Biology subjects

Verd, B.

Publications and source records attributed to Verd, B..

11 recordsLinked to original sources

bioRxiv2026

Whole body elongation drives coordinated vertebral shape evolution in Lake Malawi cichlid fishes

Understanding how anatomical structures evolve requires disentangling the roles of integration and modularity in shaping morphological variation. The vertebral column, a serially repeated and regionally differentiated structure, provides a powerful system for investigating these processes. Here, we examine how vertebral morphology evolves in relation to whole-body elongation across the adaptive radiation of Lake Malawi cichlid fishes. We tested for evolutionary integration between the precaudal and caudal domains, as well as assessed the contributions of vertebral count, centrum shape, and intervertebral spacing on body elongation. We find strong evolutionary integration between the shapes of precaudal and caudal vertebrae, with both vertebral shapes varying along similar axes. Despite this, precaudal and caudal vertebral counts evolve independently, indicating a decoupling between the specification of identity and the development of their respective shapes. Whole-body elongation is significantly associated with coordinated changes in vertebral and rib morphology, including proportional increases in centrum size, posterior displacement of neural and haemal spines, and increased rib curvature. In contrast, centrum elongation and intervertebral spacing do not contribute to body elongation across the radiation. These results demonstrate that body elongation in cichlids necessitates integrated, multivariate changes in axial morphology. Our findings highlight the importance of morphological integration in facilitating coordinated evolutionary responses in anatomical systems.

evolutionary biology↗

bioRxiv2026

Evolving initial conditions: an alternative developmental route to morphological diversity

Phenotypic diversity is often thought to arise from the evolutionary modification of developmental processes. However, developmental processes are tightly coupled in space and time, with each process beginning from conditions set by the one before it. While we know from dynamical systems theory that initial conditions can significantly affect a systems out-come, their importance as a source of phenotypic evolvability has been largely overlooked. Here we show for the first time, that phenotypic evolution can proceed through changes in developmental initial conditions while the underlying developmental process remains conserved. Somitogenesis is the process by which vertebral precursors, known as somites, are periodically patterned in the pre-somitic mesoderm (PSM). Somitic count (total number of somites) is thought to diversify through the evolution of components of somitogenesis such as the tempo of the segmentation clock or the mechanisms driving axial morphogenesis. Using two closely related species of Lake Malawi cichlid fishes that differ in vertebral counts, we show that somite count evolution has happened without changes to somitogenesis itself, but instead, by altering the size of the PSM at the onset of this process. This work will expand what we consider developmental drivers of phenotypic evolution and highlight the importance of comparative studies to understand the diversification of phenotypes.

developmental biology↗

bioRxiv2025

Somitic Change Drives Changes in Vertebral Regionalisation in African Cichlids Despite Strong Canalisation of Somite Number

Vertebrae arise from somites, transient embryonic segments that rhythmically bud from the presomitic mesoderm during axial elongation. The number and identity of vertebrae are ultimately determined by somitogenesis and subsequent anterior-posterior regionalisation, largely governed by hox gene expression. Interspecific variation in vertebral count and regionalisation therefore reflects evolutionary changes in somite number and homeotic identity following species divergence. While many macroevolutionary studies have examined homeotic and non-homeotic changes in the vertebral column, few have explored these dynamics in teleosts, despite their exceptional species richness. Using African cichlids as a model, we show that shifts in vertebral regionalisation can arise through modifications to anterior-posterior patterning, but that much of the observed variation is driven by changes in somite number, with homeotic effects emerging largely as a by-product of somitic changes. Moreover, low intraspecific variation in vertebral count, lacking phylogenetic structure, suggests that somitic count variation within species is strongly canalised and has remained consistent throughout the diversification of African cichlids. In addition, we find no correlation between intraspecific variation in vertebral counts and mean vertebral counts, and this variation does not consistently scale with body aspect ratio among individuals. Therefore, intraspecific variation is decoupled from both macroevolutionary patterns of vertebral count evolution and body shape diversification. Together, our findings highlight the dynamic interplay between somitogenesis and homeotic transformations in shaping vertebral diversity and underscore the value of cichlids as a model for understanding the developmental basis of axial evolution in teleosts.

evolutionary biology↗

bioRxiv2025

African Cichlid Lake Radiations Recapitulate Riverine Axial Morphologies Through Repeated Exploration of Morphospace

Macroevolutionary theory predicts that adaptive radiations are characterised by early bursts of trait evolution followed by rate slowdowns as ecological niches are filled. However, these dynamics are rarely detected within individual clades. Here, we test whether this discrepancy arises from limited temporal sampling by comparing replicated adaptive radiations of African cichlids spanning distinct evolutionary timescales. We show that the net rates of vertebral count evolution are inversely correlated with radiation age, consistent with early burst dynamics. However, we do not detect this pattern within the individual systems. We also find evidence of changes to the underlying processes that generate disparity across the timescale of adaptive radiation, signalled by increasing importance of phylogenetic signal with radiation age. This suggests that early phenotypic expansion is both rapid and phylogenetically unstructured, but that stabilising dynamics emerge on longer timescales, amid declining rates. Despite rapid phenotypic diversification, lacustrine lineages occupy only a subset of the broader morphospace of riverine taxa, suggesting that diversification over short timescales is constrained and may largely resample the variation already present in older lineages. Our results provide strong evidence for shifting evolutionary dynamics through time in adaptive radiations, and that the apparent rarity of early-burst signals in comparative analyses may partly reflect limited statistical power due to narrow temporal sampling.

evolutionary biology↗

bioRxiv2024

Modularity of the segmentation clock and morphogenesis

Vertebrates have evolved great diversity in the number of segments dividing the trunk body, however the developmental origin of the evolvability of this trait is poorly understood. The number of segments is thought to be determined in embryogenesis as a product of morphogenesis of the pre-somitic mesoderm (PSM) and the periodicity of a molecular oscillator active within the PSM known as the segmentation clock. Here we explore whether the clock and PSM morphogenesis exhibit developmental modularity, as independent evolution of these two processes may explain the high evolvability of segment number. Using a computational model of the clock and PSM parameterised for zebrafish, we find that the clock is broadly robust to variation in morphogenetic processes such as cell ingression, motility, compaction, and cell division. We show that this robustness is in part determined by the length of the PSM and the strength of phase coupling in the clock. As previous studies report no changes to morphogenesis upon perturbing the clock, we suggest that the clock and morphogenesis of the PSM exhibit developmental modularity.

developmental biology↗

bioRxiv2023

A whole-body micro-CT scan library that captures the skeletal diversity of Lake Malawi cichlid fishes

Here we describe a dataset of freely available, readily processed, whole-body CT-scans of 56 species (116 specimens) of Lake Malawi cichlid fishes that captures a considerable majority of the morphological variation present in this remarkable adaptive radiation. We contextualise the scanned specimens within a discussion of their respective ecomorphological groupings and suggest possible macroevolutionary studies that could be conducted with these data. We also describe a methodology to efficiently CT-scan (on average) 23 specimens per hour, limiting scanning time and alleviating the financial cost whilst maintaining high resolution. We demonstrate the utility of this method by reconstructing 3D models of multiple bones from multiple specimens within the dataset. We hope this dataset will enable further morphological study of this fascinating system and permit wider-scale comparisons with other cichlid adaptive radiations.

evolutionary biology↗

bioRxiv2022

Zebrafish Neuromesodermal Progenitors Undergo a Critical State Transition in vivo

The transition state model of cell differentiation proposes that a transient window of gene expression stochasticity precedes entry into a differentiated state. As this has been assessed primarily in vitro, we sought to explore whether it can also be observed in vivo. Zebrafish neuromesodermal progenitors (NMps) differentiate into spinal cord and paraxial mesoderm at the late somitogenesis stages. We observed an increase in gene expression variability at the 24 somite stage (24ss) prior to their differentiation. From our analysis of a published 18ss scRNA-seq dataset, we showed that the NMp population possesses a signature consistent with a population undergoing a critical transition. By building in silico composite gene expression maps from our image data, we were able to assign an NM index to each in silico NMp based on the cumulative expression of its neural and mesodermal markers. With the NM index distributions, we demonstrated that cell population heterogeneity of the NMps peaked at 24ss. We then incorporated stochasticity and non-autonomy into a genetic toggle switch model and uncovered the existence of rebellious cells, which we then confirmed by reexamining the composite maps. Taken together, our work supports the transition state model within an endogenous cell fate decision making event.

developmental biology↗

bioRxiv2022

Approximated Gene Expression Trajectories (AGETs) for Gene Regulatory Network Inference on Cell Tracks

The study of pattern formation has greatly benefited from our ability to reverse-engineer gene regulatory network (GRN) structure from spatio-temporal quantitative gene expression data. Traditional approaches omit tissue morphogenesis, and focus on systems where the timescales of pattern formation and morphogenesis can be separated. In such systems, pattern forms as an emergent property of the underlying GRN and mechanistic insight can be obtained from the GRNs alone. However, this is not the case in most animal patterning systems, where patterning and morphogenesis are co-occurring and tightly linked. To address the mechanisms driving pattern formation in such systems we need to adapt our GRN inference methodologies to explicitly accommodate cell movements and tissue shape changes. In this work we present a novel framework to reverse-engineer GRNs underlying pattern formation in tissues undergoing morphogenetic changes and cell rearrangements. By integrating quantitative data from live and fixed embryos, we approximate gene expression trajectories (AGETs) in single cells and use a subset to reverse-engineer candidate GRNs using a Markov Chain Monte Carlo approach. GRN fit is assessed by simulating on cell tracks (live-modelling) and comparing the output to quantitative data-sets. This framework generates candidate GRNs that recapitulate pattern formation at the level of the tissue and the single cell. To our knowledge, this inference methodology is the first to integrate cell movements and gene expression data, making it possible to reverse-engineer GRNs patterning tissues undergoing morphogenetic changes.

systems biology↗

bioRxiv2021

Morphogenetic coupling leads to pattern emergence in the pre somitic mesoderm

As tissues elongate, cell rearrangement alters positional information in manner that must be accounted for to generate gene expression pattern. How this is achieved during paraxial mesoderm elongation is unknown. By reverse-engineering gene regulatory networks that predict single cell expression trajectories across the tissue, we find a network capable of recapitulating the full range of dynamic differentiation profiles observed both in vivo and in vitro. Simulating gene expression profiles on in toto cell tracking data sets reveal that temporal exposure to Wnt and FGF is generated by cell movement. The absence of reversal in gene expression towards a more premature gene expression state predicts the generation of aberrant tbx6 expression in the posterior progenitor zone that we then confirm by quantitative single cell imaging. Taken together, these results demonstrate cell rearrangement tunes the dynamics of mesoderm progenitor differentiation to generate pattern emergence as a function of temporal Wnt and FGF exposure.

developmental biology↗

bioRxiv2020

A deep learning approach for staging embryonic tissue isolates with small data

Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches. Author summaryThe application of machine learning approaches currently hinges on the availability of large data sets to train the models with. However, recent research has shown that large data sets might not always be required. In this work we set out to see whether we could use small confocal microscopy image data sets to train a convolutional neural network (CNN) to stage zebrafish tail buds at four different stages in their development. We found that high test accuracies can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a CNN. This work also shows that we can robustly stage the embryonic development of isolated structures, without the need to refer back to landmarks in the tail bud. This constitutes an important methodological advance for staging organoids and other 3D culture in vitro systems. This work proves that prohibitively large data sets are not always required to train CNNs, and we hope will encourage others to apply the power of machine learning to their areas of study even if data are scarce.

developmental biology↗

bioRxiv2018

Modularity, criticality and evolvability of a developmental gene regulatory network

The existence of discrete phenotypic traits suggests that the complex regulatory processes which produce them are functionally modular. These processes are usually represented by networks. Only modular networks can be partitioned into intelligible subcircuits able to evolve relatively independently. Traditionally, functional modularity is approximated by detection of modularity in network structure. However, the correlation between structure and function is loose. Many regulatory networks exhibit modular behaviour without structural modularity. Here we partition an experimentally tractable regulatory network--the gap gene system of dipteran insects--using an alternative approach. We show that this system, although not structurally modular, is composed of dynamical modules driving different aspects of whole-network behaviour. All these subcircuits share the same regulatory structure, but differ in components and sensitivity to regulatory interactions. Some subcircuits are in a state of criticality while others are not, which explains the observed differential evolvability of the various expression features in the system.

developmental biology↗