{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T01:58:40Z","timestamp":1789437520760,"version":"build-2803163510"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Cancer Institute","award":["P30CA033572"],"award-info":[{"award-number":["P30CA033572"]}]},{"name":"National Cancer Institute","award":["P30CA033572"],"award-info":[{"award-number":["P30CA033572"]}]},{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"crossref","award":["R01LM013138"],"award-info":[{"award-number":["R01LM013138"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["AG069008"],"award-info":[{"award-number":["AG069008"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>While there are currently over 40 replicated genes with mapped risk alleles for Late Onset Alzheimer\u2019s disease (LOAD), the <jats:italic>Apolipoprotein<\/jats:italic> E locus E4 haplotype is still the biggest driver of risk, with odds ratios for neuropathologically confirmed E44 carriers exceeding 30 (95% confidence interval 16.59\u201358.75). We sought to address whether the <jats:italic>APOE<\/jats:italic> E4 haplotype modifies expression globally through networks of expression to increase LOAD risk. We have used the Human Brainome data to build expression networks comparing <jats:italic>APOE<\/jats:italic> E4 carriers to non-carriers using scalable mixed-datatypes Bayesian network (BN) modeling. We have found that <jats:italic>VGF<\/jats:italic> had the greatest explanatory weight. High expression of <jats:italic>VGF<\/jats:italic> is a protective signal, even on the background of <jats:italic>APOE<\/jats:italic> E4 alleles. LOAD risk signals, considering an APOE background, include high levels of <jats:italic>SPECC1L<\/jats:italic>, <jats:italic>HLA-DRA<\/jats:italic> and <jats:italic>RANBP3L<\/jats:italic>. Our findings nominate several new transcripts, taking a combined approach to network building including known LOAD risk loci.<\/jats:p>","DOI":"10.1038\/s41598-024-65010-7","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T20:28:31Z","timestamp":1719606511000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Changes in expression of VGF, SPECC1L, HLA-DRA and RANBP3L act with APOE E4 to alter risk for late onset Alzheimer\u2019s disease"],"prefix":"10.1038","volume":"14","author":[{"given":"Sergio","family":"Branciamore","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grigoriy","family":"Gogoshin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei S.","family":"Rodin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amanda J.","family":"Myers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"65010_CR1","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1126\/science.8346443","volume":"261","author":"EH Corder","year":"1993","unstructured":"Corder, E. H. et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer\u2019s disease in late onset families. Science 261, 921\u2013923 (1993).","journal-title":"Science"},{"key":"65010_CR2","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1038\/s41467-019-14279-8","volume":"11","author":"EM Reiman","year":"2020","unstructured":"Reiman, E. M. et al. Exceptionally low likelihood of Alzheimer\u2019s dementia in APOE2 homozygotes from a 5000-person neuropathological study. Nat. Commun. 11, 667 (2020).","journal-title":"Nat. Commun."},{"key":"65010_CR3","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1016\/j.neuron.2007.05.022","volume":"54","author":"EM Reiman","year":"2007","unstructured":"Reiman, E. M. et al. GAB2 alleles modify Alzheimer\u2019s risk in APOE epsilon4 carriers. Neuron 54, 713\u2013720 (2007).","journal-title":"Neuron"},{"key":"65010_CR4","doi-asserted-by":"publisher","first-page":"1494","DOI":"10.1038\/ng.2007.16","volume":"39","author":"AJ Myers","year":"2007","unstructured":"Myers, A. J. et al. A survey of genetic human cortical gene expression. Nat Genet 39, 1494\u20131499 (2007).","journal-title":"Nat Genet"},{"key":"65010_CR5","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awy215","author":"VA Petyuk","year":"2018","unstructured":"Petyuk, V. A. et al. The human brainome: network analysis identifies HSPA2 as a novel Alzheimer\u2019s disease target. Brain https:\/\/doi.org\/10.1093\/brain\/awy215 (2018).","journal-title":"Brain"},{"key":"65010_CR6","doi-asserted-by":"publisher","first-page":"2128","DOI":"10.1021\/pr301146m","volume":"12","author":"PD Piehowski","year":"2013","unstructured":"Piehowski, P. D. et al. Sources of technical variability in quantitative LC-MS proteomics: Human brain tissue sample analysis. J. Proteome Res. 12, 2128\u20132137 (2013).","journal-title":"J. Proteome Res."},{"key":"65010_CR7","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1016\/j.ajhg.2009.03.011","volume":"84","author":"JA Webster","year":"2009","unstructured":"Webster, J. A. et al. Genetic control of human brain transcript expression in Alzheimer disease. Am. J. Hum. Genet. 84, 445\u2013458 (2009).","journal-title":"Am. J. Hum. Genet."},{"key":"65010_CR8","doi-asserted-by":"crossref","unstructured":"Pearl, J. Probabilistic reasoning in intelligent systems: Networks of plausible inference, (Morgan Kaufmann, 1988).","DOI":"10.1016\/B978-0-08-051489-5.50008-4"},{"key":"65010_CR9","doi-asserted-by":"crossref","unstructured":"McInnes, L. & Healy, J. UMAP: Uniform manifold approximation and projection for dimension reduction. ArXiv e-prints 1802.03426(2018).","DOI":"10.21105\/joss.00861"},{"key":"65010_CR10","first-page":"2579","volume":"9","author":"LJP van der Maaten","year":"2008","unstructured":"van der Maaten, L. J. P. & Hinton, G. E. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008).","journal-title":"J. Mach. Learn. Res."},{"key":"65010_CR11","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1089\/cmb.2016.0100","volume":"24","author":"G Gogoshin","year":"2017","unstructured":"Gogoshin, G., Boerwinkle, E. & Rodin, A. S. New Algorithm and software (BNOmics) for inferring and visualizing Bayesian networks from heterogeneous big biological and genetic data. J. Comput. Biol. 24, 340\u2013356 (2017).","journal-title":"J. Comput. Biol."},{"key":"65010_CR12","doi-asserted-by":"publisher","first-page":"8603","DOI":"10.3934\/mbe.2021426","volume":"18","author":"G Gogoshin","year":"2021","unstructured":"Gogoshin, G., Branciamore, S. & Rodin, A. S. Synthetic data generation with probabilistic Bayesian networks. Math. Biosci. Eng. 18, 8603\u20138621 (2021).","journal-title":"Math. Biosci. Eng."},{"key":"65010_CR13","doi-asserted-by":"publisher","first-page":"648","DOI":"10.3389\/fgene.2020.00648","volume":"11","author":"X Wang","year":"2020","unstructured":"Wang, X., Branciamore, S., Gogoshin, G., Ding, S. & Rodin, A. S. New analysis framework incorporating mixed mutual information and scalable Bayesian networks for multimodal high dimensional genomic and epigenomic cancer data. Front. Genet. 11, 648 (2020).","journal-title":"Front. Genet."},{"key":"65010_CR14","doi-asserted-by":"publisher","first-page":"3295","DOI":"10.1093\/hmg\/ddq221","volume":"19","author":"JJ Corneveaux","year":"2010","unstructured":"Corneveaux, J. J. et al. Association of CR1, CLU and PICALM with Alzheimer\u2019s disease in a cohort of clinically characterized and neuropathologically verified individuals. Hum. Mol. Genet. 19, 3295\u20133301 (2010).","journal-title":"Hum. Mol. Genet."},{"key":"65010_CR15","first-page":"270","volume":"18","author":"DL Beekly","year":"2004","unstructured":"Beekly, D. L. et al. The national Alzheimer\u2019s coordinating center (NACC) database: An Alzheimer disease database. Alzheimer Dis. Assoc. Disord 18, 270\u2013277 (2004).","journal-title":"Alzheimer Dis. Assoc. Disord"},{"key":"65010_CR16","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/0197-4580(95)00021-6","volume":"16","author":"H Braak","year":"1995","unstructured":"Braak, H. & Braak, E. Staging of Alzheimer\u2019s disease-related neurofibrillary changes. Neurobiol. Aging 16, 271\u2013278 (1995).","journal-title":"Neurobiol. Aging"},{"key":"65010_CR17","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1002\/ana.23654","volume":"72","author":"DA Bennett","year":"2012","unstructured":"Bennett, D. A., Wilson, R. S., Boyle, P. A., Buchman, A. S. & Schneider, J. A. Relation of neuropathology to cognition in persons without cognitive impairment. Ann. Neurol. 72, 599\u2013609 (2012).","journal-title":"Ann. Neurol."},{"key":"65010_CR18","doi-asserted-by":"publisher","first-page":"646","DOI":"10.2174\/156720512801322663","volume":"9","author":"DA Bennett","year":"2012","unstructured":"Bennett, D. A. et al. Overview and findings from the rush Memory and Aging Project. Curr. Alzheimer Res. 9, 646\u2013663 (2012).","journal-title":"Curr. Alzheimer Res."},{"key":"65010_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.0020190","volume":"2","author":"N Patterson","year":"2006","unstructured":"Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).","journal-title":"PLoS Genet."},{"key":"65010_CR20","doi-asserted-by":"publisher","first-page":"2316","DOI":"10.3390\/ijms22052316","volume":"22","author":"AS Rodin","year":"2021","unstructured":"Rodin, A. S. et al. Dissecting response to cancer immunotherapy by applying Bayesian network analysis to flow cytometry data. Int. J. Mol. Sci. 22, 2316 (2021).","journal-title":"Int. J. Mol. Sci."},{"key":"65010_CR21","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1186\/s12859-020-03892-w","volume":"21","author":"L de Torrente","year":"2020","unstructured":"de Torrente, L. et al. The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data. BMC Bioinf. 21, 562 (2020).","journal-title":"BMC Bioinf."},{"key":"65010_CR22","doi-asserted-by":"publisher","first-page":"3167","DOI":"10.21105\/joss.03167","volume":"6","author":"I Patil","year":"2021","unstructured":"Patil, I. Visualizations with statistical details: The \u2018ggstatsplot\u2019 approach. J. Open Sour. Softw. 6, 3167 (2021).","journal-title":"J. Open Sour. Softw."},{"key":"65010_CR23","unstructured":"Gao, W., Kannan, S., Oh, S. & Viswanath, P. Estimating mutual information for discrete-continuous mixtures. ArXiv [Preprint]. Available online at: https:\/\/arxiv.org\/abs\/1709.06212 (2018)."},{"key":"65010_CR24","doi-asserted-by":"publisher","first-page":"3942","DOI":"10.1038\/s41467-020-17405-z","volume":"11","author":"ND Beckmann","year":"2020","unstructured":"Beckmann, N. D. et al. Multiscale causal networks identify VGF as a key regulator of Alzheimer\u2019s disease. Nat. Commun. 11, 3942 (2020).","journal-title":"Nat. Commun."},{"key":"65010_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1515\/ijb-2020-0133","volume":"18","author":"Y Bu","year":"2021","unstructured":"Bu, Y. & Lederer, J. Integrating additional knowledge into the estimation of graphical models. Int. J. Biostat. 18, 1\u201317 (2021).","journal-title":"Int. J. Biostat."},{"key":"65010_CR26","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1186\/s13195-020-00733-z","volume":"12","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Wang, J., Liu, X. & Liu, H. Exploring the role of RALYL in Alzheimer\u2019s disease reserve by network-based approaches. Alzheimers Res. Ther. 12, 165 (2020).","journal-title":"Alzheimers Res. Ther."},{"key":"65010_CR27","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.3389\/fgene.2019.01196","volume":"10","author":"L Wang","year":"2019","unstructured":"Wang, L., Audenaert, P. & Michoel, T. High-dimensional bayesian network inference from systems genetics data using genetic node ordering. Front. Genet. 10, 1196 (2019).","journal-title":"Front. Genet."},{"key":"65010_CR28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004120","volume":"11","author":"SD Ghiassian","year":"2015","unstructured":"Ghiassian, S. D., Menche, J. & Barabasi, A. L. A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. PLoS Comput. Biol. 11, e1004120 (2015).","journal-title":"PLoS Comput. Biol."},{"key":"65010_CR29","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1186\/s12967-023-04516-6","volume":"21","author":"LY Fan","year":"2023","unstructured":"Fan, L. Y. et al. Integrating single-nucleus sequence profiling to reveal the transcriptional dynamics of Alzheimer\u2019s disease, Parkinson\u2019s disease, and multiple sclerosis. J. Transl. Med. 21, 649 (2023).","journal-title":"J. Transl. Med."},{"key":"65010_CR30","doi-asserted-by":"publisher","first-page":"16846","DOI":"10.1038\/s41598-022-20404-3","volume":"12","author":"F Conte","year":"2022","unstructured":"Conte, F. & Paci, P. Alzheimer\u2019s disease: insights from a network medicine perspective. Sci. Rep. 12, 16846 (2022).","journal-title":"Sci. Rep."},{"key":"65010_CR31","doi-asserted-by":"publisher","first-page":"1358998","DOI":"10.3389\/fnins.2024.1358998","volume":"18","author":"Z Zhang","year":"2024","unstructured":"Zhang, Z. et al. A review and analysis of key biomarkers in Alzheimer\u2019s disease. Front. Neurosci. 18, 1358998 (2024).","journal-title":"Front. Neurosci."},{"key":"65010_CR32","doi-asserted-by":"publisher","first-page":"5369","DOI":"10.1038\/s41467-021-25680-7","volume":"12","author":"N Beebe-Wang","year":"2021","unstructured":"Beebe-Wang, N. et al. Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer\u2019s disease neuropathologies. Nat. Commun. 12, 5369 (2021).","journal-title":"Nat. Commun."},{"key":"65010_CR33","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1186\/s12920-023-01675-9","volume":"16","author":"S Jemimah","year":"2023","unstructured":"Jemimah, S. & AlShehhi, A. c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer\u2019s disease. BMC Med. Genom. 16, 244 (2023).","journal-title":"BMC Med. Genom."},{"key":"65010_CR34","doi-asserted-by":"publisher","first-page":"1486","DOI":"10.1002\/pmic.200300470","volume":"3","author":"O Carrette","year":"2003","unstructured":"Carrette, O. et al. A panel of cerebrospinal fluid potential biomarkers for the diagnosis of Alzheimer\u2019s disease. Proteomics 3, 1486\u20131494 (2003).","journal-title":"Proteomics"},{"key":"65010_CR35","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1097\/WAD.0000000000000328","volume":"33","author":"DA Llano","year":"2019","unstructured":"Llano, D. A., Devanarayan, P. & Devanarayan, V. VGF in cerebrospinal fluid combined with conventional biomarkers enhances prediction of conversion from MCI to AD. Alzheimer Dis. Assoc. Disord. 33, 307\u2013314 (2019).","journal-title":"Alzheimer Dis. Assoc. Disord."},{"key":"65010_CR36","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1186\/s40035-021-00263-0","volume":"10","author":"N Barranco","year":"2021","unstructured":"Barranco, N. et al. Dense core vesicle markers in CSF and cortical tissues of patients with Alzheimer\u2019s disease. Transl. Neurodegener 10, 37 (2021).","journal-title":"Transl. Neurodegener"},{"key":"65010_CR37","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1046\/j.1471-4159.2002.00842.x","volume":"81","author":"E Trani","year":"2002","unstructured":"Trani, E. et al. Isolation and characterization of VGF peptides in rat brain Role of PC1\/3 and PC2 in the maturation of VGF precursor. J. Neurochem. 81, 565\u2013574 (2002).","journal-title":"J. Neurochem."},{"key":"65010_CR38","doi-asserted-by":"publisher","DOI":"10.1097\/PR9.0000000000000786","volume":"4","author":"N Soliman","year":"2019","unstructured":"Soliman, N., Okuse, K. & Rice, A. S. C. VGF: a biomarker and potential target for the treatment of neuropathic pain?. Pain Rep. 4, e786 (2019).","journal-title":"Pain Rep."},{"key":"65010_CR39","doi-asserted-by":"publisher","first-page":"2244","DOI":"10.1210\/endo.136.5.7720674","volume":"136","author":"GL Ferri","year":"1995","unstructured":"Ferri, G. L. et al. The \u201cVGF\u201d protein in rat adenohypophysis: sex differences and changes during the estrous cycle and after gonadectomy. Endocrinology 136, 2244\u20132251 (1995).","journal-title":"Endocrinology"},{"key":"65010_CR40","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/1472-6793-9-19","volume":"9","author":"E Watson","year":"2009","unstructured":"Watson, E. et al. Analysis of knockout mice suggests a role for VGF in the control of fat storage and energy expenditure. BMC Physiol. 9, 19 (2009).","journal-title":"BMC Physiol."},{"key":"65010_CR41","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1002\/(SICI)1096-9861(19970210)378:2<229::AID-CNE6>3.0.CO;2-5","volume":"378","author":"JP Wisor","year":"1997","unstructured":"Wisor, J. P. & Takahashi, J. S. Regulation of the vgf gene in the golden hamster suprachiasmatic nucleus by light and by the circadian clock. J. Comp. Neurol. 378, 229\u2013238 (1997).","journal-title":"J. Comp. Neurol."},{"key":"65010_CR42","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1210\/er.2010-0027","volume":"32","author":"A Bartolomucci","year":"2011","unstructured":"Bartolomucci, A. et al. The extended granin family: structure, function, and biomedical implications. Endocr. Rev. 32, 755\u2013797 (2011).","journal-title":"Endocr. Rev."},{"key":"65010_CR43","first-page":"398","volume":"45","author":"C Haas","year":"1995","unstructured":"Haas, C., Hung, A. Y., Citron, M., Teplow, D. B. & Selkoe, D. J. beta-Amyloid, protein processing and Alzheimer\u2019s disease. Arzneimittelforschung 45, 398\u2013402 (1995).","journal-title":"Arzneimittelforschung"},{"key":"65010_CR44","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nrn2620","volume":"10","author":"G Bu","year":"2009","unstructured":"Bu, G. Apolipoprotein E and its receptors in Alzheimer\u2019s disease: pathways, pathogenesis and therapy. Nat. Rev. Neurosci. 10, 333\u2013344 (2009).","journal-title":"Nat. Rev. Neurosci."},{"key":"65010_CR45","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ajhg.2011.05.023","volume":"89","author":"I Saadi","year":"2011","unstructured":"Saadi, I. et al. Deficiency of the cytoskeletal protein SPECC1L leads to oblique facial clefting. Am. J. Hum. Genet. 89, 44\u201355 (2011).","journal-title":"Am. J. Hum. Genet."},{"key":"65010_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmg.2018.11.022","volume":"62","author":"EJ Bhoj","year":"2019","unstructured":"Bhoj, E. J. et al. Phenotypic spectrum associated with SPECC1L pathogenic variants: new families and critical review of the nosology of Teebi, Opitz GBBB, and Baraitser-Winter syndromes. Eur. J. Med. Genet. 62, 103588 (2019).","journal-title":"Eur. J. Med. Genet."},{"key":"65010_CR47","doi-asserted-by":"publisher","first-page":"17735","DOI":"10.1038\/srep17735","volume":"6","author":"NR Wilson","year":"2016","unstructured":"Wilson, N. R. et al. SPECC1L deficiency results in increased adherens junction stability and reduced cranial neural crest cell delamination. Sci. Rep. 6, 17735 (2016).","journal-title":"Sci. Rep."},{"key":"65010_CR48","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1001\/jamaneurol.2013.2152","volume":"70","author":"BV Zlokovic","year":"2013","unstructured":"Zlokovic, B. V. Cerebrovascular effects of apolipoprotein E: implications for Alzheimer disease. JAMA Neurol. 70, 440\u2013444 (2013).","journal-title":"JAMA Neurol."},{"key":"65010_CR49","first-page":"53","volume":"59","author":"PL McGeer","year":"2000","unstructured":"McGeer, P. L., McGeer, E. G. & Yasojima, K. Alzheimer disease and neuroinflammation. J. Neural Transm. Suppl. 59, 53\u201357 (2000).","journal-title":"J. Neural Transm. Suppl."},{"key":"65010_CR50","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neurobiolaging.2014.08.008","volume":"36","author":"G Lueg","year":"2015","unstructured":"Lueg, G. et al. Clinical relevance of specific T-cell activation in the blood and cerebrospinal fluid of patients with mild Alzheimer\u2019s disease. Neurobiol. Aging 36, 81\u201389 (2015).","journal-title":"Neurobiol. Aging"},{"key":"65010_CR51","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1038\/ng.2802","volume":"45","author":"JC Lambert","year":"2013","unstructured":"Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer\u2019s disease. Nat. Genet. 45, 1452\u20131458 (2013).","journal-title":"Nat. Genet."},{"key":"65010_CR52","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1038\/s41588-019-0358-2","volume":"51","author":"BW Kunkle","year":"2019","unstructured":"Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer\u2019s disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing. Nat. Genet. 51, 414\u2013430 (2019).","journal-title":"Nat. Genet."},{"key":"65010_CR53","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1007\/s12017-020-08612-4","volume":"22","author":"ZX Wang","year":"2020","unstructured":"Wang, Z. X., Wan, Q. & Xing, A. HLA in Alzheimer\u2019s disease: Genetic association and possible pathogenic roles. Neuromolecular Med. 22, 464\u2013473 (2020).","journal-title":"Neuromolecular Med."},{"key":"65010_CR54","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1016\/j.tins.2012.08.001","volume":"35","author":"BM Elmer","year":"2012","unstructured":"Elmer, B. M. & McAllister, A. K. Major histocompatibility complex class I proteins in brain development and plasticity. Trends Neurosci. 35, 660\u2013670 (2012).","journal-title":"Trends Neurosci."},{"key":"65010_CR55","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.ebiom.2018.08.026","volume":"35","author":"LM James","year":"2018","unstructured":"James, L. M. et al. The effects of human leukocyte antigen DRB1*13 and apolipoprotein E on age-related variability of synchronous neural interactions in healthy women. EBioMedicine 35, 288\u2013294 (2018).","journal-title":"EBioMedicine"},{"key":"65010_CR56","doi-asserted-by":"publisher","first-page":"263310552211148","DOI":"10.1177\/26331055221114817","volume":"17","author":"LM James","year":"2022","unstructured":"James, L. M. & Georgopoulos, A. P. At the root of 3 \u201clong\u201d diseases: Persistent antigens inflicting chronic damage on the brain and other organs in gulf war illness, long-COVID-19, and chronic fatigue syndrome. Neurosci. Insights 17, 26331055221114816 (2022).","journal-title":"Neurosci. Insights"},{"key":"65010_CR57","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1128\/MCB.00121-15","volume":"35","author":"F Chen","year":"2015","unstructured":"Chen, F. et al. Nuclear export of smads by RanBP3L regulates bone morphogenetic protein signaling and mesenchymal stem cell differentiation. Mol. Cell Biol. 35, 1700\u20131711 (2015).","journal-title":"Mol. Cell Biol."},{"key":"65010_CR58","doi-asserted-by":"crossref","first-page":"426","DOI":"10.3389\/fncel.2015.00426","volume":"9","author":"R von Bernhardi","year":"2015","unstructured":"von Bernhardi, R., Cornejo, F., Parada, G. E. & Eugenin, J. Role of TGFbeta signaling in the pathogenesis of Alzheimer\u2019s disease. Front. Cell. Neurosci. 9, 426 (2015).","journal-title":"Front. Cell. Neurosci."},{"key":"65010_CR59","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1186\/s13287-021-02399-2","volume":"12","author":"X Wu","year":"2021","unstructured":"Wu, X. et al. Photoactivation of TGFbeta\/SMAD signaling pathway ameliorates adult hippocampal neurogenesis in Alzheimer\u2019s disease model. Stem Cell Res. Ther. 12, 345 (2021).","journal-title":"Stem Cell Res. Ther."},{"key":"65010_CR60","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0053349","volume":"8","author":"D Mastroeni","year":"2013","unstructured":"Mastroeni, D. et al. Reduced RAN expression and disrupted transport between cytoplasm and nucleus; a key event in Alzheimer\u2019s disease pathophysiology. PLoS One 8, e53349 (2013).","journal-title":"PLoS One"},{"key":"65010_CR61","doi-asserted-by":"publisher","DOI":"10.1186\/s13024-023-00624-5","volume":"18","author":"L Guo","year":"2023","unstructured":"Guo, L. et al. Sex specific molecular networks and key drivers of Alzheimer's disease. Mol. Neurodegener 18, 39 (2023).","journal-title":"Mol. Neurodegener"},{"key":"65010_CR62","doi-asserted-by":"publisher","DOI":"10.3390\/cells11233880","volume":"11","author":"AL Pan","year":"2022","unstructured":"Pan, A. L. et al. Dual-specificity protein phosphatase 4 (DUSP4) overexpression improves learning behavior selectively in female 5xFAD mice, and reduces beta-amyloid load in males and females. Cells 11, 3880 (2022).","journal-title":"Cells"},{"key":"65010_CR63","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abb5398","volume":"7","author":"RA Neff","year":"2021","unstructured":"Neff, R. A. et al. Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets. Sci. Adv. 7, eabb5398 (2021).","journal-title":"Sci. Adv."},{"key":"65010_CR64","doi-asserted-by":"publisher","DOI":"10.1101\/2023.10.20.563319","author":"Q Wang","year":"2023","unstructured":"Wang, Q. et al. A public resource of single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease. bioRxiv. https:\/\/doi.org\/10.1101\/2023.10.20.563319 (2023).","journal-title":"bioRxiv"},{"key":"65010_CR65","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-023-01366-8","volume":"16","author":"SW Han","year":"2024","unstructured":"Han, S. W. et al. miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease. Alzheimers Res. Ther. 16, 5 (2024).","journal-title":"Alzheimers Res. Ther."},{"key":"65010_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1124\/molpharm.122.000641","volume":"104","author":"ML Duarte","year":"2023","unstructured":"Duarte, M. L. et al. Multiomics analyses identify proline endopeptidase-like protein as a key regulator of protein trafficking, a pathway underlying Alzheimer's disease pathogenesis. Mol. Pharmacol. 104, 1\u201316 (2023).","journal-title":"Mol. Pharmacol."},{"key":"65010_CR67","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1016\/j.biopsych.2022.06.020","volume":"93","author":"JP Kim","year":"2023","unstructured":"Kim, J. P. et al. Integrative co-methylation network analysis identifies novel DNA methylation signatures and their target genes in Alzheimer's disease. Biol. Psychiatry 93, 842\u2013851 (2023).","journal-title":"Biol. Psychiatry"},{"key":"65010_CR68","doi-asserted-by":"publisher","first-page":"3341","DOI":"10.1093\/hmg\/ddac124","volume":"31","author":"A Liu","year":"2022","unstructured":"Liu, A. et al. Identifying candidate genes and drug targets for Alzheimer's disease by an integrative network approach using genetic and brain region-specific proteomic data. Hum. Mol. Genet. 31, 3341\u20133354 (2022).","journal-title":"Hum. Mol. Genet."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-65010-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-65010-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-65010-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T20:30:26Z","timestamp":1719606626000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-65010-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":68,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["65010"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-65010-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,28]]},"assertion":[{"value":"28 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14954"}}