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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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Cited by

Sorted by citations

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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