Abstract
APOE allelic variation is critical in brain aging and Alzheimer’s disease (AD). The APOE2 allele associated with cognitive resilience and neuroprotection against AD remains understudied. We employed a multipronged approach to characterize the transition from middle to old age in mice with APOE2 allele, using behavioral assessments, image-derived morphometry and diffusion metrics, structural connectomics, and blood transcriptomics. We used sparse multiple canonical correlation analyses (SMCCA) for integrative modeling, and graph neural network predictions. Our results revealed brain sub-networks associated with biological traits, cognitive markers, and gene expression. The cingulate cortex emerged as a critical region, demonstrating age-associated atrophy and diffusion changes, with higher fractional anisotropy in males and middle-aged subjects. Somatosensory and olfactory regions were consistently highlighted, indicating age-related atrophy and sex differences. The hippocampus exhibited significant volumetric changes with age, with differences between males and females in CA3 and CA1 regions. SMCCA underscored changes in the cingulate cortex, somatosensory cortex, olfactory regions, and hippocampus in relation to cognition and blood-based gene expression. Our integrative modeling in aging APOE2 carriers revealed a central role for changes in gene pathways involved in localization and the negative regulation of cellular processes. Our results support an important role of the immune system and response to stress. This integrative approach offers novel insights into the complex interplay among brain connectivity, aging, and sex. Our study provides a foundation for understanding the impact of APOE2 allele on brain aging, the potential for detecting associated changes in blood markers, and revealing novel therapeutic intervention targets.
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Data availability
Code and data necessary to reproduce the original analyses are available from https://github.com/AD-Decode/APOE2_Mouse. The raw RNA-seq data are available from ENA database (Accession PRJEB59982).
References
Ackert-Bicknell CL, Anderson LC, Sheehan S, Hill WG, Chang B, Churchill GA, Chesler EJ, Korstanje R, Peters LL (2015) Aging research using mouse models. Curr Protoc Mouse Biol 5(2):95–133. https://doi.org/10.1002/9780470942390.MO140195
2023 Alzheimer’s disease facts and figures (2023) Alzheimer’s & Dementia 19(4):1598–1695. https://doi.org/10.1002/ALZ.13016
Anderson RJ, Cook JJ, Delpratt N, Nouls JC, Gu B, McNamara JO, Avants BB, Johnson GA, Badea A (2019) Small animal multivariate brain analysis (SAMBA)–a high throughput pipeline with a validation framework. Neuroinformatics 17(3):451–472. https://doi.org/10.1007/s12021-018-9410-0
Arfelli VC, Chang YC, Bagnoli JW, Kerbs P, Ciamponi FE, Paz LM, Pankivskyi S, de Matha Salone J, Maucuer A, Massirer KB, Enard W, Kuster B, Greif PA, Archangelo LF (2023) UHMK1 is a novel splicing regulatory kinase. J Biol Chem 299(4):103041–103041
Arnatkeviciute A, Fulcher BD, Bellgrove MA, Fornito A (2021) Where the genome meets the connectome: understanding how genes shape human brain connectivity. Neuroimage 244:118570. https://doi.org/10.1016/j.neuroimage.2021.118570
Avram AV, Sarlls JE, Barnett AS, Özarslan E, Thomas C, Irfanoglu MO, Hutchinson E, Pierpaoli C, Basser PJ (2016) Clinical feasibility of using mean apparent propagator (MAP) MRI to characterize brain tissue microstructure. Neuroimage 127:422–434. https://doi.org/10.1016/j.neuroimage.2015.11.027
Badea A, Wu W, Shuff J, Wang M, Anderson RJ, Qi Y, Johnson GA, Wilson JG, Koudoro S, Garyfallidis E, Colton CA, Dunson DB (2019) Identifying vulnerable brain networks in mouse models of genetic risk factors for late onset Alzheimer’s disease. Front Neuroinform 13:72
Badea A, Li D, Niculescu AR, Anderson RJ, Stout JA, Williams CL, Colton CA, Maeda N, Dunson DB (2022) Absolute winding number differentiates mouse spatial navigation strategies with genetic risk for Alzheimer’s disease. Front Neurosci 16:848654–848654. https://doi.org/10.3389/fnins.2022.848654
Ballard C, Gauthier S, Corbett A, Brayne C, Aarsland D, Jones E (2011) Alzheimer’s disease. The Lancet 377(9770):1019–1031. https://doi.org/10.1016/S0140-6736(10)61349-9
Bangasser DA, Wicks B (2017) Sex-specific mechanisms for responding to stress. J Neurosci Res 95(1–2):75–75. https://doi.org/10.1002/JNR.23812
Bloom JS, Hynd GW (2005) The role of the corpus callosum in interhemispheric transfer of information: excitation or inhibition? Neuropsychol Rev 15(2):59–71. https://doi.org/10.1007/S11065-005-6252-Y
Calabrese E, Badea A, Cofer G, Qi Y, Johnson GA (2015) A diffusion MRI tractography connectome of the mouse brain and comparison with neuronal tracer data. Cereb Cortex 25(11):4628–4637. https://doi.org/10.1093/cercor/bhv121
Cho HU, Kim S, Sim J, Yang S, An H, Nam MH, Jang DP, Lee CJ (2021) Redefining differential roles of MAO-A in dopamine degradation and MAO-B in tonic GABA synthesis. Exp Mol Med 53(7):1148–1148. https://doi.org/10.1038/S12276-021-00646-3
Comoli E, Favaro PDN, Vautrelle N, Leriche M, Overton PG, Redgrave P (2012) Segregated anatomical input to sub-regions of the rodent superior colliculus associated with approach and defense. Front Neuroanat. https://doi.org/10.3389/FNANA.2012.00009
Conejero-Goldberg C, Gomar JJ, Bobes-Bascaran T, Hyde TM, Kleinman JE, Herman MM, Chen S, Davies P, Goldberg TE (2014) APOE2 enhances neuroprotection against Alzheimer’s disease through multiple molecular mechanisms. Mol Psychiatry 19(11):1243–1250. https://doi.org/10.1038/mp.2013.194
Coughlan G, Laczó J, Hort J, Minihane AM, Hornberger M (2018) Spatial navigation deficits—overlooked cognitive marker for preclinical Alzheimer disease? Nat Rev Neurol 14(8):496–506. https://doi.org/10.1038/s41582-018-0031-x
Donini M, Monteiro JM, Pontil M, Hahn T, Fallgatter AJ, Shawe-Taylor J, Mourão-Miranda J (2019) Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important. Neuroimage. https://doi.org/10.1016/j.neuroimage.2019.01.053
Doty RL, Kamath V (2014) The influences of age on olfaction: a review. Front Psychol. https://doi.org/10.3389/FPSYG.2014.00020
Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W (2005) BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21(16):3439–3440. https://doi.org/10.1093/bioinformatics/bti525
Dutar P, Bassant MH, Senut MC, Lamour Y (1995) The septohippocampal pathway: structure and function of a central cholinergic system. Physiol Rev 75(2):393–427. https://doi.org/10.1152/physrev.1995.75.2.393
Eichenbaum H (2017) Prefrontal–hippocampal interactions in episodic memory. Nat Rev Neurosci 18(9):547–558. https://doi.org/10.1038/nrn.2017.74
Fanselow MS, Dong HW (2010) Are the dorsal and ventral hippocampus functionally distinct structures? Neuron 65(1):7–19. https://doi.org/10.1016/J.NEURON.2009.11.031
Fjell AM, McEvoy L, Holland D, Dale AM, Walhovd KB (2014) What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Prog Neurobiol 117:20–40. https://doi.org/10.1016/J.PNEUROBIO.2014.02.004
Flurkey K, Currer JM, Harrison DE (2007) Mouse models in aging research. Mouse Biomed Res 3:637–672. https://doi.org/10.1016/B978-012369454-6/50074-1
Fornito A, Arnatkeviciute A, Fulcher BD (2019) Bridging the gap between connectome and transcriptome. Trends Cogn Sci 23(1):34–50. https://doi.org/10.1016/j.tics.2018.10.005
Friston KJ, Worsley KJ, Frackowiak RSJ, Mazziotta JC, Evans AC (1994) Assessing the significance of focal activations using their spatial extent. Hum Brain Mapp 1(3):210–220
Gao H, Ji S (2022) Graph U-Nets. IEEE Trans Pattern Anal Mach Intell 44(9):4948–4960. https://doi.org/10.1109/TPAMI.2021.3081010
Garyfallidis E, Brett M, Amirbekian B, Rokem A, Van Der Walt S, Descoteaux M, Nimmo-Smith I (2014) Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform. https://doi.org/10.3389/fninf.2014.00008
Gaweska H, Fitzpatrick PF (2011) Structures and mechanism of the monoamine oxidase family. Biomol Concepts 2(5):365–365. https://doi.org/10.1515/BMC.2011.030
Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage. https://doi.org/10.1006/nimg.2001.0786
Grothe MJ, Sepulcre J, Gonzalez-Escamilla G, Jelistratova I, Scholl M, Hansson O, Teipel SJ, Alzheimer’s Disease Neuroimaging, I. (2018) Molecular properties underlying regional vulnerability to Alzheimer’s disease pathology. Brain 141(9):2755–2771. https://doi.org/10.1093/brain/awy189
Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere ML, Pahwa JS, Moskvina V, Dowzell K, Williams A, Jones N, Thomas C, Stretton A, Morgan AR, Lovestone S, Powell J, Proitsi P, Lupton MK, Brayne C, Williams J (2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet 41(10):1088–1093. https://doi.org/10.1038/ng.440
Harris SE, Riggio V, Evenden L, Gilchrist T, McCafferty S, Murphy L, Wrobel N, Taylor AM, Corley J, Pattie A, Cox SR, Martin-Ruiz C, Prendergast J, Starr JM, Marioni RE, Deary IJ (2017) Age-related gene expression changes and transcriptome wide association study of physical and cognitive aging traits in the Lothian Birth Cohort 1936. Aging. https://doi.org/10.18632/aging.101333
Irons JL, Hodge-Hanson K, Downs DM (2020) RidA proteins protect against metabolic damage by reactive intermediates. Microbiol Mol Biol Rev MMBR. https://doi.org/10.1128/MMBR.00024-20
Karch CM, Goate AM (2015) Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol Psychiatry 77(1):43–51. https://doi.org/10.1016/j.biopsych.2014.05.006
Karch CM, Cruchaga C, Goate AM (2014) Alzheimer’s disease genetics: from the bench to the clinic. Neuron 83(1):11–26. https://doi.org/10.1016/j.neuron.2014.05.041
Kawajiri K, Fujii-Kuriyama Y (2017) The aryl hydrocarbon receptor: a multifunctional chemical sensor for host defense and homeostatic maintenance. Exp Anim 66(2):75–75. https://doi.org/10.1538/EXPANIM.16-0092
Kim H, Devanand DP, Carlson S, Goldberg TE (2022) Apolipoprotein E Genotype e2: neuroprotection and its limits. Front Aging Neurosci. https://doi.org/10.3389/fnagi.2022.919712
Knierim JJ, Neunuebel JP, Deshmukh SS (2013) Functional correlates of the lateral and medial entorhinal cortex: objects, path integration and local-global reference frames. Philos Trans R Soc Lond Series B Biol Sci. https://doi.org/10.1098/RSTB.2013.0369
Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, Jun G, DeStefano AL, Bis JC, Beecham GW, Grenier-Boley B, Russo G, Thornton-Wells TA, Jones N, Smith AV, Chouraki V, Thomas C, Ikram MA, Zelenika D, Seshadri S (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 45(12):1452–1458. https://doi.org/10.1038/ng.2802
Lee J, Wolfgang MJ (2012) Metabolomic profiling reveals a role for CPT1c in neuronal oxidative metabolism. BMC Biochem 13(1):23. https://doi.org/10.1186/1471-2091-13-23
Leech R, Sharp DJ (2014) The role of the posterior cingulate cortex in cognition and disease. Brain 137(1):12–32. https://doi.org/10.1093/brain/awt162
Li Z, Shue F, Zhao N, Shinohara M, Bu G (2020) APOE2: protective mechanism and therapeutic implications for Alzheimer’s disease. Mol Neurodegener 15(1):63. https://doi.org/10.1186/s13024-020-00413-4
Li X, Zhou Y, Dvornek N, Zhang M, Gao S, Zhuang J, Scheinost D, Staib LH, Ventola P, Duncan JS (2021) BrainGNN: interpretable brain graph neural network for fMRI analysis. Med Image Anal 74:102233. https://doi.org/10.1016/j.media.2021.102233
Lim Y, Ku NO (2021) Revealing the roles of keratin 8/18-associated signaling proteins involved in the development of hepatocellular carcinoma. Int J Mol Sci. https://doi.org/10.3390/IJMS22126401
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. https://doi.org/10.1186/s13059-014-0550-8
Mahzarnia A, Stout JA, Anderson RJ, Moon HS, Yar Han Z, Beck K, Browndyke JN, Dunson DB, Johnson KG, O’Brien RJ, Badea A (2022) Identifying vulnerable brain networks associated with Alzheimer’s disease risk. Cereb Cortex. https://doi.org/10.1093/cercor/bhac419
Maren S (1999) Neurotoxic or electrolytic lesions of the ventral subiculum produce deficits in the acquisition and expression of Pavlovian fear conditioning in rats. Behav Neurosci 113(2):283–290. https://doi.org/10.1037//0735-7044.113.2.283
Markello RD, Shafiei G, Tremblay C, Postuma RB, Dagher A, Misic B (2021) Multimodal phenotypic axes of Parkinson’s disease. Npj Parkinson’s Disease 7(1):6. https://doi.org/10.1038/s41531-020-00144-9
Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Groop LC (2003) PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34(3):267–273. https://doi.org/10.1038/ng1180
Mulkearns EE, Cooper JA (2012) FCH domain only-2 organizes clathrin-coated structures and interacts with disabled-2 for low-density lipoprotein receptor endocytosis. Mol Biol Cell 23(7):1330–1330. https://doi.org/10.1091/MBC.E11-09-0812
Neuner SM, Tcw J, Goate AM (2020) Genetic architecture of Alzheimer’s disease. Neurobiol Dis 143:104976. https://doi.org/10.1016/j.nbd.2020.104976
Niculescu AB, Le-Niculescu H, Roseberry K, Wang S, Hart J, Kaur A, Robertson H, Jones T, Strasburger A, Williams A, Kurian SM, Lamb B, Shekhar A, Lahiri DK, Saykin AJ (2020) Blood biomarkers for memory: toward early detection of risk for Alzheimer disease, pharmacogenomics, and repurposed drugs. Mol Psychiatry 25(8):1651–1672. https://doi.org/10.1038/s41380-019-0602-2
Obeso JA, Stamelou M, Goetz CG, Poewe W, Lang AE, Weintraub D, Burn D, Halliday GM, Bezard E, Przedborski S, Lehericy S, Brooks DJ, Rothwell JC, Hallett M, DeLong MR, Marras C, Tanner CM, Ross GW, Langston JW, Stoessl AJ (2017) Past, present, and future of Parkinson’s disease: a special essay on the 200th Anniversary of the Shaking Palsy. Mov Disord off J Mov Disord Soc 32(9):1264–1310. https://doi.org/10.1002/MDS.27115
Özarslan E, Koay CG, Shepherd TM, Komlosh ME, İrfanoğlu MO, Pierpaoli C, Basser PJ (2013) Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure. Neuroimage 78:16–32. https://doi.org/10.1016/j.neuroimage.2013.04.016
Panitch R, Hu J, Xia W, Bennett DA, Stein TD, Farrer LA, Jun GR (2022) Blood and brain transcriptome analysis reveals APOE genotype-mediated and immune-related pathways involved in Alzheimer disease. Alzheimer’s Res Ther 14(1):30–30. https://doi.org/10.1186/s13195-022-00975-z
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14(4):417–419. https://doi.org/10.1038/nmeth.4197
Pomaznoy M, Ha B, Peters B (2018) GOnet: a tool for interactive gene ontology analysis. BMC Bioinformatics 19(1):470. https://doi.org/10.1186/s12859-018-2533-3
Raz N, Rodrigue KM (2006) Differential aging of the brain: Patterns, cognitive correlates and modifiers. Neurosci Biobehav Rev 30(6):730–730. https://doi.org/10.1016/J.NEUBIOREV.2006.07.001
Reuber BE, Germain-Lee E, Collins CS, Morrell JC, Ameritunga R, Moser HW, Valle D, Gould SJ (1997) Mutations in PEX1 are the most common cause of peroxisome biogenesis disorders. Nat Genet 17(4):445–448. https://doi.org/10.1038/ng1297-445
Rolls ET (2019) The orbitofrontal cortex and emotion in health and disease, including depression. Neuropsychologia 128:14–43. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2017.09.021
Rolls ET, Kesner RP (2006) A computational theory of hippocampal function, and empirical tests of the theory. Prog Neurobiol 79(1):1–48. https://doi.org/10.1016/J.PNEUROBIO.2006.04.005
Romero-Molina C, Garretti F, Andrews SJ, Marcora E, Goate AM (2022) Microglial efferocytosis: diving into the Alzheimer’s disease gene pool. Neuron 110(21):3513–3533. https://doi.org/10.1016/j.neuron.2022.10.015
Savage SR, Shi Z, Liao Y, Zhang B (2019) Graph algorithms for condensing and consolidating gene set analysis results. Mol Cell Proteomics. https://doi.org/10.1074/mcp.TIR118.001263
Saykin AJ, Shen L, Yao X, Kim S, Nho K, Risacher SL, Ramanan VK, Foroud TM, Faber KM, Sarwar N, Munsie LM, Hu X, Soares HD, Potkin SG, Thompson PM, Kauwe JS, Kaddurah-Daouk R, Green RC, Toga AW, Alzheimer’s Disease Neuroimaging, I. (2015) Genetic studies of quantitative MCI and AD phenotypes in ADNI: progress, opportunities, and plans. Alzheimers Dement 11(7):792–814. https://doi.org/10.1016/j.jalz.2015.05.009
Shavlakadze T, Morris M, Fang J, Wang SX, Zhu J, Zhou W, Tse HW, Mondragon-Gonzalez R, Roma G, Glass DJ (2019) Age-related gene expression signature in rats demonstrate early, late, and linear transcriptional changes from multiple tissues. Cell Rep. https://doi.org/10.1016/j.celrep.2019.08.043
Sheehan TP, Chambers RA, Russell DS (2004) Regulation of affect by the lateral septum: implications for neuropsychiatry. Brain Res Rev 46(1):71–117. https://doi.org/10.1016/J.BRAINRESREV.2004.04.009
Smith JB, Alloway KD (2010) Functional specificity of claustrum connections in the rat: interhemispheric communication between specific parts of motor cortex. J Neurosci 30(50):16832–16832. https://doi.org/10.1523/JNEUROSCI.4438-10.2010
Sobue A, Komine O, Hara Y, Endo F, Mizoguchi H, Watanabe S, Murayama S, Saito T, Saido TC, Sahara N, Higuchi M, Ogi T, Yamanaka K (2021) Microglial gene signature reveals loss of homeostatic microglia associated with neurodegeneration of Alzheimer’s disease. Acta Neuropathol Commun 9(1):1. https://doi.org/10.1186/s40478-020-01099-x
Stoodley CJ, Schmahmann JD (2010) Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex J Devoted Study Nerv Syst Behav 46(7):831–844. https://doi.org/10.1016/J.CORTEX.2009.11.008
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci 102(43):15545. https://doi.org/10.1073/pnas.0506580102
Sullivan PM, Mezdour H, Quarfordt SH, Maeda N (1998a) Type III hyperlipoproteinemia and spontaneous atherosclerosis in mice resulting from gene replacement of mouse Apoe with human Apoe*2. J Clin Invest 102(1):130–135. https://doi.org/10.1172/jci2673
Sullivan PM, Mezdour H, Quarfordt SH, Maeda N (1998b) Type III hyperlipoproteinemia and spontaneous atherosclerosis in mice resulting from gene replacement of mouse Apoe with human Apoe* 2. J Clin Investig 102(1):130–135
Timonidis N, Llera A, Tiesinga PHE (2021) Uncovering statistical links between gene expression and structural connectivity patterns in the mouse brain. Neuroinformatics 19(4):649–667. https://doi.org/10.1007/s12021-021-09511-0
Tournier JD, Calamante F, Connelly A (2012) MRtrix: Diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22(1):53–66. https://doi.org/10.1002/IMA.22005
Tournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, Christiaens D, Jeurissen B, Yeh CH, Connelly A (2019) MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202:116137. https://doi.org/10.1016/j.neuroimage.2019.116137
Turato C, Pontisso P (2015) SERPINB3 (serpin peptidase inhibitor, clade B (ovalbumin), member 3). Atlas Genet Cytogenet Oncol Haematol 19(3):202–202. https://doi.org/10.4267/2042/56413
Uecker M, Ong F, Tamir JI, Bahri DVP, Cheng JY, Zhang T, Lustig M (2015) Berkeley advanced reconstruction toolbox. Annual Meeting ISMRM ISMRM, Toronto
Wang N, Anderson RJ, Badea A, Cofer G, Dibb R, Qi Y, Johnson GA (2018) Whole mouse brain structural connectomics using magnetic resonance histology. Brain Struct Funct 223(9):4323–4335. https://doi.org/10.1007/s00429-018-1750-x
Watson Y, Nelson B, Kluesner JH, Tanzy C, Ramesh S, Patel Z, Kluesner KH, Singh A, Murthy V, Mitchell CS (2021) Aggregate trends of apolipoprotein E on cognition in transgenic Alzheimer’s disease mice. J Alzheimers Dis 83:435–450. https://doi.org/10.3233/JAD-210492
Witten DM, Tibshirani RJ (2009) Extensions of sparse canonical correlation analysis with applications to genomic data. Stat Appl Genet Mol Biol. https://doi.org/10.2202/1544-6115.1470
Xie T, Pan S, Zheng H, Luo Z, Tembo KM, Jamal M, Yu Z, Yu Y, Xia J, Yin Q, Wang M, Yuan W, Zhang Q, Xiong J (2018) PEG10 as an oncogene: expression regulatory mechanisms and role in tumor progression. Cancer Cell Int 18(1):1–10. https://doi.org/10.1186/S12935-018-0610-3/FIGURES/3
Yang W, Han B, Chen Y, Geng F (2022) SAAL1, a novel oncogene, is associated with prognosis and immunotherapy in multiple types of cancer. Aging (albany NY) 14(15):6316–6316. https://doi.org/10.18632/AGING.204224
Zhao N, Ren Y, Yamazaki Y, Qiao W, Li F, Felton LM, Mahmoudiandehkordi S, Kueider-Paisley A, Sonoustoun B, Arnold M, Shue F, Zheng J, Attrebi ON, Martens YA, Li Z, Bastea L, Meneses AD, Chen K, Thompson JW, Bu G (2020) Alzheimer’s risk factors age, APOE genotype, and sex drive distinct molecular pathways. Neuron 106(5):727-742 e726
Acknowledgements
The authors are grateful to Dr. Nobuyo Maeda for the initial mouse donations, and to NIH and the Bass Connections program for supporting our research.
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This work was supported by National Institutes of Health (RF1 AG057895, R01 AG066184, U24 CA220245, RF1 AG070149, and P30 AG072958).
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Statistical analysis was conducted by HSM, AM, MS, DC, AB, MWL; computation by HSM, AM, MS, DC, AN, JAS, RJA, AMF; data acquisition by HSM, JTT, ZYH, AMF, JK, AB; writing and editing by HSM, AM, MWL, AB; supervision by AAK, MWL, AB; and conceptualization and funding acquisition by MWL, AB. All authors read and approved the final manuscript.
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Moon, H.S., Mahzarnia, A., Stout, J. et al. Multivariate investigation of aging in mouse models expressing the Alzheimer’s protective APOE2 allele: integrating cognitive metrics, brain imaging, and blood transcriptomics. Brain Struct Funct 229, 231–249 (2024). https://doi.org/10.1007/s00429-023-02731-x
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DOI: https://doi.org/10.1007/s00429-023-02731-x