Choe, M. | Search bioRxiv

bioRxiv2020

A Cryptic Site of Vulnerability on the Receptor Binding Domain of the SARS-CoV-2 Spike Glycoprotein

SARS-CoV-2 is a zoonotic virus that has caused a pandemic of severe respiratory disease--COVID-19-- within several months of its initial identification. Comparable to the first SARS-CoV, this novel coronaviruss surface Spike (S) glycoprotein mediates cell entry via the human ACE-2 receptor, and, thus, is the principal target for the development of vaccines and immunotherapeutics. Molecular information on the SARS-CoV-2 S glycoprotein remains limited. Here we report the crystal structure of the SARS-CoV-2 S receptor-binding-domain (RBD) at a the highest resolution to date, of 1.95 [A]. We identified a set of SARS-reactive monoclonal antibodies with cross-reactivity to SARS-CoV-2 RBD and other betacoronavirus S glycoproteins. One of these antibodies, CR3022, was previously shown to synergize with antibodies that target the ACE-2 binding site on the SARS-CoV RBD and reduce viral escape capacity. We determined the structure of CR3022, in complex with the SARS-CoV-2 RBD, and defined a broadly reactive epitope that is highly conserved across betacoronaviruses. This epitope is inaccessible in the "closed" prefusion S structure, but is accessible in "open" conformations. This first-ever resolution of a human antibody in complex with SARS-CoV-2 and the broad reactivity of this set of antibodies to a conserved betacoronavirus epitope will allow antigenic assessment of vaccine candidates, and provide a framework for accelerated vaccine, immunotherapeutic and diagnostic strategies against SARS-CoV-2 and related betacoronaviruses. HIGHLIGHTSHigh resolution structure of the SARS-CoV-2 Receptor-Binding-Domain (RBD). Recognition of the SARS-CoV-2 RBD by SARS-CoV antibodies. Structure of the SARS-COV-2 RBD in complex with antibody CR3022. Identification of a cryptic site of vulnerability on the SARS-CoV-2 Spike.

immunology↗

bioRxiv2020

Quantitative single cell heterogeneity profiling of patient derived tumor initiating gliomaspheres reveals unique signatures of malignancy and response

BackgroundGlioblastoma is a deadly brain tumor with median patient survival of 14.6 months. At the core of this malignancy are rare, highly heterogenous malignant stem-like tumor initiating cells. Aberrant signaling across the EGFR-PTEN-AKT-mTOR signal transduction pathways are common oncogenic drivers in these cells. Though gene-level clustering has determined the importance of the EGFR signaling pathway as a treatment indicator, multiparameter protein-level analyses are necessary to discern functional attributes of signal propagation. Multiparameter single cell analyses is emerging as particularly useful in identifying such attributes. MethodsSingle cell targeted proteomic analysis of EGFR-PTEN-AKT-mTOR proteins profiled heterogeneity in a panel of fifteen patient derived gliomaspheres. A microfluidic cell array chip tool served as a low cost methodology to derive high quality quantitative single cell analytical outputs. Chip design specifications produced extremely high signal-to-noise ratios and brought experimental efficiencies of cell control and minimal cell use to accommodate experimentation with these rare and often slow-growing cell populations. Quantitative imaging software generated datasets to observe similarities and differences within and between cells and patients. Bioinformatic self-organizing maps (SOMs) and hierarchical clustering stratified patients into malignancy and responder groups which were validated by phenotypic and statistical analyses. ResultsFifteen patient dissociated gliomaspheres produced 59,464 data points from 14,866 cells. Forty-nine molecularly defined signaling phenotypes were identified across samples. Bioinformatics resolved two clusters diverging on EGFR expression (p = 0.0003) and AKT/TORC1 activation (p = 0.08 and p = 0.09 respectively). TCGA status of a subset showed genetic heterogeneity with proneural, classical and mesenchymal subtypes represented in both clusters. Phenotypic validation measures indicated drug responsive phenotypes to EGFR blocking were found in the EGFR expressing cluster. EGFR expression in the subset of drug-treated lines was statistically significant (p<.05). The EGFR expressing cluster was of lower tumor initiating potential in comparison to the AKT/TORC1 activated cluster. Though not statistically significant, EGFR expression trended with improved patient prognosis while AKT/TORC1 activated samples trended with poorer outcomes. ConclusionsQuantitative single cell heterogeneity profiling resolves signaling diversity into meaningful non-obvious phenotypic groups suggesting EGFR is decoupled from AKT/TORC1 signalling while identifying potentially valuable targets for personalized therapeutic approaches for deadly tumor-initiating cell populations.

cancer biology↗