Precision of tissue patterning is controlled by dynamical properties of gene regulatory networks

Exelby, Katherine; Herrera-Delgado, Edgar; Perez, Lorena Garcia; Pérez-Carrasco, Rubén; Sagner, Andreas; Metzis, Vicki; Sollich, Peter; Briscoe, James

doi:10.1242/dev.197566

Cited by 52 publications

(45 citation statements)

References 81 publications

(158 reference statements)

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“…For example, the authors of [ 39 ] classified the qualitative behaviors of attractors and trajectories in toggle switches with time-dependent (morphogen) inputs and subsequently used these classifications to describe the dynamic behavior of the gap gene network in fruit flies [ 15 ]. Similarly, the authors of [ 29 ] found that stochastic gene expression could result in boundary refinement over specific timescales, and confirmed the effect experimentally in a larger network [ 27 ]. Such approaches leverage a thorough understanding of simple models to make sense of more complex cases, enabling researchers to generate new insights, predictions, and perspectives along the way.…”

Section: Introductionsupporting

confidence: 52%

“…Determining whether a model accurately captures an empirical behavior requires systematic and often quantitative comparison of theoretical predictions with experimental results. For example, the mathematical effect observed in [ 29 ] to refine gene expression boundaries in stochastic models was empirically verified in vertebrate neural tube through a careful interplay of theory and experiment [ 27 ]. We encourage the community to follow the example of these authors in future efforts to validate a proposed model, or to differentiate among potentially many mathematical descriptions of the same phenomenon.…”

Section: Discussionmentioning

confidence: 99%

“…First, the precision (regularity) of boundaries increases over time, which in combination with domain shifting leads to evolution of patterns from an initial messy form to a refined final form that cannot be directly predicted from the concentrations of underlying signals [18][19][20]. Second, gene expression domains shift over time, as indicated by experimental evidence from fruit flies [18,[21][22][23][24][25], scuttle flies [26], and vertebrate neural tube [27]. To explain these observed behaviors, researchers have investigated stochastic influences [28,29], the structure of biochemical networks [30], protein diffusion [20], biomechanical forces including cell sorting or aggregation [31][32][33], and the temporal evolution of upstream patterns [15].…”

Section: Introductionmentioning

confidence: 99%

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Implications of diffusion and time-varying morphogen gradients for the dynamic positioning and precision of bistable gene expression boundaries

Perkins

2021

PLoS Comput Biol

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The earliest models for how morphogen gradients guide embryonic patterning failed to account for experimental observations of temporal refinement in gene expression domains. Following theoretical and experimental work in this area, dynamic positional information has emerged as a conceptual framework to discuss how cells process spatiotemporal inputs into downstream patterns. Here, we show that diffusion determines the mathematical means by which bistable gene expression boundaries shift over time, and therefore how cells interpret positional information conferred from morphogen concentration. First, we introduce a metric for assessing reproducibility in boundary placement or precision in systems where gene products do not diffuse, but where morphogen concentrations are permitted to change in time. We show that the dynamics of the gradient affect the sensitivity of the final pattern to variation in initial conditions, with slower gradients reducing the sensitivity. Second, we allow gene products to diffuse and consider gene expression boundaries as propagating wavefronts with velocity modulated by local morphogen concentration. We harness this perspective to approximate a PDE model as an ODE that captures the position of the boundary in time, and demonstrate the approach with a preexisting model for Hunchback patterning in fruit fly embryos. We then propose a design that employs antiparallel morphogen gradients to achieve accurate boundary placement that is robust to scaling. Throughout our work we draw attention to tradeoffs among initial conditions, boundary positioning, and the relative timescales of network and gradient evolution. We conclude by suggesting that mathematical theory should serve to clarify not just our quantitative, but also our intuitive understanding of patterning processes.

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Section: Introductionsupporting

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Implications of diffusion and time-varying morphogen gradients for the dynamic positioning and precision of bistable gene expression boundaries

Perkins

2021

PLoS Comput Biol

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“…For spatial signals, binding with membrane-bound non-signaling entities [25], regulation of gradient steepness by ligand shuttling [26,27] and self-regulated ligand uptake [28,29] can reduce spatial variation in morphogen gradients. Anti-parallel morphogens [14] and gene regulatory networks [30][31][32] that translate noisy spatial signals into cell fate decisions can also reduce patterning errors. Interestingly, noise in gene expression can counteract other stochastic effects (e.g.…”

Section: Introductionmentioning

confidence: 99%

Multiple morphogens and rapid elongation promote segmental patterning during development

et al. 2021

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The vertebrate hindbrain is segmented into rhombomeres (r) initially defined by distinct domains of gene expression. Previous studies have shown that noise-induced gene regulation and cell sorting are critical for the sharpening of rhombomere boundaries, which start out rough in the forming neural plate (NP) and sharpen over time. However, the mechanisms controlling simultaneous formation of multiple rhombomeres and accuracy in their sizes are unclear. We have developed a stochastic multiscale cell-based model that explicitly incorporates dynamic morphogenetic changes (i.e. convergent-extension of the NP), multiple morphogens, and gene regulatory networks to investigate the formation of rhombomeres and their corresponding boundaries in the zebrafish hindbrain. During pattern initiation, the short-range signal, fibroblast growth factor (FGF), works together with the longer-range morphogen, retinoic acid (RA), to specify all of these boundaries and maintain accurately sized segments with sharp boundaries. At later stages of patterning, we show a nonlinear change in the shape of rhombomeres with rapid left-right narrowing of the NP followed by slower dynamics. Rapid initial convergence improves boundary sharpness and segment size by regulating cell sorting and cell fate both independently and coordinately. Overall, multiple morphogens and tissue dynamics synergize to regulate the sizes and boundaries of multiple segments during development.

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“…Downstream of extrinsic signaling, progenitor cells employ gene regulatory networks (GRNs) to interpret morphogen gradients and regulate cell fate 16 . During neural tube development, Otx2 is expressed in anterior regions, whereas Gbx2 is expressed in posterior regions at early stages of development.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Production of Mesencephalic Dopaminergic Neurons from Lineage-Restricted Human Undifferentiated Stem Cells

Maimaitili

Chen

Febbraro³

et al. 2021

Preprint

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The differentiation of human pluripotent stem cells (hPSCs) into mesencephalic dopaminergic (mesDA) neurons requires a precise combination of extrinsic factors that recapitulates the in vivo environment and timing. Current methods are capable of generating authentic mesDA neurons after long-term culture in vitro; however, when mesDA progenitors are transplanted in vivo, the resulting mesDA neurons are only minor components of the graft. This low yield hampers the broad use of these cells in the clinic. In this study, we genetically modified pluripotent stem cells to generate a novel type of stem cells called lineage-restricted undifferentiated stem cells (LR-USCs), which robustly generate mesDA neurons. LR-USCs are prevented from differentiating into a broad range of nondopaminergic cell types by knocking out genes that are critical for the specification of cells of alternate lineages. Specifically, we target transcription factors involved in the production of spinal cord and posterior hindbrain cell types. When LR-USCs are differentiated under caudalizing condition, which normally give rise to hindbrain cell types, a large proportion adopt a midbrain identity and develop into authentic mesDA neurons. We show that the mesDA neurons are electrophysiologically active, and due to their higher purity, are capable of restoring motor behavior eight weeks after transplantation into 6-hydroxydopamine (6-OHDA)-lesioned rats. This novel strategy improves the reliability and scalability of mesDA neuron generation for clinical use.

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Precision of tissue patterning is controlled by dynamical properties of gene regulatory networks

Cited by 52 publications

References 81 publications

Implications of diffusion and time-varying morphogen gradients for the dynamic positioning and precision of bistable gene expression boundaries

Implications of diffusion and time-varying morphogen gradients for the dynamic positioning and precision of bistable gene expression boundaries

Multiple morphogens and rapid elongation promote segmental patterning during development

Enhanced Production of Mesencephalic Dopaminergic Neurons from Lineage-Restricted Human Undifferentiated Stem Cells

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