Astrocyte/Oligodendrocyte Interaction in Association with Reactive Gliosis
Part of the
Altschul Symposia Series
book series (ALSS, volume 2)
The pathologic lesion in multiple sclerosis is characterized by demyelination, loss of oligodendrocytes, and the development of an astrocytic scar (Lumsden, 1970). Analysis of lesions at different stages in the disease process, however, has suggested that the lesion may develop in several different ways (Koopmans et al., 1989; Hawkins et al., 1991; Lee et al., 1991). In the early lesion, cellular inflammation and breakdown of blood brain barrier (BBB) function is noted. In these areas, there is perivascular mononuclear cell inflammation, edema, oligodendrocyte hyperplasia, minimal myelin loss, and astrocytic hypertrophy but not fibrosis (Raine et al., 1981). As the lesion develops, the BBB repairs, the residual cellular infiltrate consists primarily of foamy macrophages and plasma cells, and areas of CNS remyelination may be observed in the vicinity of a reactive gliosis. Axonal loss is minimal and, on rare occasions, remyelination may be mediated by Schwann cell investment (Raine, 1990). Lymphocytic infiltration is essentially limited to the lesion edge. It would appear that some of these lesions attain functional recovery associated with remission of clinical symptoms. In other lesions, however, changes characteristic of a more chronic inflammatory nature are observed. These lesions consist of a dense gliotic scar, absence of myelin and oligodendrocytes, and variable loss of axons. Mononuclear cell infiltration is minimal and lymphocytes, that in the more acute lesions show a predominance of T cell receptors of the alpha-beta lineage, may show a more predominant usage of T lymphocytes bearing the gamma-delta T cell receptor (Selmaj et al., 1991a).
KeywordsMultiple Sclerosis Glial Fibrillary Acidic Protein Reactive Gliosis Hypertrophic Astrocyte Amoeboid Microglia
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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