Regulating the human HECT E3 ligases

Ubiquitination, the covalent attachment of ubiquitin to proteins, by E3 ligases of the HECT (homologous to E6AP C terminus) family is critical in controlling diverse physiological pathways. Stringent control of HECT E3 ligase activity and substrate specificity is essential for cellular health, whereas deregulation of HECT E3s plays a prominent role in disease. The cell employs a wide variety of regulatory mechanisms to control HECT E3 activity and substrate specificity. Here, we summarize the current understanding of these regulatory mechanisms that control HECT E3 function. Substrate specificity is generally determined by interactions of adaptor proteins with domains in the N-terminal extensions of HECT E3 ligases. These N-terminal domains have also been found to interact with the HECT domain, resulting in the formation of inhibitory conformations. In addition, catalytic activity of the HECT domain is commonly regulated at the level of E2 recruitment and through HECT E3 oligomerization. The previously mentioned regulatory mechanisms can be controlled through protein–protein interactions, post-translational modifications, the binding of calcium ions, and more. Functional activity is determined not only by substrate recruitment and catalytic activity, but also by the type of ubiquitin polymers catalyzed to the substrate. While this is often determined by the specific HECT member, recent studies demonstrate that HECT E3s can be modulated to alter the type of ubiquitin polymers they catalyze. Insight into these diverse regulatory mechanisms that control HECT E3 activity may open up new avenues for therapeutic strategies aimed at inhibition or enhancement of HECT E3 function in disease-related pathways

Contribution of Fdh3 and Glr1 to Glutathione Redox State, Stress Adaptation and Virulence in Candida albicans

Acknowledgments: We thank Aaron Mitchell and Dominique Sanglard for providing the C. albicans protein kinase and transposon mutant libraries, and Louise Walker for the strain CAMY203.Peer reviewedPublisher PD

A cross-species spatiotemporal proteomic analysis identifies UBE3A-dependent signaling pathways and targets

Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by the loss of neuronal E3 ligase UBE3A. Restoring UBE3A levels is a potential disease-modifying therapy for AS and has recently entered clinical trials. There is paucity of data regarding the molecular changes downstream of UBE3A hampering elucidation of disease therapeutics and biomarkers. Notably, UBE3A plays an important role in the nucleus but its targets have yet to be elucidated. Using proteomics, we assessed changes during postnatal cortical development in an AS mouse model. Pathway analysis revealed dysregulation of proteasomal and tRNA synthetase pathways at all postnatal brain developmental stages, while synaptic proteins were altered in adults. We confirmed pathway alterations in an adult AS rat model across multiple brain regions and highlighted region-specific differences. UBE3A reinstatement in AS model mice resulted in near complete and partial rescue of the proteome alterations in adolescence and adults, respectively, supporting the notion that restoration of UBE3A expression provides a promising therapeutic option. We show that the nuclear enriched transketolase (TKT), one of the most abundantly altered proteins, is a novel direct UBE3A substrate and is elevated in the neuronal nucleus of rat brains and human iPSC-derived neurons. Taken together, our study provides a comprehensive map of UBE3A-driven proteome remodeling in AS across development and species, and corroborates an early UBE3A reinstatement as a viable therapeutic option. To support future disease and biomarker research, we present an accessible large-scale multi-species proteomic resource for the AS community (https://www.angelman-proteome-project.org/)

A versatile plasmid system for reconstitution and analysis of mammalian ubiquitination cascades in yeast

Ubiquitination is a posttranslational protein modification that regulates most aspects of cellular life. The sheer number of ubiquitination enzymes that are present in a mammalian cell, over 700 in total, has thus far hampered the analysis of distinct protein ubiquitination cascades in a cellular context. To overcome this complexity we have developed a versatile vector system that allows the reconstitution of specific ubiquitination cascades in the model eukaryote Saccharomyces cerevisae (baker’s yeast). The vector system consists of 32 modular yeast shuttle plasmids allowing inducible or constitutive expression of up to four proteins of interest in a single cell. To demonstrate the validity of the system, we show that co-expression in yeast of the mammalian HECT type E3 ubiquitin ligase E6AP (E6-Associated Protein) and a model substrate faithfully recapitulates E6AP-dependent substrate ubiquitination and degradation. In addition, we show that the endogenous sumoylation pathway of S. cerevisiae can specifically sumoylate mouse PML (Promyelocytic leukemia protein). In conclusion, the yeast vector system described in this paper provides a versatile tool to study complex posttranslational modifications in a cellular setting

The Peroxisomal Targeting Signal 1 in sterol carrier protein 2 is autonomous and essential for receptor recognition

<p>Abstract</p> <p>Background</p> <p>The majority of peroxisomal matrix proteins destined for translocation into the peroxisomal lumen are recognised <it>via </it>a C-terminal Peroxisomal Target Signal type 1 by the cycling receptor Pex5p. The only structure to date of Pex5p in complex with a cargo protein is that of the C-terminal cargo-binding domain of the receptor with sterol carrier protein 2, a small, model peroxisomal protein. In this study, we have tested the contribution of a second, ancillary receptor-cargo binding site, which was found in addition to the characterised Peroxisomal Target Signal type 1.</p> <p>Results</p> <p>To investigate the function of this secondary interface we have mutated two key residues from the ancillary binding site and analyzed the level of binding first by a yeast-two-hybrid assay, followed by quantitative measurement of the binding affinity and kinetics of purified protein components and finally, by <it>in vivo </it>measurements, to determine translocation capability. While a moderate but significant reduction of the interaction was found in binding assays, we were not able to measure any significant defects <it>in vivo</it>.</p> <p>Conclusions</p> <p>Our data therefore suggest that at least in the case of sterol carrier protein 2 the contribution of the second binding site is not essential for peroxisomal import. At this stage, however, we cannot rule out that other cargo proteins may require this ancillary binding site.</p

Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology

The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. In this work, we evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology using the 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal gray zone cases. For the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 63.65% and 74.57%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4's strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates good knowledge of statistics, CNS & eye, pediatrics, biology, and physics but has limitations in bone & soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs well in diagnosis, prognosis, and toxicity but lacks proficiency in topics related to brachytherapy and dosimetry, as well as in-depth questions from clinical trials. For the gray zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Most importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts. Both evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Because of the risk of hallucination, facts provided by ChatGPT always need to be verified

A novel UBE3A sequence variant identified in eight related individuals with neurodevelopmental delay, results in a phenotype which does not match the clinical criteria of Angelman syndrome

Background: Loss of functional UBE3A, an E3 protein ubiquitin ligase, causes Angelman syndrome (AS), a neurodevelopmental disorder characterized by severe developmental delay, speech impairment, epilepsy, movement or balance disorder, and a characteristic behavioral pattern. We identified a novel UBE3A sequence variant in a large family with eight affected individuals, who did not meet the clinical AS criteria. Methods: Detailed clinical examination and genetic analysis was performed to establish the phenotypic diversity and the genetic cause. The function of the mutant UBE3A protein was assessed with respect to its subcellular localization, stability, and E3 ubiquitin ligase activity. Results: All eight affected individuals showed the presence of a novel maternally inherited UBE3A sequence variant (NM_130838.4(UBE3A):c.1018-1020del, p.(Asn340del), which is in line with a genetic AS diagnosis. Although they presented with moderate to severe intellectual disability, the phenotype did not match the clinical criteria for AS. In line with this, functional analysis of the UBE3A p.Asn340del mutant protein revealed no major deficits in UBE3A protein localization, stability, or E3 ubiquitin ligase activity. Conclusion: The p.(Asn340del) mutant protein behaves distinctly different from previously described AS-linked missense mutations in UBE3A, and causes a phenotype that is markedly different from AS. This study further extends the range of phenotypes that are associated with UBE3A loss, duplication, or mutation

Conserved UBE3A subcellular distribution between human and mice is facilitated by non-homologous isoforms

The human UBE3A gene, which is essential for normal neurodevelopment, encodes three Ubiquitin E3 ligase A (UBE3A) protein isoforms. However, the subcellular localization and relative abundance of these human UBE3A isoforms are unknown. We found, as previously reported in mice, that UBE3A is predominantly nuclear in human neurons. However, this conserved subcellular distribution is achieved by strikingly distinct cis-acting mechanisms. A single amino-acid deletion in the N-terminus of human hUBE3A-Iso3, which is homologous to cytosolic mouse mUBE3A-Iso2, results in its translocation to the nucleus. This singe amino-acid deletion is shared with apes and Old World monkeys and was preceded by the appearance of the cytosolic hUBE3A-Iso2 isoform. This hUBE3A-Iso2 isoform arose after the lineage of New World monkeys and Old World monkeys separated from the Tarsiers (Tarsiidae). Due to the loss of a s

Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology

PurposeThe potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology.MethodsThe 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4. The TXIT exam contains 300 questions covering various topics of radiation oncology. The 2022 Gray Zone collection contains 15 complex clinical cases.ResultsFor the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 62.05% and 78.77%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4’s strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates better knowledge of statistics, CNS &amp; eye, pediatrics, biology, and physics than knowledge of bone &amp; soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs better in diagnosis, prognosis, and toxicity than brachytherapy and dosimetry. It lacks proficiency in in-depth details of clinical trials. For the Gray Zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts.ConclusionBoth evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Owing to the risk of hallucinations, it is essential to verify the content generated by models such as ChatGPT for accuracy