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Results for “Evolutionary Biology”

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bioRxiv2026

Potential benefit of loss-of-function on bacterial fitness

Escherichia coli is a well-studied organism with extensive genomic and proteomic data. This study examines how gene loss reallocates cellular resources and impacts fitness. Genes were classified based on fitness measurements as essential, important, mean-effect, or fitness-enhancing. Using proteomic data, we analyzed the relationship between protein production cost and fitness, finding that genes with a high proteomic mass fraction are more likely to affect fitness, while fitness-enhancing deletions rarely improve fitness by reducing proteomic burden. We calculated the cumulative of proteome fractions encoded by genes classified as mean-effect and compared it with the results from the ME-model simulations. The mean-effect category constitutes 31-75% of the proteome, with the highest proportion LB, while enrichment analysis of core mean-effect genes highlighted transmembrane transport as the main functional category. Furthermore, we identified a subset of genes whose deletion increased fitness compared to the mean; they generally have low expression, and many have unknown functions. AI-assisted structural analyses identified domains and conserved features compatible with DNA-binding proteins, suggesting that some may represent putative transcriptional regulators requiring further validation. RpoS, stress sigma factor controlling up to 15% of the proteome is one of the transcriptional regulators in the fitness-enhancing category. Our findings suggest that the cost of being a generalist is linked to transcriptional regulation, while molecular transport represents a high burden for nutrient readiness. ImportanceThis study provides new insights into how gene loss benefits bacteria by identifying gene categories and their associated protein fractions whose disruption does not impose large fitness penalties. Additionally, it uncovers specific fitness-enhancing genes and generates hypotheses based on structural analyses for previously uncharacterized ones. Our findings suggest that several of these genes may encode putative transcriptional regulators, highlighting a potential role for regulatory complexity in cellular efficiency. By revealing how certain gene deletions enhance fitness and which gene categories are nonessential, this work advances our understanding of bacterial adaptation and genome streamlining. These insights have broad implications for evolutionary biology, metabolic engineering, and biotechnology, offering strategies to optimize microbial function by selectively reducing genetic and regulatory burden.

systems biology↗

bioRxiv2026

Percolation-inspired criticality in complement activation: universal scaling and transport-limited complement surface amplification

The collective spatial phenomenon of complement protein opsonization on nanoparticle surfaces is a key component of the immune response to viruses, engineered nanoparticles, and diseased cells. Recent work showed this opsonization follows a sharp, percolation-like transition versus the spacing d between surface-bound attachment sites, leaving two open questions: 1) whether the transition exhibits hallmarks of true criticality, such as diverging susceptibility, and 2) whether it can be distinguished from an alternative first-order cooperative (Hill-type) process producing an equally sharp threshold without true criticality. Here, we resolve both questions using a hierarchical statistical-mechanics treatment spanning stochastic, mean-field, and spatial reaction-diffusion models. The variance of two order parameters, peak complement activity and activation lifetime, diverges near threshold and sharpens systematically with system size, the defining signature of a critical point rather than a smooth cooperative response. Extending the analysis across site spacing and intrinsic kinetic rate constants traces a two-dimensional locus of critical points with consistent critical exponents throughout, establishing a single, robust universality class. The mean-field dynamic exponent for activation lifetime agrees quantitatively with the exact value predicted for the general epidemic process. Finally, a reaction-diffusion model of the nanoparticle surface shows the critical locus is set by a diffusion-limited length scale, establishing complement percolation as a fundamentally transport-limited surface reaction. These results place complement activation within the percolation universality class and identify the physical parameters, diffusion, catalysis, and decay, that govern its critical threshold, with direct implications for rational design of complement-evading nanomaterials, immunology, and evolutionary biology.

biophysics↗

bioRxiv2026

Chromosome-scale Daphnia magna genome assembly resolves scaffolding discrepancies

Daphnia magna is a widely used model organism in ecological genomics, ecotoxicology, and evolutionary biology. While a few genome assemblies of this species of freshwater crustacean are available, disagreements in their chromosomal arrangements point toward scaffolding problems. We present the most contiguous chromosome-level genome assembly of D. magna, the first based on PacBio HiFi sequencing technology. The assembly spans 184.38 Mb and has a scaffold N50 of 12.4 Mb. Eight of ten chromosomal scaffolds consist of mainly two contigs, representing one chromosome arm each. Eleven of 20 telomeric regions are fully resolved. Using a genomic map and comparative genomics, we were able to not only resolve the problems of previous assemblies, but also to demonstrate that some of the scaffolding errors followed a systematic pattern, most likely resulting from mis-joins during Hi-C scaffolding. Additionally, we provide expression profiles for annotated genes derived from different D. magna RNA-seq datasets, enabling rapid assessment of predicted gene expression and identification of potential pseudogenes. The new genomic and transcriptomic resource enables comparative genomic studies on genome architecture, structural variation, and gene family evolution in this important model organism.

genomics↗

bioRxiv2026

Genomes of three Sympatric Species of an Adaptive Radiation: Cyprinodon variegatus, C. desquamator, and C. brontotheroides

Speciation is a central process in evolutionary biology that involves the formation of reproductive barriers between diverging populations. This process can occur through multiple ecological, behavioral, and genetic mechanisms that ultimately lead to changes within a population leading to the formation of distinct species. The relatively young adaptive radiation of Cyprinodon pupfishes in the lakes of San Salvador Island provides a powerful system to study the drivers of speciation in species that exist both sympatrically and allopatrically. The three species; C. variegatus, C. desquamator, and C. brontotheroides, can be found in many lakes on the island, exhibit distinct morphological adaptations relating to feeding, and, when in sympatry, the three species have overlapping habitats and breeding grounds. Species boundaries are maintained by asymmetric mate choice by both males and females, which reinforce extreme fitness costs of hybridization. Here we present high-quality annotated reference genomes for these three species generated using PacBio hifi sequencing. The assemblies resulted in a genome size of approximately 1.2 Gb for each species with most sequences scaffolding into 24 chromosomes with BUSCO analysis indicating a >99% level of completion. The annotation of these three genomes generated over 21,500 protein coding genes with BUSCO analysis indicating a >86 completion. These genomes provide a key resource for studying the genomic architecture of ecological specialization, reproductive isolation in sympatry, and rapid speciation and will facilitate future comparative and population genomic studies on adaptive radiations.

genomics↗

bioRxiv2026

Subcellular spatial proteomics of the heterotrophic nanoflagellate, Cafeteria roenbergensis

Cafeteria roenbergensis is the best-studied free-living heterotrophic nanoflagellate, an abundant marine bacterivore and the host of the giant virus CroV, yet almost nothing is known about how its cell is organized. To provide broad insight into the localization of its proteome, we used subcellular spatial proteomics (SSP), based on a label-free version of LOPIT-DC (Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation), in which organelles are separated by differential centrifugation and each protein is assigned to a compartment from its abundance profile across fractions. We detected 5,948 proteins (5,889 protein groups), corresponding to 69.7% of the predicted C. roenbergensis proteome, and assigned high-confidence localizations to 2,945 proteins across 14 subcellular compartments, including basal body/cilia, cytosol, ER, Golgi, lysosome, membrane-trafficking, mitochondria, nucleoplasm, nucleus, peroxisome, plasma membrane, proteasome, ribosome, and spliceosome. This study serves as a proteomic dataset to resolve protein compartmentalization in a free-living heterotrophic stramenopile, enabling evolutionary, biological and functional insight and analysis. Investigators interested in using these data prior to publication are encouraged to contact us to avoid overlap and to facilitate coordinated and collaborative use of this resource.

cell biology↗

bioRxiv2026

geoGraph: Connectivity modelling across geographic space with hexagonal discrete global grids

1. Estimating the movement and connectivity of organisms across landscapes is central to research in ecology and evolutionary biology. Although graph-based approaches representing geographic space as weighted networks provide a flexible and computationally efficient framework for such analyses, existing tools either perform their computations only on rectangular raster grids, thus introducing systematic directional and area distortions at global scales, or rely on bespoke pipelines that limit the general applicability of recent hexagonal-grid analyses. 2. To overcome these shortcomings, here we present geoGraph, an R package that brings graph-based spatial modelling to discrete global hexagonal grids. By representing landscapes on an equal-area hexagonal tessellation of the sphere, geoGraph enables global-scale analysis without projection distortion while retaining the flexibility of the graph framework and the performance of the graph and RBGL/Boost libraries. The package integrates both raster- and vector-based GIS data into a single cost surface, supports custom grids at various resolutions, and provides a complete pipeline from raw GIS layers to graph-algorithmic outputs, together with tools for visualising and interactively editing graphs. 3. We illustrate geoGraph by revisiting previous studies of isolation by distance among human populations in the Human Genome Diversity Project panel. At a global scale, restricting movement to land raised the correlation between geographic and genetic distance from r = 0.74 (great-circle) to r = 0.84. On a finer grid within Asia, accounting for terrain ruggedness improved the fit, with the best-fitting models implying reduced movement across the Himalaya and Tibetan Plateau. These analyses demonstrate that geoGraph can model movement and connectivity appropriate to the desired spatial scale and that the level of geographic realism required is itself scale-dependent. 4. By combining discrete global grids with the power of graph algorithms, geoGraph enables seamless connectivity analysis from landscape to global scales. It is broadly applicable across landscape genetics, phylogeography, conservation planning, and studies of human dispersal, and is particularly suited to large-scale problems that rectangular-raster tools cannot address without distortion.

bioinformatics↗

bioRxiv2026

Comparison of evolutionary rescue via biological and cultural evolution

Rapid evolution allows populations to persist in environments where they would otherwise go extinct. This phenomenon, known as evolutionary rescue, is typically studied in the framework of biological evolution, yet adaptive traits can also arise and spread through cultural evolution. The present study developed a stochastic eco-evolutionary model to compare rescue probabilities through biological and cultural evolution. Transmission bias governed the rescue probability under cultural evolution by setting how readily a rare adaptive trait was copied. Conformity bias suppressed population persistence because a rare trait was the least likely to be copied. Content bias toward the adaptive trait enabled evolutionary rescue when social learning was rapid, but it typically yielded a lower rescue probability than biological evolution. Only anticonformity bias, together with a high social learning rate, exceeded the rescue probability of biological evolution by enabling the adaptive trait to be established more rapidly. These results demonstrate that transmission bias alters the demographic consequences of cultural evolution and highlight the importance of transmission processes in evolutionary rescue theory. Understanding how adaptive behaviours are socially transmitted may also improve predictions of animal population persistence and inform conservation efforts in rapidly changing environments.

evolutionary biology↗

bioRxiv2021

There and Back to the Present: An Evolutionary Tale on Biological Diversity

Ecologists often agree on the importance of macroevolution for niche-mediated distribution of biological diversity along environmental gradients. Yet, macroevolutionary diversification and dispersal in time and space generate uneven geographic distribution of phylogenetic pools, which affects the imprint let by macroevolution on local species pools. In this article we introduce an individual-based simulation approach coupled to Approximate Bayesian Computation (ABC) that allows to parameterize the adaptation rate of species niche positions along the evolution of a monophyletic lineage, and the intensity of dispersal limitation, associated with the distribution of biological diversity between assemblages potentially connected by dispersal (metacommunity). The analytical tool was implemented in an R package called mcfly. We evaluated the statistical performance of the analytical framework using simulated datasets, which confirmed the suitability of the analysis to estimate adaptation rate and dispersal limitation parameters. Further, we evaluated the role played by niche evolution and dispersal limitation on species diversity distribution of Phyllostomidae bats across the Neotropics. The framework proposed here shed light on the links between niche evolution, dispersal limitation and the distribution of biological diversity, and thereby improved our understanding of evolutionary imprints on ecological patterns. Perhaps more importantly, it offers new possibilities for solving the eco-evolutionary puzzle.

ecology↗

bioRxiv2024

How to validate a Bayesian evolutionary model

Biology has become a highly mathematical discipline in which probabilistic models play a central role. As a result, research in the biological sciences is now dependent on computational tools capable of carrying out complex analyses. These tools must be validated before they can be used, but what is understood as validation varies widely among methodological contributions. This may be a consequence of the still embryonic stage of the literature on statistical software validation for computational biology. Our manuscript aims to advance this literature. Here, we describe and illustrate good practices for assessing the correctness of a model implementation, with an emphasis on Bayesian methods. We also introduce a suite of functionalities for automating validation protocols. It is our hope that the guidelines presented here help sharpen the focus of discussions on (as well as elevate) expected standards of statistical software for biology.

evolutionary biology↗

bioRxiv2026

{Sigma}0-EvoCell: An AI-Native Ontology that Unifies Evolutionary and Cell Biology in Latent Space

Foundation models for biology achieve impressive pattern recognition on molecular sequences and single-cell transcriptomics, yet they fail to outperform simple linear baselines for predicting genetic perturbation effects, exposing a gap between statistical correlation and mechanistic understanding. This gap is compounded by an interface problem: biological knowledge lives in human-readable formats (FASTA, SBML, ontology triples) that must be lossily re-encoded before a neural network can reason about them. Here we introduce the Evolutionary Cell Ontology (ECO), an AI-native formal language built on the {Sigma}0 substrate that represents biological knowledge directly as vector-encoded relational graphs. ECO uses 16 structural operators that serve simultaneously as knowledge glyphs, tensor operations, and--critically--carry a dual semantics spanning both evolutionary and cellular timescales, so the same operator that denotes speciation at the phylogenetic scale denotes irreversible APC/C commitment at the cell-cycle scale. We define a Latent Space Communication Protocol (LSCP) that maps ECO graphs into the residual stream of large language models, enabling systematic auditing of the biological knowledge a model actually contains. We illustrate ECO across three domains using controlled simulations: (i) globin protein-family evolution across roughly 1.5 billion years, where ECO epistatic attention recovers long-range coevolutionary couplings and ancestral-state reconstruction reaches 82-91% accuracy graded by conservation class; (ii) mammalian cell-cycle dynamics, where a CDK-cyclin attention graph with GATE checkpoints and FUSE commitment nodes reproduces two full oscillatory cycles with a four-attractor phase portrait emerging without explicit programming; and (iii) latent-space alignment, where ECO embedding distance tracks divergence across 60 protein families (Pearson r = 0.50) and an illustrative LSCP audit projects that relational, multi-scale concepts are encoded far more weakly than sequence-level facts. ECO replaces the human-readability constraint with an AI-processing constraint and, in doing so, turns the opacity of foundation models into a measurable, navigable coverage map.

bioinformatics↗

bioRxiv2025

Automated Bacteriophage Evolution Studies with the Aionostat

Bacteriophages, the viruses that infect bacteria, are the most abundant and diverse biological entities on our planet. They play a critical role in shaping ecosystems and are increasingly recognized for their potential in treating bacterial infections. Yet, our comprehension of their biology and evolutionary dynamics is limited, largely because research has concentrated on a select few well-characterized phages or relies on broad metagenomic studies with limited follow-up analysis of individual phages. This knowledge gap hinders our capacity to exploit their therapeutic and ecological possibilities - and while some studies have attempted to bridge it, such efforts typically require a lot of manual labor, highlighting the need for high-throughput, reproducible methods for in-depth study of phage evolution. To address this gap, we introduce the Aionostat, a novel automated continuous culture device designed to facilitate bacteriophage directed evolution experiments at scale. The Aionostats potential is showcased through two example experiments. In the first, phages from the BASEL collection rapidly adapted to a challenging E. coli strain, acquiring mutations and deletions that improved their infectivity. In the second experiment, we evolved a mixture of these phages on the same E.coli strain, leading to the emergence of recombinant phages with increased fitness. By automating these experiments, the Aionostat enables faster, more reproducible studies of phage evolution that would be impractical to perform by hand, thereby opening new avenues for investigating viral dynamics, engineering phage therapies, and studying evolutionary principles in broader biological contexts.

evolutionary biology↗

bioRxiv2023

Using developmental dynamics for evolutionary prediction and control

Understanding, predicting, and controlling the phenotypic consequences of genetic and environmental change is essential to many areas of fundamental and applied biology. In evolutionary biology, the generative process of development is a major source of organismal evolvability that constrains or facilitates adaptive change by shaping the distribution of phenotypic variation that selection can act upon. While the complex interactions between genetic and environmental factors during development may appear to make it impossible to infer the consequences of perturbations, the persistent observation that many perturbations result in similar phenotypes indicates that there is a logic to what variation is generated. Here, we show that a general representation of development as a dynamical system can reveal this logic. We build a framework that allows to predict the phenotypic effects of perturbations, and conditions for when the effects of perturbations of different origin are concordant. We find that this concordance is explained by two generic features of development, namely the dynamical dependence of the phenotype on itself and the fact that all perturbations must be funneled by the same developmental process. We apply our theoretical results to classical models of development and show that it can be used to predict the evolutionary response to selection using information of plasticity, and to accelerate evolution in a desired direction. The framework we introduce provides a way to quantitatively interchange perturbations, opening a new avenue of perturbation design to control the generation of variation, and thus evolution.

evolutionary biology↗

bioRxiv2024

Pore formation in complex biological membranes: torn between evolutionary needs

The primary function of biological membranes is to enable compartmentalization among cells and organelles. Loss of integrity by the formation of membrane pores would trigger uncontrolled depolarization or influx of toxic compounds, posing a fatal thread to living cells. How the lipid complexity of biological membranes enables mechanical stability against pore formation while simultaneously allowing ongoing membrane remodeling is largely enigmatic. We performed molecular dynamics simulations of eight complex lipid membranes including the plasma membrane and membranes of the organelles ER, Golgi, lysosome, and mitochondrion. To quantify the mechanical stability of these membranes, we computed the free energies for nucleating a transmembrane pore as well as the line tension along the rim of open pores. Our simulations reveal that complex biological membranes are overall remarkably stable, however with the plasma membrane standing out as exceptionally stable, which aligns with its crucial role as a protective layer. We observe that sterol content is the main regulator for biomembrane stability, and that lateral sorting among lipid mixtures influences the energetics of membrane pores. A comparison of 25 model membranes with varying sterol content, tail length, tail saturation, and head group type shows that the pore nucleation free energy is mostly associated with the lipid tilt modulus, whereas the line tension along the pore rim is determined by the lipid intrinsic curvature. Together, our study provides an atomistic and energetic view on the role of lipid complexity on biomembrane stability. Significance statementBiomembranes have evolved to fulfill seemingly conflicting requirements. Membranes form a protective layer against bacterial or viral infection and against external mechanical and toxic stress, thus requiring mechanical stability. However, membranes are furthermore involved in ongoing remodeling for homeostasis, signaling, trafficking, and morphogenesis, necessitating a high degree of plasticity. How the chemical diversity of membranes, comprising hundreds of lipid species, contributes to enable both stability and plasticity is not well understood. We used molecular simulations and free energy calculations of pore formation in complex biomembranes to reveal how mechanical and geometric properties of lipids as well as lateral lipid sorting control the integrity of complex membranes.

biophysics↗

bioRxiv2026

EvoRMD: Integrating Biological Context and Evolutionary RNA Language Models for Interpretable Prediction of RNA Modifications

RNA modifications are essential regulators of post-transcriptional gene expression, influencing RNA stability, localization, translation, and degradation. Determining the specific modification at a given nucleotide is therefore critical for understanding its regulatory role. Most computational approaches treat each modification type as an independent binary task. This strategy provides a macro-level statistical perspective, but it does not reflect that, under a defined biochemical or cellular condition, only one modification type can occur at a specific site. Current mapping assays also report a single observed modification per site, leaving all other types unlabeled rather than truly negative. These properties motivate a framework that can reason over competing modification types. We introduce EvoRMD, a unified model for biologically contextualized and interpretable prediction of RNA modification types. EvoRMD integrates contextual sequence embeddings from a large-scale RNA language model with structured biological metadata--including species, organ, cell type, and subcellular localization. A lightweight attention mechanism highlights informative sequence positions. A shared multi-class classifier then generates a context-conditioned plausibility distribution over eleven modification types (Am, Cm, Um, Gm, D, pseudouridine, m1A, m5C, m5U, m6A, m7G), consistent with the single-positive, multiple-unlabeled nature of existing datasets. Although trained in a multi-class setting, EvoRMD also produces calibrated multi-label predictions through sigmoid-transformed logits, enabling direct comparison with existing single-modification and multi-label methods. EvoRMD achieves strong predictive performance and offers interpretable insights through attention profiles and motif analyses. Together, these components establish a biologically grounded framework for identifying and prioritizing RNA modification types from sequence and context.

bioinformatics↗

bioRxiv2019

Robust-fit to nature: an evolutionary perspective on biological (and artificial) neural networks

Evolution is a blind fitting process by which organisms, over generations, adapt to the niches of an ever-changing environment. Does the mammalian brain use similar brute-force fitting processes to learn how to perceive and act upon the world? Recent advances in training deep neural networks has exposed the power of optimizing millions of synaptic weights to map millions of observations along ecologically relevant objective functions. This class of models has dramatically outstripped simpler, more intuitive models, operating robustly in real-life contexts spanning perception, language, and action coordination. These models do not learn an explicit, human-interpretable representation of the underlying structure of the data; rather, they use local computations to interpolate over task-relevant manifolds in a high-dimensional parameter space. Furthermore, counterintuitively, over-parameterized models, similarly to evolutionary processes, can be simple and parsimonious as they provide a versatile, robust solution for learning a diverse set of functions. In contrast to traditional scientific models, where the ultimate goal is interpretability, over-parameterized models eschew interpretability in favor of solving real-life problems or tasks. We contend that over-parameterized blind fitting presents a radical challenge to many of the underlying assumptions and practices in computational neuroscience and cognitive psychology. At the same time, this shift in perspective informs longstanding debates and establishes unexpected links with evolution, ecological psychology, and artificial life.

neuroscience↗

bioRxiv2025

Active learning enables discovery of transcriptional activators across fungal evolutionary space

Biological discovery and design are increasingly being guided by predictive models in place of costly experimentation. However, existing datasets are often biased by overrepresentation from model organisms, leading to failures in evolutionary studies of non-model species. We present a hybrid framework that leverages high-throughput molecular assays and active learning to quantify biological properties across evolutionary space. We focus on transcriptional activators, which contain activation domains (ADs) that promote gene expression. ADs are intrinsically disordered and poorly conserved, which limits their study using comparative genomics. Here, we developed ADhunter, a high-capacity regression model that outperforms state-of-theart algorithms in identifying and quantifying the strength of transcriptional activators. Model uncertainty was used to guide evolutionary sampling across 7.8 million proteins from 2,400 fungal genomes. We functionally characterized 9,836 ADs from 1,071 fungal genomes, providing a 15.5-fold expansion in genome representation compared to existing datasets. Comprehensive sampling from non-model genomes improved model generalizability and provides the first functional annotation for 3,416 proteins from 670 non-model fungi. Model interpretability analysis aligns with the biophysical model of AD function and reveals novel, underrepresented protein codes, highlighting the importance of sampling from non-model organisms to build evolutionarily robust models for predicting biological properties.

genomics↗

bioRxiv2017

Multi-omic analysis of a hyper-diverse plant metabolic pathway reveals evolutionary routes to biological innovation

The diversity of life on Earth is a result of continual innovations in molecular networks influencing morphology and physiology. Plant specialized metabolism produces hundreds of thousands of compounds, offering striking examples of these innovations. To understand how this novelty is generated, we investigated the evolution of the Solanaceae family-specific, trichome-localized acylsugar biosynthetic pathway using a combination of mass spectrometry, RNA-seq, enzyme assays, RNAi and phylogenetics in non-model species. Our results reveal that hundreds of acylsugars are produced across the Solanaceae family and even within a single plant, revealing this phenotype to be hyper-diverse. The relatively short biosynthetic pathway experienced repeated cycles of innovation over the last 100 million years that include gene duplication and divergence, gene loss, evolution of substrate preference and promiscuity. This study provides mechanistic insights into the emergence of plant chemical novelty, and offers a template for investigating the [~]300,000 non-model plant species that remain underexplored.

genomics↗

bioRxiv2021

A muscle-related contractile tissue specified by MRTF-activity in Porifera

Muscle-based movement is a hallmark of animal biology, but the evolutionary origins of myocytes - the cells that comprise muscle tissues - are unknown. Sponges (Porifera) provide an opportunity to reconstruct the earliest periods of myocyte evolution. Although sponges are believed to lack muscle, they are capable of coordinated whole-body contractions that purge debris from internal water canals. This behavior has been observed for decades, but their contractile tissues remain uncharacterized; it is an open question whether they have affinity to muscle or non-muscle contractile tissues in other animals. Here, we characterize the endothelial-like lining of water canals (the endopinacoderm) as a primary contractile tissue in the sponge Ephydatia muelleri. We find tissue-wide organization of contractile actin-bundles that contain striated-muscle myosin II and transgelin, and that contractions are regulated by the release of internal Ca2+ stores upstream of the myosin-light-chain-kinase (MLCK) pathway. Further, we show that the endopinacoderm is developmentally specified by myocardin-related transcription factor (MRTF) as part of an environmentally-inducible transcriptional complex that otherwise is known to function in muscle development, plasticity, and regeneration in other animals. We conclude that both muscle tissues and the endopinacoderm evolved from a common, multifunctional contractile tissue in the animal stem-lineage. Furthermore, as an actin-regulated force-sensor, MRTF-activity offers a mechanism for how water canals dynamically remodel in response to flow and can re-form normally from stem-cells in the absence of the intrinsic positional cues characteristic of embryogenesis in other animals.

evolutionary biology↗