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Meta-Analysis
. 2022 Feb 1;12(1):47.
doi: 10.1038/s41398-022-01806-3.

Brain microRNAs are associated with variation in cognitive trajectory in advanced age

Aliza P Wingo #  1   2 , Mengli Wang #  3 , Jiaqi Liu  4 , Michael S Breen  5   6   7 , Hyun-Sik Yang  8   9 , Beisha Tang  3   10 , Julie A Schneider  11 , Nicholas T Seyfried  12 , James J Lah  4 , Allan I Levey  4 , David A Bennett  11 , Peng Jin  13 , Philip L De Jager  14   15 , Thomas S Wingo  16   17
Affiliations
Meta-Analysis

Brain microRNAs are associated with variation in cognitive trajectory in advanced age

Aliza P Wingo et al. Transl Psychiatry. .

Abstract

In advancing age, some individuals maintain a stable cognitive performance over time, while others experience a rapid decline. Such variation in cognitive trajectory is only partially explained by common neurodegenerative pathologies. Hence, we aimed to identify new molecular processes underlying variation in cognitive trajectory using brain microRNA profile followed by an integrative analysis with brain transcriptome and proteome. Individual cognitive trajectories were derived from longitudinally assessed cognitive-test scores of older-adult brain donors from four longitudinal cohorts. Postmortem brain microRNA profiles, transcriptomes, and proteomes were derived from the dorsolateral prefrontal cortex. The global microRNA association study of cognitive trajectory was performed in a discovery (n = 454) and replication cohort (n = 134), followed by a meta-analysis that identified 6 microRNAs. Among these, miR-132-3p and miR-29a-3p were most significantly associated with cognitive trajectory. They explain 18.2% and 2.0% of the variance of cognitive trajectory, respectively, and act independently of the eight measured neurodegenerative pathologies. Furthermore, integrative transcriptomic and proteomic analyses revealed that miR-132-3p was significantly associated with 24 of the 47 modules of co-expressed genes of the transcriptome, miR-29a-3p with 3 modules, and identified 84 and 214 downstream targets of miR-132-3p and miR-29a-3p, respectively, in cognitive trajectory. This is the first global microRNA study of cognitive trajectory to our knowledge. We identified miR-29a-3p and miR-132-3p as novel and robust contributors to cognitive trajectory independently of the eight known cerebral pathologies. Our findings lay a foundation for future studies investigating mechanisms and developing interventions to enhance cognitive stability in advanced age.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Overview of the study design and findings.
Individual cognitive trajectories were estimated from annual cognitive testing scores in participants of the ROS/MAP, Banner, and BLSA cohorts, respectively. These participants donated their brains at death. In ROS/MAP, global microRNAs and transcriptomes were profiled from the dorsolateral prefrontal cortex (dlPFC). Likewise, in Banner and BLSA, proteomes were profiled from the dlPFC. A global miRNA association study of cognitive trajectory was performed followed by a transcriptome-wide and proteome-wide association studies of cognitive trajectory. Next, an integrative analysis was performed to identify downstream targets of the cognitive trajectory-associated miRNAs at the transcript and protein levels.
Fig. 2
Fig. 2. Global miRNA association study of cognitive trajectory in ROS/MAP.
A Volcano plot of the global miRNA association study of cognitive trajectory in the discovery dataset. The miRNAs colored in blue are those associated with cognitive trajectory at adjusted p < 0.05. B Volcano plot for the meta-analysis of the global miRNA association studies of cognitive trajectory in the discovery and replication datasets. C Plot of slope of cognitive trajectory versus individual miR-132 and miR-29a expression in the discovery dataset. Note that the more positive the slope, the more stable the trajectory, and the more negative the slope, the faster the decline.
Fig. 3
Fig. 3. Percent variance of cognitive trajectory explained by miR-132 and miR-29a.
This figure shows the percent variance of cognitive trajectory explained by variables in the model. A Percent variance of cognitive trajectory explained by miR-132, miR-29a, and each of the eight considered pathologies (amyloid density, tangle density, Lewy bodies, macroinfarct, microinfarcts, atherosclerosis, cerebral amyloid angiopathy, and hippocampal sclerosis), B Percent variance of cognitive trajectory explained by miR-132 and miR-29a after effects of the eight cerebral pathologies have been regressed out. For all analyses, the effects of sex, age at death, PMI, RIN, study, and proportions of neurons, astrocytes, oligodendrocytes, and microglia have been regressed out in these percent variance estimates.
Fig. 4
Fig. 4. Validation of the targets of miR-132 and miR-29a.
This figure shows the results for the Renilla Luciferase assays for the 3’UTR for a selected number of cognitive trajectory-associated genes. A Relative R-luc/F-luc ratio in the co-transfection of miR-132 potential targets 3’UTR-reporter constructs with pcDNA3.1-pre-miRNA-132 vs. pcDNA3.1 (as control) in HEK 293 T cells. B Rescue experiments to further validate the targets of miR-132 using respective 3’UTR-reporter construct mutant. C Relative R-luc/F-luc ratio in the co-transfection of miR-29a potential targets 3’UTR-reporter constructs with pcDNA3.1-pre-miRNA-29a vs. pcDNA3.1 (as control) in HEK 293 T cells (N = 3, ns= not significant, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). D Rescue experiments to further validate the targets of miR-29a using respective 3’UTR-reporter construct mutant.
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References

    1. Schneider JA, Aggarwal NT, Barnes L, Boyle P, Bennett DA. The neuropathology of older persons with and without dementia from community versus clinic cohorts. J Alzheimer’s Dis: JAD. 2009;18:691–701. - PMC - PubMed
    1. Boyle PA, Yu L, Wilson RS, Leurgans SE, Schneider JA, Bennett DA. Person-specific contribution of neuropathologies to cognitive loss in old age. Ann Neurol. 2018;83:74–83. - PMC - PubMed
    1. Terry RD, Masliah E, Salmon DP, Butters N, DeTeresa R, Hill R, et al. Physical basis of cognitive alterations in Alzheimer’s disease: synapse loss is the major correlate of cognitive impairment. Ann Neurol. 1991;30:572–80. - PubMed
    1. Boyle PA, Wilson RS, Yu L, Barr AM, Honer WG, Schneider JA, et al. Much of late life cognitive decline is not due to common neurodegenerative pathologies. Ann Neurol. 2013;74:23964.. - PMC - PubMed
    1. Fabian MR, Sonenberg N. The mechanics of miRNA-mediated gene silencing: a look under the hood of miRISC. Nat Struct Mol Biol. 2012;19:586–93. - PubMed

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