Deep Time Course Proteomics of SARS-CoV- and SARS-CoV-2-Infected Human Lung Epithelial Cells (Calu-3) Reveals Strong Induction of Interferon-Stimulated Gene Expression by SARS-CoV-2 in Contrast to SARS-CoV.

Severe acute respiratory syndrome (SARS)-CoV and SARS-CoV-2 infections are characterized by remarkable differences, including infectivity and case fatality rate. The underlying mechanisms are not well understood, illustrating major knowledge gaps of coronavirus biology. In this study, protein expression of the SARS-CoV- and SARS-CoV-2-infected human lung epithelial cell line Calu-3 was analyzed using data-independent acquisition-mass spectrometry. This resulted in a comprehensive map of infection-related proteome-wide expression changes in human cells covering the quantification of 7478 proteins across four time points. Most notably, the activation of interferon type-I response was observed, which is surprisingly absent in several proteome studies. The data reveal that SARS-CoV-2 triggers interferon-stimulated gene expression much stronger than SARS-CoV, which reflects the already described differences in interferon sensitivity. Potentially, this may be caused by the enhanced abundance of the viral M protein of SARS-CoV in comparison to SARS-CoV-2, which is a known inhibitor of type I interferon expression. This study expands the knowledge on the host response to SARS-CoV-2 infections on a global scale using an infection model, which seems to be well suited to analyze the innate immunity.

Application of spectral library prediction for parallel reaction monitoring of viral peptides.

A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here, we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Viral peptides were chosen as an example, because virology is an area where in silico library generation could significantly improve PRM assay design. With both libraries a total of 1174 precursors were identified. Notably, compared to the DDA-derived library, we could identify 101 more precursors by using the Prosit-derived library. Additionally, we show that Prosit can be applied to predict tandem mass spectra of synthetic viral peptides with different collision energies. Finally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab demonstrating that both libraries are suited for peptide identification by PRM. Summarized, Prosit-derived viral spectral libraries predicted in silico can be used for PRM data analysis, making DDA analysis for library generation partially redundant in the future.

Inactivation of Coronaviruses during Sample Preparation for Proteomics Experiments.

Mass spectrometry-based proteomics is applied in SARS-CoV-2 research and is, moreover, being discussed as a novel method for SARS-CoV-2 diagnostics. However, the safe inactivation of coronaviruses by proteomics lysis buffers has not been systematically analyzed yet. Hence, for safety reasons a heating step prior to sample preparation is often performed. This step could be omitted once the safe inactivation with the typical buffers is proven. Here we test five different proteomics lysis buffers-4% SDS, 1% SDC, TFA, 6 M GdmCl, and 8 M urea-for their inactivation capacity of coronaviruses. Two representative human coronaviruses, namely HCoV-229E and HCoV-OC43, were used as surrogate for SARS-CoV-2. Lysis was performed at room temperature and at 95 °C for 5 min. Inactivation was confirmed by the absence of a cytopathic effect in MRC-5 cells, and equivocal results were further confirmed by serial passaging and quantitative real-time PCR. While at room temperature SDS, SDC, and TFA inactivated both coronaviruses, and GdmCl and urea resulted in partially incomplete inactivation. This demonstrates that care should be taken when choosing lysis buffers for proteomics analysis of coronaviruses, because some buffers do not ensure inactivation and, hence, biosafety during the further sample preparation.

Perspective on Proteomics for Virus Detection in Clinical Samples.

One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted.