Munc18c accelerates SNARE-dependent membrane fusion in the presence of regulatory proteins α-SNAP and NSF.

Intracellular vesicle fusion is driven by the soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) and their cofactors, including Sec1/Munc18 (SM), α-SNAP, and NSF. α-SNAP and NSF play multiple layers of regulatory roles in the SNARE assembly, disassembling the cis-SNARE complex and the prefusion SNARE complex. How SM proteins coupled with NSF and α-SNAP regulate SNARE-dependent membrane fusion remains incompletely understood. Munc18c, an SM protein involved in the exocytosis of the glucose transporter GLUT4, binds and activates target (t-) SNAREs to accelerate the fusion reaction through a SNARE-like peptide (SLP). Here, using an in vitro reconstituted system, we discovered that α-SNAP blocks the GLUT4 SNAREs-mediated membrane fusion. Munc18c interacts with t-SNAREs to displace α-SNAP, which overcomes the fusion inhibition. Furthermore, Munc18c shields the trans-SNARE complex from NSF/α-SNAP-mediated disassembly and accelerates SNARE-dependent fusion kinetics in the presence of NSF and α-SNAP. The SLP in domain 3a is indispensable in Munc18c-assisted resistance to NSF and α-SNAP. Together, our findings demonstrate that Munc18c protects the prefusion SNARE complex from α-SNAP and NSF, promoting SNARE-dependent membrane fusion through its SLP.

Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges.

Due to the small sample sizes in early-phase clinical trials, the toxicity and efficacy profiles of the dose-schedule regimens determined for subsequent trials may not be well established. The recent development of novel anti-tumor treatments and combination therapies further complicates the problem. Therefore, there is an increasing recognition of the essential place of optimizing dose-schedule regimens, and new strategies are now urgently needed. Bayesian adaptive designs provide a potentially effective way to evaluate several doses and schedules simultaneously in a single clinical trial with higher efficiency, but real-world implementation examples of such adaptive designs are still few. In this paper, we cover the critical factors associated with dose-schedule optimization and review the related innovative Bayesian adaptive designs. The assumptions, characteristics, limitations, and application scenarios of those designs are introduced. The review also summarizes some unresolved issues and future research opportunities for dose-schedule optimization.

Enhancing phase I dose-finding trials design through dynamic borrowing information and handling late-onset toxicity.

Introduction: In recent years, there has been a growing trend among regulatory agencies to consider the use of historical controls in clinical trials as a means of improving the efficiency of trial design. In this paper, to enhance the statistical operating characteristic of Phase I dose-finding trials, we propose a novel model-assisted design method named "MEM-Keyboard". Methods: The proposed design is based on the multisource exchangeability models (MEMs) that allows for dynamic borrowing of information from multiple supplemental data sources, including historical trial data, to inform the dose-escalation process. Furthermore, with the frequent occurrence of delayed toxicity in novel anti-cancer drugs, we extended our proposed method to handle late-onset toxicity by incorporating historical data. This extended method is referred to as "MEM-TITE-Keyboard" and aims to improve the efficiency of early clinical trials. Results: Simulation studies have indicated that the proposed methods can improve the probability of correctly selecting the maximum tolerated dose (MTD) with an acceptable level of risk, compared to designs that do not account for information borrowing and late-onset toxicity. Discussion: The MEM-Keyboard and MEM-TITE-Keyboard, easy to implement in practice, provide a useful tool for identifying MTD and accelerating drug development.

A Bayesian phase I-II clinical trial design to find the biological optimal dose on drug combination.

In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I-II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose-response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well.

ORP9 and ORP10 form a heterocomplex to transfer phosphatidylinositol 4-phosphate at ER-TGN contact sites.

Oxysterol-binding protein (OSBP) and its related proteins (ORPs) are a family of lipid transfer proteins (LTPs) that mediate non-vesicular lipid transport. ORP9 and ORP10, members of the OSBP/ORPs family, are located at the endoplasmic reticulum (ER)-trans-Golgi network (TGN) membrane contact sites (MCSs). It remained unclear how they mediate lipid transport. In this work, we discovered that ORP9 and ORP10 form a binary complex through intermolecular coiled-coil (CC) domain-CC domain interaction. The PH domains of ORP9 and ORP10 specially interact with phosphatidylinositol 4-phosphate (PI4P), mediating the TGN targeting. The ORP9-ORP10 complex plays a critical role in regulating PI4P levels at the TGN. Using in vitro reconstitution assays, we observed that while full-length ORP9 efficiently transferred PI4P between two apposed membranes, the lipid transfer kinetics was further accelerated by ORP10. Interestingly, our data showed that the PH domains of ORP9 and ORP10 participate in membrane tethering simultaneously, whereas ORDs of both ORP9 and ORP10 are required for lipid transport. Furthermore, our data showed that the depletion of ORP9 and ORP10 led to increased vesicle transport to the plasma membrane (PM). These findings demonstrate that ORP9 and ORP10 form a binary complex through the CC domains, maintaining PI4P homeostasis at ER-TGN MCSs and regulating vesicle trafficking.

Assembly-promoting protein Munc18c stimulates SNARE-dependent membrane fusion through its SNARE-like peptide.

Intracellular vesicle fusion requires the soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) and their cognate Sec1/Munc18 (SM) proteins. How SM proteins act in concert with trans-SNARE complexes to promote membrane fusion remains incompletely understood. Munc18c, a broadly distributed SM protein, selectively regulates multiple exocytotic pathways, including GLUT4 exocytosis. Here, using an in vitro reconstituted system, we discovered a SNARE-like peptide (SLP), conserved in Munc18-1 of synaptic exocytosis, is crucial to the stimulatory activity of Munc18c in vesicle fusion. The direct stimulation of the SNARE-mediated fusion reaction by SLP further supported the essential role of this fragment. Interestingly, we found SLP strongly accelerates the membrane fusion rate when anchored to the target membrane but not the vesicle membrane, suggesting it primarily interacts with t-SNAREs in cis to drive fusion. Furthermore, we determined the SLP fragment is competitive with the full-length Munc18c protein and specific to the cognate v-SNARE isoforms, supporting how it could resemble Munc18c's activity in membrane fusion. Together, our findings demonstrate that Munc18c facilitates SNARE-dependent membrane fusion through SLP, revealing that the t-SNARE-SLP binding mode might be a conserved mechanism for the stimulatory function of SM proteins in vesicle fusion.