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. Author manuscript; available in PMC: 2021 Feb 6.
Published in final edited form as: Cell Stem Cell. 2020 Jan 30;26(2):221–233.e6. doi: 10.1016/j.stem.2019.12.013

Increased neural progenitor proliferation in a hiPSC model of autism induces replication stress-associated genome instability

Meiyan Wang 1,2, Pei-Chi Wei 3,4, Christina K Lim 1, Iryna S Gallina 1, Sara Marshall 1, Maria C Marchetto 1, Frederick W Alt 3, Fred H Gage 1,2,5,*
PMCID: PMC7175642  NIHMSID: NIHMS1569131  PMID: 32004479

SUMMARY

The association between macrocephaly and autism spectrum disorder (ASD) suggests that the mechanisms underlying excessive neural growth could contribute to ASD pathogenesis. Consistently, neural progenitor cells (NPCs) derived from induced pluripotent stem cells (iPSCs) of ASD individuals with early developmental brain enlargement are inherently more proliferative than control NPCs. Here, we show that hiPSC-derived NPCs from ASD individuals with macrocephaly display an altered DNA replication program and increased DNA damage. When compared to the control NPCs, high throughput genome-wide translocation sequencing (HTGTS) demonstrates that ASD-derived NPCs harbored elevated DNA double-strand breaks in replication stress-susceptible genes, some of which are associated with ASD pathogenesis. Our results provide a mechanism linking hyperproliferation of NPCs with the pathogenesis of ASD by disrupting long neural genes involved in cell-cell adhesion and migration.

eTOC blurb

Replication stress poses threats to genome stability. Gage and colleagues show that macrocephalic ASD patient-specific neural progenitor cells show increased DNA damage in a group of long neural genes vulnerable to replication stress, some of which are associated with the pathogenesis of ASD.

Graphical Abstract

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INTRODUCTION

Brain development requires that NPCs undergo millions of cell divisions during embryonic development and early years of life to give rise to most of the 80 billion neurons in the human brain (Lui et al., 2011). DNA damage, especially DNA double-strand breaks (DSBs), can generate de novo somatic mutations during development. Somatic mutations not only contribute to genomic diversity but may also cause many human genetic disorders and cancers (Greenman et al., 2007, Poduri et al., 2013, McConnell et al., 2017). Because neurons are among the longest-living cells in the body, accumulation of somatic mutations during embryonic brain development could influence neuronal development and function (McConnell et al., 2017).

Accumulating data have demonstrated that efficient DNA repair is imperative for neural development. For example, mice deficient in certain components of the classical non-homologous end-joining (C-NHEJ) [e.g., DNA ligase IV (Lig4) and X-Ray Repair Complementing Defective Repair In Chinese Hamster Cells 4 (Xrcc4)] pathway exhibit late embryonic lethality due to extensive neuronal apoptosis (Gao et al., 1998, Barnes et al., 1998, Frank et al., 2000). Moreover, neuronal death and embryonic lethality in C-NHEJ-deficient mice are rescued by p53 deficiency (Frank et al., 2000, Gao et al., 2000), indicating a role for DNA damage-induced apoptosis in generating these phenotypes.

Recent studies have identified numerous recurrent DSB clusters (RDCs) located in genes in mouse NPCs; the majority of these clusters occur in long neural-specific genes associated with neuropsychiatric diseases and cancers, suggesting potential impacts of DNA damage on neural development and function (Wei et al., 2016, Wei et al., 2018). A subset of RDC-containing genes is found within mosaic copy number variations (CNVs) identified in human postmortem brains (McConnell et al., 2013), some of which correspond to genomic regions harboring known common fragile sites (CFSs) (Wei et al., 2016, Wei et al., 2018, Glover and Wilson, 2016). However, it remains unknown whether these RDCs occur in repair-proficient human NPCs and whether increased DNA damage in genes influences NPC function.

There has been a long-standing association between macrocephaly and ASD. The excessive brain overgrowth prior to most clinical manifestations of the disorder (Courchesne et al., 2003) suggests that excessive brain growth could play a role in the pathogenesis of ASD. Despite growing evidence that NPCs derived from ASD patients with macrocephaly undergo rapid cell cycle progression (Marchetto et al., 2017, Mariani et al., 2015), little is known about how perturbed cellular proliferation affects genome stability in NPCs and its contribution to neurodevelopmental disorders such as ASD. Here, we demonstrate that NPCs derived from induced pluripotent stem cells (iPSCs) reprogrammed from fibroblasts of ASD patients with macrocephaly exhibited accelerated S-phase progression, increased replication stress, and chronic DNA damage compared to NPCs derived from control subjects. To understand how altered replication affects genome stability, we mapped DSB sites at nucleotide resolution in NPCs. We showed that replication stress induced a plethora of DSBs in the longest genes of the genome in NPCs. Intriguingly, replication stress attenuated the expression of many of these susceptible genes involved in adherens junctions, apical polarity, cell migration, and disrupted relative functions in NPCs. Notably, ASD-derived NPCs harbored more DSBs in the same set of genes that are sensitive to replication stress and exhibited defects in cell-cell adhesion and cell migration. Collectively, these findings suggest that genome instability induced by replication stress in NPCs may contribute to neurodevelopmental disorders such as ASD.

RESULTS

ASD-derived NPCs display rapid S-phase progression

A previous study demonstrated that NPCs derived from iPSCs reprogrammed from fibroblasts of ASD subjects with macroscopic early brain overgrowth (Table S1) displayed rapid cellular proliferation (Marchetto et al., 2017). Exome sequencing revealed damaging mutations in genes in the canonical Wnt pathway, cell cycle regulation, mitotic checkpoints, and DNA repair in ASD subjects, including genes central to maintaining genome stability (e.g., ATM, BRCA1, CDK7, and ERCC4) and several components of the anaphase-promoting complex/cyclosome (e.g., ANAPC1 and CDC27) (Marchetto et al., 2017) (Table S1). Mutations that attenuate mitotic checkpoints and DNA repair promote transmission of errors that occur during DNA replication to daughter cells. Quantification of Ki-67+ cells indicated an increased percentage of proliferating cells in ASD-derived NPCs (Figure 1A and 1B). To explore whether ASD-derived NPCs had an altered replication program, asynchronously growing NPCs were pulse-labeled with 5-bromo-2’-deoxyuridine (BrdU) for 30 min and then chased for 3 hours in fresh media before fluorescence-activated cell sorting (FACS) analysis. ASD-derived NPCs exhibited a greater fraction of BrdU+ cells that reached 4N DNA content 3 hours following the labeling (Figure 1C, S1A and S1B). To validate this finding, the asynchronously growing NPCs were pulse-labeled with CldU for 30 min, chased for 3 hours, and then pulse-labeled with IdU for 30 min (Figure 1D and S1C). The ASD-derived NPCs progressed through S-phase much faster than the control NPCs (Figure 1E and 1F).

Figure 1. ASD-derived NPCs display rapid S-phase progression.

Figure 1.

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(A) Images of NPCs derived from control (left) or ASD (right) subjects (Table S1). Ki-67 (white), DAPI (blue), NESTIN (red). Arrows indicate Ki-67 negative cells. Scale bar, 10 μm.

(B) Bar plot shows the quantification of percentage of Ki-67+ cells. Each point represents one cell line. Average of six randomly selected 20x images per line. Mean ± SD. Student’s t test, two-tailed, ** p < 0.01.

(C) NPCs were pulse-labeled with 20 μM BrdU for 30 min, collected immediately or chased in fresh media for 3 h before collecting. Percentage of BrdU+ cells reaching 4N DNA content was quantified. Mean ± SD. Student’s t test, two-tailed, * p < 0.05.

(D) NPCs were pulse-labeled with 20 μM CldU (green) for 30 min, chased in fresh media for 3 h, and pulse-labeled with 20 μM IdU (red) for 30 min. S phase progression was determined by the characteristic replication foci detected by IdU or CldU staining. Representative images of S phase progression: top, early; middle, mid; bottom, late; scale bar, 10 μm. Transition of replication patterns was classified as early-early (cells that remained in early S-phase during the experiment), early-mid (cells that progressed from early to mid S-phase during the experiment), and early-late/exit (cells that progressed through S-phase).

(E) Percentage of early-early cells. n > 100 nuclei per line. Average of each cell line was used for statistical test. Mean ± SD. Student’s t test, two-tailed, * p < 0.05.

(F) Percentage of early-late/exit cells. n > 100 nuclei per line. Average of each cell line was used for statistical test. Mean ± SD. Student’s t test, two-tailed, ** p < 0.01.

(G) Foci intensity of IdU-labeled cells. n > 100 nuclei per line. Average of each cell line was used for statistical test. Mean ± SD. Student’s t test, two-tailed, * p < 0.05.

To determine whether accelerated S-phase progression altered the replication program, we examined the spatiotemporal pattern of replication factories. Analysis of the replication program showed similar proportions of cells in early, mid, and late S-phase in ASD-derived NPCs and control NPCs (Figure S1D and S1E). We analyzed IdU-labeled replication foci and found that ASD-derived NPCs exhibited increased intensity of individual foci compared to control NPCs (Figure 1G), indicating a higher number of replication forks within each focus. Collectively, these data reveal that ASD-derived NPCs displayed accelerated S-phase progression and altered nuclear organization of replication factories.

ASD-derived NPCs exhibit replication stress, activation of the ATR-CHK1 pathway, and chronic DNA damage

Rapid S-phase progression may cause perturbation of DNA replication. To directly test this possibility, we pulse-labeled NPCs with BrdU for 30 min to identify replicating regions and then carried out a DNA combing assay to examine the length of BrdU tracks (Figure 2A). The analysis revealed a greater reduction in replicative DNA fiber length in ASD-derived NPCs compared to control NPCs (Figure 2A and 2B). To identify stalled or collapsed forks, we performed a DNA combing assay to monitor the progression of replicating forks, during which replicating DNA was pulse-labeled with 5-Iodo-2’-deoxyuridine (IdU) first and then with 5-Chloro-2’-deoxyuridine (CldU) (Figure 2C). We next analyzed fork symmetry between the first and second pulse in IdU/CldU dual-labeled DNA fibers. Analysis revealed asymmetric fork progression in ASD-derived NPCs (Figure 2D). To estimate origin firing, we measured the distance between origins of replication (Figure S2A). A reduction in origin-to-origin distance was observed in ASD-derived NPCs (Figure 2E), indicating increased fork density. Collectively, these data point to DNA replication stress in ASD-derived NPCs.

Figure 2. ASD-derived NPCs display increased replication stress and chronic DNA damage.

Figure 2.

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(A) Representative images of replicating DNA labeled with BrdU by DNA combing assay. BrdU (green). Top, control NPC. Bottom, ASD NPC. Scale bar, 10 μm.

(B) Scatter plot shows the DNA fiber length. Line represents average DNA fiber length. Each bar represents one cell line. Average of each cell line was used for statistical test. Student’s t test, two-tailed, ** p < 0.01.

(C) Representative images of IdU/CldU dual-labeled DNA fibers. IdU (red), CldU (green). Top, control NPC. Bottom, ASD NPC. Scale bar, 10 μm.

(D) Scatter plot shows the fork symmetry calculated by IdU/CldU ratio. Line represents average IdU/CldU ratio. Each bar represents one cell line. Average of each cell line was used for statistical test. Student’s t test, two-tailed, * p < 0.05.

(E) Scatter plot shows the ori-ori distance. Line represents average ori-ori distance. Each bar represents one cell line. Average of each cell line was used for statistical test. Student’s t test, two-tailed, * p < 0.05.

(F) Representative images of γH2AX staining. Top, control NPC. Bottom, ASD NPC. Blue, DAPI. Red, γH2AX. Arrows indicate cells with three or more γH2AX foci. Scale bar, 10 μm.

(G) Bar plot shows the percentage of cells with three or more γH2AX foci. Each dot represents one cell line. Mean ± SD. Average of each cell line was used for statistical test. Student’s t test, two-tailed, ** p < 0.01.

Replication stress induces fork stalling and promotes genome instability (Cimprich and Cortez, 2008). Mild treatment of NPCs with aphidicolin (APH), a reversible inhibitor of eukaryotic DNA replication (Glover et al., 1984), dramatically induced DNA damage detected by the phosphorylation of histone H2AX on serine 139 (γH2AX) in control NPCs (Figure S2C and S2D). We then asked whether ASD-derived NPCs also showed elevated DNA damage. We observed a significant increase in the percentage of cells with three or more γH2AX foci in ASD-derived cells compared to control NPCs (Figure 2F and 2G) without an apparent increase in cell death (Figure S2B). We next characterized the key molecular events induced by DNA damage, i.e., the activation of the ataxia telangiectasia mutated (ATM) and ataxia telangiectasia and Rad3-related protein (ATR) pathways, which are primarily triggered by DSBs and replication stress, respectively. ASD-derived NPCs showed increased CHK1 phosphorylation at Ser345 and RPA32 phosphorylation at Ser 33 (Figure S2E and S2F), a hallmark for activation of the ATR pathway. ATR and CHK1 kinases are key for the response to replication stress and are essential for cell viability (Cimprich and Cortez, 2008). In contrast, the ATM pathway was not activated in ASD-derived cells, as indicated by the lack of ATM phosphorylation at Ser1981 and RPA32 phosphorylation at Ser 4/8 (Figure S2E and S2F). These observations are consistent with the finding that replication stress induces head-on transcription-replication conflicts that lead to fork stalling and selectively activate the ATR-CHK1 pathway but not the ATM pathway (Hamperl et al., 2017). We hypothesized that perturbation of DNA replication would induce increased sensitivity to external replication stress in ASD-derived NPCs. Indeed, ASD-derived NPCs exhibited elevated DNA damage upon mild treatment with APH (Figure S2G). These findings suggest that ASD-derived NPCs had increased replication stress that led to chronic activation of the ATR-CHK1 pathway and elevated DNA damage.

High-throughput mapping of DNA DSBs in NPCs

The faithful transmission of genetic materials to daughter cells is central to maintaining genome stability. Increased replication stress and chronic activation of DNA damage contribute to genome instability (Cimprich and Cortez, 2008). However, how perturbed replication influences genome instability remains elusive. To investigate genomic regions susceptible to DNA damage in NPCs upon replication stress, we treated control NPCs derived from human iPSCs (hiPSCs) and human embryonic stem cells (hESCs) with low doses of APH (Wei et al., 2016, Wei et al., 2018), and we performed a high-throughput genome-wide translocation sequencing (HTGTS) assay (Frock et al., 2015, Hu et al., 2016, Wei et al., 2016, Wei et al., 2018) that maps genome-wide DSBs at nucleotide resolution based on their ability to be translocated to bait DSBs (Figure S3A). We employed a Cas9:single-guide RNA (sgRNA) approach to generate a HTGTS bait DSB at 1p36.22 in the intronic region of CASZ1 (Table S7). A biotinylated primer that specifically recognizes the telomeric broken end of the bait DSB was used to amplify endogenous prey DSBs that joined the bait DSBs. After removing DSB hotspots within low complexity/repeat regions identified by the RepeatMasker track of the UCSC genome browser (http://genome.ucsc.edu/) (Table S2), six prey DSB hotspots were identified in NPCs treated with vehicle control (DMSO) (Figure S3B and S3C). SgRNA off-target (OT) analysis revealed that all of the detected DSB hotspots were Chr1-sgRNA OTs (Table S2). By comparing them to the HTGTS data of NPCs treated with APH, we found new prey DSB hotspots that were unique to APH-treated NPCs (Figure S3D and S3E). Therefore, altered replication is associated with abnormal DSB hotspots in control NPCs derived from hESCs/hiPSCs.

Replication stress induces DNA DSB hotspots in NPCs

To profile genome-wide replication stress-induced DSB hotspots (Wei et al., 2016, Wei et al., 2018) in NPCs, we carried out HTGTS analysis of two hiPSC-derived NPC lines and one hESC-derived NPC line (Figure S3F) with bait DSBs on either Chr1 or Chr11 (Figure 3A and Table S7) and performed DSB hotspot calling with a modified RDC-identification pipeline (Wei et al., 2016, Wei et al., 2018). We performed at least three independent HTGTS experiments on DMSO-or APH-treated cells for each of the baits and NPC lines. Employing the spatial clustering approach for the identification of a chromatin immunoprecipitation-enriched regions (SICER) algorithm (Table S3) (Zang et al., 2009) and focusing on common regions in multiple lines revealed by at least one bait, we identified 37 DSB hotspots (Figure S4A, S4B, Table S4, and Table S5; see STAR methods for details). The size of the DSB hotspots ranged from 120kb to 2.1Mb, with a median length of 570kb (Figure 3B). To facilitate the comparison of HTGTS libraries generated from different baits and cell lines, we calculated DSB densities defined as number of DSBs per Mb per 10,000 total DSBs and analyzed intra-chromosomal DSB hotspots (hotspots located on the same chromosome as the bait DSB) and inter-chromosomal DSB hotspots (hotspots located on chromosomes other than the bait chromosome). We observed significantly greater DSB density in 36 of the 37 DSB hotspots in APH-treated NPCs when compared to DMSO-treated NPCs (Figure 3C and 3D, ANOVA corrected for two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli, FDR < 0.05; Figure S4A and S4B). Notably, 17 of the 37 DSB hotspots located in genes and 19 of the 37 DSB hotspots partially overlapped genes (Figure 3E-F and Table S5). In summary, our results indicate that replication stress induced 37 DSB hotspots in human NPCs, 36 of which correspond to genes.

Figure 3. Replication stress induces DNA DSB hotspots in NPCs.

Figure 3.

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(A) Illustration shows the identification of replication stress-induced DSB hotspots in NPCs.

(B) Scatter plot of the size of DSB hotspots in Mb. Line represents median length (570kb).

(C) Bar plot shows the DSB densities captured by Chr1 bait and Chr11 bait within the indicated inter-chromosomal DSB hotspots. Mean ± SD. ANOVA corrected for two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli, *** FDR < 0.001, ** FDR < 0.01, * FDR < 0.05, ns, not significant.

(D) Bar plot shows the DSB densities captured by Chr1 bait (left) or Chr11 bait (right) within the indicated intra-chromosomal DSB hotspots located on Chr1 or Chr11. Mean ± SD. ANOVA corrected for two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli, *** FDR < 0.001, ** FDR < 0.01, * FDR < 0.05.

(E) Pie chart shows the location of the DSB hotspots.

(F) Scatter plot of the overlap fraction of DSB hotspot interval in genes. Line represents average overlap fraction. 19 DSB hotspots overlapping genes are plotted.

Replication stress induces DSBs in long genes in NPCs

To assess the DSB enrichment in genes, we calculated the DSB density in the 36 susceptible genes that overlapped with DSB hotspots using the gene coordinates from the hg19 reference genome. Thirty-one of the 36 susceptible genes harbored significantly greater DSB density in APH-treated NPCs than DMSO-treated NPCs (Figure 4A and 4B; ANOVA corrected for two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli, FDR < 0.05). Consistent with previously published studies of mouse cells (Wei et al., 2016, Wei et al., 2018), we found that replication stress-susceptible genes in human NPCs were also enriched in long genes (Figure 4C). To determine whether the extent of replication stress-induced DSBs in genes was directly correlated with gene length, we quantified the number of DSBs in genes longer than 100kb and interrogated the enrichment of DSBs upon replication stress (fold-change of DSB densities of genes in APH-treated versus DMSO-treated cells) versus gene length. We found length-dependent replication stress-induced DSB enrichment in NPCs, with the longest genes in the genome displaying the highest level of DSBs after induction of replication stress (Figure 4D and 4E). The length dependence of gene fragility upon replication stress was reproducibly detected in all NPC lines (Figure 4D and 4E). We verified that DSB densities of long genes (genes longer than 800kb located on chromosomes other than Chr1) in APH-treated NPCs were significantly greater than those in DMSO-treated NPCs (Figure 4F). Markedly, 30 of the 36 susceptible genes were longer than 800kb (Table S5). Furthermore, in APH-treated NPCs, long genes harbored significantly greater DSB densities than medium genes (genes between 400kb to 800kb) (Figure 4G). Taken together, our data suggest that replication stress induced DSBs in long genes.

Figure 4. Replication stress induces DSBs in long genes in NPCs.

Figure 4.

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(A) Bar plot shows the DSB densities captured by Chr1 bait and Chr11 bait within the indicated genes located on chromosomes other than Chr1 and Chr11. Mean ± SD. ANOVA corrected for two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli, *** FDR < 0.001, ** FDR < 0.01, * FDR < 0.05.

(B) Bar plot shows the DSB densities captured by Chr1 bait (NEGR1) or Chr11 bait (LRRC4C and DLG2) within the indicated genes located on Chr1 or Chr11. Mean ± SD. ANOVA corrected for two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli, *** FDR < 0.001, * FDR < 0.05.

(C) Gene length comparison of all protein coding genes that are expressed (left) and the 36 replication stress-susceptible genes (right). Box-and-whiskers plot shows the gene length in kb on a log scale. Min to max is plotted. Mann-Whitney test, two-tailed, **** p < 0.0001.

Genome-wide changes in DSB density assessed by HTGTS captured by Chr1 bait (D) or Chr11 bait (E) of APH-treated versus DMSO-treated NPCs. Lines represent mean fold-change (APH/DMSO) of DSB density in genes on a log scale for genes binned according to gene length (100 gene bins, 25 gene step); the ribbon is the SEM of each bin.

(F) Box-and-whiskers plot shows the DSB densities of genes longer than 800kb located on chromosomes other than Chr1 in DMSO-treated or APH-treated NPCs. Min to max is plotted. Wilcoxon matched-pairs signed rank test, ****p < 0.0001.

(G) Box-and-whiskers plot shows the DSB densities of genes longer than 800kb or genes between 400kb and 800kb located on chromosomes other than Chr1 in APH-treated NPCs. Min to max is plotted. Mann-Whitney test, two-tailed, ****p < 0.0001, **p < 0.01.

Replication stress induces replication-transcription conflicts in NPCs

The occurrence of DSB hotspots in genes suggests that transcription could potentially influence DSB sites. To investigate this possibility, we performed Global Run-On sequencing (GRO-seq) (Core et al., 2008) (Figure S5A). Thirty-four of the 36 susceptible genes were actively transcribed in NPCs (Figure 5A). In addition, enriched DSBs were observed at the actively transcribed region of the gene, suggesting the involvement of transcription in generating DSBs (Figure 5B, 5C and S5B). To further explore this finding, we divided the long genes into three groups based on the gene expression level from GRO-seq: high, medium, and low. We then compared the DSB densities of the genes in the high expression group to the genes in the low expression group. We found that, upon replication stress, genes in the high expression group had more DSBs than genes in the low expression group (Figure 5D). We also observed that the expression level of long genes was positively correlated with DSB density of genes (Figure 5E). Analysis of the replication program showed that APH-treated cells were accumulated in mid to late S-phase (Figure S5C-E). To investigate whether replication stress induced conflicts between replication fork and transcription machineries, we monitored their interaction by proximity ligation assay (PLA) (Hamperl et al., 2017). Antibodies against RNA polymerase II (RNAPII) and Proliferating Cell Nuclear Antigen (PCNA) were used to detect transcription machineries and replication forks, respectively (Figure S5F). This analysis revealed increased PLA foci in APH-treated NPCs compared to DMSO-treated NPCs (Figure 5F and 5G). Collectively, our data demonstrate that replication stress induced conflicts of replication fork and transcription machineries, leading to increased DNA damage in transcribed long genes.

Figure 5. Replication stress induces replication-transcription conflicts.

Figure 5.

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(A) Scatter plot shows the average RPKM of two GRO-seq replicates of hiPSC1-derived NPCs of the 36 replication stress-susceptible genes (Table S4) on a log scale. Mean ± SD.

DSB cluster within AUTS2 locus (B) and MID1 locus (C). Prey DSB junctions within the indicated region captured by Chr1 bait and Chr11 bait are shown (middle). RefGene (top) and GRO-seq (bottom) are shown (ordinate indicates normalized GRO-seq counts; reads are shown in plus [red] and minus [gray] orientations). Genomic region corresponding to the actively transcribed region of the gene detected by GRO-seq is highlighted in yellow.

(D) Long genes (>800kb) were divided into three groups based on their expression level from GRO-seq: high (1–33%), medium (34–66%), and low (67–100%). Scatter plot shows the DSB densities of the high expression group and low expression group. Median ± interquartile range. Mann-Whitney test, two-tailed, *** p < 0.001, **** p < 0.0001.

(E) XY-plot shows the correlation of DSB density and expression (log2RPKM) detected by GRO-seq. Genes longer than 800kb located on chromosomes other than Chr1 were plotted. Nonparametric Spearman correlation, correlation coefficient r = 0.572, two tailed, P < 0.0001.

(F) Images of NPCs treated with DMSO (left) or 0.25 μM APH (right) for 24 h. DAPI (blue), PCNA-RNAPII PLA (red). Scale bar, 10 μm.

(G) Scatter plot shows the number of PLA foci of NPCs treated with DMSO (left) or 0.25 μM APH (right) for 24 h. n > 100 nuclei. Mean ± SD. Mann-Whitney test, two-tailed, **** p < 0.0001.

Replication stress induces aberrant adherens junctions, apical polarity, and cell migration in NPCs

DSB repair interferes with replication and transcription. The induction of a single DSB at a human RNAPII-transcribed gene can lead to inhibition of transcription elongation and re-initiation (Pankotai et al., 2012). Moreover, increased conflicts between replication machineries and replication forks may also attenuate transcription of the genes. The 36 susceptible genes play important roles in neural function, including cell-cell adhesion and cell migration (Figure 6A). We first carried out qPCR analysis of Hues6-derived NPCs and found that many of the susceptible genes implicated in cell-cell adhesion were significantly downregulated after replication stress (Figure S6A). Neural rosettes from Hues6 NPCs showed strong expression of the neural cell adhesion molecule N-cadherin at the center of the luminal surface of each rosette (Figure 6B), representing typical formation of adherens junctions of NPCs. Interestingly, while the structure of the rosettes was largely maintained, the expression of N-cadherin was disrupted after replication stress (Figure 6C), as indicated by a scattered expression of N-cadherin (Figure 6B). Because adherens junctions are crucial for maintaining cell polarity, we asked whether the apical-basal polarity of NPCs was affected. Neural rosettes showed robust expression of the apical polarity marker (Figure 6D), atypical PKCλ, representing the typical formation of apical-basal polarity of NPCs. Upon replication stress, disruption of the PKCλ structure was observed in NPCs, as indicated by the disrupted structure or absence of PKCλ in the center of neural rosettes (Figure 6D and 6E). We observed no apparent cell death when the neural cultures were treated with 0.25 μM APH for 2 days and a slight increase in cell death at 0.5 μM APH for 2 days (Figure S6C). Aberrant cell-cell adhesion and apical polarity of the neural rosettes were observed at the low dose (Figure 6C and 6E), indicating that these phenotypes were not induced by cell death.

Figure 6. Replication stress induces aberrant adherens junctions, apical polarity, and cell migration in NPCs.

Figure 6.

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(A) Heatmap shows the P value on a log scale of selected top GO terms.

(B) Defects in adherens junctions of Hues6-derived NPCs treated with APH for 48 h. Sample confocal images of immunostaining of N-cadherin for neural rosettes are shown. Scale bar, 10 μm.

(C) Quantification of neural rosettes with complete or scattered N-cadherin in Hues6-derived NPCs treated with DMSO, 0.25 μM APH, or 0.5 μM APH for 48 h. n = 3 cultures. Mean ± SD. Student’s t test, two-tailed, **** p < 0.0001, *** p < 0.001.

(D) Defects in atypical PKCλ of Hues6-derived NPCs treated with APH for 48 h. Sample confocal images of immunostaining of PKCλ for neural rosettes are shown. Scale bar, 10 μm.

(E) Quantification of neural rosettes with complete or disrupted PKCλ in Hues6-derived NPCs treated with DMSO, 0.25 μM APH, or 0.5 μM APH for 48 h. n = 3 cultures. Mean ± SD. Student’s t test, two-tailed, *** p < 0.001, ** p < 0.01.

(F) Quantification of cell migration from neurospheres treated with DMSO or 0.25 μM APH for 48 h. Each point represents one neurosphere. Student’s t test, two-tailed, **** p < 0.0001.

N-cadherin is also involved in cell migration. A number of susceptible genes implicated in neuronal migration were significantly downregulated upon replication stress (Figure S6B). To investigate whether replication stress affected cell migration, we generated neurospheres from Hues6-derived NPCs and performed a neurosphere migration assay in the presence or absence of APH (Figure S6D). Cell body distances from the neurosphere were measured after 48 hours of treatment. We found migration defects of neurospheres treated with 0.25 μM APH (Figure 6F). Taken together, these results suggest that replication stress attenuates the expression of many of the susceptible genes and induces aberrant adherens junctions, apical polarity, and cellular migration of NPCs.

ASD-derived NPCs harbor increased DNA damage in long genes and exhibit aberrant adherens junctions, apical polarity, and cell migration

Remarkably, 20 susceptible genes were found in the SFARI Gene dataset (https://www.sfari.org/resource/sfari-gene/) that consisted of genes implicated in ASD (Figure 7A and Table S6) and 19 susceptible genes were located within CNV modules of SFARI Gene (Table S6). We next sought to ascertain, using PLA, whether ASD-derived NPCs had increased conflicts of replication fork and transcription machineries. We found increased PLA foci in ASD-derived NPCs compared to control NPCs (Figure 7B and S7A). The observation that replication stress induced increased conflicts of replication fork and transcription machineries (Figure 5G) corroborates the observation that ASD-derived NPCs with altered replication program had increased PCNA-RNAPII PLA foci (Figure 7B), leading to a hypothesis that ASD-derived NPCs harbor more DNA damage in replication stress-susceptible genes. To investigate whether the altered S-phase progression and replication program in ASD-derived NPCs induced DNA damage in long genes, we interrogated DSB sites by HTGTS (Table S7). We observed a small, yet significant increase in DSB densities in the long genes (genes longer than 800kb+ located on chromosomes other than Chr1) in ASD-derived NPCs (Figure 7C). This difference was more significant in replication stress-susceptible genes (33 susceptible genes located on chromosomes other than Chr1) (Figure 7D and Table S5).

Figure 7. ASD-derived NPCs exhibit elevated DNA DSBs in long genes and defects in adherens junctions, apical polarity, and cell migration.

Figure 7.

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(A) Bar plot shows the expected and observed number of replication stress-susceptible genes overlapping SFARI genes. Hypergeometric test; p < 9.04e-22.

(B) Scatter plot shows the PCNA-RNAPII PLA foci. Each bar represents one cell line. Mean ± SD. Average of each line was used for statistical test. Student’s t test, two-tailed, * p < 0.05.

(C) Bar plot shows the DSB densities assessed by HTGTS captured by Chr1 bait of long genes (> 800kb) located on chromosomes other than Chr1. Each bar represents one line. Mean ± SEM. Wilcoxon matched-pairs signed rank test, two-tailed, ** p < 0.01.

(D) Bar plot shows the DSB densities assessed by HTGTS captured by Chr1 bait of 33 NPC susceptible genes located on chromosomes other than Chr1 (Table S5). Each bar represents one line. Mean ± SEM. Wilcoxon matched-pairs signed rank test, two-tailed, *** p < 0.001.

(E) Defects in adherens junctions (N-cadherin, left) and apical polarity (PKCλ, right) in ASD-derived NPCs. Sample confocal images of immunostaining of N-cadherin and PKCλ for neural rosettes are shown. SOX2 (green), N-cadherin (red, left), PKCλ (red, right). Top, control neural rosette. Bottom, ASD neural rosette. Scale bar, 10 μm.

(F) Quantification of neural rosettes with complete or disrupted N-cadherin or PKCλ expression in control or ASD NPCs. Average percentage of 4 cultures per line. Each dot represents one line. Mean ± SD. Student’s t test, two-tailed, * p < 0.05.

(G) Quantification of cell migration from neurospheres generated from control or ASD NPCs after 60 h. Each point represents one neurosphere. Student’s t test, two-tailed, * p < 0.05.

Increased replication stress in neural culture led to gene expression changes and defects in neural function. To determine whether replication stress and increased DNA damage in replication stress-susceptible genes attenuated gene expression in ASD-derived NPCs, we carried out qPCR analysis of genes involved in cell-cell adhesion and neuron migration. Consistently, ASD-derived NPCs showed decreased expression of several genes in these pathways (Figure S7B and S7C), including AUTS2, which has been shown to regulate neuronal migration (Hori et al., 2014). To determine whether ASD-derived cells exhibited defects in apical polarity and adherens junctions, we generated neural rosettes using a monolayer differentiation protocol (Shi et al., 2012). A greater percentage of disrupted neural rosettes, as evidenced by both N-cadherin and PKCλ expression, were observed in ASD-derived cultures (Figure 7E and 7F). Moreover, neurosphere migration assay also revealed decreased cell migration in ASD-derived NPCs (Figure 7G). Taken together, our findings suggest that replication stress in ASD-derived NPCs induces elevated DSBs in long ASD genes and leads to expression and function defects, providing a novel mechanistic link between an abnormal replication program and defects related to ASD risk.

DISCUSSION

DNA repair by classical, non-homologous end-joining is required for neural development (Barnes et al., 1998, Gao et al., 1998, Frank et al., 2000), suggesting critical roles for DNA repair during embryonic neurogenesis. Studies of the DSB hotspots in human neural precursors are essential for elucidating the details of nervous system development and for understanding the mechanisms underlying brain somatic mosaicism and its contribution to human-specific neurodevelopmental diseases such as autism and schizophrenia (McConnell et al., 2017). We observed that, in human NPCs, replication stress induced DSBs in a number of actively transcribed long genes critical for nervous system development. Notably, 26 susceptible genes identified in human NPCs corresponded to RDC-containing genes in mouse neural precursors (Wei et al., 2016, Wei et al., 2018) (Table S5). Remarkably, 10 susceptible genes were unique to human NPCs (Table S5). Eight of the human-specific susceptible genes were reliably identified by both baits in our study (Table S4 and Table S5). SOX5, a susceptible gene in both mouse and human NPCs, contributes to reduction in regional differences in ASD based on expression analysis of the postmortem brain (Parikshak et al., 2016). RBFOX1, a major neuronal splicing regulator, is linked to isoform-level dysregulation in ASD and other psychiatric diseases (Gandal et al., 2018).

Our work reveals a previously unknown mechanism by which accelerated S-phase progression in ASD-derived NPCs may contribute to DNA damage via increased replication stress. We demonstrated that ASD-derived NPCs displayed accelerated S-phase progression accompanied by an altered replication program (Figure 1) and that perturbed S-phase progression and potentially other factors induced replication stress and activated the ATR-CHK1 pathway. Collisions between transcription and replication activate distinct DNA damage responses depending on the conflict orientation. A head-on orientation collision between the transcription machineries and replication fork leads to fork stalling and robust activation of the ATR pathway (Hamperl et al., 2017). The activation of DNA damage and ATR-CHK1 pathway in the ASD-derived NPCs corroborates the observation that replication stress induces head-on transcription-replication conflict (Hamperl et al., 2017). Consistently, ASD-derived NPCs harbor more transcription-replication conflicts, reflected by increased PCNA-RNAPII PLA foci. Head-on collision may block transcription, which can in turn lead to diminished gene expression. In fact, in both APH-treated NPC cultures and ASD-derived NPCs, gene expression was attenuated in many of the replication stress-susceptible genes. Our data suggest an intriguing mechanism by which replication stress in the ASD-derived NPCs activates the ATR pathway and chronic DNA damage and induces transcription-replication conflicts, which then leads to attenuated gene expression and functional defects.

Our study reveals that replication stress caused defects in adherens junctions, apical polarity, and migration of NPCs (Figure 6), reminiscent of what was observed in NPCs carrying 15q11.2 CNVs (Yoon et al., 2014). 15q11.2 CNVs are prominent risk factors for various neuropsychiatric disorders, including schizophrenia, ASD, and intellectual disability (Malhotra and Sebat, 2012). Knockdown of CYFIP1, a gene within 15q11.2, caused ectopic localization of radial glial cells in the developing mouse cortex (Yoon et al., 2014), similar to a recent study in which a high incidence of patches of cortical laminar disorganization in autistic brains was identified (Stoner et al., 2014). Aberrant cell migration has been reported in neuropsychiatric disorders (Penagarikano et al., 2011, Wegiel et al., 2010). Recent studies have found migration defects using NPCs derived from patients with neuropsychiatric disorders (Han et al., 2016, Brennand et al., 2015). CTNNA2, a gene robustly downregulated upon replication stress, was reported in pachygyria syndrome (Schaffer et al., 2018), where disordered cortical neuronal migration was observed in the cerebral cortex. Our study provides a mechanistic model to understand how increased replication stress of NPCs might contribute to the laminar disorganization that is linked to susceptibility to neuropsychiatric disorders.

STAR METHODS

LEAD CONTACT AND MATERIALS AVAILABILITY

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Fred H. Gage (gage@salk.edu).

All unique reagents generated in this study are available upon request by contacting the Lead Contact.

EXPERIMENTAL MODEL AND SUBJECT DETAILS

iPSC and hESC lines

All the ESC studies were done using HUES6 obtained from HSCI iPS Core. Protocols describing the use of iPSCs and hESCs were previously approved by the University of California San Diego and Salk Institute Institutional Review Board and the Embryonic Stem Cell Research Oversight Committee. Informed consent was obtained from all subjects.

Acai, Aero, and Aqua iPSCs were derived from subjects recruited through the University of California San Diego Autism Center of Excellence from a pool of volunteers formerly included in previous brain imaging studies (Marchetto et al., 2017). Cent, Clay, and Clue iPSCs were derived from control subjects selected randomly from lists of typically developing individuals who had had the magnetic resonance imaging scan when they were toddlers (Marchetto et al., 2017). Clinical assessments of the subjects are reported (Table S1) (Marchetto et al., 2017). male.

Two additional healthy individuals with normal psychiatric evaluations collected from University medical center Utrecht were included. iPSC1–926 (female, 32 years of age), iPSC2–611 (male, 35 years of age). The iPSCs were obtained from skin fibroblasts and reprogrammed with CytoTune-iPSC reprogramming kit as per manufacturer’s instruction (Thermo Fisher Scientific). Standard G-banding karyotype analysis was performed. All iPSC lines are karyotypically normal.

All the iPSC lines and Hues6 were maintained in mTeSR1 media (StemCell Technologies) in a 5% CO2 humidified atmosphere at 37°C and passaged using Collagenase IV (Thermo Fisher Scientific) onto plates coated with Matrigel (Trevigen). To generate pan NPCs, hiPSC colonies were dissociated with Collagenase IV and plated onto low-adherence dishes in mTeSR1 medium with the addition of ROCK inhibitor Y-27632 (StemCell Technologies) to generate floating embryoid bodies (EBs). To obtain NPCs, the following day the EBs were treated with 1 μM Dorsomorphin (Tocris) in Dulbecco’s modified Eagle’s medium (DMEM)/F12 (Gibco) plus N2 and B27 supplements (Thermo Fisher Scientific). The treatment was continued for 14 days followed by plating onto dishes coated with 10 μg/ml poly-ornithine (Sigma-Aldrich) and 5 μg/ml laminin (Invitrogen) in DMEM/F12 plus 1x N2 supplement, 1x B27 supplement, and laminin (Invitrogen, 1 μg/ml) to facilitate the attachment of the EBs. Within a few days, rosettes were manually collected and dissociated with Accutase (StemCell Technologies) and plated onto poly-ornithine/laminin-coated dishes. NPCs were maintained in DMEM/F12 (Gibco) supplemented with 1x N2 supplement (Thermo Fisher Scientific), 1x B27 supplement (Thermo Fisher Scientific), 20ng/ml FGF (Joint Protein Central), and 1μg/ml Laminin (Invitrogen) in a 5% CO2 humidified atmosphere at 37°C. NPCs were passaged with Accutase (StemCell Technologies) onto polyornithine/laminin-coated dishes.

METHOD DETAILS

Cell culture

Briefly, NPCs were maintained in DMEM/F12 (Gibco) supplemented with 1x N2 supplement (Thermo Fisher Scientific), 1x B27 supplement (Thermo Fisher Scientific), 20ng/ml FGF (Joint Protein Central), and 1μg/ml Laminin (Invitrogen) in a 5% CO2 humidified atmosphere at 37°C and passaged with Accutase (StemCell Technologies) onto dishes coated with 10 μg/ml poly-ornithine (Sigma-Aldrich) and 5 μg/ml laminin. Characterization of NPCs from ASD cohort was reported in a previous study (Marchetto et al., 2017). All the assays using NPCs were carried out using passage 6 to 8 NPCs. The cells were plated at 100–150 k/cm2 and cultured for two or three days for the analysis.

DSB induction

Bait DSB induction was achieved with a Cas9:sgRNA approach (Frock et al., 2015). Briefly, NPCs were culture until confluent and dissociated with Accutase. 5 million cells were nucleofected with 5 μg of Cas9:sgRNA expression vector using the Nucleofector reagent for Rat Neural Stem Cell (Lonza, VPG-1005) as per manufacturer’s instruction. Cells were plated at 200–300 k/cm2 post nucleofection and cultured for 4 days before harvesting. Replication stress was induced by the addition of 0.5 μM APH (Sigma-Aldrich) for 3 days and then 0.25 μM APH for 1 day.

Cas9:sgRNA plasmid construction

Cas9:sgRNA expression vectors were constructed as described (Cong et al., 2013). Briefly, annealed oligonucleotides (see Table S7 for details) were ligated into BbsI digested pX330-U6-Chimeric_BB-CBh-hSpCas9 vector (Addgene plasmid #42230).

HTGTS and related bioinformatic analyses

LAM-HTGTS was performed and analyzed as previously described (Hu et al., 2016). Briefly, 20–100μg genomic DNA was sheared into 200bp–2kb fragments. Sheared DNA was linearly amplified with a biotinylated primer that targets the bait sequence using Phusion High-Fidelity DNA Polymerase (Thermo Fisher Scientific). The biotin-labeled single-stranded DNA was then purified with Streptavidin C1 beads (Thermo Fisher Scientific, 65001) at room temperature for 4 h. The beads were washed with 1x B&W buffer (5mM Tris-HCl pH 7.5, 0.5mM EDTA, 1M NaCl) three times and on-beads ligation with bridge adapter containing a 6-nucleotide overhang was performed overnight using T4 DNA ligase (Promega Corporation). The adapter-ligated products were amplified by a nested primer and an adapter-complementary primer. The PCR products were then purified and prepared for Illumina MiSeq platform (Illumina) after tagging with the P5-I5 and P7-I7 sequences. PCR products between 500–1000bp was then purified and sequenced on Illumina MiSeq.

FASTQ output files were de-multiplexed, and unique reads aligned to genome build hg19 by Bowtie2 (Langmead and Salzberg, 2012) were processed through the HTGTS pipeline (Hu et al., 2016). Reads with less than 50bp bait sequence were excluded and only unique HTGTS junctions were kept. Primers used and junction yield were described in Table S7.

HTGTS junction enrichment analysis

A modified RDC identification pipeline (Wei et al., 2016) was used to identify DSB hotspot candidates. The analyses were performed by SICER (Zang et al., 2009) of concatenated control (DMSO) or treated (APH) HTGTS libraries (excluding junctions within 10kb of the bait break-site) using the following parameters (Wei et al., 2016):

SICER.sh Species-hg19; redundancy threshold-5; window size-30000; fragment size-1; effective genome fraction-0.8; gap size (bp)-90000; FDR-0.1. Only clusters with more than five junctions (more than 10 junctions if on the same chromosome as the bait DSB) from APH-treated libraries were considered.

To identify DSB hotspots in NPCs, we separated the candidates into intra-chromosomal and inter-chromosomal DSB hotspots. For intra-chromosomal DSB hotspots, clusters had to be independently identified in three NPC lines by the bait located on the same chromosome. For inter-chromosomal DSB hotspots, clusters identified in three NPC lines or identified in two NPC lines by both baits were kept. The remaining inter-chromosomal DSB hotspots identified in two NPC lines were subjected to statistical test (two-tailed Student’s t-test) and only the significant ones were reported.

Identification of recurrent translocation to Cas9:sgRNA off-target sites

Translocations between Cas9:sgRNA bait and off-target DSBs were identified as described (Frock et al., 2015) by MACS2 (Zhang et al., 2008) with the following parameters: -g hs --keep-dup all --nomodel --extsize 500 -q 0.001 --llocal 10000000. Hotspots ≥100 kb from the bait DSB break-site with an FDR-adjusted P-value threshold of 1 × 10−9 were considered translocations between Cas9:sgRNA bait and off-target DSBs if they shared >30% sequence with the bait site in multiple libraries.

Global run-on sequencing

GRO-seq libraries were prepared as previously described (Core et al., 2008) from 5 to 10 million NPC nuclei. Briefly, NPCs were washed with ice-cold PBS buffer three times and scraped off and re-suspended in SB1 buffer (0.32M sucrose, 3mM CaCl2, 2mM MgAC2, 0.1mM EDTA, 10 mM Tris-HCl pH 8.0, 0.5% (v/v) IGEPAL, 1mM DTT, and 1x EDTA-free protease inhibitors) and dounced on ice. The nuclei were then mixed with an equal volume of SB2 buffer (2M sucrose, 5mM MgAC2, 0.1mM EDTA, 10mM Tris-HCl pH 8.0, and 1mM DTT) and layer onto the SB2 buffer and subjected to centrifugation at 30,000 x g for 45 mins at 4°C. The resultant nuclei were resuspended in glycerol storage buffer (40% (v/v) glycerol, 5 mM MgCl2, 0.1 mM EDTA, and 50 mM Tris-HCl pH 8.3) before the run-on assay. For the run-on assay, re-suspended nuclear extract was mixed with an equal volume of run-on reaction buffer (10 mM Tris-HCl pH 8.0, 5 mM MgCl2, 300 mM KCl, 1 mM DTT, 200 U/ml RNaseOut, 1% Sarkosyl, 500μM ATP, 500μGTP, 500μM CTP, and 500μM Br-UTP) and incubated for 5 min at 30°C.

The reaction was stopped by adding 1mL TRIzol LS reagent (Thermo Fisher Scientific). The total nuclear RNA was extracted by acidic phenol/chloroform and precipitated by ethanol. The extracted RNA was subjected to base hydrolysis by 200mM NaOH for 18 min on ice and then neutralized by Tris-Cl buffer (pH 6.8). The RNA was cleaned with Micro Bio-Spin P-30 Gel-columns (Bio-Rad) according to manufacturer’s instructions. To prepare for immunoprecipitation, anti-BrdU agarose beads (Santa Cruz Biotechnology) were equilibrated with binding buffer (0.25X SSPE, 1 mM EDTA, 0.05% Tween-20, and 37.5 mM NaCl) and incubated with blocking buffer (1X binding buffer, 0.1% PVP, and 1 ug/ml BSA) for 1 h at room temperature. RNA and blocked beads were mixed in binding buffer (0.25X SSPE, 1 mM EDTA, 0.05% Tween-20, and 37.5 mM NaCl) for 1 h at room temperature. Beads were sequentially washed with binding buffer, low salt buffer (0.2X SSPE, 1 mM EDTA, and 0.05% Tween-20), and equal ratio of low salt buffer and high salt buffer (0.25X SSPE, 1 mM EDTA, 0.05% Tween-20, and 100 mM NaCl). Finally, BrU-incorporated RNA was eluted in elution buffer (20 mM DTT, 300 mM NaCl, 50 mM Tris-HCl pH 7.5, 1 mM EDTA, and 0.1% SDS). BrU-RNA was then sequentially subjected to decapped at the very 5’ end by tobacco acid pyrophosphatase, end repair, 5’-adaptor ligation, and 3’-adaptor ligation. RNA was next converted to cDNA by reverse transcription using SuperScript III First-Strand Synthesis System (Thermo Fisher Scientific). The cDNA products were then prepared for sequencing on the Illumina HiSeq 2500 platform. The library fraction in the size range of 200 – 500 bp was sliced and recovered. Sequencing was performed on the Illumina HiSeq 2500 platform following manufacturer’s instructions. Two biological replicates of hiPSC-1 derived NPCs were performed. GRO-seq data were aligned to human genome build hg19 by Bowtie2 (Langmead and Salzberg, 2012) (bowtie2 -x hg19 --non-deterministic) and non-redundant, uniquely mapped sequence reads were retained. Gene expression levels were analyzed by HOMER (analyzeRepeats.pl rna hg19 -count genes -strand --rpkm) (Heinz et al., 2010).

FACS analysis

Cell cycle analysis.

For BrdU pulse-chase experiment, cells were pulse-labeled with 20 μM BrdU (Sigma-Aldrich) for 30 min, collected immediately or chased for 3 h in fresh media. For cell cycle analysis, cells were pulse-labeled with 20 μM BrdU for 2 h and harvested. Cells were fixed in 70% ice cold ethanol for at least 30 min, permeabilized with 0.1% Triton X-100, and treated with 2N HCl for 30 min prior to BrdU antibody (BioLegend, 364104) labeling. Cells were washed with PBS and treated with 20 μg/ml RNase A (Thermo Fisher Scientific) and DNA was stained with 20 μg/ml propidium iodide (Invitrogen). Cells were analyzed on LSR II (Becton Dickinson) and acquired data were analyzed using FlowJo (Becton Dickinson) software.

Cell viability assay.

Cells were collected, washed in cold PBS, and stained with propidium iodide (Invitrogen). Cells were analyzed on LSR II (Becton Dickinson) and acquired data were analyzed using FlowJo (Becton Dickinson) software. Live cells were defined as cells negative for propidium iodide staining.

DNA combing assay

To measure fork speed, cells were pulse-labeled with 20 μM BrdU (Sigma-Aldrich) for 30 min and collected. To measure fork symmetry and estimate fork density, cells were sequentially labeled with 25 μM IdU (Sigma-Aldrich) for 20 min and then with 100 μM CldU (Sigma-Aldrich) for 20 min. DNA fiber spreads were prepared as previously described (Marechal et al., 2014). Briefly, 2 μl of cell suspension was spotted onto a cleaned glass slide and lysed with 7 μl of lysis buffer (50mM EDTA, 0.5% SDS, and 200mM Tris-HCl pH 7.5). Slides were tilted to allow DNA to spread slowly down the slide, followed by air-drying and fixation in methanol/acetic acid (3:1) for 10 min.

The DNA spread was then denatured in 2.5 M HCl for 80 min and then blocked with 5% BSA in PBS for 30 min. Mouse anti-BrdU antibody (BD Biosciences, 347580) was used to detect IdU, and rat anti-BrdU antibody (Accurate, OBT0030) was used to detect BrdU or CldU. SsDNA antibody (Enzo life Sciences, F7–26) was used to detect single-stranded DNA. Antibodies were diluted in blocking solution and applied to the slides followed by incubation in a humidified chamber for overnight at 4°C. After three washes with PBS, secondary antibodies were applied for 1 h at room temperature. The slides were washed and then mounted with ProLong Gold Antifade Mountant (Thermo Fisher Scientific). Images of well-spread DNA fibers were acquired using Zeiss LSM 710 or LSM 880 Laser Scanning Confocal Microscope (Carl Zeiss) and measured using the ImageJ software (NIH) (Schneider et al., 2012).

Immunofluorescence

Cells cultured on slides were fixed in 4% paraformaldehyde (10 min at room temperature), permeabilized with 0.1% Triton X-100, blocked in 5% horse serum, and incubated with primary antibodies overnight at 4 °C. After wash, cells were incubated with secondary antibodies for 1 h at room temperature, washed, incubated with DAPI (Thermo Fisher Scientific) for 10 min, and mounted using ProLong Gold Antifade Mountant (Thermo Fisher Scientific). Image acquisition was performed using Zeiss LSM 710 Laser Scanning Confocal Microscope or Zeiss LSM 880 Laser Scanning Confocal Microscope (Carl Zeiss). For the CIdU/ IdU experiment, cells were treated with 2N HCl for 30 min at room temperature prior to blocking. IdU foci intensity was analyzed using unprocessed 0.31-μm stacks with the FociPicker3D algorithm (Du et al., 2011). The primary antibodies used were γH2AX (Millipore Sigma, 05–636), mouse anti BrdU (BD Biosciences, 347580), rat anti BrdU (Accurate, OBT0030), SOX2 (Thermo Fisher Scientific, 53–9811-82), NESTIN (Millipore, AB5922), Ki-67 (MilliporeSigma, AB9260), N-cadherin (ThermoFisher Scientific, 33–3900), PKCλ (BD Biosciences, BDB610207), phospho-ATM Ser1981 (Santa Cruz, sc-47739), phospho-CHK1 Ser345 (Life technologies, PA5–34625), phospho-RPA32 Ser4/Ser8 (abcam, Ab87277), and phospho-RPA32 Ser33 (Bethyl Laboratory, A300–246A).

Proximity ligation assay

Proximity ligation assay was performed using Duolink In Situ Orange Kit (Millipore Sigma) according to manufacturer’s instruction with minor modifications. Briefly, cells were fixed 4% paraformaldehyde (10 min, room temperature), permeabilized with 0.1% Triton X-100, blocked in 2% horse serum and 2% BSA, and incubated with primary antibodies overnight at 4 °C (1:2000 rabbit PCNA (abcam, ab18197) alone; 1:1000 mouse RNAPII (CTD4H8, santa cruz biotechnology, sc-47701); or 1:2000 rabbit PCNA and 1:1000 mouse RNAPII). Cells were then processed according to manufacturer’s instructions. Slides were imaged using Zeiss LSM 710 Laser Scanning Confocal Microscope (Carl Zeiss).

Neural rosette formation assay

Neural rosettes from Hues6 hESCs were generated based on a previously published protocol (Yu et al., 2014). Briefly, EBs were formed by mechanical dissociation of Hues6 colonies using collagenase IV and cultured in low-adherent plates. For EB differentiation, floating EBs were treated with DKK1 (0.5 μg/ml), SB431542 (10 μM), Noggin (0.5 μg/ml), and cyclopamine (1 μM) in DMEM/F12 (Gibco) plus N2 and B27 supplements for 20 days. To obtain neural rosettes, EBs were plated on polyornithine and laminin-coated plates in DMEM/F12 (Gibco) plus N2 and B27 supplements and laminin (Invitrogen, 1μg/ml). Rosettes were manually collected and dissociated with Accutase after 1 week and plated onto poly-ornithine and laminin-coated plates. Rosettes were passaged again at high density with gentle dissociation with Accutase. Neural rosettes from iPSCs reprogrammed from ASD or CTRL individuals were generated following a monolayer method adapted from a previously published protocol (Shi et al., 2012). Briefly, iPSCs were cultured on Matrigel-coated plates at about 70–80% confluence. Neural induction was initiated the next day with a 1:1 mixture of N2-and B27-containing media. N2 medium consisted of DMEM/F12, N2 supplement, 5 μg/ml insulin (Sigma-Aldrich), 1 mM l-glutamine, 100 μM non-essential amino acids, and 100 μM 2-mercaptoethanol. B27 medium consisted of Neurobasal (Invitrogen), B27 with vitamin A (Thermo Fisher Scientific), and 200 μM glutamine. The medium was supplemented with 1 μM Dorsomorphin (Tocris) and 10 μM SB431542 (StemRD). Cells were cultured for 10–13 days and then passaged onto poly-ornithine and laminin-coated plates with a 1:1 mixture of N2-and B27-containing media with 20 ng/ml FGF2. Rosettes were passaged again at high density with gentle dissociation with Accutase. Only individual non-overlapped neural rosettes with typical morphology were included for quantification. The quantifications were performed with at least three independent cultures.

Neurosphere migration assay

The neurosphere migration assay was performed according to previously published protocols (Brennand et al., 2015, Marchetto et al., 2019). Briefly, NPCs were dissociated with Accutase and then cultured for 3 days in low-adherent plates to generate neurospheres. Neurospheres were then manually picked and plated in Matrigel matrix (0.5 mg Matrigel was used to coat one 24-well plate for at least 1 hour before plating). The next morning, neurospheres derived from Hues6 NPCs were treated with NPC media with DMSO or 0.25 μM APH and fixed 48 hours later to assess NPC migration. To compare cell migration of ASD-derived NPCs and control NPCs, neurospheres were manually picked and plated in Matrigel-coated plates and maintained for 60 hours in NPC media. Cell migration distance from each neurosphere was measured using Image J software (NIH).

RNA extraction and quantitative PCR

Total cellular RNA was extracted from 2–5 ×106 cells using the RNA-BEE (TEL-TEST, INC) and RNA Clean & Concentrator Kit (Zymo research), according to the manufacturer’s instructions, and reverse transcribed using SuperScript III First-Strand Synthesis System (Thermo Fisher Scientific). qPCR was done using Power SYBR Green PCR Master Mix (Thermo Fisher Scientific).

QUANTIFICATION AND STATISTICAL ANALYSIS

Data were analyzed using GraphPad Prism version 8. Group sizes and definition of error bars were indicated in figure legends. Assumptions for the correct application of standard parametric procedures were checked (e.g., normality of the data). For parametric datasets with two groups, statistical analysis was performed using a two-tailed Student’s t test. For the non-parametric dataset with two groups, an unpaired (two way) Mann-Whitney test was performed. For paired observations, paired Wilcoxon matched-pairs signed rank test was performed. ANOVA analyses were used for comparisons of data with more than two groups. Post hoc group comparisons were performed with the two-stage step-up method of Benjamini, Krieger and Yekutieli.

Spearman’s correlation coefficient was calculated to evaluate the presence of a monotonic relationship between two variables. The observed distribution and expected distribution comparison (Figure 6A and 7A) were performed with hypergeometric test.

Differences were considered statistically significant at * (p < 0.05), ** (p < 0.01), *** (p < 0.001), and **** (p < 0.0001).

Supplementary Material

1
2

Table S1. ASD subjects and matched control subjects used in this study. Related to Figures 1, 2, 7, S1, S2, and S7.

Table S3. DSB hotspot candidates identified by SICER in NPCs. Related to Figures 3 and S3.

3
4

Highlights.

  1. NPCs derived from macrocephalic ASD patients exhibit replication stress.

  2. Replication stress induces replication-transcription conflicts.

  3. Replication stress induces DSB hotspots in genes in human NPCs.

  4. ASD-derived NPCs show aberrant adherens junctions, apical polarity, and migration.

ACKNOWLEDGMENTS

We thank Y. Xu and M.L. Gage for critical reading and discussions of the manuscript, and Dr. R. Frock for making HTGTS libraries. M.W. was supported by a training grant from the California Institute for Regenerative Medicine. This study was supported by the AHA-Allen Initiative in Brain Health and Cognitive Impairment award made jointly through the American Heart Association and The Paul G. Allen Frontiers

Group (19PABH134610000), the National Institutes of Health U01# MH106882–01, the JPB Foundation, the Leona M. and Harry B. Helmsley Charitable Trust (#2012-PG-MED002), Annette C. Merle-Smith, the Robert and Mary Jane Engmann Foundation, and the March of Dimes. This work was also supported by the Waitt Advanced Biophotonics Core Facility of the Salk Institute with funding from NIH-NCI CCSG: P30 014195 and the Waitt Foundation and the Flow Cytometry Core Facility of the Salk Institute with funding from NIH-NCI CCSG: P30 014195. This study was also supported by the Harvard Brain Initiative and a Charles H. Hood Foundation Major Grant. P.-C.W. is supported by the Charles A. King Trust Postdoctoral Research Fellowship Program, Bank of America, co-trustees. F.W.A. is an investigator of HHMI.

Footnotes

DECLARATION OF INTERESTS

The authors declare no competing interests.

DATA AND CODE AVAILABILITY

The sequencing data generated in this study can be found at the Gene Expression Omnibus (GEO) under the accession number GSE115782.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1569131-supplement-1.docx (58.3KB, docx)
2

Table S1. ASD subjects and matched control subjects used in this study. Related to Figures 1, 2, 7, S1, S2, and S7.

Table S3. DSB hotspot candidates identified by SICER in NPCs. Related to Figures 3 and S3.

NIHMS1569131-supplement-2.pdf (25.6MB, pdf)
3
NIHMS1569131-supplement-3.xlsx (120.2KB, xlsx)
4
NIHMS1569131-supplement-4.xlsx (48.8KB, xlsx)

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