Boolean modeling of mechanosensitive epithelial to mesenchymal transition and its reversal

Sullivan, Emmalee; Harris, Marlayna; Bhatnagar, Arnav; Guberman, Eric; Zonfa, Ian; Regan, Erzsébet Ravasz

doi:10.1016/j.isci.2023.106321

Cited by 7 publications

(11 citation statements)

References 169 publications

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“…S4 for the Gene-Exp dataset, we find that the Yamanka factors play a central role in transdifferentiating between cell types. In addition, the prominence of multiple micro-RNA (MIR31 + , MIR34A − ), mechanotransduction (CDHR2 − , TWST1 + , VEGFC − ), and metabolic (IDH1 − , IDH2 − , ALDH1A1 − , ALDH3A1 − ) gene perturbations is consistent with the observed interplay between cancer progression, mechanosensitivity [44], and metabolic reprogramming [45].…”

Section: Prominent Genes In Transdifferentiation Transitionssupporting

confidence: 75%

Cell reprogramming design by transfer learning of functional transcriptional networks

Wytock,

Motter

2024

Proc. Natl. Acad. Sci. U.S.A.

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Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cells. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates, thereby generating a model of the network dynamics that can be transferred to specific reprogramming goals. The approach combines transcriptional responses to gene perturbations to minimize the difference between a given pair of initial and target transcriptional states. We demonstrate our approach’s versatility by applying it to a microarray dataset comprising >9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of >10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases with decreasing developmental relatedness and that fewer genes are needed to progress along developmental paths than to regress. These findings establish a proof-of-concept for our approach to computationally design control strategies and provide insights into how gene regulatory networks govern phenotype.

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Section: Prominent Genes In Transdifferentiation Transitionssupporting

confidence: 75%

Cell reprogramming design by transfer learning of functional transcriptional networks

Wytock,

Motter

2024

Proc. Natl. Acad. Sci. U.S.A.

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“…To build the Boolean model we extended our previously published cell cycle and apoptosis network [47] with pathways that control glycolysis, mitochondrial ATP production, mitochondrial morphology, and DNA damage response (a bare-bones circuit focused on p53/p21-mediated cell cycle arrest). The model synthesizes qualitative experimental data from 466 papers into a 134-node Boolean network in which all links and regulatory functions are experimentally justified (SM Table 1; black: published in [47]; gray: changes from [47] included in [50,51]; blue: new nodes/links). For details of our model construction approach, the use of synchronous vs. asynchronous update, automatic module isolation from the larger network, storing our model in Dynamically Modular Model Specification (.dmms) format (SM File 1), as well as using dynmod for all functionality except visualizing attractors in 5D and running simulations, see STAR Methods in [50].…”

Section: Boolean Model Buildingmentioning

confidence: 99%

“…The model synthesizes qualitative experimental data from 466 papers into a 134-node Boolean network in which all links and regulatory functions are experimentally justified (SM Table 1; black: published in [47]; gray: changes from [47] included in [50,51]; blue: new nodes/links). For details of our model construction approach, the use of synchronous vs. asynchronous update, automatic module isolation from the larger network, storing our model in Dynamically Modular Model Specification (.dmms) format (SM File 1), as well as using dynmod for all functionality except visualizing attractors in 5D and running simulations, see STAR Methods in [50]. Key command-line tags described in [50] include: -t --coord_colors (output .BooleanNet [SM File 2] and files Boolean_code_for_MiDAS uses to run statistics); -g (.glm format, SM File 3), -u (update coordinates from user-organized .gml into .dmms), and -s (generate formatted LaTeX document and bibliography for SM Table S1).…”

Section: Boolean Model Buildingmentioning

confidence: 99%

“…To test whether our model can reproduce irreversible MiDAS, we first examined the steady states of the isolated Mitochondrial Module (automatic module isolation method in [50,53]). As Figure 4A indicates, this module acts as a three-state switch (has three distinct steady states).…”

Section: Mitochondrial Dysfunction-induced Senescence (Midas) Is Lock...mentioning

confidence: 99%

“…Sampling the model’s state space to map its synchronous attractors. We used synchronous Boolean modeling to find every stable phenotype and/or oscillation (attractor) of the model using a stochastic sampling procedure [52] implemented in dynmod and Boolean_code_for_MiDAS [53,47,51,50]. For an exhaustive search, we ran dynmod --grid ’20 10 0.02 10’ MiDAS_Cell_Cycle_Arrests_Apoptosis.dmms to generate a 20 x 10 grid of independent sampling attempts (see [50]).…”

Section: Introductionmentioning

confidence: 99%

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Unlocking Mitochondrial Dysfunction-Associated Senescence (MiDAS) with NAD⁺– a Boolean Model of Mitochondrial Dynamics and Cell Cycle Control

Sizek,

Deritei,

Fleig

et al. 2023

Preprint

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The steady accumulation of senescent cells with aging creates tissue environments that aid cancer evolution. The secretome of senescent cells promotes chronic inflammation, contains growth and transforming signals, and causes chronic oxidative stress. The latter is primarily due to dysfunctional mitochondria often seen in senescent cells. Aging cell states are highly heterogeneous. ’Deep senescent’ cells rely on healthy mitochondria to fuel a strong proinflammatory secretome. In parallel, the triggers of deep senescence also generate cells with mitochondrial dysfunction, and sufficient energy deficit to alter their secretome – a state termed Mitochondrial Dysfunction-Associated Senescence (MiDAS). Here we offer a mechanistic explanation for the molecular processes leading to MiDAS. To do this we have built a Boolean regulatory network model able to reproduce mitochondrial dynamics during cell cycle progression (hyper-fusion at the G1/S boundary, fission in mitosis), apoptosis (fission and dysfunction) and glucose starvation (reversible hyper-fusion), as well as MiDAS in response toSIRT3knockdown or oxidative stress. Our model also recapitulates the protective role of NAD+and external pyruvate. We offer testable predictions about the growth factor- and glucose-dependence of MiDAS and its reversibility at different stages of reactive oxygen species (ROS)-induced senescence. Our model opens the door to modeling distinct stages of DNA-damage induced senescence, the relationship between senescence and epithelial-to-mesenchymal transition in cancer and building multiscale models of tissue aging.HighlightsBoolean regulatory network modelreproduces mitochondrial dynamicsduring cell cycle progression, apoptosis, and glucose starvation.Model offers a mechanistic explanation for the positive feedback loop that locks inMitochondrial Dysfunction-Associated Senescence(MiDAS), involving autophagy-resistant hyperfused but dysfunctional mitochondria.Model reproducesROS-mediated mitochondrial dysfunctionand suggests that MiDAS is part of the early phase of damage-induced senescence.Modelpredictsthat cancer-driving mutations that bypass the G1/S checkpoint generally increase the incidence of MiDAS, with the notable exception ofp53loss.

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Mechanotransduction pathways in regulating epithelial-mesenchymal plasticity

Horta,

Doan,

Yang

2023

Current Opinion in Cell Biology

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Boolean modeling of mechanosensitive epithelial to mesenchymal transition and its reversal

Cited by 7 publications

References 169 publications

Cell reprogramming design by transfer learning of functional transcriptional networks

Cell reprogramming design by transfer learning of functional transcriptional networks

Unlocking Mitochondrial Dysfunction-Associated Senescence (MiDAS) with NAD⁺– a Boolean Model of Mitochondrial Dynamics and Cell Cycle Control

Mechanotransduction pathways in regulating epithelial-mesenchymal plasticity

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Boolean modeling of mechanosensitive epithelial to mesenchymal transition and its reversal

Cited by 7 publications

References 169 publications

Cell reprogramming design by transfer learning of functional transcriptional networks

Cell reprogramming design by transfer learning of functional transcriptional networks

Unlocking Mitochondrial Dysfunction-Associated Senescence (MiDAS) with NAD+– a Boolean Model of Mitochondrial Dynamics and Cell Cycle Control

Mechanotransduction pathways in regulating epithelial-mesenchymal plasticity

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Unlocking Mitochondrial Dysfunction-Associated Senescence (MiDAS) with NAD⁺– a Boolean Model of Mitochondrial Dynamics and Cell Cycle Control