OXR1 maintains the retromer to delay brain aging under dietary restriction.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
11 Jan 2024
Historique:
received: 02 08 2023
accepted: 07 12 2023
medline: 12 1 2024
pubmed: 12 1 2024
entrez: 11 1 2024
Statut: epublish

Résumé

Dietary restriction (DR) delays aging, but the mechanism remains unclear. We identified polymorphisms in mtd, the fly homolog of OXR1, which influenced lifespan and mtd expression in response to DR. Knockdown in adulthood inhibited DR-mediated lifespan extension in female flies. We found that mtd/OXR1 expression declines with age and it interacts with the retromer, which regulates trafficking of proteins and lipids. Loss of mtd/OXR1 destabilized the retromer, causing improper protein trafficking and endolysosomal defects. Overexpression of retromer genes or pharmacological restabilization with R55 rescued lifespan and neurodegeneration in mtd-deficient flies and endolysosomal defects in fibroblasts from patients with lethal loss-of-function of OXR1 variants. Multi-omic analyses in flies and humans showed that decreased Mtd/OXR1 is associated with aging and neurological diseases. mtd/OXR1 overexpression rescued age-related visual decline and tauopathy in a fly model. Hence, OXR1 plays a conserved role in preserving retromer function and is critical for neuronal health and longevity.

Identifiants

pubmed: 38212606
doi: 10.1038/s41467-023-44343-3
pii: 10.1038/s41467-023-44343-3
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

467

Subventions

Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
ID : R01AG061165
Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
ID : R56AG070705-01
Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
ID : T32AG000266
Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
ID : 5F31AG062112
Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
ID : R01AG07326
Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
ID : U01AG072439
Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
ID : R56AG070705
Organisme : Larry L. Hillblom Foundation (Larry L. Hillblom Foundation, Inc.)
ID : 2019-A-026-FEL
Organisme : U.S. Department of Health & Human Services | NIH | National Center for Research Resources (NCRR)
ID : 1S10 OD028654

Informations de copyright

© 2024. The Author(s).

Références

Mattson, M. P. Gene-diet interactions in brain aging and neurodegenerative disorders. Ann. Intern. Med. 139, 441–444 (2003).
pubmed: 12965973 doi: 10.7326/0003-4819-139-5_Part_2-200309021-00012
Wilson, K. A. et al. Evaluating the beneficial effects of dietary restrictions: A framework for precision nutrigeroscience. Cell Metab. 33, 2142–2173 (2021).
pubmed: 34555343 pmcid: 8845500 doi: 10.1016/j.cmet.2021.08.018
Mackay, T. F. et al. The Drosophila melanogaster Genetic Reference Panel. Nature 482, 173–178 (2012).
pubmed: 22318601 pmcid: 3683990 doi: 10.1038/nature10811
Wilson, K. A. et al. GWAS for Lifespan and Decline in Climbing Ability in Flies upon Dietary Restriction Reveal decima as a Mediator of Insulin-like Peptide Production. Curr. Biol. 30, 2749–2760 e2743 (2020).
pubmed: 32502405 pmcid: 7375902 doi: 10.1016/j.cub.2020.05.020
Wang, J. et al. Loss of Oxidation Resistance 1, OXR1, Is Associated with an Autosomal-Recessive Neurological Disease with Cerebellar Atrophy and Lysosomal Dysfunction. Am. J. Hum. Genet. 105, 1237–1253 (2019).
pubmed: 31785787 pmcid: 6904826 doi: 10.1016/j.ajhg.2019.11.002
Liu, K. X. et al. Neuron-specific antioxidant OXR1 extends survival of a mouse model of amyotrophic lateral sclerosis. Brain 138, 1167–1181 (2015).
pubmed: 25753484 pmcid: 4407188 doi: 10.1093/brain/awv039
Small, S. A. & Petsko, G. A. Retromer in Alzheimer disease, Parkinson disease and other neurological disorders. Nat. Rev. Neurosci. 16, 126–132 (2015).
pubmed: 25669742 doi: 10.1038/nrn3896
Gallon, M. & Cullen, P. J. Retromer and sorting nexins in endosomal sorting. Biochem. Soc. Trans. 43, 33–47 (2015).
pubmed: 25619244 doi: 10.1042/BST20140290
Volkert, M. R., Elliott, N. A. & Housman, D. E. Functional genomics reveals a family of eukaryotic oxidation protection genes. Proc. Natl. Acad. Sci. USA 97, 14530–14535 (2000).
pubmed: 11114193 pmcid: 18953 doi: 10.1073/pnas.260495897
Xu, H. et al. Zebrafish Oxr1a Knockout Reveals Its Role in Regulating Antioxidant Defenses and Aging. Genes (Basel) 11, 1118 (2020).
Finelli, M. J., Sanchez-Pulido, L., Liu, K. X., Davies, K. E. & Oliver, P. L. The Evolutionarily Conserved Tre2/Bub2/Cdc16 (TBC), Lysin Motif (LysM), Domain Catalytic (TLDc) Domain Is Neuroprotective against Oxidative Stress. J. Biol. Chem. 291, 2751–2763 (2016).
pubmed: 26668325 doi: 10.1074/jbc.M115.685222
Slattery, M. et al. Diverse patterns of genomic targeting by transcriptional regulators in Drosophila melanogaster. Genome Res. 24, 1224–1235 (2014).
pubmed: 24985916 pmcid: 4079976 doi: 10.1101/gr.168807.113
Negre, N. et al. A cis-regulatory map of the Drosophila genome. Nature 471, 527–531 (2011).
pubmed: 21430782 pmcid: 3179250 doi: 10.1038/nature09990
Lachmann, A. et al. Massive mining of publicly available RNA-seq data from human and mouse. Nat. Commun. 9, 1366 (2018).
pubmed: 29636450 pmcid: 5893633 doi: 10.1038/s41467-018-03751-6
Maruzs, T. et al. Retromer Ensures the Degradation of Autophagic Cargo by Maintaining Lysosome Function in Drosophila. Traffic 16, 1088–1107 (2015).
pubmed: 26172538 doi: 10.1111/tra.12309
Cui, Y. et al. Retromer has a selective function in cargo sorting via endosome transport carriers. J. Cell Biol. 218, 615–631 (2019).
pubmed: 30559172 pmcid: 6363445 doi: 10.1083/jcb.201806153
Lin, G. et al. Phospholipase PLA2G6, a Parkinsonism-Associated Gene, Affects Vps26 and Vps35, Retromer Function, and Ceramide Levels, Similar to alpha-Synuclein Gain. Cell Metab. 28, 605–618 e606 (2018).
pubmed: 29909971 doi: 10.1016/j.cmet.2018.05.019
Lane, R. F. et al. Vps10 family proteins and the retromer complex in aging-related neurodegeneration and diabetes. J. Neurosci. 32, 14080–14086 (2012).
pubmed: 23055476 pmcid: 3576841 doi: 10.1523/JNEUROSCI.3359-12.2012
Wilson, K. A. The understudied links of the retromer complex to age-related pathways. Geroscience 44, 19–24 (2022).
pubmed: 34370162 doi: 10.1007/s11357-021-00430-1
Vagnozzi, A. N. & Pratico, D. Endosomal sorting and trafficking, the retromer complex and neurodegeneration. Mol. Psychiatry 24, 857–868 (2019).
pubmed: 30120416 doi: 10.1038/s41380-018-0221-3
Mecozzi, V. J. et al. Pharmacological chaperones stabilize retromer to limit APP processing. Nat. Chem. Biol. 10, 443–449 (2014).
pubmed: 24747528 pmcid: 4076047 doi: 10.1038/nchembio.1508
Yoshii, S. R. & Mizushima, N. Monitoring and Measuring Autophagy. Int J Mol Sci. 18, 1865 (2017).
Wang, S. et al. The retromer complex is required for rhodopsin recycling and its loss leads to photoreceptor degeneration. PLoS Biol. 12, e1001847 (2014).
pubmed: 24781186 pmcid: 4004542 doi: 10.1371/journal.pbio.1001847
Kusne, Y., Wolf, A. B., Townley, K., Conway, M. & Peyman, G. A. Visual system manifestations of Alzheimer’s disease. Acta. Ophthalmol. 95, e668–e676 (2017).
pubmed: 27864881 doi: 10.1111/aos.13319
Roberts, R. O. et al. Association Between Olfactory Dysfunction and Amnestic Mild Cognitive Impairment and Alzheimer Disease Dementia. JAMA Neurol. 73, 93–101 (2016).
pubmed: 26569387 pmcid: 4710557 doi: 10.1001/jamaneurol.2015.2952
Bruderer, R. et al. Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results. Mol. Cell. Proteom.: MCP 16, 2296–2309 (2017).
pubmed: 29070702 doi: 10.1074/mcp.RA117.000314
Johnson, E. C. B. et al. Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level. Nat. Neurosci. 25, 213–225 (2022).
pubmed: 35115731 pmcid: 8825285 doi: 10.1038/s41593-021-00999-y
Jia, K., Cui, C., Gao, Y., Zhou, Y. & Cui, Q. An analysis of aging-related genes derived from the Genotype-Tissue Expression project (GTEx). Cell Death Discov. 4, 26 (2018).
pubmed: 30155276 doi: 10.1038/s41420-018-0093-y
Mele, M. et al. Human genomics. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).
pubmed: 25954002 pmcid: 4547472 doi: 10.1126/science.aaa0355
Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).
pubmed: 23586463 pmcid: 3637064 doi: 10.1186/1471-2105-14-128
Simoes, S. et al. Tau and other proteins found in Alzheimer’s disease spinal fluid are linked to retromer-mediated endosomal traffic in mice and humans. Sci. Trans. Med. 12, eaba6334 (2020).
Qureshi, Y. H. et al. The neuronal retromer can regulate both neuronal and microglial phenotypes of Alzheimer’s disease. Cell Rep 38, 110262 (2022).
pubmed: 35045281 pmcid: 8830374 doi: 10.1016/j.celrep.2021.110262
Simoes, S. et al. Alzheimer’s vulnerable brain region relies on a distinct retromer core dedicated to endosomal recycling. Cell Rep. 37, 110182 (2021).
pubmed: 34965419 pmcid: 8792909 doi: 10.1016/j.celrep.2021.110182
Moulton, M. J. et al. Neuronal ROS-induced glial lipid droplet formation is altered by loss of Alzheimer’s disease-associated genes. Proc. Natl. Acad. Sci. USA. 118, e2112095118 (2021).
Asadzadeh, J. et al. Retromer deficiency in Tauopathy models enhances the truncation and toxicity of Tau. Nat. Commun. 13, 5049 (2022).
pubmed: 36030267 pmcid: 9420134 doi: 10.1038/s41467-022-32683-5
Ye, H. et al. Retromer subunit, VPS29, regulates synaptic transmission and is required for endolysosomal function in the aging brain. Elife 9, e51977 (2020).
Johnson, E. C. B. et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat. Med. 26, 769–780 (2020).
pubmed: 32284590 pmcid: 7405761 doi: 10.1038/s41591-020-0815-6
Katewa, S. D. et al. Intramyocellular fatty-acid metabolism plays a critical role in mediating responses to dietary restriction in Drosophila melanogaster. Cell Metab. 16, 97–103 (2012).
pubmed: 22768842 pmcid: 3400463 doi: 10.1016/j.cmet.2012.06.005
Morabito, M. V. et al. Hyperleucinemia causes hippocampal retromer deficiency linking diabetes to Alzheimer’s disease. Neurobiol. Dis. 65, 188–192 (2014).
pubmed: 24440570 pmcid: 4235335 doi: 10.1016/j.nbd.2013.12.017
Chae, C. W. et al. High glucose-mediated VPS26a down-regulation dysregulates neuronal amyloid precursor protein processing and tau phosphorylation. Br. J. Pharmacol. 179, 3934–3950 (2022).
pubmed: 35297035 doi: 10.1111/bph.15836
Knupp, A. et al. Depletion of the AD Risk Gene SORL1 Selectively Impairs Neuronal Endosomal Traffic Independent of Amyloidogenic APP Processing. Cell Rep 31, 107719 (2020).
pubmed: 32492427 pmcid: 7409533 doi: 10.1016/j.celrep.2020.107719
Pandey, S., Dhusia, K., Katara, P., Singh, S. & Gautam, B. An in silico analysis of deleterious single nucleotide polymorphisms and molecular dynamics simulation of disease linked mutations in genes responsible for neurodegenerative disorder. J. Biomol. Struct. Dyn. 38, 4259–4272 (2020).
Li, J. G., Chiu, J., Ramanjulu, M., Blass, B. E. & Pratico, D. A pharmacological chaperone improves memory by reducing Abeta and tau neuropathology in a mouse model with plaques and tangles. Mol. Neurodegeneration 15, 1 (2020).
doi: 10.1186/s13024-019-0350-4
Muhammad, A. et al. Retromer deficiency observed in Alzheimer’s disease causes hippocampal dysfunction, neurodegeneration, and Abeta accumulation. Proc. Natl. Acad. Sci. USA 105, 7327–7332 (2008).
pubmed: 18480253 pmcid: 2386077 doi: 10.1073/pnas.0802545105
Mir, R. et al. The Parkinson’s disease VPS35[D620N] mutation enhances LRRK2-mediated Rab protein phosphorylation in mouse and human. Biochem. J. 475, 1861–1883 (2018).
pubmed: 29743203 doi: 10.1042/BCJ20180248
Chen, X. et al. Parkinson’s disease-linked D620N VPS35 knockin mice manifest tau neuropathology and dopaminergic neurodegeneration. Proc. Natl. Acad. Sci. USA. 116, 5765–5774 (2019).
pubmed: 30842285 pmcid: 6431187 doi: 10.1073/pnas.1814909116
Zhao, Y. et al. Reduced LRRK2 in association with retromer dysfunction in post-mortem brain tissue from LRRK2 mutation carriers. Brain 141, 486–495 (2018).
pubmed: 29253086 doi: 10.1093/brain/awx344
Finan, G. M., Okada, H. & Kim, T. W. BACE1 retrograde trafficking is uniquely regulated by the cytoplasmic domain of sortilin. J. Biol. Chem. 286, 12602–12616 (2011).
pubmed: 21245145 pmcid: 3069461 doi: 10.1074/jbc.M110.170217
Cadby, G. et al. Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease. Nat. Commun. 13, 3124 (2022).
pubmed: 35668104 pmcid: 9170690 doi: 10.1038/s41467-022-30875-7
Nelson, C. S. et al. Cross-phenotype association tests uncover genes mediating nutrient response in Drosophila. BMC Genom. 17, 867 (2016).
doi: 10.1186/s12864-016-3137-9
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
pubmed: 32015543 pmcid: 7056644 doi: 10.1038/s41592-019-0686-2
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
pubmed: 24336217 doi: 10.1038/nature12820
Kulahoglu, C. & Brautigam, A. Quantitative transcriptome analysis using RNA-seq. Methods Mol. Biol. 1158, 71–91 (2014).
pubmed: 24792045 doi: 10.1007/978-1-4939-0700-7_5
Cook, K. R., Parks, A. L., Jacobus, L. M., Kaufman, T. C. & Matthews, K. A. New research resources at the Bloomington Drosophila Stock Center. Fly (Austin) 4, 88–91 (2010).
pubmed: 20160480 doi: 10.4161/fly.4.1.11230
Vissers, J. H., Manning, S. A., Kulkarni, A. & Harvey, K. F. A Drosophila RNAi library modulates Hippo pathway-dependent tissue growth. Nat. Commun. 7, 10368 (2016).
pubmed: 26758424 pmcid: 4735554 doi: 10.1038/ncomms10368
Bischof, J., Sheils, E. M., Bjorklund, M. & Basler, K. Generation of a transgenic ORFeome library in Drosophila. Nat. Protocols 9, 1607–1620 (2014).
pubmed: 24922270 doi: 10.1038/nprot.2014.105
Zid, B. M. et al. 4E-BP extends lifespan upon dietary restriction by enhancing mitochondrial activity in Drosophila. Cell 139, 149–160 (2009).
pubmed: 19804760 pmcid: 2759400 doi: 10.1016/j.cell.2009.07.034
Katewa, S. D. et al. Peripheral Circadian Clocks Mediate Dietary Restriction-Dependent Changes in Lifespan and Fat Metabolism in Drosophila. Cell Metab. 23, 143–154 (2016).
pubmed: 26626459 doi: 10.1016/j.cmet.2015.10.014
Osterwalder, T., Yoon, K. S., White, B. H. & Keshishian, H. A conditional tissue-specific transgene expression system using inducible GAL4. Proc. Natl. Acad. Sci. USA 98, 12596–12601 (2001).
pubmed: 11675495 pmcid: 60099 doi: 10.1073/pnas.221303298
Arya, G. H. et al. The genetic basis for variation in olfactory behavior in Drosophila melanogaster. Chem. Senses 40, 233–243 (2015).
pubmed: 25687947 pmcid: 4398050 doi: 10.1093/chemse/bjv001
Iyer, J. et al. Quantitative Assessment of Eye Phenotypes for Functional Genetic Studies Using Drosophila melanogaster. G3 6, 1427–1437 (2016).
pubmed: 26994292 pmcid: 4856093 doi: 10.1534/g3.116.027060
Escher, C. et al. Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12, 1111–1121 (2012).
pubmed: 22577012 pmcid: 3918884 doi: 10.1002/pmic.201100463
Gillet, L. C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteom.: MCP 11, O111 016717 (2012).
pubmed: 22261725 doi: 10.1074/mcp.O111.016717
Collins, B. C. et al. Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry. Nat. Commun. 8, 291 (2017).
pubmed: 28827567 pmcid: 5566333 doi: 10.1038/s41467-017-00249-5
Burger, T. Gentle Introduction to the Statistical Foundations of False Discovery Rate in Quantitative Proteomics. J. Proteome Res. 17, 12–22 (2018).
pubmed: 29067805 doi: 10.1021/acs.jproteome.7b00170
Hou, J. et al. The Prognostic Value and the Oncogenic and Immunological Roles of Vacuolar Protein Sorting Associated Protein 26 A in Pancreatic Adenocarcinoma. Int. J. Mol. Sci. 24, 3486 (2023).

Auteurs

Kenneth A Wilson (KA)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.
Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA.

Sudipta Bar (S)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

Eric B Dammer (EB)

Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, 30322, USA.

Enrique M Carrera (EM)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

Brian A Hodge (BA)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

Tyler A U Hilsabeck (TAU)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.
Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA.

Joanna Bons (J)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

George W Brownridge (GW)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

Jennifer N Beck (JN)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

Jacob Rose (J)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

Melia Granath-Panelo (M)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

Christopher S Nelson (CS)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

Grace Qi (G)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

Akos A Gerencser (AA)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

Jianfeng Lan (J)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.
Guanxi Key Laboratory of Molecular Medicine in Liver Injury and Repair, The Afilliated Hospital of Guilin Medican University, Guilin, 541001, Guanxi, China.

Alexandra Afenjar (A)

Assistance Publique des Hôpitaux de Paris, Unité de Génétique Clinique, Hôpital Armand Trousseau, Groupe Hospitalier Universitaire, Paris, 75012, France.
Département de Génétique et Embryologie Médicale, CRMR des Malformations et Maladies Congénitales du Cervelet, GRC ConCer-LD, Sorbonne Universités, Hôpital Trousseau, Paris, 75012, France.

Geetanjali Chawla (G)

RNA Biology Laboratory, Department of Life Sciences, School of Natural Sciences, Shiv Nadar Institute of Eminence, NH91, Tehsil Dadri, G. B. Nagar, 201314, Uttar Pradesh, India.

Rachel B Brem (RB)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.
Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA.
Department of Plant and Microbial Biology, University of California, Berkeley, 111 Koshland Hall, Berkeley, CA, 94720, USA.

Philippe M Campeau (PM)

Centre Hospitalier Universitaire Saint-Justine Research Center, CHU Sainte-Justine, Montreal, QC, H3T 1J4, Canada.

Hugo J Bellen (HJ)

Departments of Molecular and Human Genetics and Neuroscience, Neurological Research Institute, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, 77030, USA.

Birgit Schilling (B)

Buck Institute for Research on Aging, Novato, CA, 94945, USA.

Nicholas T Seyfried (NT)

Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, 30322, USA.

Lisa M Ellerby (LM)

Buck Institute for Research on Aging, Novato, CA, 94945, USA. lellerby@buckinstitute.org.
Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA. lellerby@buckinstitute.org.

Pankaj Kapahi (P)

Buck Institute for Research on Aging, Novato, CA, 94945, USA. pkapahi@buckinstitute.org.
Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA. pkapahi@buckinstitute.org.

Classifications MeSH