriminated DLB from ADD (sensitivity, 74%–90%; specificity, 80%). In MCI-AD groups, ADRP was expressed, whereas DLBRP and PDRP were increased only in MCI-AD-S+, although comparatively less than in MCI-LB.ConclusionsThis study demonstrates the value of DLBRP in diagnosing prodromal and manifest DLB and distinguishing them from their AD counterparts. While overlap between patterns may reflect actual co-pathology, this possibility cannot be accepted without thorough pathological confirmation. The current findings support the use of DLBRP in patient evaluation and in future trial design.

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype ( p  = 7.18 × 10 −4 ) or temporal stage ( p  = 3.96 × 10 −5 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine. Progressive diseases tend to be heterogeneous in their underlying aetiology mechanism, disease manifestation, and disease time course. Here, Young and colleagues devise a computational method to account for both phenotypic heterogeneity and temporal heterogeneity, and demonstrate it using two neurodegenerative disease cohorts.

EFSA performed a public consultation of the draft statement of the PPR Panel on a framework for conducting the environmental exposure and risk assessment for transition metals when used as active substances in plant protection products (PPP) from 3 August to 21 September 2020. EFSA was asked by the European Commission to prepare a public consultation on the draft statement. The statement provides a framework for conducting the environmental exposure, hazard characterisation and risk assessment for transition metals when used as active substances in plant protection products according to Regulation (EC) No 1107/2009 of the European Parliament and the Council. This report presents statistics on the comments received and answers to them. These comments were taken into account when finalising the statement. This publication is linked to the following EFSA Journal article: http://onlinelibrary.wiley.com/doi/10.2903/j.efsa.2021.6498/full

We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemotron 3 Super is the first model in the Nemotron 3 family to 1) be pre-trained in NVFP4, 2) leverage LatentMoE, a new Mixture-of-Experts architecture that optimizes for both accuracy per FLOP and accuracy per parameter, and 3) include MTP layers for inference acceleration through native speculative decoding. We pre-trained Nemotron 3 Super on 25 trillion tokens followed by post-training using supervised fine tuning (SFT) and reinforcement learning (RL). The final model supports up to 1M context length and achieves comparable accuracy on common benchmarks, while also achieving up to 2.2x and 7.5x higher inference throughput compared to GPT-OSS-120B and Qwen3.5-122B, respectively. Nemotron 3 Super datasets, along with the base, post-trained, and quantized checkpoints, are open-sourced on HuggingFace.

This technical report reflects the outcome of the ecotoxicology experts meeting on general recurring issues noted during the EFSA peer reviews of pesticide active substances under Regulation (EC) No 1107/2009. General and specific issues were identified and discussed relating to risk assessment for birds and mammals, aquatic organisms, non‐target arthropods and soil organisms. Conclusions and recommendations on these topics were drawn.