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Microdomains of the C-type lectin DC-SIGN are portals for virus entry into dendritic cells.

Cambi A, de Lange F, van Maarseveen NM, Nijhuis M, Joosten B, van Dijk EM, de Bakker BI, Fransen JA, Bovee-Geurts PH, van Leeuwen FN, Van Hulst NF, Figdor CG - J. Cell Biol. (2004)

Bottom Line: The C-type lectin dendritic cell (DC)-specific intercellular adhesion molecule grabbing non-integrin (DC-SIGN; CD209) facilitates binding and internalization of several viruses, including HIV-1, on DCs, but the underlying mechanism for being such an efficient phagocytic pathogen-recognition receptor is poorly understood.During development of human monocyte-derived DCs, DC-SIGN becomes organized in well-defined microdomains, with an average diameter of 200 nm.Biochemical experiments and confocal microscopy indicate that DC-SIGN microdomains reside within lipid rafts.

View Article: PubMed Central - PubMed

Affiliation: Dept. of Tumor Immunology, Nijmegen Center for Molecular Life Sciences, University Medical Center Nijmegen, P.O. Box 9101, 6500 HB Nijmegen, Netherlands.

ABSTRACT
The C-type lectin dendritic cell (DC)-specific intercellular adhesion molecule grabbing non-integrin (DC-SIGN; CD209) facilitates binding and internalization of several viruses, including HIV-1, on DCs, but the underlying mechanism for being such an efficient phagocytic pathogen-recognition receptor is poorly understood. By high resolution electron microscopy, we demonstrate a direct relation between DC-SIGN function as viral receptor and its microlocalization on the plasma membrane. During development of human monocyte-derived DCs, DC-SIGN becomes organized in well-defined microdomains, with an average diameter of 200 nm. Biochemical experiments and confocal microscopy indicate that DC-SIGN microdomains reside within lipid rafts. Finally, we show that the organization of DC-SIGN in microdomains on the plasma membrane is important for binding and internalization of virus particles, suggesting that these multimolecular assemblies of DC-SIGN act as a docking site for pathogens like HIV-1 to invade the host.

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Quantitative analysis of the distribution of gold particles labeling DC-SIGN. The digital images of electron micrographs were processed by a custom-written software based on Labview. Gold labels were counted, and coordinates were assigned to each feature. Subsequently, interparticles distances were calculated using a nearest neighbor distance algorithm. Nearest neighbor distance values were calculated for each image, and the data of several independent experiments were pooled. Subsequently, the nearest neighbor distances were divided into three classes 0–50 nm (gray bar), 50–150 nm (black bar), and >150 nm (white bar), and the percentage of nearest neighbor distance values falling into each class was plotted (A). The partitioning of gold labels in clusters of various size (i.e.: number of particles/cluster) was also quantified. Clusters were defined when gold particles were <50 nm apart from a neighboring particle. The percentage of gold particles involved in the formation of a certain cluster size was calculated for (B) K-DC-SIGN, (C) immature DC, and (D) intermediate DC. The insets are three representative processed digital images, where each type of cluster is shown in a different color. For K-DC-SIGN, one representative experiment out of two is shown; for immature DC, one representative experiment out of six is shown; and for intermediate DCs, one representative experiment out of three is shown.
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fig4: Quantitative analysis of the distribution of gold particles labeling DC-SIGN. The digital images of electron micrographs were processed by a custom-written software based on Labview. Gold labels were counted, and coordinates were assigned to each feature. Subsequently, interparticles distances were calculated using a nearest neighbor distance algorithm. Nearest neighbor distance values were calculated for each image, and the data of several independent experiments were pooled. Subsequently, the nearest neighbor distances were divided into three classes 0–50 nm (gray bar), 50–150 nm (black bar), and >150 nm (white bar), and the percentage of nearest neighbor distance values falling into each class was plotted (A). The partitioning of gold labels in clusters of various size (i.e.: number of particles/cluster) was also quantified. Clusters were defined when gold particles were <50 nm apart from a neighboring particle. The percentage of gold particles involved in the formation of a certain cluster size was calculated for (B) K-DC-SIGN, (C) immature DC, and (D) intermediate DC. The insets are three representative processed digital images, where each type of cluster is shown in a different color. For K-DC-SIGN, one representative experiment out of two is shown; for immature DC, one representative experiment out of six is shown; and for intermediate DCs, one representative experiment out of three is shown.

Mentions: To quantitatively describe the DC-SIGN distribution pattern, the nearest neighbor distance values among the gold particles were calculated applying a spatial-point pattern analysis. A quantitative comparison of DC-SIGN clustering among intermediate DCs, immature DCs, and K-DC-SIGN is shown in Fig. 4 A. On immature DCs, as well as on K-DC-SIGN, almost 70 and 80% of the gold particles reside within a 50-nm distance from its nearest neighbor, respectively. In contrast, on intermediate DCs, only 30% of the nearest neighbor distance values are found in the same category, and the majority of interparticle distances rather fall within the 50–150 nm range. It has to be noted that the intermediate and the immature DCs compared by spatial-point pattern analysis had a similar amount of gold particles per micrometer squared, confirming the comparable expression levels of DC-SIGN on the two cell types.


Microdomains of the C-type lectin DC-SIGN are portals for virus entry into dendritic cells.

Cambi A, de Lange F, van Maarseveen NM, Nijhuis M, Joosten B, van Dijk EM, de Bakker BI, Fransen JA, Bovee-Geurts PH, van Leeuwen FN, Van Hulst NF, Figdor CG - J. Cell Biol. (2004)

Quantitative analysis of the distribution of gold particles labeling DC-SIGN. The digital images of electron micrographs were processed by a custom-written software based on Labview. Gold labels were counted, and coordinates were assigned to each feature. Subsequently, interparticles distances were calculated using a nearest neighbor distance algorithm. Nearest neighbor distance values were calculated for each image, and the data of several independent experiments were pooled. Subsequently, the nearest neighbor distances were divided into three classes 0–50 nm (gray bar), 50–150 nm (black bar), and >150 nm (white bar), and the percentage of nearest neighbor distance values falling into each class was plotted (A). The partitioning of gold labels in clusters of various size (i.e.: number of particles/cluster) was also quantified. Clusters were defined when gold particles were <50 nm apart from a neighboring particle. The percentage of gold particles involved in the formation of a certain cluster size was calculated for (B) K-DC-SIGN, (C) immature DC, and (D) intermediate DC. The insets are three representative processed digital images, where each type of cluster is shown in a different color. For K-DC-SIGN, one representative experiment out of two is shown; for immature DC, one representative experiment out of six is shown; and for intermediate DCs, one representative experiment out of three is shown.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2171967&req=5

fig4: Quantitative analysis of the distribution of gold particles labeling DC-SIGN. The digital images of electron micrographs were processed by a custom-written software based on Labview. Gold labels were counted, and coordinates were assigned to each feature. Subsequently, interparticles distances were calculated using a nearest neighbor distance algorithm. Nearest neighbor distance values were calculated for each image, and the data of several independent experiments were pooled. Subsequently, the nearest neighbor distances were divided into three classes 0–50 nm (gray bar), 50–150 nm (black bar), and >150 nm (white bar), and the percentage of nearest neighbor distance values falling into each class was plotted (A). The partitioning of gold labels in clusters of various size (i.e.: number of particles/cluster) was also quantified. Clusters were defined when gold particles were <50 nm apart from a neighboring particle. The percentage of gold particles involved in the formation of a certain cluster size was calculated for (B) K-DC-SIGN, (C) immature DC, and (D) intermediate DC. The insets are three representative processed digital images, where each type of cluster is shown in a different color. For K-DC-SIGN, one representative experiment out of two is shown; for immature DC, one representative experiment out of six is shown; and for intermediate DCs, one representative experiment out of three is shown.
Mentions: To quantitatively describe the DC-SIGN distribution pattern, the nearest neighbor distance values among the gold particles were calculated applying a spatial-point pattern analysis. A quantitative comparison of DC-SIGN clustering among intermediate DCs, immature DCs, and K-DC-SIGN is shown in Fig. 4 A. On immature DCs, as well as on K-DC-SIGN, almost 70 and 80% of the gold particles reside within a 50-nm distance from its nearest neighbor, respectively. In contrast, on intermediate DCs, only 30% of the nearest neighbor distance values are found in the same category, and the majority of interparticle distances rather fall within the 50–150 nm range. It has to be noted that the intermediate and the immature DCs compared by spatial-point pattern analysis had a similar amount of gold particles per micrometer squared, confirming the comparable expression levels of DC-SIGN on the two cell types.

Bottom Line: The C-type lectin dendritic cell (DC)-specific intercellular adhesion molecule grabbing non-integrin (DC-SIGN; CD209) facilitates binding and internalization of several viruses, including HIV-1, on DCs, but the underlying mechanism for being such an efficient phagocytic pathogen-recognition receptor is poorly understood.During development of human monocyte-derived DCs, DC-SIGN becomes organized in well-defined microdomains, with an average diameter of 200 nm.Biochemical experiments and confocal microscopy indicate that DC-SIGN microdomains reside within lipid rafts.

View Article: PubMed Central - PubMed

Affiliation: Dept. of Tumor Immunology, Nijmegen Center for Molecular Life Sciences, University Medical Center Nijmegen, P.O. Box 9101, 6500 HB Nijmegen, Netherlands.

ABSTRACT
The C-type lectin dendritic cell (DC)-specific intercellular adhesion molecule grabbing non-integrin (DC-SIGN; CD209) facilitates binding and internalization of several viruses, including HIV-1, on DCs, but the underlying mechanism for being such an efficient phagocytic pathogen-recognition receptor is poorly understood. By high resolution electron microscopy, we demonstrate a direct relation between DC-SIGN function as viral receptor and its microlocalization on the plasma membrane. During development of human monocyte-derived DCs, DC-SIGN becomes organized in well-defined microdomains, with an average diameter of 200 nm. Biochemical experiments and confocal microscopy indicate that DC-SIGN microdomains reside within lipid rafts. Finally, we show that the organization of DC-SIGN in microdomains on the plasma membrane is important for binding and internalization of virus particles, suggesting that these multimolecular assemblies of DC-SIGN act as a docking site for pathogens like HIV-1 to invade the host.

Show MeSH
Related in: MedlinePlus