Pinto, Dalila

Abstract Osteogenic differentiation is essential for bone development, metabolism, and repair; however, the underlying regulatory relationships among genes remain poorly understood. To elucidate the transcriptomic changes and identify novel regulatory genes involved in osteogenic differentiation, we differentiated mesenchymal stem cells (MSCs) derived from 20 human iPSC lines into preosteoblasts (preOBs) and osteoblasts (OBs). We then performed transcriptome profiling of MSCs, preOBs and OBs. The iPSC-derived MSCs and OBs showed similar transcriptome profiles to those of primary human MSCs and OBs, respectively. Differential gene expression analysis revealed global changes in the transcriptomes from MSCs to preOBs, and then to OBs, including the differential expression of 840 genes encoding transcription factors (TFs). TF regulatory network analysis uncovered a network comprising 451 TFs, organized into five interactive modules. Multiscale embedded gene co-expression network analysis (MEGENA) identified gene co-expression modules and key network regulators (KNRs). From these analyses, KLF16 emerged as an important TF in osteogenic differentiation. We demonstrate that overexpression of Klf16 in vitro inhibited osteogenic differentiation and mineralization, while Klf16+/- mice exhibited increased bone mineral density, trabecular number, and cortical bone area. Our study underscores the complexity of osteogenic differentiation and identifies novel regulatory genes such as KLF16, which plays an inhibitory role in osteogenic differentiation both in vitro and in vivo.

Abstract Osteogenic differentiation is essential for bone development, metabolism, and repair; however, the underlying regulatory relationships among genes remain poorly understood. To elucidate the transcriptomic changes and identify novel regulatory genes involved in osteogenic differentiation, we differentiated mesenchymal stem cells (MSCs) derived from 20 human iPSC lines into preosteoblasts (preOBs) and osteoblasts (OBs). We then performed transcriptome profiling of MSCs, preOBs and OBs. The iPSC-derived MSCs and OBs showed similar transcriptome profiles to those of primary human MSCs and OBs, respectively. Differential gene expression analysis revealed global changes in the transcriptomes from MSCs to preOBs, and then to OBs, including the differential expression of 840 genes encoding transcription factors (TFs). TF regulatory network analysis uncovered a network comprising 451 TFs, organized into five interactive modules. Multiscale embedded gene co-expression network analysis (MEGENA) identified gene co-expression modules and key network regulators (KNRs). From these analyses, KLF16 emerged as an important TF in osteogenic differentiation. We demonstrate that overexpression of Klf16 in vitro inhibited osteogenic differentiation and mineralization, while Klf16+/- mice exhibited increased bone mineral density, trabecular number, and cortical bone area. Our study underscores the complexity of osteogenic differentiation and identifies novel regulatory genes such as KLF16, which plays an inhibitory role in osteogenic differentiation both in vitro and in vivo.

Abstract Eating disorders and substance use disorders frequently co‐occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [ r g ], twin‐based = 0.23‐0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome‐wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance‐use‐related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism‐based genetic correlations between eating disorder‐ and substance‐use‐related phenotypes. Significant positive genetic associations emerged between AUD and AN ( r g = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN ( r g = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating ( r g = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating ( r gs = −0.19 to −0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co‐varying for major depressive disorder loci. The patterns of association between eating disorder‐ and substance‐use‐related phenotypes highlights the potentially complex and substance‐specific relationships among these behaviors.

INTRODUCTION The human cerebral cortex has undergone an extraordinary increase in size and complexity during mammalian evolution. Cortical cell lineages are specified in the embryo, and genetic and epidemiological evidence implicates early cortical development in the etiology of neuropsychiatric disorders such as autism spectrum disorder (ASD), intellectual disabilities, and schizophrenia. Most of the disease-implicated genomic variants are located outside of genes, and the interpretation of noncoding mutations is lagging behind owing to limited annotation of functional elements in the noncoding genome. RATIONALE We set out to discover gene-regulatory elements and chart their dynamic activity during prenatal human cortical development, focusing on enhancers, which carry most of the weight upon regulation of gene expression. We longitudinally modeled human brain development using human induced pluripotent stem cell (hiPSC)–derived cortical organoids and compared organoids to isogenic fetal brain tissue. RESULTS Fetal fibroblast–derived hiPSC lines were used to generate cortically patterned organoids and to compare oganoids’ epigenome and transcriptome to that of isogenic fetal brains and external datasets. Organoids model cortical development between 5 and 16 postconception weeks, thus enabling us to study transitions from cortical stem cells to progenitors to early neurons. The greatest changes occur at the transition from stem cells to progenitors. The regulatory landscape encompasses a total set of 96,375 enhancers linked to target genes, with 49,640 enhancers being active in organoids but not in mid-fetal brain, suggesting major roles in cortical neuron specification. Enhancers that gained activity in the human lineage are active in the earliest stages of organoid development, when they target genes that regulate the growth of radial glial cells. Parallel weighted gene coexpression network analysis (WGCNA) of transcriptome and enhancer activities defined a number of modules of coexpressed genes and coactive enhancers, following just six and four global temporal patterns that we refer to as supermodules, likely reflecting fundamental programs in embryonic and fetal brain. Correlations between gene expression and enhancer activity allowed stratifying enhancers into two categories: activating regulators (A-regs) and repressive regulators (R-regs). Several enhancer modules converged with gene modules, suggesting that coexpressed genes are regulated by enhancers with correlated patterns of activity. Furthermore, enhancers active in organoids and fetal brains were enriched for ASD de novo variants that disrupt binding sites of homeodomain, Hes1, NR4A2, Sox3, and NFIX transcription factors. CONCLUSION We validated hiPSC-derived cortical organoids as a suitable model system for studying gene regulation in human embryonic brain development, evolution, and disease. Our results suggest that organoids may reveal how noncoding mutations contribute to ASD etiology. Summary of the study, analyses, and main results. Data were generated for iPSC-derived human telencephalic organoids and isogenic fetal cortex. Organoids modeled embryonic and early fetal cortex and show a larger repertoire of enhancers. Enhancers could be divided into activators and repressors of gene expression. We derived networks of modules and supermodules with correlated gene and enhancer activities, some of which were implicated in autism spectrum disorders (ASD).