Modeling signaling-dependent pluripotent cell states with boolean logic can predict cell fate transitions
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ABSTRACT:
PROVIDER: PRJNA349131 | ENA |
REPOSITORIES: ENA
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Project description:Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The process by which pluripotency is either maintained or destabilized to initiate specific developmental programs is poorly understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to combinations of input signals. While previous attempts to model PSC fate have been limited to static cell compositions, our approach enables simulations of dynamic heterogeneity by combining an Asynchronous Boolean Simulation (ABS) strategy with simulated single cell fate transitions using a Strongly Connected Components (SCCs). This computational framework was applied to a reverse-engineered and curated core GRN for mouse embryonic stem cells (mESCs) to simulate responses to LIF, Wnt/β-catenin, FGF/ERK, BMP4, and Activin A/Nodal pathway activation. For these input signals, our simulations exhibit strong predictive power for gene expression patterns, cell population composition, and nodes controlling cell fate transitions. The model predictions extend into early PSC differentiation, demonstrating, for example, that a Cdx2-high/Oct4-low state can be efficiently generated from mESCs residing in a naïve and signal-receptive state sustained by combinations of signaling activators and inhibitors.
2018-02-06 | GSE88927 | GEO
Project description:Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The process by which pluripotency is either maintained or destabilized to initiate specific developmental programs is poorly understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to combinations of input signals. While previous attempts to model PSC fate have been limited to static cell compositions, our approach enables simulations of dynamic heterogeneity by combining an Asynchronous Boolean Simulation (ABS) strategy with simulated single cell fate transitions using a Strongly Connected Components (SCCs). This computational framework was applied to a reverse-engineered and curated core GRN for mouse embryonic stem cells (mESCs) to simulate responses to LIF, Wnt/β-catenin, FGF/ERK, BMP4, and Activin A/Nodal pathway activation. For these input signals, our simulations exhibit strong predictive power for gene expression patterns, cell population composition, and nodes controlling cell fate transitions. The model predictions extend into early PSC differentiation, demonstrating, for example, that a Cdx2-high/Oct4-low state can be efficiently generated from mESCs residing in a naïve and signal-receptive state sustained by combinations of signaling activators and inhibitors.
2018-02-06 | GSE88926 | GEO
Project description:Modeling signaling-dependent pluripotent cell states with boolean logic can predict cell fate transitions [II]
| PRJNA349134 | ENA
Project description:Modeling signaling-dependent pluripotent cell states with boolean logic can predict cell fate transitions [I]
| PRJNA349135 | ENA
Project description: Not available
| S-EPMC5787708 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-02-05 | GSE88928 | GEO
Project description:A systems biology approach in which qualitative modeling based on combining boolean networks and in silico perturbation experiments were employed to identify 15 pluripotency regulating cytokines or cytokine related genes. IL-11 was validated as a novel factor capable of maintaing the undifferentiated state of human embryonic stem cells in the absence of exogenously added bFGF to the culture acting via a different mechanims than bFGF. Transcriptomic microarray data of eight overexpression and knock-down experiments were used for qualitative modeling based on boolean networks to predict novel factors - mainly cytokines - that maintain pluripotent human embryonic stem cells in the absence of bFGF in culture. The culture was maintained for at least 9 passages using and stained positive for alcaline phosphatase staining, OCT3/4, SOX2, NANOG, TRA1-60. Microarray based gene expression profiling showed that the IL-11 treatment occupies an intermediate state, between bFGF treatment and the negative control which is no cytokine treatment as judged by hierarchical clustering. Moreover, KEGG pathway analysis indicates common and additional distinct mechanisms of bFGF and IL-11 dependant pluripotency dependant mechanisms.
2013-09-26 | E-GEOD-51159 | biostudies-arrayexpress
Project description:While CAR T cells have altered the treatment landscape for B cell malignancies, the risk of on-target, off-tumor toxicity has hampered their development for solid tumors because most target antigens are shared with normal cells. Researchers have attempted to apply Boolean logic gating to CAR T cells to prevent on-target, off-tumor toxicity; however, a truly safe and effective logic-gated CAR has remained elusive. Here, we describe a novel approach to CAR engineering in which we replace traditional ITAM-containing CD3ζ domains with intracellular proximal T cell signaling molecules. We demonstrate that certain proximal signaling CARs, such as a ZAP-70 CAR, can activate T cells and eradicate tumors in vivo while bypassing upstream signaling proteins such as CD3ζ. The primary role of ZAP-70 is to phosphorylate LAT and SLP-76, which form a scaffold for the propagation of T cell signaling. We leveraged the cooperative role of LAT and SLP-76 to engineer Logic-gated Intracellular NetworK (LINK) CAR, a rapid and reversible Boolean-logic AND-gated CAR T cell platform that outperforms other systems in both efficacy and the prevention of on-target, off-tumor toxicity. LINK CAR will dramatically expand the number and types of molecules that can be targeted with CAR T cells, enabling the deployment of these powerful therapeutics for solid tumors and diverse diseases such as autoimmunity9 and fibrosis10. In addition, this work demonstrates that the internal signaling machinery of cells can be repurposed into surface receptors, a finding that could have broad implications for new avenues of cellular engineering.
2022-12-13 | GSE216286 | GEO
Project description:Enhancers play key roles in gene regulation. However, comprehensive enhancer discovery is challenging because most enhancers, especially those affected in complex diseases, have weak effects on gene expression. Through gene regulatory network modeling, we identified that dynamic cell state transitions, a critical missing component in prevalent enhancer discovery strategies, can be utilized to improve the cells’ sensitivity to enhancer perturbation. Guided by the modeling results, we performed a mid-transition CRISPRi-based enhancer screen utilizing human embryonic stem cell definitive endoderm differentiation as a dynamic transition system. The screen discovered a comprehensive set of enhancers (4 to 9 per locus) for each of the core lineage-specifying transcription factors (TFs), including many enhancers with weak to moderate effects. Integrating the screening results with enhancer activity measurements (ATAC-seq, H3K27ac ChIP-seq) and three-dimensional enhancer-promoter interaction information (CTCF looping, Hi-C), we were able to develop a CTCF loop-constrained Interaction Activity (CIA) model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Together, our dynamic network-guided enhancer screen and the CIA enhancer prediction model provide generalizable strategies for sensitive and more comprehensive enhancer discovery in both normal and pathological cell state transitions.
2023-06-17 | PXD043070 | Pride
Project description:Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to “static” transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach that integrates kinetic transcription data and the theory of attractor dynamics analysis to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, all of which do not incorporate kinetic transcriptional data in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in S. cerevisiae. Moreover, we have shown the potential of our method to predict unknown transcription profiles that would be produced upon genetic perturbation of the GRN governing a two-state phenotypic switch in C. albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation and the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that accurately describes the structure and dynamics of the in vivo GRN.
2023-06-23 | GSE217461 | GEO