Article | Published:

Somatic APP gene recombination in Alzheimer’s disease and normal neurons

Naturevolume 563pages639645 (2018) | Download Citation


The diversity and complexity of the human brain are widely assumed to be encoded within a constant genome. Somatic gene recombination, which changes germline DNA sequences to increase molecular diversity, could theoretically alter this code but has not been documented in the brain, to our knowledge. Here we describe recombination of the Alzheimer’s disease-related gene APP, which encodes amyloid precursor protein, in human neurons, occurring mosaically as thousands of variant ‘genomic cDNAs’ (gencDNAs). gencDNAs lacked introns and ranged from full-length cDNA copies of expressed, brain-specific RNA splice variants to myriad smaller forms that contained intra-exonic junctions, insertions, deletions, and/or single nucleotide variations. DNA in situ hybridization identified gencDNAs within single neurons that were distinct from wild-type loci and absent from non-neuronal cells. Mechanistic studies supported neuronal ‘retro-insertion’ of RNA to produce gencDNAs; this process involved transcription, DNA breaks, reverse transcriptase activity, and age. Neurons from individuals with sporadic Alzheimer’s disease showed increased gencDNA diversity, including eleven mutations known to be associated with familial Alzheimer’s disease that were absent from healthy neurons. Neuronal gene recombination may allow ‘recording’ of neural activity for selective ‘playback’ of preferred gene variants whose expression bypasses splicing; this has implications for cellular diversity, learning and memory, plasticity, and diseases of the human brain.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Data availability

Fastq files of SMRT sequences performed on a PacBio Sequel and Illumina sequences on NextSeq500 have been deposited in NCBI Sequence Read Archive (BioProject ID: PRJNA493258). The PacBio produced RNA-seq data sets from whole brain and temporal lobe supporting the findings of this study are available at, and from the authors upon reasonable request and with permission of PacBio, respectively. The source codes of the customized algorithms are available on GitHub ( and

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Dreyer, W. J., Gray, W. R. & Hood, L. The genetic, molecular and cellular basis of antibody formation: some facts and a unifying hypothesis. Cold Spring Harb. Symp. Quant. Biol. 32, 353–367 (1967).

  2. 2.

    Hozumi, N. & Tonegawa, S. Evidence for somatic rearrangement of immunoglobulin genes coding for variable and constant regions. Proc. Natl Acad. Sci. USA 73, 3628–3632 (1976).

  3. 3.

    Chun, J. J., Schatz, D. G., Oettinger, M. A., Jaenisch, R. & Baltimore, D. The recombination activating gene-1 (RAG-1) transcript is present in the murine central nervous system. Cell 64, 189–200 (1991).

  4. 4.

    Buck, L. & Axel, R. A novel multigene family may encode odorant receptors: a molecular basis for odor recognition. Cell 65, 175–187 (1991).

  5. 5.

    Rohrback, S., Siddoway, B., Liu, C. S. & Chun, J. Genomic mosaicism in the developing and adult brain. Dev. Neurobiol. (2018).

  6. 6.

    Rehen, S. K. et al. Chromosomal variation in neurons of the developing and adult mammalian nervous system. Proc. Natl Acad. Sci. USA 98, 13361–13366 (2001).

  7. 7.

    Rehen, S. K. et al. Constitutional aneuploidy in the normal human brain. J. Neurosci. 25, 2176–2180 (2005).

  8. 8.

    Westra, J. W. et al. Neuronal DNA content variation (DCV) with regional and individual differences in the human brain. J. Comp. Neurol. 518, 3981–4000 (2010).

  9. 9.

    McConnell, M. J. et al. Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network. Science 356, eaal1641 (2017).

  10. 10.

    Bushman, D. M. et al. Genomic mosaicism with increased amyloid precursor protein (APP) gene copy number in single neurons from sporadic Alzheimer’s disease brains. eLife 4, (2015).

  11. 11.

    Selkoe, D. J. & Hardy, J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 8, 595–608 (2016).

  12. 12.

    Murrell, J., Farlow, M., Ghetti, B. & Benson, M. D. A mutation in the amyloid precursor protein associated with hereditary Alzheimer’s disease. Science 254, 97–99 (1991).

  13. 13.

    Hooli, B. V. et al. Rare autosomal copy number variations in early-onset familial Alzheimer’s disease. Mol. Psychiatry 19, 676–681 (2014).

  14. 14.

    Wiseman, F. K. et al. A genetic cause of Alzheimer disease: mechanistic insights from Down syndrome. Nat. Rev. Neurosci. 16, 564–574 (2015).

  15. 15.

    Rohrback, S. et al. Submegabase copy number variations arise during cerebral cortical neurogenesis as revealed by single-cell whole-genome sequencing. Proc. Natl Acad. Sci. USA (2018).

  16. 16.

    Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).

  17. 17.

    Dawkins, E. & Small, D. H. Insights into the physiological function of the β-amyloid precursor protein: beyond Alzheimer’s disease. J. Neurochem. 129, 756–769 (2014).

  18. 18.

    Mucke, L. et al. High-level neuronal expression of Aβ1–42 in wild-type human amyloid protein precursor transgenic mice: synaptotoxicity without plaque formation. J. Neurosci. 20, 4050–4058 (2000).

  19. 19.

    Ming, G. L. & Song, H. Adult neurogenesis in the mammalian brain: significant answers and significant questions. Neuron 70, 687–702 (2011).

  20. 20.

    Esnault, C., Maestre, J. & Heidmann, T. Human LINE retrotransposons generate processed pseudogenes. Nat. Genet. 24, 363–367 (2000).

  21. 21.

    Harrison, P. M., Zheng, D., Zhang, Z., Carriero, N. & Gerstein, M. Transcribed processed pseudogenes in the human genome: an intermediate form of expressed retrosequence lacking protein-coding ability. Nucleic Acids Res. 33, 2374–2383 (2005).

  22. 22.

    Vanin, E. F. Processed pseudogenes: characteristics and evolution. Annu. Rev. Genet. 19, 253–272 (1985).

  23. 23.

    Kalyana-Sundaram, S. et al. Expressed pseudogenes in the transcriptional landscape of human cancers. Cell 149, 1622–1634 (2012).

  24. 24.

    Evrony, G. D., Lee, E., Park, P. J. & Walsh, C. A. Resolving rates of mutation in the brain using single-neuron genomics. eLife 5, e12966 (2016).

  25. 25.

    Muotri, A. R. et al. Somatic mosaicism in neuronal precursor cells mediated by L1 retrotransposition. Nature 435, 903–910 (2005).

  26. 26.

    Upton, K. R. et al. Ubiquitous L1 mosaicism in hippocampal neurons. Cell 161, 228–239 (2015).

  27. 27.

    Cleary, J. D. & Ranum, L. P. Repeat associated non-ATG (RAN) translation: new starts in microsatellite expansion disorders. Curr. Opin. Genet. Dev. 26, 6–15 (2014).

  28. 28.

    Jain, A. & Vale, R. D. RNA phase transitions in repeat expansion disorders. Nature 546, 243–247 (2017).

  29. 29.

    McClintock, B. The significance of responses of the genome to challenge. Science 226, 792–801 (1984).

  30. 30.

    Egan, M. F. et al. Randomized trial of verubecestat for mild-to-moderate Alzheimer’s disease. N. Engl. J. Med. 378, 1691–1703 (2018).

  31. 31.

    Brouwers, N. et al. Genetic risk and transcriptional variability of amyloid precursor protein in Alzheimer’s disease. Brain 129, 2984–2991 (2006).

  32. 32.

    Huang, Y. A., Zhou, B., Wernig, M. & Sudhof, T. C. ApoE2, ApoE3, and ApoE4 differentially stimulate APP transcription and Aβ secretion. Cell 168, 427–441.e21 (2017).

  33. 33.

    Turner, R. S. et al. An individual with human immunodeficiency virus, dementia, and central nervous system amyloid deposition. Alzheimers Dement. (Amst.) 4, 1–5 (2016).

  34. 34.

    Centers for Disease Control and Prevention. HIV Surveillance Report, 2016 Vol. 28 (2017).

  35. 35.

    Suberbielle, E. et al. Physiologic brain activity causes DNA double-strand breaks in neurons, with exacerbation by amyloid-β. Nat. Neurosci. 16, 613–621 (2013).

  36. 36.

    Mortimer, J. A., French, L. R., Hutton, J. T. & Schuman, L. M. Head injury as a risk factor for Alzheimer’s disease. Neurology 35, 264–267 (1985).

  37. 37.

    Pravdenkova, S. V., Basnakian, A. G., James, S. J. & Andersen, B. J. DNA fragmentation and nuclear endonuclease activity in rat brain after severe closed head injury. Brain Res. 729, 151–155 (1996).

  38. 38.

    Nhan, H. S., Chiang, K. & Koo, E. H. The multifaceted nature of amyloid precursor protein and its proteolytic fragments: friends and foes. Acta Neuropathol. 129, 1–19 (2015).

  39. 39.

    Guzman-Karlsson, M. C., Meadows, J. P., Gavin, C. F., Hablitz, J. J. & Sweatt, J. D. Transcriptional and epigenetic regulation of Hebbian and non-Hebbian plasticity. Neuropharmacology 80, 3–17 (2014).

  40. 40.

    Hattori, D., Millard, S. S., Wojtowicz, W. M. & Zipursky, S. L. Dscam-mediated cell recognition regulates neural circuit formation. Annu. Rev. Cell Dev. Biol. 24, 597–620 (2008).

  41. 41.

    Madabhushi, R. et al. Activity-induced DNA breaks govern the expression of neuronal early-response genes. Cell 161, 1592–1605 (2015).

  42. 42.

    West, A. E. & Greenberg, M. E. Neuronal activity-regulated gene transcription in synapse development and cognitive function. Cold Spring Harb. Perspect. Biol. 3, a005744 (2011).

  43. 43.

    Hebert, P. D. N. et al. A Sequel to Sanger: amplicon sequencing that scales. BMC Genomics 19, 219 (2018).

  44. 44.

    Eid, J. et al. Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138 (2009).

  45. 45.

    Zhang, H., Susanto, T. T., Wan, Y. & Chen, S. L. Comprehensive mutagenesis of the fimS promoter regulatory switch reveals novel regulation of type 1 pili in uropathogenic Escherichia coli. Proc. Natl Acad. Sci. USA 113, 4182–4187 (2016).

  46. 46.

    Roberts, R. J., Carneiro, M. O. & Schatz, M. C. The advantages of SMRT sequencing. Genome Biol. 14, 405 (2013).

  47. 47.

    Lovatt, A. et al. High throughput detection of retrovirus-associated reverse transcriptase using an improved fluorescent product enhanced reverse transcriptase assay and its comparison to conventional detection methods. J. Virol. Methods 82, 185–200 (1999).

  48. 48.

    Ma, Y. K. & Khan, A. S. Evaluation of different RT enzyme standards for quantitation of retroviruses using the single-tube fluorescent product-enhanced reverse transcriptase assay. J. Virol. Methods 157, 133–140 (2009).

Download references


We thank D. Schatz, C. Murre and J.-P. Changeux for discussions; the UCSD ADRC and the UCI MIND for human brain specimens, along with the donors and families who shared these precious materials; flow cytometry core colleagues B. Seegers, M. Haynes (TSRI) and Y. Altman (SBP); and M. Wang (ACD), D. J. Weiss (Agilent) and H. Lee (PacBio) for technical assistance. Support was provided by The Shaffer Family Foundation, The Bruce Ford & Anne Smith Bundy Foundation, a UCSD pilot grant (NIH P50AG00513) and SBP institutional funds (J.C.); a PRAP fellowship from the Ministry of Science and Technology, Taiwan (105-2917-I-564-085, M.-H.L.); and NIH training grant 5T32AG000216-24 (G.E.K.).

Reviewer information

Nature thanks L. Feuk, F. Gage and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Benjamin Siddoway, Gwendolyn E. Kaeser, Igor Segota


  1. Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA

    • Ming-Hsiang Lee
    • , Benjamin Siddoway
    • , Gwendolyn E. Kaeser
    • , Igor Segota
    • , Richard Rivera
    • , William J. Romanow
    • , Christine S. Liu
    • , Chris Park
    • , Grace Kennedy
    • , Tao Long
    •  & Jerold Chun
  2. Biomedical Sciences Program, School of Medicine, University of California, San Diego, La Jolla, CA, USA

    • Gwendolyn E. Kaeser
    • , Christine S. Liu
    •  & Chris Park


  1. Search for Ming-Hsiang Lee in:

  2. Search for Benjamin Siddoway in:

  3. Search for Gwendolyn E. Kaeser in:

  4. Search for Igor Segota in:

  5. Search for Richard Rivera in:

  6. Search for William J. Romanow in:

  7. Search for Christine S. Liu in:

  8. Search for Chris Park in:

  9. Search for Grace Kennedy in:

  10. Search for Tao Long in:

  11. Search for Jerold Chun in:


J.C. conceived the project. M.-H.L. and J.C. designed and analysed experiments. M.-H.L. (nuclei sorting, RT–PCR, Southern blot, cloning and Sanger sequencing, genomic DNA PCR, RNA and DNA in situ hybridization, targeted DNA pull-down and Illumina sequencing, SMRT sequencing, gencDNA induction in culture and cytotoxicity assay), B.S. (SMRT sequencing), R.R. (genotyping, breeding and maintenance of mouse lines and DNA concatemer preparation and virus infection), W.J.R. (nucleus sorting, in vitro reverse transcriptase activity assay and western blot), C.P. (genomic DNA PCR) and G.K. (RNA and DNA in situ hybridization) completed experiments. M.-H.L. and G.E.K. performed statistical analyses and made figures. B.S., I.S., C.S.L. and T.L. performed informatics analyses. M.-H.L., B.S., G.E.K. and J.C. prepared the manuscript. Key experiments were repeated by G.E.K., W.J.R., C.P. and other researchers in our laboratory.

Competing interests

Sanford Burnham Prebys Medical Discovery Institute has filed the following patent applications on the subject matter of this publication: (1) PCT application number PCT/US2018/030520 entitled “Methods of diagnosing and treating Alzheimer’s disease” filed 1 May 2018, which claims priority to US provisional application 62/500,270 filed 2 May 2017; and (2) US provisional application number 62/687,428 entitled “Anti-retroviral therapies and reverse transcriptase inhibitors for treatment of Alzheimer’s disease” filed 20 June 2018.

Corresponding author

Correspondence to Jerold Chun.

Extended data figures and tables

  1. Extended Data Fig. 1 RT–PCR on bulk and sorted nuclei, and RISH.

    a, RT–PCR products from bulk brain tissue samples from three individuals with SAD and three without. Canonical APP splice variants and non-APP products were identified. b, Representative gels showing the presence of canonical APP splice variants (red arrows, n = 2 independent experiments). c, No APP variants were identified in NeuN-negative nuclei from individuals with or without SAD. The 18S rRNA control verified the presence of RNA. Novel APP RNA variants were identified from oligo-dT primed cDNA libraries from 50-cell populations of neuronal nuclei (d, n = 3 biological replicates) and brains from individuals with Alzheimer’s disease (e, commercially produced PacBio cDNA libraries). f, RISH3/16 signal from antisense probes showed cytoplasmic distribution of APP 3/16 RNA. Negative control sense probes and a probe targeting the bacterial gene DapB showed no signal. g, PSEN1 RT–PCR on populations of 50 nuclei from the brains of three individuals with SAD and three without showed no PSEN1 RNA variants. The positive control (PC) is amplified from RNA extracted from bulk brain tissue. 18S rRNA control verified the presence of RNA.

  2. Extended Data Fig. 2 APP gencDNA detection by genomic DNA PCR, DISH, and targeted genomic pull-down.

    a, Duplicate gel from Fig. 2b, with more sensitive thresholds to show the clear absence of PSEN1 bands. b, Nested PCR was used with alternative APP primers (three total sets: APP 1–18, APP 1–18N, and APP 2–17). c, Cloning and Sanger sequencing of indicated bands (red numbers in b) revealed novel APP gencDNAs (see Fig. 1 for legend and nomenclature). d, APP 1–18 DNA PCR showed no products in non-neuronal cell types: IMR-90 (human lung fibroblast), HEK (human embryonic kidney) and non-neuronal (NeuN-negative) genomic brain DNA from individuals with and without SAD. RNaseP was used as a positive control. e, APP mRNA is expressed in HEK-293 and IMR-90 cells; 18S rRNA used as a positive control. f, Digestion with the off-target restriction enzyme XbaI did not affect DISH3/16 or DISH16/17 signals. g, h, Synthetic DNA containing 16/17 (g) or 3/16 (h) target sequences (target), or wild-type human genomic APP sequences lacking IEJs and exon–exon junctions (mutant target) were introduced by retroviral transduction into NIH-3T3 cells. DISH16/17 and DISH3/16 signals from both sense and antisense probes were detected only in target infected cells. i, Schematic of Agilent SureSelect targeted DNA pull-down. j, Agilent SureSelect hybridization enrichment targeted the entire genomic locus of APP and showed unbiased sequencing depth across the full genomic locus. Exons and introns are shown on two scales. Source data

  3. Extended Data Fig. 3 APP gencDNA reading frame analysis.

    a, Colour key for all gencDNAs with junctions identified by SMRT sequencing. b, c, Percentage of unique in-frame reads from brains of individual with SAD (b; 6,299 unique reads) or without SAD (c; 1,084 unique reads). Source data

  4. Extended Data Fig. 4 APP gencDNA and RNA variant formation in CHO cells.

    a, Time line of CHO cell experiments modified from Fig. 4a. After transfection and gencDNA induction, serum was added and CHO cell cultures were passaged for 7 days. Cells were harvested, and DNA and RNA were extracted for analyses. b, c, PCR of genomic DNA (b; gDNA) and RT–PCR with APP 1 and 18 primers (c; n = 2 independent experiments). Note that APP plasmid is no longer detected (compare to Fig. 4b). DNA breaks during cell proliferation might contribute to variant formation in cells without DNA damage (no H2O2). Reverse transcriptase inhibitor (RTi, AZT + ABC) treatment prevents formation of APP RNA variants, indicating the dependence of RNA variants on gencDNAs. d, Induced APP variants with IEJs observed in b, c.

  5. Extended Data Fig. 5 Data from six individual brains for each brain from individuals with or without SAD represented as averages in Fig. 5, and variant cytotoxicity.

    a, d, Nuclei sorted from cortices of six individuals with SAD and six without were analysed by DISH16/17 (a) and DISH3/16 (d). Cumulative frequency distribution plots and average numbers of foci per nucleus show statistical significance (non-parametric Kruskal–Wallis test with Dunn’s correction for multiple comparisons) between all paired brain sets. Numbers above bars indicate number of nuclei analysed. NS, not significant. Error bars show s.e.m. b, c, e, f, Detailed P values for Fig. 5b (b), Fig. 5c (c), Fig. 5e (e) and Fig. 5f (f). g, APP-751, three coding and one non-coding APP variant in constructs containing haemagglutinin (HA) tags were transfected into HEK-293 cells. Cell lysates from all three coding variants and full-length APP-751 displayed protein products of the expected size by western blot. α-tubulin was used as a loading control. h, Three coding APP variants were transfected into SH-SY5Y cells individually and cell viability was measured by WST-1 seven days after transfection under serum-deprived conditions. Means of three independent experiments were analysed using ordinary one-way ANOVA with uncorrected Fisher’s LSD for multiple comparisons (*P = 0.0477, ****P < 0.0001). Source data

  6. Extended Data Fig. 6 DISH3/16 and DISH16/17 data analyses.

    a, DISH3/16 data from individual J20 and wild-type mouse cortices represented as an average in Fig. 5h; numbers above bars represent number of nuclei analysed. b, No DISH16/17 signal was detected in wild-type mouse nuclei. c, Detailed statistical significance of DISH16/17 signal across all mice in Fig. 5j (non-parametric Kruskal–Wallis with Dunn’s multiple comparisons test). **P < 0.01, ***P < 0.001, ****P < 0.0001. NS, not significant. df, Synthetic DNA targets containing the exon 16/17 junction sequence were introduced by retroviral transduction into NIH-3T3 cells, and the target sequence (provirus) identified by DISH16/17. A concatamer (×2) showed increased focus size, represented as a cumulative frequency distribution plot (e) and a box and whisker plot (f). Line, median; box, 75th–25th percentiles; whiskers, 90th–10th percentiles. Statistical significance was calculated using non-parametric Kruskal–Wallis test with Dunn’s correction for multiple comparisons. ****P < 0.0001. Source data

  7. Extended Data Table 1 Nine distinct experimental approaches supporting APP recombination
  8. Extended Data Table 2 Human postmortem brain information
  9. Extended Data Table 3 APP variants information
  10. Extended Data Table 4 DISH and RISH experiments and validation list

Supplementary information

  1. Supplementary Information

    This file contains a Supplementary Discussion, Supplementary References and Supplementary Figure 1, the uncropped blots.

  2. Reporting Summary

  3. Supplementary Tables 1-6

    Supplementary Table 1 contains sequences of the probes and primers used, Supplementary Table 2 contains identified APP variant sequences by Sanger sequencing, Supplementary Table 3 shows known FAD mutation analysis on SAD, Supplementary Table 4 shows known FAD mutation analysis on ND, Supplementary Table 5 shows the fraction of unique APP variants among total gencDNA identified in SAD and Supplementary Table 6 shows the fraction of unique APP variants among total gencDNA identified in ND.

Source data

About this article

Publication history






By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.