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Cited by 439

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Cellular autophagy: surrender, avoidance and subversion by microorganisms

Open access
Kirkegaard, Karla, Taylor, Matthew P., Jackson, William T.·2004·Nature Reviews Microbiology

Abstract

Intracellular bacteria and viruses must survive the vigorous antimicrobial responses of their hosts to replicate successfully. The cellular process of autophagy — in which compartments bound by double membranes engulf portions of the cytosol and then mature to degrade their cytoplasmic contents — is likely to be one such host-cell response. Several lines of evidence show that both bacteria and viruses are vulnerable to autophagic destruction and that successful pathogens have evolved strategies to avoid autophagy, or to actively subvert its components, to promote their own replication. The molecular mechanisms of the avoidance and subversion of autophagy by microorganisms will be the subject of much future research, not only to study their roles in the replication of these microorganisms, but also because they will provide — as bacteria and viruses so often have — unique tools to study the cellular process itself.

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Metadata

Authors
Kirkegaard, K., Taylor, M. P., & Jackson, W. T.
Venue
Nature Reviews Microbiology
Published
2004-04-01
Volume
2
Issue
4
Pages
301-314
ISSN
  • 1740-1526 (ISSN-L)
  • 1740-1534
OpenAlex
W2034172998
Cited by
439
References
6
FWCI
4.59
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Cellular autophagy: surrender, avoidance and subversion by microorganisms. https://app.cochat.ai/papers/10.1038/nrmicro865
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Kirkegaard, K., Taylor, M. P., & Jackson, W. T. (2004). Cellular autophagy: surrender, avoidance and subversion by microorganisms. Nature Reviews Microbiology. https://doi.org/10.1038/nrmicro865

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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.