Rotavirus, a leading cause of severe gastroenteritis and diarrhoea in young children, accounts for around 215,000 deaths annually worldwide1. Rotavirus specifically infects the intestinal epithelial cells in the host small intestine and has evolved strategies to antagonize interferon and NF-κB signalling2–5, raising the question as to whether other host factors participate in antiviral responses in intestinal mucosa. The mechanism by which enteric viruses are sensed and restricted in vivo, especially by NOD-like receptor (NLR) inflammasomes, is largely unknown. Here we uncover and mechanistically characterize the NLR Nlrp9b that is specifically expressed in intestinal epithelial cells and restricts rotavirus infection. Our data show that, via RNA helicase Dhx9, Nlrp9b recognizes short double-stranded RNA stretches and forms inflammasome complexes with the adaptor proteins Asc and caspase-1 to promote the maturation of interleukin (Il)-18 and gasdermin D (Gsdmd)-induced pyroptosis. Conditional depletion of Nlrp9b or other inflammasome components in the intestine in vivo resulted in enhanced susceptibility of mice to rotavirus replication. Our study highlights an important innate immune signalling pathway that functions in intestinal epithelial cells and may present useful targets in the modulation of host defences against viral pathogens.

Biomedical researchers are generating high-throughput, high-dimensional single-cell 5 data at a staggering rate. As costs of data generation decrease, experimental design is mov-6 ing towards measurement of many different single-cell samples in the same dataset. These 7 samples can correspond to different patients, conditions, or treatments. While scalability of 8 methods to datasets of these sizes is a challenge on its own, dealing with large-scale exper-9 imental design presents a whole new set of problems, including batch effects and sample 10 1 .

Handling the vast amounts of single-cell RNA-sequencing and CyTOF data, which are now being generated in patient cohorts, presents a computational challenge due to the noise, complexity, sparsity and batch effects present. Here, we propose a unified deep neural network-based approach to automatically process and extract structure from these massive datasets. Our unsupervised architecture, called SAUCIE (Sparse Autoencoder for Unsupervised Clustering, Imputation, and Embedding), simultaneously performs several key tasks for single-cell data analysis including 1) clustering, 2) batch correction, 3) visualization, and 4) denoising/imputation. SAUCIE is trained to recreate its own input after reducing its dimensionality in a 2-D embedding layer which can be used to visualize the data. Additionally, it uses two novel regularizations: (1) an information dimension regularization to penalize entropy as computed on normalized activation values of the layer, and thereby encourage binary-like encodings that are amenable to clustering and (2) a Maximal Mean Discrepancy penalty to correct batch effects. Thus SAUCIE has a single architecture that denoises, batch-corrects, visualizes and clusters data using a unified 1 . CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a

doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Antibody dependent enhancement (ADE) of dengue virus (DENV) infection is identified as the main risk factor of severe Dengue diseases. Through opsonization by subneutralizing or non-neutralizing antibodies, DENV infection suppresses innate cell immunity to facilitate viral replication. However, it is largely unknown whether suppression of type-I IFN is necessary for a successful ADE infection. Here, we report that both DENV and DENV-ADE infection induce an early ISG (NOS2) expression through RLR-MAVS signalling axis independent of the IFNs signaling. Besides, DENV-ADE suppress this early antiviral response through increased autophagy formation rather than induction of IL-10 secretion. The early induced autophagic proteins ATG5-ATG12 participate in suppression of MAVS mediated ISGs induction. Our findings suggest a mechanism for DENV to evade the early antiviral response before IFN signalling activation. Altogether, these results add knowledge about the complexity of ADE infection and contribute further to research on therapeutic strategies.