Systematic review and meta-analysis of human transcriptomics reveals neuroinflammation, deficient energy metabolism, and proteostasis failure across neurodegeneration
Open accessAbstract
Neurodegenerative disorders such as Alzheimer's disease (AD), Lewy body diseases (LBD), and the amyotrophic lateral sclerosis and frontotemporal dementia (ALS-FTD) spectrum are defined by the accumulation of specific misfolded protein aggregates. However, the mechanisms by which each proteinopathy leads to neurodegeneration remain elusive. We hypothesized that there is a common "pan-neurodegenerative" gene expression signature driving pathophysiology across these clinically and pathologically diverse proteinopathies. To test this hypothesis, we performed a systematic review of human CNS transcriptomics datasets from AD, LBD, and ALS-FTD patients and age-matched controls in the Gene Expression Omnibus (GEO) and ArrayExpress databases, followed by consistent processing of each dataset, meta-analysis, pathway enrichment, and overlap analyses. After applying pre-specified eligibility criteria and stringent data pre-processing, a total of 2600 samples from 26 AD, 21 LBD, and 13 ALS-FTD datasets were included in the meta-analysis. The pan-neurodegenerative gene signature is characterized by an upregulation of innate immunity, cytoskeleton, and transcription and RNA processing genes, and a downregulation of the mitochondrial electron transport chain. Pathway enrichment analyses also revealed the upregulation of neuroinflammation (including Toll-like receptor, TNF, and NFκB signaling) and phagocytosis, and the downregulation of mitochondrial oxidative phosphorylation, lysosomal acidification, and ubiquitin-proteasome pathways. Our findings suggest that neuroinflammation and a failure in both neuronal energy metabolism and protein degradation systems are consistent features underlying neurodegenerative diseases, despite differences in the extent of neuronal loss and brain regions involved.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Previous bulk transcriptomic analyses have highlighted energy metabolism deficits as a feature of AD and other neurodegenerative diseases.
“previous bulk transcriptomic analyses have highlighted energy metabolism deficits as a feature of AD and other neurodegenerative diseases43,44”
From Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease (2024) · cites this paper as [43]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Part of this paper’s reference list could not be split into single entries.
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930 · see citing works
arrayQualityMetrics—a bioconductor package for quality assessment of microarray data
Kauffmann, A., Gentleman, R., & Huber, W. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btn647 · Cited by 946 · see citing works
Tau PTM Profiles Identify Patient Heterogeneity and Stages of Alzheimer’s Disease
Wesseling, H., Mair, W., Kumar, M., Schlaffner, C. N., Tang, S., Beerepoot, P., Fatou, B., Guise, A. J., Cheng, L., Takeda, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.029 · Cited by 688 · cited 1× · see citing works
“, lack of a stable tertiary structure), which would render them prone to misfolding and subsequent aggregation in pathological conditions (Jarosz and Khurana, 2017), for example upon certain abnormal post-translational modifications (Toth-Petroczy et al., 2016; Wesseling et al., 2020).”
Self-citationSelective autophagy as a potential therapeutic target for neurodegenerative disorders
Scrivo, A., Bourdenx, M., Pampliega, O., & Cuervo, A. M. (2018) · The Lancet Neurology · doi:10.1016/s1474-4422(18)30238-2 · Cited by 355 · see citing works
Damisah, E. C., Hill, R. A., Rai, A., Chen, F., Rothlin, C. V., Ghosh, S., & Grutzendler, J. (2020) · Science Advances · doi:10.1126/sciadv.aba3239 · Cited by 301 · see citing works
Wan, Y.-W., Al-Ouran, R., Mangleburg, C. G., Perumal, T. M., Lee, T. V., Allison, K., Swarup, V., Funk, C. C., Gaiteri, C., Allen, M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107908 · Cited by 295 · see citing works
Flagged first, then by citations
Research works printed but not linked: 53
- Alshikho MJ, Zürcher NR, Loggia ML, Cernasov P, Reynolds B, Pijanowski O, Chonde DB, Izquierdo Garcia D, Mainero C, Catana C, Chan J, Babu S, Paganoni S, Hooker JM, Atassi N, 2018 Integrated magnetic resonance imaging and [11 C]-PBR28 positron emission tomographic imaging in amyotrophic lateral sclerosis. Ann. Neurol 83, 1186– 1197. 10.1002/ana.25251. 29740862 PMC6105567 · DOI
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G, 2000 Gene ontology: tool for the unification of biology. Nat. Genet 25, 25– 29. 10.1038/75556. 10802651 PMC3037419 · DOI · Paper page
- Brion JP, Flament-Durand J, Dustin P, 1986 Alzheimer’s disease and tau proteins. Lancet Lond. Engl 2, 1098 10.1016/s0140-6736(86)90495-2. 2877247 · DOI
- Carvalho BS, Irizarry RA, 2010 A framework for oligonucleotide microarray preprocessing. Bioinformatics 26, 2363– 2367. 10.1093/bioinformatics/btq431. 20688976 PMC2944196 · DOI
- Ciryam P, Kundra R, Freer R, Morimoto RI, Dobson CM, Vendruscolo M, 2016 A transcriptional signature of Alzheimer’s disease is associated with a metastable subproteome at risk for aggregation. Proc. Natl. Acad. Sci. U. S. A 113, 4753– 4758. 10.1073/pnas.1516604113. 27071083 PMC4855616 · DOI
- Das S, Li Z, Noori A, Hyman BT, Serrano-Pozo A, 2020 Meta-analysis of mouse transcriptomic studies supports a context-dependent astrocyte reaction in acute CNS injury versus neurodegeneration. J. Neuroinflammation 17, 227 10.1186/s12974-020-01898-y. 32736565 PMC7393869 · DOI · Paper page
- Durrenberger PF, Fernando FS, Kashefi SN, Bonnert TP, Seilhean D, Nait-Oumesmar B, Schmitt A, Gebicke-Haerter PJ, Falkai P, Grünblatt E, Palkovits M, Arzberger T, Kretzschmar H, Dexter DT, Reynolds R, 2015 Common mechanisms in neurodegeneration and neuroinflammation: A BrainNet Europe gene expression microarray study. J. Neural Transm. Vienna Austria 1996 122, 1055– 1068. 10.1007/s00702-014-1293-0. 25119539 · DOI
- Fan Z, Aman Y, Ahmed I, Chetelat G, Landeau B, Ray Chaudhuri K, Brooks DJ, Edison P, 2015 Influence of microglial activation on neuronal function in Alzheimer’s and Parkinson’s disease dementia. Alzheimers Dement. J. Alzheimers Assoc 11, 608– 621 e7. 10.1016/j.jalz.2014.06.016. 25239737 · DOI · Paper page
- Fuhrmann M, Bittner T, Jung CKE, Burgold S, Page RM, Mitteregger G, Haass C, LaFerla FM, Kretzschmar H, Herms J, 2010 Microglial Cx3cr1 knockout prevents neuron loss in a mouse model of Alzheimer’s disease. Nat. Neurosci 13, 411– 413. 10.1038/nn.2511. 20305648 PMC4072212 · DOI · Paper page
- Gao F-B, Almeida S, Lopez-Gonzalez R, 2017 Dysregulated molecular pathways in amyotrophic lateral sclerosis-frontotemporal dementia spectrum disorder. EMBO J. 36, 2931– 2950. 10.15252/embj.201797568. 28916614 PMC5641681 · DOI
- Gerhard A, Pavese N, Hotton G, Turkheimer F, Es M, Hammers A, Eggert K, Oertel W, Banati RB, Brooks DJ, 2006 In vivo imaging of microglial activation with [11C](R)-PK11195 PET in idiopathic Parkinson’s disease. Neurobiol. Dis 21, 404– 412. 10.1016/j.nbd.2005.08.002. 16182554 · DOI · Paper page
- Glenner GG, Wong CW, 1984 Alzheimer’s disease: initial report of the purification and characterization of a novel cerebrovascular amyloid protein. Biochem. Biophys. Res. Commun 120, 885– 890. 10.1016/s0006-291x(84)80190-4. 6375662 · DOI · Paper page
- Grundke-Iqbal I, Iqbal K, Quinlan M, Tung YC, Zaidi MS, Wisniewski HM, 1986 Microtubule-associated protein tau. A component of Alzheimer paired helical filaments. J. Biol. Chem 261, 6084– 6089. 3084478
- Gui Y, Marks JD, Das S, Hyman BT, Serrano-Pozo A, 2020 Characterization of the 18 kDa translocator protein (TSPO) expression in post-mortem normal and Alzheimer’s disease brains. Brain Pathol. Zurich Switz 30, 151– 164. 10.1111/bpa.12763. PMC6904423 31276244 · DOI · Paper page
- Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP, 2003 Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostat. Oxf. Engl 4, 249– 264. 10.1093/biostatistics/4.2.249. 12925520 · DOI · Paper page
- Jarosz DF, Khurana V, 2017 Specification of physiologic and disease states by distinct proteins and protein conformations. Cell 171, 1001– 1014. 10.1016/j.cell.2017.10.047. 29149602 · DOI
- Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, Sidiropoulos K, Cook J, Gillespie M, Haw R, Loney F, May B, Milacic M, Rothfels K, Sevilla C, Shamovsky V, Shorser S, Varusai T, Weiser J, Wu G, Stein L, Hermjakob H, D’Eustachio P, 2020 The reactome pathway knowledge base. Nucleic Acids Res. 48, D498– D503. 10.1093/nar/gkz1031. 31691815 PMC7145712 · DOI
- Johnson ECB, Dammer EB, Duong DM, Ping L, Zhou M, Yin L, Higginbotham LA, Guajardo A, White B, Troncoso JC, Thambisetty M, Montine TJ, Lee EB, Trojanowski JQ, Beach TG, Reiman EM, Haroutunian V, Wang M, Schadt E, Zhang B, Dickson DW, Ertekin-Taner N, Golde TE, Petyuk VA, De Jager PL, Bennett DA, Wingo TS, Rangaraju S, Hajjar I, Shulman JM, Lah JJ, Levey AI, Seyfried NT, 2020 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. 10.1038/s41591-020-0815-6. 32284590 PMC7405761 · DOI · Paper page
- Kauffmann A, Huber W, 2010 Microarray data quality control improves the detection of differentially expressed genes. Genomics 95, 138– 142. 10.1016/j.ygeno.2010.01.003. 20079422 · DOI
- Kelly J, Moyeed R, Carroll C, Albani D, Li X, 2019 Gene expression meta-analysis of Parkinson’s disease and its relationship with Alzheimer’s disease. Mol. Brain 12, 16 10.1186/s13041-019-0436-5. 30819229 PMC6396547 · DOI
- Kwiatkowski TJ, Bosco DA, Leclerc AL, Tamrazian E, Vanderburg CR, Russ C, Davis A, Gilchrist J, Kasarskis EJ, Munsat T, Valdmanis P, Rouleau GA, Hosler BA, Cortelli P, de Jong PJ, Yoshinaga Y, Haines JL, Pericak-Vance MA, Yan J, Ticozzi N, Siddique T, McKenna-Yasek D, Sapp PC, Horvitz HR, Landers JE, Brown RH, 2009 Mutations in the FUS/TLS gene on chromosome 16 cause familial amyotrophic lateral sclerosis. Science 323, 1205– 1208. 10.1126/science.1166066. 19251627 · DOI
- Labadorf A, Choi SH, Myers RH, 2018 Evidence for a pan-neurodegenerative disease response in Huntington’s and Parkinson’s disease expression profiles. Front. Mol. Neurosci 10 10.3389/fnmol.2017.00430. PMC5768647 29375298 · DOI
- Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD, 2012 The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882– 883. 10.1093/bioinformatics/bts034. 22257669 PMC3307112 · DOI · Paper page
- Li MD, Burns TC, Morgan AA, Khatri P, 2014 Integrated multi-cohort transcriptional meta-analysis of neurodegenerative diseases. Acta Neuropathol. Commun 2, 93 10.1186/s40478-014-0093-y. 25187168 PMC4167139 · DOI
- Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, Clarke M, Devereaux PJ, Kleijnen J, Moher D, 2009 The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. The BMJ 339. PMC2714672 19622552
- Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP, 2011 Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739– 1740. 10.1093/bioinformatics/btr260. 21546393 PMC3106198 · DOI · Paper page
- López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G, 2013 The hallmarks of aging. Cell 153, 1194– 1217. 10.1016/j.cell.2013.05.039. 23746838 PMC3836174 · DOI · Paper page
- Maglott D, Ostell J, Pruitt KD, Tatusova T, 2007 Entrez gene: Gene-centered information at NCBI. Nucleic Acids Res. 35, D26– D31. 10.1093/nar/gkl993. 17148475 PMC1761442 · DOI · Paper page
- Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, Menon M, He L, Abdurrob F, Jiang X, Martorell AJ, Ransohoff RM, Hafler BP, Bennett DA, Kellis M, Tsai L-H, 2019 Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332– 337. 10.1038/s41586-019-1195-2. 31042697 PMC6865822 · DOI · Paper page
- McKeith IG, Boeve BF, Dickson DW, Halliday G, Taylor J-P, Weintraub D, Aarsland D, Galvin J, Attems J, Ballard CG, Bayston A, Beach TG, Blanc F, Bohnen N, Bonanni L, Bras J, Brundin P, Burn D, Chen-Plotkin A, Duda JE, El-Agnaf O, Feldman H, Ferman TJ, Ffytche D, Fujishiro H, Galasko D, Goldman JG, Gomperts SN, Graff-Radford NR, Honig LS, Iranzo A, Kantarci K, Kaufer D, Kukull W, Lee VMY, Leverenz JB, Lewis S, Lippa C, Lunde A, Masellis M, Masliah E, McLean P, Mollenhauer B, Montine TJ, Moreno E, Mori E, Murray M, O’Brien JT, Orimo S, Postuma RB, Ramaswamy S, Ross OA, Salmon DP, Singleton A, Taylor A, Thomas A, Tiraboschi P, Toledo JB, Trojanowski JQ, Tsuang D, Walker Z, Yamada M, Kosaka K, 2017 Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology 89, 88– 100. 10.1212/WNL.0000000000004058. 28592453 PMC5496518 · DOI · Paper page
- McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH, 2011 The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. J. Alzheimers Assoc 7, 263– 269. 10.1016/j.jalz.2011.03.005. PMC3312024 21514250 · DOI · Paper page
- Mori K, Weng S-M, Arzberger T, May S, Rentzsch K, Kremmer E, Schmid B, Kretzschmar HA, Cruts M, Van Broeckhoven C, Haass C, Edbauer D, 2013 The C9orf72 GGGGCC repeat is translated into aggregating dipeptide-repeat proteins in FTLD/ALS. Science 339, 1335– 1338. 10.1126/science.1232927. 23393093 · DOI
- Neumann M, Sampathu DM, Kwong LK, Truax AC, Micsenyi MC, Chou TT, Bruce J, Schuck T, Grossman M, Clark CM, McCluskey LF, Miller BL, Masliah E, Mackenzie IR, Feldman H, Feiden W, Kretzschmar HA, Trojanowski JQ, Lee VM-Y, 2006 Ubiquitinated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Science 314, 130– 133. 10.1126/science.1134108. 17023659 · DOI · Paper page
- Oerton E, Bender A, 2017 Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson’s disease: A comparison of 33 human and animal studies. BMC Neurol. 17, 58 10.1186/s12883-017-0838-x. 28335819 PMC5364698 · DOI
- Patel A, Malinovska L, Saha S, Wang J, Alberti S, Krishnan Y, Hyman AA, 2017 ATP as a biological hydrotrope. Science 356, 753– 756. 10.1126/science.aaf6846. 28522535 · DOI
- Patel H, Dobson RJB, Newhouse SJ, 2019 A meta-analysis of Alzheimer’s disease brain transcriptomic data. J. Alzheimers Dis 68, 1635– 1656. 10.3233/JAD-181085. 30909231 PMC6484273 · DOI
- Phipson B, Lee S, Majewski IJ, Alexander WS, Smyth GK, 2016 Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Ann. Appl. Stat 10, 946– 963. 10.1214/16-AOAS920. 28367255 PMC5373812 · DOI
- Rascovsky K, Hodges JR, Knopman D, Mendez MF, Kramer JH, Neuhaus J, van Swieten JC, Seelaar H, Dopper EGP, Onyike CU, Hillis AE, Josephs KA, Boeve BF, Kertesz A, Seeley WW, Rankin KP, Johnson JK, Gorno-Tempini M-L, Rosen H, Prioleau-Latham CE, Lee A, Kipps CM, Lillo P, Piguet O, Rohrer JD, Rossor MN, Warren JD, Fox NC, Galasko D, Salmon DP, Black SE, Mesulam M, Weintraub S, Dickerson BC, Diehl-Schmid J, Pasquier F, Deramecourt V, Lebert F, Pijnenburg Y, Chow TW, Manes F, Grafman J, Cappa SF, Freedman M, Grossman M, Miller BL, 2011 Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain J. Neurol 134, 2456– 2477. 10.1093/brain/awr179. PMC3170532 21810890 · DOI · Paper page
- Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK, 2015 Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 10.1093/nar/gkv007. 25605792 PMC4402510 · DOI · Paper page
- Sjöstedt E, Zhong W, Fagerberg L, Karlsson M, Mitsios N, Adori C, Oksvold P, Edfors F, Limiszewska A, Hikmet F, Huang J, Du Y, Lin L, Dong Z, Yang L, Liu X, Jiang H, Xu X, Wang J, Yang H, Bolund L, Mardinoglu A, Zhang C, von Feilitzen K, Lindskog C, Pontén F, Luo Y, Hökfelt T, Uhlén M, Mulder J, 2020 An atlas of the protein-coding genes in the human, pig, and mouse brain. Science 367 10.1126/science.aay5947. 32139519 · DOI · Paper page
- Spillantini MG, Schmidt ML, Lee VM, Trojanowski JQ, Jakes R, Goedert M, 1997 Alpha-synuclein in Lewy bodies. Nature 388, 839– 840. 10.1038/42166. 9278044 · DOI · Paper page
- Subramanian A, Kuehn H, Gould J, Tamayo P, Mesirov JP, 2007 GSEA-P: A desktop application for gene set enrichment analysis. Bioinform. Oxf. Engl 23, 3251– 3253. 10.1093/bioinformatics/btm369. 17644558 · DOI
- Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, von Mering C, 2019 STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607– D613. 10.1093/nar/gky1131. 30476243 PMC6323986 · DOI · Paper page
- The Gene Ontology Consortium, 2019 The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330– D338. 10.1093/nar/gky1055. 30395331 PMC6323945 · DOI · Paper page
- Toth-Petroczy A, Palmedo P, Ingraham J, Hopf TA, Berger B, Sander C, Marks DS, 2016 Structured states of disordered proteins from genomic sequences. Cell 167, 158– 170 e12. 10.1016/j.cell.2016.09.010. 27662088 PMC5451116 · DOI
- Vilchez D, Saez I, Dillin A, 2014 The role of protein clearance mechanisms in organismal ageing and age-related diseases. Nat. Commun 5, 5659 10.1038/ncomms6659. 25482515 · DOI
- Walsh CJ, Hu P, Batt J, Santos CCD, 2015 Microarray meta-analysis and cross-platform normalization: Integrative genomics for robust biomarker discovery. Microarrays Basel Switz. 4, 389– 406. 10.3390/microarrays4030389. PMC4996376 27600230 · DOI
- Walther DM, Kasturi P, Zheng M, Pinkert S, Vecchi G, Ciryam P, Morimoto RI, Dobson CM, Vendruscolo M, Mann M, Hartl FU, 2017 Widespread proteome remodeling and aggregation in aging C. elegans. Cell 168, 944 10.1016/j.cell.2016.12.041. 28235202 · DOI
- Wang X, Lin Y, Song C, Sibille E, Tseng GC, 2012 Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: With application to major depressive disorder. BMC Bioinform. 13, 52 10.1186/1471-2105-13-52. PMC3342232 22458711 · DOI
- Wang Q, Zhang Y, Wang M, Song W-M, Shen Q, McKenzie A, Choi I, Zhou X, Pan P-Y, Yue Z, Zhang B, 2019 The landscape of multiscale transcriptomic networks and key regulators in Parkinson’s disease. Nat. Commun 10, 5234 10.1038/s41467-019-13144-y. 31748532 PMC6868244 · DOI · Paper page
- Zaykin DV, 2011 Optimally weighted Z-test is a powerful method for combining probabilities in meta-analysis. J. Evol. Biol 24, 1836– 1841. 10.1111/j.1420-9101.2011.02297.x. 21605215 PMC3135688 · DOI
- Zhang Y, Sloan SA, Clarke LE, Caneda C, Plaza CA, Blumenthal PD, Vogel H, Steinberg GK, Edwards MSB, Li G, Duncan JA, Cheshier SH, Shuer LM, Chang EF, Grant GA, Gephart MGH, Barres BA, 2016 Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37– 53. 10.1016/j.neuron.2015.11.013. 26687838 PMC4707064 · DOI · Paper page
- Zheng B, Liao Z, Locascio JJ, Lesniak KA, Roderick SS, Watt ML, Eklund AC, Zhang-James Y, Kim PD, Hauser MA, Grünblatt E, Moran LB, Mandel SA, Riederer P, Miller RM, Federoff HJ, Wüllner U, Papapetropoulos S, Youdim MB, Cantuti-Castelvetri I, Young AB, Vance JM, Davis RL, Hedreen JC, Adler CH, Beach TG, Graeber MB, Middleton FA, Rochet J-C, Scherzer CR, Global PD Gene Expression (GPEX) Consortium, 2010 PGC-1α, a potential therapeutic target for early intervention in Parkinson’s disease. Sci. Transl. Med 2, 52– 73. 10.1126/scitranslmed.3001059. PMC3129986 20926834 · DOI
Cited by
Sorted by citations
Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease
Serrano-Pozo, A., Li, H., Li, Z., Muñoz-Castro, C., Jaisa-aad, M., Healey, M. A., Welikovitch, L. A., Jayakumar, R., Bryant, A. G., Noori, A., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01791-4 · Cited by 140 · see citing works
Self-citationLiu, Z., Fan, Q., Wu, S., Wan, Y., & Lei, Y. (2021) · Lipids in Health and Disease · doi:10.1186/s12944-021-01462-4 · Cited by 112 · see citing works
Axonal energy metabolism, and the effects in aging and neurodegenerative diseases
Yang, S., Park, J. H., & Lu, H.-C. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00634-3 · Cited by 94 · see citing works
The role of glial autophagy in Alzheimer’s disease
Litwiniuk, A., Juszczak, G. R., Stankiewicz, A. M., & Urbańska, K. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02242-5 · Cited by 54 · see citing works
Artificial intelligence for dementia genetics and omics
Bettencourt, C., Skene, N., Bandres‐Ciga, S., Anderson, E., Winchester, L. M., Foote, I. F., Schwartzentruber, J., Botia, J. A., Nalls, M., Singleton, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13427 · Cited by 30 · see citing works
- Authors
- Noori, A., Mezlini, A. M., Hyman, B. T., Serrano-Pozo, A., & Das, S.
- Affiliations
- Harvard University (US)
- Corresponding author
- Das, Sudeshna
- Venue
- Neurobiology of Disease
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 149
- Pages
- 105225
- ISSN
- 0969-9961 (ISSN-L)
- PMC
- PMC7856076
- OpenAlex
- W3116928997
- Cited by
- 98
- References
- 7
- Citations by year
- 2026: 19 · 2025: 10 · 2024: 16 · 2023: 16 · 2022: 20 · 2021: 17
- FWCI
- 1.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Neurobiology of Disease (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, other-oa)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Massachusetts Life Sciences Center
- National Institute on Aging
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Neurology › Amyotrophic Lateral Sclerosis Research
- Keywords
- neurodegeneration, Alzheimer's disease, Lewy body disease, amyotrophic lateral sclerosis, frontotemporal dementia, neuroinflammation, neuronal energy metabolism, proteostasis, protein aggregation, mitochondrial oxidative phosphorylation, ubiquitin-proteasome system, lysosomal acidification, innate immunity, phagocytosis, gene expression signatures
- MeSH terms
- Proteostasis, Alzheimer Disease, Amyotrophic Lateral Sclerosis, Brain, Energy Metabolism, Humans, Inflammation, Inflammation Mediators, Neurodegenerative Diseases, Lewy Body Disease, Frontotemporal Dementia, Transcriptome
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 ›
Are you an author, or do you see a problem with this page? Request a review