Dysregulation in mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells

Appelberg, Sofia; Gupta, Soham; Ambikan, Anoop T.; Mikaeloff, Flora; Végvári, Ákos; Akusjärvi, Sara Svensson; Benfeitas, Rui; Sperk, Maike; Ståhlberg, Marie; Krishnan, Shuba; Singh, Kamal; Penninger, Josef; Mirazimi, Alì; Neogi, Ujjwal

doi:10.1101/2020.04.30.070383

Cited by 17 publications

(25 citation statements)

References 33 publications

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“…Consistent with a robust signature for the glucocorticoid receptor across all CoVs (GR; q-values: SARS1, 3e-35; SARS2, 1e-35; MERS, 7e-32), while this paper was under review, studies were published showing the GR agonist dexamethasone was a successful therapeutic for SARS2 infection 31 . Finally, and also while this paper was under review, in vitro analyses confirmed our predictions of the modulation by SARS2 infection of ErbB/EGFR 20,32 and TGFBR 16,32 signaling systems (Fig. 2).…”

Section: To Illuminate Human Signaling Pathways Orchestrating the Trasupporting

confidence: 77%

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Ochsner

Pillich

McKenna

2020

Preprint

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15Identifying transcriptional responses that are most consistently associated with 16 experimental coronavirus (CoV) infection can help illuminate human cellular signaling 17 pathways impacted by CoV infection. Here, we distilled over 3,000,000 data points from 18 publically archived CoV infection transcriptomic datasets into consensus regulatory 19 signatures, or consensomes, that rank genes based on their transcriptional 20 responsiveness to infection of human cells by MERS, SARS-CoV-1 and SARS-CoV-2 21 subtypes. We computed overlap between genes with elevated rankings in the CoV 22 consensomes against those from transcriptomic and ChIP-Seq consensomes for nearly 23 880 cellular signaling pathway nodes. Validating the CoV infection consensomes, we 24 identified robust overlap between their highly ranked genes and high confidence targets 25 of signaling pathway nodes with known roles in CoV infection. We then developed a 26 series of use cases that illustrate the utility of the CoV consensomes for hypothesis 27 generation around mechanistic aspects of the cellular response to CoV infection. We 28 make the CoV infection consensomes and their universe of underlying data points freely 29 accessible through the Signaling Pathways Project web knowledgebase. 30 31 65 points in a series of use cases illuminates previously uncharacterized intersections 66 between CoV infection and human cellular signaling pathways. The CoV infection 67 consensome and its underlying datasets provide researchers with a unique and freely 68 accessible framework within which to generate and pressure test hypotheses around 69 human cellular signaling pathways impacted by CoV infection.70 71 72 73 5 Results 74Generation of the CoV consensomes 75 We first set out to generate a set of consensomes ranking human genes based on the 76 frequency of their significant differential expression in response to infection by MERS, 77 SARS1 and SARS2 CoVs. To do this we searched the Gene Expression Omnibus 78 (GEO) and ArrayExpress databases to identify datasets involving infection of human 79 cells by these species. From this initial collection of datasets, we next carried out a 80 three step quality control check as previously described (Ochsner et al., 2019), yielding 81 a total of 3,041,047 million data points in 111 experiments from 25 independent CoV 82 infection transcriptomic datasets (Supplementary information, Section 1). Using these 83 curated datasets, we next generated consensomes for each CoV species, as well as 84 one ranking genes across all CoV infection experiments (ALL CoV). As a reference 85 consensome for a virus whose transcriptional impact on human cells has been studied 86 in depth, we also generated a consensome for human influenza A virus (IAV) infection. 87The Supplementary information files contain the full human ALL CoV (Section 2), MERS 88 (Section 3), SARS1 (Section 4), SARS2 (Section 5) and IAV (Section 6) infection 89 transcriptomic consensomes. To assist researchers in inferring transcriptional regulation 90 of sig...

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Section: To Illuminate Human Signaling Pathways Orchestrating the Trasupporting

confidence: 77%

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Ochsner

Pillich

McKenna

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Unfortunately, global and protein-specific surface glycosylation profiling is still challenging but could become an important future avenue to understand better how complex glycan patterns both of the virus as well as host cells determine the tropism of SARS-CoV-2. A rapidly rising number of reports on SARS-CoV-2 implicate intracellular phosphorylation events in a plethora of pathways from growth factor signaling to interferons and interleukins, HIF1A/mTOR signaling, PIKFYVE kinase inhibition, and JAK1 signaling ( 39 , 64 , 65 , 66 , 67 , 68 , 69 ), many of which are implicated in innate immunity. The full extent of phosphorylation regulation (or other PTMs for that matter) by SARS-CoV-2 remains to be elucidated.…”

Section: Discussionmentioning

confidence: 99%

Data, Reagents, Assays and Merits of Proteomics for SARS-CoV-2 Research and Testing

Zecha

Lee

Bayer

et al. 2020

Molecular & Cellular Proteomics

142

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As the SARS-CoV-2 pandemic continues to spread, thousands of scientists around the globe have changed research direction to understand better how the virus works and to find out how it may be tackled. The number of manuscripts on preprint servers is soaring and peer-reviewed publications using mass spectrometry-based proteomics are beginning to emerge. To facilitate proteomic research on SARS-CoV-2, this report presents deep-scale proteomes (10,000 proteins; >130,000 peptides) of common cell line models, notably Vero E6, Calu-3, Caco-2, and ACE2-A549 that characterize their protein expression profiles including viral entry factors such as ACE2 or TMPRSS2. Using the 9 kDa protein SRP9 and the breast cancer oncogene BRCA1 as examples, we show how the proteome expression data can be used to refine the annotation of protein-coding regions of the African green monkey and the Vero cell line genomes. Monitoring changes of the proteome upon viral infection revealed widespread expression changes including transcriptional regulators, protease inhibitors, and proteins involved in innate immunity. Based on a library of 98 stable-isotope labeled synthetic peptides representing 11 SARS-CoV-2 proteins, we developed PRM (parallel reaction monitoring) assays for nano-flow and micro-flow LC-MS/MS. We assessed the merits of these PRM assays using supernatants of virus-infected Vero E6 cells and challenged the assays by analyzing two diagnostic cohorts of 24 (+30) SARS-CoV-2 positive and 28 (+9) negative cases. In light of the results obtained and including recent publications or manuscripts on preprint servers, we critically discuss the merits of mass spectrometry-based proteomics for SARS-CoV-2 research and testing.

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“…mTORC1, which is hyperactive in LAM, promotes protein synthesis and cellular growth, and is believed to be used by SARS-CoV-2 to favor replication 13 . LARP1, an mTOR-regulated translational repressor, is among the 332 virus-interacting proteins identified by Gordon et al 11 , and Appelberg et al identified crosstalk between SARS-CoV-2 and mTOR/HIF-1 signaling 14 .…”

Section: To the Editormentioning

confidence: 99%

mTORC1 hyperactivation in lymphangioleiomyomatosis leads toACE2upregulation in type II pneumocytes: implications for COVID-19

2020

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Dysregulation in mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells

Cited by 17 publications

References 33 publications

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Data, Reagents, Assays and Merits of Proteomics for SARS-CoV-2 Research and Testing

mTORC1 hyperactivation in lymphangioleiomyomatosis leads toACE2upregulation in type II pneumocytes: implications for COVID-19

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