Application of proteomics in brain's aging research
Abstract
Proteomics is one of the commonly used techniques to explore the protein composition or protein modification status in various healthy or diseased brain tissues in the past decades. Aging is an extremely complex biological process including physiological function decline with age increasing. To have a better understanding of protein changes along with aging, proteomics has been applied in aging-associated research trying to uncover protein changes or post-translational modification (PTM) occurs in aging with the advantage of screening proteins on a large scale. In this review, we summarized protein expression differences detected by proteomics in human or animal brains at different age stages. Protein differences among species or brain regions are obvious, which reminds us to carefully consider these factors in brain aging research. Important protein changes have been found in multiple brain regions in the aging process and these differentially expressed proteins are mainly involved in cellular components, activities of metabolism, mitochondria changes, oxidative modification and some specific signaling pathways.
References
- Adler, P., Chiang, C. K., Mayne, J., Ning, Z., Zhang, X., Xu, B., Cheng, H. M., & Figeys, D. (2020). Aging Disrupts the Circadian Patterns of Protein Expression in the Murine Hippocampus. Frontiers in aging neuroscience, 11, 368. https://doi.org/10.3389/fnagi.2019.00368
- Adlimoghaddam, A., Benson, T., & Albensi, B. C. (2022). Mitochondrial Transfusion Improves Mitochondrial Function Through Up-regulation of Mitochondrial Complex II Protein Subunit SDHB in the Hippocampus of Aged Mice. Molecular neurobiology, 59(10), 6009–6017. https://doi.org/10.1007/s12035-022-02937-w
- Arrázola, M. S., Lira, M., Véliz-Valverde, F., Quiroz, G., Iqbal, S., Eaton, S. L., Lamont, D. J., Huerta, H., Ureta, G., Bernales, S., Cárdenas, J. C., Cerpa, W., Wishart, T. M., & Court, F. A. (2023). Necroptosis inhibition counteracts neurodegeneration, memory decline, and key hallmarks of aging, promoting brain rejuvenation. Aging cell, 22(5), e13814. https://doi.org/10.1111/acel.13814
- Azam, S., Haque, M. E., Balakrishnan, R., Kim, I. S., & Choi, D. K. (2021). The Ageing Brain: Molecular and Cellular Basis of Neurodegeneration. Frontiers in cell and developmental biology, 9, 683459. https://doi.org/10.3389/fcell.2021.683459
- Bai, B., Vanderwall, D., Li, Y., Wang, X., Poudel, S., Wang, H., Dey, K. K., Chen, P. C., Yang, K., & Peng, J. (2021). Proteomic landscape of Alzheimer's Disease: novel insights into pathogenesis and biomarker discovery. Molecular neurodegeneration, 16(1), 55. https://doi.org/10.1186/s13024-021-00474-z
- Bai, B., Wang, X., Li, Y., Chen, P. C., Yu, K., Dey, K. K., Yarbro, J. M., Han, X., Lutz, B. M., Rao, S., Jiao, Y., Sifford, J. M., Han, J., Wang, M., Tan, H., Shaw, T. I., Cho, J. H., Zhou, S., Wang, H., Niu, M., … Peng, J. (2020). Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer's Disease Progression. Neuron, 105(6), 975–991.e7. https://doi.org/10.1016/j.neuron.2019.12.015
- Birkisdóttir, M. B., Van't Sant, L. J., Brandt, R. M. C., Barnhoorn, S., Hoeijmakers, J. H. J., Vermeij, W. P., & Jaarsma, D. (2023). Purkinje-cell-specific DNA repair-deficient mice reveal that dietary restriction protects neurons by cell-intrinsic preservation of genomic health. Frontiers in aging neuroscience, 14, 1095801. https://doi.org/10.3389/fnagi.2022.1095801
- Burbaud, P., Courtin, E., Ribot, B., & Guehl, D. (2022). Basal ganglia: From the bench to the bed. European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society, 36, 99–106. https://doi.org/10.1016/j.ejpn.2021.12.002
- Burke, S. N., & Barnes, C. A. (2006). Neural plasticity in the ageing brain. Nature reviews. Neuroscience, 7(1), 30–40. https://doi.org/10.1038/nrn1809
- Carlyle, B. C., Kitchen, R. R., Kanyo, J. E., Voss, E. Z., Pletikos, M., Sousa, A. M. M., Lam, T. T., Gerstein, M. B., Sestan, N., & Nairn, A. C. (2017). A multiregional proteomic survey of the postnatal human brain. Nature neuroscience, 20(12), 1787–1795. https://doi.org/10.1038/s41593-017-0011-2
- Chandra, P. K., Cikic, S., Rutkai, I., Guidry, J. J., Katakam, P. V. G., Mostany, R., & Busija, D. W. (2022). Effects of aging on protein expression in mice brain microvessels: ROS scavengers, mRNA/protein stability, glycolytic enzymes, mitochondrial complexes, and basement membrane components. GeroScience, 44(1), 371–388. https://doi.org/10.1007/s11357-021-00468-1
- Chen, Y. C., Chang, Y. W., & Huang, Y. S. (2019). Dysregulated Translation in Neurodevelopmental Disorders: An Overview of Autism-Risk Genes Involved in Translation. Developmental neurobiology, 79(1), 60–74. https://doi.org/10.1002/dneu.22653
- Chen, Y., Wang, X., Xu, B. (2022). Advances in human brain proteomics analysis of neurodegenerative diseases. Human Brain, 1(1), 21–45 https://doi.org/10.37819/hb.001.001.0197
- Creecy, A., Brown, K. L., Rose, K. L., Voziyan, P., & Nyman, J. S. (2021). Post-translational modifications in collagen type I of bone in a mouse model of aging. Bone, 143, 115763. https://doi.org/10.1016/j.bone.2020.115763
- Dai, S. K., Liu, P. P., Li, X., Jiao, L. F., Teng, Z. Q., & Liu, C. M. (2022). Dynamic profiling and functional interpretation of histone lysine crotonylation and lactylation during neural development. Development (Cambridge, England), 149(14), dev200049. https://doi.org/10.1242/dev.200049
- Dan, X., Yang, B., McDevitt, R. A., Gray, S., Chu, X., Claybourne, Q., Figueroa, D. M., Zhang, Y., Croteau, D. L., & Bohr, V. A. (2023). Loss of smelling is an early marker of aging and is associated with inflammation and DNA damage in C57BL/6J mice. Aging cell, 22(4), e13793. https://doi.org/10.1111/acel.13793
- Das, S., Li, Z., Wachter, A., Alla, S., Noori, A., Abdourahman, A., Tamm, J. A., Woodbury, M. E., Talanian, R. V., Biber, K., Karran, E. H., Hyman, B. T., & Serrano-Pozo, A. (2023). Distinct transcriptomic responses to Aβ plaques, neurofibrillary tangles, and APOE in Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association, 10.1002/alz.13387. Advance online publication. https://doi.org/10.1002/alz.13387
- De Benedictis, A., Rossi-Espagnet, M. C., de Palma, L., Carai, A., & Marras, C. E. (2022). Networking of the Human Cerebellum: From Anatomo-Functional Development to Neurosurgical Implications. Frontiers in neurology, 13, 806298. https://doi.org/10.3389/fneur.2022.806298
- de Graaf, E. L., Vermeij, W. P., de Waard, M. C., Rijksen, Y., van der Pluijm, I., Hoogenraad, C. C., Hoeijmakers, J. H., Altelaar, A. F., & Heck, A. J. (2013). Spatio-temporal analysis of molecular determinants of neuronal degeneration in the aging mouse cerebellum. Molecular & cellular proteomics : MCP, 12(5), 1350–1362. https://doi.org/10.1074/mcp.M112.024950
- de la Fuente, A. G., Queiroz, R. M. L., Ghosh, T., McMurran, C. E., Cubillos, J. F., Bergles, D. E., Fitzgerald, D. C., Jones, C. A., Lilley, K. S., Glover, C. P., & Franklin, R. J. M. (2020). Changes in the Oligodendrocyte Progenitor Cell Proteome with Ageing. Molecular & cellular proteomics : MCP, 19(8), 1281–1302. https://doi.org/10.1074/mcp.RA120.002102
- Domínguez, M., de Oliveira, E., Odena, M. A., Portero, M., Pamplona, R., & Ferrer, I. (2016). Redox proteomic profiling of neuroketal-adducted proteins in human brain: Regional vulnerability at middle age increases in the elderly. Free radical biology & medicine, 95, 1–15. https://doi.org/10.1016/j.freeradbiomed.2016.02.034
- Donega, V., Burm, S. M., van Strien, M. E., van Bodegraven, E. J., Paliukhovich, I., Geut, H., van de Berg, W. D. J., Li, K. W., Smit, A. B., Basak, O., & Hol, E. M. (2019). Transcriptome and proteome profiling of neural stem cells from the human subventricular zone in Parkinson's disease. Acta neuropathologica communications, 7(1), 84. https://doi.org/10.1186/s40478-019-0736-0
- Drulis-Fajdasz, D., Gizak, A., Wójtowicz, T., Wiśniewski, J. R., & Rakus, D. (2018). Aging-associated changes in hippocampal glycogen metabolism in mice. Evidence for and against astrocyte-to-neuron lactate shuttle. Glia, 66(7), 1481–1495. https://doi.org/10.1002/glia.23319
- Drulis-Fajdasz, D., Gostomska-Pampuch, K., Duda, P., Wiśniewski, J. R., & Rakus, D. (2021). Quantitative Proteomics Reveals Significant Differences between Mouse Brain Formations in Expression of Proteins Involved in Neuronal Plasticity during Aging. Cells, 10(8), 2021. https://doi.org/10.3390/cells10082021
- Drulis-Fajdasz, D., Gostomska-Pampuch, K., Duda, P., Wiśniewski, J. R., & Rakus, D. (2021). Quantitative Proteomics Reveals Significant Differences between Mouse Brain Formations in Expression of Proteins Involved in Neuronal Plasticity during Aging. Cells, 10(8), 2021. https://doi.org/10.3390/cells10082021
- Drulis-Fajdasz, D., Rakus, D., Wiśniewski, J. R., McCubrey, J. A., & Gizak, A. (2018). Systematic analysis of GSK-3 signaling pathways in aging of cerebral tissue. Advances in biological regulation, 69, 35–42. https://doi.org/10.1016/j.jbior.2018.06.001
- Duda, P., Wójcicka, O., Wiśniewski, J. R., & Rakus, D. (2018). Global quantitative TPA-based proteomics of mouse brain structures reveals significant alterations in expression of proteins involved in neuronal plasticity during aging. Aging, 10(7), 1682–1697. https://doi.org/10.18632/aging.101501
- Ferrari, F., Gorini, A., & Villa, R. F. (2015). Energy metabolism of synaptosomes from different neuronal systems of rat cerebellum during aging: a functional proteomic characterization. Neurochemical research, 40(1), 172–185. https://doi.org/10.1007/s11064-014-1482-0
- Furukawa, A., Oikawa, S., Hasegawa-Ishii, S., Chiba, Y., Kawamura, N., Takei, S., Yoshikawa, K., Hosokawa, M., Kawanishi, S., & Shimada, A. (2010). Proteomic analysis of aging brain in SAMP10 mouse: a model of age-related cerebral degeneration. Mechanisms of ageing and development, 131(6), 379–388. https://doi.org/10.1016/j.mad.2010.05.002
- Gao, Y., Liu, J., Wang, J., Liu, Y., Zeng, L. H., Ge, W., & Ma, C. (2022). Proteomic analysis of human hippocampal subfields provides new insights into the pathogenesis of Alzheimer's disease and the role of glial cells. Brain pathology (Zurich, Switzerland), 32(4), e13047. https://doi.org/10.1111/bpa.13047
- Gant, J. C., Blalock, E. M., Chen, K. C., Kadish, I., Thibault, O., Porter, N. M., & Landfield, P. W. (2018). FK506-Binding Protein 12.6/1b, a Negative Regulator of [Ca2+], Rescues Memory and Restores Genomic Regulation in the Hippocampus of Aging Rats. The Journal of neuroscience : the official journal of the Society for Neuroscience, 38(4), 1030–1041. https://doi.org/10.1523/JNEUROSCI.2234-17.2017
- Gómez-Gálvez, Y., Fuller, H. R., Synowsky, S., Shirran, S. L., & Gates, M. A. (2020). Quantitative proteomic profiling of the rat substantia nigra places glial fibrillary acidic protein at the hub of proteins dysregulated during aging: Implications for idiopathic Parkinson's disease. Journal of neuroscience research, 98(7), 1417–1432. https://doi.org/10.1002/jnr.24622
- Guo, Z., Shao, C., Zhang, Y., Qiu, W., Li, W., Zhu, W., Yang, Q., Huang, Y., Pan, L., Dong, Y., Sun, H., Xiao, X., Sun, W., Ma, C., & Zhang, L. (2022). A Global Multiregional Proteomic Map of the Human Cerebral Cortex. Genomics, proteomics & bioinformatics, 20(4), 614–632. https://doi.org/10.1016/j.gpb.2021.08.008
- Gostomska-Pampuch, K., Drulis-Fajdasz, D., Gizak, A., Wiśniewski, J. R., & Rakus, D. (2021). Absolute Proteome Analysis of Hippocampus, Cortex and Cerebellum in Aged and Young Mice Reveals Changes in Energy Metabolism. International journal of molecular sciences, 22(12), 6188. https://doi.org/10.3390/ijms22126188
- Graham, L. C., Naldrett, M. J., Kohama, S. G., Smith, C., Lamont, D. J., McColl, B. W., Gillingwater, T. H., Skehel, P., Urbanski, H. F., & Wishart, T. M. (2019). Regional Molecular Mapping of Primate Synapses during Normal Healthy Aging. Cell reports, 27(4), 1018–1026.e4. https://doi.org/10.1016/j.celrep.2019.03.096
- Gray, D. T., Khattab, S., Meltzer, J., McDermott, K., Schwyhart, R., Sinakevitch, I., Härtig, W., & Barnes, C. A. (2023). Retrosplenial cortex microglia and perineuronal net densities are associated with memory impairment in aged rhesus macaques. Cerebral cortex (New York, N.Y. : 1991), 33(8), 4626–4644. https://doi.org/10.1093/cercor/bhac366
- Griffioen G. (2023). Calcium Dyshomeostasis Drives Pathophysiology and Neuronal Demise in Age-Related Neurodegenerative Diseases. International journal of molecular sciences, 24(17), 13243. https://doi.org/10.3390/ijms241713243
- Hanseeuw, B. J., Jacobs, H. I., Schultz, A. P., Buckley, R. F., Farrell, M. E., Guehl, N. J., Becker, J. A., Properzi, M., Sanchez, J. S., Quiroz, Y. T., Vannini, P., Sepulcre, J., Yang, H. S., Chhatwal, J. P., Gatchel, J., Marshall, G. A., Amariglio, R., Papp, K., Rentz, D. M., Normandin, M., … Johnson, K. A. (2023). Association of pathological and volumetric biomarker changes with cognitive decline in clinically normal adults: Harvard Aging Brain Study. Neurology, 10.1212/WNL.0000000000207962. Advance online publication. https://doi.org/10.1212/WNL.0000000000207962
- Hou, Y., Dan, X., Babbar, M., Wei, Y., Hasselbalch, S. G., Croteau, D. L., & Bohr, V. A. (2019). Ageing as a risk factor for neurodegenerative disease. Nature reviews. Neurology, 15(10), 565–581. https://doi.org/10.1038/s41582-019-0244-7
- Huang, Z., Chen, B., Liu, X., Li, H., Xie, L., Gao, Y., Duan, R., Li, Z., Zhang, J., Zheng, Y., & Su, W. (2021). Effects of sex and aging on the immune cell landscape as assessed by single-cell transcriptomic analysis. Proceedings of the National Academy of Sciences of the United States of America, 118(33), e2023216118. https://doi.org/10.1073/pnas.2023216118
- Huitinga, I., de Goeij, M., & Klioueva, N. (2019). Legal and Ethical Issues in Brain Banking. Neuroscience bulletin, 35(2), 267–269. https://doi.org/10.1007/s12264-018-0305-8
- Jasien, J. M., Daimon, C. M., Wang, R., Shapiro, B. K., Martin, B., & Maudsley, S. (2014). The effects of aging on the BTBR mouse model of autism spectrum disorder. Frontiers in aging neuroscience, 6, 225. https://doi.org/10.3389/fnagi.2014.00225
- Jayathirtha, M., Jayaweera, T., Whitham, D., Petre, B. A., Neagu, A. N., & Darie, C. C. (2023). Two-Dimensional Polyacrylamide Gel Electrophoresis Coupled with Nanoliquid Chromatography-Tandem Mass Spectrometry-Based Identification of Differentially Expressed Proteins and Tumorigenic Pathways in the MCF7 Breast Cancer Cell Line Transfected for Jumping Translocation Breakpoint Protein Overexpression. International journal of molecular sciences, 24(19), 14714. https://doi.org/10.3390/ijms241914714
- Jia, Y., Wang, X., Chen, Y., Qiu, W., Ge, W., & Ma, C. (2021). Proteomic and Transcriptomic Analyses Reveal Pathological Changes in the Entorhinal Cortex Region that Correlate Well with Dysregulation of Ion Transport in Patients with Alzheimer's Disease. Molecular neurobiology, 58(8), 4007–4027. https://doi.org/10.1007/s12035-021-02356-3
- Johnson, E. C. B., Carter, E. K., Dammer, E. B., Duong, D. M., Gerasimov, E. S., Liu, Y., Liu, J., Betarbet, R., Ping, L., Yin, L., Serrano, G. E., Beach, T. G., Peng, J., De Jager, P. L., Haroutunian, V., Zhang, B., Gaiteri, C., Bennett, D. A., Gearing, M., Wingo, T. S., … Seyfried, N. T. (2022). Large-scale deep multi-layer analysis of Alzheimer's disease brain reveals strong proteomic disease-related changes not observed at the RNA level. Nature neuroscience, 25(2), 213–225. https://doi.org/10.1038/s41593-021-00999-y
- Kashem, M. A., Ahmed, S., Sultana, N., Ahmed, E. U., Pickford, R., Rae, C., Šerý, O., McGregor, I. S., & Balcar, V. J. (2016). Metabolomics of Neurotransmitters and Related Metabolites in Post-Mortem Tissue from the Dorsal and Ventral Striatum of Alcoholic Human Brain. Neurochemical research, 41(1-2), 385–397. https://doi.org/10.1007/s11064-016-1830-3
- Kisaretova, P., Tsybko, A., Bondar, N., & Reshetnikov, V. (2023). Molecular Abnormalities in BTBR Mice and Their Relevance to Schizophrenia and Autism Spectrum Disorders: An Overview of Transcriptomic and Proteomic Studies. Biomedicines, 11(2), 289. https://doi.org/10.3390/biomedicines11020289
- Kluever, V., Russo, B., Mandad, S., Kumar, N. H., Alevra, M., Ori, A., Rizzoli, S. O., Urlaub, H., Schneider, A., & Fornasiero, E. F. (2022). Protein lifetimes in aged brains reveal a proteostatic adaptation linking physiological aging to neurodegeneration. Science advances, 8(20), eabn4437. https://doi.org/10.1126/sciadv.abn4437
- Kochunov, P., Rogers, W., Mangin, J. F., & Lancaster, J. (2012). A library of cortical morphology analysis tools to study development, aging and genetics of cerebral cortex. Neuroinformatics, 10(1), 81–96. https://doi.org/10.1007/s12021-011-9127-9
- Lathe, R., & St Clair, D. (2023). Programmed ageing: decline of stem cell renewal, immunosenescence, and Alzheimer's disease. Biological reviews of the Cambridge Philosophical Society, 98(4), 1424–1458. https://doi.org/10.1111/brv.12959
- Libertini, G., & Ferrara, N. (2016). Aging of perennial cells and organ parts according to the programmed aging paradigm. Age (Dordrecht, Netherlands), 38(2), 35. https://doi.org/10.1007/s11357-016-9895-0
- Liu, F., Yin, X., Cong, C., Wang, Y., Ma, C. (2022). Human Brain Banking as a Convergence Platform of Neuroscience and Neuropsychiatric Research. Human Brain, (2022) 1(1). https://doi.org/10.37819/hb.001.001.0204
- Liu, P., Yang, Q., Yu, N., Cao, Y., Wang, X., Wang, Z., Qiu, W. Y., & Ma, C. (2021). Phenylalanine Metabolism Is Dysregulated in Human Hippocampus with Alzheimer's Disease Related Pathological Changes. Journal of Alzheimer's disease : JAD, 83(2), 609–622. https://doi.org/10.3233/JAD-210461
- López-Otín, C., Pietrocola, F., Roiz-Valle, D., Galluzzi, L., & Kroemer, G. (2023). Meta-hallmarks of aging and cancer. Cell metabolism, 35(1), 12–35. https://doi.org/10.1016/j.cmet.2022.11.001
- Lubec, J., Smidak, R., Malikovic, J., Feyissa, D. D., Korz, V., Höger, H., & Lubec, G. (2019). Dentate Gyrus Peroxiredoxin 6 Levels Discriminate Aged Unimpaired From Impaired Rats in a Spatial Memory Task. Frontiers in aging neuroscience, 11, 198. https://doi.org/10.3389/fnagi.2019.00198
- Luo, D., Li, J., Liu, H., Wang, J., Xia, Y., Qiu, W., Wang, N., Wang, X., Wang, X., Ma, C., & Ge, W. (2023). Integrative Transcriptomic Analyses of Hippocampal-Entorhinal System Subfields Identify Key Regulators in Alzheimer's Disease. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 10(22), e2300876. https://doi.org/10.1002/advs.202300876
- Ma, C., Bao, A. M., Yan, X. X., & Swaab, D. F. (2019). Progress in Human Brain Banking in China. Neuroscience bulletin, 35(2), 179–182. https://doi.org/10.1007/s12264-019-00350-3
- Mansour L, S., Di Biase, M. A., Smith, R. E., Zalesky, A., & Seguin, C. (2023). Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource. NeuroImage, 283, 120407. https://doi.org/10.1016/j.neuroimage.2023.120407
- Mao, L., Römer, I., Nebrich, G., Klein, O., Koppelstätter, A., Hin, S. C., Hartl, D., & Zabel, C. (2010). Aging in mouse brain is a cell/tissue-level phenomenon exacerbated by proteasome loss. Journal of proteome research, 9(7), 3551–3560. https://doi.org/10.1021/pr100059j
- Mao, L., Zabel, C., Wacker, M. A., Nebrich, G., Sagi, D., Schrade, P., Bachmann, S., Kowald, A., & Klose, J. (2006). Estimation of the mtDNA mutation rate in aging mice by proteome analysis and mathematical modeling. Experimental gerontology, 41(1), 11–24. https://doi.org/10.1016/j.exger.2005.09.012
- Martinez-Val, A., Guzmán, U. H., & Olsen, J. V. (2022). Obtaining Complete Human Proteomes. Annual review of genomics and human genetics, 23, 99–121. https://doi.org/10.1146/annurev-genom-112921-024948
- McDonald, K. R., Pearson, J. M., & Huettel, S. A. (2020). Dorsolateral and dorsomedial prefrontal cortex track distinct properties of dynamic social behavior. Social cognitive and affective neuroscience, 15(4), 383–393. https://doi.org/10.1093/scan/nsaa053
- McGinn, M. J., Colello, R. J., & Sun, D. (2012). Age-related proteomic changes in the subventricular zone and their association with neural stem/progenitor cell proliferation. Journal of neuroscience research, 90(6), 1159–1168. https://doi.org/10.1002/jnr.23012
- Mehan, N. D., & Strauss, K. I. (2012). Combined age- and trauma-related proteomic changes in rat neocortex: a basis for brain vulnerability. Neurobiology of aging, 33(9), 1857–1873. https://doi.org/10.1016/j.neurobiolaging.2011.09.029
- Mott, N. N., Pinceti, E., Rao, Y. S., Przybycien-Szymanska, M. M., Prins, S. A., Shults, C. L., Yang, X., Glucksman, M. J., Roberts, J. L., & Pak, T. R. (2014). Age-dependent Effects of 17β-estradiol on the dynamics of estrogen receptor β (ERβ) protein-protein interactions in the ventral hippocampus. Molecular & cellular proteomics : MCP, 13(3), 760–779. https://doi.org/10.1074/mcp.M113.031559
- Nerattini, M., Jett, S., Andy, C., Carlton, C., Zarate, C., Boneu, C., Battista, M., Pahlajani, S., Loeb-Zeitlin, S., Havryulik, Y., Williams, S., Christos, P., Fink, M., Brinton, R. D., & Mosconi, L. (2023). Systematic review and meta-analysis of the effects of menopause hormone therapy on risk of Alzheimer's disease and dementia. Frontiers in aging neuroscience, 15, 1260427. https://doi.org/10.3389/fnagi.2023.1260427
- Orock, A., Logan, S., & Deak, F. (2020). Age-Related Cognitive Impairment: Role of Reduced Synaptobrevin-2 Levels in Deficits of Memory and Synaptic Plasticity. The journals of gerontology. Series A, Biological sciences and medical sciences, 75(9), 1624–1632. https://doi.org/10.1093/gerona/glz013
- Ottis, P., Topic, B., Loos, M., Li, K. W., de Souza, A., Schulz, D., Smit, A. B., Huston, J. P., & Korth, C. (2013). Aging-induced proteostatic changes in the rat hippocampus identify ARP3, NEB2 and BRAG2 as a molecular circuitry for cognitive impairment. PloS one, 8(9), e75112. https://doi.org/10.1371/journal.pone.0075112
- Pabba, M., Scifo, E., Kapadia, F., Nikolova, Y. S., Ma, T., Mechawar, N., Tseng, G. C., & Sibille, E. (2017). Resilient protein co-expression network in male orbitofrontal cortex layer 2/3 during human aging. Neurobiology of aging, 58, 180–190. https://doi.org/10.1016/j.neurobiolaging.2017.06.023
- Park, S. B., Koh, B., Kwon, H. S., Kim, Y. E., Kim, S. S., Cho, S. H., Kim, T. Y., Bae, M. A., Kang, D., Kim, C. H., & Kim, K. Y. (2023). Quantitative and Qualitative Analysis of Neurotransmitter and Neurosteroid Production in Cerebral Organoids during Differentiation. ACS chemical neuroscience, 14(20), 3761–3771. https://doi.org/10.1021/acschemneuro.3c00246
- Pawlyk, A. C., Ferber, M., Shah, A., Pack, A. I., & Naidoo, N. (2007). Proteomic analysis of the effects and interactions of sleep deprivation and aging in mouse cerebral cortex. Journal of neurochemistry, 103(6), 2301–2313. https://doi.org/10.1111/j.1471-4159.2007.04949.x
- Perluigi, M., Di Domenico, F., Giorgi, A., Schininà, M. E., Coccia, R., Cini, C., Bellia, F., Cambria, M. T., Cornelius, C., Butterfield, D. A., & Calabrese, V. (2010). Redox proteomics in aging rat brain: involvement of mitochondrial reduced glutathione status and mitochondrial protein oxidation in the aging process. Journal of neuroscience research, 88(16), 3498–3507. https://doi.org/10.1002/jnr.22500
- Pike, K. E., Cavuoto, M. G., Li, L., Wright, B. J., & Kinsella, G. J. (2022). Subjective Cognitive Decline: Level of Risk for Future Dementia and Mild Cognitive Impairment, a Meta-Analysis of Longitudinal Studies. Neuropsychology review, 32(4), 703–735. https://doi.org/10.1007/s11065-021-09522-3
- Poon, H. F., Castegna, A., Farr, S. A., Thongboonkerd, V., Lynn, B. C., Banks, W. A., Morley, J. E., Klein, J. B., & Butterfield, D. A. (2004). Quantitative proteomics analysis of specific protein expression and oxidative modification in aged senescence-accelerated-prone 8 mice brain. Neuroscience, 126(4), 915–926. https://doi.org/10.1016/j.neuroscience.2004.04.046
- Poon, H. F., Shepherd, H. M., Reed, T. T., Calabrese, V., Stella, A. M., Pennisi, G., Cai, J., Pierce, W. M., Klein, J. B., & Butterfield, D. A. (2006). Proteomics analysis provides insight into caloric restriction mediated oxidation and expression of brain proteins associated with age-related impaired cellular processes: Mitochondrial dysfunction, glutamate dysregulation and impaired protein synthesis. Neurobiology of aging, 27(7), 1020–1034. https://doi.org/10.1016/j.neurobiolaging.2005.05.014
- Poon, H. F., Vaishnav, R. A., Butterfield, D. A., Getchell, M. L., & Getchell, T. V. (2005). Proteomic identification of differentially expressed proteins in the aging murine olfactory system and transcriptional analysis of the associated genes. Journal of neurochemistry, 94(2), 380–392. https://doi.org/10.1111/j.1471-4159.2005.03215.x
- Poon, H. F., Vaishnav, R. A., Getchell, T. V., Getchell, M. L., & Butterfield, D. A. (2006). Quantitative proteomics analysis of differential protein expression and oxidative modification of specific proteins in the brains of old mice. Neurobiology of aging, 27(7), 1010–1019. https://doi.org/10.1016/j.neurobiolaging.2005.05.006
- Raffel, S., Klimmeck, D., Falcone, M., Demir, A., Pouya, A., Zeisberger, P., Lutz, C., Tinelli, M., Bischel, O., Bullinger, L., Thiede, C., Flörcken, A., Westermann, J., Ehninger, G., Ho, A. D., Müller-Tidow, C., Gu, Z., Herrmann, C., Krijgsveld, J., Trumpp, A., … Hansson, J. (2020). Quantitative proteomics reveals specific metabolic features of acute myeloid leukemia stem cells. Blood, 136(13), 1507–1519. https://doi.org/10.1182/blood.2019003654
- Ratovitski, T., O'Meally, R. N., Jiang, M., Chaerkady, R., Chighladze, E., Stewart, J. C., Wang, X., Arbez, N., Roby, E., Alexandris, A., Duan, W., Vijayvargia, R., Seong, I. S., Lavery, D. J., Cole, R. N., & Ross, C. A. (2017). Post-Translational Modifications (PTMs), Identified on Endogenous Huntingtin, Cluster within Proteolytic Domains between HEAT Repeats. Journal of proteome research, 16(8), 2692–2708. https://doi.org/10.1021/acs.jproteome.6b00991
- Rozanova, S., Uszkoreit, J., Schork, K., Serschnitzki, B., Eisenacher, M., Tönges, L., Barkovits-Boeddinghaus, K., & Marcus, K. (2023). Quality Control-A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome. Biomolecules, 13(3), 491. https://doi.org/10.3390/biom13030491
- Saia-Cereda, V. M., Cassoli, J. S., Schmitt, A., Falkai, P., Nascimento, J. M., & Martins-de-Souza, D. (2015). Proteomics of the corpus callosum unravel pivotal players in the dysfunction of cell signaling, structure, and myelination in schizophrenia brains. European archives of psychiatry and clinical neuroscience, 265(7), 601–612. https://doi.org/10.1007/s00406-015-0621-1
- Santín-Márquez, R., Ramírez-Cordero, B., Toledo-Pérez, R., Luna-López, A., López-Diazguerrero, N. E., Hernández-Arciga, U., Pérez-Morales, M., Ortíz-Retana, J. J., García-Servín, M., Alcauter, S., Hernández-Godínez, B., Ibañez-Contreras, A., Concha, L., Gómez-González, B., & Königsberg, M. (2021). Sensory and memory processing in old female and male Wistar rat brain, and its relationship with the cortical and hippocampal redox state. GeroScience, 43(4), 1899–1920. https://doi.org/10.1007/s11357-021-00353-x
- Schrötter, A., Oberhaus, A., Kolbe, K., Seger, S., Mastalski, T., El Magraoui, F., Hoffmann-Posorske, E., Bohnert, M., Deckert, J., Braun, C., Graw, M., Schmitz, C., Arzberger, T., Loosse, C., Heinsen, H., Meyer, H. E., & Müller, T. (2017). LMD proteomics provides evidence for hippocampus field-specific motor protein abundance changes with relevance to Alzheimer's disease. Biochimica et biophysica acta. Proteins and proteomics, 1865(6), 703–714. https://doi.org/10.1016/j.bbapap.2017.03.013
- Seefeldt, I., Nebrich, G., Römer, I., Mao, L., & Klose, J. (2006). Evaluation of 2-DE protein patterns from pre- and postnatal stages of the mouse brain. Proteomics, 6(18), 4932–4939. https://doi.org/10.1002/pmic.200600188
- Shepherd, C. E., Alvendia, H., & Halliday, G. M. (2019). Brain Banking for Research into Neurodegenerative Disorders and Ageing. Neuroscience bulletin, 35(2), 283–288. https://doi.org/10.1007/s12264-018-0326-3
- Shine J. M. (2019). Neuromodulatory Influences on Integration and Segregation in the Brain. Trends in cognitive sciences, 23(7), 572–583. https://doi.org/10.1016/j.tics.2019.04.002
- Short, M. I., Fohner, A. E., Skjellegrind, H. K., Beiser, A., Gonzales, M. M., Satizabal, C. L., Austin, T. R., Longstreth, W. T., Bis, J. C., Lopez, O., Hveem, K., Selbæk, G., Larson, M. G., Yang, Q., Aparicio, H. J., McGrath, E. R., Gerszten, R. E., DeCarli, C. S., Psaty, B. M., Vasan, R. S., … Seshadri, S. (2023). Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging. Journal of Alzheimer's disease : JAD, 10.3233/JAD-230145. Advance online publication. https://doi.org/10.3233/JAD-230145
- Shumaker, S. A., Legault, C., Kuller, L., Rapp, S. R., Thal, L., Lane, D. S., Fillit, H., Stefanick, M. L., Hendrix, S. L., Lewis, C. E., Masaki, K., Coker, L. H., & Women's Health Initiative Memory Study (2004). Conjugated equine estrogens and incidence of probable dementia and mild cognitive impairment in postmenopausal women: Women's Health Initiative Memory Study. JAMA, 291(24), 2947–2958. https://doi.org/10.1001/jama.291.24.2947
- Simandi, Z., Pajer, K., Karolyi, K., Sieler, T., Jiang, L. L., Kolostyak, Z., Sari, Z., Fekecs, Z., Pap, A., Patsalos, A., Contreras, G. A., Reho, B., Papp, Z., Guo, X., Horvath, A., Kiss, G., Keresztessy, Z., Vámosi, G., Hickman, J., Xu, H., … Nagy, L. (2018). Arginine Methyltransferase PRMT8 Provides Cellular Stress Tolerance in Aging Motoneurons. The Journal of neuroscience : the official journal of the Society for Neuroscience, 38(35), 7683–7700. https://doi.org/10.1523/JNEUROSCI.3389-17.2018
- Song, Q., Hou, Y., Zhang, Y., Liu, J., Wang, Y., Fu, J., Zhang, C., Cao, M., Cui, Y., Zhang, X., Wang, X., Zhang, J., Liu, C., Zhang, Y., & Wang, P. (2022). Integrated multi-omics approach revealed cellular senescence landscape. Nucleic acids research, 50(19), 10947–10963. https://doi.org/10.1093/nar/gkac885
- Spaak, E., & de Lange, F. P. (2020). Hippocampal and Prefrontal Theta-Band Mechanisms Underpin Implicit Spatial Context Learning. The Journal of neuroscience : the official journal of the Society for Neuroscience, 40(1), 191–202. https://doi.org/10.1523/JNEUROSCI.1660-19.2019
- Srivastava, K., & Mishra, R. (2023). Pax6 affects Ras-Raf-ERK1/2 in mouse aging brain. Biogerontology, 24(6), 901–912. https://doi.org/10.1007/s10522-023-10044-z
- Stastna, M., Abraham, M. R., & Van Eyk, J. E. (2009). Cardiac stem/progenitor cells, secreted proteins, and proteomics. FEBS letters, 583(11), 1800–1807. https://doi.org/10.1016/j.febslet.2009.03.026
- Stauch, K. L., Purnell, P. R., & Fox, H. S. (2014). Aging synaptic mitochondria exhibit dynamic proteomic changes while maintaining bioenergetic function. Aging, 6(4), 320–334. https://doi.org/10.18632/aging.100657
- Stühler, K., Pfeiffer, K., Joppich, C., Stephan, C., Jung, K., Müller, M., Schmidt, O., van Hall, A., Hamacher, M., Urfer, W., Meyer, H. E., & Marcus, K. (2006). Pilot study of the Human Proteome Organisation Brain Proteome Project: applying different 2-DE techniques to monitor proteomic changes during murine brain development. Proteomics, 6(18), 4899–4913. https://doi.org/10.1002/pmic.200600089
- Theves, S., Neville, D. A., Fernández, G., & Doeller, C. F. (2021). Learning and Representation of Hierarchical Concepts in Hippocampus and Prefrontal Cortex. The Journal of neuroscience : the official journal of the Society for Neuroscience, 41(36), 7675–7686. https://doi.org/10.1523/JNEUROSCI.0657-21.2021
- Toader, C., Dobrin, N., Brehar, F. M., Popa, C., Covache-Busuioc, R. A., Glavan, L. A., Costin, H. P., Bratu, B. G., Corlatescu, A. D., Popa, A. A., & Ciurea, A. V. (2023). From Recognition to Remedy: The Significance of Biomarkers in Neurodegenerative Disease Pathology. International journal of molecular sciences, 24(22), 16119. https://doi.org/10.3390/ijms242216119
- Tooley, U. A., Park, A. T., Leonard, J. A., Boroshok, A. L., McDermott, C. L., Tisdall, M. D., Bassett, D. S., & Mackey, A. P. (2022). The Age of Reason: Functional Brain Network Development during Childhood. The Journal of neuroscience : the official journal of the Society for Neuroscience, 42(44), 8237–8251. https://doi.org/10.1523/JNEUROSCI.0511-22.2022
- Tsumagari, K., Sato, Y., Aoyagi, H., Okano, H., & Kuromitsu, J. (2023). Proteomic characterization of aging-driven changes in the mouse brain by co-expression network analysis. Scientific reports, 13(1), 18191. https://doi.org/10.1038/s41598-023-45570-w
- Tuttle, C. S. L., Waaijer, M. E. C., Slee-Valentijn, M. S., Stijnen, T., Westendorp, R., & Maier, A. B. (2020). Cellular senescence and chronological age in various human tissues: A systematic review and meta-analysis. Aging cell, 19(2), e13083. https://doi.org/10.1111/acel.13083
- Tzeng, W. Y., Figarella, K., & Garaschuk, O. (2021). Olfactory impairment in men and mice related to aging and amyloid-induced pathology. Pflugers Archiv : European journal of physiology, 473(5), 805–821. https://doi.org/10.1007/s00424-021-02527-0
- Urrutia, P. J., & Bórquez, D. A. (2023). Expanded bioinformatic analysis of Oximouse dataset reveals key putative processes involved in brain aging and cognitive decline. Free radical biology & medicine, 207, 200–211. https://doi.org/10.1016/j.freeradbiomed.2023.07.018
- van Hoorn, J., Shablack, H., Lindquist, K. A., & Telzer, E. H. (2019). Incorporating the social context into neurocognitive models of adolescent decision-making: A neuroimaging meta-analysis. Neuroscience and biobehavioral reviews, 101, 129–142. https://doi.org/10.1016/j.neubiorev.2018.12.024
- Vinaiphat, A., & Sze, S. K. (2022). Proteomics for comprehensive characterization of extracellular vesicles in neurodegenerative disease. Experimental neurology, 355, 114149. https://doi.org/10.1016/j.expneurol.2022.114149
- Wang, J., Clauson, C. L., Robbins, P. D., Niedernhofer, L. J., & Wang, Y. (2012). The oxidative DNA lesions 8,5'-cyclopurines accumulate with aging in a tissue-specific manner. Aging cell, 11(4), 714–716. https://doi.org/10.1111/j.1474-9726.2012.00828.x
- Wang, L., Pang, K., Zhou, L., Cebrián-Silla, A., González-Granero, S., Wang, S., Bi, Q., White, M. L., Ho, B., Li, J., Li, T., Perez, Y., Huang, E. J., Winkler, E. A., Paredes, M. F., Kovner, R., Sestan, N., Pollen, A. A., Liu, P., Li, J., … Kriegstein, A. R. (2023). A cross-species proteomic map reveals neoteny of human synapse development. Nature, 622(7981), 112–119. https://doi.org/10.1038/s41586-023-06542-2
- Wang, L., Xia, Y., Chen, Y., Dai, R., Qiu, W., Meng, Q., Kuney, L., & Chen, C. (2019). Brain Banks Spur New Frontiers in Neuropsychiatric Research and Strategies for Analysis and Validation. Genomics, proteomics & bioinformatics, 17(4), 402–414. https://doi.org/10.1016/j.gpb.2019.02.002
- Wang, Q., Zhao, X., He, S., Liu, Y., An, M., & Ji, J. (2010). Differential proteomics analysis of specific carbonylated proteins in the temporal cortex of aged rats: the deterioration of antioxidant system. Neurochemical research, 35(1), 13–21. https://doi.org/10.1007/s11064-009-0023-8
- Wang, X., Wu, J, Wang, N., Zhang, D., Chen, Z., Zhang, H., Zhu, K., Bao, A.,
- Zhang, J., Shen,Y., Qian X., Qiu W. (2022). Standardized Operational Protocol for the China Human Brain Bank Consortium. Human Brain, 1(1), 92–106. https://doi.org/10.37819/hb.001.001.0209
- Watson, T. C., Obiang, P., Torres-Herraez, A., Watilliaux, A., Coulon, P., Rochefort, C., & Rondi-Reig, L. (2019). Anatomical and physiological foundations of cerebello-hippocampal interaction. eLife, 8, e41896. https://doi.org/10.7554/eLife.41896
- Wesseling, H., Chan, M. K., Tsang, T. M., Ernst, A., Peters, F., Guest, P. C., Holmes, E., & Bahn, S. (2013). A combined metabonomic and proteomic approach identifies frontal cortex changes in a chronic phencyclidine rat model in relation to human schizophrenia brain pathology. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 38(12), 2532–2544. https://doi.org/10.1038/npp.2013.160
- Xiao, H., Jedrychowski, M. P., Schweppe, D. K., Huttlin, E. L., Yu, Q., Heppner, D. E., Li, J., Long, J., Mills, E. L., Szpyt, J., He, Z., Du, G., Garrity, R., Reddy, A., Vaites, L. P., Paulo, J. A., Zhang, T., Gray, N. S., Gygi, S. P., & Chouchani, E. T. (2020). A Quantitative Tissue-Specific Landscape of Protein Redox Regulation during Aging. Cell, 180(5), 968–983.e24. https://doi.org/10.1016/j.cell.2020.02.012
- Ximerakis, M., Lipnick, S. L., Innes, B. T., Simmons, S. K., Adiconis, X., Dionne, D., Mayweather, B. A., Nguyen, L., Niziolek, Z., Ozek, C., Butty, V. L., Isserlin, R., Buchanan, S. M., Levine, S. S., Regev, A., Bader, G. D., Levin, J. Z., & Rubin, L. L. (2019). Single-cell transcriptomic profiling of the aging mouse brain. Nature neuroscience, 22(10), 1696–1708. https://doi.org/10.1038/s41593-019-0491-3
- Xu, B., Gao, Y., Zhan, S., Xiong, F., Qiu, W., Qian, X., Wang, T., Wang, N., Zhang, D., Yang, Q., Wang, R., Bao, X., Dou, W., Tian, R., Meng, S., Gai, W. P., Huang, Y., Yan, X. X., Ge, W., & Ma, C. (2016). Quantitative protein profiling of hippocampus during human aging. Neurobiology of aging, 39, 46–56. https://doi.org/10.1016/j.neurobiolaging.2015.11.029
- Xu, B., Xiong, F., Tian, R., Zhan, S., Gao, Y., Qiu, W., Wang, R., Ge, W., & Ma, C. (2016). Temporal lobe in human aging: A quantitative protein profiling study of samples from Chinese Human Brain Bank. Experimental gerontology, 73, 31–41. https://doi.org/10.1016/j.exger.2015.11.016
- Yang, S., Liu, T., Li, S., Zhang, X., Ding, Q., Que, H., Yan, X., Wei, K., & Liu, S. (2008). Comparative proteomic analysis of brains of naturally aging mice. Neuroscience, 154(3), 1107–1120. https://doi.org/10.1016/j.neuroscience.2008.04.012
- Yang, S., Park, J. H., & Lu, H. C. (2023). Axonal energy metabolism, and the effects in aging and neurodegenerative diseases. Molecular neurodegeneration, 18(1), 49. https://doi.org/10.1186/s13024-023-00634-3
- Yang, Y., Tapias, V., Acosta, D., Xu, H., Chen, H., Bhawal, R., Anderson, E. T., Ivanova, E., Lin, H., Sagdullaev, B. T., Chen, J., Klein, W. L., Viola, K. L., Gandy, S., Haroutunian, V., Beal, M. F., Eliezer, D., Zhang, S., & Gibson, G. E. (2022). Altered succinylation of mitochondrial proteins, APP and tau in Alzheimer's disease. Nature communications, 13(1), 159. https://doi.org/10.1038/s41467-021-27572-2
- Yin, P., Tu, Z., Yin, A., Zhao, T., Yan, S., Guo, X., Chang, R., Zhang, L., Hong, Y., Huang, X., Zhou, J., Wang, Y., Li, S., & Li, X. J. (2015). Aged monkey brains reveal the role of ubiquitin-conjugating enzyme UBE2N in the synaptosomal accumulation of mutant huntingtin. Human molecular genetics, 24(5), 1350–1362. https://doi.org/10.1093/hmg/ddu544
- Ying, Y., & Li, H. (2022). Recent progress in the analysis of protein deamidation using mass spectrometry. Methods (San Diego, Calif.), 200, 42–57. https://doi.org/10.1016/j.ymeth.2020.06.009
- Yousefzadeh, M. J., Flores, R. R., Zhu, Y., Schmiechen, Z. C., Brooks, R. W., Trussoni, C. E., Cui, Y., Angelini, L., Lee, K. A., McGowan, S. J., Burrack, A. L., Wang, D., Dong, Q., Lu, A., Sano, T., O'Kelly, R. D., McGuckian, C. A., Kato, J. I., Bank, M. P., Wade, E. A., … Niedernhofer, L. J. (2021). An aged immune system drives senescence and ageing of solid organs. Nature, 594(7861), 100–105. https://doi.org/10.1038/s41586-021-03547-7
- Yu, A. Q., Wang, J., Jiang, S. T., Yuan, L. Q., Ma, H. Y., Hu, Y. M., Han, X. M., Tan, L. M., & Wang, Z. X. (2021). SIRT7-Induced PHF5A Decrotonylation Regulates Aging Progress Through Alternative Splicing-Mediated Downregulation of CDK2. Frontiers in cell and developmental biology, 9, 710479. https://doi.org/10.3389/fcell.2021.710479
- Yuan, J. J., Zhang, Q., Gong, C. X., Wang, F. X., Huang, J. C., Yang, G. Q., Liu, L., Zhou, K., Xu, R., Chen, Q., Zhou, Y., Xiong, X. Y., & Yang, Q. W. (2019). Young plasma ameliorates aging-related acute brain injury after intracerebral hemorrhage. Bioscience reports, 39(5), BSR20190537. https://doi.org/10.1042/BSR20190537
- Zhang, M., Flury, S., Kim, C. K., Chung, W. C. J., Kirk, J. A., & Pak, T. R. (2021). Absolute Quantification of Phosphorylated ERβ Amino Acids in the Hippocampus of Women and in A Rat Model of Menopause. Endocrinology, 162(9), bqab122. https://doi.org/10.1210/endocr/bqab122
- Zhang, S., Zhu, R., Pan, B., Xu, H., Olufemi, M. F., Gathagan, R. J., Li, Y., Zhang, L., Zhang, J., Xiang, W., Kagan, E. M., Cao, X., Yuan, C., Kim, S. J., Williams, C. K., Magaki, S., Vinters, H. V., Lashuel, H. A., Garcia, B. A., James Petersson, E., … Peng, C. (2023). Post-translational modifications of soluble α-synuclein regulate the amplification of pathological α-synuclein. Nature neuroscience, 26(2), 213–225. https://doi.org/10.1038/s41593-022-01239-7
- Zheng, Y., Tao, S., Liu, Y., Liu, J., Sun, L., Zheng, Y., Tian, Y., Su, P., Zhu, X., & Xu, F. (2022). Basal Forebrain-Dorsal Hippocampus Cholinergic Circuit Regulates Olfactory Associative Learning. International journal of molecular sciences, 23(15), 8472. https://doi.org/10.3390/ijms23158472
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