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Brain DNA Methylation Patterns in CLDN5 Associated With Cognitive Decline

Published:February 03, 2021DOI:https://doi.org/10.1016/j.biopsych.2021.01.015

      Abstract

      Background

      Cognitive trajectory varies widely and can distinguish people who develop dementia from people who remain cognitively normal. Variation in cognitive trajectory is only partially explained by traditional neuropathologies. We sought to identify novel genes associated with cognitive trajectory using DNA methylation profiles from human postmortem brain.

      Methods

      We performed a brain epigenome-wide association study of cognitive trajectory in 636 participants from the ROS (Religious Orders Study) and MAP (Rush Memory and Aging Project) using DNA methylation profiles of the dorsolateral prefrontal cortex. To maximize our power to detect epigenetic associations, we used the recently developed Gene Association with Multiple Traits test to analyze the 5 measured cognitive domains simultaneously.

      Results

      We found an epigenome-wide association for differential methylation of sites in the CLDN5 locus and cognitive trajectory (p = 9.96 × 10−7) that was robust to adjustment for cell type proportions (p = 8.52 × 10−7). This association was primarily driven by association with declines in episodic (p = 4.65 × 10−6) and working (p = 2.54 × 10−7) memory. This association between methylation in CLDN5 and cognitive decline was significant even in participants with no or little signs of amyloid-β and neurofibrillary tangle pathology.

      Conclusions

      Differential methylation of CLDN5, a gene that encodes an important protein of the blood-brain barrier, is associated with cognitive trajectory beyond traditional Alzheimer’s disease pathologies. The association between CLDN5 methylation and cognitive trajectory in people with low pathology suggests an early role for CLDN5 and blood-brain barrier dysfunction in cognitive decline and Alzheimer’s disease.

      Keywords

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      References

        • Batty G.D.
        • Deary I.J.
        • Zaninotto P.
        Association of cognitive function with cause-specific mortality in middle and older age: Follow-up of participants in the English Longitudinal Study of Ageing.
        Am J Epidemiol. 2016; 183: 183-190
        • Van Cauwenberghe C.
        • Van Broeckhoven C.
        • Sleegers K.
        The genetic landscape of Alzheimer disease: Clinical implications and perspectives.
        Genet Med. 2016; 18: 421-430
        • Möller H.J.
        • Graeber M.B.
        The case described by Alois Alzheimer in 1911. Historical and conceptual perspectives based on the clinical record and neurohistological sections.
        Eur Arch Psychiatry Clin Neurosci. 1998; 248: 111-122
        • Boyle P.A.
        • Wilson R.S.
        • Yu L.
        • Barr A.M.
        • Honer W.G.
        • Schneider J.A.
        • Bennett D.A.
        Much of late life cognitive decline is not due to common neurodegenerative pathologies.
        Ann Neurol. 2013; 74: 478-489
        • Boyle P.A.
        • Yu L.
        • Wilson R.S.
        • Leurgans S.E.
        • Schneider J.A.
        • Bennett D.A.
        Person-specific contribution of neuropathologies to cognitive loss in old age.
        Ann Neurol. 2018; 83: 74-83
        • Schneider J.A.
        • Aggarwal N.T.
        • Barnes L.
        • Boyle P.
        • Bennett D.A.
        The neuropathology of older persons with and without dementia from community versus clinic cohorts.
        J Alzheimers Dis. 2009; 18: 691-701
        • Terry R.D.
        • Masliah E.
        • Salmon D.P.
        • 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: 572-580
        • Li P.
        • Marshall L.
        • Oh G.
        • Jakubowski J.L.
        • Groot D.
        • He Y.
        • et al.
        Epigenetic dysregulation of enhancers in neurons is associated with Alzheimer’s disease pathology and cognitive symptoms.
        Nat Commun. 2019; 10: 2246
        • Altuna M.
        • Urdánoz-Casado A.
        • Sánchez-Ruiz De Gordoa J.
        • Zelaya M.V.
        • Labarga A.
        • Lepesant J.M.J.
        • et al.
        DNA methylation signature of human hippocampus in Alzheimer’s disease is linked to neurogenesis.
        Clin Epigenetics. 2019; 11: 91
        • Lunnon K.
        • Smith R.
        • Hannon E.
        • De Jager P.L.
        • Srivastava G.
        • Volta M.
        • et al.
        Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer’s disease.
        Nat Neurosci. 2014; 17: 1164-1170
        • De Jager P.L.
        • Srivastava G.
        • Lunnon K.
        • Burgess J.
        • Schalkwyk L.C.
        • Yu L.
        • et al.
        Alzheimer’s disease: Early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci.
        Nat Neurosci. 2014; 17: 1156-1163
        • Humphries C.E.
        • Kohli M.A.
        • Nathanson L.
        • Whitehead P.
        • Beecham G.
        • Martin E.
        • et al.
        Integrated whole transcriptome and DNA methylation analysis identifies gene networks specific to late-onset Alzheimer’s disease.
        J Alzheimers Dis. 2015; 44: 977-987
        • Watson C.T.
        • Roussos P.
        • Garg P.
        • Ho D.J.
        • Azam N.
        • Katsel P.L.
        • et al.
        Genome-wide DNA methylation profiling in the superior temporal gyrus reveals epigenetic signatures associated with Alzheimer’s disease.
        Genome Med. 2016; 8: 5
        • Bakulski K.M.
        • Dolinoy D.C.
        • Sartor M.A.
        • Paulson H.L.
        • Konen J.R.
        • Lieberman A.P.
        • et al.
        Genome-wide DNA methylation differences between late-onset Alzheimer’s disease and cognitively normal controls in human frontal cortex.
        J Alzheimers Dis. 2012; 29: 571-588
        • Marioni R.E.
        • McRae A.F.
        • Bressler J.
        • Colicino E.
        • Hannon E.
        • Li S.
        • et al.
        Meta-analysis of epigenome-wide association studies of cognitive abilities.
        Mol Psychiatry. 2018; 23: 2133-2144
        • Broadaway K.A.
        • Cutler D.J.
        • Duncan R.
        • Moore J.L.
        • Ware E.B.
        • Jhun M.A.
        • et al.
        A statistical approach for testing cross-phenotype effects of rare variants.
        Am J Hum Genet. 2016; 98: 525-540
        • Holleman A.M.
        • Broadaway K.A.
        • Duncan R.
        • Todor A.
        • Almli L.M.
        • Bradley B.
        • et al.
        Powerful and efficient strategies for genetic association testing of symptom and questionnaire data in psychiatric genetic studies.
        Sci Rep. 2019; 9: 7523
        • Bennett D.A.
        • Buchman A.S.
        • Boyle P.A.
        • Barnes L.L.
        • Wilson R.S.
        • Schneider J.A.
        Religious Orders Study and Rush Memory and Aging Project.
        J Alzheimers Dis. 2018; 64: S161-S189
        • 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. 2012; 72: 599-609
        • Braak H.
        • Braak E.
        Neuropathological stageing of Alzheimer-related changes.
        Acta Neuropathol. 1991; 82: 239-259
        • De Jager P.L.
        • Shulman J.M.
        • Chibnik L.B.
        • Keenan B.T.
        • Raj T.
        • Wilson R.S.
        • et al.
        A genome-wide scan for common variants affecting the rate of age-related cognitive decline.
        Neurobiol Aging. 2012; 33: 1017.e1-1017.e15
        • Mathys H.
        • Davila-Velderrain J.
        • Peng Z.
        • Gao F.
        • Mohammadi S.
        • Young J.Z.
        • et al.
        Single-cell transcriptomic analysis of Alzheimer’s disease.
        Nature. 2019; 570: 332-337
        • Paajanen T.
        • Hänninen T.
        • Tunnard C.
        • Hallikainen M.
        • Mecocci P.
        • Sobow T.
        • et al.
        CERAD neuropsychological compound scores are accurate in detecting prodromal Alzheimer’s disease: A prospective AddNeuroMed study.
        J Alzheimers Dis. 2014; 39: 679-690
        • Larson N.B.
        • Chen J.
        • Schaid D.J.
        A review of kernel methods for genetic association studies.
        Genet Epidemiol. 2019; 43: 122-136
        • Davies R.B.
        Algorithm AS 155: The distribution of a linear combination of χ2 random variables.
        J R Stat Soc Ser C Appl Stat. 1980; 29: 323-333
        • Laird P.W.
        Principles and challenges of genome-wide DNA methylation analysis.
        Nat Rev Genet. 2010; 11: 191-203
        • Mcgregor K.
        • Bernatsky S.
        • Colmegna I.
        • Hudson M.
        • Pastinen T.
        • Labbe A.
        • Greenwood C.M.T.
        An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies.
        Genome Biol. 2016; 17: 84
        • Barfield R.T.
        • Almli L.M.
        • Kilaru V.
        • Smith A.K.
        • Mercer K.B.
        • Duncan R.
        • et al.
        Accounting for population stratification in DNA methylation studies.
        Genet Epidemiol. 2014; 38: 231-241
        • Guintivano J.
        • Aryee M.J.
        • Kaminsky Z.A.
        A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression.
        Epigenetics. 2013; 8: 290-302
        • Ritchie M.E.
        • Phipson B.
        • Wu D.
        • Hu Y.
        • Law C.W.
        • Shi W.
        • Smyth G.K.
        limma powers differential expression analyses for RNA-sequencing and microarray studies.
        Nucleic Acids Res. 2015; 43: e47
        • Wang J.
        • Zhao Q.
        cate: High dimensional factor analysis and confounder adjusted testing [no. R package version 1.1.1].
        (Available at:) (Accessed March 23, 2021)
        • Imai K.
        • Keele L.
        • Tingley D.
        A general approach to causal mediation analysis.
        Psychol Methods. 2010; 15: 309-334
        • Marques F.
        • Sousa J.C.
        • Sousa N.
        • Palha J.A.
        Blood-brain-barriers in aging and in Alzheimer’s disease.
        Mol Neurodegener. 2013; 8: 38
        • Farrall A.J.
        • Wardlaw J.M.
        Blood-brain barrier: Ageing and microvascular disease—systematic review and meta-analysis.
        Neurobiol Aging. 2009; 30: 337-352
        • Viggars A.P.
        • Wharton S.B.
        • Simpson J.E.
        • Matthews F.E.
        • Brayne C.
        • Savva G.M.
        • et al.
        Alterations in the blood brain barrier in ageing cerebral cortex in relationship to Alzheimer-type pathology: A study in the MRC-CFAS population neuropathology cohort.
        Neurosci Lett. 2011; 505: 25-30
        • Weiss N.
        • Miller F.
        • Cazaubon S.
        • Couraud P.O.
        The blood-brain barrier in brain homeostasis and neurological diseases.
        Biochim Biophys Acta. 2009; 1788: 842-857
        • Montagne A.
        • Barnes S.R.
        • Sweeney M.D.
        • Halliday M.R.
        • Sagare A.P.
        • Zhao Z.
        • et al.
        Blood-brain barrier breakdown in the aging human hippocampus.
        Neuron. 2015; 85: 296-302
        • Nation D.A.
        • Sweeney M.D.
        • Montagne A.
        • Sagare A.P.
        • D’Orazio L.M.
        • Pachicano M.
        • et al.
        Blood-brain barrier breakdown is an early biomarker of human cognitive dysfunction.
        Nat Med. 2019; 25: 270-276
        • Dudek K.A.
        • Dion-Albert L.
        • Lebel M.
        • LeClair K.
        • Labrecque S.
        • Tuck E.
        • et al.
        Molecular adaptations of the blood-brain barrier promote stress resilience vs. depression.
        Proc Natl Acad Sci U S A. 2020; 117: 3326-3336
        • Keaney J.
        • Walsh D.M.
        • O’Malley T.
        • Hudson N.
        • Crosbie D.E.
        • Loftus T.
        • et al.
        Autoregulated paracellular clearance of amyloid-β across the blood-brain barrier.
        Sci Adv. 2015; 1e1500472
        • Shimizu F.
        • Sano Y.
        • Saito K.
        • Abe M.
        • Maeda T.
        • Haruki H.
        • Kanda T.
        Pericyte-derived glial cell line-derived neurotrophic factor increase the expression of claudin-5 in the blood-brain barrier and the blood-nerve barrier.
        Neurochem Res. 2012; 37: 401-409
        • Yamazaki Y.
        • Shinohara M.
        • Shinohara M.
        • Yamazaki A.
        • Murray M.E.
        • Liesinger A.M.
        • et al.
        Selective loss of cortical endothelial tight junction proteins during Alzheimer’s disease progression.
        Brain. 2019; 142: 1077-1092
        • Boyle P.A.
        • Yu L.
        • Leurgans S.E.
        • Wilson R.S.
        • Brookmeyer R.
        • Schneider J.A.
        • Bennett D.A.
        Attributable risk of Alzheimer’s dementia attributed to age-related neuropathologies.
        Ann Neurol. 2019; 85: 114-124
        • Gatev E.
        • Gladish N.
        • Mostafavi S.
        • Kobor M.S.
        CoMeBack: DNA methylation array data analysis for co-methylated regions.
        Bioinformatics. 2020; 36: 2675-2683
        • Deaton A.M.
        • Bird A.
        CpG islands and the regulation of transcription.
        Genes Dev. 2011; 25: 1010-1022
        • Daneman R.
        • Prat A.
        The blood-brain barrier.
        Cold Spring Harb Perspect Biol. 2015; 7: a020412

      Linked Article

      • Candidate Epigenetic Biomarker of Cognitive Trajectory: The Chicken or the Egg?
        Biological PsychiatryVol. 91Issue 4
        • Preview
          Brain health throughout the life course depends upon both genetic and epigenetic features. The most widely studied epigenetic mark in the brain has been DNA methylation owing to its stability and convenience to assay from widely available DNA biospecimens. Evidence suggests that distinct DNA methylation patterns serve as a wide-ranging biomarker including but not limited to disease status, disease risk, predictor of life expectancy and mortality, aging, and prior environmental exposures. Emerging research is suggesting the utility of DNA methylation biomarkers in neuroimaging and neurocognition epigenetics.
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