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Article . 2005
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Radboud Repository
Article . 2005
Data sources: Radboud Repository
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How C-type lectins detect pathogens.

Authors: Cambi, A.; Koopman, M.; Figdor, C.G.;

How C-type lectins detect pathogens.

Abstract

Glycosylation of proteins has proven extremely important in a variety of cellular processes, including enzyme trafficking, tissue homing and immune functions. In the past decade, increasing interest in carbohydrate-mediated mechanisms has led to the identification of novel carbohydrate-recognizing receptors expressed on cells of the immune system. These non-enzymatic lectins contain one or more carbohydrate recognition domains (CRDs) that determine their specificity. In addition to their cell adhesion functions, lectins now also appear to play a major role in pathogen recognition. Depending on their structure and mode of action, lectins are subdivided in several groups. In this review, we focus on the calcium (Ca(2+))-dependent lectin group, known as C-type lectins, with the dendritic cell-specific ICAM-3 grabbing non-integrin (DC-SIGN) as a prototype type II C-type lectin organized in microdomains, and their role as pathogen recognition receptors in sensing microbes. Moreover, the cross-talk of C-type lectins with other receptors, such as Toll-like receptors, will be discussed, highlighting the emerging model that microbial recognition is based on a complex network of interacting receptors.

Contains fulltext : 47731.pdf (Publisher’s version ) (Open Access)

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Netherlands
Related Organizations
Keywords

NCMLS 2: Immune Regulation, Bacteria, NCMLS 1: Immunity, infection and tissue repair, Fungi, Receptors, Cell Surface, Dendritic Cells, Communicable Diseases, Mice, ONCOL 3: Translational research, SDG 3 - Good Health and Well-being, Viruses, DC-Specific ICAM-3 Grabbing Nonintegrin, Animals, Humans, Lectins, C-Type, Cell Adhesion Molecules

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
345
Top 1%
Top 1%
Top 1%
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gold