Many Gram-negative bacteria, including causative agents of dysentery, plague, and typhoid fever, rely on a type III secretion system - a multi-membrane spanning syringe-like apparatus - for their pathogenicity. The cytosolic ATPase complex of this injectisome is proposed to play an important role in energizing secretion events and substrate recognition. We present the 3.3 Å resolution cryo-EM structure of the enteropathogenic Escherichia coli ATPase EscN in complex with its central stalk EscO. The structure shows an asymmetric pore with different functional states captured in its six catalytic sites, details directly supporting a rotary catalytic mechanism analogous to that of the heterohexameric F/V-ATPases despite its homohexameric nature. Situated at the C-terminal opening of the EscN pore is one molecule of EscO, with primary interaction mediated through an electrostatic interface. The EscN-EscO structure provides significant atomic insights into how the ATPase contributes to type III secretion, including torque generation and binding of chaperone/substrate complexes.

The epigenetic dynamics of induced pluripotent stem cell (iPSC) reprogramming in correctly reprogrammed cells at high resolution and throughout the entire process remain largely undefined. Here, we characterize conversion of mouse fibroblasts into iPSCs using Gatad2a-Mbd3/NuRD-depleted and highly efficient reprogramming systems. Unbiased high-resolution profiling of dynamic changes in levels of gene expression, chromatin engagement, DNA accessibility, and DNA methylation were obtained. We identified two distinct and synergistic transcriptional modules that dominate successful reprogramming, which are associated with cell identity and biosynthetic genes. The pluripotency module is governed by dynamic alterations in epigenetic modifications to promoters and binding by Oct4, Sox2, and Klf4, but not Myc. Early DNA demethylation at certain enhancers prospectively marks cells fated to reprogram. Myc activity drives expression of the essential biosynthetic module and is associated with optimized changes in tRNA codon usage. Our functional validations highlight interweaved epigenetic- and Myc-governed essential reconfigurations that rapidly commission and propel deterministic reprogramming toward naive pluripotency.

Starting a new research campus is a leap of faith. Only later, in the full measure of time, is it possible to take stock of what has worked and what could have been done better or differently. The Janelia Research Campus opened its doors 12 years ago. What has it achieved? What has it taught us? And where does Janelia go from here?

Short-term memories link events separated in time, such as past sensation and future actions. Short-term memories are correlated with slow neural dynamics, including selective persistent activity, which can be maintained over seconds. In a delayed response task that requires short-term memory, neurons in the mouse anterior lateral motor cortex (ALM) show persistent activity that instructs future actions. To determine the principles that underlie this persistent activity, here we combined intracellular and extracellular electrophysiology with optogenetic perturbations and network modelling. We show that during the delay epoch, the activity of ALM neurons moved towards discrete end points that correspond to specific movement directions. These end points were robust to transient shifts in ALM activity caused by optogenetic perturbations. Perturbations occasionally switched the population dynamics to the other end point, followed by incorrect actions. Our results show that discrete attractor dynamics underlie short-term memory related to motor planning.

Understanding information processing in the brain requires us to monitor neural activity in vivo at high spatiotemporal resolution. Using an ultrafast two-photon fluorescence microscope (2PFM) empowered by all-optical laser scanning, we imaged neural activity in vivo at 1,000 frames per second and submicron spatial resolution. This ultrafast imaging method enabled monitoring of electrical activity down to 300 μm below the brain surface in head fixed awake mice.

Open-source software development has skyrocketed in part due to community tools like github.com, which allows publication of code as well as the ability to create branches and push accepted modifications back to the original repository. As the number and size of EM-based datasets increases, the connectomics community faces similar issues when we publish snapshot data corresponding to a publication. Ideally, there would be a mechanism where remote collaborators could modify branches of the data and then flexibly reintegrate results via moderated acceptance of changes. The DVID system provides a web-based connectomics API and the first steps toward such a distributed versioning approach to EM-based connectomics datasets. Through its use as the central data resource for Janelia's FlyEM team, we have integrated the concepts of distributed versioning into reconstruction workflows, allowing support for proofreader training and segmentation experiments through branched, versioned data. DVID also supports persistence to a variety of storage systems from high-speed local SSDs to cloud-based object stores, which allows its deployment on laptops as well as large servers. The tailoring of the backend storage to each type of connectomics data leads to efficient storage and fast queries. DVID is freely available as open-source software with an increasing number of supported storage options.

Nicotine dependence is thought to arise in part because nicotine permeates into the endoplasmic reticulum (ER), where it binds to nicotinic receptors (nAChRs) and begins an "inside-out" pathway that leads to up-regulation of nAChRs on the plasma membrane. However, the dynamics of nicotine entry into the ER are unquantified. Here, we develop a family of genetically encoded fluorescent biosensors for nicotine, termed iNicSnFRs. The iNicSnFRs are fusions between two proteins: a circularly permutated GFP and a periplasmic choline-/betaine-binding protein engineered to bind nicotine. The biosensors iNicSnFR3a and iNicSnFR3b respond to nicotine by increasing fluorescence at [nicotine] <1 µM, the concentration in the plasma and cerebrospinal fluid of a smoker. We target iNicSnFR3 biosensors either to the plasma membrane or to the ER and measure nicotine kinetics in HeLa, SH-SY5Y, N2a, and HEK293 cell lines, as well as mouse hippocampal neurons and human stem cell-derived dopaminergic neurons. In all cell types, we find that nicotine equilibrates in the ER within 10 s (possibly within 1 s) of extracellular application and leaves as rapidly after removal from the extracellular solution. The [nicotine] in the ER is within twofold of the extracellular value. We use these data to run combined pharmacokinetic and pharmacodynamic simulations of human smoking. In the ER, the inside-out pathway begins when nicotine becomes a stabilizing pharmacological chaperone for some nAChR subtypes, even at concentrations as low as ∼10 nM. Such concentrations would persist during the 12 h of a typical smoker's day, continually activating the inside-out pathway by >75%. Reducing nicotine intake by 10-fold decreases activation to ∼20%. iNicSnFR3a and iNicSnFR3b also sense the smoking cessation drug varenicline, revealing that varenicline also permeates into the ER within seconds. Our iNicSnFRs enable optical subcellular pharmacokinetics for nicotine and varenicline during an early event in the inside-out pathway.

Nicotine dependence is thought to arise in part because nicotine permeates into the endoplasmic reticulum (ER), where it binds to nicotinic receptors (nAChRs) and begins an "inside-out" pathway that leads to up-regulation of nAChRs on the plasma membrane. However, the dynamics of nicotine entry into the ER are unquantified. Here, we develop a family of genetically encoded fluorescent biosensors for nicotine, termed iNicSnFRs. The iNicSnFRs are fusions between two proteins: a circularly permutated GFP and a periplasmic choline-/betaine-binding protein engineered to bind nicotine. The biosensors iNicSnFR3a and iNicSnFR3b respond to nicotine by increasing fluorescence at [nicotine] <1 µM, the concentration in the plasma and cerebrospinal fluid of a smoker. We target iNicSnFR3 biosensors either to the plasma membrane or to the ER and measure nicotine kinetics in HeLa, SH-SY5Y, N2a, and HEK293 cell lines, as well as mouse hippocampal neurons and human stem cell-derived dopaminergic neurons. In all cell types, we find that nicotine equilibrates in the ER within 10 s (possibly within 1 s) of extracellular application and leaves as rapidly after removal from the extracellular solution. The [nicotine] in the ER is within twofold of the extracellular value. We use these data to run combined pharmacokinetic and pharmacodynamic simulations of human smoking. In the ER, the inside-out pathway begins when nicotine becomes a stabilizing pharmacological chaperone for some nAChR subtypes, even at concentrations as low as ∼10 nM. Such concentrations would persist during the 12 h of a typical smoker's day, continually activating the inside-out pathway by >75%. Reducing nicotine intake by 10-fold decreases activation to ∼20%. iNicSnFR3a and iNicSnFR3b also sense the smoking cessation drug varenicline, revealing that varenicline also permeates into the ER within seconds. Our iNicSnFRs enable optical subcellular pharmacokinetics for nicotine and varenicline during an early event in the inside-out pathway.

During sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys either performed a familiar motor task (movement-state) or learned to control the firing rate of a target neuron using a brain-machine interface (BMI-state). Before learning, monkeys were placed in an observation-state, where no action was required. We found that neuronal patterns during the BMI-state were markedly different from the movement-state patterns. BMI-state patterns were initially similar to those in the observation-state and evolved to produce an increase in the firing rate of the target neuron. The overall activity of the non-target neurons remained similar after learning, suggesting that excitatory-inhibitory balance was maintained. Indeed, a novel neural-level reinforcement-learning network model operating in a chaotic regime of balanced excitation and inhibition predicts our results in detail. We conclude that during BMI learning, the brain can adapt patterns corresponding to the current action-perception state to gain rewards. Moreover, our results show that we can predict activity changes that occur during learning based on the pre-learning activity. This new finding may serve as a key step toward clinical brain-machine interface applications to modify impaired brain activity.

Enzymes that cut proteins inside membranes regulate diverse cellular events, including cell signaling, homeostasis, and host-pathogen interactions. Adaptations that enable catalysis in this exceptional environment are poorly understood. We visualized single molecules of multiple rhomboid intramembrane proteases and unrelated proteins in living cells (human and ) and planar lipid bilayers. Notably, only rhomboid proteins were able to diffuse above the Saffman-Delbrück viscosity limit of the membrane. Hydrophobic mismatch with the irregularly shaped rhomboid fold distorted surrounding lipids and propelled rhomboid diffusion. The rate of substrate processing in living cells scaled with rhomboid diffusivity. Thus, intramembrane proteolysis is naturally diffusion-limited, but cells mitigate this constraint by using the rhomboid fold to overcome the "speed limit" of membrane diffusion.