How severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections engage cellular host pathways and innate immunity in infected cells remains largely elusive. We performed an integrative proteo-transcriptomics analysis in SARS-CoV-2 infected Huh7 cells to map the cellular response to the invading virus over time. We identified four pathways, ErbB, HIF-1, mTOR and TNF signaling, among others that were markedly modulated during the course of the SARS-CoV-2 infection in vitro. Western blot validation of the downstream effector molecules of these pathways revealed a dose-dependent activation of Akt, mTOR, S6K1 and 4E-BP1 at 24 hours post infection (hpi). However, we found a significant inhibition of HIF-1α through 24hpi and 48hpi of the infection, suggesting a crosstalk between the SARS-CoV-2 and the Akt/mTOR/HIF-1 signaling pathways. Inhibition of the mTOR signaling pathway using Akt inhibitor MK-2206 showed a significant reduction in virus production. Further investigations are required to better understand the molecular sequelae in order to guide potential therapy in the management of severe coronavirus disease 2019 (COVID-19) patients.

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Viruses hijack host metabolic pathways for their replicative advantage. In this study, using patient-derived multi-omics data and in vitro infection assays, we aimed to understand the role of key metabolic pathways that can regulate severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) reproduction and their association with disease severity. We used multi-omics platforms (targeted and untargeted proteomics and untargeted metabolomics) on patient samples and cell line models along with immune phenotyping of metabolite transporters in patient blood to understand viral-induced metabolic modulations. We also modulated key metabolic pathways that were identified using multi-omics data to regulate the viral reproduction in vitro . COVID-19 disease severity was characterized by increased plasma glucose and mannose levels. Immune phenotyping identified altered expression patterns of carbohydrate transporter, GLUT1, in CD8 + T-cells, intermediate and non-classical monocytes, and amino acid transporter, xCT, in classical, intermediate, and non-classical monocytes. In in vitro lung epithelial cell (Calu-3) infection model we found that glycolysis and glutaminolysis are essential for virus replication and blocking these metabolic pathways caused significant reduction in virus production. Taken together, we therefore hypothesized that SARS-CoV-2 utilizes and rewires pathways governing central carbon metabolism leading to the efflux of toxic metabolites and associated with disease severity. Thus, the host metabolic perturbation could be an attractive strategy to limit the viral replication and disease severity.

The commonly used laboratory cell lines are the first line of experimental models to study the pathogenicity and performing antiviral assays for emerging viruses. Here, we assessed the tropism and cytopathogenicity of the first Swedish isolate of SARS-CoV-2 in six different human cell lines, compared their growth characteristics and performed quantitative proteomics for the susceptible cell lines. Overall, Calu-3, Caco2, Huh7, and 293FT cell lines showed a high to moderate level of susceptibility to SARS-CoV-2. In Caco2 cells the virus can achieve high titers in the absence of any prominent cytopathic effect. The protein abundance profile during SARS-CoV-2 infection revealed cell-type-specific regulation of cellular pathways. Type-I interferon signaling was identified as the common dysregulated cellular response in Caco2, Calu-3 and Huh7 cells. Together, our data shows cell-type specific variability for cytopathogenicity, susceptibility and cellular response to SARS-CoV-2 and provide important clues to guide future studies.

Deconvolution of targets and action mechanisms of anticancer compounds is fundamental in drug development. Here, we report on ProTargetMiner as a publicly available expandable proteome signature library of anticancer molecules in cancer cell lines. Based on 287 A549 adenocarcinoma proteomes affected by 56 compounds, the main dataset contains 7,328 proteins and 1,307,859 refined protein-drug pairs. These proteomic signatures cluster by compound targets and action mechanisms. The targets and mechanistic proteins are deconvoluted by partial least square modeling, provided through the website http://protargetminer.genexplain.com. For 9 molecules representing the most diverse mechanisms and the common cancer cell lines MCF-7, RKO and A549, deep proteome datasets are obtained. Combining data from the three cell lines highlights common drug targets and cell-specific differences. The database can be easily extended and merged with new compound signatures. ProTargetMiner serves as a chemical proteomics resource for the cancer research community, and can become a valuable tool in drug discovery.