A common molecular logic determines embryonic stem cell self-renewal and reprogramming

OneSC: a computational platform for recapitulating cell state transitions

MOTIVATION

Computational modeling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology, and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a lab. Recent advancements in single-cell RNA-sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico "synthetic" cells that faithfully mimic the temporal trajectories.

RESULTS

Here we present OneSC, a platform that can simulate cell state transitions using systems of stochastic differential equations govern by a regulatory network of core transcription factors (TFs). Different from many current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and terminal cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes, and monocytes). Finally, through the in silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.

AVAILABILITY AND IMPLEMENTATION

OneSC is implemented as a Python package on GitHub (https://github.com/CahanLab/oneSC) and on Zenodo (https://zenodo.org/records/14052421).

Canalizing kernel for cell fate determination

The tendency for cell fate to be robust to most perturbations, yet sensitive to certain perturbations raises intriguing questions about the existence of a key path within the underlying molecular network that critically determines distinct cell fates. Reprogramming and trans-differentiation clearly show examples of cell fate change by regulating only a few or even a single molecular switch. However, it is still unknown how to identify such a switch, called a master regulator, and how cell fate is determined by its regulation. Here, we present CAESAR, a computational framework that can systematically identify master regulators and unravel the resulting canalizing kernel, a key substructure of interconnected feedbacks that is critical for cell fate determination. We demonstrate that CAESAR can successfully predict reprogramming factors for de-differentiation into mouse embryonic stem cells and trans-differentiation of hematopoietic stem cells, while unveiling the underlying essential mechanism through the canalizing kernel. CAESAR provides a system-level understanding of how complex molecular networks determine cell fates.

Transcriptomic and epigenomic landscapes of muscle growth during the postnatal period of broilers

BACKGROUND

Broilers stand out as one of the fastest-growing livestock globally, making a substantial contribution to animal meat production. However, the molecular and epigenetic mechanisms underlying the rapid growth and development of broiler chickens are still unclear. This study aims to explore muscle development patterns and regulatory networks during the postnatal rapid growth phase of fast-growing broilers. We measured the growth performance of Cornish (CC) and White Plymouth Rock (RR) over a 42-d period. Pectoral muscle samples from both CC and RR were randomly collected at day 21 after hatching (D21) and D42 for RNA-seq and ATAC-seq library construction.

RESULTS

The consistent increase in body weight and pectoral muscle weight across both breeds was observed as they matured, with CC outpacing RR in terms of weight at each stage of development. Differential expression analysis identified 398 and 1,129 genes in the two dimensions of breeds and ages, respectively. A total of 75,149 ATAC-seq peaks were annotated in promoter, exon, intron and intergenic regions, with a higher number of peaks in the promoter and intronic regions. The age-biased genes and breed-biased genes of RNA-seq were combined with the ATAC-seq data for subsequent analysis. The results spotlighted the upregulation of ACTC1 and FDPS at D21, which were primarily associated with muscle structure development by gene cluster enrichment. Additionally, a noteworthy upregulation of MUSTN1, FOS and TGFB3 was spotted in broiler chickens at D42, which were involved in cell differentiation and muscle regeneration after injury, suggesting a regulatory role of muscle growth and repair.

CONCLUSIONS

This work provided a regulatory network of postnatal broiler chickens and revealed ACTC1 and MUSTN1 as the key responsible for muscle development and regeneration. Our findings highlight that rapid growth in broiler chickens triggers ongoing muscle damage and subsequent regeneration. These findings provide a foundation for future research to investigate the functional aspects of muscle development.

OneSC: A computational platform for recapitulating cell state transitions

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.

Boolean Network Models of Human Preimplantation Development

Single-cell transcriptomic studies of differentiating systems allow meaningful understanding, especially in human embryonic development and cell fate determination. We present an innovative method aimed at modeling these intricate processes by leveraging scRNAseq data from various human developmental stages. Our implemented method identifies pseudo-perturbations, since actual perturbations are unavailable due to ethical and technical constraints. By integrating these pseudo-perturbations with prior knowledge of gene interactions, our framework generates stage-specific Boolean networks (BNs). We apply our method to medium and late trophectoderm developmental stages and identify 20 pseudo-perturbations required to infer BNs. The resulting BN families delineate distinct regulatory mechanisms, enabling the differentiation between these developmental stages. We show that our program outperforms existing pseudo-perturbation identification tool. Our framework contributes to comprehending human developmental processes and holds potential applicability to diverse developmental stages and other research scenarios.

MiR-290 Family Maintains Pluripotency and Self-Renewal by Regulating MAPK Signaling Pathway in Intermediate Pluripotent Stem Cells

Mouse embryonic stem cells (ESCs) and epiblast stem cells (EpiSCs) are derived from pre- and post-implantation embryos, representing the initial "naïve" and final "primed" states of pluripotency, respectively. In this study, novel reprogrammed pluripotent stem cells (rPSCs) were induced from mouse EpiSCs using a chemically defined medium containing mouse LIF, BMP4, CHIR99021, XAV939, and SB203580. The rPSCs exhibited domed clones and expressed key pluripotency genes, with both X chromosomes active in female cells. Furthermore, rPSCs differentiated into cells of all three germ layers in vivo through teratoma formation. Regarding epigenetic modifications, the DNA methylation of Oct4, Sox2, and Nanog promoter regions and the mRNA levels of Dnmt3a, Dnmt3b, and Dnmt1 were reduced in rPSCs compared with EpiSCs. However, the miR-290 family was significantly upregulated in rPSCs. After removing SB203580, an inhibitor of the p38 MAPK pathway, the cell colonies changed from domed to flat, with a significant decrease in the expression of pluripotency genes and the miR-290 family. Conversely, overexpression of pri-miR-290 reversed these changes. In addition, Map2k6 was identified as a direct target gene of miR-291b-3p, indicating that the miR-290 family maintains pluripotency and self-renewal in rPSCs by regulating the MAPK signaling pathway.

KLF2 controls proliferation and apoptosis of human spermatogonial stem cells via targeting GJA1

Human spermatogonial stem cells (SSCs) are essential for spermatogenesis and male fertility. However, molecular mechanisms regulating fate determinations of human SSCs remain elusive. In this study, we revealed that KLF2 decreased the proliferation, DNA synthesis, and colonization of human SSCs as well as increased apoptosis of these cells. We identified and demonstrated that GJA1 was a target gene for KLF2 in human SSCs. Notably, KLF2 overexpression rescued the reduction of proliferation of human SSCs caused by GJA1 silencing as well as the enhancement of apoptosis of human SSCs. Abnormalities in the higher level of KLF2 and/or KIF2 mutations might lead to male infertility. Collectively, these results implicate that KLF2 inhibits proliferation of human SSCs and enhances their apoptosis by targeting GJA1. This study thus provides novel genetic mechanisms underlying human spermatogenesis and azoospermia, and it offers new endogenous targets for treating male infertility.

Modeling unveils sex differences of signaling networks in mouse embryonic stem cells

For a short period during early development of mammalian embryos, both X chromosomes in females are active, before dosage compensation is ensured through X-chromosome inactivation. In female mouse embryonic stem cells (mESCs), which carry two active X chromosomes, increased X-dosage affects cell signaling and impairs differentiation. The underlying mechanisms, however, remain poorly understood. To dissect X-dosage effects on the signaling network in mESCs, we combine systematic perturbation experiments with mathematical modeling. We quantify the response to a variety of inhibitors and growth factors for cells with one (XO) or two X chromosomes (XX). We then build models of the signaling networks in XX and XO cells through a semi-quantitative modeling approach based on modular response analysis. We identify a novel negative feedback in the PI3K/AKT pathway through GSK3. Moreover, the presence of a single active X makes mESCs more sensitive to the differentiation-promoting Activin A signal and leads to a stronger RAF1-mediated negative feedback in the FGF-triggered MAPK pathway. The differential response to these differentiation-promoting pathways can explain the impaired differentiation propensity of female mESCs.

Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics

Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.

Rat post-implantation epiblast-derived pluripotent stem cells produce functional germ cells

In mammals, pluripotent cells transit through a continuum of distinct molecular and functional states en route to initiating lineage specification. Capturing pluripotent stem cells (PSCs) mirroring in vivo pluripotent states provides accessible in vitro models to study the pluripotency program and mechanisms underlying lineage restriction. Here, we develop optimal culture conditions to derive and propagate post-implantation epiblast-derived PSCs (EpiSCs) in rats, a valuable model for biomedical research. We show that rat EpiSCs (rEpiSCs) can be reset toward the naive pluripotent state with exogenous Klf4, albeit not with the other five candidate genes (Nanog, Klf2, Esrrb, Tfcp2l1, and Tbx3) effective in mice. Finally, we demonstrate that rat EpiSCs retain competency to produce authentic primordial germ cell-like cells that undergo functional gametogenesis leading to the birth of viable offspring. Our findings in the rat model uncover principles underpinning pluripotency and germline competency across species.

Esrrb guides naive pluripotent cells through the formative transcriptional programme

During embryonic development, naive pluripotent epiblast cells transit to a formative state. The formative epiblast cells form a polarized epithelium, exhibit distinct transcriptional and epigenetic profiles and acquire competence to differentiate into all somatic and germline lineages. However, we have limited understanding of how the transition to a formative state is molecularly controlled. Here we used murine embryonic stem cell models to show that ESRRB is both required and sufficient to activate formative genes. Genetic inactivation of Esrrb leads to illegitimate expression of mesendoderm and extra-embryonic markers, impaired formative expression and failure to self-organize in 3D. Functionally, this results in impaired ability to generate formative stem cells and primordial germ cells in the absence of Esrrb. Computational modelling and genomic analyses revealed that ESRRB occupies key formative genes in naive cells and throughout the formative state. In so doing, ESRRB kickstarts the formative transition, leading to timely and unbiased capacity for multi-lineage differentiation.

Towards AI-driven longevity research: An overview

While in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g., proteomics, lipidomics, and other omics), to understand the interactions between human body and the external environment. This is especially relevant as external factors have been shown to have a key role in aging. As the field of computational systems biology keeps improving and new biomarkers of aging are being developed, artificial intelligence promises to become a major ally of aging research.

NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity

A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.

CELLoGeNe - An energy landscape framework for logical networks controlling cell decisions

Experimental and computational efforts are constantly made to elucidate mechanisms controlling cell fate decisions during development and reprogramming. One powerful computational method is to consider cell commitment and reprogramming as movements in an energy landscape. Here, we develop Computation of Energy Landscapes of Logical Gene Networks (CELLoGeNe), which maps Boolean implementation of gene regulatory networks (GRNs) into energy landscapes. CELLoGeNe removes inadvertent symmetries in the energy landscapes normally arising from standard Boolean operators. Furthermore, CELLoGeNe provides tools to visualize and stochastically analyze the shapes of multi-dimensional energy landscapes corresponding to epigenetic landscapes for development and reprogramming. We demonstrate CELLoGeNe on two GRNs governing different aspects of induced pluripotent stem cells, identifying experimentally validated attractors and revealing potential reprogramming roadblocks. CELLoGeNe is a general framework that can be applied to various biological systems offering a broad picture of intracellular dynamics otherwise inaccessible with existing methods.

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CELLoGeNe – Computation of Energy Landscapes of Logical Gene Networks

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Cell states as landscape attractors

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Maintenance and acquisition of cell pluripotency applications

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Single cell stochastic landscape navigation and visualization tool

BMP4 drives primed to naïve transition through PGC-like state

Multiple pluripotent states have been described in mouse and human stem cells. Here, we apply single-cell RNA-seq to a newly established BMP4 induced mouse primed to naïve transition (BiPNT) system and show that the reset is not a direct reversal of cell fate but goes through a primordial germ cell-like cells (PGCLCs) state. We first show that epiblast stem cells bifurcate into c-Kit⁺ naïve and c-Kit^- trophoblast-like cells, among which, the naïve branch undergoes further transition through a PGCLCs intermediate capable of spermatogenesis in vivo. Mechanistically, we show that DOT1L inhibition permits the transition from primed pluripotency to PGCLCs in part by facilitating the loss of H3K79me2 from Gata3/6. In addition, Prdm1/Blimp1 is required for PGCLCs and naïve cells, while Gata2 inhibits PGC-like state by promoting trophoblast-like fate. Our work not only reveals an alternative route for primed to naïve transition, but also gains insight into germ cell development.

Cnot8 eliminates naïve regulation networks and is essential for naïve-to-formative pluripotency transition

Mammalian early epiblasts at different phases are characterized by naïve, formative, and primed pluripotency states, involving extensive transcriptome changes. Here, we report that deadenylase Cnot8 of Ccr4-Not complex plays essential roles during the transition from naïve to formative state. Knock out (KO) Cnot8 resulted in early embryonic lethality in mice, but Cnot8 KO embryonic stem cells (ESCs) could be established. Compared with the cells differentiated from normal ESCs, Cnot8 KO cells highly expressed a great many genes during their differentiation into the formative state, including several hundred naïve-like genes enriched in lipid metabolic process and gene expression regulation that may form the naïve regulation networks. Knockdown expression of the selected genes of naïve regulation networks partially rescued the differentiation defects of Cnot8 KO ESCs. Cnot8 depletion led to the deadenylation defects of its targets, increasing their poly(A) tail lengths and half-life, eventually elevating their expression levels. We further found that Cnot8 was involved in the clearance of targets through its deadenylase activity and the binding of Ccr4-Not complex, as well as the interacting with Tob1 and Pabpc1. Our results suggest that Cnot8 eliminates naïve regulation networks through mRNA clearance, and is essential for naïve-to-formative pluripotency transition.

Disabling de novo DNA methylation in embryonic stem cells allows an illegitimate fate trajectory

Genome remethylation is essential for mammalian development but specific reasons are unclear. Here we examined embryonic stem (ES) cell fate in the absence of de novo DNA methyltransferases. We observed that ES cells deficient for both Dnmt3a and Dnmt3b are rapidly eliminated from chimeras. On further investigation we found that in vivo and in vitro the formative pluripotency transition is derailed toward production of trophoblast. This aberrant trajectory is associated with failure to suppress activation of Ascl2Ascl2 encodes a bHLH transcription factor expressed in the placenta. Misexpression of Ascl2 in ES cells provokes transdifferentiation to trophoblast-like cells. Conversely, Ascl2 deletion rescues formative transition of Dnmt3a/b mutants and improves contribution to chimeric epiblast. Thus, de novo DNA methylation safeguards against ectopic activation of Ascl2 However, Dnmt3a/b-deficient cells remain defective in ongoing embryogenesis. We surmise that multiple developmental transitions may be secured by DNA methylation silencing potentially disruptive genes.

The combined action of Esrrb and Nr5a2 is essential for murine naïve pluripotency

The maintenance of pluripotency in mouse embryonic stem cells (ESCs) is governed by the action of an interconnected network of transcription factors. Among them, only Oct4 and Sox2 have been shown to be strictly required for the self-renewal of ESCs and pluripotency, particularly in culture conditions in which differentiation cues are chemically inhibited. Here, we report that the conjunct activity of two orphan nuclear receptors, Esrrb and Nr5a2, parallels the importance of that of Oct4 and Sox2 in naïve mouse ESCs. By occupying a large common set of regulatory elements, these two factors control the binding of Oct4, Sox2 and Nanog to DNA. Consequently, in their absence the pluripotency network collapses and the transcriptome is substantially deregulated, leading to the differentiation of ESCs. Altogether, this work identifies orphan nuclear receptors, previously thought to be performing supportive functions, as a set of core regulators of naïve pluripotency.

Summary: Esrrb and Nr5a2, two orphan nuclear receptors, are identified as essential regulators of pluripotency in mouse embryonic stem cells.

Computational systems-biology approaches for modeling gene networks driving epithelial-mesenchymal transitions

Epithelial-mesenchymal transition (EMT) is an important biological process through which epithelial cells undergo phenotypic transitions to mesenchymal cells by losing cell-cell adhesion and gaining migratory properties that cells use in embryogenesis, wound healing, and cancer metastasis. An important research topic is to identify the underlying gene regulatory networks (GRNs) governing the decision making of EMT and develop predictive models based on the GRNs. The advent of recent genomic technology, such as single-cell RNA sequencing, has opened new opportunities to improve our understanding about the dynamical controls of EMT. In this article, we review three major types of computational and mathematical approaches and methods for inferring and modeling GRNs driving EMT. We emphasize (1) the bottom-up approaches, where GRNs are constructed through literature search; (2) the top-down approaches, where GRNs are derived from genome-wide sequencing data; (3) the combined top-down and bottom-up approaches, where EMT GRNs are constructed and simulated by integrating bioinformatics and mathematical modeling. We discuss the methodologies and applications of each approach and the available resources for these studies.

Computational Stem Cell Biology: Open Questions and Guiding Principles

Computational biology is enabling an explosive growth in our understanding of stem cells and our ability to use them for disease modeling, regenerative medicine, and drug discovery. We discuss four topics that exemplify applications of computation to stem cell biology: cell typing, lineage tracing, trajectory inference, and regulatory networks. We use these examples to articulate principles that have guided computational biology broadly and call for renewed attention to these principles as computation becomes increasingly important in stem cell biology. We also discuss important challenges for this field with the hope that it will inspire more to join this exciting area.

DNA methylation and the core pluripotency network

From the onset of fertilization, the genome undergoes cell division and differentiation. All of these developmental transitions and differentiation processes include cell-specific signatures and gradual changes of the epigenome. Understanding what keeps stem cells in the pluripotent state and what leads to differentiation are fascinating and biomedically highly important issues. Numerous studies have identified genes, proteins, microRNAs and small molecules that exert essential effects. Notably, there exists a core pluripotency network that consists of several transcription factors and accessory proteins. Three eminent transcription factors, OCT4, SOX2 and NANOG, serve as hubs in this core pluripotency network. They bind to the enhancer regions of their target genes and modulate, among others, the expression levels of genes that are associated with Gene Ontology terms related to differentiation and self-renewal. Also, much has been learned about the epigenetic rewiring processes during these changes of cell fate. For example, DNA methylation dynamics is pivotal during embryonic development. The main goal of this review is to highlight an intricate interplay of (a) DNA methyltransferases controlling the expression levels of core pluripotency factors by modulation of the DNA methylation levels in their enhancer regions, and of (b) the core pluripotency factors controlling the transcriptional regulation of DNA methyltransferases. We discuss these processes both at the global level and in atomistic detail based on information from structural studies and from computer simulations.

Transcriptional Profiling of Stem Cells: Moving from Descriptive to Predictive Paradigms

Transcriptional profiling is a powerful tool commonly used to benchmark stem cells and their differentiated progeny. As the wealth of stem cell data builds in public repositories, we highlight common data traps, and review approaches to combine and mine this data for new cell classification and cell prediction tools. We touch on future trends for stem cell profiling, such as single-cell profiling, long-read sequencing, and improved methods for measuring molecular modifications on chromatin and RNA that bring new challenges and opportunities for stem cell analysis.

A transcriptome-wide antitermination mechanism sustaining identity of embryonic stem cells

Eukaryotic gene expression relies on extensive crosstalk between transcription and RNA processing. Changes in this composite regulation network may provide an important means for shaping cell type-specific transcriptomes. Here we show that the RNA-associated protein Srrt/Ars2 sustains embryonic stem cell (ESC) identity by preventing premature termination of numerous transcripts at cryptic cleavage/polyadenylation sites in first introns. Srrt interacts with the nuclear cap-binding complex and facilitates recruitment of the spliceosome component U1 snRNP to cognate intronic positions. At least in some cases, U1 recruited in this manner inhibits downstream cleavage/polyadenylation events through a splicing-independent mechanism called telescripting. We further provide evidence that the naturally high expression of Srrt in ESCs offsets deleterious effects of retrotransposable sequences accumulating in its targets. Our work identifies Srrt as a molecular guardian of the pluripotent cell state.

Besides its role in splicing, U1 snRNP can suppress pre-mRNA cleavage and polyadenylation. The authors show that the nuclear cap-binding complex component Srrt/Ars2 maintains embryonic stem cell identity by promoting U1 recruitment to first introns and preventing premature termination of multiple transcripts.

Zfp281 orchestrates interconversion of pluripotent states by engaging Ehmt1 and Zic2

Developmental cell fate specification is a unidirectional process that can be reverted in response to injury or experimental reprogramming. Whether differentiation and de-differentiation trajectories intersect mechanistically is unclear. Here, we performed comparative screening in lineage-related mouse naïve embryonic stem cells (ESCs) and primed epiblast stem cells (EpiSCs), and identified the constitutively expressed zinc finger transcription factor (TF) Zfp281 as a bidirectional regulator of cell state interconversion. We showed that subtle chromatin binding changes in differentiated cells translate into activation of the histone H3 lysine 9 (H3K9) methyltransferase Ehmt1 and stabilization of the zinc finger TF Zic2 at enhancers and promoters. Genetic gain-of-function and loss-of-function experiments confirmed a critical role of Ehmt1 and Zic2 downstream of Zfp281 both in driving exit from the ESC state and in restricting reprogramming of EpiSCs. Our study reveals that cell type-invariant chromatin association of Zfp281 provides an interaction platform for remodeling the cis-regulatory network underlying cellular plasticity.

A gene regulatory network controls the balance between mesendoderm and ectoderm at pluripotency exit

During embryogenesis, differentiation of pluripotent cells into somatic cell types depends both on signaling cues and intrinsic gene expression programs. While the molecular underpinnings of pluripotency are well mapped, much less is known on how mouse embryonic stem cells (mESCs) differentiate. Using RNA-Seq profiling during specification to the three germ layers, we showed that mESCs switched on condition-specific gene expression programs from the onset of the differentiation procedure and that primed pluripotency did not constitute an obligatory intermediate state. After inferring the gene network controlling mESC differentiation, we tested the role of the highly connected nodes by deleting them in a triple knock-in Sox1-Brachyury-Eomes mESC line reporting on ectoderm, mesoderm, and endoderm fates. This led to the identification of regulators of mESC differentiation that acted at several levels: Sp1 as a global break on differentiation, Nr5a2 controlling ectoderm specification, and notably Fos:Jun and Zfp354c as opposite switches between ectoderm and mesendoderm fate.