Bronstein, M. | Search bioRxiv

bioRxiv2021

Human nasal and lung tissues infected ex vivo with SARS-CoV-2 provide insights into differential tissue-specific and virus-specific innate immune responses in the upper and lower respiratory tract

The nasal-mucosa constitutes the primary entry site for respiratory viruses including SARS-CoV-2. While the imbalanced innate immune response of end-stage COVID-19 has been extensively studied, the earliest stages of SARS-CoV-2 infection at the mucosal entry site have remained unexplored. Here we employed SARS-CoV-2 and influenza virus infection in native multi-cell-type human nasal turbinate and lung tissues ex vivo, coupled with genome-wide transcriptional analysis, to investigate viral susceptibility and early patterns of local-mucosal innate immune response in the authentic milieu of the human respiratory tract. SARS-CoV-2 productively infected the nasal turbinate tissues, predominantly targeting respiratory epithelial cells, with rapid increase in tissue-associated viral sub-genomic mRNA, and secretion of infectious viral progeny. Importantly, SARS-CoV-2 infection triggered robust antiviral and inflammatory innate immune responses in the nasal mucosa. The upregulation of interferon stimulated genes, cytokines and chemokines, related to interferon signaling and immune-cell activation pathways, was broader than that triggered by influenza virus infection. Conversely, lung tissues exhibited a restricted innate immune response to SARS-CoV-2, with a conspicuous lack of type I and III interferon upregulation, contrasting with their vigorous innate immune response to influenza virus. Our findings reveal differential tissue-specific innate immune responses in the upper and lower respiratory tract, that are distinct to SARS-CoV-2. The studies shed light on the role of the nasal-mucosa in active viral transmission and immune defense, implying a window of opportunity for early interventions, whereas the restricted innate immune response in early-SARS-CoV-2-infected lung tissues could underlie the unique uncontrolled late-phase lung damage of advanced COVID-19. IMPORTANCEIn order to reduce the late-phase morbidity and mortality of COVID-19, there is a need to better understand and target the earliest stages of SARS-CoV-2 infection in the human respiratory tract. Here we have studied the initial steps of SARS-CoV-2 infection and the consequent innate immune responses within the natural multicellular complexity of human nasal-mucosal and lung tissues. Comparing the global innate response patterns of nasal and lung tissues, infected in parallel with SARS-CoV-2 and influenza virus, we have revealed distinct virus-host interactions in the upper and lower respiratory tract, which could determine the outcome and unique pathogenesis of SARS-CoV-2 infection. Studies in the nasal-mucosal infection model can be employed to assess the impact of viral evolutionary changes, and evaluate new therapeutic and preventive measures against SARS-CoV-2 and other human respiratory pathogens.

microbiology↗

bioRxiv2020

Fast end-to-end learning on protein surfaces

Proteins biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional sites, such as binding targets for potential drugs. Unfortunately though, the use of meshes as the underlying representation for protein structure has multiple drawbacks including the need to pre-compute the input features and mesh connectivities. This becomes a bottleneck for many important tasks in protein science. In this paper, we present a new framework for deep learning on protein structures that addresses these limitations. Among the key advantages of our method are the computation and sampling of the molecular surface on-the-fly from the underlying atomic point cloud and a novel efficient geometric convolutional layer. As a result, we are able to process large collections of proteins in an end-to-end fashion, taking as the sole input the raw 3D coordinates and chemical types of their atoms, eliminating the need for any hand-crafted pre-computed features. To showcase the performance of our approach, we test it on two tasks in the field of protein structural bioinformatics: the identification of interaction sites and the prediction of protein-protein interactions. On both tasks, we achieve state-of-the-art performance with much faster run times and fewer parameters than previous models. These results will considerably ease the deployment of deep learning methods in protein science and open the door for end-to-end differentiable approaches in protein modeling tasks such as function prediction and design.

bioinformatics↗