Native mechano-regulative matrix properties stabilize alternans dynamics and reduce spiral wave stabilization in cardiac tissue.

The stability of wave conduction in the heart is strongly related to the proper interplay between the electrophysiological activation and mechanical contraction of myocytes and extracellular matrix (ECM) properties. In this study, we statistically compare bioengineered cardiac tissues cultured on soft hydrogels ( E ≃ 12 kPa) and rigid glass substrates by focusing on the critical threshold of alternans, network-physiological tissue properties, and the formation of stable spiral waves that manifest after wave breakups. For the classification of wave dynamics, we use an improved signal oversampling technique and introduce simple probability maps to identify and visualize spatially concordant and discordant alternans as V- and X-shaped probability distributions. We found that cardiac tissues cultured on ECM-mimicking soft hydrogels show a lower variability of the calcium transient durations among cells in the tissue. This lowers the likelihood of forming stable spiral waves because of the larger dynamical range that tissues can be stably entrained with to form alternans and larger spatial spiral tip movement that increases the chance of self-termination on the tissue boundary. Conclusively, we show that a dysfunction in the excitation-contraction coupling dynamics facilitates life-threatening arrhythmic states such as spiral waves and, thus, highlights the importance of the network-physiological interplay between contractile myocytes and the ECM.

A cortical field theory - dynamics and symmetries.

We characterise cortical dynamics using partial differential equations (PDEs), analysing various connectivity patterns within the cortical sheet. This exploration yields diverse dynamics, encompassing wave equations and limit cycle activity. We presume balanced equations between excitatory and inhibitory neuronal units, reflecting the ubiquitous oscillatory patterns observed in electrophysiological measurements. Our derived dynamics comprise lowest-order wave equations (i.e., the Klein-Gordon model), limit cycle waves, higher-order PDE formulations, and transitions between limit cycles and near-zero states. Furthermore, we delve into the symmetries of the models using the Lagrangian formalism, distinguishing between continuous and discontinuous symmetries. These symmetries allow for mathematical expediency in the analysis of the model and could also be useful in studying the effect of symmetrical input from distributed cortical regions. Overall, our ability to derive multiple constraints on the fields - and predictions of the model - stems largely from the underlying assumption that the brain operates at a critical state. This assumption, in turn, drives the dynamics towards oscillatory or semi-conservative behaviour. Within this critical state, we can leverage results from the physics literature, which serve as analogues for neural fields, and implicit construct validity. Comparisons between our model predictions and electrophysiological findings from the literature - such as spectral power distribution across frequencies, wave propagation speed, epileptic seizure generation, and pattern formation over the cortical surface - demonstrate a close match. This study underscores the importance of utilizing symmetry preserving PDE formulations for further mechanistic insights into cortical activity.

Statistical Thermodynamic Description of Self-Assembly of Large Inclusions in Biological Membranes.

Recent studies revealed anomalous underscreening in concentrated electrolytes, and we suggest that the underscreened electrostatic forces between membrane proteins play a significant role in the process of self-assembly. In this work, we assumed that the underscreened electrostatic forces compete with the thermodynamic Casimir forces induced by concentration fluctuations in the lipid bilayer, and developed a simplified model for a binary mixture of oppositely charged membrane proteins with different preference to liquid-ordered and liquid-disordered domains in the membrane. In the model, like macromolecules interact with short-range Casimir attraction and long-range electrostatic repulsion, and the cross-interaction is of the opposite sign. We determine energetically favored patterns in a system in equilibrium with a bulk reservoir of the macromolecules. Different patterns consisting of clusters and stripes of the two components and of vacancies are energetically favorable for different values of the chemical potentials. Effects of thermal flutuations at low temperature are studied using Monte Carlo simulations in grand canonical and canonical ensembles. For fixed numbers of the macromolecules, a single two-component cluster with a regular pattern coexists with dispersed small one-component clusters, and the number of small clusters depends on the ratio of the numbers of the molecules of the two components. Our results show that the pattern formation is controlled by the shape of the interactions, the density of the proteins, and the proportion of the components.

Bounded risk disposition explains Turing patterns and tipping points during spatial contagions.

Spatial contagions, such as pandemics, opinion polarization, infodemics and civil unrest, exhibit non-trivial spatio-temporal patterns and dynamics driven by complex human behaviours and population mobility. Here, we propose a concise generic framework to model different contagion types within a suitably defined contagion vulnerability space. This space comprises risk disposition, considered in terms of bounded risk aversion and adaptive responsiveness and a generalized susceptibility acquisition. We show that resultant geospatial contagion configurations follow intricate Turing patterns observed in reaction-diffusion systems. Pattern formation is shown to be highly sensitive to changes in underlying vulnerability parameters. The identified critical regimes (tipping points) imply that slight changes in susceptibility acquisition and perceived local risks can significantly alter the population flow and resultant contagion patterns. We examine several case studies using Australian datasets (COVID-19 pandemic; crime incidence; conflict exposure during COVID-19 protests; real estate businesses and residential building approvals) and demonstrate that these spatial contagions generated Turing patterns in accordance with the proposed model.

Self-organized Patterns in Non-reciprocal Active Droplet Systems.

Non-equilibrium patterns are widespread in nature and often arise from the self-organization of constituents through nonreciprocal chemotactic interactions. In this study, we demonstrate how active oil-in-water droplet mixtures with predator-prey interactions can result in a variety of self-organized patterns. By manipulating physical parameters, the droplet diameter ratio and number ratio, we identify distinct classes of patterns within a binary droplet system, rationalize the pattern formation, and quantify motilities. Experimental results are recapitulated in numerical simulations using a minimal computational model that solely incorporates chemotactic interactions and steric repulsion among the constituents. The time evolution of the patterns is investigated and chemically explained. We also investigate how patterns vary with differing interaction strength by altering surfactant composition. Leveraging insights from the binary droplet system, the framework is extended to a ternary droplet mixture composed of multiple chasing droplet pairs to create chemically directed hierarchical organization. Our findings demonstrate how rationalizable, self-organized patterns can be programmed in a chemically minimal system and provide the basis for exploration of emergent organization and higher order complexity in active colloids.

Robust tissue pattern formation by coupling morphogen signal and cell adhesion.

Morphogens, locally produced signaling molecules, form a concentration gradient to guide tissue patterning. Tissue patterns emerge as a collaboration between morphogen diffusion and responsive cell behaviors, but the mechanisms through which diffusing morphogens define precise spatial patterns amidst biological fluctuations remain unclear. To investigate how cells respond to diffusing proteins to generate tissue patterns, we develop SYMPLE3D, a 3D culture platform. By engineering gene expression responsive to artificial morphogens, we observe that coupling morphogen signals with cadherin-based adhesion is sufficient to convert a morphogen gradient into distinct tissue domains. Morphogen-induced cadherins gather activated cells into a single domain, removing ectopically activated cells. In addition, we reveal a switch-like induction of cadherin-mediated compaction and cell mixing, homogenizing activated cells within the morphogen gradient to form a uniformly activated domain with a sharp boundary. These findings highlight the cooperation between morphogen gradients and cell adhesion in robust tissue patterning and introduce a novel method for tissue engineering to develop new tissue domains in organoids.

Antibody-Mediated Protein Knockdown Reveals Distal-less Functions for Eyespots and Parafocal Elements in Butterfly Wing Color Pattern Development.

One of the important genes for eyespot development in butterfly wings is Distal-less. Its function has been evaluated via several methods, including CRISPR/Cas9 genome editing. However, functional inhibition may be performed at the right time at the right place using a different method. Here, we used a novel protein delivery method for pupal wing tissues in vivo to inactivate a target protein, Distal-less, with a polyclonal anti-Distal-less antibody using the blue pansy butterfly Junonia orithya. We first demonstrated that various antibodies including the anti-Distal-less antibody were delivered to wing epithelial cells in vivo in this species. Treatment with the anti-Distal-less antibody reduced eyespot size, confirming the positive role of Distal-less in eyespot development. The treatment eliminated or deformed a parafocal element, suggesting a positive role of Distal-less in the development of the parafocal element. This result also suggested the integrity of an eyespot and its corresponding parafocal element as the border symmetry system. Taken together, these findings demonstrate that the antibody-mediated protein knockdown method is a useful tool for functional assays of proteins, such as Distal-less, expressed in pupal wing tissues, and that Distal-less functions for eyespots and parafocal elements in butterfly wing color pattern development.

The Lego hypothesis of tissue morphogenesis: stereotypic organization of parallel orientational cell adhesions for epithelial self-assembly.

How tissues develop distinct structures remains poorly understood. We propose herein the Lego hypothesis of tissue morphogenesis, which states that during tissue morphogenesis, the topographical properties of cell surface adhesion molecules can be dynamically altered and polarised by regulating the spatiotemporal expression and localization of orientational cell adhesion (OCA) molecules cell-autonomously and non-cell-autonomously, thus modulating cells into unique Lego pieces for self-assembling into distinct cytoarchitectures. This concept can be exemplified by epithelial morphogenesis, in which cells are coalesced into a sheet by many types of adhesions. Among them, parallel OCAs (pOCAs) at the lateral cell membranes are essential for configuring cells in parallel. Major pOCAs include Na+/K+-ATPase-mediated adhesions, Crumbs-mediated adhesions, tight junctions, adherens junctions, and desmosomes. These pOCAs align in stereotypical orders along the apical-to-basal axis, and their absolute positioning is also regulated. Such spatial organization of pOCAs underlies proper epithelial morphogenesis. Thus, a key open question about tissue morphogenesis is how to regulate OCAs to make compatible adhesive cellular Lego pieces for tissue construction.

The pentatricopeptide repeat protein DG1 promotes the transition to bilateral symmetry during Arabidopsis embryogenesis through GUN1-mediated plastid signals.

During Arabidopsis embryogenesis, the transition of the embryo's symmetry from radial to bilateral between the globular and heart stage is a crucial event, involving the formation of cotyledon primordia and concurrently the establishment of a shoot apical meristem (SAM). However, a coherent framework of how this transition is achieved remains to be elucidated. In this study, we investigated the function of DELAYED GREENING 1 (DG1) in Arabidopsis embryogenesis using a newly identified dg1-3 mutant. The absence of chloroplast-localized DG1 in the mutants led to embryos being arrested at the globular or heart stage, accompanied by an expansion of WUSCHEL (WUS) and SHOOT MERISTEMLESS (STM) expression. This finding pinpoints the essential role of DG1 in regulating the transition to bilateral symmetry. Furthermore, we showed that this regulation of DG1 may not depend on its role in plastid RNA editing. Nevertheless, we demonstrated that the DG1 function in establishing bilateral symmetry is genetically mediated by GENOMES UNCOUPLED 1 (GUN1), which represses the transition process in dg1-3 embryos. Collectively, our results reveal that DG1 functionally antagonizes GUN1 to promote the transition of the Arabidopsis embryo's symmetry from radial to bilateral and highlight the role of plastid signals in regulating pattern formation during plant embryogenesis.

Pattern Formation and Bistability in a Synthetic Intercellular Genetic Toggle.

Differentiation within multicellular organisms is a complex process that helps to establish spatial patterning and tissue formation within the body. Often, the differentiation of cells is governed by morphogens and intercellular signaling molecules that guide the fate of each cell, frequently using toggle-like regulatory components. Synthetic biologists have long sought to recapitulate patterned differentiation with engineered cellular communities, and various methods for differentiating bacteria have been invented. Here, we couple a synthetic corepressive toggle switch with intercellular signaling pathways to create a "quorum-sensing toggle". We show that this circuit not only exhibits population-wide bistability in a well-mixed liquid environment but also generates patterns of differentiation in colonies grown on agar containing an externally supplied morphogen. If coupled to other metabolic processes, circuits such as the one described here would allow for the engineering of spatially patterned, differentiated bacteria for use in biomaterials and bioelectronics.

Unveiling Hidden Hyperuniformity: Radial Turing Pattern Formation of Marangoni-Driven SiO2 Nanoparticles on Liquid Metal Surface.

Mastering the self-organization of nanoparticle morphologies is pivotal in soft matter physics and film growth. Silicon dioxide (SiO2) nanoparticles are an archetypical model of nanomotor in soft matter. Here, the emphasis is on the self-organizing behavior of SiO2 nanoparticles under extreme conditions. It is unveiled that manipulating the states of the metal substrate profoundly dictates the motion characteristics of SiO2 nanoparticles. This manipulation triggers the emergence of intricate morphologies and distinctive patterns. Employing a reaction-diffusion model, the fundamental roles played by Brownian motion and Marangoni-driven motion in shaping fractal structures and radial Turing patterns are demonstrated, respectively. Notably, these radial Turing patterns showcase hyperuniform order, challenging conventional notions of film morphology. These discoveries pave the way for crafting non-equilibrium morphological materials, poised with the potential for self-healing, adaptability, and innovative applications.

Controlled Protein-Membrane Interactions Modulate Self-Organization of Min Protein Patterns.

Self-organizing protein patterns are crucial for living systems, governing important cellular processes such as polarization and division. While the field of protein self-organization has reached a point where basic pattern-forming mechanisms can be reconstituted in vitro using purified proteins, understanding how cells can dynamically switch and modulate these patterns, especially when transiently needed, remains an interesting frontier. Here, we demonstrate the efficient regulation of self-organizing protein patterns through the modulation of simple biophysical membrane parameters. Our investigation focuses on the impact of membrane affinity changes on Min protein patterns at lipid membranes composed of Escherichia coli lipids or minimal lipid compositions, and we present three major results. First, we observed the emergence of a diverse array of pattern phenotypes, ranging from waves over flower-shaped patterns to snowflake-like structures. Second, we demonstrated the dependency of these patterns on the density of protein-membrane linkers. Finally, we demonstrate that the shape of snowflake-like patterns is fine-tuned by membrane charge. Our results demonstrate the significant influence of membrane linkage as a straightforward biophysical parameter governing protein pattern formation. Our research points towards a simple yet intriguing mechanism by which cells can adeptly tune and switch protein patterns on the mesoscale.

Instabilities and self-organization in spatiotemporal epidemic dynamics driven by nonlinearity and noise.

Theoretical analysis of epidemic dynamics has attracted significant attention in the aftermath of the COVID-19 pandemic. In this article, we study dynamic instabilities in a spatiotemporal compartmental epidemic model represented by a stochastic system of coupled partial differential equations (SPDE). Saturation effects in infection spread-anchored in physical considerations-lead to strong nonlinearities in the SPDE. Our goal is to study the onset of dynamic, Turing-type instabilities, and the concomitant emergence of steady-state patterns under the interplay between three critical model parameters-the saturation parameter, the noise intensity, and the transmission rate. Employing a second-order perturbation analysis to investigate stability, we uncover both diffusion-driven and noise-induced instabilities and corresponding self-organized distinct patterns of infection spread in the steady state. We also analyze the effects of the saturation parameter and the transmission rate on the instabilities and the pattern formation. In summary, our results indicate that the nuanced interplay between the three parameters considered has a profound effect on the emergence of dynamical instabilities and therefore on pattern formation in the steady state. Moreover, due to the central role played by the Turing phenomenon in pattern formation in a variety of biological dynamic systems, the results are expected to have broader significance beyond epidemic dynamics.

Adsorption on a Spherical Colloidal Particle from a Mixture of Nanoparticles with Competing Interactions.

Adsorption of nanoparticles on a spherical colloidal particle is studied by molecular dynamics simulations. We consider a generic model for a mixture of nanoparticles with energetically favored self-assembly into alternating layers of the two components. When both components are attracted to the colloidal particle, the adsorbed nanoparticles self-assemble either into alternating parallel tori and clusters at the two poles of the colloidal particle, or into alternating spirals wrapped around the spherical surface. The long-lived metastable states obtained in simulations follow from the spherical shape of the adsorbing surface and the requirement that the neighboring chains of the nanoparticles are composed of different components. A geometrical construction leading to all such patterns is presented. When the second component particles are repelled from the colloidal particle and the attraction of the first component is strong, the attracted particles form a monolayer at the surface of the colloidal particle that screens the repulsion of the second component. The subsequent adsorbed alternating spherical layers of the two components form together a thick shell. This structure leads to the adsorption that is larger than in the case of the same attraction of the two components to the colloidal particle.

A distinct foliar pigmentation pattern formed by activator-repressor gradients upstream of an anthocyanin-activating R2R3-MYB.

The emergence of novel traits is often preceded by a potentiation phase, when all the genetic components necessary for producing the trait are assembled. However, elucidating these potentiating factors is challenging. We have previously shown that an anthocyanin-activating R2R3-MYB, STRIPY, triggers the emergence of a distinct foliar pigmentation pattern in the monkeyflower Mimulus verbenaceus. Here, using forward and reverse genetics approaches, we identify three potentiating factors that pattern STRIPY expression: MvHY5, a master regulator of light signaling that activates STRIPY and is expressed throughout the leaf, and two leaf developmental regulators, MvALOG1 and MvTCP5, that are expressed in opposing gradients along the leaf proximodistal axis and negatively regulate STRIPY. These results provide strong empirical evidence that phenotypic novelties can be potentiated through incorporation into preexisting genetic regulatory networks and highlight the importance of positional information in patterning the novel foliar stripe.

Gestational VPA exposure reduces the density of juxtapositions between TH+ axons and calretinin or calbindin expressing cells in the ventrobasal forebrain of neonatal mice.

Gestational exposure to valproic acid (VPA) is a valid rodent model of human autism spectrum disorder (ASD). VPA treatment is known to bring about specific behavioral deficits of sociability, matching similar alterations in human autism. Previous quantitative morphometric studies from our laboratory showed a marked reduction and defasciculation of the mesotelencephalic dopaminergic pathway of VPA treated mice, along with a decrease in tissue dopamine in the nucleus accumbens (NAc), but not in the caudatoputamen (CPu). In the present study, the correlative distribution of tyrosine hydroxylase positive (TH+) putative axon terminals, presynaptic to the target neurons containing calretinin (CR) or calbindin (CB), was assessed using double fluorescent immunocytochemistry and confocal laser microscopy in two dopamine recipient forebrain regions, NAc and olfactory tubercle (OT) of neonatal mice (mothers injected with VPA on ED13.5, pups investigated on PD7). Representative image stacks were volumetrically analyzed for spatial proximity and abundance of presynaptic (TH+) and postsynaptic (CR+, CB+) structures with the help of an Imaris (Bitplane) software. In VPA mice, TH/CR juxtapositions were reduced in the NAc, whereas the TH/CB juxtapositions were impoverished in OT. Volume ratios of CR+ and CB+ elements remained unchanged in NAc, whereas that of CB+ was markedly reduced in OT; here the abundance of TH+ axons was also diminished. CR and CB were found to partially colocalize with TH in the VTA and SN. In VPA exposed mice, the abundance of CR+ (but not CB+) perikarya increased both in VTA and SN, however, this upregulation was not mirrored by an increase of the number of CR+/TH+ double labeled cells. The observed reduction of total CB (but not of CB+ perikarya) in the OT of VPA exposed animals signifies a diminished probability of synaptic contacts with afferent TH+ axons, presumably by reducing the available synaptic surface. Altered dopaminergic input to ventrobasal forebrain targets during late embryonic development will likely perturb the development and consolidation of neural and synaptic architecture, resulting in lasting changes of the neuronal patterning (detected here as reduced synaptic input to dopaminoceptive interneurons) in ventrobasal forebrain regions specifically involved in motivation and reward.

Interspecific dispersal constraints suppress pattern formation in metacommunities.

Decisions to disperse from a habitat stand out among organismal behaviours as pivotal drivers of ecosystem dynamics across scales. Encounters with other species are an important component of adaptive decision-making in dispersal, resulting in widespread behaviours like tracking resources or avoiding consumers in space. Despite this, metacommunity models often treat dispersal as a function of intraspecific density alone. We show, focusing initially on three-species network motifs, that interspecific dispersal rules generally drive a transition in metacommunities from homogeneous steady states to self-organized heterogeneous spatial patterns. However, when ecologically realistic constraints reflecting adaptive behaviours are imposed-prey tracking and predator avoidance-a pronounced homogenizing effect emerges where spatial pattern formation is suppressed. We demonstrate this effect for each motif by computing master stability functions that separate the contributions of local and spatial interactions to pattern formation. We extend this result to species-rich food webs using a random matrix approach, where we find that eventually, webs become large enough to override the homogenizing effect of adaptive dispersal behaviours, leading once again to predominately pattern-forming dynamics. Our results emphasize the critical role of interspecific dispersal rules in shaping spatial patterns across landscapes, highlighting the need to incorporate adaptive behavioural constraints in efforts to link local species interactions and metacommunity structure. This article is part of the theme issue 'Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics'.

Geo-evolutionary feedbacks: integrating rapid evolution and landscape change.

We develop a conceptual framework for geo-evolutionary feedbacks which describes the mutual interplay between landscape change and the evolution of traits of organisms residing on the landscape, with an emphasis on contemporary timeframes. Geo-evolutionary feedbacks can be realized via the direct evolution of geomorphic engineering traits or can be mediated by the evolution of trait variation that affects the population size and distribution of the specific geomorphic engineering organisms involved. Organisms that modify their local environments provide the basis for patch-scale geo-evolutionary feedbacks, whereas spatial self-organization provides a mechanism for geo-evolutionary feedbacks at the landscape scale. Understanding these likely prevalent geo-evolutionary feedbacks, that occur at timescales similar to anthropogenic climate change, will be essential to better predict landscape adaptive capacity and change.

Chronobiology of Viscum album L.: a time series of daily metabolomic fingerprints spanning 27 years.

Introduction: European mistletoe (Viscum album L.) has been gaining increasing interest in the field of oncology as a clinically relevant adjunctive treatment in many forms of cancer. In the field of phytopharmacology, harvesting time is pivotal. In the last century, a form of metabolomic fingerprinting based on pattern formation was proposed as a way to determine optimal harvesting times to ensure high quality of mistletoe as raw material for pharmaceutical use. In order to further evaluate the information obtained with this metabolomic fingerprinting method, we analysed a large time series of previously undigitised daily mistletoe chromatograms dating back to the 1950s. Methods: These chromatograms were scanned and evaluated using computerized image analysis, resulting in 12 descriptors for each individual chromatogram. We performed a statistical analysis of the data obtained, investigating statistical distributions, cross-correlations and time self-correlations. Results: The analysed dataset spanning about 27 years, contains 19,037 evaluable chromatograms in daily resolution. Based on the distribution and cross-correlation analyses, the 12 descriptors could be clustered into six independent groups describing different aspects of the chromatograms. One descriptor was found to mirror the annual rhythm being well correlated with temperature and a phase shift of 10 days. The time self-correlation analysis showed that most other descriptors had a characteristic self-correlation of ∼50 days, which points to further infradian rhythms (i.e., more than 24 h). Discussion: To our knowledge, this dataset is the largest of its type. The combination of this form of metabolomic fingerprinting with the proposed computer analysis seems to be a promising tool to characterise biological variations of mistletoe. Additional research is underway to further analyse the different rhythms present in this dataset.

The molecular and cellular choreography of early mammalian lung development.

Mammalian lung development starts from a specific cluster of endodermal cells situated within the ventral foregut region. With the orchestrating of delicate choreography of transcription factors, signaling pathways, and cell-cell communications, the endodermal diverticulum extends into the surrounding mesenchyme, and builds the cellular and structural basis of the complex respiratory system. This review provides a comprehensive overview of the current molecular insights of mammalian lung development, with a particular focus on the early stage of lung cell fate differentiation and spatial patterning. Furthermore, we explore the implications of several congenital respiratory diseases and the relevance to early organogenesis. Finally, we summarize the unprecedented knowledge concerning lung cell compositions, regulatory networks as well as the promising prospect for gaining an unbiased understanding of lung development and lung malformations through state-of-the-art single-cell omics.