Abstract

Williams syndrome (WS) is a neurodevelopmental disorder caused by a genomic deletion of ∼28 genes that results in a cognitive and behavioral profile marked by overall intellectual impairment with relative strength in expressive language and hypersocial behavior. Advancements in protocols for neuron differentiation from induced pluripotent stem cells allowed us to elucidate the molecular circuitry underpinning the ontogeny of WS. In patient-derived stem cells and neurons, we determined the expression profile of the Williams–Beuren syndrome critical region-deleted genes and the genome-wide transcriptional consequences of the hemizygous genomic microdeletion at chromosome 7q11.23. Derived neurons displayed disease-relevant hallmarks and indicated novel aberrant pathways in WS neurons including over-activated Wnt signaling accompanying an incomplete neurogenic commitment. We show that haploinsufficiency of the ATP-dependent chromatin remodeler, BAZ1B, which is deleted in WS, significantly contributes to this differentiation defect. Chromatin-immunoprecipitation (ChIP-seq) revealed BAZ1B target gene functions are enriched for neurogenesis, neuron differentiation and disease-relevant phenotypes. BAZ1B haploinsufficiency caused widespread gene expression changes in neural progenitor cells, and together with BAZ1B ChIP-seq target genes, explained 42% of the transcriptional dysregulation in WS neurons. BAZ1B contributes to regulating the balance between neural precursor self-renewal and differentiation and the differentiation defect caused by BAZ1B haploinsufficiency can be rescued by mitigating over-active Wnt signaling in neural stem cells. Altogether, these results reveal a pivotal role for BAZ1B in neurodevelopment and implicate its haploinsufficiency as a likely contributor to the neurological phenotypes in WS.

Introduction

Williams syndrome (WS [MIM 194050]) is a neurodevelopmental disorder genetically defined by a typical hemizygous 7q11.23 microdeletion of 1.55 million base pairs (Mb) that encompasses approximately 28 genes (1). Individuals with this deletion exhibit a specific physical, cognitive and behavioral profile characterized by hypersociality and a relative preservation of language function despite overall intellectual disability (ID). Although the phenotypes of WS have been described for half a century and the exact genotype known for nearly two decades, the molecular circuitry linking specific genetic changes with the neurological features remains largely undetermined (2). Hemizygosity of elastin (ELN), located in the Williams–Beuren critical region deletion (WSΔ), causing supravalvular aortic stenosis (SVAS [MIM 185500]) is the only firmly established genotype–phenotype correlation in WS (3). Despite important efforts to build a cognitive map from the genome architecture of patients with atypical patient deletions (4) and the use of animal models to dissect the genetic networks underlying the phenotypes, these systems are faced with limitations that hamper discovery of clear-cut phenotype–genotype correlations (5) and the failure of mouse models to capture certain aspects of human physiology (6).

The technical breakthrough of reprogramming mature cells into self-renewing, induced pluripotent stem cells (iPSCs) enabled the generation of disease-specific human iPSC lines (7–9). WS iPSCs have been recently used to model the vascular phenotype and profile the transcriptional landscape of WS derivatives (10, 11); however, neurons were not investigated in these studies. Building upon these studies, we have generated iPSCs from fibroblasts of two individuals carrying the typical WS 7q11.23 microdeletion and directly induced these cells into neurons. Genome-wide transcriptional profiling allowed us to measure the expression of WSΔ genes and assess the transcriptional consequences of the microdeletion specifically in neurons. Thus, the goal of these studies is to link the WS microdeletion to a transcriptional profile.

During neural induction of WS iPSCs, we identified a differentiation defect characterized by aberrant cell cycle and activated Wnt signaling. We investigated the contributions of BAZ1B ((bromodomain adjacent to zinc finger domain, 1B), also known as Williams syndrome transcription factor, WSTF), an evolutionarily conserved protein tyrosine kinase and member of the ISWI ATP-dependent chromatin remodeler complex subfamily encoded in the WSΔ and expressed throughout neurodevelopment, to these defects. Genome-wide identification of BAZ1B binding sites implicated this gene in neurodevelopment and several WS phenotypes. Knocking down BAZ1B to haploinsufficient levels in neural stem cells recapitulated the transcriptional dysregulation we observed in WS and caused a neural differentiation defect. This defect was reversed by antagonizing Wnt signaling, thus implicating this gene and pathway in WS pathology. WS iPSCs serve as a powerful platform to study the neurodevelopmental pathology of the disease and to gain insights into the relationship between gene deletions and uniquely human neurocognitive functions.

Results

WSΔ gene expression and transcriptional rewiring in WS iPS cells and induced neurons

We generated and characterized iPSCs from two individuals carrying the typical WS 7q11.23 microdeletion and two euploid controls. We derived one of the control lines from an individual with Angelman syndrome (AS [MIM 105830]), a neurological disorder characterized by autistic features, ID and the absence of speech that served as a control with another form developmental cognitive dysfunction (12). In addition to euploidy at the WSΔ locus, the controls were matched for age, gender and ethnicity to the WS individuals. We induced neurons directly from the iPSC lines through viral transduction of the neurogenic transcription factor, NEUROD1, allowing us to consistently and reproducibly obtain highly pure human-induced neurons (iNs) suitable for gene expression profiling (13). Gene expression profiles obtained from this neuron induction protocol were highly reproducible (r2 ≥ 0.98) for all pairs of biological replicates (Supplementary Material, Fig. S1A and S1B). Hierarchical clustering of gene expression profiles by genes with high variation or the expression profile of the WS locus showed that the two WS lines behaved similarly, as did the two control lines (Supplementary Material, Fig. S1C and S1D).

To determine the local and global consequences of the WS microdeletion on gene expression, we performed transcriptional profiling of WS cells and euploid controls of iPS cells and iNs (Fig. 1A). WS iPSCs exhibited typical embryonic stem cell morphology and stained positively for typical pluripotent stem cell markers including OCT4, SOX2 and NANOG (Fig. 1B and Supplementary Material, Fig. S2) and were validated for other stem cell characteristics (Supplementary Material, Fig. S3). Six of the WS deleted genes were significantly downregulated in WS iPSCs (Fig. 1D), despite few overall transcriptional differences between the different iPS cell lines (Fig. 1E and Supplementary Material, Table S1). Direct induction of neurons yielded highly pure populations of neurons staining positively for MAP2 and TUJ1 (Fig. 1C). High purity neuronal induction was equivalent between all lines, and no gross morphological differences distinguished WS neurons from controls (Supplementary Material, Fig. S4A,B), although further characterization of these neurons may reveal differences. When plated on a multielectrode array, neurons induced by this method were capable of firing action potentials that were blocked in the presence of tetrodotoxin (Supplementary Material, Fig. S4C,D). In contrast to the iPSCs, neurons induced from WS lines showed widespread transcriptional dysregulation compared with controls with 2272 differentially expressed probes corresponding to 778 genes downregulated in WS and 1008 upregulated genes (Fig. 1F and Supplementary Material, Table S2). Fourteen of the WS-deleted genes were expressed in neurons (log2 normalized expression > 8), and all of these were statistically downregulated to about half of control levels (average fold decrease = −1.8 ± 0.1; Fig. 1D). The expression profile of the WSΔ genes is therefore consistent with a lack of dosage compensation for the WS microdeletion, in line with findings reported in other human microdeletions (14).

Figure 1.

Transcriptional profiling of WS-iPSCs and induced neurons reveals disease-relevant gene expression changes. (A) Schematic overview of transcriptional profiling experimental design. (B) WS-iPS cells express canonical pluripotency markers OCT4 and NANOG. (C) WS-induced neurons exhibit neuronal morphology and express neuronal markers MAP2 and β3-tubulin (TUJ1). (D) The WSΔ gene expression profiles in iPS cells and induced neurons suggest the lack of dosage compensation for this microdeletion. All expressed WSΔ in neurons are significantly downregulated to around half of control levels. (E) MA plot of log ratio versus mean average shows few differentially expressed gene probes in WS iPSC compared to controls. (F) MA plot highlights widespread transcriptional dysregulation in WS induced neurons compared to controls. (G) GO and pathway enrichment of terms up- and downregulated in WS neurons capture disease-relevant terms. WS, Williams syndrome; AS, Angelman syndrome; CTRL, control; iPSC, induced pluripotent stem cell; FDR, false-discovery rate; WSΔ, Williams syndrome critical region deleted; GO, gene ontology.

Figure 1.

Transcriptional profiling of WS-iPSCs and induced neurons reveals disease-relevant gene expression changes. (A) Schematic overview of transcriptional profiling experimental design. (B) WS-iPS cells express canonical pluripotency markers OCT4 and NANOG. (C) WS-induced neurons exhibit neuronal morphology and express neuronal markers MAP2 and β3-tubulin (TUJ1). (D) The WSΔ gene expression profiles in iPS cells and induced neurons suggest the lack of dosage compensation for this microdeletion. All expressed WSΔ in neurons are significantly downregulated to around half of control levels. (E) MA plot of log ratio versus mean average shows few differentially expressed gene probes in WS iPSC compared to controls. (F) MA plot highlights widespread transcriptional dysregulation in WS induced neurons compared to controls. (G) GO and pathway enrichment of terms up- and downregulated in WS neurons capture disease-relevant terms. WS, Williams syndrome; AS, Angelman syndrome; CTRL, control; iPSC, induced pluripotent stem cell; FDR, false-discovery rate; WSΔ, Williams syndrome critical region deleted; GO, gene ontology.

Enrichment analysis of genes downregulated in WS neurons revealed widespread transcriptional dysregulation that included markedly downregulated genes implicated in cognition, synaptic transmission and ID (CACNA1C, GABRG2, GRIN3A, NLGN3; Fig. 1G and Supplementary Material, Table S3). Also among these downregulated terms was an enriched interaction network centered on the WSΔ gene STX1A (Supplementary Material, Fig. S5). All of the genes presented in this figure were significantly downregulated in the WS neurons. STX1A encodes syntaxin-1a, a protein highly abundant in the synaptic bouton (15) whose haploinsufficiency may contribute to the WS phenotypes (16), but in itself is not a satisfactory explanation for the complex cognitive phenotype. Based on many transcriptional profiling experiments in neurological diseases, downregulated synaptic transcripts are expected and may be a general feature of neurological conditions that affect cognition. On the other hand, the enriched upregulated pathways in WS neurons suggested novel upstream pathogenic mechanisms. The upregulated terms included cell cycle terms (cell cycle, mitosis) and Wnt-activated receptor genes, and suggested an underlying basis for a neurodevelopmental facet of the pathology that precedes synaptogenesis and may more directly underlie defects in neural circuitry.

Interestingly, we found that canonical neural stem cell markers (ASCL1, HES5, PAX6, SOX2) were upregulated in WS iNs, suggesting the transcriptional state of WS neurons was more immature compared with controls. Neuroepigenetic chromatin remodelers were also dysregulated in WS neurons, with three neuron-specific chromatin remodeling subunits downregulated in WS iNs and a neural progenitor-specific chromatin remodeler upregulated in these cells (17) (Supplementary Material, Fig. S6). Together this suggested a possible neuroepigenetic constriction point in the path to post-mitotic commitment of WS-induced neurons. Consistent with this observation, a recent mouse model recapitulating key features of WS revealed an increased proportion of immature neurons, further supporting this as a relevant cellular phenotype of WS (18). Moreover, as this neuroepigenetic switch is essential for proper synaptic development and neuronal plasticity (19, 20), an incomplete neurogenic commitment of WS iNs is a strong candidate event contributing to the widespread transcriptional deficits of synaptic genes we observed.

Prioritization of BAZ1B as a candidate contributor to WS transcriptional dysregulation

After finding all of the expressed WSΔ gene levels at roughly half the control levels in neurons, we next sought to identify which of these may contribute to the observed widespread transcriptional dysregulation and neurogenic defect in WS iNs. Six genes within the WSΔ locus are predicted to function in transcriptional regulation or epigenetics (21), and three of these are predicted to be completely intolerant to loss of function mutations in healthy humans (22): GTF2I, GTF2IRD1 and BAZ1B. Extensive studies have linked the general transcription factors (GTFs) to neurocognitive aspects of WS (23–29), but the contributions of BAZ1B are not well studied.

BAZ1B is a component of the ISWI chromatin remodeling complex (30), which predominantly labels proliferating cell populations during early mouse brain development (31), and helps to establish the gene expression programs controlling neural maturation (32). BAZ1B regulates gene expression by modulating chromatin and BAZ1B knockout clones of human cells have large numbers of genes both up- and downregulated (33). In an expression dataset of cortical neurons derived from human pluripotent stem cells, BAZ1B levels are associated with cortical specification, and genes with similar co-expression patterns were enriched for cell-cycle roles (34). Thus BAZ1B is a compelling candidate contributor to the neuroepigenetic phenotype of WS neurons.

Genome-wide identification of BAZ1B binding sites implicates BAZ1B in neurogenesis and WS phenotypes

We used a human neural progenitor cell line as a platform to ascertain the role of BAZ1B in neural stem cell differentiation. Given its DNA and chromatin binding capabilities, we first sought to identify the genomic binding targets of BAZ1B in neural stem cell self-renewal and in neuronal differentiation. To this end, we performed chromatin-immunoprecipitation followed by next-generation sequencing (ChIP-seq) in these two conditions. In self-renewal conditions, we identified 1211 BAZ1B peaks and mapped these to the nearest transcription start sites to identify 1068 genes putatively regulated by BAZ1B (Fig. 2A and Supplementary Material, Table S4). Enriched gene ontology (GO) terms of BAZ1B target genes were related to neurogenesis, implicating BAZ1B as a regulator of neurodevelopment (Fig. 2B). BAZ1B target genes in self-renewal conditions were enriched for 161 target genes involved in nervous system development and 47 synaptic genes (Fig. 2B left and Supplementary Material, Table S5). Wnt signaling was enriched among BAZ1B target genes.

Figure 2.

Genome-wide identification of BAZ1B binding sites implicates BAZ1B in neurogenesis and WS phenotypes. (A) ChIP-seq identifies BAZ1B binding sites in neural progenitor cells in self-renewal and differentiation conditions. (B) Gene ontology (GO) enrichment of BAZ1B target genes in self-renewal conditions highlights pathways in nervous system development (left). GO enrichment of BAZ1B target genes in differentiation conditions highlights neuron-specific processes (right). (C) Disease enrichment of BAZ1B target genes implicates BAZ1B in specific WS phenotypes, including neurological symptoms. Hypergeometric P-values for enriched terms are shown in parentheses. (D) Human phenotype enrichment of BAZ1B target genes further implicates BAZ1B in WS-specific phenotypes. NSD, nervous system development; LTP, long-term potentiation; PSD, post-synaptic density; ADHD, attention deficit hyperactivity disorder.

Figure 2.

Genome-wide identification of BAZ1B binding sites implicates BAZ1B in neurogenesis and WS phenotypes. (A) ChIP-seq identifies BAZ1B binding sites in neural progenitor cells in self-renewal and differentiation conditions. (B) Gene ontology (GO) enrichment of BAZ1B target genes in self-renewal conditions highlights pathways in nervous system development (left). GO enrichment of BAZ1B target genes in differentiation conditions highlights neuron-specific processes (right). (C) Disease enrichment of BAZ1B target genes implicates BAZ1B in specific WS phenotypes, including neurological symptoms. Hypergeometric P-values for enriched terms are shown in parentheses. (D) Human phenotype enrichment of BAZ1B target genes further implicates BAZ1B in WS-specific phenotypes. NSD, nervous system development; LTP, long-term potentiation; PSD, post-synaptic density; ADHD, attention deficit hyperactivity disorder.

ChIP-seq in differentiated neurons further supported a role for BAZ1B in neurodevelopment. We identified 3043 BAZ1B peaks mapping to the nearest transcription start sites of 2339 genes in differentiated neurons (Fig. 2A and Supplementary Material, Table S6). This 2-fold expansion of target genes in differentiation over the precursor state suggests an expanding role for BAZ1B in neuronal differentiation. Important pathways in neuron differentiation, such as axon guidance and the formation of neuronal projections, were enriched among these targets (Fig. 2B right). Compared with targets in self-renewal conditions, BAZ1B target genes in differentiation conditions were further enriched for synaptic genes and less enriched in genes in nervous system development. This observation suggests a progressive shift in BAZ1B function from a role in neural progenitor cell cycle regulation to neuronal differentiation and synaptic gene activation. Of the 390 overlapping genes between precursor cells and differentiated cells, 59 genes were involved in nervous system development and 20 genes were synaptic. Taken together, BAZ1B appears to coordinate a set of neurogenic genes with cell cycle genes to control the final population of terminally differentiated neurons.

Disease enrichment of BAZ1B-regulated genes predicted several characteristic WS phenotypes, including anxiety and attention deficit hyperactivity disorder (ADHD [MIM 143465]) (Fig. 2C). BAZ1B target genes were also enriched for Simons Foundation Autism Research Initiative (SFARI) curated human genes (35). Of the 385 manually curated and scored genes in the human gene module of SFARI, 44 and 103 BAZ1B target genes in self-renewal and differentiation conditions respectively overlapped these autism-associated genes (P = 2.6 × 10−7, P = 1.9 × 10−18, hypergeometric test). The predicted functionality of BAZ1B target genes extended beyond the neural lineage to the musculoskeletal and cardiovascular phenotypes of WS. In particular, our results predict BAZ1B target genes also contribute to craniofacial dysmorphology in WS, consistent with mouse studies (36). Specific phenotypes regulated by BAZ1B were further predicted by the Human Phenotype Ontology (HPO) (Fig. 2D) (37). These predicted phenotypes, including short nose, small sella turcica and optic nerve dysplasia have been observed in WS (38,39). Strikingly, BAZ1B target genes delta-catenin (CTNND2) and KANSL1 are associated with the HPO term ‘conspicuously happy disposition’ implicating BAZ1B as a possible regulator of the WS behavioral profile. Altogether, BAZ1B target genes predict physical, behavioral and disease-relevant phenotypes, and allow us to link novel and specific genotype–phenotype correlations in WS.

BAZ1B knock-down to haploinsufficient levels recapitulates WS transcriptional dysregulation and a Wnt-dependent neurogenic commitment defect

Having shown the direct binding of the haploinsufficient WS gene, BAZ1B, to neurogenic and neuron differentiation genes, we next wanted to determine the transcriptional consequences of BAZ1B haploinsufficiency on neural stem cell differentiation. We selected two independent short-hairpin RNAs (shRNA) targeting BAZ1B to haploinsufficient levels. These shRNAs reduced mRNA expression levels to 62 ± 1 and 57 ± 3% of control levels (Fig. 3A, P < 0.001), resembling the levels we observed in WS iNs. We infected neural progenitor cells with either BAZ1B shRNAs or scrambled shRNA, and grew these cells under differentiation conditions (Fig. 3B). Transcriptional profiling of these cells revealed widespread expression changes, in agreement with previous BAZ1B knock-down studies (33). Compared with scrambled shRNA differentiated neurons, thousands of genes were affected by BAZ1B haploinsufficiency, including 1697 unique genes upregulated and 1199 downregulated (Supplementary Material, Fig. S7 and Table S7). Hierarchical clustering of these cells by the most variably expressed genes clustered BAZ1B knock-down cells as an outgroup to neural progenitors cells and differentiated neurons, demonstrating widespread transcriptional consequences attributable to dosage reduction of BAZ1B (Fig. 3C). As anticipated, the set of genes perturbed by BAZ1B haploinsufficiency was enriched for direct target genes identified in our ChIP-seq analysis. Remarkably, GO analysis of the transcriptional consequences of dosage reduction of BAZ1B to haploinsufficient levels recapitulated two transcriptional hallmarks we observed in WS neurons, namely upregulated mitotic genes and downregulated genes enriched for roles in nervous system development and disease (Fig. 3D). This concordance of GO terms was driven by a strong overlap of dysregulated genes in both WS neurons and BAZ1B knock-down neurons. In fact, direct and indirect BAZ1B targets identified by ChIP-seq and knock-down explained a combined 42% of all transcriptional dysregulation in WS iNs (P = 3.4 × 10−79, hypergeometric test; Fig. 3E). Included in this overlap were an enrichment of Wnt-activated receptor genes and canonical neural stem cell markers (Supplementary Material, Table S8), consistent with a role for BAZ1B in regulating these pathways.

Figure 3.

BAZ1B haploinsufficiency recapitulates WS transcriptional dysregulation. (A) BAZ1B shRNA targeting efficiency resembles haploinsufficient levels. (B) Schematic overview of transcriptional profiling experimental design. (C) Hierarchical clustering and heat map of most variably expressed genes shows widespread transcriptional differences in BAZ1B knock-down. (D) Gene ontology (GO) enrichment of genes dysregulated by BAZ1B knock-down matches pathways dysregulated in WS neurons. (E) BAZ1B ChIP-seq target genes and genes dysregulated by BAZ1B knock-down overlap genes dysregulated in WS neurons. scRNA, scrambled control shRNA; shRNA, short-hairpin RNA; GO, gene ontology; FDR, false-discovery rate; rep, biological replicate; DE, differentially expressed; ChIP, chromatin-immunoprecipitation, KD, knock-down, ***P < 0.001.

Figure 3.

BAZ1B haploinsufficiency recapitulates WS transcriptional dysregulation. (A) BAZ1B shRNA targeting efficiency resembles haploinsufficient levels. (B) Schematic overview of transcriptional profiling experimental design. (C) Hierarchical clustering and heat map of most variably expressed genes shows widespread transcriptional differences in BAZ1B knock-down. (D) Gene ontology (GO) enrichment of genes dysregulated by BAZ1B knock-down matches pathways dysregulated in WS neurons. (E) BAZ1B ChIP-seq target genes and genes dysregulated by BAZ1B knock-down overlap genes dysregulated in WS neurons. scRNA, scrambled control shRNA; shRNA, short-hairpin RNA; GO, gene ontology; FDR, false-discovery rate; rep, biological replicate; DE, differentially expressed; ChIP, chromatin-immunoprecipitation, KD, knock-down, ***P < 0.001.

Brightfield microscopy showed that both shRNAs targeting BAZ1B dramatically blocked the differentiation of neural progenitors, whereas scrambled control shRNA had no effect (Supplementary Material, Fig. S8). This profound delay of differentiation was confirmed by staining for proliferation marker Ki67 (Fig. 4A), showing a significantly elevated proportion of BAZ1B haploinsufficient neural progenitors continued to proliferate. Quantifying these results, 43.8 ± 2% of BAZ1B shRNA-treated cells remained proliferative even after 6 days of differentiation conditions compared with <1% of cells treated with a scrambled control (P = 1 × 10−05). Expression of MAP2 and other neuronal marker genes was also blocked in these cells (Fig. 4B and Supplementary Material, Fig. S8B). Cell cycle analysis of these cells in differentiation conditions showed a 4-fold increase of cells in S phase (P < 0.001) and a 2.25-fold increase in G2/M cells (P < 5 × 10−5) upon BAZ1B knock-down, indicative of an increased proliferative capacity in the population versus differentiation (Fig. 4C). These results implicate haploinsufficiency of BAZ1B as altering the balance between progenitor self-renewal and neuronal differentiation.

Figure 4.

BAZ1B haploinsufficiency causes a Wnt-dependent neurogenic commitment defect. (A) Staining of proliferation marker Ki67 in BAZ1B knock-down neural progenitors shows a high fraction of proliferative cells. Quantification and significance values are indicated. (B) Staining with neuronal marker MAP2 shows a block in differentiation in BAZ1B knock-down. Quantification and significance are indicated. (C) FACS analysis of cell-cycle shows a significant increase of cells in S and G2/M upon BAZ1B knock-down. (D) Brightfield microscopy of BAZ1B knock-down cells treated with Wnt pathway antagonist (XAV939) shows a partial rescue of differentiation defect visible by neurite outgrowth. (E) XAV939 treatment allows for the expression of neuronal marker MAP2 in BAZ1B knock-down cells. (F) Quantification of Ki67 + cells in BAZ1B knock-down neural progenitors after treatment with Wnt-antagonist shows a dramatic and dose-dependent decrease in proliferating cells upon treatment. **P < 0.01, ***P < 0.001.

Figure 4.

BAZ1B haploinsufficiency causes a Wnt-dependent neurogenic commitment defect. (A) Staining of proliferation marker Ki67 in BAZ1B knock-down neural progenitors shows a high fraction of proliferative cells. Quantification and significance values are indicated. (B) Staining with neuronal marker MAP2 shows a block in differentiation in BAZ1B knock-down. Quantification and significance are indicated. (C) FACS analysis of cell-cycle shows a significant increase of cells in S and G2/M upon BAZ1B knock-down. (D) Brightfield microscopy of BAZ1B knock-down cells treated with Wnt pathway antagonist (XAV939) shows a partial rescue of differentiation defect visible by neurite outgrowth. (E) XAV939 treatment allows for the expression of neuronal marker MAP2 in BAZ1B knock-down cells. (F) Quantification of Ki67 + cells in BAZ1B knock-down neural progenitors after treatment with Wnt-antagonist shows a dramatic and dose-dependent decrease in proliferating cells upon treatment. **P < 0.01, ***P < 0.001.

Given the well-established role of Wnt signaling in regulating neurogenesis (40, 41), and the enrichment of Wnt signaling terms in both WS neuron upregulated genes and BAZ1B ChIP-seq target genes, we asked whether we could rescue the differentiation defect induced by BAZ1B haploinsufficiency by inhibiting this pathway. We antagonized Wnt/β-catenin signaling in BAZ1B knock-down neural progenitor cells during differentiation using the small molecule XAV939 which stimulates β-catenin degradation by stabilizing axin (42). Wnt/β-catenin pathway inhibition was able to partially rescue the differentiation defect induced by BAZ1B haploinsufficiency, visibly increasing neurite outgrowth in the cells (Fig. 4D) and partially restoring the expression of the neuron marker MAP2 (Fig. 4E). As shown by Ki67 staining, a dose-dependent decrease in proliferating cells could be achieved by increasing Wnt-antagonist concentration (Fig. 4F). A modest dose of XAV939 (0.5 μm) lowered the percentage of proliferative cells from around 28% of the total population to under 5% (P < 0.01). A higher dose (1 μm) further reduced the proliferative proportion to near wild-type levels. These effects were independent of apoptosis, which did not increase upon BAZ1B knock-down or treatment with XAV939 (Supplementary Material, Fig. S8C). Overall, these results confirm that the BAZ1B haploinsufficiency-mediated imbalance in neural progenitor cell cycle exit depends on Wnt/β-catenin signaling and can be rescued by inhibiting this pathway. An overactivation of this pathway, as observed in the WS iNs transcriptomes, may be a novel and targetable pathway in WS.

Discussion

WS, with a well-defined deletion and characteristic neurocognitive and behavioral profile, offers one of the most powerful systems to connect specific gene deletions to the molecular mechanisms underlying human neurocognitive phenotypes. Patients with atypical deletions have been used to try to link specific genes to WS phenotypes (5, 43, 44), and in fact, partial deletions of just an ∼950 kb interval that spans ABHD11 on the centromeric side to GTF21 on the telomeric side have been reported (43, 45). However, these rare partial deletions cannot exclude a position effect on the neighboring chromosome region (46) or mutations not detectable with conventional fluorescence in situ hybridization (FISH) analysis.

WS iPSCs now provide an unprecedented vantage to understand the effects of the 7q11.23 microdeletion and serve as a novel model to identify disease-relevant cellular phenotypes. Recent work has examined the vascular phenotype and transcriptional dysregulation caused by this microdeletion in WS iPSC derivatives (10, 11), but neither study generated neurons, the most relevant cell type to investigate neurological phenotypes of WS. The latter study implicated a dosage imbalance in GTF2I, a GTF deleted at the WS locus. However, this gene was not studied in the context of WS neurons, is encoded in a highly redundant manner with many closely related genes and pseudogenes on chromosome 7, and only accounted for 10–20% of the widespread transcriptional dysregulation observed in the WS IPSCs (11). A lack of major transcriptional consequences for the duplication or deletion of Gtf2i was reported in mouse primary cortical neurons (47) suggesting that other WS-deleted genes may contribute to transcriptional dysregulation. Our patient-derived neurons enabled us to discover novel WS pathogenic molecular mechanisms connected to the neurological phenotypes of WS and exhibited disease-relevant hallmarks such as the downregulation of key genes involved in synaptic transmission and cognition. With all detected WSΔ genes expressed at only around half the control levels, a combination of these genes, including STX1A, likely contributes to the neurological aspects of WS. Upregulated pathways in WS neurons, including cell cycle terms and Wnt signaling, informed novel mechanistic insights into WS pathology. These pathways indicated an incomplete developmental transition from neural progenitors to differentiated neurons in WS, the same cellular phenotype recently observed in a mouse model of WS (18).

A limitation to our study is that we generated iPS cells from only two individuals with WS and two controls. However, the reproducibility between differentiations and the similarity of gene expression between the two samples suggest the transcriptional consequences of the WS microdeletion are profound and robustly detectible. While previous iPS cell-derived neurons of AS individuals have reported thousands of transcriptional differences in these cells (48), the use of AS iPSCs and neurons as controls in our experiment may enhance our ability to detect transcriptional changes specific to WS versus other neurodevelopmental disorders. Comparing the WS iNs with either AS or healthy control iNs, we found largely overlapping gene sets, including cell cycle, Wnt signaling and neural stem cell genes (Supplementary Material, Fig. S9), further supporting these pathways as transcriptional signatures of WS iNs. The full datasets generated in this work are openly available, allowing further exploration of the transcriptional dysregulation we observed in WS neurons.

We have identified BAZ1B, a component of the ISWI subfamily of ATP-dependent chromatin remodelers, as a likely critical contributor to the unique neurocognitive phenotypic features of WS. Systematic overexpression (49) or deletion experiments (50) have shown the vast majority of eukaryotic genes are not dosage sensitive, and gene regulatory networks are typically robust to the failure of random individual nodes (51). Yet, certain genes within the WSΔ locus at 7q11.23 are dosage sensitive, and their haploinsufficiency manifests as multiple systems-level phenotypes. Critical determinants of haploinsufficiency include high and developmentally regulated expression, evolutionary conservation, and high connectivity in protein–protein interaction networks (52). BAZ1B fits these criteria. Confirming the essentiality of BAZ1B to normal human development is a significant depletion of loss of function mutations in this gene in over 60 000 human exomes from the Exome Aggregation Consortium (ExAC) (22). Consistent with a pivotal role in neurodevelopment and WS, ChIP-seq identified thousands of genomic loci bound by BAZ1B proximal to genes enriched for functions in neurogenesis, neuron differentiation and specific WS phenotypes. Among these were several cell-cycle-related genes including cyclin D1 (CCND1) that can increase neurogenesis and reduce basal progenitor population when inhibited, resulting in a reduced surface area of the cerebral cortex (53). BAZ1B dosage reduction to haploinsufficient levels in neural progenitor cells elicited widespread transcriptional dysregulation and induced a differentiation defect resembling our observations in WS-derived neurons. Remarkably, when knock-down experiments brought BAZ1B to haploinsufficient levels in a neural progenitor cell line, the resultant dysregulation overlapped a plurality (42%) of all the transcriptional changes we observed in WS neurons. These results implicate BAZ1B as a novel regulator of neurogenesis and support an etiological role for BAZ1B haploinsufficiency as contributory to the WS neurocognitive phenotypes. The size of BAZ1B and its exquisite dosage sensitivity when deleted or duplicated precluded facile rescue experiments, which could definitively establish the causality of BAZ1B haploinsufficiency underlying the transcriptional changes we observed in WS neurons. Despite this omission, our knock-down experiment in neuronal progenitors and our ChIP-seq data independently support a pivotal role for BAZ1B in neurodevelopment and prioritize this gene for future studies.

The identification of dosage sensitivity of the chromatin-remodeling protein BAZ1B in widespread transcriptional dysregulation of neurodevelopmental pathways arrives at an emerging theme in the literature implicating chromatin remodeling genes underlying autism spectrum disorder (ASD) and ID (54, 55). Recent genome-wide association and exome-sequencing studies have identified causative mutations for a range of neurodevelopmental disorders in several ATP-dependent chromatin remodeling proteins (56–64). These complexes overlap at target sites to coordinate gene expression (65) and epigenetically orchestrate the complexity and dynamism of the neurodevelopmental transcription program characterized by the activation and precise spatio-temporal regulation of an estimated 86% of the total coding transcriptome (66). In a comprehensive functional genomics approach integrating ChIP-seq, knock-down experiments and transcriptomics, we have elucidated the effects of BAZ1B dosage on neurodevelopmental pathways. BAZ1B target genes are enriched for Wnt signaling genes and this pathway is activated in WS neurons. Wnt signaling regulates the balance between neuronal progenitor proliferation and differentiation, with loss-of-function and overexpression causing reciprocal effects of depleting or expanding the progenitor pool (67, 68). By antagonizing this pathway, we can rescue the switch defect caused by BAZ1B haploinsufficiency in neural progenitor differentiation. These results suggest that BAZ1B haploinsufficiency in neural progenitors is sufficient to tip the balance away from neuron differentiation toward prolonged self-renewal. Precise control of cell cycle exit in neural precursors regulates cortical histogenesis (67), and even slight alterations of this control result in profound downstream consequences on brain size, regionalization and micro-circuitry (69). An approximate 10–15% reduction in brain volume has been documented in WS (70), with regions of increased cortical thickness and complexity (71, 72). These findings support a Wnt-mediated cell-cycle exit defect caused by BAZ1B haploinsufficiency as a plausible mechanism contributing to the abnormalities in brain volume, thickness and complexity in WS. Notably, FZD9 and BCL7B, two additional genes deleted in WS, are contiguous with BAZ1B and also function in Wnt signaling (73, 74). Both these genes are expressed at roughly half the levels of control neurons in the WS iNs in this study and may contribute to the Wnt-mediated cell-cycle abnormalities we observed.

The genetic pathways identified are particularly insightful, because symmetrical copy number variations of 7q11.23 display symmetrically opposite phenotypes with regard to the social facets of brain function. Whereas WS is often called the opposite of autism, the WS-locus duplication (MIM 609757) is associated with ASD (75, 76), confirming that this region contains one or more genes whose dosage contributes to these specific cognitive phenotypes. A set of brain functions mediated by a genetically based brain circuit may bundle seemingly diverse behaviors, which in the case of 7q11.23-mediated social behavior include excessive friendliness, unreserved behavior around strangers, an engaging personality, striking verbal ability and an affinity for music. All these could be construed as facets of social behavior under the control of a genetic program that precisely times developmental progression of neural progenitors and their integration into mature neural circuits. We hypothesize that BAZ1B and the regulatory node identified here follow the corresponding symmetrically divergent pattern in which delayed cell cycle exit and concomitant effects on chromatin remodeling mediates the WS hypersocial features, and early cell cycle exit mediates the impaired social behaviors of ASD. This hypothesis dovetails with orthogonal findings that neurological disorders affecting language and intelligence exhibit premature differentiation of neural progenitor cells (77).

Materials and Methods

Generation and characterization of iPSCs

Skin biopsies were collected from two females diagnosed with WS at the Alexandru Obregia Clinical Hospital of Psychiatry in Bucharest, Romania, pursuant to approval by institutional review boards at Alexandru Obregia Clinical Hospital of Psychiatry and Victor Babes National Institute of Pathology. Both lines carried the typical 1.55 Mb hemizygous WS deletion, confirmed by FISH probes spanning the locus. A clinical description of these individuals is provided in Supplementary Material. Epidermal fibroblasts were reprogrammed to iPSCs using integrating retroviral ‘Yamanaka factors’ (7). Apparently, healthy, young female donor dermal fibroblasts were purchased from Cell Applications Inc. (San Diego, CA) and reprogrammed to iPSC as a control. Fibroblasts obtained from an age, gender and ethnically matched control with AS (MIM 105830) were also collected and reprogrammed.

All iPSCs exhibited typical embryonic stem cell morphology and stained positively for typical pluripotent stem cell markers (Supplementary Material, Fig. S2). Derived iPSCs expressed pluripotency genes and formed three germ layers during embryoid body differentiation (Supplementary Material, Fig. S3A and S3B). iPSCs stably retained normal (46XX) karyotypes and underwent demethylation of the OCT4 promoter upon reprogramming to pluripotency (Supplementary Material, Fig. S3C and S3D). G-banded karyotyping was performed by WiCell (WiCell Research Institute Inc., Madison, WI). iPSCs were grown in feeder-free conditions on Matrigel (BD Biosciences, San Jose, CA) in mTeSR1 media (StemCell Technologies, Inc., Vancouver) and manually passaged every 5–7 days.

Direct neural induction of iPSCs

We induced neurons directly from the iPSC lines through viral transduction of a master neurogenic transcription factor, NEUROD1 (13, 78). In brief, iPSCs were passaged as single cells with Accutase (StemCell Technologies), and 500 000 cells were plated in one well of a 6-well plate on Matrigel in the presence of ROCK inhibitor Y-27632 (StemCell Technologies). On the next day, cells were transduced with lentiviral particles encoding NEUROD1-ires-eGFP-PuroR driven by a tetracycline inducible promoter and doxycycline (1 µg/ml, Sigma) was added to the media. Two days later, cells were re-plated at a dilution of 1:6 onto Matrigel and puromycin (1 µg/ml) was added to the media to remove non-transduced cells. This was designated as day 0. Cytosine beta-d-arabinofuranoside (Ara-C, Sigma) was added at a low concentration for days 2–6 to remove any proliferative cells that escaped selection to ensure pure non-dividing neuronal cultures. Purity of this protocol exceeded 90% by Tuj1 staining and did not differ across lines (Supplementary Material, Fig. S4B). Induced neurons were maintained in N2/B27 media (Gibco-Life Technologies, Carlsbad, CA) without additional neurotrophic factors. Cells were cultured for 14 days before RNA extraction. Doxycycline was withdrawn from the media after day 7 to minimize the effect of transgene overexpression on neuronal transcriptomes at the time of analysis.

Transcriptome profiling and differential gene expression analysis

RNA samples were collected using the mirVana kit (Life Technologies), and RNA expression was measured with Illumina HumanHT-12 Expression BeadChips (Illumina, Inc., San Diego, CA) by expert technicians at the microarray core facility at Sanford–Burnham Medical Research Institute. Data were analyzed using the beadarray (79) and limma (80) packages in R (v2.15.2) (81). Expression data were quantile normalized and log2 transformed for analysis. All samples were profiled in biological triplicate, i.e. three independent replicates per line. Gene expression levels across biological replicates were highly reproducible, with r2 ≥ 0.96 in all cases (Supplementary Material, Fig. S1); therefore, gene expression levels for the replicates were averaged. Differentially expressed genes were determined by fitting linear models to the expression data and performing empirical Bayes testing. P-values were adjusted for multiple testing according to Benjamini–Hochberg method (82). All differentially expressed gene probes (P < 0.01) in iPSCs and neurons are provided in Supplementary Material, Table S1 and Table S2.

Gene ontology and pathway enrichment analysis

For GO analysis, gene lists for enrichment analysis were obtained from PharmGKB (83), KEGG pathways from the Encyclopedia of Genes and Genomes (84) and GO terms from AmiGO (85). P-values were calculated from a cumulative hypergeometric distribution, calculated using the phyper command in R and adjusted for multiple testing. The total population size was set to 21 462, representing the number of genes detectable using the Illumina HumanHT-12 Expression BeadChips. Additional GO analysis was performed with WebGestalt (86).

Human neural progenitor cell culture and differentiation

We used human neural progenitor cells which were derived from the ventral mesencephalon of human fetal brain (ReNcells) purchased from EMD Millipore (Billerica, MA, SCC008). ReNcells were maintained on Matrigel in ReNcell NSC maintenance medium (EMD Millipore) with 20 ng/ml human FGF2 (Peprotech, Rocky Hill, NY) and 20 ng/ml human EGF (Peprotech). For differentiation, ReNcells were seeded on Matrigel-coated plates and induced to differentiate by withdrawing growth factors in ReNcell NSC media. To antagonize Wnt/β-catenin signaling, XAV939 (Selleckchem, 1 µm) was added to differentiating ReNcells.

Immunofluorescence assays

Cells were fixed with 4% paraformaldehyde, permeabilized with 0.25% Triton X-100 and then blocked with 10% fetal bovine serum (FBS). Samples were stained with primary antibodies Ki67 (Cell Signaling 9129), Tau (T46, Life Technologies 13-6400), TuJ1 (Covance MMS-435P), MAP2 (Millipore AB5622) overnight at 4°C. Secondary antibodies used include Alexa Fluor 555-goat anti-mouse IgG, and Alexa Fluor 488 or 555-goat anti-rabbit IgG (Invitrogen). Images were acquired using an Olympus IX71 fluorescence microscope with MetaMorph software (Molecular Devices).

RNA isolation and quantitative real-time PCR

Total RNA was extracted using the mirVana kit (Life Technologies) and reverse transcribed with the SuperScript III First-Strand Synthesis System (Life Technologies). Real-time quantitative PCR was performed using a QuantStudio 12K Real-Time PCR System (Life Technologies) with Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA). HPRT or GAPDH mRNA levels were used as a normalization control.

BAZ1B knock-down

Lentiviral vectors (pLKO.1) containing shRNA targeting BAZ1B were purchased from Sigma (Sigma-Aldrich, St. Louis, MO). Lentiviral vector plasmids were transfected into HEK293T cells along with psPAX2 (Addgene #12260) and pMD2.G (Addgene #12259). After 48 h, supernatants were harvested, filtered through a 0.45 μm filter and concentrated by ultracentrifugation. Human neural progenitor cells were infected with virus, and stably integrated shRNA lines were selected using puromycin.

Cell cycle analysis

Cells were dissociated as single cells with Accutase (Life Technologies) and fixed with ethanol for 1 h on ice, followed by RNase treatment. Propidium iodide (Sigma, 10 μg/ml) was added to the samples and data were acquired by BD Accuri C6 flow cytometry (BD Biosciences). Cell cycle analyses were performed in biological triplicate.

Apoptosis assays

Annexin V staining was performed with Dead Cell Apoptosis Kit (Life Technologies) according to the manufacturer's instruction. Briefly, differentiated ReNcells were washed with annexin-binding buffer and incubated with Alexa 488-anti-annexin V antibody at room temperature for 15 min. After additional washing with annexin-binding buffer, images were acquired using an Olympus IX71 fluorescence microscope with MetaMorph software (Molecular Devices).

Chromatin-immunoprecipitation and sequencing

ChIP-seq was performed in accordance with ENCODE guidelines (87). ChIP was performed using the EZ-ChIP kit (Millipore) according to manufacturer's instructions. Briefly, ReNcells grown under either self-renewal conditions or differentiation for 6 days were crosslinked with 1% paraformaldehyde for 10 min. Chromatin was isolated and sonicated using a Covaris S2 (Covaris, Woburn, MA). Sheared chromatin was incubated with anti-BAZ1B antibody (Abcam ab50850) overnight at 4°C followed by Protein G pulldown.

Ion Xpress Plus Fragment Libraries were prepared and sequenced on an Ion Proton PI chip on the Ion Proton System (Life Technologies, Carlsbad, CA) according to company supported protocols. DNA reads were aligned to the human genome (hg19) using bowtie2 (v 2.1.0) (88). ChIP-seq peaks were called using MACS (v 1.4.0) (89) and annotated using custom R scripts. BAZ1B pulldown was performed in duplicate, and replicates were combined for analysis. RNA-polymerase II (POL2) was included as a positive control. Input chromatin was used as a negative control.

Multi-electrode array recording

A glial bed was prepared from C57BL/6 mice. Briefly, cortices were dissected from postnatal day 1 mice, dissociated using papain and trituration, then plated at a density of 700 cells/mm2. Cells were incubated at 37°C with 5% CO2 and maintained in MEM supplemented with 5% heat-inactivated fetal calf serum and Mito + serum extender. At 7 days after plating, primary neurons were killed using a solution composed of 200 uM glutamate. NEUROD1-transduced cells were plated on glial bed 2 days after initiating doxycycline treatment and grown for 2 weeks. Extracellular action potentials were recorded using a Multi Channel Systems MEA 2100 system (MCS, Reutlingen, Germany) with a 120 count multi-electrode array with 100 μm interelectrode spacing and 30 um electrode diameter, at a sampling frequency of 20 kHz. 1 uM tetrodotoxin was added to verify biological origin of action potentials. All animal usage was performed following University of California, Santa Barbara Institutional Animal Care and Use Committee approved protocols.

Accession number

Sequencing data have been deposited to GEO with accession number GSE71664.

Web resources

AmiGO, http://amigo1.geneontology.org/cgi-bin/amigo/term_enrichment.

ExAC Exome Aggregation Consortium Browser, http://exac.broadinstitute.org/.

Human Phenotype Ontology, http://www.human-phenotype-ontology.org/.

KEGG, http://www.genome.jp/kegg/.

OMIM, http://www.omim.org/.

PharmGKB, https://www.pharmgkb.org/index.jsp.

SFARI, https://gene.sfari.org/.

WebGestalt, http://bioinfo.vanderbilt.edu/webgestalt/login.php.

Supplementary Material

Supplementary Material is available at HMG online.

Acknowledgements

Above all, we gratefully acknowledge the fibroblast donors who enabled this study. We also acknowledge Dr Hyung-Seok Kim for the generous help with deriving some of the iPSC lines in this study. We gratefully acknowledge Dr Kang Liu and the Sanford Burnham Medical Research Institute's Microarray Core facility. We acknowledge the use of the Laboratory for Stem Cell Biology and Engineering at UC Santa Barbara, which is supported by the California Institute for Regenerative Medicine (CIRM) Grant CL1-00521-1. J.J. was supported by CIRM fellowship TG2-01151. We gratefully acknowledge support from the Dr Miriam and Sheldon Adelson Medical Research Foundation.

Conflict of Interest statement: The authors declare no conflicts of interest.

References

1
Bayes
M.
,
Magano
L.F.
,
Rivera
N.
,
Flores
R.
,
Perez Jurado
L.A.
(
2003
)
Mutational mechanisms of Williams–Beuren syndrome deletions
.
Am. J. Hum. Genet.
 ,
73
,
131
151
.
2
Morris
C.A.
,
Lenhoff
H.M.
,
Wang
P.P.
(
2006
)
Williams–Beuren Syndrome: Research, Evaluation, and Treatment
 .
The Johns Hopkins University Press
,
Baltimore, MD
.
3
Li
D.Y.
,
Toland
A.E.
,
Boak
B.B.
,
Atkinson
D.L.
,
Ensing
G.J.
,
Morris
C.A.
,
Keating
M.T.
(
1997
)
Elastin point mutations cause an obstructive vascular disease, supravalvular aortic stenosis
.
Hum. Mol. Genet.
 ,
6
,
1021
1028
.
4
Korenberg
J.R.
,
Chen
X.N.
,
Hirota
H.
,
Lai
Z.
,
Bellugi
U.
,
Burian
D.
,
Roe
B.
,
Matsuoka
R.
(
2000
)
VI. Genome structure and cognitive map of Williams syndrome
.
J. Cogn. Neurosci.
 ,
1
,
89
107
.
5
Fusco
C.
,
Micale
L.
,
Augello
B.
,
Teresa Pellico
M.
,
Menghini
D.
,
Alfieri
P.
,
Cristina Digilio
M.
,
Mandriani
B.
,
Carella
M.
,
Palumbo
O.
et al
. (
2014
)
Smaller and larger deletions of the Williams–Beuren syndrome region implicate genes involved in mild facial phenotype, epilepsy and autistic traits
.
Eur. J. Hum. Genet.
 ,
22
,
64
70
.
6
Osborne
L.R.
(
2010
)
Animal models of Williams syndrome
.
Am. J. Med. Genet. C Semin. Med. Genet.
 ,
15
,
209
219
.
7
Takahashi
K.
,
Tanabe
K.
,
Ohnuki
M.
,
Narita
M.
,
Ichisaka
T.
,
Tomoda
K.
,
Yamanaka
S.
(
2007
)
Induction of pluripotent stem cells from adult human fibroblasts by defined factors
.
Cell
 ,
131
,
861
872
.
8
Dimos
J.T.
,
Rodolfa
K.T.
,
Niakan
K.K.
,
Weisenthal
L.M.
,
Mitsumoto
H.
,
Chung
W.
,
Croft
G.F.
,
Saphier
G.
,
Leibel
R.
,
Goland
R.
et al
. (
2008
)
Induced pluripotent stem cells generated from patients with ALS can be differentiated into motor neurons
.
Science
 ,
321
,
1218
1221
.
9
Park
I.H.
,
Arora
N.
,
Huo
H.
,
Maherali
N.
,
Ahfeldt
T.
,
Shimamura
A.
,
Lensch
M.W.
,
Cowan
C.
,
Hochedlinger
K.
,
Daley
G.Q.
(
2008
)
Disease-specific induced pluripotent stem cells
.
Cell
 ,
134
,
877
886
.
10
Kinnear
C.
,
Chang
W.Y.
,
Khattak
S.
,
Hinek
A.
,
Thompson
T.
,
de Carvalho Rodrigues
D.
,
Kennedy
K.
,
Mahmut
N.
,
Pasceri
P.
,
Stanford
W.L.
et al
. (
2013
)
Modeling and rescue of the vascular phenotype of Williams-Beuren syndrome in patient induced pluripotent stem cells
.
Stem Cells Transl. Med.
 ,
2
,
2
15
.
11
Adamo
A.
,
Atashpaz
S.
,
Germain
P.L.
,
Zanella
M.
,
D'Agostino
G.
,
Albertin
V.
,
Chenoweth
J.
,
Micale
L.
,
Fusco
C.
,
Unger
C.
et al
. (
2015
)
7q11.23 dosage-dependent dysregulation in human pluripotent stem cells affects transcriptional programs in disease-relevant lineages
.
Nat. Genet.
 ,
47
,
132
141
.
12
Angelman
H.
(
1965
)
‘Puppet’ children a report on three cases
.
Dev. Med. Child. Neurol.
 ,
7
,
681
688
.
13
Zhang
Y.
,
Pak
C.
,
Han
Y.
,
Ahlenius
H.
,
Zhang
Z.
,
Chanda
S.
,
Marro
S.
,
Patzke
C.
,
Acuna
C.
,
Covy
J.
et al
. (
2013
)
Rapid single-step induction of functional neurons from human pluripotent stem cells
.
Neuron
 ,
78
,
785
798
.
14
Blumenthal
I.
,
Ragavendran
A.
,
Erdin
S.
,
Klei
L.
,
Sugathan
A.
,
Guide
J.R.
,
Manavalan
P.
,
Zhou
J.Q.
,
Wheeler
V.C.
,
Levin
J.Z.
et al
. (
2014
)
Transcriptional consequences of 16p11.2 deletion and duplication in mouse cortex and multiplex autism families
.
Am. J. Hum. Genet.
 ,
94
,
870
883
.
15
Wilhelm
B.G.
,
Mandad
S.
,
Truckenbrodt
S.
,
Krohnert
K.
,
Schafer
C.
,
Rammner
B.
,
Koo
S.J.
,
Classen
G.A.
,
Krauss
M.
,
Haucke
V.
et al
. (
2014
)
Composition of isolated synaptic boutons reveals the amounts of vesicle trafficking proteins
.
Science
 ,
344
,
1023
1028
.
16
Gao
M.C.
,
Bellugi
U.
,
Dai
L.
,
Mills
D.L.
,
Sobel
E.M.
,
Lange
K.
,
Korenberg
J.R.
(
2010
)
Intelligence in Williams Syndrome is related to STX1A, which encodes a component of the presynaptic SNARE complex
.
PLoS ONE
 ,
5
,
0010292
.
17
Lessard
J.
,
Wu
J.I.
,
Ranish
J.A.
,
Wan
M.
,
Winslow
M.M.
,
Staahl
B.T.
,
Wu
H.
,
Aebersold
R.
,
Graef
I.A.
,
Crabtree
G.R.
(
2007
)
An essential switch in subunit composition of a chromatin remodeling complex during neural development
.
Neuron
 ,
55
,
201
215
.
18
Segura-Puimedon
M.
,
Sahun
I.
,
Velot
E.
,
Dubus
P.
,
Borralleras
C.
,
Rodrigues
A.J.
,
Valero
M.C.
,
Valverde
O.
,
Sousa
N.
,
Herault
Y.
et al
. (
2014
)
Heterozygous deletion of the Williams-Beuren syndrome critical interval in mice recapitulates most features of the human disorder
.
Hum. Mol. Genet.
 ,
23
,
6481
6494
.
19
Wu
J.I.
,
Lessard
J.
,
Olave
I.A.
,
Qiu
Z.
,
Ghosh
A.
,
Graef
I.A.
,
Crabtree
G.R.
(
2007
)
Regulation of dendritic development by neuron-specific chromatin remodeling complexes
.
Neuron
 ,
56
,
94
108
.
20
Vogel-Ciernia
A.
,
Matheos
D.P.
,
Barrett
R.M.
,
Kramar
E.A.
,
Azzawi
S.
,
Chen
Y.
,
Magnan
C.N.
,
Zeller
M.
,
Sylvain
A.
,
Haettig
J.
et al
. (
2013
)
The neuron-specific chromatin regulatory subunit BAF53b is necessary for synaptic plasticity and memory
.
Nat. Neurosci.
 ,
16
,
552
561
.
21
Strong
E.
,
Butcher
D.T.
,
Singhania
R.
,
Mervis
C.B.
,
Morris
C.A.
,
De Carvalho
D.
,
Weksberg
R.
,
Osborne
L.R.
(
2015
)
Symmetrical Dose-Dependent DNA-Methylation Profiles in Children with Deletion or Duplication of 7q11.23
.
Am. J. Hum. Genet.
 ,
97
,
216
227
.
22
Lek
M.
,
Karczewski
K.
,
Minikel
E.
,
Samocha
K.
,
Banks
E.
,
Fennell
T.
,
O'Donnell-Luria
A.
,
Ware
J.
,
Hill
A.
,
Cummings
B.
et al
. (
2015
)
Analysis of protein-coding genetic variation in 60,706 humans
.
In press
.
23
Antonell
A.
,
Del Campo
M.
,
Magano
L.F.
,
Kaufmann
L.
,
de la Iglesia
J.M.
,
Gallastegui
F.
,
Flores
R.
,
Schweigmann
U.
,
Fauth
C.
,
Kotzot
D.
et al
. (
2010
)
Partial 7q11.23 deletions further implicate GTF2I and GTF2IRD1 as the main genes responsible for the Williams-Beuren syndrome neurocognitive profile
.
J. Med. Genet.
 ,
47
,
312
320
.
24
Morris
C.A.
,
Mervis
C.B.
,
Hobart
H.H.
,
Gregg
R.G.
,
Bertrand
J.
,
Ensing
G.J.
,
Sommer
A.
,
Moore
C.A.
,
Hopkin
R.J.
,
Spallone
P.A.
et al
. (
2003
)
GTF2I hemizygosity implicated in mental retardation in Williams syndrome: genotype-phenotype analysis of five families with deletions in the Williams syndrome region
.
Am. J. Med. Genet. A
 ,
15
,
45
59
.
25
Sakurai
T.
,
Dorr
N.P.
,
Takahashi
N.
,
McInnes
L.A.
,
Elder
G.A.
,
Buxbaum
J.D.
(
2011
)
Haploinsufficiency of Gtf2i, a gene deleted in Williams Syndrome, leads to increases in social interactions
.
Autism Res.
 ,
4
,
28
39
.
26
Malenfant
P.
,
Liu
X.
,
Hudson
M.L.
,
Qiao
Y.
,
Hrynchak
M.
,
Riendeau
N.
,
Hildebrand
M.J.
,
Cohen
I.L.
,
Chudley
A.E.
,
Forster-Gibson
C.
et al
. (
2012
)
Association of GTF2i in the Williams-Beuren syndrome critical region with autism spectrum disorders
.
J. Autism Dev. Disord.
 ,
42
,
1459
1469
.
27
Hirota
H.
,
Matsuoka
R.
,
Chen
X.N.
,
Salandanan
L.S.
,
Lincoln
A.
,
Rose
F.E.
,
Sunahara
M.
,
Osawa
M.
,
Bellugi
U.
,
Korenberg
J.R.
(
2003
)
Williams syndrome deficits in visual spatial processing linked to GTF2IRD1 and GTF2I on chromosome 7q11.23
.
Genet. Med.
 ,
5
,
311
321
.
28
O'Leary
J.
,
Osborne
L.R.
(
2011
)
Global analysis of gene expression in the developing brain of Gtf2ird1 knockout mice
.
PLoS ONE
 ,
6
,
31
.
29
Mervis
C.B.
,
Dida
J.
,
Lam
E.
,
Crawford-Zelli
N.A.
,
Young
E.J.
,
Henderson
D.R.
,
Onay
T.
,
Morris
C.A.
,
Woodruff-Borden
J.
,
Yeomans
J.
et al
. (
2012
)
Duplication of GTF2I results in separation anxiety in mice and humans
.
Am. J. Hum. Genet.
 ,
90
,
1064
1070
.
30
Barnett
C.
,
Krebs
J.E.
(
2011
)
WSTF does it all: a multifunctional protein in transcription, repair, and replication
.
Biochem. Cell. Biol.
 ,
89
,
12
23
.
31
Lazzaro
M.A.
,
Picketts
D.J.
(
2001
)
Cloning and characterization of the murine Imitation Switch (ISWI) genes: differential expression patterns suggest distinct developmental roles for Snf2h and Snf2l
.
J. Neurochem.
 ,
77
,
1145
1156
.
32
Alvarez-Saavedra
M.
,
De Repentigny
Y.
,
Lagali
P.S.
,
Raghu Ram
E.V.
,
Yan
K.
,
Hashem
E.
,
Ivanochko
D.
,
Huh
M.S.
,
Yang
D.
,
Mears
A.J.
et al
. (
2014
)
Snf2h-mediated chromatin organization and histone H1 dynamics govern cerebellar morphogenesis and neural maturation
.
Nat. Commun.
 ,
5
,
4181
.
33
Culver-Cochran
A.E.
,
Chadwick
B.P.
(
2013
)
Loss of WSTF results in spontaneous fluctuations of heterochromatin formation and resolution, combined with substantial changes to gene expression
.
BMC Genomics
 ,
14
,
1471
2164
.
34
van de Leemput
J.
,
Boles
N.C.
,
Kiehl
T.R.
,
Corneo
B.
,
Lederman
P.
,
Menon
V.
,
Lee
C.
,
Martinez
R.A.
,
Levi
B.P.
,
Thompson
C.L.
et al
. (
2014
)
CORTECON: a temporal transcriptome analysis of in vitro human cerebral cortex development from human embryonic stem cells
.
Neuron
 ,
83
,
51
68
.
35
Basu
S.N.
,
Kollu
R.
,
Banerjee-Basu
S.
(
2009
)
AutDB: a gene reference resource for autism research
.
Nucleic Acids Res.
 ,
37
,
10
.
36
Ashe
A.
,
Morgan
D.K.
,
Whitelaw
N.C.
,
Bruxner
T.J.
,
Vickaryous
N.K.
,
Cox
L.L.
,
Butterfield
N.C.
,
Wicking
C.
,
Blewitt
M.E.
,
Wilkins
S.J.
et al
. (
2008
)
A genome-wide screen for modifiers of transgene variegation identifies genes with critical roles in development
.
Genome Biol.
 ,
9
,
2008
2009
.
37
Kohler
S.
,
Doelken
S.C.
,
Mungall
C.J.
,
Bauer
S.
,
Firth
H.V.
,
Bailleul-Forestier
I.
,
Black
G.C.
,
Brown
D.L.
,
Brudno
M.
,
Campbell
J.
et al
. (
2014
)
The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data
.
Nucleic Acids Res.
 ,
42
,
11
.
38
Axelsson
S.
,
Storhaug
K.
,
Kjaer
I.
(
2004
)
Post-natal size and morphology of the sella turcica in Williams syndrome
.
Eur. J. Orthod.
 ,
26
,
613
621
.
39
Mansour
A.M.
,
Bitar
F.F.
,
Traboulsi
E.I.
,
Kassak
K.M.
,
Obeid
M.Y.
,
Megarbane
A.
,
Salti
H.I.
(
2005
)
Ocular pathology in congenital heart disease
.
Eye
 ,
19
,
29
34
.
40
Hirabayashi
Y.
,
Itoh
Y.
,
Tabata
H.
,
Nakajima
K.
,
Akiyama
T.
,
Masuyama
N.
,
Gotoh
Y.
(
2004
)
The Wnt/beta-catenin pathway directs neuronal differentiation of cortical neural precursor cells
.
Development
 ,
131
,
2791
2801
.
41
Woodhead
G.J.
,
Mutch
C.A.
,
Olson
E.C.
,
Chenn
A.
(
2006
)
Cell-autonomous beta-catenin signaling regulates cortical precursor proliferation
.
J. Neurosci.
 ,
26
,
12620
12630
.
42
Huang
S.M.
,
Mishina
Y.M.
,
Liu
S.
,
Cheung
A.
,
Stegmeier
F.
,
Michaud
G.A.
,
Charlat
O.
,
Wiellette
E.
,
Zhang
Y.
,
Wiessner
S.
et al
. (
2009
)
Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling
.
Nature
 ,
461
,
614
620
.
43
Heller
R.
,
Rauch
A.
,
Luttgen
S.
,
Schroder
B.
,
Winterpacht
A.
(
2003
)
Partial deletion of the critical 1.5 Mb interval in Williams-Beuren syndrome
.
J. Med. Genet.
 ,
40
,
e99
.
44
Karmiloff-Smith
A.
,
Broadbent
H.
,
Farran
E.K.
,
Longhi
E.
,
D'Souza
D.
,
Metcalfe
K.
,
Tassabehji
M.
,
Wu
R.
,
Senju
A.
,
Happe
F.
et al
. (
2012
)
Social cognition in williams syndrome: genotype/phenotype insights from partial deletion patients
.
Front. Psychol.
 ,
3
,
168
.
45
Botta
A.
,
Novelli
G.
,
Mari
A.
,
Novelli
A.
,
Sabani
M.
,
Korenberg
J.
,
Osborne
L.R.
,
Digilio
M.C.
,
Giannotti
A.
,
Dallapiccola
B.
(
1999
)
Detection of an atypical 7q11.23 deletion in Williams syndrome patients which does not include the STX1A and FZD3 genes
.
J. Med. Genet.
 ,
36
,
478
480
.
46
Duba
H.C.
,
Doll
A.
,
Neyer
M.
,
Erdel
M.
,
Mann
C.
,
Hammerer
I.
,
Utermann
G.
,
Grzeschik
K.H.
(
2002
)
The elastin gene is disrupted in a family with a balanced translocation t(7;16)(q11.23;q13) associated with a variable expression of the Williams-Beuren syndrome
.
Eur. J. Hum. Genet.
 ,
10
,
351
361
.
47
Brimble
E.
(
2014
)
Investigating the function of GTF2I and its contribution to the Williams–Beuren syndrome neurological profile
.
In press
.
48
Germain
N.D.
,
Chen
P.F.
,
Plocik
A.M.
,
Glatt-Deeley
H.
,
Brown
J.
,
Fink
J.J.
,
Bolduc
K.A.
,
Robinson
T.M.
,
Levine
E.S.
,
Reiter
L.T.
et al
. (
2014
)
Gene expression analysis of human induced pluripotent stem cell-derived neurons carrying copy number variants of chromosome 15q11-q13.1
.
Mol. Autism
 ,
5
,
2040
2392
.
49
Sopko
R.
,
Huang
D.
,
Preston
N.
,
Chua
G.
,
Papp
B.
,
Kafadar
K.
,
Snyder
M.
,
Oliver
S.G.
,
Cyert
M.
,
Hughes
T.R.
et al
. (
2006
)
Mapping pathways and phenotypes by systematic gene overexpression
.
Mol. Cell
 ,
21
,
319
330
.
50
Springer
M.
,
Weissman
J.S.
,
Kirschner
M.W.
(
2010
)
A general lack of compensation for gene dosage in yeast
.
Mol. Syst. Biol.
 ,
6
,
19
.
51
Albert
R.
,
Jeong
H.
,
Barabasi
A.L.
(
2000
)
Error and attack tolerance of complex networks
.
Nature
 ,
406
,
378
382
.
52
Huang
N.
,
Lee
I.
,
Marcotte
E.M.
,
Hurles
M.E.
(
2010
)
Characterising and predicting haploinsufficiency in the human genome
.
PLoS Genet.
 ,
6
,
1001154
.
53
Lange
C.
,
Huttner
W.B.
,
Calegari
F.
(
2009
)
Cdk4/cyclinD1 overexpression in neural stem cells shortens G1, delays neurogenesis, and promotes the generation and expansion of basal progenitors
.
Cell Stem Cell
 ,
5
,
320
331
.
54
De Rubeis
S.
,
He
X.
,
Goldberg
A.P.
,
Poultney
C.S.
,
Samocha
K.
,
Cicek
A.E.
,
Kou
Y.
,
Liu
L.
,
Fromer
M.
,
Walker
S.
et al
. (
2014
)
Synaptic, transcriptional and chromatin genes disrupted in autism
.
Nature
 ,
515
,
209
215
.
55
Pinto
D.
,
Delaby
E.
,
Merico
D.
,
Barbosa
M.
,
Merikangas
A.
,
Klei
L.
,
Thiruvahindrapuram
B.
,
Xu
X.
,
Ziman
R.
,
Wang
Z.
et al
. (
2014
)
Convergence of genes and cellular pathways dysregulated in autism spectrum disorders
.
Am. J. Hum. Genet.
 ,
94
,
677
694
.
56
Bernier
R.
,
Golzio
C.
,
Xiong
B.
,
Stessman
H.A.
,
Coe
B.P.
,
Penn
O.
,
Witherspoon
K.
,
Gerdts
J.
,
Baker
C.
,
Vulto-van Silfhout
A.T.
et al
. (
2014
)
Disruptive CHD8 mutations define a subtype of autism early in development
.
Cell
 ,
158
,
263
276
.
57
Helsmoortel
C.
,
Vulto-van Silfhout
A.T.
,
Coe
B.P.
,
Vandeweyer
G.
,
Rooms
L.
,
van den Ende
J.
,
Schuurs-Hoeijmakers
J.H.
,
Marcelis
C.L.
,
Willemsen
M.H.
,
Vissers
L.E.
et al
. (
2014
)
A SWI/SNF-related autism syndrome caused by de novo mutations in ADNP
.
Nat. Genet.
 ,
46
,
380
384
.
58
Hoyer
J.
,
Ekici
A.B.
,
Endele
S.
,
Popp
B.
,
Zweier
C.
,
Wiesener
A.
,
Wohlleber
E.
,
Dufke
A.
,
Rossier
E.
,
Petsch
C.
et al
. (
2012
)
Haploinsufficiency of ARID1B, a member of the SWI/SNF-a chromatin-remodeling complex, is a frequent cause of intellectual disability
.
Am. J. Hum. Genet.
 ,
90
,
565
572
.
59
Kosho
T.
,
Okamoto
N.
,
Ohashi
H.
,
Tsurusaki
Y.
,
Imai
Y.
,
Hibi-Ko
Y.
,
Kawame
H.
,
Homma
T.
,
Tanabe
S.
,
Kato
M.
et al
. (
2013
)
Clinical correlations of mutations affecting six components of the SWI/SNF complex: detailed description of 21 patients and a review of the literature
.
Am. J. Med. Genet. A
 ,
161A
,
1221
1237
.
60
Neale
B.M.
,
Kou
Y.
,
Liu
L.
,
Ma'ayan
A.
,
Samocha
K.E.
,
Sabo
A.
,
Lin
C.F.
,
Stevens
C.
,
Wang
L.S.
,
Makarov
V.
et al
. (
2012
)
Patterns and rates of exonic de novo mutations in autism spectrum disorders
.
Nature
 ,
485
,
242
245
.
61
O'Roak
B.J.
,
Vives
L.
,
Girirajan
S.
,
Karakoc
E.
,
Krumm
N.
,
Coe
B.P.
,
Levy
R.
,
Ko
A.
,
Lee
C.
,
Smith
J.D.
et al
. (
2012
)
Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations
.
Nature
 ,
485
,
246
250
.
62
Santen
G.W.
,
Aten
E.
,
Vulto-van Silfhout
A.T.
,
Pottinger
C.
,
van Bon
B.W.
,
van Minderhout
I.J.
,
Snowdowne
R.
,
van der Lans
C.A.
,
Boogaard
M.
,
Linssen
M.M.
et al
. (
2013
)
Coffin-Siris syndrome and the BAF complex: genotype-phenotype study in 63 patients
.
Hum. Mutat.
 ,
34
,
1519
1528
.
63
Van Houdt
J.K.
,
Nowakowska
B.A.
,
Sousa
S.B.
,
van Schaik
B.D.
,
Seuntjens
E.
,
Avonce
N.
,
Sifrim
A.
,
Abdul-Rahman
O.A.
,
van den Boogaard
M.J.
,
Bottani
A.
et al
. (
2012
)
Heterozygous missense mutations in SMARCA2 cause Nicolaides-Baraitser syndrome
.
Nat. Genet.
 ,
44
,
445
449
.
64
Miyake
N.
,
Tsurusaki
Y.
,
Matsumoto
N.
(
2014
)
Numerous BAF complex genes are mutated in Coffin-Siris syndrome
.
Am. J. Med. Genet. C Semin. Med. Genet.
 ,
3
,
257
261
.
65
Morris
S.A.
,
Baek
S.
,
Sung
M.H.
,
John
S.
,
Wiench
M.
,
Johnson
T.A.
,
Schiltz
R.L.
,
Hager
G.L.
(
2014
)
Overlapping chromatin-remodeling systems collaborate genome wide at dynamic chromatin transitions
.
Nat. Struct. Mol. Biol.
 ,
21
,
73
81
.
66
Kang
H.J.
,
Kawasawa
Y.I.
,
Cheng
F.
,
Zhu
Y.
,
Xu
X.
,
Li
M.
,
Sousa
A.M.
,
Pletikos
M.
,
Meyer
K.A.
,
Sedmak
G.
et al
. (
2011
)
Spatio-temporal transcriptome of the human brain
.
Nature
 ,
478
,
483
489
.
67
Chenn
A.
,
Walsh
C.A.
(
2002
)
Regulation of cerebral cortical size by control of cell cycle exit in neural precursors
.
Science
 ,
297
,
365
369
.
68
Zechner
D.
,
Fujita
Y.
,
Hulsken
J.
,
Muller
T.
,
Walther
I.
,
Taketo
M.M.
,
Crenshaw
E.B.
3rd
,
Birchmeier
W.
,
Birchmeier
C.
(
2003
)
beta-Catenin signals regulate cell growth and the balance between progenitor cell expansion and differentiation in the nervous system
.
Dev. Biol.
 ,
258
,
406
418
.
69
Fang
W.Q.
,
Chen
W.W.
,
Jiang
L.
,
Liu
K.
,
Yung
W.H.
,
Fu
A.K.
,
Ip
N.Y.
(
2014
)
Overproduction of upper-layer neurons in the neocortex leads to autism-like features in mice
.
Cell Rep.
 ,
9
,
1635
1643
.
70
Reiss
A.L.
,
Eliez
S.
,
Schmitt
J.E.
,
Straus
E.
,
Lai
Z.
,
Jones
W.
,
Bellugi
U.
(
2000
)
IV. Neuroanatomy of Williams syndrome: a high-resolution MRI study
.
J. Cogn. Neurosci.
 ,
1
,
65
73
.
71
Kippenhan
J.S.
,
Olsen
R.K.
,
Mervis
C.B.
,
Morris
C.A.
,
Kohn
P.
,
Meyer-Lindenberg
A.
,
Berman
K.F.
(
2005
)
Genetic contributions to human gyrification: sulcal morphometry in Williams syndrome
.
J. Neurosci.
 ,
25
,
7840
7846
.
72
Thompson
P.M.
,
Lee
A.D.
,
Dutton
R.A.
,
Geaga
J.A.
,
Hayashi
K.M.
,
Eckert
M.A.
,
Bellugi
U.
,
Galaburda
A.M.
,
Korenberg
J.R.
,
Mills
D.L.
et al
. (
2005
)
Abnormal cortical complexity and thickness profiles mapped in Williams syndrome
.
J. Neurosci.
 ,
25
,
4146
4158
.
73
Albers
J.
,
Schulze
J.
,
Beil
F.T.
,
Gebauer
M.
,
Baranowsky
A.
,
Keller
J.
,
Marshall
R.P.
,
Wintges
K.
,
Friedrich
F.W.
,
Priemel
M.
et al
. (
2011
)
Control of bone formation by the serpentine receptor Frizzled-9
.
J. Cell. Biol.
 ,
192
,
1057
1072
.
74
Uehara
T.
,
Kage-Nakadai
E.
,
Yoshina
S.
,
Imae
R.
,
Mitani
S.
(
2015
)
The Tumor Suppressor BCL7B Functions in the Wnt Signaling Pathway
.
PLoS Genet.
 ,
11
,
e1004921
.
75
Somerville
M.J.
,
Mervis
C.B.
,
Young
E.J.
,
Seo
E.J.
,
del Campo
M.
,
Bamforth
S.
,
Peregrine
E.
,
Loo
W.
,
Lilley
M.
,
Perez-Jurado
L.A.
et al
. (
2005
)
Severe expressive-language delay related to duplication of the Williams-Beuren locus
.
N. Engl. J. Med.
 ,
353
,
1694
1701
.
76
Berg
J.S.
,
Brunetti-Pierri
N.
,
Peters
S.U.
,
Kang
S.H.
,
Fong
C.T.
,
Salamone
J.
,
Freedenberg
D.
,
Hannig
V.L.
,
Prock
L.A.
,
Miller
D.T.
et al
. (
2007
)
Speech delay and autism spectrum behaviors are frequently associated with duplication of the 7q11.23 Williams-Beuren syndrome region
.
Genet. Med.
 ,
9
,
427
441
.
77
Chen
E.S.
,
Gigek
C.O.
,
Rosenfeld
J.A.
,
Diallo
A.B.
,
Maussion
G.
,
Chen
G.G.
,
Vaillancourt
K.
,
Lopez
J.P.
,
Crapper
L.
,
Poujol
R.
et al
. (
2014
)
Molecular convergence of neurodevelopmental disorders
.
Am. J. Hum. Genet.
 ,
95
,
490
508
.
78
Pataskar
A.
,
Jung
J.
,
Smialowski
P.
,
Noack
F.
,
Calegari
F.
,
Straub
T.
,
Tiwari
V.K.
(
2015
)
NeuroD1 reprograms chromatin and transcription factor landscapes to induce the neuronal program
.
EMBO J.
 ,
35
,
24
45
.
79
Dunning
M.J.
,
Smith
M.L.
,
Ritchie
M.E.
,
Tavare
S.
(
2007
)
beadarray: R classes and methods for Illumina bead-based data
.
Bioinformatics
 ,
23
,
2183
2184
.
80
Smyth
G.K.
(
2005
)
Limma: linear models for microarray data
.
In Bioinformatics and Computational Biology Solutions Using R and Bioconductor
 .
Springer
,
New York
, pp.
397
420
.
81
R Core Team
. (
2012
)
R: A Language and Environment for Statistical Computing
 .
R Foundation for Statistical Computing
,
Vienna, Austria
,
2012
.
82
Benjamini
Y.
,
Hochberg
Y.
(
1995
)
Controlling the false discovery rate: a practical and powerful approach to multiple testing
.
J. Royal Stat. Soc. B (Methodological)
 ,
57
,
289
300
.
83
Whirl-Carrillo
M.
,
McDonagh
E.M.
,
Hebert
J.M.
,
Gong
L.
,
Sangkuhl
K.
,
Thorn
C.F.
,
Altman
R.B.
,
Klein
T.E.
(
2012
)
Pharmacogenomics knowledge for personalized medicine
.
Clin. Pharmacol. Ther.
 ,
92
,
414
417
.
84
Kanehisa
M.
,
Goto
S.
(
2000
)
KEGG: Kyoto encyclopedia of genes and genomes
.
Nucleic Acids Res.
 ,
28
,
27
30
.
85
Gene Ontology Consortium
. (
2015
)
Gene Ontology Consortium: going forward
.
Nucleic Acids Res.
 ,
43
,
26
.
86
Wang
J.
,
Duncan
D.
,
Shi
Z.
,
Zhang
B.
(
2013
)
WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013
.
Nucleic Acids Res.
 ,
41
,
23
.
87
Landt
S.G.
,
Marinov
G.K.
,
Kundaje
A.
,
Kheradpour
P.
,
Pauli
F.
,
Batzoglou
S.
,
Bernstein
B.E.
,
Bickel
P.
,
Brown
J.B.
,
Cayting
P.
et al
. (
2012
)
ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia
.
Genome Res.
 ,
22
,
1813
1831
.
88
Langmead
B.
,
Salzberg
S.L.
(
2012
)
Fast gapped-read alignment with Bowtie 2
.
Nat. Methods
 ,
9
,
357
359
.
89
Zhang
Y.
,
Liu
T.
,
Meyer
C.A.
,
Eeckhoute
J.
,
Johnson
D.S.
,
Bernstein
B.E.
,
Nusbaum
C.
,
Myers
R.M.
,
Brown
M.
,
Li
W.
et al
. (
2008
)
Model-based analysis of ChIP-Seq (MACS)
.
Genome Biol.
 ,
9
,
2008
2009
.

Author notes

These authors contributed equally to this work.