Impact of regulatory variation across human iPSCs and differentiated cells

Fundamentals for predicting transcriptional regulations from DNA sequence patterns

Cell-type-specific regulatory elements, cataloged through extensive experiments and bioinformatics in large-scale consortiums, have enabled enrichment analyses of genetic associations that primarily utilize positional information of the regulatory elements. These analyses have identified cell types and pathways genetically associated with human complex traits. However, our understanding of detailed allelic effects on these elements' activities and on-off states remains incomplete, hampering the interpretation of human genetic study results. This review introduces machine learning methods to learn sequence-dependent transcriptional regulation mechanisms from DNA sequences for predicting such allelic effects (not associations). We provide a concise history of machine-learning-based approaches, the requirements, and the key computational processes, focusing on primers in machine learning. Convolution and self-attention, pivotal in modern deep-learning models, are explained through geometrical interpretations using dot products. This facilitates understanding of the concept and why these have been used for machine learning for DNA sequences. These will inspire further research in this genetics and genomics field.

Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures

Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell-types and developmental stages remain underexplored. Here we harnessed the potential of heterogeneous differentiating cultures ( HDCs ), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell-types. We generated HDCs for 53 human donors and collected single-cell RNA-sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell-types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues, and dynamic regulatory effects associated with a range of complex traits.

Shaping immunity: The influence of natural selection on population immune diversity

Humans exhibit considerable variability in their immune responses to the same immune challenges. Such variation is widespread and affects individual and population-level susceptibility to infectious diseases and immune disorders. Although the factors influencing immune response diversity are partially understood, what mechanisms lead to the wide range of immune traits in healthy individuals remain largely unexplained. Here, we discuss the role that natural selection has played in driving phenotypic differences in immune responses across populations and present-day susceptibility to immune-related disorders. Further, we touch on future directions in the field of immunogenomics, highlighting the value of expanding this work to human populations globally, the utility of modeling the immune response as a dynamic process, and the importance of considering the potential polygenic nature of natural selection. Identifying loci acted upon by evolution may further pinpoint variants critically involved in disease etiology, and designing studies to capture these effects will enrich our understanding of the genetic contributions to immunity and immune dysregulation.

Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases

Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.

Chromatin accessibility variation provides insights into missing regulation underlying immune-mediated diseases

Most genetic loci associated with complex traits and diseases through genome-wide association studies (GWAS) are noncoding, suggesting that the causal variants likely have gene regulatory effects. However, only a small number of loci have been linked to expression quantitative trait loci (eQTLs) detected currently. To better understand the potential reasons for many trait-associated loci lacking eQTL colocalization, we investigated whether chromatin accessibility QTLs (caQTLs) in lymphoblastoid cell lines (LCLs) explain immune-mediated disease associations that eQTLs in LCLs did not. The power to detect caQTLs was greater than that of eQTLs and was less affected by the distance from the transcription start site of the associated gene. Meta-analyzing LCL eQTL data to increase the sample size to over a thousand led to additional loci with eQTL colocalization, demonstrating that insufficient statistical power is still likely to be a factor. Moreover, further eQTL colocalization loci were uncovered by surveying eQTLs of other immune cell types. Altogether, insufficient power and context-specificity of eQTLs both contribute to the 'missing regulation.'

Cell-type-specific and disease-associated expression quantitative trait loci in the human lung

Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis. Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA sequencing of lung tissue from 66 individuals with pulmonary fibrosis and 48 unaffected donors. Using a pseudobulk approach, we mapped expression quantitative trait loci (eQTLs) across 38 cell types, observing both shared and cell-type-specific regulatory effects. Furthermore, we identified disease interaction eQTLs and demonstrated that this class of associations is more likely to be cell-type-specific and linked to cellular dysregulation in pulmonary fibrosis. Finally, we connected lung disease risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression and implicates context-specific eQTLs as key regulators of lung homeostasis and disease.

Genetic and molecular architecture of complex traits

Human genetics has emerged as one of the most dynamic areas of biology, with a broadening societal impact. In this review, we discuss recent achievements, ongoing efforts, and future challenges in the field. Advances in technology, statistical methods, and the growing scale of research efforts have all provided many insights into the processes that have given rise to the current patterns of genetic variation. Vast maps of genetic associations with human traits and diseases have allowed characterization of their genetic architecture. Finally, studies of molecular and cellular effects of genetic variants have provided insights into biological processes underlying disease. Many outstanding questions remain, but the field is well poised for groundbreaking discoveries as it increases the use of genetic data to understand both the history of our species and its applications to improve human health.

Guided Differentiation of Pluripotent Stem Cells for Cardiac Cell Diversity

We have developed a guided differentiation protocol for induced pluripotent stem cells (iPSCs) that rapidly generates a temporally and functionally diverse set of cardiac-relevant cell types. By leveraging techniques used in embryoid body and cardiac organoid generation, we produce both progenitor and terminal cardiac cell types concomitantly in just 10 days. Our results show that guided differentiation generates functionally relevant cardiac cell types that closely align with the transcriptional profiles of cells from differentiation time-course collections, mature cardiac organoids, and in vivo heart tissue. Guided differentiation prioritizes simplicity by minimizing the number of reagents and steps required, thereby enabling rapid and cost-effective experimental throughput. We expect this approach will provide a scalable cardiac model for population-level studies of gene regulatory variation and gene-by-environment interactions.

Anthracyclines induce cardiotoxicity through a shared gene expression response signature

TOP2 inhibitors (TOP2i) are effective drugs for breast cancer treatment. However, they can cause cardiotoxicity in some women. The most widely used TOP2i include anthracyclines (AC) Doxorubicin (DOX), Daunorubicin (DNR), Epirubicin (EPI), and the anthraquinone Mitoxantrone (MTX). It is unclear whether women would experience the same adverse effects from all drugs in this class, or if specific drugs would be preferable for certain individuals based on their cardiotoxicity risk profile. To investigate this, we studied the effects of treatment of DOX, DNR, EPI, MTX, and an unrelated monoclonal antibody Trastuzumab (TRZ) on iPSC-derived cardiomyocytes (iPSC-CMs) from six healthy females. All TOP2i induce cell death at concentrations observed in cancer patient serum, while TRZ does not. A sub-lethal dose of all TOP2i induces limited cellular stress but affects calcium handling, a function critical for cardiomyocyte contraction. TOP2i induce thousands of gene expression changes over time, giving rise to four distinct gene expression response signatures, denoted as TOP2i early-acute, early-sustained, and late response genes, and non-response genes. There is no drug- or AC-specific signature. TOP2i early response genes are enriched in chromatin regulators, which mediate AC sensitivity across breast cancer patients. However, there is increased transcriptional variability between individuals following AC treatments. To investigate potential genetic effects on response variability, we first identified a reported set of expression quantitative trait loci (eQTLs) uncovered following DOX treatment in iPSC-CMs. Indeed, DOX response eQTLs are enriched in genes that respond to all TOP2i. Next, we identified 38 genes in loci associated with AC toxicity by GWAS or TWAS. Two thirds of the genes that respond to at least one TOP2i, respond to all ACs with the same direction of effect. Our data demonstrate that TOP2i induce thousands of shared gene expression changes in cardiomyocytes, including genes near SNPs associated with inter-individual variation in response to DOX treatment and AC-induced cardiotoxicity.

Single-cell long-read sequencing in human cerebral organoids uncovers cell-type-specific and autism-associated exons

Dysregulation of alternative splicing has been repeatedly associated with neurodevelopmental disorders, but the extent of cell-type-specific splicing in human neural development remains largely uncharted. Here, single-cell long-read sequencing in induced pluripotent stem cell (iPSC)-derived cerebral organoids identifies over 31,000 uncatalogued isoforms and 4,531 cell-type-specific splicing events. Long reads uncover coordinated splicing and cell-type-specific intron retention events, which are challenging to study with short reads. Retained neuronal introns are enriched in RNA splicing regulators, showing shorter lengths, higher GC contents, and weaker 5' splice sites. We use this dataset to explore the biological processes underlying neurological disorders, focusing on autism. In comparison with prior transcriptomic data, we find that the splicing program in autistic brains is closer to the progenitor state than differentiated neurons. Furthermore, cell-type-specific exons harbor significantly more de novo mutations in autism probands than in siblings. Overall, these results highlight the importance of cell-type-specific splicing in autism and neuronal gene regulation.

Identifying key underlying regulatory networks and predicting targets of orphan C/D box SNORD116 snoRNAs in Prader-Willi syndrome

Prader-Willi syndrome (PWS) is a rare neurodevelopmental disorder characterized principally by initial symptoms of neonatal hypotonia and failure-to-thrive in infancy, followed by hyperphagia and obesity. It is well established that PWS is caused by loss of paternal expression of the imprinted region on chromosome 15q11-q13. While most PWS cases exhibit megabase-scale deletions of the paternal chromosome 15q11-q13 allele, several PWS patients have been identified harboring a much smaller deletion encompassing primarily SNORD116. This finding suggests SNORD116 is a direct driver of PWS phenotypes. The SNORD116 gene cluster is composed of 30 copies of individual SNORD116 C/D box small nucleolar RNAs (snoRNAs). Many C/D box snoRNAs have been shown to guide chemical modifications of other RNA molecules, often ribosomal RNA (rRNA). However, SNORD116 snoRNAs are termed 'orphans' because no verified targets have been identified and their sequences show no significant complementarity to rRNA. It is crucial to identify the targets and functions of SNORD116 snoRNAs because all reported PWS cases lack their expression. To address this, we engineered two different deletions modelling PWS in two distinct human embryonic stem cell (hESC) lines to control for effects of genetic background. Utilizing an inducible expression system enabled quick, reproducible differentiation of these lines into neurons. Systematic comparisons of neuronal gene expression across deletion types and genetic backgrounds revealed a novel list of 42 consistently dysregulated genes. Employing the recently described computational tool snoGloBe, we discovered these dysregulated genes are significantly enriched for predicted SNORD116 targeting versus multiple control analyses. Importantly, our results showed it is critical to use multiple isogenic cell line pairs, as this eliminated many spuriously differentially expressed genes. Our results indicate a novel gene regulatory network controlled by SNORD116 is likely perturbed in PWS patients.

Human-specific genetics: new tools to explore the molecular and cellular basis of human evolution

Our ancestors acquired morphological, cognitive and metabolic modifications that enabled humans to colonize diverse habitats, develop extraordinary technologies and reshape the biosphere. Understanding the genetic, developmental and molecular bases for these changes will provide insights into how we became human. Connecting human-specific genetic changes to species differences has been challenging owing to an abundance of low-effect size genetic changes, limited descriptions of phenotypic differences across development at the level of cell types and lack of experimental models. Emerging approaches for single-cell sequencing, genetic manipulation and stem cell culture now support descriptive and functional studies in defined cell types with a human or ape genetic background. In this Review, we describe how the sequencing of genomes from modern and archaic hominins, great apes and other primates is revealing human-specific genetic changes and how new molecular and cellular approaches - including cell atlases and organoids - are enabling exploration of the candidate causal factors that underlie human-specific traits.

Reliable multiplex generation of pooled induced pluripotent stem cells

Reprogramming somatic cells into pluripotent stem cells (iPSCs) enables the study of systems in vitro. To increase the throughput of reprogramming, we present induction of pluripotency from pooled cells (iPPC)-an efficient, scalable, and reliable reprogramming procedure. Using our deconvolution algorithm that employs pooled sequencing of single-nucleotide polymorphisms (SNPs), we accurately estimated individual donor proportions of the pooled iPSCs. With iPPC, we concurrently reprogrammed over one hundred donor lymphoblastoid cell lines (LCLs) into iPSCs and found strong correlations of individual donors' reprogramming ability across multiple experiments. Individual donors' reprogramming ability remains consistent across both same-day replicates and multiple experimental runs, and the expression of certain immunoglobulin precursor genes may impact reprogramming ability. The pooled iPSCs were also able to differentiate into cerebral organoids. Our procedure enables a multiplex framework of using pooled libraries of donor iPSCs for downstream research and investigation of in vitro phenotypes.

The relationship between regulatory changes in cis and trans and the evolution of gene expression in humans and chimpanzees

BACKGROUND

Comparative gene expression studies in apes are fundamentally limited by the challenges associated with sampling across different tissues. Here, we used single-cell RNA sequencing of embryoid bodies to collect transcriptomic data from over 70 cell types in three humans and three chimpanzees.

RESULTS

We find hundreds of genes whose regulation is conserved across cell types, as well as genes whose regulation likely evolves under directional selection in one or a handful of cell types. Using embryoid bodies from a human-chimpanzee fused cell line, we also infer the proportion of inter-species regulatory differences due to changes in cis and trans elements between the species. Using the cis/trans inference and an analysis of transcription factor binding sites, we identify dozens of transcription factors whose inter-species differences in expression are affecting expression differences between humans and chimpanzees in hundreds of target genes.

CONCLUSIONS

Here, we present the most comprehensive dataset of comparative gene expression from humans and chimpanzees to date, including a catalog of regulatory mechanisms associated with inter-species differences.

Engineering considerations of iPSC-based personalized medicine

Personalized medicine aims to provide tailored medical treatment that considers the clinical, genetic, and environmental characteristics of patients. iPSCs have attracted considerable attention in the field of personalized medicine; however, the inherent limitations of iPSCs prevent their widespread use in clinical applications. That is, it would be important to develop notable engineering strategies to overcome the current limitations of iPSCs. Such engineering approaches could lead to significant advances in iPSC-based personalized therapy by offering innovative solutions to existing challenges, from iPSC preparation to clinical applications. In this review, we summarize how engineering strategies have been used to advance iPSC-based personalized medicine by categorizing the development process into three distinctive steps: 1) the production of therapeutic iPSCs; 2) engineering of therapeutic iPSCs; and 3) clinical applications of engineered iPSCs. Specifically, we focus on engineering strategies and their implications for each step in the development of iPSC-based personalized medicine.

An intrinsically interpretable neural network architecture for sequence-to-function learning

MOTIVATION

Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called totally interpretable sequence-to-function model (tiSFM). tiSFM improves upon the performance of standard multilayer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multilayer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs.

RESULTS

We analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context-specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM's model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition.

AVAILABILITY AND IMPLEMENTATION

The source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv, implemented in Python.

Cell type-specific and disease-associated eQTL in the human lung

Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis (PF). Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA-sequencing of lung tissue from 67 PF and 49 unaffected donors. Employing a pseudo-bulk approach, we mapped expression quantitative trait loci (eQTL) across 38 cell types, observing both shared and cell type-specific regulatory effects. Further, we identified disease-interaction eQTL and demonstrated that this class of associations is more likely to be cell-type specific and linked to cellular dysregulation in PF. Finally, we connected PF risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression, and implicates context-specific eQTL as key regulators of lung homeostasis and disease.

Evidence for the role of transcription factors in the co-transcriptional regulation of intron retention

BACKGROUND

Alternative splicing is a widespread regulatory phenomenon that enables a single gene to produce multiple transcripts. Among the different types of alternative splicing, intron retention is one of the least explored despite its high prevalence in both plants and animals. The recent discovery that the majority of splicing is co-transcriptional has led to the finding that chromatin state affects alternative splicing. Therefore, it is plausible that transcription factors can regulate splicing outcomes.

RESULTS

We provide evidence for the hypothesis that transcription factors are involved in the regulation of intron retention by studying regions of open chromatin in retained and excised introns. Using deep learning models designed to distinguish between regions of open chromatin in retained introns and non-retained introns, we identified motifs enriched in IR events with significant hits to known human transcription factors. Our model predicts that the majority of transcription factors that affect intron retention come from the zinc finger family. We demonstrate the validity of these predictions using ChIP-seq data for multiple zinc finger transcription factors and find strong over-representation for their peaks in intron retention events.

CONCLUSIONS

This work opens up opportunities for further studies that elucidate the mechanisms by which transcription factors affect intron retention and other forms of splicing.

AVAILABILITY

Source code available at https://github.com/fahadahaf/chromir.

Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease

The causal variants and genes underlying thousands of cardiac GWAS signals have yet to be identified. Here, we leverage spatiotemporal information on 966 RNA-seq cardiac samples and perform an expression quantitative trait locus (eQTL) analysis detecting eQTLs considering both eGenes and eIsoforms. We identify 2,578 eQTLs associated with a specific developmental stage-, tissue- and/or cell type. Colocalization between eQTL and GWAS signals of five cardiac traits identified variants with high posterior probabilities for being causal in 210 GWAS loci. Pulse pressure GWAS loci are enriched for colocalization with fetal- and smooth muscle- eQTLs; pulse rate with adult- and cardiac muscle- eQTLs; and atrial fibrillation with cardiac muscle- eQTLs. Fine mapping identifies 79 credible sets with five or fewer SNPs, of which 15 were associated with spatiotemporal eQTLs. Our study shows that many cardiac GWAS variants impact traits and disease in a developmental stage-, tissue- and/or cell type-specific fashion.

In vitro methods to ensure absence of residual undifferentiated human induced pluripotent stem cells intermingled in induced nephron progenitor cells

Cell therapies using human induced pluripotent stem cell (hiPSC)-derived nephron progenitor cells (NPCs) are expected to ameliorate acute kidney injury (AKI). However, using hiPSC-derived NPCs clinically is a challenge because hiPSCs themselves are tumorigenic. LIN28A, ESRG, CNMD and SFRP2 transcripts have been used as a marker of residual hiPSCs for a variety of cell types undergoing clinical trials. In this study, by reanalyzing public databases, we found a baseline expression of LIN28A, ESRG, CNMD and SFRP2 in hiPSC-derived NPCs and several other cell types, suggesting LIN28A, ESRG, CNMD and SFRP2 are not always reliable markers for iPSC detection. As an alternative, we discovered a lncRNA marker gene, MIR302CHG, among many known and unknown iPSC markers, as highly differentially expressed between hiPSCs and NPCs, by RNA sequencing and quantitative RT-PCR (qRT-PCR) analyses. Using MIR302CHG as an hiPSC marker, we constructed two assay methods, a combination of magnetic bead-based enrichment and qRT-PCR and digital droplet PCR alone, to detect a small number of residual hiPSCs in NPC populations. The use of these in vitro assays could contribute to patient safety in treatments using hiPSC-derived cells.

Lamin A/C-dependent chromatin architecture safeguards naïve pluripotency to prevent aberrant cardiovascular cell fate and function

Tight control of cell fate choices is crucial for normal development. Here we show that lamin A/C plays a key role in chromatin organization in embryonic stem cells (ESCs), which safeguards naïve pluripotency and ensures proper cell fate choices during cardiogenesis. We report changes in chromatin compaction and localization of cardiac genes in Lmna-/- ESCs resulting in precocious activation of a transcriptional program promoting cardiomyocyte versus endothelial cell fate. This is accompanied by premature cardiomyocyte differentiation, cell cycle withdrawal and abnormal contractility. Gata4 is activated by lamin A/C loss and Gata4 silencing or haploinsufficiency rescues the aberrant cardiovascular cell fate choices induced by lamin A/C deficiency. We uncover divergent functions of lamin A/C in naïve pluripotent stem cells and cardiomyocytes, which have distinct contributions to the transcriptional alterations of patients with LMNA-associated cardiomyopathy. We conclude that disruption of lamin A/C-dependent chromatin architecture in ESCs is a primary event in LMNA loss-of-function cardiomyopathy.

Monosomy X in isogenic human iPSC-derived trophoblast model impacts expression modules preserved in human placenta

Mammalian sex chromosomes encode homologous X/Y gene pairs that were retained on the Y chromosome in males and escape X chromosome inactivation (XCI) in females. Inferred to reflect X/Y pair dosage sensitivity, monosomy X is a leading cause of miscarriage in humans with near full penetrance. This phenotype is shared with many other mammals but not the mouse, which offers sophisticated genetic tools to generate sex chromosomal aneuploidy but also tolerates its developmental impact. To address this critical gap, we generated X-monosomic human induced pluripotent stem cells (hiPSCs) alongside otherwise isogenic euploid controls from male and female mosaic samples. Phased genomic variants in these hiPSC panels enable systematic investigation of X/Y dosage-sensitive features using in vitro models of human development. Here, we demonstrate the utility of these validated hiPSC lines to test how X/Y-linked gene dosage impacts a widely used model for human syncytiotrophoblast development. While these isogenic panels trigger a GATA2/3- and TFAP2A/C-driven trophoblast gene circuit irrespective of karyotype, differential expression implicates monosomy X in altered levels of placental genes and in secretion of placental growth factor (PlGF) and human chorionic gonadotropin (hCG). Remarkably, weighted gene coexpression network modules that significantly reflect these changes are also preserved in first-trimester chorionic villi and term placenta. Our results suggest monosomy X may skew trophoblast cell type composition and function, and that the combined haploinsufficiency of the pseudoautosomal region likely plays a key role in these changes.

Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels

There is increasing appreciation that, in addition to being shaped by an individual's genotype and environment, most complex traits are also determined by poorly understood interactions between these two factors. So-called "genotype × environment" (G×E) interactions remain difficult to map at the organismal level but can be uncovered using molecular phenotypes. To do so at large scale, we used TM3'seq to profile transcriptomes across 12 cellular environments in 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression levels across environments and revealed a context-dependent genetic architecture: The average heritability of gene expression levels increased in treatment relative to control conditions, and on average, each treatment revealed new expression quantitative trait loci (eQTLs) at 11% of genes. Across our experiments, 22% of all identified eQTLs were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses suggested that positive selection has shaped G×E loci involved in responding to immune challenges and hormones but not to man-made chemicals. We hypothesize that this reflects a reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposure's evolutionary history when studying and interpreting G×E interactions, and provides new insight into the evolutionary mechanisms that maintain G×E loci in human populations.

Functional genomics and the future of iPSCs in disease modeling

Induced pluripotent stem cells (iPSCs) are valuable in disease modeling because of their potential to expand and differentiate into virtually any cell type and recapitulate key aspects of human biology. Functional genomics are genome-wide studies that aim to discover genotype-phenotype relationships, thereby revealing the impact of human genetic diversity on normal and pathophysiology. In this review, we make the case that human iPSCs (hiPSCs) are a powerful tool for functional genomics, since they provide an in vitro platform for the study of population genetics. We describe cutting-edge tools and strategies now available to researchers, including multi-omics technologies, advances in hiPSC culture techniques, and innovations in drug development. Functional genomics approaches based on hiPSCs hold great promise for advancing drug discovery, disease etiology, and the impact of genetic variation on human biology.

Chromatin accessibility profiling by ATAC-seq

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) provides a simple and scalable way to detect the unique chromatin landscape associated with a cell type and how it may be altered by perturbation or disease. ATAC-seq requires a relatively small number of input cells and does not require a priori knowledge of the epigenetic marks or transcription factors governing the dynamics of the system. Here we describe an updated and optimized protocol for ATAC-seq, called Omni-ATAC, that is applicable across a broad range of cell and tissue types. The ATAC-seq workflow has five main steps: sample preparation, transposition, library preparation, sequencing and data analysis. This protocol details the steps to generate and sequence ATAC-seq libraries, with recommendations for sample preparation and downstream bioinformatic analysis. ATAC-seq libraries for roughly 12 samples can be generated in 10 h by someone familiar with basic molecular biology, and downstream sequencing analysis can be implemented using benchmarked pipelines by someone with basic bioinformatics skills and with access to a high-performance computing environment.

Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin

BACKGROUND

Evolutionary conservation is an invaluable tool for inferring functional significance in the genome, including regions that are crucial across many species and those that have undergone convergent evolution. Computational methods to test for sequence conservation are dominated by algorithms that examine the ability of one or more nucleotides to align across large evolutionary distances. While these nucleotide alignment-based approaches have proven powerful for protein-coding genes and some non-coding elements, they fail to capture conservation of many enhancers, distal regulatory elements that control spatial and temporal patterns of gene expression. The function of enhancers is governed by a complex, often tissue- and cell type-specific code that links combinations of transcription factor binding sites and other regulation-related sequence patterns to regulatory activity. Thus, function of orthologous enhancer regions can be conserved across large evolutionary distances, even when nucleotide turnover is high.

RESULTS

We present a new machine learning-based approach for evaluating enhancer conservation that leverages the combinatorial sequence code of enhancer activity rather than relying on the alignment of individual nucleotides. We first train a convolutional neural network model that can predict tissue-specific open chromatin, a proxy for enhancer activity, across mammals. Next, we apply that model to distinguish instances where the genome sequence would predict conserved function versus a loss of regulatory activity in that tissue. We present criteria for systematically evaluating model performance for this task and use them to demonstrate that our models accurately predict tissue-specific conservation and divergence in open chromatin between primate and rodent species, vastly out-performing leading nucleotide alignment-based approaches. We then apply our models to predict open chromatin at orthologs of brain and liver open chromatin regions across hundreds of mammals and find that brain enhancers associated with neuron activity have a stronger tendency than the general population to have predicted lineage-specific open chromatin.

CONCLUSION

The framework presented here provides a mechanism to annotate tissue-specific regulatory function across hundreds of genomes and to study enhancer evolution using predicted regulatory differences rather than nucleotide-level conservation measurements.

Multi-omic rejuvenation of human cells by maturation phase transient reprogramming

Ageing is the gradual decline in organismal fitness that occurs over time leading to tissue dysfunction and disease. At the cellular level, ageing is associated with reduced function, altered gene expression and a perturbed epigenome. Recent work has demonstrated that the epigenome is already rejuvenated by the maturation phase of somatic cell reprogramming, which suggests full reprogramming is not required to reverse ageing of somatic cells. Here we have developed the first "maturation phase transient reprogramming" (MPTR) method, where reprogramming factors are selectively expressed until this rejuvenation point then withdrawn. Applying MPTR to dermal fibroblasts from middle-aged donors, we found that cells temporarily lose and then reacquire their fibroblast identity, possibly as a result of epigenetic memory at enhancers and/or persistent expression of some fibroblast genes. Excitingly, our method substantially rejuvenated multiple cellular attributes including the transcriptome, which was rejuvenated by around 30 years as measured by a novel transcriptome clock. The epigenome was rejuvenated to a similar extent, including H3K9me3 levels and the DNA methylation ageing clock. The magnitude of rejuvenation instigated by MPTR appears substantially greater than that achieved in previous transient reprogramming protocols. In addition, MPTR fibroblasts produced youthful levels of collagen proteins, and showed partial functional rejuvenation of their migration speed. Finally, our work suggests that optimal time windows exist for rejuvenating the transcriptome and the epigenome. Overall, we demonstrate that it is possible to separate rejuvenation from complete pluripotency reprogramming, which should facilitate the discovery of novel anti-ageing genes and therapies.

A guide for the diagnosis of rare and undiagnosed disease: beyond the exome

Rare diseases affect 30 million people in the USA and more than 300–400 million worldwide, often causing chronic illness, disability, and premature death. Traditional diagnostic techniques rely heavily on heuristic approaches, coupling clinical experience from prior rare disease presentations with the medical literature. A large number of rare disease patients remain undiagnosed for years and many even die without an accurate diagnosis. In recent years, gene panels, microarrays, and exome sequencing have helped to identify the molecular cause of such rare and undiagnosed diseases. These technologies have allowed diagnoses for a sizable proportion (25–35%) of undiagnosed patients, often with actionable findings. However, a large proportion of these patients remain undiagnosed. In this review, we focus on technologies that can be adopted if exome sequencing is unrevealing. We discuss the benefits of sequencing the whole genome and the additional benefit that may be offered by long-read technology, pan-genome reference, transcriptomics, metabolomics, proteomics, and methyl profiling. We highlight computational methods to help identify regionally distant patients with similar phenotypes or similar genetic mutations. Finally, we describe approaches to automate and accelerate genomic analysis. The strategies discussed here are intended to serve as a guide for clinicians and researchers in the next steps when encountering patients with non-diagnostic exomes.

Predicting genotype-specific gene regulatory networks

Understanding how each person's unique genotype influences their individual patterns of gene regulation has the potential to improve our understanding of human health and development, and to refine genotype-specific disease risk assessments and treatments. However, the effects of genetic variants are not typically considered when constructing gene regulatory networks, despite the fact that many disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding. We developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, expression quantitative trait locus (eQTL) data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses a technique known as message passing to integrate this prior network with gene expression and TF protein–protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer gene regulatory networks for two blood-derived cell lines and identified genotype-associated, cell line–specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET networks for three cell types from each of 119 individuals and identified cell type–specific regulatory differences associated with diseases related to those cell types. EGRET is, to our knowledge, the first method that infers networks reflective of individual genetic variation in a way that provides insight into the genetic regulatory associations driving complex phenotypes.

Human embryoid bodies as a novel system for genomic studies of functionally diverse cell types

Practically all studies of gene expression in humans to date have been performed in a relatively small number of adult tissues. Gene regulation is highly dynamic and context-dependent. In order to better understand the connection between gene regulation and complex phenotypes, including disease, we need to be able to study gene expression in more cell types, tissues, and states that are relevant to human phenotypes. In particular, we need to characterize gene expression in early development cell types, as mutations that affect developmental processes may be of particular relevance to complex traits. To address this challenge, we propose to use embryoid bodies (EBs), which are organoids that contain a multitude of cell types in dynamic states. EBs provide a system in which one can study dynamic regulatory processes at an unprecedentedly high resolution. To explore the utility of EBs, we systematically explored cellular and gene expression heterogeneity in EBs from multiple individuals. We characterized the various cell types that arise from EBs, the extent to which they recapitulate gene expression in vivo, and the relative contribution of technical and biological factors to variability in gene expression, cell composition, and differentiation efficiency. Our results highlight the utility of EBs as a new model system for mapping dynamic inter-individual regulatory differences in a large variety of cell types.

One major goal of human genetics is to understand how changes in the way genes are regulated affect human traits, including disease susceptibility. To date, most studies of gene regulation have been performed in adult tissues, such as liver or kidney tissue, that were collected at a single time point. Yet, gene regulation is highly dynamic and context-dependent, meaning that it is important to gather data from a greater variety of cell types at different stages of their development. Additionally, observing which genes switch on and off in response to external treatments can shed light on how genetic variation can drive errors in gene regulation and cause diseases.

Stem cells can produce more cells like themselves or differentiate – acquire the characteristics – of many cell types. These cells have been used in the laboratory to research gene regulation. Unfortunately, these studies often fail to capture the complex spatial and temporal dynamics of stem cell differentiation; in particular, these studies are unable to observe gene regulation in the transient cell types that appear early in embryonic development. To overcome these limitations, scientists developed systems such as embryoid bodies: three-dimensional aggregates of stem cells that, when grown under certain conditions, spontaneously develop into a variety of cell types.

Rhodes, Barr et al. wanted to assess the utility of embryoid bodies as a model to study how genes are dynamically regulated in different cell types, by different individuals who have distinct genetic makeups. To do this, they grew embryoid bodies made from human stem cells from different individuals to examine which genes switched on and off as the stem cells that formed the embryoid bodies differentiated into different types of cells. The results showed that it was possible to grow embryoid bodies derived from genetically distinct individuals that consistently produce diverse cell types, similar to those found during human fetal development.

Rhodes, Barr et al.’s findings suggest that embryoid bodies are a useful model to study gene regulation across individuals with different genetic backgrounds. This could accelerate research into how genetics are associated with disease by capturing gene regulatory dynamics at an unprecedentedly high spatial and temporal resolution. Additionally, embryoid bodies could be used to explore how exposure to different environmental factors during early development affect disease-related outcomes in adulthood in different individuals.

Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation

Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.

Chromatin accessibility and gene expression during adipocyte differentiation identify context-dependent effects at cardiometabolic GWAS loci

Chromatin accessibility and gene expression in relevant cell contexts can guide identification of regulatory elements and mechanisms at genome-wide association study (GWAS) loci. To identify regulatory elements that display differential activity across adipocyte differentiation, we performed ATAC-seq and RNA-seq in a human cell model of preadipocytes and adipocytes at days 4 and 14 of differentiation. For comparison, we created a consensus map of ATAC-seq peaks in 11 human subcutaneous adipose tissue samples. We identified 58,387 context-dependent chromatin accessibility peaks and 3,090 context-dependent genes between all timepoint comparisons (log2 fold change>1, FDR<5%) with 15,919 adipocyte- and 18,244 preadipocyte-dependent peaks. Adipocyte-dependent peaks showed increased overlap (60.1%) with Roadmap Epigenomics adipocyte nuclei enhancers compared to preadipocyte-dependent peaks (11.5%). We linked context-dependent peaks to genes based on adipocyte promoter capture Hi-C data, overlap with adipose eQTL variants, and context-dependent gene expression. Of 16,167 context-dependent peaks linked to a gene, 5,145 were linked by two or more strategies to 1,670 genes. Among GWAS loci for cardiometabolic traits, adipocyte-dependent peaks, but not preadipocyte-dependent peaks, showed significant enrichment (LD score regression P<0.005) for waist-to-hip ratio and modest enrichment (P < 0.05) for HDL-cholesterol. We identified 659 peaks linked to 503 genes by two or more approaches and overlapping a GWAS signal, suggesting a regulatory mechanism at these loci. To identify variants that may alter chromatin accessibility between timepoints, we identified 582 variants in 454 context-dependent peaks that demonstrated allelic imbalance in accessibility (FDR<5%), of which 55 peaks also overlapped GWAS variants. At one GWAS locus for palmitoleic acid, rs603424 was located in an adipocyte-dependent peak linked to SCD and exhibited allelic differences in transcriptional activity in adipocytes (P = 0.003) but not preadipocytes (P = 0.09). These results demonstrate that context-dependent peaks and genes can guide discovery of regulatory variants at GWAS loci and aid identification of regulatory mechanisms.

Cardiovascular and metabolic diseases are widespread, and an increased understanding of genetic mechanisms behind these diseases could improve treatment. Chromatin accessibility and gene expression in relevant cell contexts can guide identification of regulatory elements and genetic mechanisms for disease traits. A relevant context for cardiovascular and metabolic disease traits is adipocyte differentiation. To identify regulatory elements and genes that display differences in activity during adipocyte differentiation, we profiled chromatin accessibility and gene expression in a human cell model of preadipocytes and adipocytes. We identified chromatin regions that change accessibility during differentiation and predicted genes they may affect. We also linked these chromatin regions to genetic variants associated with risk of disease. At one genomic region linked to fatty acids, a chromatin region more accessible in adipocytes linked to a fatty acid synthesis gene and exhibited allelic differences in transcriptional activity in adipocytes but not preadipocytes. These results demonstrate that chromatin regions and genes that change during cell context can guide discovery of regulatory variants and aid identification of disease mechanisms.

Comparative epigenetic analysis of tumour initiating cells and syngeneic EPSC-derived neural stem cells in glioblastoma

Epigenetic mechanisms which play an essential role in normal developmental processes, such as self-renewal and fate specification of neural stem cells (NSC) are also responsible for some of the changes in the glioblastoma (GBM) genome. Here we develop a strategy to compare the epigenetic and transcriptional make-up of primary GBM cells (GIC) with patient-matched expanded potential stem cell (EPSC)-derived NSC (iNSC). Using a comparative analysis of the transcriptome of syngeneic GIC/iNSC pairs, we identify a glycosaminoglycan (GAG)-mediated mechanism of recruitment of regulatory T cells (Tregs) in GBM. Integrated analysis of the transcriptome and DNA methylome of GBM cells identifies druggable target genes and patient-specific prediction of drug response in primary GIC cultures, which is validated in 3D and in vivo models. Taken together, we provide a proof of principle that this experimental pipeline has the potential to identify patient-specific disease mechanisms and druggable targets in GBM.

A functional genomics pipeline identifies pleiotropy and cross-tissue effects within obesity-associated GWAS loci

Genome-wide association studies (GWAS) have identified many disease-associated variants, yet mechanisms underlying these associations remain unclear. To understand obesity-associated variants, we generate gene regulatory annotations in adipocytes and hypothalamic neurons across cellular differentiation stages. We then test variants in 97 obesity-associated loci using a massively parallel reporter assay and identify putatively causal variants that display cell type specific or cross-tissue enhancer-modulating properties. Integrating these variants with gene regulatory information suggests genes that underlie obesity GWAS associations. We also investigate a complex genomic interval on 16p11.2 where two independent loci exhibit megabase-range, cross-locus chromatin interactions. We demonstrate that variants within these two loci regulate a shared gene set. Together, our data support a model where GWAS loci contain variants that alter enhancer activity across tissues, potentially with temporally restricted effects, to impact the expression of multiple genes. This complex model has broad implications for ongoing efforts to understand GWAS.

A self-attention model for inferring cooperativity between regulatory features

Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information from the trained models. While there has been much recent work on developing feature attribution methods that discover the most important features for a given sequence, inferring cooperativity between regulatory elements, which is the hallmark of phenomena such as gene expression, remains an open problem. We present SATORI, a Self-ATtentiOn based model to detect Regulatory element Interactions. Our approach combines convolutional layers with a self-attention mechanism that helps us capture a global view of the landscape of interactions between regulatory elements in a sequence. A comprehensive evaluation demonstrates the ability of SATORI to identify numerous statistically significant TF-TF interactions, many of which have been previously reported. Our method is able to detect higher numbers of experimentally verified TF-TF interactions than existing methods, and has the advantage of not requiring a computationally expensive post-processing step. Finally, SATORI can be used for detection of any type of feature interaction in models that use a similar attention mechanism, and is not limited to the detection of TF-TF interactions.

Contiguous erosion of the inactive X in human pluripotency concludes with global DNA hypomethylation

Female human pluripotent stem cells (hPSCs) routinely undergo inactive X (Xi) erosion. This progressive loss of key repressive features follows the loss of XIST expression, the long non-coding RNA driving X inactivation, and causes reactivation of silenced genes across the eroding X (Xe). To date, the sporadic and progressive nature of erosion has obscured its scale, dynamics, and key transition events. To address this problem, we perform an integrated analysis of DNA methylation (DNAme), chromatin accessibility, and gene expression across hundreds of hPSC samples. Differential DNAme orders female hPSCs across a trajectory from initiation to terminal Xi erosion. Our results identify a cis-regulatory element crucial for XIST expression, trace contiguously growing reactivated domains to a few euchromatic origins, and indicate that the late-stage Xe impairs DNAme genome-wide. Surprisingly, from this altered regulatory landscape emerge select features of naive pluripotency, suggesting that its link to X dosage may be partially conserved in human embryonic development.

Reactivation of the silenced X in human female iPSC/ESCs compromises their utility. Bansal et al. perform an integrated genomics analysis to reveal a prevalent X erosion trajectory that they validate in long-term culture. Starting with XIST loss, this trajectory indicates that reactivation may spread contiguously from escapees to silenced genes.

Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions

Genetic effects on gene expression and splicing can be modulated by cellular and environmental factors; yet interactions between genotypes, cell type, and treatment have not been comprehensively studied together. We used an induced pluripotent stem cell system to study multiple cell types derived from the same individuals and exposed them to a large panel of treatments. Cellular responses involved different genes and pathways for gene expression and splicing and were highly variable across contexts. For thousands of genes, we identified variable allelic expression across contexts and characterized different types of gene-environment interactions, many of which are associated with complex traits. Promoter functional and evolutionary features distinguished genes with elevated allelic imbalance mean and variance. On average, half of the genes with dynamic regulatory interactions were missed by large eQTL mapping studies, indicating the importance of exploring multiple treatments to reveal previously unrecognized regulatory loci that may be important for disease.

Harnessing pluripotent stem cells as models to decipher human evolution

The study of human evolution, long constrained by a lack of experimental model systems, has been transformed by the emergence of the induced pluripotent stem cell (iPSC) field. iPSCs can be readily established from noninvasive tissue sources, from both humans and other primates; they can be maintained in the laboratory indefinitely, and they can be differentiated into other tissue types. These qualities mean that iPSCs are rapidly becoming established as viable and powerful model systems with which it is possible to address questions in human evolution that were until now logistically and ethically intractable, especially in the quest to understand humans' place among the great apes, and the genetic basis of human uniqueness. In this review, we discuss the key lessons and takeaways of this nascent field; from the types of research, iPSCs make possible to lingering challenges and likely future directions. We provide a comprehensive overview of how the seemingly unlikely combination of iPSCs and explicit evolutionary frameworks is transforming what is possible in our understanding of humanity's past and present.

Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%-20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics

Induced pluripotent stem cells (iPSCs) are an established cellular system to study the impact of genetic variants in derived cell types and developmental contexts. However, in their pluripotent state, the disease impact of genetic variants is less known. Here, we integrate data from 1,367 human iPSC lines to comprehensively map common and rare regulatory variants in human pluripotent cells. Using this population-scale resource, we report hundreds of novel colocalization events for human traits specific to iPSCs, and find increased power to identify rare regulatory variants compared with somatic tissues. Finally, we demonstrate how iPSCs enable the identification of causal genes for rare diseases.

Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation

Studying the function of common genetic variants in primary human tissues and during development is challenging. To address this, we use an efficient multiplexing strategy to differentiate 215 human induced pluripotent stem cell (iPSC) lines toward a midbrain neural fate, including dopaminergic neurons, and use single-cell RNA sequencing (scRNA-seq) to profile over 1 million cells across three differentiation time points. The proportion of neurons produced by each cell line is highly reproducible and is predictable by robust molecular markers expressed in pluripotent cells. Expression quantitative trait loci (eQTL) were characterized at different stages of neuronal development and in response to rotenone-induced oxidative stress. Of these, 1,284 eQTL colocalize with known neurological trait risk loci, and 46% are not found in the Genotype-Tissue Expression (GTEx) catalog. Our study illustrates how coupling scRNA-seq with long-term iPSC differentiation enables mechanistic studies of human trait-associated genetic variants in otherwise inaccessible cell states.

Dynamic effects of genetic variation on gene expression revealed following hypoxic stress in cardiomyocytes

One life-threatening outcome of cardiovascular disease is myocardial infarction, where cardiomyocytes are deprived of oxygen. To study inter-individual differences in response to hypoxia, we established an in vitro model of induced pluripotent stem cell-derived cardiomyocytes from 15 individuals. We measured gene expression levels, chromatin accessibility, and methylation levels in four culturing conditions that correspond to normoxia, hypoxia, and short- or long-term re-oxygenation. We characterized thousands of gene regulatory changes as the cells transition between conditions. Using available genotypes, we identified 1,573 genes with a cis expression quantitative locus (eQTL) in at least one condition, as well as 367 dynamic eQTLs, which are classified as eQTLs in at least one, but not in all conditions. A subset of genes with dynamic eQTLs is associated with complex traits and disease. Our data demonstrate how dynamic genetic effects on gene expression, which are likely relevant for disease, can be uncovered under stress.

Genetics of Anthracycline Cardiomyopathy in Cancer Survivors: JACC: CardioOncology State-of-the-Art Review

Anthracyclines are an integral part of chemotherapy regimens used to treat a variety of childhood-onset and adult-onset cancers. However, the development of cardiac dysfunction and heart failure often compromises the clinical utility of anthracyclines. The risk of cardiac dysfunction increases with anthracycline dose. This anthracycline-cardiac dysfunction association is modified by several demographic and clinical factors, such as age at anthracycline exposure (<4 years and ≥65 years); female sex; chest radiation; presence of cardiovascular risk factors (diabetes, hypertension); and concurrent use of cyclophosphamide, paclitaxel, and trastuzumab. However, the clinical variables alone yield modest predictive power in detecting cardiac dysfunction. Recently, attention has focused on the molecular basis of anthracycline-related cardiac dysfunction, providing an initial understanding of the mechanism of anthracycline-related cardiomyopathy. This review describes the current state of knowledge with respect to the pathogenesis of anthracycline-related cardiomyopathy and identifies the critical next steps to mitigate this problem.

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Anthracycline chemotherapy results in an increased risk of cardiac dysfunction.

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Most recent studies have suggested that there is a genetic basis for anthracycline-related cardiac dysfunction.

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Integration of genetics with the clinical characteristics may be used to enhance the ability to predict the risk for anthracycline-related cardiomyopathy.

Modelling the developmental spliceosomal craniofacial disorder Burn-McKeown syndrome using induced pluripotent stem cells

The craniofacial developmental disorder Burn-McKeown Syndrome (BMKS) is caused by biallelic variants in the pre-messenger RNA splicing factor gene TXNL4A/DIB1. The majority of affected individuals with BMKS have a 34 base pair deletion in the promoter region of one allele of TXNL4A combined with a loss-of-function variant on the other allele, resulting in reduced TXNL4A expression. However, it is unclear how reduced expression of this ubiquitously expressed spliceosome protein results in craniofacial defects during development. Here we reprogrammed peripheral mononuclear blood cells from a BMKS patient and her unaffected mother into induced pluripotent stem cells (iPSCs) and differentiated the iPSCs into induced neural crest cells (iNCCs), the key cell type required for correct craniofacial development. BMKS patient-derived iPSCs proliferated more slowly than both mother- and unrelated control-derived iPSCs, and RNA-Seq analysis revealed significant differences in gene expression and alternative splicing. Patient iPSCs displayed defective differentiation into iNCCs compared to maternal and unrelated control iPSCs, in particular a delay in undergoing an epithelial-to-mesenchymal transition (EMT). RNA-Seq analysis of differentiated iNCCs revealed widespread gene expression changes and mis-splicing in genes relevant to craniofacial and embryonic development that highlight a dampened response to WNT signalling, the key pathway activated during iNCC differentiation. Furthermore, we identified the mis-splicing of TCF7L2 exon 4, a key gene in the WNT pathway, as a potential cause of the downregulated WNT response in patient cells. Additionally, mis-spliced genes shared common sequence properties such as length, branch point to 3’ splice site (BPS-3’SS) distance and splice site strengths, suggesting that splicing of particular subsets of genes is particularly sensitive to changes in TXNL4A expression. Together, these data provide the first insight into how reduced TXNL4A expression in BMKS patients might compromise splicing and NCC function, resulting in defective craniofacial development in the embryo.

Human Stem Cell Resources Are an Inroad to Neandertal DNA Functions

Induced pluripotent stem cells (iPSCs) from diverse humans offer the potential to study human functional variation in controlled culture environments. A portion of this variation originates from an ancient admixture between modern humans and Neandertals, which introduced alleles that left a phenotypic legacy on individual humans today. Here, we show that a large iPSC repository harbors extensive Neandertal DNA, including alleles that contribute to human phenotypes and diseases, encode hundreds of amino acid changes, and alter gene expression in specific tissues. We provide a database of the inferred introgressed Neandertal alleles for each individual iPSC line, together with the annotation of the predicted functional variants. We also show that transcriptomic data from organoids generated from iPSCs can be used to track Neandertal-derived RNA over developmental processes. Human iPSC resources provide an opportunity to experimentally explore Neandertal DNA function and its contribution to present-day phenotypes, and potentially study Neandertal traits.

Properties of structural variants and short tandem repeats associated with gene expression and complex traits

Structural variants (SVs) and short tandem repeats (STRs) comprise a broad group of diverse DNA variants which vastly differ in their sizes and distributions across the genome. Here, we identify genomic features of SV classes and STRs that are associated with gene expression and complex traits, including their locations relative to eGenes, likelihood of being associated with multiple eGenes, associated eGene types (e.g., coding, noncoding, level of evolutionary constraint), effect sizes, linkage disequilibrium with tagging single nucleotide variants used in GWAS, and likelihood of being associated with GWAS traits. We identify a set of high-impact SVs/STRs associated with the expression of three or more eGenes via chromatin loops and show that they are highly enriched for being associated with GWAS traits. Our study provides insights into the genomic properties of structural variant classes and short tandem repeats that are associated with gene expression and human traits.

Global Characterization of X Chromosome Inactivation in Human Pluripotent Stem Cells

Dosage compensation of sex-chromosome gene expression between male and female mammals is achieved via X chromosome inactivation (XCI) by employing epigenetic modifications to randomly silence one X chromosome during early embryogenesis. Human pluripotent stem cells (hPSCs) were reported to present various states of XCI that differ according to the expression of the long non-coding RNA XIST and the degree of X chromosome silencing. To obtain a comprehensive perspective on XCI in female hPSCs, we performed a large-scale analysis characterizing different XCI parameters in more than 700 RNA high-throughput sequencing samples. Our findings suggest differences in XCI status between most published samples of embryonic stem cells (ESCs) and induced PSCs (iPSCs). While the majority of iPSC lines maintain an inactive X chromosome, ESC lines tend to silence the expression of XIST and upregulate distal chromosomal regions. Our study highlights significant epigenetic heterogeneity within hPSCs, which may bear implications for their use in research and regenerative therapy.

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

Cellular heterogeneity in gene expression is driven by cellular processes, such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). By using these data, we developed a novel approach to characterize cell cycle progression. Although standard methods assign cells to discrete cell cycle stages, our method goes beyond this and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell's position on the cell cycle continuum to within 14% of the entire cycle and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

Effect for Human Genomic Variation During the BMP4-Induced Conversion From Pluripotent Stem Cells to Trophoblast

The role of genomic variation in differentiation is currently not well understood. Here, the genomic variations were determined with the whole-genome sequencing for three pairs of pluripotent stem cell lines and their corresponding BMP4-induced trophoblast cell lines. We identified ∼3,500 single nucleotide variations and ∼4,500 indels by comparing the genome sequenced data between the stem cell lines and the matched BMP4-induced trophoblast cell lines and annotated them by integrating the epigenomic and transcriptomic datasets. Relatively, introns enrich more variations. We found ∼45% (42 genes) of the differentially expressed genes in trophoblasts that associate genomic variations. Six variations, located at transcription factor binding sites where H3K4me3 and H3K27ac are enriched in both H1 and H1_BMP4, were identified. The epigenetic status around the genomic variations in H1 was similar to that in H1_BMP4. This means that the variation-associated gene’s expression change can not be attributed to epigenetic alteration. The genes associated with the six variations were upregulated in differentiation. We inferred that during the differentiation, an increased in the expression level of the MEF2C gene is due to a genomic variation in chromosomes 5: 88179358 A > G, which is at a binding site of TFs KLF16, NR2C2, and ZNF740 to MEF2C. Allele G shows a higher affinity to the TFs in the induced cells. The increased expression of MEF2C leads to an increased expression of TF MEF2C’s target genes, subsequently affecting the differentiation. Although genomic variation should not be a dominant factor in differentiation, we believe that genomic variation could indeed play a role in the differentiation from stem cells into trophoblast.

Contribution of unfixed transposable element insertions to human regulatory variation

Thousands of unfixed transposable element (TE) insertions segregate in the human population, but little is known about their impact on genome function. Recently, a few studies associated unfixed TE insertions to mRNA levels of adjacent genes, but the biological significance of these associations, their replicability across cell types and the mechanisms by which they may regulate genes remain largely unknown. Here, we performed a TE-expression QTL analysis of 444 lymphoblastoid cell lines (LCL) and 289 induced pluripotent stem cells using a newly developed set of genotypes for 2743 polymorphic TE insertions. We identified 211 and 176 TE-eQTL acting in cis in each respective cell type. Approximately 18% were shared across cell types with strongly correlated effects. Furthermore, analysis of chromatin accessibility QTL in a subset of the LCL suggests that unfixed TEs often modulate the activity of enhancers and other distal regulatory DNA elements, which tend to lose accessibility when a TE inserts within them. We also document a case of an unfixed TE likely influencing gene expression at the post-transcriptional level. Our study points to broad and diverse cis-regulatory effects of unfixed TEs in the human population and underscores their plausible contribution to phenotypic variation. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.