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Autoantibodies to Synaptic Receptors and Neuronal Cell Surface Proteins in Autoimmune Diseases of the Central Nervous System
Open accessAbstract
Investigations in the last 10 years have revealed a new category of neurological diseases mediated by antibodies against cell surface and synaptic proteins. There are currently 16 such diseases all characterized by autoantibodies against neuronal proteins involved in synaptic signaling and plasticity. In clinical practice these findings have changed the diagnostic and treatment approach to potentially lethal, but now treatable, neurological and psychiatric syndromes previously considered idiopathic or not even suspected to be immune-mediated. Studies show that patients' antibodies can impair the surface dynamics of the target receptors eliminating them from synapses (e.g., NMDA receptor), block the function of the antigens without changing their synaptic density (e.g., GABAb receptor), interfere with synaptic protein-protein interactions (LGI1, Caspr2), alter synapse formation (e.g., neurexin-3α), or by unclear mechanisms associate to a new form of tauopathy (IgLON5). Here we first trace the process of discovery of these diseases, describing the triggers and symptoms related to each autoantigen, and then review in detail the structural and functional alterations caused by the autoantibodies with special emphasis in those (NMDA receptor, amphiphysin) that have been modeled in animals.
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Metadata
- Authors
- Dalmau, J., Geis, C., & Graus, F.
- Venue
- Physiological Reviews
- Publisher
- American Physiological Society
- Published
- 2017-04
- Volume
- 97
- Issue
- 2
- Pages
- 839-887
- ISSN
- 0031-9333 (ISSN-L)
- 1522-1210
- OpenAlex
- W2595114075
- Cited by
- 628
- References
- 330
- FWCI
- 7.39
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
