Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 902

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Host cell proteases: Critical determinants of coronavirus tropism and pathogenesis

Open access
Millet, Jean Kaoru, Whittaker, Gary R.·2015·Virus Research

Abstract

Coronaviruses are a large group of enveloped, single-stranded positive-sense RNA viruses that infect a wide range of avian and mammalian species, including humans. The emergence of deadly human coronaviruses, severe acute respiratory syndrome coronavirus (SARS-CoV), and Middle East respiratory syndrome coronavirus (MERS-CoV) have bolstered research in these viral and often zoonotic pathogens. While coronavirus cell and tissue tropism, host range, and pathogenesis are initially controlled by interactions between the spike envelope glycoprotein and host cell receptor, it is becoming increasingly apparent that proteolytic activation of spike by host cell proteases also plays a critical role. Coronavirus spike proteins are the main determinant of entry as they possess both receptor binding and fusion functions. Whereas binding to the host cell receptor is an essential first step in establishing infection, the proteolytic activation step is often critical for the fusion function of spike, as it allows for controlled release of the fusion peptide into target cellular membranes. Coronaviruses have evolved multiple strategies for proteolytic activation of spike, and a large number of host proteases have been shown to proteolytically process the spike protein. These include, but are not limited to, endosomal cathepsins, cell surface transmembrane protease/serine (TMPRSS) proteases, furin, and trypsin. This review focuses on the diversity of strategies coronaviruses have evolved to proteolytically activate their fusion protein during spike protein biosynthesis and the critical entry step of their life cycle, and highlights important findings on how proteolytic activation of coronavirus spike influences tissue and cell tropism, host range and pathogenicity.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.virusres.2014.11.021

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Cited by

Metadata

Authors
Millet, J. K., & Whittaker, G. R.
Venue
Virus Research
Publisher
Elsevier BV
Published
2015-04
Volume
202
Pages
120-134
ISSN
0168-1702 (ISSN-L)
OpenAlex
W1994915168
Cited by
902
References
4
FWCI
6.92
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.