Dube U, Del-Aguila JL, Li Z, Budde JP, Jiang S, Hsu S, Ibanez L, Fernandez MV, Farias F, Norton J, Gentsch J, Wang F, Dominantly Inherited Alzheimer Network (DIAN), Salloway S, Masters CL, Lee JH, Graff-Radford NR, Chhatwal JP, Bateman RJ, Morris JC, Karch CM, Harari O, Cruchaga C. An atlas of cortical circular RNA expression in Alzheimer disease brains demonstrates clinical and pathological associations. Nat Neurosci. 2019 Nov;22(11):1903-1912. Epub 2019 Oct 7 PubMed.

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  1. In this manuscript, “An atlas of cortical circular RNA expression demonstrates clinical and pathological associations with Alzheimer disease,” Dube et al. evaluated parietal RNA-Seq data from 13 control and 83 AD samples from Washington University and an independent AD RNA-Seq data from a Mount Sinai Brain Bank data set of 195 individuals and four cortical regions (inferior frontal gyrus, frontal pole, superior temporal gyrus, parahippocampal gyrus) to quantify circular RNA (circRNA) expression. They evaluated 3,547 circRNAs in the discovery and 3,924 circRNAs in the replication cohort. The authors performed associations between brain circRNA levels, AD status, neuropathology (Braak scores) and clinical impairment (clinical dementia rating scale).

    They identified 37 circRNAs that associated with at least one of these outcomes in the discovery cohort. Meta-analysis of the findings after inclusion of the replication cohort using the inferior frontal gyrus data revealed 164 circRNAs that associated with one of the AD traits and survived Bonferroni correction. Three circRNAs were consistently associated with all AD traits across all meta-analysis involving all four cortical regions of the replication cohort; and 11 such circRNAs associated with at least two of the traits, suggesting replicability of this subset across brain regions. The authors evaluated associations in a small subset of individuals (12 AD versus 53 controls) with pathologic evidence of AD but clinical evidence of at most mild dementia (CDR≤0.5) and found consistencies with those from the WashU and Mount Sinai cohorts, including those with CDR>0.5 (214 AD). This is not surprising since the underlying neuropathology in both subsets is AD neuropathology regardless of the clinical severity observed.

    Investigation of RNA-Seq data from 21 autosomal-dominant AD parietal cortex samples revealed 59 circRNAs that were differentially expressed even after correcting for Braak stage in both this and the discovery data sets, where the effect sizes were greater in the former. The top 10 circRNAs were found to explain more of the proportion of variation in the traits assessed than APOE4 or estimated neuronal proportions. Two of the AD-associated circRNAs (circCORO1C and circHOMER1) reside in co-expression networks that also include some AD risk genes. The authors also identified putative miRNA binding sites for microRNAs within circRNAs.

    This study adds to the growing body of literature which highlights gene expression differences between the brains of AD patients versus controls or those with other neurodegenerative diseases (Allen et al., 2018; Zhang et al., 2013; McKenzie et al., 2017; Raj et al., 2018). These studies identified differentially expressed genes and networks that are implicated in biological processes, including innate immunity and myelination. Some of these networks were also found to be enriched for genes that harbor risk variants for AD. Collectively, these studies pinpoint biological pathways and key molecules in these pathways that may drive AD and therefore be potential therapeutic targets.

    An important potential caveat in studies that utilize autopsy tissue is the inability to discern cause versus effect, as the detected changes in the transcriptome and proteome (Seyfried et al., 2017) could be a consequence, rather than a cause of the neuropathology. Cellular proportion changes secondary to the disease process can also account for the detected differential expression. Some studies address this concern by adjusting for cellular proportions using surrogate markers (Allen et al., 2018) and/or incorporating data from brain regions that are essentially unaffected by the gross disease pathology (Allen et al., 2018; Allen et al., 2018). The study by Dube et al. indicates their adjustment for cell proportion changes, which partially alleviates this concern. It will be important to determine in future studies whether these findings replicate in brain regions that are devoid of neuropathology, which will support their potential causality in the disease process.

    The findings also need validation through both microRNA and protein studies from the same brain samples to determine whether circRNAs indeed influence microRNA levels and whether these effects are translated into changes in protein levels in the brain. Integration of genomic data will help determine whether genetic variation accounts for any of the observed cirRNA changes and whether such genetic variants also account for AD risk, which will garner support for an AD causal effect for this mechanism.

    Finally, this study is another example of the significant utility of the multi-omics data generated and shared by the Accelerating Medicines Partnership-Alzheimer’s Disease (AMP-AD) community (Allen et al., 2016; Wang et al., 2018; Ping et al., 2018; St John-Williams et al., 2017; De Jager et al., 2018). Dube et al. utilized the brain transcriptome data generated and shared by the AMP-AD Mount Sinai team. The available data from AMP-AD should enable replication of these findings in additional and larger data sets.

    References:

    Allen M, Wang X, Burgess JD, Watzlawik J, Serie DJ, Younkin CS, Nguyen T, Malphrus KG, Lincoln S, Carrasquillo MM, Ho C, Chakrabarty P, Strickland S, Murray ME, Swarup V, Geschwind DH, Seyfried NT, Dammer EB, Lah JJ, Levey AI, Golde TE, Funk C, Li H, Price ND, Petersen RC, Graff-Radford NR, Younkin SG, Dickson DW, Crook JR, Asmann YW, Ertekin-Taner N. Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases. Alzheimers Dement. 2018 Mar;14(3):352-366. Epub 2017 Oct 31 PubMed.

    Zhang B, Gaiteri C, Bodea LG, Wang Z, McElwee J, Podtelezhnikov AA, Zhang C, Xie T, Tran L, Dobrin R, Fluder E, Clurman B, Melquist S, Narayanan M, Suver C, Shah H, Mahajan M, Gillis T, Mysore J, MacDonald ME, Lamb JR, Bennett DA, Molony C, Stone DJ, Gudnason V, Myers AJ, Schadt EE, Neumann H, Zhu J, Emilsson V. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell. 2013 Apr 25;153(3):707-20. PubMed.

    McKenzie AT, Moyon S, Wang M, Katsyv I, Song WM, Zhou X, Dammer EB, Duong DM, Aaker J, Zhao Y, Beckmann N, Wang P, Zhu J, Lah JJ, Seyfried NT, Levey AI, Katsel P, Haroutunian V, Schadt EE, Popko B, Casaccia P, Zhang B. Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer's disease. Mol Neurodegener. 2017 Nov 6;12(1):82. PubMed.

    Raj T, Li YI, Wong G, Humphrey J, Wang M, Ramdhani S, Wang YC, Ng B, Gupta I, Haroutunian V, Schadt EE, Young-Pearse T, Mostafavi S, Zhang B, Sklar P, Bennett DA, De Jager PL. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer's disease susceptibility. Nat Genet. 2018 Nov;50(11):1584-1592. Epub 2018 Oct 8 PubMed.

    Seyfried NT, Dammer EB, Swarup V, Nandakumar D, Duong DM, Yin L, Deng Q, Nguyen T, Hales CM, Wingo T, Glass J, Gearing M, Thambisetty M, Troncoso JC, Geschwind DH, Lah JJ, Levey AI. A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer's Disease. Cell Syst. 2017 Jan 25;4(1):60-72.e4. Epub 2016 Dec 15 PubMed.

    Allen M, Wang X, Serie DJ, Strickland SL, Burgess JD, Koga S, Younkin CS, Nguyen TT, Malphrus KG, Lincoln SJ, Alamprese M, Zhu K, Chang R, Carrasquillo MM, Kouri N, Murray ME, Reddy JS, Funk C, Price ND, Golde TE, Younkin SG, Asmann YW, Crook JE, Dickson DW, Ertekin-Taner N. Divergent brain gene expression patterns associate with distinct cell-specific tau neuropathology traits in progressive supranuclear palsy. Acta Neuropathol. 2018 Nov;136(5):709-727. Epub 2018 Aug 22 PubMed.

    Allen M, Carrasquillo MM, Funk C, Heavner BD, Zou F, Younkin CS, Burgess JD, Chai HS, Crook J, Eddy JA, Li H, Logsdon B, Peters MA, Dang KK, Wang X, Serie D, Wang C, Nguyen T, Lincoln S, Malphrus K, Bisceglio G, Li M, Golde TE, Mangravite LM, Asmann Y, Price ND, Petersen RC, Graff-Radford NR, Dickson DW, Younkin SG, Ertekin-Taner N. Human whole genome genotype and transcriptome data for Alzheimer's and other neurodegenerative diseases. Sci Data. 2016 Oct 11;3:160089. PubMed.

    Wang M, Beckmann ND, Roussos P, Wang E, Zhou X, Wang Q, Ming C, Neff R, Ma W, Fullard JF, Hauberg ME, Bendl J, Peters MA, Logsdon B, Wang P, Mahajan M, Mangravite LM, Dammer EB, Duong DM, Lah JJ, Seyfried NT, Levey AI, Buxbaum JD, Ehrlich M, Gandy S, Katsel P, Haroutunian V, Schadt E, Zhang B. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer's disease. Sci Data. 2018 Sep 11;5:180185. PubMed.

    Ping L, Duong DM, Yin L, Gearing M, Lah JJ, Levey AI, Seyfried NT. Global quantitative analysis of the human brain proteome in Alzheimer's and Parkinson's Disease. Sci Data. 2018 Mar 13;5:180036. PubMed.

    St John-Williams L, Blach C, Toledo JB, Rotroff DM, Kim S, Klavins K, Baillie R, Han X, Mahmoudiandehkordi S, Jack J, Massaro TJ, Lucas JE, Louie G, Motsinger-Reif AA, Risacher SL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Metabolomics Consortium, Saykin AJ, Kastenmüller G, Arnold M, Koal T, Moseley MA, Mangravite LM, Peters MA, Tenenbaum JD, Thompson JW, Kaddurah-Daouk R. Targeted metabolomics and medication classification data from participants in the ADNI1 cohort. Sci Data. 2017 Oct 17;4:170140. PubMed.

    De Jager PL, Ma Y, McCabe C, Xu J, Vardarajan BN, Felsky D, Klein HU, White CC, Peters MA, Lodgson B, Nejad P, Tang A, Mangravite LM, Yu L, Gaiteri C, Mostafavi S, Schneider JA, Bennett DA. A multi-omic atlas of the human frontal cortex for aging and Alzheimer's disease research. Sci Data. 2018 Aug 7;5:180142. PubMed.

    View all comments by Nilufer Ertekin-Taner

  2. Although the biogenesis of Aβ in familial AD is well-documented, Aβ production in sporadic AD is not well understood. We identified circAβ-a and analyzed its formation in prefrontal cortex samples of sporadic AD patients, non-dementia controls, and transient transfection assays.

    According to circbase, circAβ-a (hsa_circ_0007556) can be found in various human brain tissues (diencephalon, cerebellum, occipital lobe, frontal cortex, parietal lobe, temporal lobe). We also identified 16 additional circAβ forms both in frontal lobe and hippocampus. With the aid of intron-mediated enhancement, we confirmed that circAβ-a served as a template for the biosynthesis of an Aβ-related protein (Aβ175) in HEK293 cells.

    In addition, the unique C-terminal peptide of Aβ175 was identified in human brain samples. Furthermore, Aβ175 was processed into Aβ peptides, representing a novel route of Aβ generation that might give rise to new perspectives on the molecular mechanisms leading to the manifestation of Alzheimer’s disease.

    Recently, circRNA expression and its correlation to AD pathology was investigated. With extensive RNA-sequencing of AD brain samples, Dube et al. report that 10 circRNAs have strong pathological associations with AD. Quite surprisingly, circAβ-a and its other isoforms were not included. This could be due to insufficient depth of sequencing with respect to circAβ isoforms as a result of bias in library construction.

    Furthermore, the involvement of circAβ variants in AD pathology may lie in their translation activation, rather than changes in their circRNA levels. Interestingly, the mouse ortholog of circRTN4, one of the circRNAs with strong correlation to AD, has been previously reported to produce proteins (both monomer and repeating multimers by rolling cycle translation). Meanwhile, RTN4 protein (NogoA) is known to play a role in AD through BACE1 activity regulation. Thus, circRTN4-derived polypeptides may have a regulatory role in AD akin to its NogoA protein counterpart.

    Additional polypeptide-producing circRNAs with strong AD association could possibly play a role. In any event, the investigation of biological roles of circAβ-a offers new perspectives in the search for underlying mechanisms of sporadic Alzheimer’s disease, which could ultimately lead to the design of disease-modifying drugs.

    References:

    Mo D, Li X, Raabe CA, Rozhdestvensky TS, Skryabin BV, Brosius J. Circular RNA Encoded Amyloid Beta peptides-A Novel Putative Player in Alzheimer's Disease. Cells. 2020 Sep 29;9(10) PubMed.

    Mo D, Li X, Raabe CA, Cui D, Vollmar JF, Rozhdestvensky TS, Skryabin BV, Brosius J. A universal approach to investigate circRNA protein coding function. Sci Rep. 2019 Aug 12;9(1):11684. PubMed.

    Mehta SL, Dempsey RJ, Vemuganti R. Role of circular RNAs in brain development and CNS diseases. Prog Neurobiol. 2020 Mar;186:101746. Epub 2020 Jan 10 PubMed.

    View all comments by Dingding Mo

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