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APP gene copy number changes reflect exogenous contamination

Nature volume 584, pages E20–E28 (2020)Cite this article

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Matters Arising to this article was published on 19 August 2020

The Original Article was published on 21 November 2018

arising from M. H. Lee et al. Nature https://doi.org/10.1038/s41586-018-0718-6 (2018)

Various types of somatic mutations occur in cells of the human body and cause human diseases, including cancer and some neurological disorders1. Recently, Lee et al.2 (hereafter ‘the Lee study’) reported somatic copy number gains of the APP gene, a known risk locus for Alzheimer’s disease (AD), in 69% and 25% of neurons of AD patients and controls, respectively, and argued that the mechanism of these copy number gains was somatic integration of APP mRNA into the genome, creating what they called genomic cDNA (gencDNA). Our reanalysis of the data from the Lee study and two additional whole-exome sequencing (WES) data sets by the authors of the Lee study3 and Park et al.4 revealed evidence that APP gencDNA originates mainly from exogenous contamination by APP recombinant vectors, nested PCR products, and human and mouse mRNA, respectively, rather than from true somatic integration of endogenous APP. We further present our own single-cell whole-genome sequencing (scWGS) data that show no evidence for somatic APP retrotransposition in neurons from individuals with AD or from healthy individuals of various ages.

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Fig. 1: APP vector contamination in the Lee study.
Fig. 2: APP cDNA-supporting reads originate from exogenous PCR products and genome-wide human and mouse mRNA contamination.
Fig. 3: Absence of somatic APP retrogene insertions in our scWGS data.

Data availability

APP vector PCR sequences have been deposited in the NCBI SRA (PRJNA577966). Single-cell whole-genome sequencing data from control individuals have been deposited in the NCBI SRA (PRJNA245456) and dbGAP (phs001485.v1.p1). Single-cell whole-genome sequencing data from patients with AD are available upon request for genomic regions of APP and source pseudogene SKA3 and ZNF100.

Code availability

Implemented custom code for the estimation of clipped read fractions and the detection of intra-exon junctions (IEJs) is available at https://sourceforge.net/projects/somatic-app-analysis/.

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Acknowledgements

E.A.L. is supported by grants from the NIA (K01AG051791), the Suh Kyungbae Foundation, and the Charles H. Hood foundation. This work was also supported by the Paul G. Allen Frontiers Group (C.A.W., E.A.L.), NINDS grant R01NS032457-20S1 (C.A.W.), DOD grant W18XWH2010028 (J.K., E.A.L., C.A.W.), Manton Center Pilot Project Award and Rare Disease Research Fellowship (B.Z.), NIH grants T32HL007627 and K08AG065502 (M.B.M.), and NIH grant AG054748 (M.A.L). C.A.W. is an Investigator of the Howard Hughes Medical Institute.

Author information

Author notes

  1. Michael A. Lodato

    Present address: Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, MA, USA

Authors and Affiliations

  1. Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA, USA

    Junho Kim, Boxun Zhao, August Yue Huang, Michael B. Miller, Michael A. Lodato, Christopher A. Walsh & Eunjung Alice Lee

  2. Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    Junho Kim, Boxun Zhao, August Yue Huang, Michael B. Miller, Michael A. Lodato, Christopher A. Walsh & Eunjung Alice Lee

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Junho Kim, Boxun Zhao, August Yue Huang, Michael B. Miller, Michael A. Lodato, Christopher A. Walsh & Eunjung Alice Lee

  4. Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, USA

    Michael B. Miller, Michael A. Lodato & Christopher A. Walsh

  5. Department of Neurology, Harvard Medical School, Boston, MA, USA

    Michael B. Miller, Michael A. Lodato & Christopher A. Walsh

  6. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Michael B. Miller

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Contributions

J.K. and E.A.L. conceived and designed the study. J.K. and B.Z. designed the APP vector PCR and sequencing, and B.Z. performed the PCR and sequencing. M.B.M. and M.A.L. performed single-neuron sorting and sequencing. J.K. and A.Y.H. performed bioinformatic analyses. E.A.L and C.A.W supervised the study. J.K., B.Z., M.B.M., M.A.L., C.A.W., and E.A.L. wrote the manuscript.

Corresponding authors

Correspondence to Christopher A. Walsh or Eunjung Alice Lee.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Pervasive recombinant vector contamination in next-generation sequencing.

a, Schematic of a retrogene insertion and the characteristics expected to be captured in sequencing data: increased exonic read-depth, discordant reads spanning exons, clipped reads at exon junctions, 3′ poly-A tail, target site duplication (TSD) at the new genomic insertion site, and clipped reads spanning the retrocopy and insertion sites. b, Recombinant vector contamination found in the Walsh laboratory data. Four single human neurons (1286_PFC_02, 1762_PFC_04, 5379_PFC_01, 5416_PFC_06) in our previous publication showed contamination by a mouse Nin recombinant vector15. The homologous human gene region (NIN) is visualized by the IGV browser for a vector-contaminated cell (top) and an unaffected control cell (bottom). Contamination characteristics were identified, including increased exonic read-depth and exon-spanning discordant reads (reads coloured in red) with numerous mismatches to the human genome reference (coloured vertical bars in the read depth track). c, Mouse single-neuron WGS data from the Chun laboratory7 contaminated by the same APP recombinant vector detected in the Lee study2 and an additional APP plasmid vector (magnified panel).

Extended Data Fig. 2 Evidence that recombinant vector contamination is the major source of APP gencDNA.

a, Schematic of the DNA fragment size distribution for each APP source (source APP, APP retrocopy, APP vector). Fragments from APP vectors are expected to be more homogeneous and smaller than those from other sources owing to the fixed and relatively small vector size. b, DNA fragment (or insert) size estimation. Sequence reads mapped to APP exon junctions were divided into two groups: source APP (reads containing intron sequences) and APP gencDNA (reads clipped at the exon junction) supporting reads. gencDNA supporting reads were remapped to the APP reference transcript sequence (APP-751) to estimate insert sizes. c, Comparison of insert size distribution between source and gencDNA supporting reads. n, number of read pairs in each group; centre line, median; box limits, first and third quartiles; whiskers, 1.5 × interquartile range.

Extended Data Fig. 3 New APP variants with intra-exon junctions as PCR artefacts.

a, Electrophoresis of PCR products from the vector APP inserts (APP-751, APP-695) showing novel APP variants as artefacts. All combinations of two PCR enzymes (FastStart PCR master mix and Platinum SuperFi DNA polymerase; OneStep Ahead RT–PCR in Fig. 1c) with three primer sets generated new bands smaller than the expected PCR product. b, PCR-induced IEJs with homologous sequences at each junction identified by Illumina sequencing. Twelve IEJs from our vector PCR sequencing showed exactly the same sequence homologies and genomic coordinates as IEJs reported by Lee et al2. For two IEJs, IGV browser images show pre- (left) and post-junction sites (right) connected by split reads spanning the IEJ (red arc). Because IGV displays forward strand sequences of the human reference genome, all IEJ sequences were also reverse complemented for consistent visualization.

Supplementary information

Supplementary Information

This file contains Supplementary Methods, and Supplementary Figure 1 and 2.

Reporting Summary

Supplementary Table 1

This table shows identified intra-exon junctions (IEJs) generated by PCR artifacts.

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Kim, J., Zhao, B., Huang, A.Y. et al. APP gene copy number changes reflect exogenous contamination. Nature 584, E20–E28 (2020). https://doi.org/10.1038/s41586-020-2522-3

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