Astrocyte–neuron interplay in maladaptive plasticity.
Abstract
The complexity of neuronal networks cannot only be explained by neuronal activity so neurobiological research in the last decade has focused on different components of the central nervous system: the glia. Glial cells are fundamental elements for development and maintenance of physiological brain work. New data confirm that glia significantly influences neuronal communication through specific molecules, named "gliotransmitters", and their related receptors. This new approach to the traditional model of the way synapses work is also supported by changes occurring in pathological conditions, such as neurodegenerative diseases or toxic/traumatic injury to nervous system. Experimental models have revealed that glial cells are the starting point of damage progression that subsequently involves neurons. The "bedside to bench" approach has demonstrated that clinical phenotypes are strictly related to neuronal death, however it is conceivable that the disease begins earlier, years before clinical onset. This temporal gap is necessary to determine complex changes in the neuro-glial network organization and produce a "maladaptive plasticity". We review the function of glial cells in health and disease, pointing the putative mechanisms of maladaptive plasticity, suggesting that glial cells may represent a fascinating therapeutic target to prevent irreversible neuronal cell death.
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Maladaptive synaptic plasticity provides aberrant inputs that exacerbate glial reactivity, while cytokines feedback to impair LTP and promote synaptic loss.
“Similarly, maladaptive synaptic plasticity provides aberrant inputs that exacerbate glial reactivity, while cytokines feedback to impair LTP and promote synaptic loss [93].”
From Toward a Unified Framework in Molecular Neurobiology of Alzheimer’s Disease: Revisiting the Pathophysiological Hypotheses (2026) · cites this paper as [93]
Neuro-inflammation may be crucial in understanding MMP-9 dysregulation in CNS disorders.
“these may be crucial in understanding MMP-9 dysregulation in CNS disorders”
From The Perineuronal ‘Safety’ Net? Perineuronal Net Abnormalities in Neurological Disorders (2018) · cites this paper as Papa et al., 2014
Neuro-inflammation is evidenced in injury models, AD, epilepsy, and schizophrenia.
“there is evidence for neuro-inflammation in injury models, AD, epilepsy, and schizophrenia (Papa et al., 2014).”
From The Perineuronal ‘Safety’ Net? Perineuronal Net Abnormalities in Neurological Disorders (2018) · cites this paper as Papa et al., 2014
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Verleysdonk, S., & Hamprecht, B. (2000) · Glia · doi:10.1002/(sici)1098-1136(200003)30:1 · Cited by 37 · see citing works
Hatton, G. I., & Yang, Q. Z. (1994) · Brain Research · doi:10.1016/0006-8993(94)90207-0 · Cited by 37 · see citing works
McKenzie, J. C., Juan, Y.-W., Thomas, C. R., Berman, N. E. J., & Klein, R. M. (2001) · Journal of Histochemistry & Cytochemistry · doi:10.1177/002215540104901113 · Cited by 37 · see citing works
Benkler, C., Ben‐Zur, T., Barhum, Y., & Offen, D. (2012) · Glia · doi:10.1002/glia.22428 · Cited by 36 · see citing works
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Modulation of learning and memory by cytokines: Signaling mechanisms and long term consequences
Donzis, E. J., & Tronson, N. C. (2014) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2014.08.008 · Cited by 254 · see citing works
The Perineuronal ‘Safety’ Net? Perineuronal Net Abnormalities in Neurological Disorders
Wen, T. H., Binder, D. K., Ethell, I. M., & Razak, K. A. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00270 · Cited by 170 · see citing works
Hein, Z. M., Karikalan, B., Gopalakrishna, P. K., Dhevi, K., Alkatiri, A., Hussan, F., Mohd Moklas, M. A., Jagadeesan, S., Che Ramli, M. D., Che Mohd Nassir, C. M. N., et al. (2026) · Molecular Neurobiology · doi:10.1007/s12035-025-05602-0 · Cited by 43 · see citing works
Cirillo, G., Colangelo, A. M., De Luca, C., Savarese, L., Barillari, M. R., Alberghina, L., & Papa, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152750 · Cited by 27 · see citing works
Self-citation
- Authors
- Papa, M., De Luca, C., Petta, F., Alberghina, L., & Cirillo, G.
- Affiliations
- University of Campania "Luigi Vanvitelli" (IT)
- University of Milano-Bicocca (IT)
- Venue
- Neuroscience & Biobehavioral Reviews
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 42
- Pages
- 35-54
- ISSN
- 0149-7634 (ISSN-L)
- OpenAlex
- W2089397496
- Cited by
- 104
- References
- 215
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 3 · 2023: 5 · 2022: 11 · 2021: 7 · 2020: 12 · 2019: 21 · 2018: 6 · 2017: 4 · 2016: 8 · 2015: 13 · 2014: 5
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neuroscience & Biobehavioral Reviews (publishedVersion)
- PubMed (publishedVersion)
- Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli" (submittedVersion)
- Funders
- Regione Campania
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Health Sciences › Medicine › Physiology › Pain Mechanisms and Treatments
- Keywords
- astrocytes, glial cells, gliotransmitters, astrocyte-neuron interaction, maladaptive plasticity, neurodegenerative diseases, traumatic brain injury, neuroinflammation, neuronal cell death, therapeutic target
- MeSH terms
- Animals, Astrocytes, Gliosis, Humans, Nervous System Diseases, Neuronal Plasticity, Neurons
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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