Perineuronal net formation and structure in aggrecan knockout mice

K A Giamanco, M Morawski, R T Matthews
Neuroscience 2010 November 10, 170 (4): 1314-27
Perineuronal nets (PNNs) are specialized substructures of the neural extracellular matrix (ECM) which envelop the cell soma and proximal neurites of particular sets of neurons with apertures at sites of synaptic contact. Previous studies have shown that PNNs are enriched with chondroitin sulfate proteoglycans (CSPGs) and hyaluronan, however, a complete understanding of their precise molecular composition has been elusive. In addition, identifying which specific PNN components are critical to the formation of this structure has not been demonstrated. Previous work in our laboratory has demonstrated that the CSPG, aggrecan, is a key activity-dependent component of PNNs in vivo. In order to assess the contribution of aggrecan to PNN formation, we utilized cartilage matrix deficiency (cmd) mice, which lack aggrecan. Herein, we utilized an in vitro model, dissociated cortical culture, and an ex vivo model, organotypic slice culture, to specifically investigate the role aggrecan plays in PNN formation. Our work demonstrates that staining with the lectin, Wisteria floribunda agglutinin (WFA), considered a broad PNN marker, is eliminated in the absence of aggrecan, suggesting the loss of PNNs. However, in contrast, we found that the expression patterns of other PNN markers, including hyaluronan and proteoglycan link protein 1 (HAPLN1), tenascin-R, brevican, and hyaluronan are unaffected by the absence of aggrecan. Lastly, we determined that while all PNN components are bound to the surface in a hyaluronan-dependent manner, only HAPLN1 remains attached to the cell surface when neurons are treated with chondroitinase. These results suggest a different model for the molecular association of PNNs to the cell surface. Together our work has served to assess the contribution of aggrecan to PNN formation while providing key evidence concerning the molecular composition of PNNs in addition to determining how these components ultimately form PNNs.

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