Parietal cortex RNA-sequencing (RNA-seq) data were generated from individuals with and without Alzheimer disease (AD; ncontrol = 13; nAD = 83) from the Knight Alzheimer Disease Research Center (Knight ADRC). Using this and an independent (Mount Sinai Brain Bank (MSBB)) AD RNA-seq dataset, cortical circular RNA (circRNA) expression was quantified in the context of AD. Significant associations were identified between circRNA expression and AD diagnosis, clinical dementia severity and neuropathological severity. It was demonstrated that most circRNA–AD associations are independent of changes in cognate linear messenger RNA expression or estimated brain cell-type proportions. Evidence was provided for circRNA expression changes occurring early in presymptomatic AD and in autosomal dominant AD. It was also observed that AD-associated circRNAs co-expressed with known AD genes. Finally, potential microRNA-binding sites were identified in AD-associated circRNAs for miRNAs predicted to target AD genes. Together, these results highlight the importance of analyzing non-linear RNAs and support future studies exploring the potential roles of circRNAs in AD pathogenesis.
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We thank all the participants and their families, as well as the many institutions and their staff. This work was supported by grants from the National Institutes of Health (NIH: grant nos R01AG044546, P01AG003991, RF1AG053303, R01AG058501, U01AG058922, RF1AG058501 and R01AG057777), the Alzheimer Association (grant nos NIRG-11-200110, BAND-14-338165, AARG-16-441560 and BFG-15-362540), grant no. NIH AG046374 (C.M.K.), Tau Consortium (C.M.K.) and grant no. K23 AG049087 (J.P.C.). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P50 AG05681, P01 AG03991, and P01 AG026276. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine. The results published here are in part based on data obtained from the AMP-AD Knowledge Portal accessed via the cited accession numbers. The MSBB data were generated from postmortem brain tissue collected through the Mount Sinai VA Medical Center Brain Bank and were provided by E. Schadt from Mount Sinai School of Medicine. Data collection and sharing for this project were supported by DIAN (UF1AG032438) funded by the National Institute on Aging, the German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, with partial support by the Research and Development Grants for Dementia from the Japan Agency for Medical Research and Development, and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute. This manuscript has been reviewed by DIAN study investigators for scientific content and consistency of data interpretation with previous DIAN study publications. We acknowledge the altruism of the participants and their families and contributions of the DIAN research and support staff at each of the participating sites for their contributions to this study.
C.C. receives research support from Biogen, EISAI, Alector and Parabon. The funders of the study had no role in the collection, analysis or interpretation of data, in the writing of the report or the decision to submit the paper for publication. C.C. is a member of the advisory board of Vivid genetics, Halia Therapeutics and ADx Healthcare.
Peer review information Nature Neuroscience thanks G. Goodall 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.
Integrated supplementary information
Supplementary Figure 1 Venn diagram depicting the overlap of high-confidence circRNAs called in the different cortical RNA-seq datasets.
Parietal: Knight ADRC and DIAN parietal cortex dataset (nsample = 170); BM10: MSBB: Brodmann Area 10 dataset (nsample = 265); BM22: MSBB: Brodmann Area 22 dataset (nsample = 264); BM36: MSBB: Brodmann Area 36 dataset (nsample = 267); BM44: MSBB: Brodmann Area 44 dataset (nsample = 230).
Supplementary Figure 2 Pearson’s correlation plots demonstrating the imperfect correlation between a neuropathological diagnosis of definite AD (AD_case), a clinical measure of dementia—CDR—and a neuropathological measure of tau tangle pathology—Braak score (Braak).
Parietal: Knight ADRC parietal cortex, discovery dataset (nCDR = 96, nBraak = 86, ncontrol = 13, nAD = 83); BM10: MSBB: Brodmann Area 10 replication dataset (nCDR = 218, nBraak = 209, ncontrol = 46, nDefinite AD = 108); BM22: MSBB: Brodmann Area 22 replication dataset (nCDR = 202, nBraak = 195, ncontrol = 38, nDefinite AD = 97); BM36: MSBB: Brodmann Area 36 replication dataset (nCDR = 187, nBraak = 177, ncontrol = 41, nDefinite AD = 91); BM44: MSBB: Brodmann Area 44 replication dataset (nCDR = 195, nBraak = 188, ncontrol = 40, nDefinite AD = 89).
GAPDH-normalized deltaCt values for 13 Knight ADRC discovery dataset RNA samples (ncontrol = 3, nPreSympAD = 3, nAD = 7) versus RNA-seq-derived counts for the same individuals. A negative correlation is expected since circRNA transcripts with greater expression levels will reach the cycle threshold (Ct) sooner than those with lower expression and consequently have lower DeltaCt values. Shaded areas represent the 95% confidence level interval for predictions from the linear model. Corr: Pearson correlation estimates with significance determined by a two-tailed t-test. Box plot elements: center line (median), box (first and third quartiles), whiskers (quartile ± 1.5 × interquartile range), dots (outlier points as defined by falling outside of whiskers).
Supplementary Figure 4 Overlap between circRNAs significantly associated with different AD traits in the Knight ADRC, parietal discovery dataset.
All circRNAs were significantly associated with the AD trait at a significance less than the false discovery rate threshold of 0.05. CDR: clinical dementia rating, a clinical measure of AD severity; Braak: Braak score, a neuropathological measure of AD severity. Sample size: nCDR = 96, nBraak = 86, ncontrol = 13, nAD = 83.
Supplementary Figure 5 Overlap between circRNAs significantly associated with different AD traits in the meta-analysis of the Knight ADRC parietal discovery and the MSBB BM44 replication datasets.
All circRNAs were significantly associated with the AD trait at a significance less than the false discovery rate threshold of 0.05. CDR: clinical dementia rating, a clinical measure of AD severity; Braak: Braak score, a neuropathological measure of AD severity measuring the number of tau tangles. Discovery, parietal sample size: nCDR = 96, nBraak = 86, ncontrol = 13, nAD = 83; Replication, BM44 sample size: nCDR = 195, nBraak = 188, ncontrol = 40, nDefinite AD = 89.
Supplementary Figure 6 Overlap between circRNAs significantly associated with different AD traits in the meta-analyses of the Knight ADRC parietal discovery and all four cortical regions of the MSBB replication dataset.
Clinical dementia rating (CDR), Braak score, and AD case-control status. PCtx: Knight ADRC parietal cortex, discovery dataset (nCDR = 96, nBraak = 86, ncontrol = 13, nAD = 83); BM10: MSBB: Brodmann Area 10 replication dataset (nCDR = 218, nBraak = 209, ncontrol = 46, nDefinite AD = 108); BM22: MSBB: Brodmann Area 22 replication dataset (nCDR = 202, nBraak = 195, ncontrol = 38, nDefinite AD = 97); BM36: MSBB: Brodmann Area 36 replication dataset (nCDR = 187, nBraak = 177, ncontrol = 41, nDefinite AD = 91); BM44: MSBB: Brodmann Area 44 replication dataset (nCDR = 195, nBraak = 188, ncontrol = 40, nDefinite AD = 89).
Supplementary Figure 7 Overlap between circRNAs significantly associated with mean number of amyloid plaques, a neuropathological measure of AD severity, in the four cortical regions of the MSBB replication dataset.
All circRNAs were significantly associated with mean number of plaques at the false discovery rate threshold of 0.05. BM10: MSBB Brodmann Area 10 replication dataset (nPlaqueMean= 218); BM22: MSBB Brodmann Area 22 replication dataset (nPlaqueMean= 202); BM36: MSBB Brodmann Area 36 replication dataset (nPlaqueMean= 187); BM44: MSBB Brodmann Area 44 replication dataset (nPlaqueMean= 195).
Supplementary Figure 8 Overlap between circRNAs significantly associated with ADAD versus Braak-score-adjusted AD and versus controls (CO).
ADAD versus AD* analysis was adjusted for neuropathological severity, measured by Braak score. All circRNAs were significantly associated at the false discovery rate threshold of 0.05. Sample sizes: ADADvsCO (nADAD = 17, ncontrol = 13); ADADvsAD* (samples with available Braak score: nADAD = 17, nAD = 73).
Supplementary Figure 9 AD-associated circRNAs explain more of the observed variation in Braak score compared with number of APOE4 alleles or the estimated proportion of neurons.
Percent of variation in Braak score (Braak) explained by the top 10, most meta-analysis significant Braak-associated circRNAs compared to known contributors: number of APOE4 alleles – the most common genetic risk factor for AD – and the estimated proportion of neurons. Knight ADRC: PCtx – parietal discovery dataset (nBraak = 86); MSBB BM44 – inferior frontal gyrus replication dataset (nBraak = 188).
Supplementary Figure 10 AD-associated circRNAs improve sensitivity and specificity of logistic models predicting AD case status.
The base, genetic-demographic model includes the differential expression covariates (post mortem interval, transcript integrity number, age of death, batch, sex, genetic ancestry) as well as number of APOE4 alleles. The Circ model includes normalized counts for the top 10 circRNAs most significantly associated with CDR on meta-analysis. The Circ+Base model combined the previous two models together. PCtx: Parietal discovery dataset (ncontrol = 13, nAD = 83); BM10: Brodmann Area 10 replication dataset (ncontrol = 46, nDefinite AD = 108); BM22: Brodmann Area 22 replication dataset (ncontrol = 38, nDefinite AD = 97); BM36: Brodmann Area 36 replication dataset (ncontrol = 41, nDefinite AD = 91); BM44: Brodmann Area 44 replication dataset (ncontrol = 40, nDefinite AD = 89).
Supplementary Figure 11 Backsplice junction filtering to remove artifactual junctions and generate a set of high confidence circRNA counts.
Backsplice junctions detected in spiked-in, linear ERCC RNA in the Knight ADRC parietal, discovery dataset (nsamples = 170) are artifactual. Two levels of filtering are depicted. The minimum number of samples filter indicates how many different samples a particular backsplice has to be observed in to be included. The minimum circular to linear (Circ:Linear) filter indicates the minimum percentage of reads classified as circular, compared to linear, a backsplice must be supported by to be included. Graph points and lines are depicted with jitter for clarity in observing overlapping points and lines.