Live-attenuated RNA hybrid vaccine technology provides single-dose protection against Chikungunya virus.

We present a live-attenuated RNA hybrid vaccine technology that uses an RNA vaccine delivery vehicle to deliver in vitro-transcribed, full-length, live-attenuated viral genomes to the site of vaccination. This technology allows ready manufacturing in a cell-free environment, regardless of viral attenuation level, and it promises to avoid many safety and manufacturing challenges of traditional live-attenuated vaccines. We demonstrate this technology through development and testing of a live-attenuated RNA hybrid vaccine against Chikungunya virus (CHIKV), comprised of an in vitro-transcribed, highly attenuated CHIKV genome delivered by a highly stable nanostructured lipid carrier (NLC) formulation as an intramuscular injection. We demonstrate that single-dose immunization of immunocompetent C57BL/6 mice results in induction of high CHIKV-neutralizing antibody titers and protection against mortality and footpad swelling after lethal CHIKV challenge.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

A Nanostructured Lipid Carrier for Delivery of a Replicating Viral RNA Provides Single, Low-Dose Protection against Zika.

Since the first demonstration of in vivo gene expression from an injected RNA molecule almost two decades ago,1 the field of RNA-based therapeutics is now taking significant strides, with many cancer and infectious disease targets entering clinical trials.2 Critical to this success has been advances in the knowledge and application of delivery formulations. Currently, various lipid nanoparticle (LNP) platforms are at the forefront,3 but the encapsulation approach underpinning LNP formulations offsets the synthetic and rapid-response nature of RNA vaccines.4 Second, limited stability of LNP formulated RNA precludes stockpiling for pandemic readiness.5 Here, we show the development of a two-vialed approach wherein the delivery formulation, a highly stable nanostructured lipid carrier (NLC), can be manufactured and stockpiled separate from the target RNA, which is admixed prior to administration. Furthermore, specific physicochemical modifications to the NLC modulate immune responses, either enhancing or diminishing neutralizing antibody responses. We have combined this approach with a replicating viral RNA (rvRNA) encoding Zika virus (ZIKV) antigens and demonstrated a single dose as low as 10 ng can completely protect mice against a lethal ZIKV challenge, representing what might be the most potent approach to date of any Zika vaccine.

PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks.

Task-trained artificial recurrent neural networks (RNNs) provide a computational modeling framework of increasing interest and application in computational, systems, and cognitive neuroscience. RNNs can be trained, using deep-learning methods, to perform cognitive tasks used in animal and human experiments and can be studied to investigate potential neural representations and circuit mechanisms underlying cognitive computations and behavior. Widespread application of these approaches within neuroscience has been limited by technical barriers in use of deep-learning software packages to train network models. Here, we introduce PsychRNN, an accessible, flexible, and extensible Python package for training RNNs on cognitive tasks. Our package is designed for accessibility, for researchers to define tasks and train RNN models using only Python and NumPy, without requiring knowledge of deep-learning software. The training backend is based on TensorFlow and is readily extensible for researchers with TensorFlow knowledge to develop projects with additional customization. PsychRNN implements a number of specialized features to support applications in systems and cognitive neuroscience. Users can impose neurobiologically relevant constraints on synaptic connectivity patterns. Furthermore, specification of cognitive tasks has a modular structure, which facilitates parametric variation of task demands to examine their impact on model solutions. PsychRNN also enables task shaping during training, or curriculum learning, in which tasks are adjusted in closed-loop based on performance. Shaping is ubiquitous in training of animals in cognitive tasks, and PsychRNN allows investigation of how shaping trajectories impact learning and model solutions. Overall, the PsychRNN framework facilitates application of trained RNNs in neuroscience research.

Intramuscular Delivery of Replicon RNA Encoding ZIKV-117 Human Monoclonal Antibody Protects against Zika Virus Infection.

Monoclonal antibody (mAb) therapeutics are an effective modality for the treatment of infectious, autoimmune, and cancer-related diseases. However, the discovery, development, and manufacturing processes are complex, resource-consuming activities that preclude the rapid deployment of mAbs in outbreaks of emerging infectious diseases. Given recent advances in nucleic acid delivery technology, it is now possible to deliver exogenous mRNA encoding mAbs for in situ expression following intravenous (i.v.) infusion of lipid nanoparticle-encapsulated mRNA. However, the requirement for i.v. administration limits the application to settings where infusion is an option, increasing the cost of treatment. As an alternative strategy, and to enable intramuscular (IM) administration of mRNA-encoded mAbs, we describe a nanostructured lipid carrier for delivery of an alphavirus replicon encoding a previously described highly neutralizing human mAb, ZIKV-117. Using a lethal Zika virus challenge model in mice, our studies show robust protection following alphavirus-driven expression of ZIKV-117 mRNA when given by IM administration as pre-exposure prophylaxis or post-exposure therapy.

Integrated pipeline for the accelerated discovery of antiviral antibody therapeutics.

The emergence and re-emergence of highly virulent viral pathogens with the potential to cause a pandemic creates an urgent need for the accelerated discovery of antiviral therapeutics. Antiviral human monoclonal antibodies (mAbs) are promising candidates for the prevention and treatment of severe viral diseases, but their long development timeframes limit their rapid deployment and use. Here, we report the development of an integrated sequence of technologies, including single-cell mRNA-sequence analysis, bioinformatics, synthetic biology and high-throughput functional analysis, that enables the rapid discovery of highly potent antiviral human mAbs, the activity of which we validated in vivo. In a 78-d study modelling the deployment of a rapid response to an outbreak, we isolated more than 100 human mAbs that are specific to Zika virus, assessed their function, identified that 29 of these mAbs have broadly neutralizing activity, and verified the therapeutic potency of the lead candidates in mice and non-human primate models of infection through the delivery of an antibody-encoding mRNA formulation and of the respective IgG antibody. The pipeline provides a roadmap for rapid antibody-discovery programmes against viral pathogens of global concern.

nor-BNI Antagonism of Kappa Opioid Agonist-Induced Reinstatement of Ethanol-Seeking Behavior.

Recent work suggests that the dynorphin (DYN)/kappa opioid receptor (KOR) system may be a key mediator in the behavioral effects of alcohol. The objective of the present study was to examine the ability of the KOR antagonist norbinaltorphimine (nor-BNI) to attenuate relapse to ethanol seeking due to priming injections of the KOR agonist U50,488 at time points consistent with KOR selectivity. Male Wistar rats were trained to self-administer a 10% ethanol solution, and then responding was extinguished. Following extinction, rats were injected with U50,488 (0.1-10 mg/kg, i.p.) or saline and were tested for the reinstatement of ethanol seeking. Next, the ability of the nonselective opioid receptor antagonist naltrexone (0 or 3.0 mg/kg, s.c.) and nor-BNI (0 or 20.0 mg/kg, i.p.) to block U50,488-induced reinstatement was examined. Priming injections U50,488 reinstated responding on the previously ethanol-associated lever. Pretreatment with naltrexone reduced the reinstatement of ethanol-seeking behavior. nor-BNI also attenuated KOR agonist-induced reinstatement, but to a lesser extent than naltrexone, when injected 24 hours prior to injections of U50,488, a time point that is consistent with KOR selectivity. While these results suggest that activation of KORs is a key mechanism in the regulation of ethanol-seeking behavior, U50,488-induced reinstatement may not be fully selective for KORs.