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An atlas of cortical circular RNA expression in Alzheimer disease brains demonstrates clinical and pathological associations

Nature Neuroscience volume 22, pages 1903–1912 (2019)Cite this article

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Abstract

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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Fig. 1: Cortical circRNAs are associated with AD traits.
Fig. 2: Changes in cortical circRNA expression tracks with AD clinical severity.
Fig. 3: AD-associated circRNAs explain more of the observed variation in clinical dementia rating compared with number of APOE4 alleles or the estimated proportion of neurons.
Fig. 4: AD-associated circRNAs co-express with AD-relevant genes.

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Data availability

Knight ADRC dataset: NG00083. Sequencing information derived from ADAD samples is protected and requires additional authorization from DIAN for access. Mount Sinai Brain Bank replication dataset: syn3159438.

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Acknowledgements

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.

Author information

Authors and Affiliations

  1. Medical Scientist Training Program, Washington University School of Medicine, St. Louis, MO, USA

    Umber Dube

  2. Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave. CB8134, St. Louis, MO, USA

    Umber Dube, Jorge L Del-Aguila, Zeran Li, John P Budde, Shan Jiang, Simon Hsu, Laura Ibanez, Maria Victoria Fernandez, Fabiana Farias, Joanne Norton, Jen Gentsch, Fengxian Wang, Celeste M Karch, Oscar Harari & Carlos Cruchaga

  3. Department of Neurology, Washington University School of Medicine, St Louis, MO, USA

    Umber Dube, Randall J Bateman, John C Morris & Carlos Cruchaga

  4. Alpert Medical School of Brown University, 345 Blackstone Boulevard, Providence, RI, USA

    Stephen Salloway

  5. The Florey Institute, the University of Melbourne. Level 1, Howard Florey Laboratories, Royal Parade, Parkville, VIC, Australia

    Colin L Masters

  6. Department of Neurology, University of Ulsan College of Medicine, Seoul, Korea

    Jae-Hong Lee

  7. Department of Neurology, Mayo Clinic, Jacksonville, FL, USA

    Neill R Graff-Radford

  8. Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA, USA

    Jasmeer P Chhatwal

  9. Hope Center for Neurological Disorders. Washington University School of Medicine, St. Louis, MO, USA

    Celeste M Karch & Carlos Cruchaga

  10. NeuroGenomics and Informatics, Washington University School of Medicine, St. Louis, MO, USA

    Carlos Cruchaga

  11. FLENI Institute of Neurological Research (Fundacion para la Lucha contra las Enfermedades Neurologicas de la Infancia), Buenos Aires, Argentina

    Ricardo Allegri, Patricio Chrem & Noelia Edigo

  12. Washington University in St. Louis School of Medicine, St. Louis, MO, USA

    Fatima Amtashar, Tammie Benzinger, Susan Brandon, Virginia Buckles, Tamara Donahue, Anne Fagan, Angela Farrar, Gigi Flynn, Erin Franklin, Cortaiga Gant, Brian Gordon, Julia Gray, Jenny Gurney, Jason Hassenstab, David Holtzman, Russ Hornbeck, Gina Jerome, Daniel Marcus, Denise Maue-Dreyfus, Eric McDade, Joanne Norton, Richard Perrin, Marc Raichle, Wendy Sigurdson, Laura Swisher, Peter Wang, Mary Wolfsberger, Chengjie Xiong & Xiong Xu

  13. University of Pittsburgh, Pittsburgh, PA, USA

    Sarah Berman, Snezana Ikonomovic & William Klunk

  14. Brown University-Butler Hospital, Providence, RI, USA

    Courtney Bodge

  15. Neuroscience Research Australia, Sydney, Australia

    William Brooks, Mirelle D’Mello & Peter Schofield

  16. Indiana University, Bloomington, IN, USA

    Jill Buck, Marty Farlow & Bernardino Ghetti

  17. Mayo Clinic Jacksonville, Jacksonville, FL, USA

    Sochenda Chea, Jack Clifford & Nilufer Erekin-Taner

  18. University of Southern California, Los Angeles, CA, USA

    Helena Chui, Lucy Montoya & John Ringman

  19. University College London, London, UK

    Jake Cinco, Jane Douglas & Nick Fox

  20. University of California San Diego, San Diego, CA, USA

    Howard Feldman

  21. Osaka City University, Osaka, Japan

    Hisako Fujii, Hiroshi Mori & Hiroyuki Shimada

  22. Edith Cowan University, Perth, Australia

    Samantha Gardener, Ralph Martins, Hamid Sohrabi & Kevin Taddei

  23. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Alison Goate & Jen Wang

  24. Columbia University, New York, NY, USA

    Jill Goldman, Katie Neimeyer & James Noble

  25. Hirosaki University, Hirosaki, Japan

    Mie Hirohara, Takeshi Kawarabayashi, Tomoyo Shiroto & Mikio Shoji

  26. German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany

    Siri Houeland DiBari & Johannes Levin

  27. Niigata University, Niigata, Japan

    Takeshi Ikeuchi & Kensaku Kasuga

  28. German Center for Neurodegnerative Diseases (DZNE) Tubingen, Tubingen, Germany

    Mathias Jucker, Elke Kuder-Buletta & Christoph Laske

  29. University of Michigan, Ann Arbor, MI, USA

    Robert Koeppe

  30. University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    Neal Scott Mason

  31. Tokyo University, Bunkyo, Japan

    Akem Nagamatsu & Kazushi Suzuki

  32. Asan Medical Center, Seoul, South Korea

    Jee Hoon Roh

  33. Brigham and Women’s Hospital, Boston, MA, USA

    Paige Sparks

  34. University of California San Francisco, San Francisco, CA, USA

    Mike Weiner

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  1. Umber Dube
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  2. Jorge L Del-Aguila
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  3. Zeran Li
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  4. John P Budde
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  5. Shan Jiang
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  7. Laura Ibanez
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  8. Maria Victoria Fernandez
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  9. Fabiana Farias
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  10. Joanne Norton
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  11. Jen Gentsch
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  12. Fengxian Wang
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  13. Stephen Salloway
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  14. Colin L Masters
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  19. John C Morris
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  20. Celeste M Karch
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Consortia

the Dominantly Inherited Alzheimer Network (DIAN)

  • Ricardo Allegri
  • , Fatima Amtashar
  • , Tammie Benzinger
  • , Sarah Berman
  • , Courtney Bodge
  • , Susan Brandon
  • , William Brooks
  • , Jill Buck
  • , Virginia Buckles
  • , Sochenda Chea
  • , Patricio Chrem
  • , Helena Chui
  • , Jake Cinco
  • , Jack Clifford
  • , Mirelle D’Mello
  • , Tamara Donahue
  • , Jane Douglas
  • , Noelia Edigo
  • , Nilufer Erekin-Taner
  • , Anne Fagan
  • , Marty Farlow
  • , Angela Farrar
  • , Howard Feldman
  • , Gigi Flynn
  • , Nick Fox
  • , Erin Franklin
  • , Hisako Fujii
  • , Cortaiga Gant
  • , Samantha Gardener
  • , Bernardino Ghetti
  • , Alison Goate
  • , Jill Goldman
  • , Brian Gordon
  • , Julia Gray
  • , Jenny Gurney
  • , Jason Hassenstab
  • , Mie Hirohara
  • , David Holtzman
  • , Russ Hornbeck
  • , Siri Houeland DiBari
  • , Takeshi Ikeuchi
  • , Snezana Ikonomovic
  • , Gina Jerome
  • , Mathias Jucker
  • , Kensaku Kasuga
  • , Takeshi Kawarabayashi
  • , William Klunk
  • , Robert Koeppe
  • , Elke Kuder-Buletta
  • , Christoph Laske
  • , Johannes Levin
  • , Daniel Marcus
  • , Ralph Martins
  • , Neal Scott Mason
  • , Denise Maue-Dreyfus
  • , Eric McDade
  • , Lucy Montoya
  • , Hiroshi Mori
  • , Akem Nagamatsu
  • , Katie Neimeyer
  • , James Noble
  • , Joanne Norton
  • , Richard Perrin
  • , Marc Raichle
  • , John Ringman
  • , Jee Hoon Roh
  • , Peter Schofield
  • , Hiroyuki Shimada
  • , Tomoyo Shiroto
  • , Mikio Shoji
  • , Wendy Sigurdson
  • , Hamid Sohrabi
  • , Paige Sparks
  • , Kazushi Suzuki
  • , Laura Swisher
  • , Kevin Taddei
  • , Jen Wang
  • , Peter Wang
  • , Mike Weiner
  • , Mary Wolfsberger
  • , Chengjie Xiong
  •  & Xiong Xu

Contributions

U.D. conceived the project, designed the study, collected the data, performed the analyses, interpreted the results and wrote the manuscript. J.L.D.A., Z.L., J.P.B., S.J., S.H., L.I., M.V.F., F.F., J.N., J.G., F.W., R.J.B., J.C.M., C.M.K. and O.H. contributed to data collection, data processing, quality control and cleaning. C.M.K., S.S., C.L.M., J.H.L., N.R.G.R., J.P.C., R.J.B., J.C.M. and C.C. contributed samples and/or data to DIAN. C.C. designed the study, collected the data, supervised the analyses, interpreted the results and wrote the manuscript. All authors read and contributed to the final manuscript.

Corresponding author

Correspondence to Carlos Cruchaga.

Ethics declarations

Competing interests

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.

Additional information

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).

Supplementary Figure 3 Quantitative PCR validation of RNA-seq counts and direction of effect.

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.

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Dube, U., Del-Aguila, J.L., Li, Z. et al. An atlas of cortical circular RNA expression in Alzheimer disease brains demonstrates clinical and pathological associations. Nat Neurosci 22, 1903–1912 (2019). https://doi.org/10.1038/s41593-019-0501-5

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