Saltatory Conduction along Myelinated Axons Involves a Periaxonal Nanocircuit.

The propagation of electrical impulses along axons is highly accelerated by the myelin sheath and produces saltating or "jumping" action potentials across internodes, from one node of Ranvier to the next. The underlying electrical circuit, as well as the existence and role of submyelin conduction in saltatory conduction remain, however, elusive. Here, we made patch-clamp and high-speed voltage-calibrated optical recordings of potentials across the nodal and internodal axolemma of myelinated neocortical pyramidal axons combined with electron microscopy and experimentally constrained cable modeling. Our results reveal a nanoscale yet conductive periaxonal space, incompletely sealed at the paranodes, which separates the potentials across the low-capacitance myelin sheath and internodal axolemma. The emerging double-cable model reproduces the recorded evolution of voltage waveforms across nodes and internodes, including rapid nodal potentials traveling in advance of attenuated waves in the internodal axolemma, revealing a mechanism for saltation across time and space.

A Growth-Based Framework for Leaf Shape Development and Diversity.

How do genes modify cellular growth to create morphological diversity? We study this problem in two related plants with differently shaped leaves: Arabidopsis thaliana (simple leaf shape) and Cardamine hirsuta (complex shape with leaflets). We use live imaging, modeling, and genetics to deconstruct these organ-level differences into their cell-level constituents: growth amount, direction, and differentiation. We show that leaf shape depends on the interplay of two growth modes: a conserved organ-wide growth mode that reflects differentiation; and a local, directional mode that involves the patterning of growth foci along the leaf edge. Shape diversity results from the distinct effects of two homeobox genes on these growth modes: SHOOTMERISTEMLESS broadens organ-wide growth relative to edge-patterning, enabling leaflet emergence, while REDUCED COMPLEXITY inhibits growth locally around emerging leaflets, accentuating shape differences created by patterning. We demonstrate the predictivity of our findings by reconstructing key features of C. hirsuta leaf morphology in A. thaliana. VIDEO ABSTRACT.

The dynamic mechanism of 4E-BP1 recognition and phosphorylation by mTORC1.

The activation of cap-dependent translation in eukaryotes requires multisite, hierarchical phosphorylation of 4E-BP by the 1 MDa kinase mammalian target of rapamycin complex 1 (mTORC1). To resolve the mechanism of this hierarchical phosphorylation at the atomic level, we monitored by NMR spectroscopy the interaction of intrinsically disordered 4E binding protein isoform 1 (4E-BP1) with the mTORC1 subunit regulatory-associated protein of mTOR (Raptor). The N-terminal RAIP motif and the C-terminal TOR signaling (TOS) motif of 4E-BP1 bind separate sites in Raptor, resulting in avidity-based tethering of 4E-BP1. This tethering orients the flexible central region of 4E-BP1 toward the mTORC1 kinase site for phosphorylation. The structural constraints imposed by the two tethering interactions, combined with phosphorylation-induced conformational switching of 4E-BP1, explain the hierarchy of 4E-BP1 phosphorylation by mTORC1. Furthermore, we demonstrate that mTORC1 recognizes both free and eIF4E-bound 4E-BP1, allowing rapid phosphorylation of the entire 4E-BP1 pool and efficient activation of translation. Finally, our findings provide a mechanistic explanation for the differential rapamycin sensitivity of the 4E-BP1 phosphorylation sites.

Deterministic and probabilistic fate decisions co-exist in a single retinal lineage.

Correct nervous system development depends on the timely differentiation of progenitor cells into neurons. While the output of progenitor differentiation is well investigated at the population and clonal level, how stereotypic or variable fate decisions are during development is still more elusive. To fill this gap, we here follow the fate outcome of single neurogenic progenitors in the zebrafish retina over time using live imaging. We find that neurogenic progenitor divisions produce two daughter cells, one of deterministic and one of probabilistic fate. Interference with the deterministic branch of the lineage affects lineage progression. In contrast, interference with fate probabilities of the probabilistic branch results in a broader range of fate possibilities than in wild-type and involves the production of any neuronal cell type even at non-canonical developmental stages. Combining the interference data with stochastic modelling of fate probabilities revealed that a simple gene regulatory network is able to predict the observed fate decision probabilities during wild-type development. These findings unveil unexpected lineage flexibility that could ensure robust development of the retina and other tissues.

Mutation of p53 increases the competitive ability of pluripotent stem cells.

During development, the rate of tissue growth is determined by the relative balance of cell division and cell death. Cell competition is a fitness quality-control mechanism that contributes to this balance by eliminating viable cells that are less fit than their neighbours. The mutations that confer cells with a competitive advantage and the dynamics of the interactions between winner and loser cells are not well understood. Here, we show that embryonic cells lacking the tumour suppressor p53 are 'super-competitors' that eliminate their wild-type neighbours through the direct induction of apoptosis. This elimination is context dependent, as it does not occur when cells are pluripotent and it is triggered by the onset of differentiation. Furthermore, by combining mathematical modelling and cell-based assays we show that the elimination of wild-type cells is not through competition for space or nutrients, but instead is mediated by short-range interactions that are dependent on the local cell neighbourhood. This highlights the importance of the local cell neighbourhood and the competitive interactions within this neighbourhood for the regulation of proliferation during early embryonic development.

A mathematical framework for measuring and tuning tempo in developmental gene regulatory networks.

Embryo development is a dynamic process governed by the regulation of timing and sequences of gene expression, which control the proper growth of the organism. Although many genetic programmes coordinating these sequences are common across species, the timescales of gene expression can vary significantly among different organisms. Currently, substantial experimental efforts are focused on identifying molecular mechanisms that control these temporal aspects. In contrast, the capacity of established mathematical models to incorporate tempo control while maintaining the same dynamical landscape remains less understood. Here, we address this gap by developing a mathematical framework that links the functionality of developmental programmes to the corresponding gene expression orbits (or landscapes). This unlocks the ability to find tempo differences as perturbations in the dynamical system that preserve its orbits. We demonstrate that this framework allows for the prediction of molecular mechanisms governing tempo, through both numerical and analytical methods. Our exploration includes two case studies: a generic network featuring coupled production and degradation, with a particular application to neural progenitor differentiation; and the repressilator. In the latter, we illustrate how altering the dimerisation rates of transcription factors can decouple the tempo from the shape of the resulting orbits. We conclude by highlighting how the identification of orthogonal molecular mechanisms for tempo control can inform the design of circuits with specific orbits and tempos.

Local and global changes in cell density induce reorganisation of 3D packing in a proliferating epithelium.

Tissue morphogenesis is intimately linked to the changes in shape and organisation of individual cells. In curved epithelia, cells can intercalate along their own apicobasal axes, adopting a shape named 'scutoid' that allows energy minimization in the tissue. Although several geometric and biophysical factors have been associated with this 3D reorganisation, the dynamic changes underlying scutoid formation in 3D epithelial packing remain poorly understood. Here, we use live imaging of the sea star embryo coupled with deep learning-based segmentation to dissect the relative contributions of cell density, tissue compaction and cell proliferation on epithelial architecture. We find that tissue compaction, which naturally occurs in the embryo, is necessary for the appearance of scutoids. Physical compression experiments identify cell density as the factor promoting scutoid formation at a global level. Finally, the comparison of the developing embryo with computational models indicates that the increase in the proportion of scutoids is directly associated with cell divisions. Our results suggest that apico-basal intercalations appearing immediately after mitosis may help accommodate the new cells within the tissue. We propose that proliferation in a compact epithelium induces 3D cell rearrangements during development.

Two-way feedback between chromatin compaction and histone modification state explains Saccharomyces cerevisiae heterochromatin bistability.

Compact chromatin is closely linked with gene silencing in part by sterically masking access to promoters, inhibiting transcription factor binding and preventing polymerase from efficiently transcribing a gene. However, a broader hypothesis suggests that chromatin compaction can be both a cause and a consequence of the locus histone modification state, with a tight bidirectional interaction underpinning bistable transcriptional states. To rigorously test this hypothesis, we developed a mathematical model for the dynamics of the HMR locus in Saccharomyces cerevisiae, that incorporates activating histone modifications, silencing proteins, and a dynamic, acetylation-dependent, three-dimensional locus size. Chromatin compaction enhances silencer protein binding, which in turn feeds back to remove activating histone modifications, leading to further compaction. The bistable output of the model was in good agreement with prior quantitative data, including switching rates from expressed to silent states (and vice versa), and protein binding/histone modification levels within the locus. We then tested the model by predicting changes in switching rates as the genetic length of the locus was increased, which were then experimentally verified. Such bidirectional feedback between chromatin compaction and the histone modification state may be a widespread and important regulatory mechanism given the hallmarks of many heterochromatic regions: physical chromatin compaction and dimerizing (or multivalent) silencing proteins.

Disconnection flow-mediated grain rotation.

Grain rotation is commonly observed during the evolution of microstructures in polycrystalline materials of different kinds, including metals, ceramics, and colloidal crystals. It is widely accepted that interface migration in these systems is mediated by the motion of line defects with step and dislocation character, i.e., disconnections. We propose a crystallography-respecting continuum model for arbitrarily curved grain boundaries or heterophase interfaces, accounting for the disconnections' role in grain rotation. Numerical simulations demonstrate that changes in grain orientations, as well as interface morphology and internal stress field, are associated with disconnection flow. Our predictions agree with molecular dynamics simulation results for pure capillarity-driven evolution of grain boundaries and are interpreted through an extended Cahn-Taylor model.

Toward a predictive understanding of epithelial cell death.

Epithelial cell death is highly prevalent during development and tissue homeostasis. While we have a rather good understanding of the molecular regulators of programmed cell death, especially for apoptosis, we still fail to predict when, where, how many and which specific cells will die in a tissue. This likely relies on the much more complex picture of apoptosis regulation in a tissular and epithelial context, which entails cell autonomous but also non-cell autonomous factors, diverse feedback and multiple layers of regulation of the commitment to apoptosis. In this review, we illustrate this complexity of epithelial apoptosis regulation by describing these different layers of control, all demonstrating that local cell death probability is a complex emerging feature. We first focus on non-cell autonomous factors that can locally modulate the rate of cell death, including cell competition, mechanical input and geometry as well as systemic effects. We then describe the multiple feedback mechanisms generated by cell death itself. We also outline the multiple layers of regulation of epithelial cell death, including the coordination of extrusion and regulation occurring downstream of effector caspases. Eventually, we propose a roadmap to reach a more predictive understanding of cell death regulation in an epithelial context.

Perspectives in collective cell migration - moving forward.

Collective cell migration, where cells move as a cohesive unit, is a vital process underlying morphogenesis and cancer metastasis. Thanks to recent advances in imaging and modelling, we are beginning to understand the intricate relationship between a cell and its microenvironment and how this shapes cell polarity, metabolism and modes of migration. The use of biophysical and mathematical models offers a fresh perspective on how cells migrate collectively, either flowing in a fluid-like state or transitioning to more static states. Continuing to unite researchers in biology, physics and mathematics will enable us to decode more complex biological behaviours that underly collective cell migration; only then can we understand how this coordinated movement of cells influences the formation and organisation of tissues and directs the spread of metastatic cancer. In this Perspective, we highlight exciting discoveries, emerging themes and common challenges that have arisen in recent years, and possible ways forward to bridge the gaps in our current understanding of collective cell migration.

Developmental mechanisms understood quantitatively.

Across developmental systems, quantitative and imaging-based approaches have provided unprecedented resolution of dynamic changes in gene regulation and cell fate specification, along with complex changes in tissue morphology. This has set the stage for a wealth of comprehensive theoretical models, parameterised by experimental data, able to reproduce key aspects of biological behaviour and jointly enabling a higher level of abstraction, going from the identification of the molecular components to understanding complex functional relationships between these components. Despite these successes, gaining a cross-scale understanding of developmental systems will require further collaboration between disciplines, from developmental biology to bioengineering, systems biology and biophysics. We highlight the exciting multi-disciplinary research discussed at The Company of Biologists workshop 'Fostering quantitative modelling and experimentation in Developmental Biology'.

On the origins of developmental robustness: modeling buffering mechanisms against cell-level noise.

During development, cells are subject to stochastic fluctuations in their positions (i.e. cell-level noise) that can potentially lead to morphological noise (i.e. stochastic differences between morphologies that are expected to be equal, e.g. the right and left sides of bilateral organisms). In this study, we explore new and existing hypotheses on buffering mechanisms against cell-level noise. Many of these hypotheses focus on how the boundaries between territories of gene expression remain regular and well defined, despite cell-level noise and division. We study these hypotheses and how irregular territory boundaries lead to morphological noise. To determine the consistency of the different hypotheses, we use a general computational model of development: EmbryoMaker. EmbryoMaker can implement arbitrary gene networks regulating basic cell behaviors (contraction, adhesion, etc.), signaling and tissue biomechanics. We found that buffering mechanisms based on the orientation of cell divisions cannot lead to regular boundaries but that other buffering mechanisms can (homotypic adhesion, planar contraction, non-dividing boundaries, constant signaling and majority rule hypotheses). We also explore the effects of the shape and size of the territories on morphological noise.

The rise of taxon-specific epitope predictors.

Computational predictors of immunogenic peptides, or epitopes, are traditionally built based on data from a broad range of pathogens without consideration for taxonomic information. While this approach may be reasonable if one aims to develop one-size-fits-all models, it may be counterproductive if the proteins for which the model is expected to generalize are known to come from a specific subset of phylogenetically related pathogens. There is mounting evidence that, for these cases, taxon-specific models can outperform generalist ones, even when trained with substantially smaller amounts of data. In this comment, we provide some perspective on the current state of taxon-specific modelling for the prediction of linear B-cell epitopes, and the challenges faced when building and deploying these predictors.

Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism.

Cancer metabolism is a marvellously complex topic, in part, due to the reprogramming of its pathways to self-sustain the malignant phenotype in the disease, to the detriment of its healthy counterpart. Understanding these adjustments can provide novel targeted therapies that could disrupt and impair proliferation of cancerous cells. For this very purpose, genome-scale metabolic models (GEMs) have been developed, with Human1 being the most recent reconstruction of the human metabolism. Based on GEMs, we introduced the genetic Minimal Cut Set (gMCS) approach, an uncontextualized methodology that exploits the concepts of synthetic lethality to predict metabolic vulnerabilities in cancer. gMCSs define a set of genes whose knockout would render the cell unviable by disrupting an essential metabolic task in GEMs, thus, making cellular proliferation impossible. Here, we summarize the gMCS approach and review the current state of the methodology by performing a systematic meta-analysis based on two datasets of gene essentiality in cancer. First, we assess several thresholds and distinct methodologies for discerning highly and lowly expressed genes. Then, we address the premise that gMCSs of distinct length should have the same predictive power. Finally, we question the importance of a gene partaking in multiple gMCSs and analyze the importance of all the essential metabolic tasks defined in Human1. Our meta-analysis resulted in parameter evaluation to increase the predictive power for the gMCS approach, as well as a significant reduction of computation times by only selecting the crucial gMCS lengths, proposing the pertinency of particular parameters for the peak processing of gMCS.

Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics.

When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.

Forgotten food crops in sub-Saharan Africa for healthy diets in a changing climate.

As climate changes in sub-Saharan Africa (SSA), Africa's "forgotten" food crops offer a wide range of options to diversify major staple production as a key measure toward achieving zero hunger and healthy diets. So far, however, these forgotten food crops have been neglected in SSA's climate-change adaptation strategies. Here, we quantified their capacity to adapt cropping systems of SSA's major staples of maize, rice, cassava, and yams to changing climates for the four subregions of West, Central, East, and Southern Africa. We used climate-niche modeling to explore their potential for crop diversification or the replacement of these major staples by 2070, and assessed the possible effects on micronutrient supply. Our results indicated that approximately 10% of the present production locations of these four major staples in SSA may experience novel climate conditions in 2070, ranging from a high of almost 18% in West Africa to a low of less than 1% in Southern Africa. From an initial candidate panel of 138 African forgotten food crops embracing leafy vegetables, other vegetables, fruits, cereals, pulses, seeds and nuts, and roots and tubers, we selected those that contributed most to covering projected future and contemporary climate conditions of the major staples' production locations. A prioritized shortlist of 58 forgotten food crops, able to complement each other in micronutrient provision, was determined, which covered over 95% of assessed production locations. The integration of these prioritized forgotten food crops in SSA's cropping systems will support the "double-win" of more climate-resilient and nutrient-sensitive food production in the region.

Analysis of the risk and pre-emptive control of viral outbreaks accounting for within-host dynamics: SARS-CoV-2 as a case study.

In the era of living with COVID-19, the risk of localised SARS-CoV-2 outbreaks remains. Here, we develop a multiscale modelling framework for estimating the local outbreak risk for a viral disease (the probability that a major outbreak results from a single case introduced into the population), accounting for within-host viral dynamics. Compared to population-level models previously used to estimate outbreak risks, our approach enables more detailed analysis of how the risk can be mitigated through pre-emptive interventions such as antigen testing. Considering SARS-CoV-2 as a case study, we quantify the within-host dynamics using data from individuals with omicron variant infections. We demonstrate that regular antigen testing reduces, but may not eliminate, the outbreak risk, depending on characteristics of local transmission. In our baseline analysis, daily antigen testing reduces the outbreak risk by 45% compared to a scenario without antigen testing. Additionally, we show that accounting for heterogeneity in within-host dynamics between individuals affects outbreak risk estimates and assessments of the impact of antigen testing. Our results therefore highlight important factors to consider when using multiscale models to design pre-emptive interventions against SARS-CoV-2 and other viruses.

Oceanographic connectivity explains the intra-specific diversity of mangrove forests at global scales.

The distribution of mangrove intra-specific biodiversity can be structured by historical demographic processes that enhance or limit effective population sizes. Oceanographic connectivity (OC) may further structure intra-specific biodiversity by preserving or diluting the genetic signatures of historical changes. Despite its relevance for biogeography and evolution, the role of oceanographic connectivity in structuring the distribution of mangrove's genetic diversity has not been addressed at global scale. Here we ask whether connectivity mediated by ocean currents explains the intra-specific diversity of mangroves. A comprehensive dataset of population genetic differentiation was compiled from the literature. Multigenerational connectivity and population centrality indices were estimated with biophysical modeling coupled with network analyses. The variability explained in genetic differentiation was tested with competitive regression models built upon classical isolation-by-distance (IBD) models considering geographic distance. We show that oceanographic connectivity can explain the genetic differentiation of mangrove populations regardless of the species, region, and genetic marker (significant regression models in 95% of cases, with an average R-square of 0.44 ± 0.23 and Person's correlation of 0.65 ± 0.17), systematically improving IBD models. Centrality indices, providing information on important stepping-stone sites between biogeographic regions, were also important in explaining differentiation (R-square improvement of 0.06 ± 0.07, up to 0.42). We further show that ocean currents produce skewed dispersal kernels for mangroves, highlighting the role of rare long-distance dispersal events responsible for historical settlements. Overall, we demonstrate the role of oceanographic connectivity in structuring mangrove intra-specific diversity. Our findings are critical for mangroves' biogeography and evolution, but also for management strategies considering climate change and genetic biodiversity conservation.

The Afrotropical breeding grounds of the Palearctic-African migratory painted lady butterflies (Vanessa cardui).

Migratory insects are key players in ecosystem functioning and services, but their spatiotemporal distributions are typically poorly known. Ecological niche modeling (ENM) may be used to predict species seasonal distributions, but the resulting hypotheses should eventually be validated by field data. The painted lady butterfly (Vanessa cardui) performs multigenerational migrations between Europe and Africa and has become a model species for insect movement ecology. While the annual migration cycle of this species is well understood for Europe and northernmost Africa, it is still unknown where most individuals spend the winter. Through ENM, we previously predicted suitable breeding grounds in the subhumid regions near the tropics between November and February. In this work, we assess the suitability of these predictions through i) extensive field surveys and ii) two-year monitoring in six countries: a large-scale monitoring scheme to study butterfly migration in Africa. We document new breeding locations, year-round phenological information, and hostplant use. Field observations were nearly always predicted with high probability by the previous ENM, and monitoring demonstrated the influence of the precipitation seasonality regime on migratory phenology. Using the updated dataset, we built a refined ENM for the Palearctic-African range of V. cardui. We confirm the relevance of the Afrotropical region and document the missing natural history pieces of the longest migratory cycle described in butterflies.