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miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer’s disease

Han, Sang-Won, Pyun, Jung-Min, Bice, Paula J., Bennett, David A., Saykin, Andrew J., Kim, Sang Yun, Park, Young Ho, Nho, Kwangsik·2024·Alzheimer's Research & Therapy
DOI 10.1186/s13195-023-01366-8PMC PMC10775662

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Abstract

Background: Alzheimer’s dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers. Methods: We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) ( N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset ( N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification. Results: Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs. Conclusions: Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

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Fig. 1 Schematic overview of the workflow of our analysis. AD Alzheimer’s dementia, MAP Memory and Aging Project, miRNAs microRNAs, mRNAs messenger RNAs, NCI no cognitive impairment, ROS Religious Orders Study, 3′-UTR 3′-untranslated region
Fig. 1 Schematic overview of the workflow of our analysis. AD Alzheimer’s dementia, MAP Memory and Aging Project, miRNAs microRNAs, mRNAs messenger RNAs, NCI no cognitive impairment, ROS Religious Orders Study, 3′-UTR 3′-untranslated regionCC BY · PubMed Central
Fig. 2 Heatmap of association analysis results of miRNAs with diagnosis and clinical and pathological traits. The numbers represent the logistic and linear regression coefficients of association between miRNAs and traits. Stars indicate significant associations with FDR-corrected p value < 0.05. Red colors represent positive correlations, while blue colors denote negative correlations. The darker the color, the stronger the association. AD Alzheimer’s dementia, CERAD Consortium to Establish a Registry for Alzheimer’s Disease, FDR false discovery rate, miRNAs microRNAs. Note: significance stars indicating the p values of the correlations adjusted for multiple comparisons. *P-value < 0.05. **P-value < 0.01. *** P-value < 0.001
Fig. 2 Heatmap of association analysis results of miRNAs with diagnosis and clinical and pathological traits. The numbers represent the logistic and linear regression coefficients of association between miRNAs and traits. Stars indicate significant associations with FDR-corrected p value < 0.05. Red colors represent positive correlations, while blue colors denote negative correlations. The darker the color, the stronger the association. AD Alzheimer’s dementia, CERAD Consortium to Establish a Registry for Alzheimer’s Disease, FDR false discovery rate, miRNAs microRNAs. Note: significance stars indicating the p values of the correlations adjusted for multiple comparisons. *P-value < 0.05. **P-value < 0.01. *** P-value < 0.001CC BY · PubMed Central
Fig. 3 Association analysis results of module eigengenes with diagnosis and clinical and pathological traits. A The numbers represent the logistic and linear regression coefficients of association between module eigengenes and traits. Stars indicate significant associations with FDR-corrected p value < 0.05. Red colors represent positive correlations, while blue colors denote negative correlations. The darker the color, the stronger the association. B Violin and box plots represent the M3 module eigengene values in the diagnosis, amyloidopathy, and tauopathy groups from the ROS/MAP cohort. C The x-axis represents years before the last clinical evaluation, while the y-axis indicates global cognitive Z scores. The two lines represent the different slopes of global cognitive changes of subjects with 1st quartile (green color) versus 4th quartile (red color) stratified by ME values of AD-associated M3 module. AD Alzheimer’s dementia, CERAD Consortium to Establish a Registry for Alzheimer’s Disease, FDR false discovery rate, MAP Memory and Aging Project, ME module eigengene, NCI no cognitive impairment, ROS Religious Orders Study. Note: significance stars indicating the p values of the correlations adjustment for multiple comparisons. *P-value < 0.05. **P-value < 0.01
Fig. 3 Association analysis results of module eigengenes with diagnosis and clinical and pathological traits. A The numbers represent the logistic and linear regression coefficients of association between module eigengenes and traits. Stars indicate significant associations with FDR-corrected p value < 0.05. Red colors represent positive correlations, while blue colors denote negative correlations. The darker the color, the stronger the association. B Violin and box plots represent the M3 module eigengene values in the diagnosis, amyloidopathy, and tauopathy groups from the ROS/MAP cohort. C The x-axis represents years before the last clinical evaluation, while the y-axis indicates global cognitive Z scores. The two lines represent the different slopes of global cognitive changes of subjects with 1st quartile (green color) versus 4th quartile (red color) stratified by ME values of AD-associated M3 module. AD Alzheimer’s dementia, CERAD Consortium to Establish a Registry for Alzheimer’s Disease, FDR false discovery rate, MAP Memory and Aging Project, ME module eigengene, NCI no cognitive impairment, ROS Religious Orders Study. Note: significance stars indicating the p values of the correlations adjustment for multiple comparisons. *P-value < 0.05. **P-value < 0.01CC BY · PubMed Central
Fig. 4 Association analysis results of top 10 candidate hub miRNAs with diagnosis and clinical and pathological traits. A The numbers represent the logistic and linear regression coefficients of association between miRNAs and traits. Stars indicate significant associations with FDR-corrected p value < 0.05. Red colors represent positive correlations, while blue colors denote negative correlations. The darker the color, the stronger the association. B, C The x-axis represents years before the last clinical evaluation, while the y-axis indicates global cognitive Z scores. The two lines represent the different slopes of global cognitive changes of subjects with 1st quartile (green color) versus 4th quartile (red color) stratified by expression levels of miR-129-5p (B) and miR-433 (C), respectively. AD Alzheimer’s dementia, CERAD Consortium to Establish a Registry for Alzheimer’s Disease, FDR false discovery rate, miRNAs microRNAs. Note: significance stars indicating the p values of the correlations adjustment for multiple comparisons. *P-value < 0.05. **P-value < 0.01. ***P-value < 0.001
Fig. 4 Association analysis results of top 10 candidate hub miRNAs with diagnosis and clinical and pathological traits. A The numbers represent the logistic and linear regression coefficients of association between miRNAs and traits. Stars indicate significant associations with FDR-corrected p value < 0.05. Red colors represent positive correlations, while blue colors denote negative correlations. The darker the color, the stronger the association. B, C The x-axis represents years before the last clinical evaluation, while the y-axis indicates global cognitive Z scores. The two lines represent the different slopes of global cognitive changes of subjects with 1st quartile (green color) versus 4th quartile (red color) stratified by expression levels of miR-129-5p (B) and miR-433 (C), respectively. AD Alzheimer’s dementia, CERAD Consortium to Establish a Registry for Alzheimer’s Disease, FDR false discovery rate, miRNAs microRNAs. Note: significance stars indicating the p values of the correlations adjustment for multiple comparisons. *P-value < 0.05. **P-value < 0.01. ***P-value < 0.001CC BY · PubMed Central
Fig. 5 Construction of consensus module eigengene networks. A, B Heatmaps showing the adjacencies of eigengene for each of the eigengene networks (A, ROS/MAP cohort; B, GSE157239 cohort). Each row and column corresponds to an eigengene labeled by the consensus module or diagnosis. Red colors represent positive correlations, while blue colors denote negative correlations. The darker the color, the stronger the association. C Adjacency heatmap displaying the pairwise preservation between the two consensus eigengene networks. Each row and column corresponds to an eigengene labeled by the consensus module or diagnosis. Red colors represent adjacency. The darker the color, the higher the adjacency. D Bar plot showing the preservation of correlation of consensus module eigengene between the two networks. Each colored bar corresponds to the eigengene of the associated consensus module. The height of the bar (y-axis) represents the eigengene preservation measure. The D value indicates the mean preservation of eigengene networks across the datasets. The high density value of D (= 0.88) denotes high overall preservation between the two networks. AD Alzheimer’s dementia, CM consensus module, M module, MAP Memory and Aging Project, ROS Religious Orders Study
Fig. 5 Construction of consensus module eigengene networks. A, B Heatmaps showing the adjacencies of eigengene for each of the eigengene networks (A, ROS/MAP cohort; B, GSE157239 cohort). Each row and column corresponds to an eigengene labeled by the consensus module or diagnosis. Red colors represent positive correlations, while blue colors denote negative correlations. The darker the color, the stronger the association. C Adjacency heatmap displaying the pairwise preservation between the two consensus eigengene networks. Each row and column corresponds to an eigengene labeled by the consensus module or diagnosis. Red colors represent adjacency. The darker the color, the higher the adjacency. D Bar plot showing the preservation of correlation of consensus module eigengene between the two networks. Each colored bar corresponds to the eigengene of the associated consensus module. The height of the bar (y-axis) represents the eigengene preservation measure. The D value indicates the mean preservation of eigengene networks across the datasets. The high density value of D (= 0.88) denotes high overall preservation between the two networks. AD Alzheimer’s dementia, CM consensus module, M module, MAP Memory and Aging Project, ROS Religious Orders StudyCC BY · PubMed Central
Fig. 6 Association analysis results of consensus module eigengenes with diagnosis in the ROS/MAP cohort, GEO (GSE157239) cohort, and across ROS/MAP and GEO datasets. A, B Heatmaps showing module-trait relationships in the ROS/MAP (A) and GEO (GSE157239) (B) cohorts. C Heatmaps showing consensus module-trait relationships across the ROS/MAP and GSE157239 cohorts. The numbers in the table indicate the coefficients (top) and its p value (bottom) of association between consensus module eigengenes and diagnosis. Red colors represent positive correlations, while green colors denote negative correlations. The darker the color, the stronger the association. Missing (NA) entry indicates the failure of forming a consensus because the directions of correlations in the ROS/MAP and GSE157239 datasets are opposite. AD Alzheimer’s dementia, CM consensus module, GEO Gene Expression Omnibus, M module, MAP Memory and Aging Project, NA not applicable, ROS Religious Orders Study
Fig. 6 Association analysis results of consensus module eigengenes with diagnosis in the ROS/MAP cohort, GEO (GSE157239) cohort, and across ROS/MAP and GEO datasets. A, B Heatmaps showing module-trait relationships in the ROS/MAP (A) and GEO (GSE157239) (B) cohorts. C Heatmaps showing consensus module-trait relationships across the ROS/MAP and GSE157239 cohorts. The numbers in the table indicate the coefficients (top) and its p value (bottom) of association between consensus module eigengenes and diagnosis. Red colors represent positive correlations, while green colors denote negative correlations. The darker the color, the stronger the association. Missing (NA) entry indicates the failure of forming a consensus because the directions of correlations in the ROS/MAP and GSE157239 datasets are opposite. AD Alzheimer’s dementia, CM consensus module, GEO Gene Expression Omnibus, M module, MAP Memory and Aging Project, NA not applicable, ROS Religious Orders StudyCC BY · PubMed Central
Fig. 7 The ROC curves and mean AUC of machine learning approach using penalized logistic regression. Sensitivity is on the y-axis and 1-specificity is on the x-axis. 10-fold cross validation was used to investigate and compare the classification performance of five different classification models for differentiating AD from NCI. The gray zone around the mean ROC curve represents ± 1 standard deviation. Five different classification models are shown per following training features. A Training features include age, sex, and apolipoprotein E (APOE) ε4 carrier status. B Training features include five AD-associated hub miRNAs. C Training features include age, sex, APOE ε4 carrier status, and five AD-associated hub miRNAs. D Training features include all 309 miRNAs. E Training features include age, sex, APOE ε4 carrier status, and all 309 miRNAs. AD Alzheimer’s dementia, AUC area under the curve, miRNAs microRNAs, NCI no cognitive impairment, ROC receiver operating characteristic
Fig. 7 The ROC curves and mean AUC of machine learning approach using penalized logistic regression. Sensitivity is on the y-axis and 1-specificity is on the x-axis. 10-fold cross validation was used to investigate and compare the classification performance of five different classification models for differentiating AD from NCI. The gray zone around the mean ROC curve represents ± 1 standard deviation. Five different classification models are shown per following training features. A Training features include age, sex, and apolipoprotein E (APOE) ε4 carrier status. B Training features include five AD-associated hub miRNAs. C Training features include age, sex, APOE ε4 carrier status, and five AD-associated hub miRNAs. D Training features include all 309 miRNAs. E Training features include age, sex, APOE ε4 carrier status, and all 309 miRNAs. AD Alzheimer’s dementia, AUC area under the curve, miRNAs microRNAs, NCI no cognitive impairment, ROC receiver operating characteristicCC BY · PubMed Central

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Cites

Research works printed but not linked: 73

  1. Association As. 2012 Alzheimer’s disease facts and figures. Alzheimers Dement. 2012;8(2):131–68. · DOI
  2. Hampel H, Frank R, Broich K, Teipel SJ, Katz RG, Hardy J, et al. Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives. Nat Rev Drug Discov. 2010;9(7):560–74. · DOI
  3. Kou X, Chen D, Chen N. The regulation of microRNAs in Alzheimer's disease. Front Neurol. 2020;11:288. · DOI · Paper page
  4. Wang M, Qin L, Tang B. MicroRNAs in Alzheimer’s disease. Front Genet. 2019;10:153. · DOI
  5. Patrick E, Rajagopal S, Wong H-KA, McCabe C, Xu J, Tang A, et al. Dissecting the role of non-coding RNAs in the accumulation of amyloid and tau neuropathologies in Alzheimer’s disease. Mol Neurodegener. 2017;12(1):1–13. · DOI · Paper page
  6. Winter J, Jung S, Keller S, Gregory RI, Diederichs S. Many roads to maturity: microRNA biogenesis pathways and their regulation. Nat Cell Biol. 2009;11(3):228–34. · DOI
  7. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. cell. 2004;116(2):281–97. · DOI
  8. Arora T, Prashar V, Singh R, Barwal TS, Changotra H, Sharma A, et al. Dysregulated miRNAs in progression and pathogenesis of Alzheimer’s disease. Mol Neurobiol. 2022;59(10):1–18. · DOI · Paper page
  9. Miya Shaik M, Tamargo IA, Abubakar MB, Kamal MA, Greig NH, Gan SH. The role of microRNAs in Alzheimer’s disease and their therapeutic potentials. Genes. 2018;9(4):174. · DOI
  10. Lau P, Frigerio CS, De Strooper B. Variance in the identification of microRNAs deregulated in Alzheimer's disease and possible role of lincRNAs in the pathology: the need of larger datasets. Ageing Res Rev. 2014;17:43–53. · DOI
  11. Carter SL, Brechbühler CM, Griffin M, Bond AT. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics. 2004;20(14):2242–50. · DOI · Paper page
  12. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998;95(25):14863–8. · DOI · Paper page
  13. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9(1):1–13. · DOI · Paper page
  14. Pedersen HK, Forslund SK, Gudmundsdottir V, Petersen AØ, Hildebrand F, Hyötyläinen T, et al. A computational framework to integrate high-throughput ‘-omics’ datasets for the identification of potential mechanistic links. Nat Protoc. 2018;13(12):2781–800. · DOI
  15. Zhang T, Shen Y, Guo Y, Yao J. Identification of key transcriptome biomarkers based on a vital gene module associated with pathological changes in Alzheimer’s disease. Aging (Albany NY). 2021;13(11):14940. · DOI
  16. Bennett DA, Schneider JA, Aggarwal NT, Arvanitakis Z, Shah RC, Kelly JF, et al. Decision rules guiding the clinical diagnosis of Alzheimer’s disease in two community-based cohort studies compared to standard practice in a clinic-based cohort study. Neuroepidemiology. 2006;27(3):169–76. · DOI
  17. Braak H, Alafuzoff I, Arzberger T, Kretzschmar H, Del Tredici K. Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol. 2006;112(4):389–404. · DOI · Paper page
  18. Mirra SS, Heyman A, McKeel D, Sumi S, Crain BJ, Brownlee L, et al. The Consortium to Establish a Registry for Alzheimer's Disease (CERAD): part II. Standardization of the neuropathologic assessment of Alzheimer's disease. Neurology. 1991;41(4):479–9. · DOI · Paper page
  19. Henriques AD, Machado-Silva W, Leite RE, Suemoto CK, Leite KR, Srougi M, et al. Genome-wide profiling and predicted significance of post-mortem brain microRNA in Alzheimer’s disease. Mech Ageing Dev. 2020;191:111352. · DOI
  20. Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42(D1):D68–73. · DOI · Paper page
  21. De Jager PL, Ma Y, McCabe C, Xu J, Vardarajan BN, Felsky D, et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci Data. 2018;5(1):1–13. · DOI · Paper page
  22. Zhang Y, Parmigiani G, Johnson WE. ComBat-seq: batch effect adjustment for RNA-seq count data. Genom Bioinform. 2020;2(3):lqaa078. · DOI · Paper page
  23. Karpenko O, Bahroos N, Chukhman M, Dong X, Kanabar P, Arbieva Z, et al. Galaxy high throughput genotyping pipeline for GeneTitan. AMIA Jt Summits Transl Sci Proc. 2013;2013:102–2.
  24. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4(2):249–64. · DOI · Paper page
  25. Wang M, Beckmann ND, Roussos P, Wang E, Zhou X, Wang Q, et al. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer's disease. Sci Data. 2018;5(1):1–16. · DOI · Paper page
  26. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7(3):562–78. · DOI
  27. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):1–10. · DOI · Paper page
  28. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:1–16. · DOI · Paper page
  29. Wilson RS, Segawa E, Boyle PA, Anagnos SE, Hizel LP, Bennett DA. The natural history of cognitive decline in Alzheimer's disease. Psychol Aging. 2012;27(4):1008. · DOI
  30. Nam J-W, Rissland OS, Koppstein D, Abreu-Goodger C, Jan CH, Agarwal V, et al. Global analyses of the effect of different cellular contexts on microRNA targeting. Mol Cell. 2014;53(6):1031–43. · DOI
  31. Wong N, Wang X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res. 2015;43(D1):D146–52. · DOI
  32. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. · DOI · Paper page
  33. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. · DOI · Paper page
  34. Botstein D, Cherry JM, Ashburner M, Ball CA, Blake JA, Butler H, et al. Gene Ontology: tool for the unification of biology. Nat genet. 2000;25(1):25–9. · DOI · Paper page
  35. Li J, Han X, Wan Y, Zhang S, Zhao Y, Fan R, et al. TAM 2.0: tool for MicroRNA set analysis. Nucleic Acids Res. 2018;46(W1):W180–5. · DOI
  36. Neyman J, Pearson ES. On the use and interpretation of certain test criteria for purposes of statistical inference: part I. Biometrika. 1928;20A(1-2):175–240. · DOI
  37. Langfelder P, Horvath S. Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol. 2007;1:1–17. · DOI · Paper page
  38. Mueller AJ, Canty-Laird EG, Clegg PD, Tew SR. Cross-species gene modules emerge from a systems biology approach to osteoarthritis. NPJ Syst Biol Appl. 2017;3(1):13. · DOI
  39. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995;57(1):289–300. · DOI · Paper page
  40. Urbanowicz RJ, Zhang R, Cui Y, Suri P. STREAMLINE: a simple, transparent, end-to-end automated machine learning pipeline facilitating data analysis and algorithm comparison. ArXiv220612002 2022. · DOI
  41. Teipel SJ, Grothe MJ, Metzger CD, Grimmer T, Sorg C, Ewers M, et al. Robust detection of impaired resting state functional connectivity networks in Alzheimer's disease using elastic net regularized regression. Front Aging Neurosci. 2017;8:318. · DOI
  42. Bouckaert RR, Frank E. Evaluating the replicability of significance tests for comparing learning algorithms. Pacific-Asia conference on knowledge discovery and data mining; 2004: Springer; 2004: 3-12. · DOI
  43. Lau P, Bossers K, Rs J, Salta E, Frigerio CS, Barbash S, et al. Alteration of the micro RNA network during the progression of Alzheimer's disease. EMBO Mol Med. 2013;5(10):1613–34. · DOI · Paper page
  44. Liu Z, Hu C, Tang D, Luo G. miR-129 Promotes the proliferation of Alzheimer’s neuronal cells by binding the 3′ untranslated regions (3′ UTR) of amyloid precursor protein (APP). J Biomater Tissue Eng. 2022;12(3):551–7. · DOI
  45. Li Z, Chen Q, Liu J, Du Y. Physical exercise ameliorates the cognitive function and attenuates the neuroinflammation of Alzheimer’s disease via miR-129–5p. Dement Geriatr Cogn Disord. 2020;49(2):163–9. · DOI · Paper page
  46. Sun W, Zhao J, Li C. Dexmedetomidine provides protection against hippocampal neuron apoptosis and cognitive impairment in mice with Alzheimer’s disease by mediating the miR-129/YAP1/JAG1 axis. Mol Neurobiol. 2020;57(12):5044–55. · DOI
  47. Gupta R, Kumar P. Integrative analysis of OIP5-AS1/miR-129-5p/CREBBP axis as a potential therapeutic candidate in the pathogenesis of metal toxicity-induced Alzheimer's disease. Gene Rep. 2022;26:101442. · DOI
  48. Zhang Y-Y, Bao H-L, Dong L-X, Liu Y, Zhang G-W, An F-M. Silenced lncRNA H19 and up-regulated microRNA-129 accelerates viability and restrains apoptosis of PC12 cells induced by Aβ25-35 in a cellular model of Alzheimer’s disease. Cell Cycle. 2021;20(1):112–25. · DOI
  49. Manzine PR, Pelucchi S, Horst MA, Vale FA, Pavarini SC, Audano M, et al. microRNA 221 targets ADAM10 mRNA and is downregulated in Alzheimer’s disease. J Alzheimers Dis. 2018;61(1):113–23. · DOI
  50. Biglari N, Khalaj-Kondori M, Ghasemi T. Potential of hsa-miR200a-3p and hsa-miR502-3p as blood-based biomarker for Alzheimer’s disease. Mol Biol Rep. 2022;49(12):1–8. · DOI
  51. Chanda K, Jana NR, Mukhopadhyay D. Long non-coding RNA MALAT1 protects against Aβ1–42 induced toxicity by regulating the expression of receptor tyrosine kinase EPHA2 via quenching miR-200a/26a/26b in Alzheimer's disease. Life Sci. 2022;302(8):120652. · DOI
  52. Zhang Q-S, Liu W, Lu G-X. miR-200a-3p promotes β-Amyloid-induced neuronal apoptosis through down-regulation of SIRT1 in Alzheimer’s disease. J Biosci. 2017;42(3):397–404. · DOI
  53. Schipper HM, Maes OC, Chertkow HM, Wang E. MicroRNA expression in Alzheimer blood mononuclear cells. Gene Regul Syst Bio. 2007;1:GRSB.S361. · DOI
  54. Nagaraj S, Laskowska-Kaszub K, Dębski KJ, Wojsiat J, Dąbrowski M, Gabryelewicz T, et al. Profile of 6 microRNA in blood plasma distinguish early stage Alzheimer’s disease patients from non-demented subjects. Oncotarget. 2017;8(10):16122. · DOI
  55. Wang R, Zhang J. Clinical significance of miR-433 in the diagnosis of Alzheimer's disease and its effect on Aβ-induced neurotoxicity by regulating JAK2. Exp Gerontol. 2020;141:111080. · DOI
  56. Jiang Q, Chen S, Hu C, Huang P, Shen H, Zhao W. Neuregulin-1 (Nrg1) signaling has a preventive role and is altered in the frontal cortex under the pathological conditions of Alzheimer's disease. Mol Med Rep. 2016;14(3):2614–24. · DOI
  57. Yang Q, Zhao Q, Yin Y. miR-133b is a potential diagnostic biomarker for Alzheimer's disease and has a neuroprotective role. Exp Ther Med. 2019;18(4):2711–8.
  58. Chen S, Velardez MO, Warot X, Yu Z-X, Miller SJ, Cros D, et al. Neuregulin 1–erbB signaling is necessary for normal myelination and sensory function. J Neurosci. 2006;26(12):3079–86. · DOI
  59. Barbato C, Giacovazzo G, Albiero F, Scardigli R, Scopa C, Ciotti M, et al. Cognitive decline and modulation of alzheimer’s disease-related genes after inhibition of MicroRNA-101 in mouse hippocampal neurons. Mol Neurobiol. 2020;57:3183–94. · DOI
  60. Chang F, Zhang LH, Xu WP, Jing P, Zhan PY. microRNA-9 attenuates amyloidβ-induced synaptotoxicity by targeting calcium/calmodulin-dependent protein kinase kinase 2. Mol Med Rep. 2014;9(5):1917–22. · DOI · Paper page
  61. Li S, Yan Y, Jiao Y, Gao Z, Xia Y, Kong L, et al. Neuroprotective effect of osthole on neuron synapses in an Alzheimer’s disease cell model via upregulation of microRNA-9. J Mol Neurosci. 2016;60:71–81. · DOI · Paper page
  62. Muraleedharan R, Dasgupta B. AMPK in the brain: its roles in glucose and neural metabolism. FEBS J. 2022;289(8):2247–62. · DOI
  63. Zhang S, Lachance BB, Mattson MP, Jia X. Glucose metabolic crosstalk and regulation in brain function and diseases. Prog Neurobiol. 2021;204:102089. · DOI
  64. Lopez M, Nogueiras R, Tena-Sempere M, Dieguez C. Hypothalamic AMPK: a canonical regulator of whole-body energy balance. Nat Rev Endocrinol. 2016;12(7):421–32. · DOI
  65. Li Q, Wang Y, Peng W, Jia Y, Tang J, Li W, et al. MicroRNA-101a regulates autophagy phenomenon via the MAPK pathway to modulate Alzheimer’s-associated pathogenesis. Cell Transplant. 2019;28(8):1076–84. · DOI
  66. Hernandez-Rapp J, Rainone S, Goupil C, Dorval V, Smith PY, Saint-Pierre M, et al. microRNA-132/212 deficiency enhances Aβ production and senile plaque deposition in Alzheimer’s disease triple transgenic mice. Sci Rep. 2016;6(1):30953. · DOI
  67. Deng Y, Zhang J, Sun X, Ma G, Luo G, Miao Z, et al. miR-132 improves the cognitive function of rats with Alzheimer's disease by inhibiting the MAPK1 signal pathway. Exp Ther Med. 2020;20(6):1–1. · DOI
  68. Zhang W, Yang Y, Xiang Z, Cheng J, Yu Z, Wang W, et al. MRTF-A-mediated protection against amyloid-β-induced neuronal injury correlates with restoring autophagy via miR-1273g-3p/mTOR axis in Alzheimer models. Aging (Albany NY). 2022;14(10):4305. · DOI
  69. Higaki S, Muramatsu M, Matsuda A, Matsumoto K, Satoh J-i, Michikawa M, et al. Defensive effect of microRNA-200b/c against amyloid-beta peptide-induced toxicity in Alzheimer's disease models. PLoS One. 2018;13(5):e0196929. · DOI
  70. Ye X, Luo H, Chen Y, Wu Q, Xiong Y, Zhu J, et al. MicroRNAs 99b-5p/100-5p regulated by endoplasmic reticulum stress are involved in abeta-induced pathologies. Front Aging Neurosci. 2015;7:210. · DOI
  71. Liang C, Mu Y, Tian H, Wang D, Zhang S, Wang H, et al. MicroRNA-140 silencing represses the incidence of Alzheimer's disease. Neurosci Lett. 2021;758:135674. · DOI · Paper page
  72. Angelucci F, Cechova K, Valis M, Kuca K, Zhang B, Hort J. MicroRNAs in Alzheimer’s disease: diagnostic markers or therapeutic agents? Front Pharmacol. 2019;10:665. · DOI
  73. Liu S, Fan M, Zheng Q, Hao S, Yang L, Qi C, et al. MicroRNAs in Alzheimer's disease: potential diagnostic markers and therapeutic targets. Biomed Pharmacother. 2022;148:112681. · DOI

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Metadata

Authors
Han, S.-W., Pyun, J.-M., Bice, P. J., Bennett, D. A., Saykin, A. J., Kim, S. Y., Park, Y. H., & Nho, K.
Venue
Alzheimer's Research & Therapy
Publisher
Springer Science and Business Media LLC
Published
2024-01-09
Volume
16
Issue
1
ISSN
1758-9193
DOI
10.1186/s13195-023-01366-8
PMC
PMC10775662
Cited by
33
References
11
Language
en
Licence

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 ›

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