Cerebral atherosclerosis contributes to dementia via unclear processes. We performed proteomic sequencing of dorsolateral prefrontal cortex in 438 older individuals and found associations between cerebral atherosclerosis and reduced synaptic signaling and RNA splicing and increased oligodendrocyte development and myelination. Consistently, single-cell RNA sequencing showed cerebral atherosclerosis associated with higher oligodendrocyte abundance. A subset of proteins and modules associated with cerebral atherosclerosis was also associated with Alzheimer’s disease, suggesting shared mechanisms.
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Phenotype data are available at: https://www.radc.rush.edu. Proteomic data from the discovery dataset (ROS/MAP cohorts) are available at https://www.synapse.org/#!Synapse:syn17015098. Proteomic data from the replication dataset (BLSA cohort) are available at https://www.synapse.org/#!Synapse:syn11209141.
Qureshi, A. I. & Caplan, L. R. Intracranial atherosclerosis. Lancet 383, 984–998 (2014).
Roher, A. E. et al. Intracranial atherosclerosis as a contributing factor to Alzheimer’s disease dementia. Alzheimer’s Dement. 7, 436–444 (2011).
Zlokovic, B. V. Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nat. Rev. Neurosci. 12, 723–738 (2011).
Iturria-Medina, Y., Sotero, R. C., Toussaint, P. J., Mateos-Perez, J. M. & Evans, A. C. Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis. Nat. Commun. 7, 11934 (2016).
Sweeney, M. D. et al. Vascular dysfunction—the disregarded partner of Alzheimer’s disease. Alzheimer’s Dement. 15, 158–167 (2019).
Snyder, H. M. et al. Vascular contributions to cognitive impairment and dementia including Alzheimer’s disease. Alzheimer’s Dement. 11, 710–717 (2015).
Hofman, A. et al. Atherosclerosis, apolipoprotein E, and prevalence of dementia and Alzheimer’s disease in the Rotterdam Study. Lancet 349, 151–154 (1997).
Beach, T. G. et al. Circle of Willis atherosclerosis: association with Alzheimer’s disease, neuritic plaques and neurofibrillary tangles. Acta Neuropathol. 113, 13–21 (2007).
Arvanitakis, Z., Capuano, A. W., Leurgans, S. E., Bennett, D. A. & Schneider, J. A. Relation of cerebral vessel disease to Alzheimer’s disease dementia and cognitive function in elderly people: a cross-sectional study. Lancet Neurol. 15, 934–943 (2016).
Dawe, R. J. et al. Postmortem MRI: a novel window into the neurobiology of late life cognitive decline. Neurobiol. Aging 45, 169–177 (2016).
Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).
Vemuri, P. et al. Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly. Brain 138, 761–771 (2015).
Gustavsson, A. M. et al. Midlife Atherosclerosis and development of Alzheimer or vascular dementia. Ann. Neurol. 87, 52–62 (2020).
Gottesman, R. F. et al. Association of intracranial atherosclerotic disease with brain β-amyloid deposition: secondary analysis of the ARIC study. JAMA Neurol. 77, 350–357 (2020).
Soldan, A. et al. White matter hyperintensities and CSF Alzheimer disease biomarkers in preclinical Alzheimer disease. Neurology 94, e950–e960 (2020).
Khalil, M. et al. Neurofilaments as biomarkers in neurological disorders. Nat. Rev. Neurol. 14, 577–589 (2018).
Zetterberg, H. et al. Association of cerebrospinal fluid neurofilament light concentration with Alzheimer disease progression. JAMA Neurol. 73, 60–67 (2016).
Preische, O. et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat. Med. 25, 277–283 (2019).
Olsson, B. et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Lancet Neurol. 15, 673–684 (2016).
Bennett, D. A. et al. Religious Orders Study and Rush Memory and Aging Project. J. Alzheimer’s Dis. 64, S161–s189 (2018).
McKhann, G. et al. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939–944 (1984).
Schneider, J. A. et al. Relation of cerebral infarctions to dementia and cognitive function in older persons. Neurology 60, 1082–1088 (2003).
Arvanitakis, Z., Leurgans, S. E., Barnes, L. L., Bennett, D. A. & Schneider, J. A. Microinfarct pathology, dementia, and cognitive systems. Stroke 42, 722–727 (2011).
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, 599–609 (2012).
Schneider, J. A. et al. Cognitive impairment, decline and fluctuations in older community-dwelling subjects with Lewy bodies. Brain 135, 3005–3014 (2012).
Nag, S. et al. Hippocampal sclerosis and TDP-43 pathology in aging and Alzheimer disease. Ann. Neurol. 77, 942–952 (2015).
Wilson, R. S. et al. TDP-43 pathology, cognitive decline, and dementia in old age. JAMA Neurol. 70, 1418–1424 (2013).
Boyle, P. A. et al. Cerebral amyloid angiopathy and cognitive outcomes in community-based older persons. Neurology 85, 1930–1936 (2015).
Schneider, J. A., Arvanitakis, Z., Leurgans, S. E. & Bennett, D. A. The neuropathology of probable Alzheimer disease and mild cognitive impairment. Ann. Neurol. 66, 200–208 (2009).
Dawe, R. J. et al. Ex vivo T2 relaxation: associations with age-related neuropathology and cognition. Neurobiol. Aging 35, 1549–1561 (2014).
Dawe, R. J. et al. Postmortem brain MRI is related to cognitive decline, independent of cerebral vessel disease in older adults. Neurobiol. Aging 69, 177–184 (2018).
Dawe, R. J., Bennett, D. A., Schneider, J. A., Vasireddi, S. K. & Arfanakis, K. Postmortem MRI of human brain hemispheres: T2 relaxation times during formaldehyde fixation. Magn. Reson. Med. 61, 810–818 (2009).
Friston, K. J., Worsley, K. J., Frackowiak, R. S., Mazziotta, J. C. & Evans, A. C. Assessing the significance of focal activations using their spatial extent. Hum. Brain Mapp. 1, 210–220 (1994).
Worsley, K. J. et al. A unified statistical approach for determining significant signals in images of cerebral activation. Hum. Brain Mapp. 4, 58–73 (1996).
Ping, L. et al. Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s disease. Sci. Data 5, 180036 (2018).
Johnson, E. C. B. et al. Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease. Mol. Neurodegener. 13, 52 (2018).
Mertins, P. et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry. Nat. Protoc. 13, 1632–1661 (2018).
McAlister, G. C. et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150–7158 (2014).
Wingo, T. S. et al. Integrating next-generation genomic sequencing and mass spectrometry to estimate allele-specific protein abundance in human brain. J. Proteome Res. 16, 3336–3347 (2017).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Sharma, K. et al. Cell type- and brain region-resolved mouse brain proteome. Nat. Neurosci. 18, 1819–1831 (2015).
Landgraf, P. et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401–1414 (2007).
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Zambon, A. C. et al. GO-Elite: a flexible solution for pathway and ontology over-representation. Bioinformatics 28, 2209–2210 (2012).
Seddighi, S. et al. SPARCL1 accelerates symptom onset in Alzheimer’s disease and influences brain structure and function during aging. J. Alzheimer’s Dis. 61, 401–414 (2018).
Dolan, H. et al. Atherosclerosis, dementia, and Alzheimer disease in the Baltimore Longitudinal Study of Aging cohort. Ann. Neurol. 68, 231–240 (2010).
O’Brien, R. J. et al. Neuropathologic studies of the Baltimore Longitudinal Study of Aging (BLSA). J. Alzheimer’s Dis. 18, 665–675 (2009).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001).
We thank the participants of the ROS and MAP studies and participants in the Baltimore Longitudinal Study of Aging for their time, participation and invaluable contributions. Support was provided by NIH grant no. R01 AG056533, grant no. R01 AG053960, the Accelerating Medicine Partnership for AD (grant nos. U01 AG046152, U01 AG046161, U01 AG061356 and U01 AG061357), the Emory Alzheimer’s Disease Research Center (grant no. P50 AG025688), the NINDS Emory Neuroscience Core (grant no. P30 NS055077), the Johns Hopkins Alzheimer’s Disease Research Center (grant no. P50 AG005146), and NIH grant nos. R01 AG017917 (D.A.B.), R01 AG015819 (D.A.B.), RC2 AG036547 (D.A.B.), P30 AG10161 (D.A.B.), R01 AG042210 (J.A.S.), R01 AG064233 (J.A.S), UH2-3 NS100599 (J.A.S.), U19 AG033655, R21 AG055844 (J.C.T.), and in part by the intramural program of the NIH, National Institute on Aging. A.P.W. is also supported by grant nos. U01 MH115484 and I01 BX003853. T.S.W. is also supported by NIH grant nos. RF1 AG057470, R56 AG062256, R56 AG060757 and R56 AG062633. N.T.S. is also supported by the Alzheimer’s Association, Alzheimer’s Research UK, The Michael J. Fox Foundation for Parkinson’s Research, the Weston Brain Institute Biomarkers Across Neurodegenerative Diseases (grant no. 11060), NIH grant no. R01 AG061800 and NIH grant no. R01 AG057911.
The authors declare no competing interests.
Peer review information Nature Neuroscience thanks Costantino Iadecola and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This figure summarizes the association between CA and oligodendrocyte abundance estimated from single-cell nuclear RNA sequencing of human dorsolateral prefrontal cortex in an independent dataset. Higher cerebral atherosclerosis was associated with higher oligodendrocyte abundance in the dorsolateral prefrontal cortex after adjusting for sex, age, and 8 other measured pathologies using linear regression (p = 0.003, β = 0.077, N = 48). For each box plot, the box reflects the first and third quartile, the dark horizontal line reflects the median, the vertical lines extending from the boxes show the 1.5x the interquartile range, and points beyond the lines are outliers.
Extended Data Fig. 2 Association between NEFL or NEFM brain protein and cerebral atherosclerosis, AD.
(a) Boxplot of dorsolateral prefrontal cortex NEFL protein level for each level of CA (that is, none to severe). NEFL protein levels were identified as associated with CA using linear regression adjusted for relevant covariates and 8 other pathologies (N = 375; p = 0.0002; adjusted p = 0.034; Fig. 1). (b) Boxplot of dorsolateral prefrontal cortex NEFL protein level by clinical diagnosis (that is, cognitive normal control [control], mild cognitive impairment [MCI], and AD). MCI is generally regarded as an intermediate stage between cognitively normal and AD. NEFL protein levels were identified as associated with AD by logistic regression adjusted for relevant covariates (N = 383; p = 0.0025; adjusted p = 0.0322; Fig. 1). (c) Boxplot of dorsolateral prefrontal cortex NEFM protein level for each level of CA (that is, none to severe). NEFM protein levels were identified as associated with CA using linear regression adjusted for relevant covariates and 8 other pathologies (N = 375; p = 0.0006; adjusted p = 0.045). (d) Boxplot of dorsolateral prefrontal cortex NEFM protein level by clinical diagnosis (that is, cognitive normal control [control], mild cognitive impairment [MCI], and AD). NEFM protein levels were identified as associated with AD by logistic regression adjusted for relevant covariates (N = 383; p = 0.0019; adjusted p = 0.028). For each boxplot, the box reflects the first and third quartile, the dark horizontal line reflects the median, the vertical lines extending from the boxes show the 1.5x the interquartile range, and points beyond the lines are outliers. (e) Proteome-wide adjusted p-values for associations between NEFL and NEFM brain protein levels and each of the 9 measured pathologies adjusting for the remaining 8 measured pathologies. Associations were tested with regression and the provided p-values were adjusted for multiple testing using Benjamin-Hochberg false discovery rate.
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Wingo, A.P., Fan, W., Duong, D.M. et al. Shared proteomic effects of cerebral atherosclerosis and Alzheimer’s disease on the human brain. Nat Neurosci 23, 696–700 (2020). https://doi.org/10.1038/s41593-020-0635-5
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