SUMMARY Dynamin is a 100 kDa GTPase that organizes into helical assemblies at the base of nascent clathrin-coated vesicles. Formation of these oligomers stimulates the intrinsic GTPase activity of dynamin, which is necessary for efficient membrane fission during endocytosis. Recent evidence suggests that the transition-state of dynamin's GTP hydrolysis reaction serves as a key determinant of productive fission. Here we present the structure of a transition-state-defective dynamin mutant, K44A, trapped in a pre-fission state, at 12.5 Å resolution. This structure constricts to 3.7 nm, reaching the theoretical limit required for spontaneous membrane fission. Computational docking indicates that the ground state conformation of the dynamin polymer is sufficient to achieve this super-constricted pre-fission state and reveals how a 2-start helical symmetry promotes the most efficient packing of dynamin tetramers around the membrane neck. These data suggest a new model for the assembly and regulation of the minimal dynamin fission machine.

Background: Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges. Methods: In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. Results: While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases. Conclusions: We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.

Numerous vesiculation processes throughout the eukaryotic cell are dependent on the protein dynamin, a large GTPase that constricts lipid bilayers. We have combined X-ray crystallography and cryo-electron microscopy (cryo-EM) data to generate a coherent model of dynamin-mediated membrane constriction. GTPase and pleckstrin homology domains of dynamin were fit to cryo-EM structures of human dynamin helices bound to lipid in nonconstricted and constricted states. Proteolysis and immunogold labeling experiments confirm the topology of dynamin domains predicted from the helical arrays. Based on the fitting, an observed twisting motion of the GTPase, middle, and GTPase effector domains coincides with conformational changes determined by cryo-EM. We propose a corkscrew model for dynamin constriction based on these motions and predict regions of sequence important for dynamin function as potential targets for future mutagenic and structural studies.

Regulation of transcription requires mechanisms to both activate and terminate transcription factor activity. GATA-1 is a key haemopoietic transcription factor whose activity is increased by acetylation. We show here that acetylated GATA-1 is targeted for degradation via the ubiquitin/proteasome pathway. Acetylation positively signals ubiquitination, suggesting that activation by acetylation simultaneously marks GATA-1 for degradation. Promoter-specific MAPK phosphorylation then cooperates with acetylation to execute protein loss. The requirement for both modifications is novel and suggests a way by which degradation of the active protein can be specifically regulated in response to external phosphorylation-mediated signalling. As many transcription factors are activated by acetylation, we suggest that this might be a general mechanism to control transcription factor activity.

SummaryThe Azotobacter vinelandii NIFL regulatory flavoprotein responds to the redox, energy and nitrogen status of the cell to inhibit transcriptional activation by the s N -dependent enhancer binding protein, NIFA, via the formation of a NIFL±NIFA protein complex. The NIFA protein contains three domains: an Nterminal domain of unknown function; a central catalytic domain required to couple nucleotide hydrolysis to activation of the s N -RNA polymerase holoenzyme; and a C-terminal DNA-binding domain. We report that truncated NIFA proteins that either lack the amino-terminal domain or contain only the isolated central domain remain responsive to inhibition by NIFL but, in contrast to native NIFA, continue to hydrolyse nucleotides when NIFL is present. We also report that NIFL is competent to inhibit the DNAbinding function of NIFA. Taken together, these results suggest that NIFL inhibits NIFA via a concerted mechanism in which DNA binding, catalytic activity and, potentially, interaction with the polymerase are controlled by NIFL in order to prevent transcriptional activation under detrimental environmental conditions.

The intracellular bacterial pathogen Legionella pneumophila (Lp) evades destruction in macrophages by camouflaging in a specialized organelle, the Legionella-containing vacuole (LCV), where it replicates. The LCV maturates by incorporating ER vesicles, which are diverted by effectors that Lp injects to take control of host cell membrane transport processes. One of these effectors, RalF, recruits the trafficking small GTPase Arf1 to the LCV. LpRalF has a Sec7 domain related to host ArfGEFs, followed by a capping domain that intimately associates with the Sec7 domain to inhibit GEF activity. How RalF is activated to function as a LCV-specific ArfGEF is unknown. We combined the reconstitution of Arf activation on artificial membranes with cellular expression and Lp infection assays, to analyze how auto-inhibition is relieved for LpRalF to function in vivo. We find that membranes activate LpRalF by about 1000 fold, and identify the membrane-binding region as the region that inhibits the Sec7 active site. It is enriched in aromatic and positively charged residues, which establish a membrane sensor to control the GEF activity in accordance with specific lipid environments. A similar mechanism of activation is found in RalF from Rickettsia prowazekii (Rp), with a different aromatic/charged residues ratio that results in divergent membrane preferences. The membrane sensor is the primary determinant of the localization of LpRalF on the LCV, and drives the timing of Arf activation during infection. Finally, we identify a conserved motif in the capping domain, remote from the membrane sensor, which is critical for RalF activity presumably by organizing its active conformation. These data demonstrate that RalF proteins are regulated by a membrane sensor that functions as a binary switch to derepress ArfGEF activity when RalF encounters a favorable lipid environment, thus establishing a regulatory paradigm to ensure that Arf GTPases are efficiently activated at specific membrane locations.

Supplementary data are available at Bioinformatics online.

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