Genomic mosaicism with increased amyloid precursor protein (APP) gene copy number in single neurons from sporadic Alzheimer's disease brains

The instructions for living cells are contained in certain stretches of DNA, called genes, and these instructions have been largely considered to be invariant, such that every cell in the body has the same DNA. However, research has revealed that many neurons in the human brain can contain different amounts of DNA compared to other cells. When cells with varied DNA are present in the same person, it is referred to as mosaicism. The effects of this mosaicism are unknown, although by altering the instructions in brain cells, it is suspected to influence both the normal and diseased brain.

The brains of patients with Alzheimer's disease often contain deposits of proteins called amyloids. The precursor of the protein that makes up most of these deposits is produced from a gene called the amyloid precursor protein gene, or APP. Having an extra copy of the APP gene can cause rare ‘familial’ Alzheimer's disease, wherein the APP duplication can be passed on genetically and is present in all the cells of a patient's body. By contrast, ‘sporadic’ Alzheimer's disease, which constitutes around 95% of cases, does not show any difference in the number of APP genes found in tissue samples, including whole brain. The early studies that discovered this were conducted before an appreciation of brain mosaicism, and thus single neurons were not investigated. This raises the possibility that the number of APP genes may be mosaically increased, which would not be detected by examining non-brain or bulk brain tissue.

Bushman, Kaeser et al. used five different types of experiments to examine the DNA content of single neurons and investigate whether mosaicism could explain the discrepancy in the results of the previous studies. The neurons from people with Alzheimer's disease contained more DNA—on average, hundreds of millions of DNA base pairs more—and more copies of the APP gene, with some neurons containing up to 12 copies.

Bushman, Kaeser et al.'s findings present evidence of a way that mosaicism can affect how the brain works by altering the number of gene copies, and how this impacts the most common form of Alzheimer's disease. Many questions arise from the work, including when does mosaicism arise, and what promotes its formation? How does this relate to age? What parts of the genome are changed, what genes are affected, and how do these changes alter neuronal function?

Furthermore, Bushman, Kaeser et al.'s work suggests that mosaicism may also play a role in other brain diseases, and could also provide new insights into the normal, complex functions of the brain. In the future, this knowledge could help to identify new treatments for brain diseases; for example, by identifying new molecular targets for therapy hidden in the extra DNA or by understanding how to alter mosaicism.

The genome has been classically viewed as being constant from cell to cell in the same individual, with genomic differences passed on through the germline. However, within neurons of the brain, numerous studies have reported somatic variability producing complex genomic mosaicism but having unknown function. Identified forms of somatically arising genomic mosaicism include aneuploidy (reviewed in Bushman and Chun, 2013), LINE elements (Muotri et al., 2005; Baillie et al., 2011; Evrony et al., 2012), copy number variations (CNVs) (Gole et al., 2013; McConnell et al., 2013; Cai et al., 2014), and DNA content variation (DCV) (Westra et al., 2010; Fischer et al., 2012).

AD is the most common form of dementia and is characterized by the presence of amyloid plaques, synaptic loss, and cell death (Alzheimer's Association, 2013), notably affecting the prefrontal cortex. The major component of these plaques is β-amyloid (Aβ), a protein encoded by APP (Goldgaber et al., 1987; St George-Hyslop et al., 1987; Tanzi et al., 1987). Familial AD accounts for less than 5% of all cases and has been genetically linked to mutations in APP and two presenilins (PSEN), PSEN1 and PSEN2, the catalytic components of γ-secretase and the units responsible for cleavage of APP (Price and Sisodia, 1998; Bertram et al., 2010). In addition, APP gene dosage is strongly associated with AD pathogenesis based on multiple lines of evidence. First, Down syndrome (DS), with three copies of APP, produces neuropathology virtually identical to AD (Glenner and Wong, 1984; Delabar et al., 1987) and APP locus duplications are sufficient to cause familial AD (Rovelet-Lecrux et al., 2006; Sleegers et al., 2006; McNaughton et al., 2012). Moreover, AD-protective effects have been reported in DS with APP deletion via partial trisomy (Prasher et al., 1998), as well as in familial AD with an APP partial loss-of-function mutation (Jonsson et al., 2012). However, seminal studies in the 1980s failed to detect evidence of APP amplification in sporadic Alzheimer's disease peripheral blood and whole brain (Podlisny et al., 1987; St George-Hyslop et al., 1987; Tanzi et al., 1987) despite strong linkage in familial AD, thus linkage between sporadic AD and APP remains unclear.

The existence of region specific genomic mosaicism in the normal brain (Westra et al., 2010) raised the possibility that DCV, defined as variations in the total DNA amount present in a single cell or population, might play a functional role in sporadic brain diseases by altering pathogenic loci in individual cells. The validated pathogenicity of APP in familial AD suggested that mosaic alterations in APP copy number within single neurons may play a role in producing sporadic AD. Through the use of five independent experimental approaches, we report increased somatic genomic variation within individual sporadic AD neurons involving mosaic increases in both DNA content and APP copy number.

Methodologies to identify genomic mosaicism in AD brain nuclei utilized multiple independent approaches (Figure 1). First, neuronal nuclei were isolated from paired prefrontal cortex and cerebellum of postmortem human brains as previously described (Figure 1A) (Westra et al., 2008, 2010). Nuclei were analyzed for: DNA content changes (Figure 1B), APP CNVs using qPCR in small populations (Figure 1C) and single-nuclei (Figure 1D), trisomy 21 using standard fluorescence in situ hybridization (FISH) (Figure 1E), and amplified APP loci using PNA-FISH (Figure 1F).

Figure 1

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AD cortices show increased DNA content variation (DCV)

Previous work identified DNA content changes with regional variability in the non-diseased brain, where prefrontal cortical nuclei—particularly neuronal nuclei—displayed increased DCV compared to nuclei of the cerebellum and non-brain controls (Westra et al., 2010). The current study utilizes the same techniques for DNA content analysis by flow cytometry using propidium iodide (PI). PI staining is the predominant methodology for quantitatively differentiating nuclei or cells with variable DNA content and is routinely utilized as a gold standard in multiple fields including genomic comparisons across species in botany (Dolezel et al., 2007), studies of the cell cycle (Krishan, 1975), and DNA degradation produced by apoptosis (Riccardi and Nicoletti, 2006). Prior analyses ruled out the effects of DNA dyes, nuclear size, mitochondrial contamination, and autofluorescent lipofuscin on DNA content and validated genomic increases in DNA content using quantitation of CENP-B PNA probes against human centromere repeats (Westra et al., 2010) which have been further substantiated by identification of copy number gains in single human neurons (Gole et al., 2013; McConnell et al., 2013).

To further validate DCV in human neurons, whole genome amplification (WGA) was used on cortical neuronal nuclei sorted into populations of high or low DNA content based upon PI intensity. Nuclei with high or low DNA content were sorted into 12 replicates of 1000, 500, or 100 and were then subject to DNA content assessment by WGA to assess starting amounts of DNA template in each sample (Figure 2A). Nuclei were denatured and amplified by multiple displacement amplification (MDA) during which DNA synthesis was continually measured by SYBR Green fluorescence (Figure 2B). In every case, nuclei with high PI intensity also showed increased DNA synthesis over those with low PI intensity. These results independently confirm, as expected, that PI staining intensity faithfully reports DNA content.

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DNA Index (DI) and percent change values and statistics.

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AD neuropathology strongly affects the prefrontal cortex. We therefore first interrogated the DNA content of pathologically confirmed prefrontal cortices (N = 32), using previously described methodologies for DCV analyses in neuronal nuclei (Figure 1B) (Westra et al., 2010). In control experiments, AD cerebellar nuclei showed DNA content profiles similar to lymphocytes, characterized by histograms with sharp peaks and narrow bases (Figure 2C,E). By comparison, AD cortices displayed high variability characterized by right hand shoulders (Figure 2D,E [AD-7]), large right hand peak shifts (Figure 2D,E [AD-6]), and wide bases. Of the AD cortex samples examined, greater than 90% displayed a net DNA content increase, averaging approximately 8% gain over human lymphocyte controls (Figure 2F–H). Notably, the AD cortex displayed increases beyond those observed in non-diseased cortices, with an average gain of approximately 6% over age and sex-matched samples (Figure 2F–H). AD cortices also displayed an increased coefficient of variation over non-diseased cortical nuclei and all cerebellar nuclei (Figure 2I) as well as a consistent skewed distribution compared to AD cerebellum (Figure 2—figure supplement 1A). In addition, we examined 14 paired sets of AD cortex and cerebellum from the same individual (Figure 3A) and 12 paired sets from non-diseased individuals (Figure 3B). Each AD cortex showed unique cortical histograms and increases in total cortical DNA compared to the cerebellum. In AD and non-diseased brain samples, DNA content changes did not correlate with age, Braak score, or postmortem index (Figure 2—figure supplement 1B–H), and DNA content was independent of nuclear size (Figure 2—figure supplement 2) (Westra et al., 2010). Importantly, the increase observed was significantly less than a 4N tetraploid genome (∼12,800 Mb), yet significantly more than what would be expected from a hypersomy of even the largest chromosome (chr1: ∼250 Mb or 3.9%). These results indicate that neuroanatomically selective increases in DNA content represent a distinct, reproducible, and prevalent characteristic of the AD brain.

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Neurons produce DNA content increases

In the non-diseased cortex, neurons were identified as the predominant cell type contributing to increased DCV (Westra et al., 2010). To determine whether neurons were also responsible for increased DCV in sporadic AD, nuclei were immunolabeled for the neuronal nuclear antigen, NeuN, and flow cytometry was used to analyze DCV in NeuN-positive and NeuN-negative nuclei (Figure 4A–C). NeuN-positive AD cortical neurons showed right hand DNA content shifts (Figure 4D) and increased DCV, displaying ∼9% gain over that observed in NeuN-negative nuclei (Figure 4E). A comparison of NeuN-positive nuclei in paired samples from the same brain also showed significantly increased DNA content in cortical neurons over cerebellar neurons (Figure 4F). These distinctions do not rule out AD-specific effects on DCV from non-neuronal cells, but do implicate neurons as the major cellular locus for increased DCV.

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APP copy number variably increases in small cell cohorts

The DNA content increases in sporadic AD neurons raised the question of what genomic loci may be specifically increased in AD. The most highly validated gene associated with AD is APP, wherein constitutive gains have previously been linked to AD through familial AD and DS (Prasher et al., 1998; Rovelet-Lecrux et al., 2006; Jonsson et al., 2012; McNaughton et al., 2012). However, prior large-scale analyses in peripheral, non-neural tissues from sporadic AD cases have not reported APP copy number changes (Bertram and Tanzi, 2009; Harold et al., 2009). The normal existence of genomic mosaicism might produce ‘hot-spots’ of cells with increased APP copy number, and we therefore interrogated small cohorts of brain nuclei from paired cortex and cerebellum. Three of six AD samples displayed significant increases in APP copy number (3.9–6.3 copies compared to reference genes) over paired cerebellar samples (Figure 5A (AD-1, AD-3, AD-6)). Similar APP amplification was not detected in non-diseased brains (Figure 5B), and DS controls along with reference gene assessments demonstrated expected results (Figure 5—figure supplement 1A,B). Notably, AD samples displayed substantial variability in APP copies (Figure 5C), revealing inherent limitations in precisely quantifying copy numbers in small, genomically mosaic cell populations and highlighting the necessity for single-cell analyses.

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Raw dual point-paint probe FISH counts.

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APP copy number is increased and enriched in single AD neurons

Mosaic single-cell increases in APP copy number could explain variations observed in small cohort qPCR data. To assess APP copy number in single neurons, an optimized microfluidic protocol to assess genomic copy number via qPCR using a Biomark HD 48.48 Dynamic Array integrated fluidic circuit (IFC) (Fluidigm, South San Francisco) was adapted from gene expression protocols. Nuclei were isolated and interrogated at two reference genes (see Materials and methods) and two APP exons that flanked the majority of the coding sequence at the 5′ and 3′ ends of APP (exons 3 and 14). Samples were run in triplicate and assays in sextuplicate, which generated high numbers of replicate data points allowing for improved copy number resolution to an extent not possible by conventional qPCR (Weaver et al., 2010; Whale et al., 2012). A total of 154 neuronal nuclei were individually examined from three AD and three non-diseased brains, within which nuclei from the cerebellum and prefrontal cortex were separately analyzed. In single AD cortical nuclei, significant increases in APP copy number, ranging up to 12 copies in a single nucleus (Figure 6A,B), were observed with a high concordance rate between exons (Figure 6—figure supplement 1). AD cortical nuclei displayed increased average copy numbers (∼3.8–4 copies) over control samples (∼1.7–2.2 copies) (Figure 6A,B), with increased frequencies of high copy number nuclei (six or more copies) (Figures 6C and 5E, red lines) primarily occurring in prefrontal cortex samples (Figure 6C–F). The AD cortex showed an approximately fourfold increase over the non-diseased cortex of nuclei with greater than 2 APP copies (55–66% vs 12–15%) (Figure 6G,I). In addition, nuclei with fewer than 2 APP copies were also identified. In neuronal nuclei with greater than two copies (Figure 6G,I, gold bars), AD cortical nuclei showed statistically significant increases (∼5 copies for both exons) over the other samples (∼3 copies for both exons) (Figure 6H,J). These data identify mosaic, neuroanatomically enriched and disease-associated increases in APP copy number in single, sporadic AD neurons.

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Single Cell qPCR Data and Statistics.

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APP amplified loci can be visualized by PNA-FISH

The identification of high APP copy number nuclei by single-cell qPCR suggested the possibility that amplified loci could produce detectable FISH signals. In chromosome 21 non-quantitative regional point probe FISH experiments, we occasionally observed variable puncta sizes (Figure 5H,I) (Rehen et al., 2005) that were initially dismissed as technical hybridization variability but which might potentially indicate sub-chromosomal variation of loci containing target sequences. However, conventional point-probe FISH analysis, while useful for assessments of aneusomy or rearrangements, cannot quantitatively evaluate copy number changes occurring as contiguous copies in close proximity to one another. Moreover, other conventional techniques for identifying copy number in single cells, such as single cell sequencing, rely heavily on genome amplification, which may introduce bias or mask CNVs. We therefore developed a detection assay based upon PNA chemistry. PNA probes hybridize with single base discrimination and have been used to quantify short repeat sequences such as those found on telomeres (Buchardt et al., 1993; Lansdorp et al., 1996). This raised the possibility that amplified copies of APP could be identified using multiple PNA probes simultaneously hybridized to unique gene sequences, provided that amplification occurred in a physically constrained locus rather than being dispersed throughout the genome.

Nine PNA probes of 12–18 residues, each conjugated to a single Alexa-488 fluor, were designed against multiple sites of the same APP exons examined by single-cell qPCR, and validated both in silico and by blotting for specificity and linear quantitative behaviors (Figure 7A, Figure 7—figure supplement 1A–C). Relative probe sequence locations were also designed to avoid fluorochrome proximity quenching. Single PNA probes did not produce a detectable signal in any samples using standard fluorescence microscopy used for aneusomy FISH (Rehen et al., 2001, 2005), contrasting with clearly detectable telomeric signals, which led to evaluation of more sensitive microscopic techniques including confocal, deconvolution (Westra et al., 2010), and ultimately super resolution structured illumination microscopy (N-SIM, Nikon) (Gustafsson, 2000), which all failed to detect possible signals in AD nuclei. However, hybridization of increasing numbers of distinct PNA probes combined with N-SIM visualization resulted in the empirical determination of a threshold that identified one, and rarely two, punctate signals that were much more frequent in AD nuclei (Figure 7B), and with a frequency consistent with single-cell qPCR data (Figure 6). AD neuronal nuclei showed single purely green puncta (Figure 7B, #1 arrow, Video 1–6) and rarer two puncta signals (Figure 7B, #3 arrow, Video 6), all of which could be differentiated from lipofuscin puncta that fluoresced in all channels (Figure 7B, #2 arrow, Video 5). Internal positive control signals identified telomeres (Figure 7, red fluorescent puncta). Use of 3D-SIM enabled acquisition and analysis of super resolution projections of individual green puncta (Figure 7B) revealing a range of morphologies and intensities (Figure 7B,D, Figure 7—figure supplement 1D,E) consistent with varied APP copy number and possibly distinct, intrachromosomal genomic organization. This threshold detection using multiple PNA probes targeting APP visualized green puncta in 56% of AD neuronal nuclei compared to 22% in non-diseased and 14% in DS (Figure 7C). There was no evidence of single-copy detection in any sample, contrasting with standard FISH data (Figure 5D–I), despite always showing telomeric signals. Compared to single-cell qPCR data, which reports both endogenous alleles in addition to amplification, copy numbers at a single locus below ∼2 were not visualized with PNA-FISH, allowing focused analyses on positive profiles resulting in fluorescence intensity plots ( Figure 7D and Figure 7—figure supplement 1D) that were highly reminiscent of APP copy number plots produced by single-cell qPCR (Figure 6).

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Data and statistics for PNA-FISH counts.

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Our results support a mechanism for the development of sporadic AD whereby the validated familial pathogenic APP gene is mosaically amplified in neurons. Mosaicism was documented based on multiple independent criteria: increased DCV in 90% of examined AD brains, varied and increased APP copy number in small cohorts, increased APP CNVs in single neurons identified by single-cell qPCR, and APP copy number increases in single nuclei visualized by PNA-FISH. These data are consistent with our inability to substantiate increases in trisomy 21 in cells of the sporadic AD brain. The concomitant increase in DCV suggests effects on other loci that are also altered in AD, the most obvious of which would be copy gains, but which could also involve losses based upon the presence of increased DCV range observed in AD; such loci might contribute to the progeric presentation of sporadic AD and possibly familial forms of AD that still require decades for disease to manifest. Our working hypothesis ties the currently accepted pathogenicity of APP gene dosage, established by familial AD and DS, to sporadic AD through a mechanism of somatic, mosaically increased APP copy number in some neurons. Our data do not exclude the possibility that the changes observed are a downstream effect of causative factors in the disease. However, if mosaic genomic changes are downstream of disease onset, APP copy number changes would likely play a significant role in disease progression.

Advances in single-cell genomic sequencing had initially suggested its use for identifying APP CNVs in single AD neurons. However, existent technology is limited to published sequence resolution between ∼2-5 Mb and ∼0.025X genome coverage (Evrony et al., 2012; Gole et al., 2013; McConnell et al., 2013; Cai et al., 2014). Considering the <0.3 Mb size of the APP locus, single cell sequencing would be incapable of identifying all but the rarest APP CNVs observed here. In addition, whole genome single cell sequencing is limited by throughput, demonstrated by the low number of neurons reported in this field, and is complicated further by variable results (Cai et al. N = 82 (QC neurons), Evrony et al. N = 6, Gole et al. N = 6, McConnell et al. N = 110). Notably, DNA losses predominated in both Cai et al. (2014) and McConnell et al. (2013) however, Gole et al. (2013) observed that two thirds of somatic CNVs were gains, consistent with increases in DNA content reported previously, and all three studies support a range of DCV amongst neurons, although none of these prior studies assessed AD neurons. While future advances will improve sequence resolution, throughput, and amplification fidelity in single cells, the distinct single cell strategy employed here allowed copy number assessment of a single targeted gene, APP, an approach which may be generalizable to other loci and diseases. Compared to the prior single-neuron reports, 320 single, neuronal nuclei were assessed here for APP CNVs wherein single cell qPCR data markedly resembled PNA-FISH data. Notably, these single cell techniques also possess limitations. For example, single cell qPCR requires normalization to single copy reference genes and control populations, which presents unique difficulties in assessing mosaic neuronal populations. For example, changes in copy number could reflect changes in both the reference and target gene, which might result in artifactual increases (or decreases) in a target gene. Similarly, assessment of copy number from an amplified template could also produce artifactual changes, and thus results require independent techniques for verification, especially since the original template is consumed in the single cell reaction and cannot be further assessed. The use of PNA-FISH does not require any amplification or normalization, and yet PNA-FISH and single-cell qPCR produced a highly similar distribution of APP copy number amplifications. The employed techniques were not capable of assessing intact neurons or their histological organization, which will require technological modifications. Similarly, assessments of genomic and expression data in a single neuron also await technological advancements. Finally, these techniques are currently capable of interrogating only parts of the entire APP locus; therefore, at this time the precise structure of APP CNVs and the mechanisms leading to their existence are unknown.

Our data have bearing on at least 3 AD hypotheses. First, the prevailing amyloid hypothesis in AD posits that Aβ deposition drives AD (Hardy and Selkoe, 2002). Increased incidences of amyloid senile plaques are observed in all forms of AD and appear to be directly linked to APP dosage in DS and familial AD. The mosaic increases in neuronal APP copy number reported here provide an explanation for the universal presence of Aβ senile plaques in sporadic forms—indeed, all forms—of AD despite an absence of constitutive copy number gain. The presence of rare neurons with APP amplifications in non-diseased brains also provides an explanation for the observed presence of senile plaques in otherwise normal, aged brains (Gibson, 1983; Cras et al., 1991; Mackenzie et al., 1996), consistent with the ages of non-diseased brains examined here, which exceeded 75 years. Along with the occurrence and augmentation of DCV, APP amplification in AD is consistent with dysregulation of normally occurring processes in the etiology of AD pathogenesis. A second hypothesis proposes increased APP dosage through trisomy 21 based largely upon the neuropathology of DS (Heston and Mastri, 1977; Potter, 1991; Geller and Potter, 1999). However, dual chromosome point-paint FISH for chromosome 21 using liberal calling criteria on ∼5000 AD brain cells (N = 14 brains) showed no relationship between total mosaic aneusomies and AD. Moreover, the lack of observed increases of trisomy 21 in sporadic AD are consistent with prior studies in both peripheral non-brain and intact brain tissues, which reported APP levels approximating 2N (Podlisny et al., 1987; St George-Hyslop et al., 1987; Tanzi et al., 1987; Bertram et al., 2010), and is further consistent with mosaic CNVs observed here. The purposeful use of liberal counting criteria resulted in generally higher percentages of aneusomies than reported previously (Rehen et al., 2005; Iourov et al., 2009), however the increases were observed in both AD and non-diseased samples without linkage to AD. Our results do not eliminate roles for mosaic aneuploidy in AD, but do not support trisomy 21 as a specific mechanism for increasing APP copy number in sporadic AD. A third hypothesis is that abnormal cell cycle reentry in post-mitotic neurons contributes to AD (Yang et al., 2001; Herrup and Arendt, 2002; Kruman II et al., 2004; Copani et al., 2006; Mosch et al., 2007; Herrup, 2012). Our DCV analyses detected maximum gains of ∼21% and average gains of ∼8.2% over lymphocytes, representing a fraction of the 100% increase expected in a 4N cell. The subgenomic increases observed here are also consistent with the reported absence of adult neurogenesis in the normal cerebral cortex (Rakic, 2002; Bhardwaj et al., 2006), and could also be relevant to reports of nucleotide incorporation in adult cortical neurons (Gould et al., 1999). Concepts involving changes in DNA synthesis in AD without cell-cycle progression or neurogenesis could be rectified through subgenomic DNA synthesis in post-mitotic neurons that might involve DNA synthetic proteins reported in AD (Copani et al., 2002, 2006).

The observed genomic alterations in AD could occur through both developmental as well as aging processes as reported for at least one form of mosaicism, aneuploidies (Bushman and Chun, 2013) that have been linked to caspase-mediated cell death (Peterson et al., 2012) and could have relevance to developmental APP functions (Nikolaev et al., 2009). Perhaps tellingly, these developmental cell death processes also involve DNA fragmentation and double strand breaks (Blaschke et al., 1996; Staley et al., 1997; Blaschke et al., 1998) that have recently been reported in neurons exposed to physiological and AD stimuli (Suberbielle et al., 2013). It is possible that somatic mosaicism may arise through dysfunction of normally operative DNA repair mechanisms that produce DCV in the normal brain, with augmentation in the diseased brain. We speculate that increased DCV and APP CNVs observed in AD may in part reflect neurons that have delayed or averted cell death, wherein somatic genomic changes could provide a survival advantage at a cost of altered neurophysiological functions.

The DNA content changes observed here showed not only disease relationships but also varied with brain region and cell type. It is important to note that the interrogated neurons are likely terminal in their fate: they will not divide further, contrasting with lymphocytes that, perhaps surprisingly, showed the least amount of DCV despite being a proliferating population. Unlike neurons, proliferating lymphocytes may be similar to stem and progenitor populations that can maintain germline genomes to promote population expansion. By contrast, neurons are by definition post-mitotic, and therefore not subject to this possible requirement. We think it likely that many dividing cells that deviate significantly from the germline genome—and thus would be incapable of further division—are eliminated by cell death and would be removed from the population interrogated for DNA content. Support for this view includes reports on aneuploidies in neuroprogenitor cells, whereby the most extremely aneuploid forms appear to be eliminated by caspase-mediated cell death (Blaschke et al., 1996, 1998; Peterson et al., 2011; Peterson et al., 2012). DNA content changes may therefore be contextual, enabling diverse functions in different cell types. The large differences observed even between brain regions and amongst neuronal nuclei would suggest the existence of distinct neuronal functions produced by DCV, and possibly specific processes or stimuli that can genomically alter neurons.

Diverse stimuli previously implicated as AD risk factors including age and trauma (Sparks et al., 1990; Plassman et al., 2000), might share common endpoints via influences on genomic mosaicism. The consequences of somatic APP amplification in AD may support functionality of other somatically altered genomic loci observed in single neurons that could contribute to the progeric presentation of AD as well as aspects of the disease itself. We further hypothesize that other sporadic or idiopathic brain diseases could arise through altered genomic mosaicism that includes somatic variations at both known and unknown pathogenic loci.

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Author details

1. Diane M Bushman

   1. Department of Molecular and Cellular Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, United States
   2. Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, United States

   Contribution

   DMB, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Gwendolyn E Kaeser

   Competing interests

   The authors declare that no competing interests exist.

2. Gwendolyn E Kaeser

   1. Department of Molecular and Cellular Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, United States
   2. Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, United States

   Contribution

   GEK, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Diane M Bushman

   Competing interests

   The authors declare that no competing interests exist.

3. Benjamin Siddoway

   Department of Molecular and Cellular Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, United States

   Contribution

   BS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Jurgen W Westra

   Department of Molecular and Cellular Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, United States

   Present address

   Genedata Inc., Lexington, United States

   Contribution

   JWW, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Richard R Rivera

   Department of Molecular and Cellular Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, United States

   Contribution

   RRR, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Stevens K Rehen

   Department of Molecular and Cellular Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, United States

   Present address

   National Laboratory for Embryonic Stem Cell Research, Federal University of Rio de Janeiro, Institute of Biomedical Sciences, Rio de Janeiro, Brazil

   Contribution

   SKR, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Yun C Yung

   Department of Molecular and Cellular Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, United States

   Contribution

   YCY, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Jerold Chun

   Department of Molecular and Cellular Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, United States

   Contribution

   JC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   jchun@scripps.edu

   Competing interests

   The authors declare that no competing interests exist.

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