CHD2 Dosage Ties Autolysosomal Pathway to Cortical Maturation in Disease and Evolution

CHD2 Dosage Ties Autolysosomal Pathway to Cortical Maturation in Disease and Evolution | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var 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Culotta , View ORCID Profile Ruizhi Deng , Linda Haazen , View ORCID Profile Katrin Linda , Marian Majoie , View ORCID Profile Annika Mordelt , View ORCID Profile Aurelio Ortale , Astrid Oudakker , View ORCID Profile Filippo Prazzoli , View ORCID Profile Sofia Puvogel , View ORCID Profile Nicky Scheefhals , Helenius J Schelhaas , Chantal Schoenmaker , View ORCID Profile Eline van Hugte , View ORCID Profile Hans van Bokhoven , Judith Verhoeven , View ORCID Profile Alessandro Vitriolo , View ORCID Profile Cedric Boeckx , View ORCID Profile Giuseppe Testa , View ORCID Profile Nael Nadif Kasri doi: https://doi.org/10.1101/2025.01.21.634145 Oliviero Leonardi 1 Human Technopole, Viale Rita Levi-Montalcini 1 , 20157, Milan, Italy 2 Department of Oncology and Hemato-Oncology, University of Milan , Milan, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Oliviero Leonardi Elly Lewerissa 3 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour , 6500 HB Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elly Lewerissa Davide Aprile 1 Human Technopole, Viale Rita Levi-Montalcini 1 , 20157, Milan, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Davide Aprile Tahsin Stefan Barakat 4 Department of Clinical Genetics, Erasmus MC University Medical Center , Rotterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tahsin Stefan Barakat Lorenza Culotta 1 Human Technopole, Viale Rita Levi-Montalcini 1 , 20157, Milan, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lorenza Culotta Ruizhi Deng 4 Department of Clinical Genetics, Erasmus MC University Medical Center , Rotterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ruizhi Deng Linda Haazen 3 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour , 6500 HB Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katrin Linda 3 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour , 6500 HB Nijmegen, the Netherlands 5 VIB-KU Leuven Center for Brain & Disease Research , Leuven, Belgium 6 KU Leuven, Department of Neurosciences, Leuven Brain Institute , Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katrin Linda Marian Majoie 7 ACE Kempenhaeghe, Department of Epileptology , 5591 VE Heeze, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Annika Mordelt 3 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour , 6500 HB Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Annika Mordelt Aurelio Ortale 1 Human Technopole, Viale Rita Levi-Montalcini 1 , 20157, Milan, Italy 8 Department of Cellular Biophysics, Max Planck Institute for Medical Research , Heidelberg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Aurelio Ortale Astrid Oudakker 3 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour , 6500 HB Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Filippo Prazzoli 1 Human Technopole, Viale Rita Levi-Montalcini 1 , 20157, Milan, Italy 9 Dipartimento di Bioscienze, Università degli Studi di Milano , Milano, Via Celoria 26, 20133, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Filippo Prazzoli Sofia Puvogel 3 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour , 6500 HB Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sofia Puvogel Nicky Scheefhals 3 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour , 6500 HB Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nicky Scheefhals Helenius J Schelhaas 10 Stiching Epilepsie Instellingen Nederland (SEIN) , 2103 SW Heemstede, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chantal Schoenmaker 3 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour , 6500 HB Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eline van Hugte 3 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour , 6500 HB Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eline van Hugte Hans van Bokhoven 3 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour , 6500 HB Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hans van Bokhoven Judith Verhoeven 7 ACE Kempenhaeghe, Department of Epileptology , 5591 VE Heeze, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alessandro Vitriolo 1 Human Technopole, Viale Rita Levi-Montalcini 1 , 20157, Milan, Italy 2 Department of Oncology and Hemato-Oncology, University of Milan , Milan, Italy 11 Department of Experimental Oncology, European Institute of Oncology IRCCS , Via Adamello 16, 20139, Milan, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alessandro Vitriolo Cedric Boeckx 12 University of Barcelona , 08007 Barcelona, Spain 13 University of Barcelona Institute of Complex Systems , 08007 Barcelona, Spain 14 University of Barcelona Institute of Neurosciences , 08007 Barcelona, Spain 15 Catalan Institute for Research and Advanced Studies (ICREA) , Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cedric Boeckx For correspondence: nael.nadifkasri{at}radboudumc.nl giuseppe.testa{at}fht.org cedric.boeckx{at}ub.edu Giuseppe Testa 1 Human Technopole, Viale Rita Levi-Montalcini 1 , 20157, Milan, Italy 2 Department of Oncology and Hemato-Oncology, University of Milan , Milan, Italy 11 Department of Experimental Oncology, European Institute of Oncology IRCCS , Via Adamello 16, 20139, Milan, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Giuseppe Testa For correspondence: nael.nadifkasri{at}radboudumc.nl giuseppe.testa{at}fht.org cedric.boeckx{at}ub.edu Nael Nadif Kasri 3 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour , 6500 HB Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nael Nadif Kasri For correspondence: nael.nadifkasri{at}radboudumc.nl giuseppe.testa{at}fht.org cedric.boeckx{at}ub.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract The relatively slow pace of cortical development in humans has long been a topic of investigation. Studies seeking to understand the underlying mechanisms have mostly focused on neurogenetic comparisons with extant species. Here we ask if developmental tempo differences may have also existed between us and our extinct relatives for whom genomes are available. To do so, we nominate a sapiens -specific derived allele, virtually fixed in contemporary populations, which resides in an enhancer region active during early cortical development. The single nucleotide variant is predicted to significantly affect CHD2 expression, a chromatin remodeler known to play an important role in neural development and for which haploinsufficiency is associated with epilepsy and autism. We leverage patient induced pluripotent stem lines (iPSC) and engineered iPSCs in which we reintroduced the ancestral allele and generated heterozygous loss-of-function mutations. We reveal that CHD2 deficiency impairs lysosomal acidification and autophagosome flux. In contrast, ancestralized lines, which we find express higher levels of CHD2, exhibit enhanced lysosomal function and consequently accelerated autophagosome flux, consistent with our observations in chimpanzee and bonobo lines. This set of findings demonstrates that CHD2 dosage critically regulates the autolysosomal pathway. Through deep phenotyping of cortical organoid and neuron cultures, we show that the CHD2-modulated autolysosomal pathway impacts the timing of developmental programs, acquisition of neuronal functional properties and circuit maturation. Finally, we validate an estrogen-dependent rewiring of CHD2 regulation in the evolution of our lineage, providing a mechanistic understanding of how a single nucleotide variant in a regulatory region contributed to the modern pace of neuronal development and maturation. Together, our findings establish CHD2 as a regulator in setting neurodevelopmental tempo via the autolysosomal pathway. Main The relatively slow pace at which our brain develops has long been recognized as a key characteristic of human brain development 1 . This extended developmental timeline, alluded to as neoteny/bradychrony, is hypothesized to have contributed to the distinct cognitive and behavioral capacities of our species, significantly shaping human uniqueness 2 – 4 . In recent years, much of our understanding of the molecular and cellular mechanisms underlying these extended developmental timelines, has emerged from the functional characterization of lineage-specific genetic modifiers, such as human-specific gene duplications that have occurred during separation between us and our closest living relatives 5 . These modifiers influence several aspects of neuronal development, from the timing and complexity of cortical neurogenesis to synaptogenesis and the assembly of cortical circuits 6 – 8 . Beyond specific genetic changes, metabolic processes implicating mitochondrial biogenesis and epigenetic barriers have recently been identified as key determinants of the cell-intrinsic pace of neuronal development 9 , 10 . Up until now, these developmental differences have been framed in comparisons between modern humans and living relatives, either close (chimpanzee) or distant (mouse). But more subtle genomic differences exist between us and our closest extinct relatives, the Neanderthals and Denisovans, for whom high-coverage genomes are available 11 – 14 . These may also affect the rate of brain maturation, as suggested in previous work 15 based on the first draft of an extinct hominin 16 . Although endocranial volume studies establish that Neanderthal and modern human adults reached similar overall brain sizes, recent data point to differences in neocortical neurogenesis and brain metabolism 17 – 20 . While these studies demonstrate that single nucleotide changes can impact neuronal development, they have only focused on mutations in protein-coding regions, which account for only a very small fraction of the genetic differences between Neanderthals/Denisovans and contemporary humans. Much less is known about the impact of lineage-specific mutations in regulatory regions, such as enhancers and promoters, despite the long-anticipated relevance of such changes 21 , and the prominence of such changes in regions of the human genome associated with signals of positive selection 22 or depleted of Neanderthal/Denisovan introgression 23 . Here we focus on a single nucleotide variant (SNV), located upstream of the chromodomain helicase DNA binding protein 2 (CHD2) gene. The sapiens -specific SNV, with the derived allele virtually fixed in contemporary populations (Major Allele Frequency > 99.9%) 24 , resides in an enhancer region active during early cortical development and is predicted to affect CHD2 expression 25 . Like many other chromatin remodelers, CHD2 plays an important role in neural development and function, impacting a wide range of processes along the developmental trajectory of neurogenesis, from progenitor expansion to differentiation, cell type specification, cell migration, synapse formation, and circuit maturation 26 , 27 . Crucially, CHD2 has been shown to have bidirectional dosage sensitivity in human neurodevelopmental disorders. Haploinsufficiency is associated with epilepsy and autism, whereas increased expression of CHD2 is associated with severe developmental delay 28 . Therefore, investigating the SNV in CHD2’s regulatory region could offer valuable insights into a possible factor influencing the neurodevelopmental trajectory of our species. CHD2 dosage in evolution and disease Sapiens- specific changes have previously been identified in regulatory regions of 212 genes that are active at early stages of cortical development 25 . We resorted to BRAIN-MAGNET, a convolutional neural network that predicts the activity of these regulatory regions based on DNA sequence composition 29 to test the impact of all these SNVs. We found that an SNV located upstream of CHD2 ranks among the top 5% of all changes examined in terms of predicted impact ( Extended Fig. 1a ). We also leveraged publicly available genome-wide maps of histone modifications profiled across different human cell lines and primary tissues to comprehensively characterize the epigenetic signature of the region. The region is marked by histone modifications of transcriptionally active enhancers, including mono and trimethylation of lysine 4 (H3K4me1, H3K4me3), and acetylation of lysine 9 and 27 (H3K9ac and H3K27ac) (Fig.1a), both in pluripotent stem cells and neuronal tissues ( Extended Fig. 1c , Suppl. Fig. 1). Notably, the SNV is predicted to be one of the most essential nucleotides for the enhancer activity of that region ( Extended Fig. 1b ), indicating that the derived variant is likely to significantly impact enhancer function. The SNV located upstream of CHD2 is a polymorphic site where virtually all contemporary humans carry a C, but Neanderthals and Denisovans carry the ancestral T, also found in chimpanzee, bonobo, among other primates ( Fig. 1a ). To validate the functional importance of the SNV, we used CRISPR-Cas9 technology to introduce the ancestral variant into the genome of induced pluripotent stem cells (iPSCs) derived from two healthy human individuals ( Extended Fig. 1d ). For clarity, we refer to these modern human alleles as Ctrl and to their ancestralized derivatives as Ctrl An/An . To extend our allelic series to contemporary human variation, we also included two iPSC lines derived from patients with CHD2 mutations (patient 1 and patient 2; Suppl. Table 1) alongside isogenic heterozygous loss-of-function deletions in CHD2 generated by CRISPR-Cas9 (referred to as CHD2 +/− ) ( Extended Fig. 1d ). We confirmed successful genetic editing of the lines through Sanger sequencing (Suppl. Fig. 2a, Suppl. Fig. 3a, Suppl. Fig 4a). We then excluded off-targets events at genomic positions carrying up to three mismatches to the guide RNA sequence via PCR amplification followed by Sanger sequencing (Suppl. Fig. 2b-c, Suppl. Fig.4b). Finally, we confirmed the preservation of genomic integrity through digital droplet PCR and whole genome sequencing (Suppl. Fig.3b, Suppl. Fig.4c, Suppl. Fig. 5, Suppl. Table 1-3). Download figure Open in new tab Figure 1. CHD2 dosage in evolution and disease (a) Location of the SNV in the enhancer region of CHD2 marked with the indicated histone modifications. The phylogenetic tree depicts the SNV present in non-human primates, archaic species, and modern humans. (b) (top) Representative Western blot showing differential CHD2 expression in Ctrl An/An , Ctrl , and CHD2-deficient ( CHD2 +/− , patient 1, and patient 2 ) iPSCs. (bottom) Quantification of CHD2 expression in iPSCs. (c) (top) Representative Western blot showing differential CHD2 expression in Ctrl An/An , Ctrl , and CHD2-deficient ( Ctrl-CHD2 +/− , patient 1, and patient 2 ) Ngn2 -driven cortical neurons. (bottom) Quantification of CHD2 expression in neurons. (d) (top) Representative fluorescent microscope images showing CHD2 expression (in dotted circle) in Ctrl An/An , Ctrl , and CHD2-deficient ( CHD2 +/− , patient 1, and patient 2 ) iPSCs. (bottom) Quantification of CHD2 expression in iPSCs. (e) (top) Representative fluorescent microscope images showing CHD2 expression (in dotted circle) in Ctrl An/An , Ctrl , and CHD2-deficient ( CHD2 +/− , patient 1, and patient 2 ) Ngn2 -driven cortical neurons. (bottom) Quantification of CHD2 expression in neurons. Scale bar represents 10 μm. Data represent means ±SEM. *p<0.05, ***p<0.001, ****p<0.0001, one-way ANOVA with post hoc Bonferroni correction. Supplementary table 6 provides a detailed overview of the sample sizes and the statistical methods employed. Given the SNV’s predicted effect, we first compared CHD2 expression levels between the Ctrl An/An , Ctrl and CHD2 haploinsufficient-( CHD2 +/− , patient 1, patient 2) cells ( Fig. 1b-e ). In nucleus-enriched cell lysates the CHD2 protein level was significantly increased in Ctrl An/An iPSCs and iPSC-derived neurons compared to their isogenic controls. As expected, CHD2 +/− and patient cells showed about 50% reduction compared to unedited controls ( Fig. 1b-c ). These differences in CHD2 expression levels were also observed by immunocytochemistry in iPSCs and iPSC-derived neurons ( Fig. 1d-e ). We also compared the expression of CHD2 between human Ctrl and chimpanzees and bonobos. We found a similar level of CHD2 increase as in Ctrl An/An , at the protein and mRNA levels, when compared to human Ctrl lines (Suppl. Fig. 6a-c). Our observations are in line with publicly available snRNA-seq data of human, chimpanzee and macaque cortical organoids 30 , in which the levels of CHD2 mRNA were lowest in human (Suppl. Fig. 6d). CHD2 modulates lysosomal dynamics To determine the functional consequences of differential CHD2 expression levels, we analyzed bulk RNA-sequencing data from CHD2 +/− and Ctrl An/An iPSC lines, comparing these to their isogenic control. Overall, CHD2 +/− cells showed more pronounced transcriptomic changes than Ctrl An/An cells: about 2000 vs 25 differentially expressed genes (DEGs) (FC ≥ |1.5| and FDR ≤ 0.05). Gene Set Enrichment Analysis (GSEA) highlighted a significant enrichment of lysosomal and core autophagy-related genes in both, Ctrl An/An and CHD2 +/− cells ( Fig. 2a ). This finding aligns with prior research showing that several chromodomain helicase DNA (CHD) binding proteins are critical regulators of genes involved in the autolysosomal pathway 31 , 32 . Download figure Open in new tab Figure 2. CHD2-dosage modulates lysosomal dynamics (a) Gene set enrichment analysis for autophagy core- and lysosomal genes in Ctrl An/An and CHD2 +/− iPSCs (dot color represents the q value and dot size corresponds to the normalized enrichment score (NES). (b) Schematic representation of autophagosome flux assay. (c) Representative images of autophagosomes (yellow) and autolysosomes (red) at steady-state (0h) and after 200 nM Rap (3h) treatment in iPSCs (circled by white stripes) derived from Ctrl An/An , Ctrl , and CHD2-deficient ( CHD2 +/− , patient 1, and patient 2). (d) Quantification of autolysosomes per iPSC at 0h and after Rap treatment. (right) Table shows flux rate ( J nAL ) and transition state ( t nAL ). (e) (left) Representative images of autophagosomes (yellow) and autolysosomes (red) at steady-state in Ctrl and Ctrl iPSCs with ectopic expression of CHD2. (right) Linear regression analysis showing correlation between CHD2 and number of autolysosomes. (f) Schematic of the Lysosensor green assay (top), where pH > 6 reduces fluorescence. Representative images of Lysosensor in cortical neurons derived from Ctrl An/An , Ctrl and CHD2 +/− (circled by white stripes) are shown (bottom). (right) Quantification of Lysosensor intensity in neurons. (g) (top) Schematic representation of the Magic Red assay, where pH 4.5-6 activates cathepsin B, producing fluorescence. (bottom) Representative Magic red images in neurons from Ctrl An/An , Ctrl and CHD2 +/− (circled by white stripes). (right) Quantification of Magic red intensity in. (h) (top) Schematic of Lysotracker, a dye for lysosome staining. (bottom) Representative Lysotracker images in neurons derived from Ctrl An/An , Ctrl and CHD2 +/− (circled by white stripes). (right) Quantification of Magic red intensity in neurons. Scale bar represents 10 μm. Data represent means ±SEM. *p<0.05, **p<0.01, ****p<0.0001, one-way ANOVA with post hoc Bonferroni correction. Supplementary table 7 and 8 provide a detailed overview of the sample sizes and the statistical methods employed. Since the rate of cortical neuronal development is linked to species-specific metabolic processes 33 , and such processes are regulated in part by autolysosomal pathways, particularly during progenitor cell differentiation 34 , we further assessed the functional impact of CHD2 dosage on the autolysosomal pathway across species. This pathway involves autophagosome formation and subsequent fusion with lysosomes ( Fig. 2b ). In these so-called autolysosomes cargo degradation occurs, a process known as autophagosome flux 35 . To monitor autophagosome flux, we used tandem mCherry-GFP-LC3 fusion protein, which allows to distinguish between autophagosomes and autolysosomes. In autophagosomes, both tags emit fluorescence, but the acidic pH in autolysosomes quenches the GFP signal ( Fig. 2b ). The number of autolysosomes was quantified to determine the autophagosome flux, J , which represents the time-dependent change in the total autolysosomal pool size before and after activation of the autolysosomal pathway (e.g., the formation of autolysosomes/cell/h). Additionally, we calculated the transition time, t nAL , which reflects the time required for the autolysosomal pathway to turn over its autolysosomal pool. In Ctrl cells, approximately 10 autolysosomes were formed per hour upon activation of the autolysosomal pathway ( Fig. 2c-d ; Suppl. Fig. 7a). In contrast, Ctrl An/An lines showed a significantly higher basal (steady-state) autolysosomal count compared to Ctrl lines (Suppl. Fig. 7b). Conversely, CHD2-deficient cells ( CHD2 +/− , patient 1, patient 2) displayed a markedly reduced basal autolysosome count (Suppl. Fig. 7b). Upon induction of the autolysosomal pathway Ctrl An/An cells exhibited a substantial increase in autolysosome formation in response to rapamycin treatment (± 20 autolysosomes/cell/h), whereas CHD2 -deficient cells showed a mirrored effect with significantly lower autolysosome formation (± 2 autolysosomes/cell/h) ( Fig. 2c-d ). Strikingly, autophagosome flux was much faster in chimpanzee and bonobo cells compared to all lines, with rates reaching approximately 45 autolysosomes per cell per hour ( Fig.2 c-d ; Extended Fig. 2a–c ). These findings highlight interspecies-specific differences in protein degradation rates that correlate with CHD2 expression levels. To further validate the link between CHD2 expression and autophagosome flux, we ectopically expressed CHD2 in Ctrl cells and observed a significant positive correlation between CHD2 expression and number of autolysosomes at steady-state ( Fig. 2e ). To examine which aspect of the autolysosomal pathway might be compromised and could account for the observed deficiency in autophagosome flux, we performed Western blot analysis to test whether the initiation of autophagy through mTOR and ULK1 inhibition is affected (Suppl. Fig. 8a). Following rapamycin induction we observed a similar reduction of mTOR (Ser2448) or ULK1 (Ser757) phosphorylation in both, Ctrl and CHD2 +/− cells (Suppl. Fig. 8b,e), indicating that the initiation of autophagy is not affected. In addition, we screened for key autophagy markers, including p62, LC3-II, and LAMP1 (Suppl. Fig. 8c,f). Upon induction of the autolysosomal pathway, we observed elevated levels of LAMP1 and p62 in CHD2 +/− cells, but no differences in LC3-II levels (Suppl. Fig. 8d-f). This indicates that the formation of mature autophagosome formation is not impaired, but that autophagosomes (e.g., high p62 level) are not degraded, possibly through dysfunctional lysosomes (e.g., increased LAMP1). Since autophagosome flux relies heavily on lysosomal function, we further assessed lysosomal activity in developing neurons by comparing Ctrl An/An , Ctrl , and CHD2 +/− neurons. Using Lysosensor, a pH-sensitive dye that fluoresces more brightly in acidic environments ( Fig. 2f ), we observed that lysosomes from Ctrl An/An neurons were the most acidified, while those from CHD2 +/− neurons showed reduced acidification. We then used Magic Red, a fluorogenic substrate that detects cathepsin B activity, to measure lysosomal enzymatic function ( Fig. 2g ). Although cathepsin B activity was comparable between Ctrl An/An and Ctrl neurons, CHD2 +/− neurons exhibited reduced cathepsin B activity ( Fig. 2g ). Next, we quantified lysosomal abundance using Lysotracker ( Fig. 2h ) and found a larger lysosomal pool in Ctrl An/An neurons compared to Ctrl neurons, consistent with presence of more functional lysosomes in the Ctrl An/An line. CHD2 +/− neurons also had an increased lysosomal pool, likely due to compensatory mechanisms for the reduced functional lysosomes ( Fig. 2h ), a finding further corroborated by transmission electron microscopy (Suppl. Fig. 8g-h). Importantly, this lysosomal effect was specific, as no genotype-related differences were observed in endosome levels (Suppl. Fig. 8i-j). Consistent with these findings, several subunits of the H + -ATPase (v-ATPase) pumps, which regulate lysosomal acidity, were affected in CHD2 +/− neurons. Western blot analysis revealed the downregulation of key v-ATPase subunits, including ATP6V1A, ATP6V1B2, and NCOA7, during cortical development (Suppl. Fig. 9a-b), suggesting a reduction in functional (auto)lysosomes in CHD2 +/− cells. Since NCOA7 interacts with the vacuolar (V)-ATPase’s cytoplasmic domain and is crucial for its assembly and activity 36 , we attempted to restore lysosomal acidification in CHD2 +/− cells through NCOA7 overexpression. This intervention successfully restored lysosomal acidification, as measured by Lysosensor (Suppl. Fig. 9d), and normalized autophagosome flux as well (Suppl. Fig. 9e-f.). Together, our findings demonstrate that CHD2 dosage critically regulates the autolysosomal pathway, with CHD2 deficiency impairing lysosomal acidification and autophagosome flux, while Ctrl An/An cells exhibit enhanced lysosomal function, underscoring both, CHD2’s essential role and interspecies differences in autophagic processes. CHD2 dosage impacts corticogenesis We next probed the effect of CHD2-dosage on cortical development through brain organoids from Ctrl An/An , Ctrl and CHD2 +/− lines by longitudinal imaging, bulkRNA-sequencing at 25 and 50 days and single cell RNA-sequencing at 25, 50 and 90 days of differentiation ( Fig 3 , Extended Fig. 3a ). Immunostainings confirmed organoids from all lines display the expected cytoarchitecture and makers localization at the ventricular and subventricular areas ( Extended Fig.3b ). Differential expression analysis (DEA) revealed that CHD2 dosage affects the transcriptional landscape in cortical organoids ( Fig. 3a , Extended Fig.3c ). We observed that CHD2 +/− organoids exhibit a stronger differential expression profile compared to Ctrl An/An organoids at both day 25 and day 50. Furthermore, consistent with what we observed in iPSCs and neurons, GSEA analysis identified a significant enrichment of autophagy core and lysosomal genes ( Fig. 3b ). Additionally, we observed enrichment of mTOR pathway-related genes across both conditions. To dissect common and distinct changes in gene expression profiles, we performed a comparative analysis of shared and unique DEGs across the four conditions ( Fig. 3c ). A significant enrichment for positive regulation of synapse assembly and homophilic cell adhesion was found for a set of 40 genes uniquely shared between day 25 CHD2 +/− and day 50 Ctrl An/An organoids ( Extended Fig. 3d ), revealing a role for CHD2 dosage in the transcriptional regulation of genes relevant for synaptic organization. GO analysis also revealed condition-specific terms ( Extended Fig. 3e ), with day 25 CHD2 An/An organoids showing enrichment for cilium assembly and response to glucose, likely reflecting metabolic adaptations in neural progenitor cells 9 , 37 . By contrast, terms related to extracellular matrix and collagen fibril organization were uniquely enriched in day 25 and day 50 CHD2 +/− organoids. In particular, cysteine cathepsins are crucial for the extracellular matrix 38 , which could reflect the deficient activation of cathepsins found in cortical neurons. Given the connection of synaptic-related genes with both ASD and epileptic encephalopathy, and the involvement of these clinical manifestations in CHD2 -related neurodevelopmental disorders, we assessed the intersection between genes annotated in the SFARI database and DisGeNET (for ASD and epilepsy, respectively) and the DEGs of each of our organoid conditions. We found a significant overlap, particularly pronounced in day 25 CHD2 +/− and day 50 Ctrl An/An organoids ( Fig. 3d ). Focusing on these two sets of DEGs, we examined their trend of expression and found that most were upregulated in day 25 CHD2 +/− but downregulated in day 50 Ctrl An/An organoids ( Fig. 3e-f ). Notably, 15 ASD-associated genes in the SFARI intersection, including neural markers for deep-cortical layers BCL11A , NFIB and FOXP2 , showed opposite log 2 FC value in day 25 CHD2 +/− and day 50 Ctrl An/An CBOs ( Fig. 3g ). To better understand the contrast between these two conditions, we performed GO enrichment analysis on upregulated and downregulated DEGs. The alluvial plot revealed categories with a coherent trend, including nervous system development and Wnt signaling or with an inverse trend, such as regulation of synaptic assembly and transmission ( Extended Fig. 3f ). Download figure Open in new tab Figure 3. CHD2 impacts organoid development (a) Volcano plots of DEGs from bulk RNA-seq of organoids ( Ctrl1 isogenic pairs) at day 25 and 50 (red = upregulated, blue = downregulated genes with FC > |1.5| & FDR < 0. 05) (b) Dotplot of GSEA results. Dot size corresponds to the normalized enrichment score (NES) and color to the q-value of the enrichment. (c) Venn diagram showing the intersection of DEGs in organoids at day 25 and 50. (d) Dotplot of the intersection between SFARI and Epilepsy gene lists with DEGs at day 25 and 50. Dot size corresponds to the odds ratio and the color to the (-log10(p-value)) based on a contingency table and Fisher’s exact test. (e-f) Number of DEGs in the intersection with SFARI and Epilepsy genes lists. (g) Heatmap of the log 2 FC value of genes in the SFARI intersection having opposite expression trends in day 25 ancestralized and day 50 heterozygous organoids. (h) Force-directed graphs of high-quality cells annotated by their lineage in the two datasets. (i) Butterfly plot showing relative changes in lineage proportions between heterozygous or ancestral conditions and the respective control from the same experiment. For each lineage, bars represent the difference in proportion (%). (j) Barplot of normalized ratios of cell cycle phase proportions (%) normalized to control. Ratios above or below 1 indicate a indicate a higher or lower proportion compared to control, respectively. (k-l) CHD2-centric gene regulatory network reconstruction in cycling progenitors performed on CHD2 An/An and CHD2 +/− , respectively. Only TFs directly regulating or regulated by CHD2 are visualized. Nodes borders are colored by log 2 FC calculated by differential expression between Ctrl and CHD2 An/An or CHD2 +/− respectively. Edge color and shapes represent activation (red, pointing arrow) or inhibition (blue, T shaped lines) and color is proportional to regulation coefficient measured by CellOracle from −1 (dark blue) to +1 (dark red). To further investigate the effects of CHD2 dosage, we performed single-cell RNA sequencing on control, CHD2 +/− , and CHD2 An/An organoids at 25, 50, and 90 days. Leiden clustering was performed to identify cell states and group them into coherent cell types across the two datasets (Suppl. Fig. 10a-c). We leveraged Leiden-specific differential expression and traced the expression of key cortical markers to annotate cell types (Suppl. Fig. 11a-b.). Draw graphs were generated by leveraging PAGA algorithm and compared with diffusion maps to reconstruct differentiation trajectories and lineages ( Fig. 3h , Suppl. Fig. 11c). Looking longitudinally at cell type proportion differences ( Fig. 3i ), we observed a consistent depletion of radial glia in the CHD2 +/− condition, coupled with an increase of intermediate progenitors and a cluster of mature excitatory neurons strongly expressing markers like FOXP2 and NEUROD6. This is fully in line with previous results from Chd2 -KO mice 39 . Our findings are also consistent with the impact of endolysosome inheritance during cell division on radial glia and intermediate progenitors 40 , 41 . Ctrl An/An organoids exhibited similar (albeit less pronounced) trends as CHD2 +/− organoids at Day 25 ( Fig. 3i ). However, at later time points, salient directional changes emerged, with an increase in radial glia in Ctrl An/An , and, especially at Day 90, a significant increase of (deep-layer) neurons expressing TBR1 and NEUROD6. Given these differences, we quantified the proportion of proliferative cells in G1, G2M, and S phases of the cell cycle compared to Ctrl within cyclic progenitors, radial glia, and intermediate progenitors ( Fig. 3j ). At day 25, G1 phase proportions were similar between Ctrl An/An and CHD2 +/− organoids. G2M cells increased by 20% in Ctrl An/An but decreased by 20% in CHD2 +/− organoids. By day 50, the proportion of G2M and S phase cells was significantly higher in Ctrl An/An organoids and considerably lower in CHD2 +/− ones, indicating that reduced CHD2 dosage accelerates differentiation and depletes the proliferative pool, while increased dosage boost neurogenesis, with a marked effect on deep-layer neuron production. To probe the downstream effects of CHD2 dosage in both directions, we inferred cell-type specific gene-regulatory networks (see Methods) and reconstructed the regulatory landscape underlying differential expression patterns in cycling progenitors (Suppl. Fig. 12a-b). Given the cell cycle contrasts abovementioned, we focused here on cycling progenitors. In CHD2 +/− organoids ( Fig. 3k ), we observed a smaller number of putative mediators of the differential expression but a stronger effect compared to Ctrl An/An organoids, corroborating the hypothesis that DEGs in the CHD2 +/− condition are directly impacted by CHD2 dosage at regulatory sites, since gene dosage is clearly halved and CHD2 DNA binding is expected to happen at a lower frequency. By contrast, the differential expression of TFs interacting with CHD2 in the CHD2 An/An showed a milder fold-change, while the number of affected TFs was higher, notably implicating deep-cortical layer markers already highlighted above (TBR1, FEZF2, FOXP2, BLC11A/B) ( Fig. 3l ). CHD2 dosage modulates developmental timing In light of the prominence of GO terms related to neuronal development found in the bulkRNA-sequencing of our organoids, we expected the differential availability of functional lysosomes in our CHD2 conditions to influence the developmental timing of cortical neurons 34 , 40 . To test this prediction, we tracked the morphometric characteristic of cortical neurons during development at different days in vitro (DIV) (Fig.4a). CHD2 +/− neurons exhibited reduced maturation compared to Ctrl neurons, as evidenced by a decrease in total neurite length, reduced neurite arborization complexity, and increased soma area at DIV 5 (Fig.4b-c). Ctrl An/An neurons exhibited an opposite phenotype compared to CHD2 +/− neurons. We observed a significant increase in both total neurite length and complexity at DIV 5 and DIV 14 compared to Ctrl neurons (Fig.4b-c). By DIV 21, these differences were however no longer observed, indicating that the Ctrl neurons caught up and suggest a temporary acceleration of neuronal maturation in Ctrl An/An neurons. A similar temporary acceleration in maturation was also observed in chimpanzee-derived cortical neurons 42 ( Extended Fig. 4a-b ), pointing to a neotenous effect observed in human control neurons. We next evaluated the effect of CHD2-dosage on subsequent stages of neuronal maturation: excitatory synapse development and neuronal networks. Ctrl An/An neurons exhibited an increase in functional excitatory synapses at DIV 21, as indicated by an increased number of co-localizing Synapsin1 and Homer1 puncta ( Fig. 4e-f ). Again, this difference is transient: by DIV 28, Ctrl An/An neurons showed no differences in synapse density compared to Ctrl neurons ( Fig. 4e-f ). In contrast, CHD2 +/− neurons showed a reduced number of functional excitatory synapses, which persisted into mature stages (DIV28) ( Fig. 4e-f ). To assess the effects of CHD2-dosage at the neuronal network level, we measured spontaneous neuronal activity of Ctrl An/An , Ctrl and CHD2-deficient ( CHD2 +/− , patient 1, patient 2) neurons cultured on micro-electrode arrays (MEAs) ( Fig. 4g ). From DIV 28 onwards, Ctrl neurons exhibited spontaneously active networks that synchronized into rhythmic, synchronous network bursts consisting of closely timed action potentials across all electrodes 43 . From DIV 28 onwards, chimpanzee and Ctrl An/An neuronal networks exhibited increased global activity compared to control networks, as reflected by an increased network burst rate and an increased burst rate ( Fig. 4h-i ). Conversely, CHD2-deficient networks exhibited decreased global activity, indicated by a reduced number of network bursts and firing rates. UMAP analysis of MEA network parameters further revealed distinct clustering of the Ctrl An/An , Ctrl , and CHD2-deficient groups. Additionally, we found that chimpanzee-derived networks clustered more closely with the Ctrl An/An group ( Extended Fig. 4c-e ). In summary, our data suggest that the modulation of CHD2 expression influences the developmental timing of cortical neurons, with long-term consequences for neuronal network dynamics ( Fig. 4j ). Download figure Open in new tab Figure 4. CHD2 dosage modulates developmental timing (a) Representative somatodendritic reconstructions of Ctrl An/An , Ctrl and CHD2 +/− neurons and (b) Sholl analysis at DIV 5, 14 and 21 (c-d) Main morphological parameters in reconstruction for neurons at DIV 5, 14, and 21. (e) Representative images of immunocytochemistry stained for glutamatergic synapse: Synapsin1 as a presynaptic marker and Homer1 as a postsynaptic marker. Scale bar = 10 µm. (f) Quantification of the density of co-localized Synapsin1/Homer1 puncta (number per 10 µm dendrite). (g) Representative raster plots (50 sec) of electrophysiological activity measured by MEA from Ctrl An/An , Ctrl- and CHD2 +/− -neuronal networks at DIV 49. (h-i) Quantification of network (h) and electrode (i) parameters as indicated from DIV 28 and DIV 49. Group with high CHD2 expression ( Ctrl An/An -indicated in green box) and low CHD2 expression ( CHD2 +/− , patient 1, and patient 2 – indicated in yellow box) were compared to the group with balanced CHD2 ( Ctrl1 and Ctrl2 – indicated in white box). (j) Graphical summary of developmental trajectories of Ctrl An/An , Ctrl and CHD2 +/− -deficient cortical neurons at different levels (neurite outgrowth, synapse development, and neuronal network development). Data represent means ±SEM. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, two-way ANOVA with post hoc Bonferroni correction (b-d,f,h). Supplementary tables 9-11 provide a detailed overview of the sample sizes and the statistical methods employed. Enhanced sensitivity to estrogen in sapiens Finally, we investigated how the SNV affects CHD2 dosage by analyzing its impact on transcription factor (TF) binding at the enhancer region. Using ChIP-seq-derived position weight matrices from the JASPAR2020 database, we scanned a 103-nucleotide window centered on the SNV across contemporary and ancestral sequences ( Extended Fig. 5a ). This identified 18 TF binding sites overlapping the SNV, corresponding to 14 unique TFs, including ESR2 and MAFG on the forward strand, and ESR2 and MAFK on the reverse strand ( Fig. 5a ). Download figure Open in new tab Figure 5. Enhanced sensitivity to estrogen in sapiens (a) Reconstruction of the TF binding site landscape at the SNV position in the presence of the sapiens -derived allele (C/G, top) or ancestral allele (T/A, bottom). Horizontal lines represent the TF binding motifs. Only TF binding sites overlapping the SNV are shown. (b) (top) Barplot of the binding affinity of ESR2 for the target sequence containing the derived or ancestral allele; (below) the TF motif used for the analysis, with the SNV highlighted by the black dashed box. (c) ESR2 motif used for motifbreakR analysis. (d) Representative western blot of DIV5 Ctrl or Ctrl An/An neurons treated with 10 nM b-estradiol or vehicle (ethanol). (e) Quantification of CHD2 expression upon treatment with 10nm b-estradiol, normalized to vehicle-treated condition, for each genotype. Data represent means ±SEM. **p<0.01, two-way ANOVA with post hoc Bonferroni correction. Supplementary table 12 provides a detailed overview of the sample sizes and the statistical methods employed. On the forward strand, the Ctrl An/An sequence features overlapping TFBSs for MAFG and ESR2 within a 15 bp DNA region, creating competition for binding (affinity scores: MAFG = 8.25, ESR2 = 7.55) (Fig.5b). The sapiens -derived allele (T>C) disrupts the MAFG TFBS, abolishing the competition and increasing ESR2 affinity to 9.75 ( Fig. 5b , Extended Fig. 5b ). Similarly, on the reverse strand of the sapiens -derived allele, the MAFK binding site (affinity score = 9.89) is equally lost. MotifbreakR confirmed the prediction that the SNV significantly impacts ESR2 binding ( Fig. 5c ), for which we gained further evidence by leveraging publicly-available ChIP-seq experiments for ESR1 collected in the ReMap database and finding peaks overlapping the SNV coordinates across multiple experiments and cell lines ( Extended Fig. 5c ), confirming the presence of a genuine binding site for estrogen receptor overlapping the SNV. To validate these analyses experimentally, we treated Ctrl and Ctrl An/An cortical neurons with estradiol and measured CHD2 expression ( Fig. 5d ). Estradiol significantly downregulated CHD2 expression in Ctrl neurons but not in Ctrl An/An neurons ( Fig. 5e ), consistent with the stronger ESR2 binding predicted in the modern sequence and the ensuing rewiring of CHD2 regulation under ESR2 control. Discussion The combined study of neurodevelopmental conditions and evolutionary variants provides unprecedented insight into the impact of CHD2 dosage during early brain development, enabling us to draw conclusions about both atypical development and evolutionary changes. First, we uncover CHD2 haploinsufficiency as a lysosomal disorder, extending the relevance of lysosomal dynamics in cortical development 34 , 40 to the pathophysiological consequences of its sensitivity to tight chromatin remodeling dosage 31 . We provide strong empirical evidence for the critical role of autophagosome flux rates, influenced by the number of functional lysosomes, in determining developmental tempo, reinforcing the emerging notion that lysosomal acidification 41 and the distribution of lysosomal subtypes 40 impact the cell fate of human neural stem cells during cortical brain development. From an evolutionary perspective, our findings align higher numbers of functional lysosomes to accelerated rate of cortical brain development, adding lysosomal dynamics to the list of (mostly metabolic) cellular traits controlling species-specific developmental programs 32 , 36 . While we have focused on our differences with our most closely related extinct relatives, our cross-species results on autophagosome flux point to additional modifications of the autolysosome pathways in the Homo lineage. The differences in lysosomal signalling and cell cycle progression reported in 44 could serve in the future as a valuable point of entry into understanding this extra layer of evolutionary modifications. Our work also reinforces the relevance of detecting transient phenotypic changes with the ensuing developmental cascades (whereby despite their only transient nature, earlier phenotypes prime the emergence of additional later differences). Alongside what was previously showed for CHD8 , the other key member of the CHD family of chromatin remodellers causative of neurodevelopmental conditions 45 , 46 , these findings expose transient trajectory alterations as a shared pathophysiological endophenotype of chromatin remodelling dosage sensitivity. Second, our findings underscore how a single regulatory variant can exert a significant causative role in setting divergent developmental courses in closely related species. As we examined the role of a single SNV and obviously no variant or gene is an island, our results lead us to anticipate additional changes in genes implicates in lysosomal dynamics and autolysosomal pathway to show important differences between us and our extinct relatives. Genes like RB1CC1 and SETD1A , already highlighted in the paleogenomic literature 25 , are strong candidates for further study. Likewise, variants impacting the cell cycle and neurogenesis, already a focus of the ancient DNA literature, 17 bear directly on our results. Furthermore, our results point to the value – and feasibility – of prioritizing, from the vast amount of sapiens nearly fixed regulatory variants and the much smaller core of protein-coding changes, specific combinations to be probed for their functional synergy. A case in point is the nearly-fixed missense mutation that was acquired in SLITRK1 during the evolution of Homo sapiens and that leads to neurite related changes in vitro 47 . This gene is among the DEGs in our Day 50 organoids, as are several genes found in large introgression deserts ( FOXP2 , KCND2 ), known to be expressed in deep-layer neurons and connected subcortical structures, such as the thalamus and the cerebellum 48 . Such structures have been prominently implicated in the modulation of vocal learning abilities 49 and neurodivergent conditions like autism 50 . Focusing on deep-layer neuron markers already highlighted in our analysis, such as FOXP2 , NFIB , and NEUROD6 , which our earlier work 25 also found to be associated with sapiens -derived changes in enhancer regions active during early cortical development, could thus provide an ideal entry point to explore this research question further. Finally, our findings of an estrogen-responsive sapiens variant that rewires dosage-sensitive chromatin remodeling identifies an important domain of recent evolutionary change orchestrating early brain development 51 – 53 and offers new insights into the mechanisms of activity-dependent brain development. Methods Generation of human induced pluripotent stem cells All experiments involving material from patients were carried out after informed consent was obtained and with approval from the medical ethical committee of Radboud University Medical Center, Nijmegen (2018 – 4525). Peripheral mononuclear blood cells (PBMCs) were isolated from patients via blood samples taken during routine diagnostic testing. CHD2 patient 1 was a male patient who had a heterozygous de novo mutation in exon 17 (c.2077_2078dup p.Glu694*). CHD2 patient 2 was a female patient with a missense mutation in exon 9 (c.948C>A p.Tyr316*). The clinical history of all patients is described in the Supplementary table 1. All lines were reprogrammed using episomal vectors with the Yamanaka transcription factors Oct4, c-Myc, Sox2 and Kfl4 and had normal karyotypes. CRISPR/Cas9 gene editing of CHD2 CRISPR/Cas9 technology was used to create a heterozygous indel variant in exon 4 of CHD2 in two independent hiPSC lines derived from a healthy male and female donor, which were obtained from the Corriel Institute (USCFi001-A, GM25256, indicated as Ctrl1 in this study) and Center for iPS cell Research and Application (CIRAAi007-A, indicated as Ctrl2 in this study). In brief, a short guide RNA (sgRNA) was designed (ATCCTGATGTTTATGGGGTC) and cloned into pSpCas9(BB)-2A-Puro (PX459) V2.0 (Addgene, #62988) according to previous studies. 8×10 5 single hiPSCs were nucleofected with 5 µg of the generated pSpCas9-sgRNA plasmid using the P3 Primary Cell 4D-Nucleofector Kit (Lonza, #V4XP-3024) in combination with the 4D Nucleofector Unit X (Lonza, #AAF-1002X). After nucleofection, cells were resuspended in E8 Flex supplemented with RevitaCell (Thermo Fisher Scientific, #A2644501) and seeded on biolaminin 521 (Biolamina, #LN521) pre-coated wells. 24 hours after nucleofection, 0.5 µg/ml puromycin was added for 24h. Puromycin resistant colonies were manually picked and send for Sanger Sequence to ensure heterozygous editing of exon 4. Genomic stability was assessed by detection of recurrent abnormalities using the iCS-digital TM PSC test, provided as a service by Stem Genomics ( https://www.stemgenomics.com/ ) (Suppl. Fig. 4c). We observed a loss of X chromosome in CHD2 +/− generated in Ctrl2 , which was also confirmed by karyotyping (Suppl. Fig. 4d). Whole genome sequencing and off-target analysis was performed to confirm genetic integrity after genomic editing (Suppl. Fig. 4b, Suppl. Fig. 5). CRISPR/Cas9 gene editing in enhancer region of CHD2 Isogenic clones harboring the ancestral allele in the enhancer region were generated from the GM25256 cell line. Briefly, a sgRNA (CACCTGCACTCAGAGTCAGCTTGT) was cloned into BbsI-HF-digested pSpCas9(BB)-2A-Puro (PX459) V2.0 (Addgene, #62988). A single cell suspension of 8×105 hiPSCs was nucleofected with 5 µg of the pSpCas9-sgRNA plasmid and 200 pmol of single-stranded oligodeoxynucleotides (ssODN; sequence: CTTTTTCCCTTGCTGTACCCGGGACACATTGATTTACATTTTAGATCTGCATCTGT GTCAAGATTAGACCAGCTATTCCAGTCAAAGAGTCAGCTTGCATTCAGAGTCAGC TTGTTGGCCCACCCTTTCCACAGACATAATAATGAATCAGATAGTGGGAAGCCAG GAGAAGGTATTGTCAAATGCTGGGGGTTAAATCC) using the P3 Primary Cell 4D-Nucleofector Kit (Lonza, #V4XP-3024) with the 4D Nucleofector Unit X (Lonza, #AAF-1002X). Cells were then resuspended in mTeSR1 (Stemcell Technologies, # 85850) supplemented with 10 µM Y-27632 dihydrochloride (R&D Systems, #1254) and plated on Matrigel (Corning, #354277) pre-coated wells. 24 hours after nucleofection, 0.25 µg/ml puromycin was added for 24h. Puromycin resistant colonies were manually picked and sent for Sanger sequencing to ensure homozygous editing. Genomic stability was assessed through whole genome sequencing (Suppl. Fig.5 ). As in the female Ctrl2 line we observed a loss of X chromosome in the Ctrl2 An/An line (Suppl. Fig. 3b). To check for off-target activity, we leveraged CasOFFinder (10.1093/bioinformatics/btu048) to predict off-targets with up to 3 mismatches (Suppl. Fig. 2b-c, Suppl. Table 4) with the sgRNA sequences. We then designed primer for each off-target (Suppl. Table 4) and performed PCR followed by Sanger sequencing to exclude off-target activity. Human induced pluripotent stem cells All CHD2 deficient hiPSC lines were cultured on biolaminin 521 (Biolamina, #LN521), and Ctrl An/An lines under feeder-free conditions either on Matrigel (Corning, #354277) coated plates or 0,125mg/cm 2 of iMatrix-511 (ReproCell, #NP892-012). Chimpanzee derived iPSCs (male) and bonobo derived iPSCs (female) were obtained from MPI EVA from Svante Pääbo lab. Cells were cultured in essential 8 (E8) flex medium (Gibco, #A2858401) supplemented with primocin (0.1 µg/mL, Invivogen, #ant-pm-2) or mTeSR1 (Stemcell Technologies, #85850) medium supplemented with 100 U/mL penicillin and 100 ug/mL streptomycin (Euroclone, #ECB3001D). Cells were passaged before reaching 70% confluency using an enzyme-free reagent (ReLeSR, Stemcell, #05872) or a 0.5 mM EDTA solution and not kept for more than 10 passages. All hiPSCs were regularly tested for mycoplasma contamination using MycoAlert PLUS (Lonza, #LT07-703) and their identity was confirmed by short tandem repeat profiling. To make Neurogenin 2 ( Ngn2 ) doxycycline-inducible excitatory neurons, cells were infected with lentiviral Ngn2 and TRE3G constructs using the transfer vector pLV[TetOn]-Puro-TRE3G>mNeurog2 (Addgene plasmid # 198754) and pLV-EF1A>Tet3G:IRES:Neo (Addgene plasmid # 198756), respectively. Selection was performed by G418 (50 µg/mL, Sigma-Aldrich, #G8168) for Ngn2 , and by puromycin (0.5 µg/mL) for TRE3G . hiPSCs were kept at 37°C/5%/CO 2 . The use of iPSC lines was approved by the ethical committee of the University of Milan, and RadboudUMC Nijmegen. Ngn2 neuronal differentiation HiPSCs were differentiated into iNeurons as previously described 43 , 54 , 55 . Briefly, hiPSCs were directly differentiated into excitatory cortical layer 2/3 neurons by overexpressing Ngn2 upon doxycycline treatment (1 mg/mL). Neuronal maturation was supported by E18 rodent astrocytes, which were added to the culture in a 1:1 ratio two days after hiPSC plating. Cell plating was adjusted to ensure equal cell densities. Well of neuronal cultures that had an unequal density or showed clustering were excluded Cortical brain organoids Cortical brain organoids were generated as previously described 56 . Briefly, 2×10 4 hiPSCs were plated in 96 V-bottom ultra-low attachment plates (S-Bio, #MS-9096VZ) in 100 µl of stem cell medium supplanted with 5 µm Y-27632 dihydrochloride (R&D Systems, #1254) and centrifuged for 3 minutes at 150 rcf to promote spheroids aggregation. Starting from day 0, medium was replaced daily with neural induction medium containing 80% DMEM/F12 medium (Gibco, #11330057), 20% Knockout serum (Gibco, #10828028), non-essential amino acids 1:100 (Euroclone, #ECB3054D), 0.1 mM cell culture grade 2-mercaptoethanol solution (ThermoFisher Scientific, #31350010), GlutaMax 1:100 (Gibco, #35050061), penicillin 100 U/mL and streptomycin 100 µg/mL (Euroclone, #ECB3001D), 5 µM Dorsomprhin (RayBiotech, #331-21079-2) and 10 µM TGFβ-inhibitor SB431542 (MedChem Express, #HY-10431). On day 6 medium was changed to complete Neurobasal medium (Gibco, #12348017) with B27 supplement without vitamin A 1:50 (Gibco, #12587001), GlutaMax 1:100 (Gibco, #35050061), penicillin 100 U/mL and streptomycin 100 μg/mL (Euroclone, #ECB3001D), 0.1 mM cell culture grade 2-mercaptoethanol solution (ThermoFisher Scientific, #31350010) supplemented with 20 ng/mL FGF2 (Peprotech, #100-18B) and 20 ng/mL EGF (Peprotech, #AF-100-15). On day 12, organoids were transferred to 90mm ultra-low attachment dishes (S-Bio, #MS-90900Z) on a standard orbital shaker (VWR Standard Orbital Shaker, Model 1000) at 55 rpm. From day 12 onwards, medium was changed every other day. On day 25, FGF and EGF were replaced with 20 ng/mL of BDNF (PeproTech, #450-02) and 20 ng/mL neurotrophin-3 (Peprotech, #450-03). From day 42 onwards, complete Neurobasal medium without growth factors was used and changed 3 times a week. Western Blot For Western Blot assays, a total of 350, 000 cells were seeded on a six-well plate. Proteins were extracted in cold RIPA lysis buffer consisting of 50 mM Tris HCl (pH 7.5), 150 mM NaCl, 1% NP-40, 0.10% SDS, 0.50% sodium deoxycholate, 1 mM EDTA, diluted in H 2 O, or by NE-PER TM Nuclear and Cytoplasmic Extraction Reagents (Thermo Fisher, #78835). Pierce BCA protein assay kit (ThermoFisher, #23225) was used to determine protein concentration. Protein lysates were loaded on Mini-PROTEAN TGX stain-free gels (Bio-Rad, #4568084) before transfer to 0.2 µm PVDF membrane (Bio-Rad, #1704156), or nitrocellulose (Bio-Rad, #1704158). The membrane was blocked with 5% blotto non-fat dry milk (ChemCruz, #sc-2325) or 5% bovine albumin serum (Sigma, #A7906-100G) (in 1X TBST 0.1%) for 1h at room temperature. Primary antibodies were incubated on the membrane for overnight at 4°C. The next day, membranes were washed three times in TBS-T0.1% followed by incubation with corresponding secondary antibodies for 1h at room temperature. Membranes were washed five times in TBS-T 0.1%, before ECL detection with the SuperSignal Tm West Femto kit (Thermo Scientific, #34095). The membranes were imaged using the Bio-Rad Gel Imaging system (ChemiDoc TM Touch Imaging System Bio-Rad). The following primary antibodies were used: rabbit anti-CHD2 (1:1000, Cell Signaling Technology, #4170S), mouse anti-β-actin (1:5000; Invitrogen, #MA1-140), mTOR (1:1000, Cell Signaling, #2972), p-mTOR (1:1000, Cell Signaling, #2971), ULK1 (1:1000, Cell Signaling, #8054), p-ULK1 (1:1000, Cell Signaling, #14202), LC3 (1:200, NanoTools, 0231-100/LC3-5F10), P62 (1:200; Sigma, p0067), g-tubulin (1:1000, BioLegend, #MMS-435P), NCOA7 (1:1000, Proteintech, #23092-1-AP), ATP6V1A, (1:1000, Proteintech, #17115-1-AP) and ATP6V1B2 (1:1000, Proteintech, #15097-1-AP). Secondary antibodies, conjugated to horseradish peroxidase (HRP), were diluted in 5% milk and added for 1h at room temperature. The following secondary antibodies were used: goat anti-mouse (1:50 000, Jackson ImmunoResearch Laboratories, 115-035-062) and goat anti-rabbit (1:50 000, Invitrogen, #G21234). Quantitative polymerase chain reaction RNA samples were isolated from iPSCs using the Nucleospin RNA isolation kit (740955, Machery Nagel) according to the manufactures’ instructions. Synthesis of cDNA was performed by iScript cDNA synthesis kit (1708890, Bio-Rad). PCR reactions were done by GoTaq master mix 2x with SYBR Green (A6002, Promega) according to the manufacturer’s protocol. PCR reactions were performed in a 96-well 0.2 ml format (Thermo Fisher Scientific) and ran on the QuantStudio TM Real-Time PCR systems (Thermo Fisher Scientific). All samples were analyzed in duplicate in the same run. Reverse transcriptase-negative conditions and no template-controls were used as a negative control. We used the arithmetic mean of the C t values of the technical replicates to calculate the relative mRNA expression levels of CHD2 . The mRNA expression level was calculated using the 2 -DDCt method with standardization to PPIA (Peptidylprolyl Isomerase A), GAPDH (glyceraldehyde-3-phosphate dehydrogenase) and TBP (TATA-Box Binding protein). Primers used to quantify mRNA expression of CHD2 are Fw (5’-3’) CTGTGACAGGTGGGGAAGAG and Rv (5’-3’) TTCCAAGCCATCATCTTTCA. Primers used for housekeeping mRNAs are: PPIA Fw (5’-3’) CATGTTTTCCTTGTTCCCTCC and Rv (5’-3’) CAACACTCTTAACTCAAACGAGGA; GAPDH Fw (5’-3’) GTGGACCTGACCTGCCGTCT and Rv (5’-3’) GGAGGAGTGGGTGTCGCTGT; TBP Fw (5’-3’) GAGCCAAGAGTGAAGAACAGTC and Rv (5’-3’) GCTCCCCACCATATTCTGAATCT. Immunocytochemistry Cultured cells were fixed with 4% paraformaldehyde supplemented with 4% sucrose for 15 min at RT, washed three times with 1XPBS, and permeabilized with 0.2% triton diluted in 1XPBS for 10 min at RT. Nonspecific binding sites were blocked with blocking buffer (5% normal goat serum diluted in 1XPBS) for 1h at room temperature. Primary antibodies were diluted in blocking buffer and incubated overnight at 4°C. Secondary antibodies conjugated to either Alexa 488, Alexa 568 or Alexa 647 (Invitrogen) were incubated for 1h at 1:1000 or 1:300 dilution in blocking buffer for neurons or organoids, respectively. Cell nuclei were stained with Fluoromount-G Mounting medium (with DAPI) at 1:50 000 dilution in 1XPBS for 10 minutes at RT. Organoids collected for imaging were washed twice in PBS and fixed in 4% PFA overnight hours at 4°C and then dehydrated for 24-48 hours in 30% sucrose in PBS 1X. For cryopreservation, organoids are transferred to plastic molds filled with a 1:1 solution of optimal cutting temperature compound (OCT) and 30% sucrose and snap-frozen by immersion in cold isopentane. 15 µm-thick slices were obtained through cryostat sectioning and placed on superfrost glass slices. Antigen retrieval was performed by heating the slices at 70 °C for 40 minutes in Dako Target Retrieval Solution 10X, Citrate Ph6 (Agilent Technologies, S236984-2) diluted 1:10 in PBS. Permeabilization was performed for 30 minutes at room temperature in 0.5% Triton-X in PBS 1x followed by incubation in blocking solution (0.3% Triton-X + 5% Normal Donkey Serum in PBS 1x) at room temperature for 1 hour. The following primary antibodies and dilutions were used: rabbit anti-CHD2 1:1000 (4170S, Cell Signaling Technology); guinea pig anti-MAP2 647 1:1000 or 1:5000 in neurons or organoids, respectively (188004, Synaptic Systems); rabbit anti-Synapsin I 1:500 (AB1543P, Sigma-Aldrich); Homer1b/c 1:200 (160111, Synaptic Systems); mouse anti-LAMP1 1:200 (15665S, Cell Signaling); goat anti-SOX2 1:300 (AF2018, R&D Systems), rabbit anti-TBR1 1:200 (ab183032, Abcam), mouse anti-SMI312 1:300 (837904, Biolegend), rabbit anti-HuD 1:200 (ab184267, Abcam), mouse anti-KI67 1:100 (9449S, Cell Signaling), mouse anti-TUJ1 (Tubulin beta 3) 1:300 (801202, Biolegend). Autophagosome flux assay hiPSCs were transfected with a lentiviral vector expressing tandem mCherry/GFP-LC3 for 7h, and then washed with 1XPBS. Two days after transfection, medium was refreshed 3h prior to rapamycin treatment. Cells were treated with 200 nM rapamycin (sc-3504A, Santa Cruz Biotechnology) for 10 min. at 37°C/CO 2 and washed with 1XPBS. After 3h, 5h, and 7h of autophagy induction, the cells were fixed with 4% PFA/sucrose diluted in 1XPBS for 15 min. at RT and stained with Fluoromount-G Mounting medium (with DAPI) at 1:50 000 dilution in 1XPBS for 10 min. at RT. Quantification and calculation of the autophagosome flux ( J ) and autolysosome transition state ( t nAL ) is performed according to an earlier publication 35 . Shortly, J is the calculated as the initial slope after autolysosomal induction. T nAL is calculated based on the steady-state (the number of autolysosomes before induction [v=1] is equal to the number after induction [v=2]) divided by J . Lysosomal assays Lysotracker Red (L7528, Invitrogen) was used to stain lysosomes and Lysosensor Green (L7435, Invitrogen) to monitor the lysosomal pH on fixed cells. A working concentration of 50 nM for Lysotracker and 500 nM for Lysosensor was used. Both dyes were diluted in prewarmed conditional (1:1) culture medium corresponding to the cell type used. After 1h of light protected incubation at 37°C/CO 2 the cells were washed with ice cold 1XPBS and subsequently processed with immunocytochemistry. To measure Cathepsin B activity we used Magic Red Cathepsin B substrate MR-(RR) 2 (6134, ImmunoChemistry). According to the manufacturer protocol, the Magic Red probe was first reconstituted with 50 ml DMSO and further diluted 1:10 in H 2 O prior to use. The diluted Magic Red was added into the cell culture medium at a ratio of 1:25, and incubated for 1h at 37°C/CO 2 . Afterwards, the cells were washed with ice cold 1XPBS and followed by immunocytochemistry to identify neurons. Ectopic expression of CHD2 and NCOA7 The following plasmids were used to ectopic CHD2: pRP-CBh>hCHD2[NM_001271.4](ns):T2A:Puro and pRP-EF1α>hCHD2[NM_00127.4]:BGHpA-hPGK>Puro. 8×10 5 single iPSCs were nucleofected with 5 μg of the plasmid using the P3 Primary Cell 4D-Nucleofector Kit in combination with the 4D Nucleofector Unit X. The nucleofected cells were selected with 0.25 µg/ml puromycin after 24 hours, and the next day autophagosome flux was performed. To ectopic express NCOA7, we used the following lentiviral plasmid: pLV-EF1α>hNCOA7[NM_001199619.2]>TagBFP2. The plasmid was packaged into lentiviral particles using the packaging vectors psPAX lentiviral packaging vector (Addgene #12260) and pMD2.G lentiviral packaging vector (Addgene #12259). To measure Lysosensor and autophagosome flux, 20.000 single iPSCs were transduced with the plasmid. The plasmids were constructed as a service by VectorBuilder. Transmission electron microscopy Cells were fixed in 2% glutaraldehyde in 0.1M sodium cacodylate (pH 7.4) buffer for 1 hr at RT, washed in buffer and post-fixed for 1h at RT in 1% osmium tetroxide and 1% potassium ferrocyanide in 0.1 M cacodylate buffer. After being washed in buffer, cells were dehydrated in an ascending series of aqueous ethanol and were subsequently transferred via a mixture of ethanol and Epon to pure Epon as embedding medium. Ultrathin sections (80 nm) were cut, contrasted with 2% aqueous uranyl acetate, counterstained with lead citrate and examined in a JEOL JEM 1400 electron microscope operating at 60 kV. RNA extraction For total RNA extraction, organoids were transferred into 1,5 ml tubes, washed twice with cold PBS and snap frozen in dry ice for long-term storage at −80°C. For each RNA extraction, 3 organoids were lysed in 1000 μl of QIAzol Lysis Reagent (Qiagen, #79306). Then, 200 μl of chloroform was added per 1 ml of QIAzol Lysis Reagent used and the samples were mixed thoroughly. After a 3-4 minutes incubation, samples were centrifuged at 15’000g for 15 minutes at 4°C. The aqueous phase was transferred into a clean 1,5 ml tube and 1 μl of GlycoBlue coprecipitant (Thermo Scientific, #AM951) was added. After 5 minutes of incubation, the RNA was precipitated by adding 500 μl of isopropanol. Samples were incubated overnight at −80°C to facilitate RNA precipitation. The following day, samples were centrifuged at 15’000g for 15 minutes at 4°C. The supernatant was discarded, and RNA pellets were washed twice with 500 μl of ice-cold 75% ethanol. Finally, pellets were air-dried at RT and resuspended in the appropriate amount of Nuclease-free water (Thermo Fisher Scientific, #AM9937). Purified RNA was quantified using a Nanodrop spectrophotometer and RNA quality was checked with an Agilent 2100 Bioanalyzer using the RNA nano kit (Agilent, 5067-1512). Genomic DNA was extracted through salting out following the protocol published by McManus lab ( https://mcmanuslab.ucsf.edu/protocol/dna-isolation-es-cells-96-well-plate ). Library preparation for RNA-seq Library preparation for RNA-sequencing was performed according to the stranded standard input TruSeq Total RNA sample preparation protocol (Illumina). cDNA library quality was assessed on an Agilent TapeStation using the High Sensitivity D1000 kit. Libraries were sequenced on a NovaSeq machine (Illunina) at a read length of 50 bp paired-end and a coverage of 20 million reads per sample. Whole Genome Sequencing and variant calling Library preparation was performed with the Illumina DNA PCR-free Library Prep and Tegmentation kit (Illumina, #20041795) using 300 ng of gDNA as input. Libraries were sequenced on a NovaSeq platform using a 2×150 configuration. Variant calling was performed using DeepVar ( https://doi.org/10.1038/nbt.4235 ) included in Nextflow (10.1038/s41587-020-0439-x) Sarek pipeline (10.1093/nargab/lqae031, 12688/f1000research.16665.2) v.3.1.1. RNA-seq data analysis RNA-seq FASTQ files for organoids data were quantified at the gene level using Salmon with the seqBias, gcBias, validateMappings flags. The GRCh38 human reference (gencode v35) was used for annotation and gene-level quantification. For RNA-seq FASTQ files of iNeurons co-cultured with rat astrocytes, reads were aligned with STAR v.2.7.9a (10.1093/bioinformatics/bts635) to retain only the reads uniquely mapping to the human reference. Briefly, a combined index was generated using the human GRCh38 primary genome assembly and Rattus norvegicus 6.0 (release 104) references and gtf annotations introducing a unique identifier for the species, and reads were aligned in quantMode=GeneCounts and retaining only uniquely mapped reads. Finally, reads were disambiguated based on their mapping to the reference of either species. Differential expression analyses were performed independently for hiPSCs, neurons and organoids, comparing heterozygous or ancestralized conditions to the isogenic control. Genes with expression levels higher than 2 cpm in at least 3 samples were analyzed for differential expression. After normalization and estimation of dispersion, differential expression was tested with DESeq2 v.1.34.0 (10.1186/s13059-014-0550-8). When analyzing data from organoids, the information about differentiation rounds was used as a covariate in the statistical model. Differentially expressed genes (DEGs) were selected imposing as thresholds FDR 1.5. Gene ontology enrichment analyses were performed with TopGO v.2.46.0 using the ‘weight01’ algorithm and ‘fisher’ statistics. Gene set enrichment analyses were performed using the GSEA function in the clusterProfiler library. Significance for DEGs overlap was tested with the GeneOverlap R library v.1.30.0. For enrichment analyses, all genes tested for differential expression were used as universe. Epigenomic analysis An atlas of putative active enhancers was generated starting from Roadmap Epigenomics data and leveraging H3K4me1, H3K27ac, H3K27me3 and H3K4me3 to define regulatory regions. We collected data from Roadmap Epigenomics for 29 histone modification profiled across H1- and H9-derived embryonic stem cells, neuronal precursors, glutamatergic neurons and primary neuronal tissues. Active enhancers were identified as intergenic and intronic regions bearing H3K4me1 and H3K27ac and devoid of H3K4me3 and H3K27me3. Intersections were performed using the bedtools v2.31.0 suite. Transcription factor binding affinity Transcription factor binding sites prediction was performed using the TFBSTools R package v.1.31.2, leveraging motif position weight matrices (PWMs) obtained from the JASPAR database. Ancestral and contemporary alleles were analyzed to identify differential binding, focusing on sequence-specific transcription factor binding at enhancers. Motifs were matched to genomic sequences using strand-specific scoring. Confidence thresholds were set at 80%, and filtering based on p-values was applied. Organoids dissociation and fixation for scRNA-seq Organoids were collected in a 24 multiwell plate, washed twice with HBSS (Stemcell Technologies, # 37150 ) and buffers freshly prepared. Papain powder was activated in a solution composed by 1.1 mM EDTA, 0.067 mM β-mercaptoethanol (ThermoFisher Scientific, #31350-010) and 5.5 mM L-cysteine (Sigma-Aldrich, #C7880-100G) for 30 minutes at 37 °C. The correct amount of papain powder was reconstituted in the activation solution having considered the lot specific % of protein and activity. To prepare the dissociation solution, activated papain solution and DNAse I (Stemcell Technologies - #07900) were diluted in Hank’s Balanced Salt Solution (HBSS, Stemcell Technologies - #37150) to the final concentrations of 30 units/mL of papain and 125 units/mL of DNase I. Organoids were incubated for 40 minutes at 37 °C on an orbital shaker at 120 rpm and then triturated 5/6 times at RT with a p1000 to obtain a suspension consisting primarily of cell aggregates. The cell aggregates suspension was then incubated again at 37°C for 15 minutes on an orbital shaker set to 120 rpm, triturated 4/5 times at RT to obtain a suspension consisting primarily of single cells, transferred to a 15 ml tube containing 3 ml of ice-cold ovomucoid protease inhibitor solution (Worthington Biochemical, #LK003182) and centrifuged at 300 g at 4 °C for 5 minutes. Remaining cell aggregates were removed filtering the cell suspension through a 70 μm strainer (Sigma/Merk, #H13680-0070). In order to fix the samples for the Chromium fixed RNA profiling protocol (10X Genomics, # 1000414), cells were centrifuged at 200 g for 10 minutes at 4°C, the supernatant was removed, the pellet resuspended with 1 ml of fixation buffer and incubated for 20 hours at 4°C. Cells were then centrifuged at 850 g for 5 minutes at RT and the pellet resuspended with 1 ml of quenching buffer. The single cells solution was supplemented with 100 μl of pre-warmed enhancer (10x genomics, PN-2000482) and 275 μl of 50% glycerol for long-term storage at −80 °C. Single cell RNA-seq data preprocessing All analyses were performed within the scanpy (10.1186/s13059-017-1382-0) single cell analysis framework. Feature matrices were imported from Cell Ranger and basic filtering strategies were applied. Droplets with potential technical issues or containing more than one cells were filtered out. Only cells with less than 10% mitochondrial genes counts were retained. Cells with less than 200 detected genes and genes detected in less than 100 cells were excluded from the analysis. Cell counts were then normalised and log transformed using sc.pp.normalize_total and sc.pp.log1p scanpy functions. We regressed out the effect of total counts via sc.pp.regress_out and scaled the data via sc.pp.scale functions. All functions were run with default parameters. HVGs were detected with the sc.pp.highly_variable_genes scanpy function specifying the experimental replicate as batch_key, with min_mean=0.0125, max_mean=5 and min_disp=0.5. PCA was computed on defined HVGs, and the samples within each dataset were integrated via scanpy’s harmony implementation. Cells’ neighbourhood graph was computed on the top 12 or 20 principal components (pcs) with neighbourhood size (n) = 50 for the ancestralized and heterozygous organoids dataset, respectively. All scRNA-seq analyses were done using scanpy v.1.9.1, anndata v.0.9.2 and umap v.0.5.3; the matplotlib v.1.20.2 and seaborn v.0.11.1 packages were used for UMAPs and visualization of gene expression at single cell level; pandas v.1.2.4, numpy v.1.20.2, scipy 1.10.1, scikit-learn v1.1.1 statsmodels v.0.13.2 were used for data handling. Gene Regulatory Network reconstruction with CellOracle Gene regulatory networks were built using CellOracle (10.1038/s41586-022-05688-9, v.0.10.12). All peaks in the previously generated promoter-enhancer association lists were scanned for the presence of TF motifs from the gimmemotifs database (gimme.vertebrate.v.5.0). Base GRNs were then generated for each combination of cell type and genotype, connecting each TF to the genes whose enhancers or promoters have the TF binding motif. CellOracle leverage co-expression patterns to generate GRNs based on celltype specific gene expression derived from scRNAseq. Edges in the resulting networks are drawn based on the concurrence of the following criteria: presence of the source TF’s binding motif in the gene promoter or its enhancers, co-expression of the TF and the gene, and the discernible impact of TF expression on the transcript levels of the gene, inferred by a Bagging Ridge ML model. The GRNs were then processed using custom scripts and visualized with Cytoscape (10.1101/gr.1239303, v.3.10.0), selecting the yFiles Orthogonal layout algorithm. Pseudobulk RNA-seq analysis from single cell RNA-seq experiments To perform differential expression on scRNA-seq data and compare control lines with matched heterozygous or ancestralized lines, we generated pseudobulks. Raw reads were aggregated by cell type and replicate using adpbulk (github.com/noamteyssier/adpbulk). Genes found in at least 2 replicates with at least 5 reads were kept for differential expression analysis. Differential expression analysis was performed using edgeR as per state-of-the-art benchmarking (doi.org/10.1038/s41467-021-25960-2; doi.org/10.1093/nar/gkw448). Differential expression analysis was performed using previously published edgeR wrapper for bulk RNA-seq “edg1” (10.1126/sciadv.aaw7908), using estimateDisp function (robust=TRUE) for dispersion estimation. FDR = 1.5 were used as thresholds. Gene Ontology (GO) enrichments were performed with enrichGO (10.1089/omi.2011.0118) filtering false discovery rate (FDR) at 0.05. Somatodendritic reconstructions Imaging of MAP2-stained neurons was done by using Zeiss Axio Imager Z1. Somatodendritic reconstructions were performed digitally by using the NeuroLucida 360 software (Version 11, MBF-Bioscience, Williston, ND, USA). We quantified the number of dendrites, the number of dendritic ends, the soma area, and the total and mean dendritic length. To investigate the complexity of dendrites in regard to their distance from the soma, Sholl analysis was performed. We quantified the number of intersections, the number of nodes and the total dendritic length per 10 mm interval. Synapse quantification Synaptic images were acquired using the Zeiss Axio Imager Z1 microscope. Regions of interest (ROIs) were traced along the proximal dendrites of neurons using MAP2 staining in ImageJ. Synaptic puncta were analyzed with the SynBot plugin (Savage et al., 2024), using the Ilastik thresholding method to detect presynaptic Synapsin1 and postsynaptic Homer1b/c puncta, followed by co-localization analysis to detect functional synapses. Functional synaptic puncta within the traced dendrite ROIs were quantified using a custom-written ImageJ macro, calculated per 10 µm dendrite length, and averaged across dendrites for each neuron. These values were then used for statistical analysis and data visualization. Micro-electrode array recording and analysis MEA recordings were performed as previously described 43 , 54 , 55 . Time-stamps per electrode were generated from Axion Biosystems raw.spk files using custom code wrote in MATLAB. Electrode bursts were detected using Maximum Spike Interval algorithm, and setting maximum spike interval to 0.05 s. Electrode bursts were restricted to be constituted by 5 spikes at least and to be longer than 0.1 ms. Consecutive electrode bursts separated by less than 0.1 s were merged. Network bursts (NB) were detected using a modified version of calculate_network_bursts function from meaRtools R package 57 . NB intervals were identified by thresholding the combined signal with the Otsu global thresholding algorithm 58 , requiring the involvement of at least 40% of the electrodes and a duration of more than 0.1 s. In addition, we imposed a restriction on NB, requiring a minimum firing rate of 9 Hz in at least 0.25 of the actively participating electrodes. Principal component and UMAP analysis Principal component (PCA) and UMAP analysis were performed considering all activity and burst-related variables in networks from DIV28 onwards. Neuronal networks showing < 3 NB were excluded. We determined the mean value of each parameter by grouping them based on cell line and DIV. Then, PCA was performed on the averaged parameters with prcomp function. UMAP was performed on the first 3 PC with umap function, and setting random_state = 123. The distance between the averaged samples was calculated based on the two UMAP dimensions. This distance was then employed for hierarchical clustering using the pheatmap function from the pheatmap R package Statistical analyses All analyses performed with R were performed with R version 4.1.1 except for the analysis of transcription factor binding motifs performed using R version 4.2.2. All analyses performed with python were performed with python version 3.8.10. Statistical details for every experiment can be found in Supplementary Tables 6-12 and experiments related to Figure 3 on https://github.com/GiuseppeTestaLab/CHD2_release Author contributions O.L. and E.L. contributed equally, are listed in alphabetical order and have the right to list their name first in their CV and public communications. N.N.K., G.T. and C.B. contributed equally and conceived the project. N.N.K., G.T., C.B, H.v.B and A.V. supervised the project. O.L. and E.L. conceived and performed most experiments, generated CRISPR and patient lines, collected and analyzed data. O.L. has driven bioinformatic analyses. F.P. contributed to bioinformatics analyses. A.V. supervised and contributed to computational analyses. D.A. and A.O. contributed to acquisition and analysis of imaging data. T.S.B. and R.D. performed BRAIN-MAGNET analysis. E.L., L.H., K.L., and A.M. designed and performed the (auto)lysosomal assays and reconstructed neurons. M.M, H.J.S and J.V recruited the CHD2 patients. A.O. performed Western Blot; S.P and E.v.H analyzed MEA data; N.S imaged and analyzed synapses in neurons; C.S. helped with iPSC cell culture. O.L., E.L., N.N.K., G.T and C.B, wrote the paper with input from all co-authors. All authors read and approved the final paper. Competing interest declaration We have no competing interests Additional information Supplementary information Supplementary tables Table S2. Information on sgRNA, ssODNs and primers used to make CHD2 -related CRISPRs Table S3. DeepVar analysis Table S4. Off-targets sequences and oligos Table S5. STR profile of CRISPR-edited lines Raw sequencing data have been deposited on ArrayExpress (EMBL-EBI) and are available under accession number E-MTAB-14758 Codes and statistics related to Figure 3 : https://github.com/GiuseppeTestaLab/CHD2_release Extended figures Download figure Open in new tab Extended Data Fig. 1 Computational prediction of SNV on regulatory region of CHD2 and iPSC models generated in this study. (a) Percentile scores based on BRAIN-MAGNET 29 for 212 human-specific SNVs located in regulatory region (grey dots). Red dot represents the SNV identified in the enhancer region of CHD2 with a BRAIN-MAGNET score of 95.5%. (b) (top) Sequence logo representation of individual nucleotide contribution to the enhancer activity computed by the BRAIN-MAGNET algorithm at the genomic region chr15:92822037–92822902; (bottom) magnification of the highlighted blue region encompassing the SNV (black dashed box). (c) Characterization of epigenetic signature of regulatory region of CHD2 in neuronal tissues. (d) iPSCs models used in this study and the corresponding CRISPR/Cas9-mediate genome editings performed on them. Download figure Open in new tab Extended Data Fig. 2. Autophagosome flux in sapiens , bonobo , and chimpanzee (a) Quantification of number of autolysosomes per cell at steady-stage (0h) and after autophagy induction (3h-5h-7h) in chimpanzee - and bonobo -derived iPSCs compared to two independent human controls ( Ctrl1 and Ctrl2 ). (b) Table provides the autophagosome flux ( J ) and time of transition state ( t nAL ) observed in chimpanzee - and bonobo -derived iPSCs compared to Ctrl1 - and Ctrl2 -derived iPSCs. (c) Representative images of autophagosome flux assay in Chimpanzee - and bonobo -derived iPSCs compared to Ctrl1 and Ctrl2 -derived iPSCs. Download figure Open in new tab Extended Data Fig. 3. Characterization of Ctrl An/An and CHD2 +/− organoids at 25 and 50 days. (a) Schematic representation of cortical organoids differentiation workflow. (b) Representative immunostaining images of organoids showing relevant markers for lysosomal function (LAMP1), proliferation of neuronal progenitors (KI67, SOX2) and cortical development (SMI312, HuD, TBR1 and TUJ1) at 25 and 50 days. (c) Principal component analysis (PCA) of bulk RNA-seq data from 25 and 50 days organoids. (d) Dotplot of GO categories for biological processes enriched by the DEGs shared by day 50 Ctrl An/An and day 25 CHD2 +/− , with dot size corresponding to the enrichment ratio. (e) Barplot of GO categories for biological processes enriched by DEGs of Ctrl An/An and CHD2 +/− organoids at 25 and 50 days. (g) Ridge plot of GO categories with coherent or inverse trends enriched by upregulated or downregulated DEGs of Ctrl An/An and CHD2 +/− organoids at 25 and 50 days. Download figure Open in new tab Extended Data Fig. 4. Somatodendritic reconstructions and neural network in sapiens and chimpanzee (a-b) Representative somatodendritic reconstruction of Chimpanzee and modern humans ( Ctrl1 and Ctrl2 ), and (b) Sholl analysis at DIV 5 and 14 (n = ±31 for chimpanzee , n = 30 for Ctrl1 , n = 30 for Ctrl2 ). (c) UMAP analysis based from 27 MEA parameters across three groups (balanced CHD2 [ Ctrl1 and Ctrl2 ], high CHD2 [ chimpanzee, Ctrl1 An/An1 , Ctrl1 An/An2 ] and low CHD2 [ CHD2 +/− , patient 1, patient 2]. (d) Representative raster plots (50 sec) of electrophysiological activity measured by MEA from chimpanzee- and Ctrl1 -neuronal networks at DIV 49. (e) Quantification of network (left) and electrode burst rate (right) parameters as indicated from DIV 28 and DIV 49. Group with high CHD2 expression ( chimpanzee and Ctrl An/An in green box) and low CHD2 expression ( CHD2 +/− , patient 1, and patient 2 – indicated in yellow box) were compared to the group with balanced CHD2 ( Ctrl1 and Ctrl2 – indicated in white box). Data represent means ±SEM. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, two-way ANOVA with post hoc Bonferroni correction (b-d,f,h). Download figure Open in new tab Extended Data Fig. 5. ESR2 binds to the enhancer region in presence of the SNV. (a) Frequency of all TFBS predicted in the 103bp window centered on the SNV. (b) Scatter plots of sapiens versus ancestral scores for TFs on forward (left) and reverse (right) DNA strands. TFBS falling on the diagonal (red dashed line) have equal modern and ancestral scores. TF labels are positioned to avoid overlap. Axes display normalized scores for modern (x-axis) and ancestral (y-axis) conditions. (c) Representative view of the peaks from publicly available ChIP-seq data included the ReMap project for ESR1. The blue vertical line indicates the SNV. Acknowledgements We thank Svante Pääbo for providing the chimpanzee and bonobo iPSCs. This work was supported by the Netherlands Organisation for Health Research and Development ZonMw grant 91217055 (to H.v.B., N.N.K., M.M., J.V. and H.J.S.), Simons Foundation (SFARI) Grant 890042 (to N.N.K.). C. B. was supported by Project PID2023-146627NB-I00 (Spanish Ministry of Science, Innovation, and Universities CIENCIA/AEI), project 2021-SGR-313 (AGAUR/Generalitat de Catalunya) and a Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. T.S.B was supported by the Netherlands Organisation for Scientific Research (ZonMw Vidi, grant 09150172110002). RD was supported by a China Scholarship Council (CSC) PhD Fellowship (201906300026 to RD) for her PhD studies at the Erasmus Medical Center, Rotterdam, The Netherlands. Work carried out in G.T.’s laboratory at Human Technopole and IEO was funded by the European Union’s Horizon 2020 research and innovation program grants RE-MEND (101057604) and R2D2-MH (101057385). We thank the European School of Molecular Medicine (SEMM), where O.L. is enrolled as a PhD student in Systems Medicine, and F. Marinaro for project management support. Funding bodies did not have any influence on study design, results, and data interpretation or final manuscript. Footnotes ↵ § These authors jointly supervised the work in the Testa lab. ↵ * Co-last authors Main references 1. ↵ Gould , S. J. Ontogeny and phylogeny . Harvard U Press ( 1977 ). 2. ↵ Gopnik , A . Childhood as a solution to explore–exploit tensions . Philosophical Transactions of the Royal Society B: Biological Sciences 375 , ( 2020 ). 3. Lindhout , F. W. , Krienen , F. M. , Pollard , K. S. & Lancaster , M. A . A molecular and cellular perspective on human brain evolution and tempo . 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