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.

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.

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.

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.

Divergence in alternative polyadenylation contributes to gene regulatory differences between humans and chimpanzees.

While comparative functional genomic studies have shown that inter-species differences in gene expression can be explained by corresponding inter-species differences in genetic and epigenetic regulatory mechanisms, co-transcriptional mechanisms, such as alternative polyadenylation (APA), have received little attention. We characterized APA in lymphoblastoid cell lines from six humans and six chimpanzees by identifying and estimating the usage for 44,432 polyadenylation sites (PAS) in 9518 genes. Although APA is largely conserved, 1705 genes showed significantly different PAS usage (FDR 0.05) between species. Genes with divergent APA also tend to be differentially expressed, are enriched among genes showing differences in protein translation, and can explain a subset of observed inter-species protein expression differences that do not differ at the transcript level. Finally, we found that genes with a dominant PAS, which is used more often than other PAS, are particularly enriched for differentially expressed genes.

Fully interpretable deep learning model of transcriptional control.

MOTIVATION: The universal expressibility assumption of Deep Neural Networks (DNNs) is the key motivation behind recent worksin the systems biology community to employDNNs to solve important problems in functional genomics and moleculargenetics. Typically, such investigations have taken a 'black box' approach in which the internal structure of themodel used is set purely by machine learning considerations with little consideration of representing the internalstructure of the biological system by the mathematical structure of the DNN. DNNs have not yet been applied to thedetailed modeling of transcriptional control in which mRNA production is controlled by the binding of specific transcriptionfactors to DNA, in part because such models are in part formulated in terms of specific chemical equationsthat appear different in form from those used in neural networks. RESULTS: In this paper, we give an example of a DNN whichcan model the detailed control of transcription in a precise and predictive manner. Its internal structure is fully interpretableand is faithful to underlying chemistry of transcription factor binding to DNA. We derive our DNN from asystems biology model that was not previously recognized as having a DNN structure. Although we apply our DNNto data from the early embryo of the fruit fly Drosophila, this system serves as a test bed for analysis of much larger datasets obtained by systems biology studies on a genomic scale. . AVAILABILITY AND IMPLEMENTATION: The implementation and data for the models used in this paper are in a zip file in the supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Discovery of N-(Indazol-3-yl)piperidine-4-carboxylic Acids as RORγt Allosteric Inhibitors for Autoimmune Diseases.

The clinical success of anti-IL-17 monoclonal antibodies (i.e., Cosentyx and Taltz) has validated Th17 pathway modulation for the treatment of autoimmune diseases. The nuclear hormone receptor RORγt is a master regulator of Th17 cells and affects the production of a host of cytokines, including IL-17A, IL-17F, IL-22, IL-26, and GM-CSF. Substantial interest has been spurred across both academia and industry to seek small molecules suitable for RORγt inhibition. A variety of RORγt inhibitors have been reported in the past few years, the majority of which are orthosteric binders. Here we disclose the discovery and optimization of a class of inhibitors, which bind differently to an allosteric binding pocket. Starting from a weakly active hit 1, a tool compound 14 was quickly identified that demonstrated superior potency, selectivity, and off-target profile. Further optimization focused on improving metabolic stability. Replacing the benzoic acid moiety with piperidinyl carboxylate, modifying the 4-aza-indazole core in 14 to 4-F-indazole, and incorporating a key hydroxyl group led to the discovery of 25, which possesses exquisite potency and selectivity, as well as an improved pharmacokinetic profile suitable for oral dosing.

Structure-Based Design and Identification of FT-2102 (Olutasidenib), a Potent Mutant-Selective IDH1 Inhibitor.

Inhibition of mutant IDH1 is being evaluated clinically as a treatment option for oncology. Here we describe the structure-based design and optimization of quinoline lead compounds to identify FT-2102, a potent, orally bioavailable, brain penetrant, and selective mIDH1 inhibitor. FT-2102 has excellent ADME/PK properties and reduces 2-hydroxyglutarate levels in an mIDH1 xenograft tumor model. This compound has been selected as a candidate for clinical development in hematologic malignancies, solid tumors, and gliomas with mIDH1.

An in silico analysis of robust but fragile gene regulation links enhancer length to robustness.

Organisms must ensure that expression of genes is directed to the appropriate tissues at the correct times, while simultaneously ensuring that these gene regulatory systems are robust to perturbation. This idea is captured by a mathematical concept called r-robustness, which says that a system is robust to a perturbation in up to r - 1 randomly chosen parameters. r-robustness implies that the biological system has a small number of sensitive parameters and that this number can be used as a robustness measure. In this work we use this idea to investigate the robustness of gene regulation using a sequence level model of the Drosophila melanogaster gene even-skipped. We consider robustness with respect to mutations of the enhancer sequence and with respect to changes of the transcription factor concentrations. We find that gene regulation is r-robust with respect to mutations in the enhancer sequence and identify a number of sensitive nucleotides. In both natural and in silico predicted enhancers, the number of nucleotides that are sensitive to mutation correlates negatively with the length of the sequence, meaning that longer sequences are more robust. The exact degree of robustness obtained is dependent not only on DNA sequence, but also on the local concentration of regulatory factors. We find that gene regulation can be remarkably sensitive to changes in transcription factor concentrations at the boundaries of expression features, while it is robust to perturbation elsewhere.

Discovery and Optimization of Quinolinone Derivatives as Potent, Selective, and Orally Bioavailable Mutant Isocitrate Dehydrogenase 1 (mIDH1) Inhibitors.

Mutations at the arginine residue (R132) in isocitrate dehydrogenase 1 (IDH1) are frequently identified in various human cancers. Inhibition of mutant IDH1 (mIDH1) with small molecules has been clinically validated as a promising therapeutic treatment for acute myeloid leukemia and multiple solid tumors. Herein, we report the discovery and optimization of a series of quinolinones to provide potent and orally bioavailable mIDH1 inhibitors with selectivity over wild-type IDH1. The X-ray structure of an early lead 24 in complex with mIDH1-R132H shows that the inhibitor unexpectedly binds to an allosteric site. Efforts to improve the in vitro and in vivo absorption, distribution, metabolism, and excretion (ADME) properties of 24 yielded a preclinical candidate 63. The detailed preclinical ADME and pharmacology studies of 63 support further development of quinolinone-based mIDH1 inhibitors as therapeutic agents in human trials.

Aristaless Controls Butterfly Wing Color Variation Used in Mimicry and Mate Choice.

Neotropical Heliconius butterflies display a diversity of warningly colored wing patterns, which serve roles in both Müllerian mimicry and mate choice behavior. Wing pattern diversity in Heliconius is controlled by a small number of unlinked, Mendelian "switch" loci [1]. One of these, termed the K locus, switches between yellow and white color patterns, important mimicry signals as well as mating cues [2-4]. Furthermore, mate preference behavior is tightly linked to this locus [4]. K controls the distribution of white versus yellow scales on the wing, with a dominant white allele and a recessive yellow allele. Here, we combine fine-scale genetic mapping, genome-wide association studies, gene expression analyses, population and comparative genomics, and genome editing with CRISPR/Cas9 to characterize the molecular basis of the K locus in Heliconius and to infer its evolutionary history. We show that white versus yellow color variation in Heliconius cydno is due to alternate haplotypes at a putative cis-regulatory element (CRE) downstream of a tandem duplication of the homeodomain transcription factor aristaless. Aristaless1 (al1) and aristaless2 (al2) are differentially regulated between white and yellow wings throughout development with elevated expression of al1 in developing white wings, suggesting a role in repressing pigmentation. Consistent with this, knockout of al1 causes white wings to become yellow. The evolution of wing color in this group has been marked by retention of the ancestral yellow color in many lineages, a single origin of white coloration in H. cydno, and subsequent introgression of white color from H. cydno into H. melpomene.

Correction: A sequence level model of an intact locus predicts the location and function of nonadditive enhancers.

[This corrects the article DOI: 10.1371/journal.pone.0180861.].

Synthetic enhancer design by in silico compensatory evolution reveals flexibility and constraint in cis-regulation.

BACKGROUND: Models that incorporate specific chemical mechanisms have been successful in describing the activity of Drosophila developmental enhancers as a function of underlying transcription factor binding motifs. Despite this, the minimum set of mechanisms required to reconstruct an enhancer from its constituent parts is not known. Synthetic biology offers the potential to test the sufficiency of known mechanisms to describe the activity of enhancers, as well as to uncover constraints on the number, order, and spacing of motifs. RESULTS: Using a functional model and in silico compensatory evolution, we generated putative synthetic even-skipped stripe 2 enhancers with varying degrees of similarity to the natural enhancer. These elements represent the evolutionary trajectories of the natural stripe 2 enhancer towards two synthetic enhancers designed ab initio. In the first trajectory, spatially regulated expression was maintained, even after more than a third of binding sites were lost. In the second, sequences with high similarity to the natural element did not drive expression, but a highly diverged sequence about half the length of the minimal stripe 2 enhancer drove ten times greater expression. Additionally, homotypic clusters of Zelda or Stat92E motifs, but not Bicoid, drove expression in developing embryos. CONCLUSIONS: Here, we present a functional model of gene regulation to test the degree to which the known transcription factors and their interactions explain the activity of the Drosophila even-skipped stripe 2 enhancer. Initial success in the first trajectory showed that the gene regulation model explains much of the function of the stripe 2 enhancer. Cases where expression deviated from prediction indicates that undescribed factors likely act to modulate expression. We also showed that activation driven Bicoid and Hunchback is highly sensitive to spatial organization of binding motifs. In contrast, Zelda and Stat92E drive expression from simple homotypic clusters, suggesting that activation driven by these factors is less constrained. Collectively, the 40 sequences generated in this work provides a powerful training set for building future models of gene regulation.

A sequence level model of an intact locus predicts the location and function of nonadditive enhancers.

Metazoan gene expression is controlled through the action of long stretches of noncoding DNA that contain enhancers-shorter sequences responsible for controlling a single aspect of a gene's expression pattern. Models built on thermodynamics have shown how enhancers interpret protein concentration in order to determine specific levels of gene expression, but the emergent regulatory logic of a complete regulatory locus shows qualitative and quantitative differences from isolated enhancers. Such differences may arise from steric competition limiting the quantity of DNA that can simultaneously influence the transcription machinery. We incorporated this competition into a mechanistic model of gene regulation, generated efficient algorithms for this computation, and applied it to the regulation of Drosophila even-skipped (eve). This model finds the location of enhancers and identifies which factors control the boundaries of eve expression. This model predicts a new enhancer that, when assayed in vivo, drives expression in a non-eve pattern. Incorporation of chromatin accessibility eliminates this inconsistency.

Inhibition of RORγT Skews TCRα Gene Rearrangement and Limits T Cell Repertoire Diversity.

Recent studies have elucidated the molecular mechanism of RORγT transcriptional regulation of Th17 differentiation and function. RORγT was initially identified as a transcription factor required for thymopoiesis by maintaining survival of CD4+CD8+ (DP) thymocytes. While RORγ antagonists are currently being developed to treat autoimmunity, it remains unclear how RORγT inhibition may impact thymocyte development. In this study, we show that in addition to regulating DP thymocytes survival, RORγT also controls genes that regulate thymocyte migration, proliferation, and T cell receptor (TCR)α selection. Strikingly, pharmacological inhibition of RORγ skews TCRα gene rearrangement, limits T cell repertoire diversity, and inhibits development of autoimmune encephalomyelitis. Thus, targeting RORγT not only inhibits Th17 cell development and function but also fundamentally alters thymic-emigrant recognition of self and foreign antigens. The analysis of RORγ inhibitors has allowed us to gain a broader perspective of the diverse function of RORγT and its impact on T cell biology.

Identification of an allosteric binding site for RORγt inhibition.

RORγt is critical for the differentiation and proliferation of Th17 cells associated with several chronic autoimmune diseases. We report the discovery of a novel allosteric binding site on the nuclear receptor RORγt. Co-crystallization of the ligand binding domain (LBD) of RORγt with a series of small-molecule antagonists demonstrates occupancy of a previously unreported allosteric binding pocket. Binding at this non-canonical site induces an unprecedented conformational reorientation of helix 12 in the RORγt LBD, which blocks cofactor binding. The functional consequence of this allosteric ligand-mediated conformation is inhibition of function as evidenced by both biochemical and cellular studies. RORγt function is thus antagonized in a manner molecularly distinct from that of previously described orthosteric RORγt ligands. This brings forward an approach to target RORγt for the treatment of Th17-mediated autoimmune diseases. The elucidation of an unprecedented modality of pharmacological antagonism establishes a mechanism for modulation of nuclear receptors.

A synthetic biology approach to the development of transcriptional regulatory models and custom enhancer design.

Synthetic biology offers novel opportunities for elucidating transcriptional regulatory mechanisms and enhancer logic. Complex cis-regulatory sequences--like the ones driving expression of the Drosophila even-skipped gene--have proven difficult to design from existing knowledge, presumably due to the large number of protein-protein interactions needed to drive the correct expression patterns of genes in multicellular organisms. This work discusses two novel computational methods for the custom design of enhancers that employ a sophisticated, empirically validated transcriptional model, optimization algorithms, and synthetic biology. These synthetic elements have both utilitarian and academic value, including improving existing regulatory models as well as evolutionary questions. The first method involves the use of simulated annealing to explore the sequence space for synthetic enhancers whose expression output fit a given search criterion. The second method uses a novel optimization algorithm to find functionally accessible pathways between two enhancer sequences. These paths describe a set of mutations wherein the predicted expression pattern does not significantly vary at any point along the path. Both methods rely on a predictive mathematical framework that maps the enhancer sequence space to functional output.

Egr2-dependent gene expression profiling and ChIP-Seq reveal novel biologic targets in T cell anergy.

T cell anergy is one of the mechanisms contributing to peripheral tolerance, particularly in the context of progressively growing tumors and in tolerogenic treatments promoting allograft acceptance. We recently reported that early growth response gene 2 (Egr2) is a critical transcription factor for the induction of anergy in vitro and in vivo, which was identified based on its ability to regulate the expression of inhibitory signaling molecules diacylglycerol kinase (DGK)-α and -ζ. We reasoned that other transcriptional targets of Egr2 might encode additional factors important for T cell anergy and immune regulation. Thus, we conducted two sets of genome-wide screens: gene expression profiling of wild type versus Egr2-deleted T cells treated under anergizing conditions, and a ChIP-Seq analysis to identify genes that bind Egr2 in anergic cells. Merging of these data sets revealed 49 targets that are directly regulated by Egr2. Among these are inhibitory signaling molecules previously reported to contribute to T cell anergy, but unexpectedly, also cell surface molecules and secreted factors, including lymphocyte-activation gene 3 (Lag3), Class-I-MHC-restricted T cell associated molecule (Crtam), Semaphorin 7A (Sema7A), and chemokine CCL1. These observations suggest that anergic T cells might not simply be functionally inert, and may have additional functional properties oriented towards other cellular components of the immune system.