Evidence map›Paper›PMID 32591346›Full record

ArticleMolecular & cellular proteomics : MCP2020

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

Jana Zecha, Chien-Yun Lee, Florian P Bayer, Chen Meng, Vincent Grass, Johannes Zerweck, Karsten Schnatbaum, Thomas Michler, Andreas Pichlmair, Christina Ludwig and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
2.1field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

51 citing papers in PubMed, 95 citations in OpenAlex.

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  15. CovAnalytical chemistry · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

Jana ZechaChair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany.
Chien-Yun LeeChair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany.
Florian P BayerChair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany.
Chen MengBavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of Munich, Freising, Germany.
Vincent GrassInstitute of Virology, School of Medicine, Technical University of Munich, Munich, Germany; German Center for Infection Research (DZIF), Munich partner site, Germany.
Johannes ZerweckJPT Peptide Technologies GmbH, Berlin, Germany.
Karsten SchnatbaumJPT Peptide Technologies GmbH, Berlin, Germany.
Thomas MichlerInstitute of Virology, School of Medicine, Technical University of Munich, Munich, Germany.
Andreas PichlmairInstitute of Virology, School of Medicine, Technical University of Munich, Munich, Germany; German Center for Infection Research (DZIF), Munich partner site, Germany.
Christina LudwigBavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of Munich, Freising, Germany. Electronic address: tina.ludwig@tum.de.
Bernhard KusterChair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany; Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of Munich, Freising, Germany. Electronic address: kuster@tum.de.
Technical University of Munich · DEGerman Center for Infection Research · DEJPT Peptide Technologies (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the COVID-19 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 MS-based proteomics are beginning to emerge. To facilitate proteomic research on SARS-CoV-2, the virus that causes COVID-19, 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 on 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 MS-based proteomics for SARS-CoV-2 research and testing.

Indexed as

A549 CellsAmino Acid SequenceAngiotensin-Converting Enzyme 2AnimalsBetacoronavirusBRCA1 ProteinCaco-2 CellsCase-Control StudiesChlorocebus aethiopsCoronavirus InfectionsCOVID-19Gene Expression RegulationGene OntologyHost-Pathogen InteractionsHumansIndicators and ReagentsACE2 protein, humanAngiotensin-Converting Enzyme 2BRCA1 ProteinBRCA1 protein, humanIndicators and ReagentsPeptidyl-Dipeptidase ASerine EndopeptidasesSignal Recognition ParticleSRP9 protein, humanTMPRSS2 protein, humanViral ProteinsACE2clinical proteomicsCOVID-19label-free quantificationmass spectrometryparallel reaction monitoringSARS-CoV-2stable-isotope labelingtargeted mass spectrometryVero E6

Identifiers

PMID32591346
PMCPMC7780043
OpenAlexW3037235351

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.