An explanatory evo-devo model for the developmental hourglass

Phylotranscriptomic patterns of network stochasticity and pathway dynamics during embryogenesis

MOTIVATION

The hourglass model is a popular evo-devo model depicting that the developmental constraints in the middle of a developmental process are higher, and hence the phenotypes are evolutionarily more conserved, than those that occur in early and late ontogeny stages. Although this model has been supported by studies analyzing developmental gene expression data, the evolutionary explanation and molecular mechanism behind this phenomenon are not fully understood yet. To approach this problem, Raff proposed a hypothesis and claimed that higher interconnectivity among elements in an organism during organogenesis resulted in the larger constraints at the mid-developmental stage. By employing stochastic network analysis and gene-set pathway analysis, we aim to demonstrate such changes of interconnectivity claimed in Raff's hypothesis.

RESULTS

We first compared the changes of network randomness among developmental processes in different species by measuring the stochasticity within the biological network in each developmental stage. By tracking the network entropy along each developmental process, we found that the network stochasticity follows an anti-hourglass trajectory, and such a pattern supports Raff's hypothesis in dynamic changes of interconnections among biological modules during development. To understand which biological functions change during the transition of network stochasticity, we sketched out the pathway dynamics along the developmental stages and found that species may activate similar groups of biological processes across different stages. Moreover, higher interspecies correlations are found at the mid-developmental stages.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The hourglass organization of the Caenorhabditis elegans connectome

We approach the C. elegans connectome as an information processing network that receives input from about 90 sensory neurons, processes that information through a highly recurrent network of about 80 interneurons, and it produces a coordinated output from about 120 motor neurons that control the nematode's muscles. We focus on the feedforward flow of information from sensory neurons to motor neurons, and apply a recently developed network analysis framework referred to as the "hourglass effect". The analysis reveals that this feedforward flow traverses a small core ("hourglass waist") that consists of 10-15 interneurons. These are mostly the same interneurons that were previously shown (using a different analytical approach) to constitute the "rich-club" of the C. elegans connectome. This result is robust to the methodology that separates the feedforward from the feedback flow of information. The set of core interneurons remains mostly the same when we consider only chemical synapses or the combination of chemical synapses and gap junctions. The hourglass organization of the connectome suggests that C. elegans has some similarities with encoder-decoder artificial neural networks in which the input is first compressed and integrated in a low-dimensional latent space that encodes the given data in a more efficient manner, followed by a decoding network through which intermediate-level sub-functions are combined in different ways to compute the correlated outputs of the network. The core neurons at the hourglass waist represent the information bottleneck of the system, balancing the representation accuracy and compactness (complexity) of the given sensory information.

Reconstructing the Transcriptional Ontogeny of Maize and Sorghum Supports an Inverse Hourglass Model of Inflorescence Development

Assembling meaningful comparisons between species is a major limitation in studying the evolution of organismal form. To understand development in maize and sorghum, closely related species with architecturally distinct inflorescences, we collected RNA-seq profiles encompassing inflorescence body-plan specification in both species. We reconstructed molecular ontogenies from 40 B73 maize tassels and 47 BTx623 sorghum panicles and separated them into transcriptional stages. To discover new markers of inflorescence development, we used random forest machine learning to determine stage by RNA-seq. We used two descriptions of transcriptional conservation to identify hourglass-like stages during inflorescence development. Despite a relatively short 12 million years since their last common ancestor, we found maize and sorghum inflorescences are most different during their hourglass-like stages of development, following an inverse-hourglass model of development. We discuss whether agricultural selection may account for the rapid divergence signatures in these species and the observed separation of evolutionary pressure and developmental reprogramming.

Maternal mRNA input of growth and stress-response-related genes in cichlids in relation to egg size and trophic specialization

BACKGROUND

Egg size represents an important form of maternal effect determined by a complex interplay of long-term adaptation and short-term plasticity balancing egg size with brood size. Haplochromine cichlids are maternal mouthbrooders showing differential parental investment in different species, manifested in great variation in egg size, brood size and duration of maternal care. Little is known about maternally determined molecular characters of eggs in fishes and their relation to egg size and trophic specialization. Here we investigate maternal mRNA inputs of selected growth- and stress-related genes in eggs of mouthbrooding cichlid fishes adapted to different trophic niches from Lake Tanganyika, Lake Malawi, Lake Victoria and compare them to their riverine allies.

RESULTS

We first identified two reference genes, atf7ip and mid1ip1, to be suitable for cross-species quantification of mRNA abundance via qRT-PCR in the cichlid eggs. Using these reference genes, we found substantial variation in maternal mRNA input for a set of candidate genes related to growth and stress response across species and lakes. We observed negative correlation of mRNA abundance between two of growth hormone receptor paralogs (ghr1 and ghr2) across all haplochromine cichlid species which also differentiate the species in the two younger lakes, Malawi and Lake Victoria, from those in Lake Tanganyika and ancestral riverine species. Furthermore, we found correlations between egg size and maternal mRNA abundance of two growth-related genes igf2 and ghr2 across the haplochromine cichlids as well as distinct clustering of the species based on their trophic specialization using maternal mRNA abundance of five genes (ghr1, ghr2, igf2, gr and sgk1).

CONCLUSIONS

These findings indicate that variations in egg size in closely related cichlid species can be linked to differences in maternal RNA deposition of key growth-related genes. In addition, the cichlid species with contrasting trophic specialization deposit different levels of maternal mRNAs in their eggs for particular growth-related genes; however, it is unclear whether such differences contribute to differential morphogenesis at later stages of development. Our results provide first insights into this aspect of gene activation, as a basis for future studies targeting their role during ecomorphological specialization and adaptive radiation.

Development and Evolutionary Constraints in Animals

We review the evolutionary importance of developmental mechanisms in constraining evolutionary changes in animals—in other words, developmental constraints. We focus on hard constraints that can act on macroevolutionary timescales. In particular, we discuss the causes and evolutionary consequences of the ancient metazoan constraint that differentiated cells cannot divide and constraints against changes of phylotypic stages in vertebrates and other higher taxa. We conclude that in all cases these constraints are caused by complex and highly controlled global interactivity of development, the disturbance of which has grave consequences. Mutations that affect such global interactivity almost unavoidably have many deleterious pleiotropic effects, which will be strongly selected against and will lead to long-term evolutionary stasis. The discussed developmental constraints have pervasive consequences for evolution and critically restrict regeneration capacity and body plan evolution.

(Why) Does Evolution Favour Embryogenesis?

Complex multicellular organisms typically possess life cycles in which zygotes (formed by gamete fusion) and meiosis occur. Canonical animal embryogenesis describes development from zygote to birth. It involves polarisation of the egg/zygote, asymmetric cell divisions, establishment of axes, symmetry breaking, formation of organs, and parental nutrition (at least in early stages). Similar developmental patterns have independently evolved in other eukaryotic lineages, including land plants and brown algae. The question arises whether embryo-like structures and associated developmental processes recurrently emerge because they are local optima of the evolutionary landscape. To understand which evolutionary principles govern complex multicellularity, we need to analyse why and how similar processes evolve convergently - von Baer's and Haeckel's phylotypic stage revisited in other phyla.

Convergent occurrence of the developmental hourglass in plant and animal embryogenesis?

BACKGROUND

The remarkable similarity of animal embryos at particular stages of development led to the proposal of a developmental hourglass. In this model, early events in development are less conserved across species but lead to a highly conserved 'phylotypic period'. Beyond this stage, the model suggests that development once again becomes less conserved, leading to the diversity of forms. Recent comparative studies of gene expression in animal groups have provided strong support for the hourglass model. How and why might such an hourglass pattern be generated? More importantly, how might early acting events in development evolve while still maintaining a later conserved stage?

SCOPE

The discovery that an hourglass pattern may also exist in the embryogenesis of plants provides comparative data that may help us explain this phenomenon. Whether the developmental hourglass occurs in plants, and what this means for our understanding of embryogenesis in plants and animals is discussed. Models by which conserved early-acting genes might change their functional role in the evolution of gene networks, how networks buffer these changes, and how that might constrain, or confer diversity, of the body plan are also discused.

CONCLUSIONS

Evidence of a morphological and molecular hourglass in plant and animal embryogenesis suggests convergent evolution. This convergence is likely due to developmental constraints imposed upon embryogenesis by the need to produce a viable embryo with an established body plan, controlled by the architecture of the underlying gene regulatory networks. As the body plan is largely laid down during the middle phases of embryo development in plants and animals, then it is perhaps not surprising this stage represents the narrow waist of the hourglass where the gene regulatory networks are the oldest and most robust and integrated, limiting species diversity and constraining morphological space.

Evolution of bow-tie architectures in biology

Bow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network—that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved.

Many biological systems show bow-tie (also called hourglass) architecture. A bow-tie means that a large number of inputs are converted to a small number of intermediates, which then fan out to generate a large number of outputs. For example, cells use a wide variety of nutrients; process them into 12 metabolic precursors, which are then used to make all of the cells biomass. Similar principles exist in biological signaling and in the information processing in the visual system. Despite the ubiquity of bow-tie structures in biology, there is no explanation of how they evolved. Here, we find that bow-ties spontaneously evolve when the information in the evolutionary goal they evolved to satisfy can be compressed. Mathematically, this means that the matrix representing the goal has deficient rank. The maximal compression possible determines the width of the bow-tie—the narrowest part in the network (equal to the rank of the goal matrix). This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the rank of the goals under which they evolved.

Evolving gene regulatory networks into cellular networks guiding adaptive behavior: an outline how single cells could have evolved into a centralized neurosensory system

Understanding the evolution of the neurosensory system of man, able to reflect on its own origin, is one of the major goals of comparative neurobiology. Details of the origin of neurosensory cells, their aggregation into central nervous systems and associated sensory organs and their localized patterning leading to remarkably different cell types aggregated into variably sized parts of the central nervous system have begun to emerge. Insights at the cellular and molecular level have begun to shed some light on the evolution of neurosensory cells, partially covered in this review. Molecular evidence suggests that high mobility group (HMG) proteins of pre-metazoans evolved into the definitive Sox [SRY (sex determining region Y)-box] genes used for neurosensory precursor specification in metazoans. Likewise, pre-metazoan basic helix-loop-helix (bHLH) genes evolved in metazoans into the group A bHLH genes dedicated to neurosensory differentiation in bilaterians. Available evidence suggests that the Sox and bHLH genes evolved a cross-regulatory network able to synchronize expansion of precursor populations and their subsequent differentiation into novel parts of the brain or sensory organs. Molecular evidence suggests metazoans evolved patterning gene networks early, which were not dedicated to neuronal development. Only later in evolution were these patterning gene networks tied into the increasing complexity of diffusible factors, many of which were already present in pre-metazoans, to drive local patterning events. It appears that the evolving molecular basis of neurosensory cell development may have led, in interaction with differentially expressed patterning genes, to local network modifications guiding unique specializations of neurosensory cells into sensory organs and various areas of the central nervous system.

An explanatory evo-devo model for the developmental hourglass

The "developmental hourglass'' describes a pattern of increasing morphological divergence towards earlier and later embryonic development, separated by a period of significant conservation across distant species (the "phylotypic stage''). Recent studies have found evidence in support of the hourglass effect at the genomic level. For instance, the phylotypic stage expresses the oldest and most conserved transcriptomes. However, the regulatory mechanism that causes the hourglass pattern remains an open question. Here, we use an evolutionary model of regulatory gene interactions during development to identify the conditions under which the hourglass effect can emerge in a general setting. The model focuses on the hierarchical gene regulatory network that controls the developmental process, and on the evolution of a population under random perturbations in the structure of that network. The model predicts, under fairly general assumptions, the emergence of an hourglass pattern in the structure of a temporal representation of the underlying gene regulatory network. The evolutionary age of the corresponding genes also follows an hourglass pattern, with the oldest genes concentrated at the hourglass waist. The key behind the hourglass effect is that developmental regulators should have an increasingly specific function as development progresses. Analysis of developmental gene expression profiles from Drosophila melanogaster and Arabidopsis thaliana provide consistent results with our theoretical predictions.