The DOI matches its Crossref record; 286 of the 288 references it cites were found in a registry or an index.
Autophagy and Neurodegeneration: Pathogenic Mechanisms and Therapeutic Opportunities
Open accessAbstract
Autophagy is a conserved pathway that delivers cytoplasmic contents to the lysosome for degradation. Here we consider its roles in neuronal health and disease. We review evidence from mouse knockout studies demonstrating the normal functions of autophagy as a protective factor against neurodegeneration associated with intracytoplasmic aggregate-prone protein accumulation as well as other roles, including in neuronal stem cell differentiation. We then describe how autophagy may be affected in a range of neurodegenerative diseases. Finally, we describe how autophagy upregulation may be a therapeutic strategy in a wide range of neurodegenerative conditions and consider possible pathways and druggable targets that may be suitable for this objective.
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“and permanent or transient endogenous defects in autophagy have been associated with embryonic lethality, developmental defects, and multiple pathological disorders, including (but not limited to) neurodegeneration, cancer, and cardiovascular disorders [812–817].”
From Molecular mechanisms of cell death: recommendations of the nomenclature committee on cell death 2018. (2018) · cites this paper as [813]
In the citing paper
“Molecular and histopathologic analyses of patient tissues demonstrate mislocalized or accumulated autophagy machinery, pathogenic mutations underlying familial neurodegenerative diseases disable known autophagy-related factors, and genetically ablating autophagy effectors produce neurodegeneration in cell and animal model systems [74–81], supporting a role for autophagy dysfunction in the devel”
From Autophagy and ALS: mechanistic insights and therapeutic implications (2022) · cites this paper as [80]
In the citing paper
“The autophagic process is essential in preserving the homeostatic requirements of post‐mitotic neurons, both at the central and at the peripheral nervous system levels (Menzies et al, 2017; Scrivo et al, 2018; Mallucci et al, 2020)”
From Autophagy in major human diseases (2021) · cites this paper as Menzies et al, 2017
In the citing paper
“By analogy, a comparable phenotype has been described in Atg16l1 hypomorphic mice (Lv et al, 2014; Wu et al, 2016; Menzies et al, 2017).”
From Autophagy in major human diseases (2021) · cites this paper as Menzies et al, 2017
In the citing paper
“Aβ is targeted for autophagy‐dependent degradation within the lysosome, explaining why activation of autophagy reduces the burden of Aβ plaques in rodents (Boland et al, 2008; Menzies et al, 2017; Meng et al, 2019).”
From Autophagy in major human diseases (2021) · cites this paper as Menzies et al, 2017
In the citing paper
“However, autophagy appears to be causally implicated in the PSEN1 (presenilin 1)‐mediated conversion of APP (amyloid beta precursor protein) into Aβ (Yu et al, 2005), as well as in the non‐canonical secretion of Aβ into the extracellular space (Nilsson et al, 2013; Menzies et al, 2017).”
From Autophagy in major human diseases (2021) · cites this paper as Menzies et al, 2017
In the citing paper
“While autophagy proteins have been shown to impact the clearance of protein aggregates in neurons, most likely by the canonical autophagic process of aggrephagy (Frake et al., 2015; Menzies et al., 2017; Qaisiya et al., 2017; Sarkar et al., 2007; Sarkar et al., 2009; Webb et al., 2003; Yamamoto and Simonsen, 2011), effects in microglia of the CNS are more likely due to LANDO.”
From LC3-Associated Endocytosis Facilitates β-Amyloid Clearance and Mitigates Neurodegeneration in Murine Alzheimer’s Disease (2019) · cites this paper as Menzies et al., 2017
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Metadata
- Authors
- Menzies, F. M., Fleming, A., Caricasole, A., Bento, C. F., Andrews, S. P., Ashkenazi, A., Füllgrabe, J., Jackson, A., Jimenez Sanchez, M., Karabiyik, C., Licitra, F., Lopez Ramirez, A., Pavel, M., Puri, C., Renna, M., Ricketts, T., Schlotawa, L., Vicinanza, M., Won, H., Zhu, Y., Skidmore, J., & Rubinsztein, D. C.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Rubinsztein, David C.
- ORCID
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 93
- Issue
- 5
- Pages
- 1015-1034
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2592067246
- Cited by
- 1,134
- References
- 288
- Citations by year
- 2026: 68 · 2025: 96 · 2024: 107 · 2023: 119 · 2022: 153 · 2021: 138 · 2020: 145 · 2019: 159 · 2018: 108 · 2017: 39
- FWCI
- 16.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (acceptedVersion, other-oa)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Apollo (University of Cambridge) (submittedVersion, cc-by-nc-nd)
- Apollo
- Funders
- Tau Consortium
- Alzheimer’s Research UK
- Wellcome Trust (095317/Z/11/Z)
- Cambridge Institute for Medical Research (100140/Z/12/Z)
- NIHR Biomedical Research Unit in Dementia at Addenbrooke’s Hospital
- The Rosetrees Trust
- FEBS
- Addenbrooke’s Charitable Trust
- Sims Scholarship
- James Baird Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Autophagy in Disease and Therapy
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- autophagy, neurodegeneration, lysosomal degradation, protein aggregation, neurodegenerative diseases, therapeutic target, druggable targets, mouse knockout models
- MeSH terms
- Animals, Autophagy, Humans, Lysosomes, Motor Neurons, Proteins, Signal Transduction, Neurodegenerative Diseases
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.
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