A computational approach towards a gene regulatory network for the developing Nematostella vectensis gut

On the evolution and development of morphological complexity: A view from gene regulatory networks

How does morphological complexity evolve? This study suggests that the likelihood of mutations increasing phenotypic complexity becomes smaller when the phenotype itself is complex. In addition, the complexity of the genotype-phenotype map (GPM) also increases with the phenotypic complexity. We show that complex GPMs and the above mutational asymmetry are inevitable consequences of how genes need to be wired in order to build complex and robust phenotypes during development. We randomly wired genes and cell behaviors into networks in EmbryoMaker. EmbryoMaker is a mathematical model of development that can simulate any gene network, all animal cell behaviors (division, adhesion, apoptosis, etc.), cell signaling, cell and tissues biophysics, and the regulation of those behaviors by gene products. Through EmbryoMaker we simulated how each random network regulates development and the resulting morphology (i.e. a specific distribution of cells and gene expression in 3D). This way we obtained a zoo of possible 3D morphologies. Real gene networks are not random, but a random search allows a relatively unbiased exploration of what is needed to develop complex robust morphologies. Compared to the networks leading to simple morphologies, the networks leading to complex morphologies have the following in common: 1) They are rarer; 2) They need to be finely tuned; 3) Mutations in them tend to decrease morphological complexity; 4) They are less robust to noise; and 5) They have more complex GPMs. These results imply that, when complexity evolves, it does so at a progressively decreasing rate over generations. This is because as morphological complexity increases, the likelihood of mutations increasing complexity decreases, morphologies become less robust to noise, and the GPM becomes more complex. We find some properties in common, but also some important differences, with non-developmental GPM models (e.g. RNA, protein and gene networks in single cells).

High-throughput method for extracting and visualizing the spatial gene expressions from in situ hybridization images: A case study of the early development of the sea anemone Nematostella vectensis

Studying the spatial gene expression profiles from in situ hybridization images of the embryo is one of the first steps toward the comprehensive understanding of gene interactions in an organism. In the case of N. vectensis, extracting and collecting these data is a challenging task due to the difficulty of detecting the cell layer through the transparent body plan and changing morphology during the blastula and gastrula stages. Here, first, we introduce a method to algorithmically identify and track the cell layer in N. vectensis embryo from the late blastula to the late gastrula stage. With this, we will be able to extract spatial expression profiles of genes alongside the cell layer and consequently reconstructing the 1D representation of gene expression profiles. Furthermore, we use the morphological configurations of the embryo extracted from confocal images, to model the dynamics of embryos morphology during the gastrulation process in 2D. Ultimately, we provide a visualization tool for studying and comparing the extracted spatial gene expression profiles over the simulated embryo. We anticipate that our method of extraction and visualization to be a starting point for quantifying and collecting more in situ images from various sources, which can potentially accelerate our understanding of gene interactions in the early development of N. vectensis. The method allows researchers to visualize and compare the different gene expressions from different in situ images or different experiments. As an example, we were able to show the complementary expression of NvFoxA-NvSnailA and NvBra-NvErg in the central domain and central/external rings during the development which suggests the possible repression effects between each pair; as it has been discovered by functional analysis.

A novel technique to combine and analyse spatial and temporal expression datasets: A case study with the sea anemone Nematostella vectensis to identify potential gene interactions

Understanding genetic interactions during early development of a given organism, is the first step toward unveiling gene regulatory networks (GRNs) that govern a biological process of interest. Predicting such interactions from large expression datasets by performing targeted knock-down/knock-out approaches is a challenging task. We use the currently available expression datasets (in situ hybridization images & qPCR time series) for a basal anthozoan the sea anemone N. vectensis to construct continuous spatiotemporal gene expression patterns during its early development. Moreover, by combining cluster results from each dataset we develop a method that provides testable hypotheses about potential genetic interactions. We show that the analysis of spatial gene expression patterns reveals functional regions of the embryo during the gastrulation. The clustering results from qPCR time series unveils significant temporal events and highlights genes potentially involved in N. vectensis gastrulation. Furthermore, we introduce a method for merging the clustering results from spatial and temporal datasets by which we can group genes that are expressed in the same region and at the time. We demonstrate that the merged clusters can be used to identify GRN interactions involved in various processes and to predict possible activators or repressors of any gene in the dataset. Finally, we validate our methods and results by predicting the repressor effect of NvErg on NvBra in the central domain during the gastrulation that has recently been confirmed by functional analysis.

The rise of the starlet sea anemone Nematostella vectensis as a model system to investigate development and regeneration

Reverse genetics and next‐generation sequencing unlocked a new era in biology. It is now possible to identify an animal(s) with the unique biology most relevant to a particular question and rapidly generate tools to functionally dissect that biology. This review highlights the rise of one such novel model system, the starlet sea anemone Nematostella vectensis. Nematostella is a cnidarian (corals, jellyfish, hydras, sea anemones, etc.) animal that was originally targeted by EvoDevo researchers looking to identify a cnidarian animal to which the development of bilaterians (insects, worms, echinoderms, vertebrates, mollusks, etc.) could be compared. Studies in Nematostella have accomplished this goal and informed our understanding of the evolution of key bilaterian features. However, Nematostella is now going beyond its intended utility with potential as a model to better understand other areas such as regenerative biology, EcoDevo, or stress response. This review intends to highlight key EvoDevo insights from Nematostella that guide our understanding about the evolution of axial patterning mechanisms, mesoderm, and nervous systems in bilaterians, as well as to discuss briefly the potential of Nematostella as a model to better understand the relationship between development and regeneration. Lastly, the sum of research to date in Nematostella has generated a variety of tools that aided the rise of Nematostella to a viable model system. We provide a catalogue of current resources and techniques available to facilitate investigators interested in incorporating Nematostella into their research. WIREs Dev Biol 2016, 5:408–428. doi: 10.1002/wdev.222

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Analysis of functional importance of binding sites in the Drosophila gap gene network model

BACKGROUND

The statistical thermodynamics based approach provides a promising framework for construction of the genotype-phenotype map in many biological systems. Among important aspects of a good model connecting the DNA sequence information with that of a molecular phenotype (gene expression) is the selection of regulatory interactions and relevant transcription factor bindings sites. As the model may predict different levels of the functional importance of specific binding sites in different genomic and regulatory contexts, it is essential to formulate and study such models under different modeling assumptions.

RESULTS

We elaborate a two-layer model for the Drosophila gap gene network and include in the model a combined set of transcription factor binding sites and concentration dependent regulatory interaction between gap genes hunchback and Kruppel. We show that the new variants of the model are more consistent in terms of gene expression predictions for various genetic constructs in comparison to previous work. We quantify the functional importance of binding sites by calculating their impact on gene expression in the model and calculate how these impacts correlate across all sites under different modeling assumptions.

CONCLUSIONS

The assumption about the dual interaction between hb and Kr leads to the most consistent modeling results, but, on the other hand, may obscure existence of indirect interactions between binding sites in regulatory regions of distinct genes. The analysis confirms the previously formulated regulation concept of many weak binding sites working in concert. The model predicts a more or less uniform distribution of functionally important binding sites over the sets of experimentally characterized regulatory modules and other open chromatin domains.

Analysis of a spatial gene expression database for sea anemone Nematostella vectensis during early development

Background

The spatial distribution of many genes has been visualized during the embryonic development in the starlet sea anemone Nematostella vectensis in the last decade. In situ hybridization images are available in the Kahi Kai gene expression database, and a method has been developed to quantify spatial gene expression patterns of N. vectensis. In this paper, gene expression quantification is performed on a wide range of gene expression patterns from this database and descriptions of observed expression domains are stored in a separate database for further analysis.

Methods

Spatial gene expression from suitable in situ hybridization images has been quantified with the GenExp program. A correlation analysis has been performed on the resulting numerical gene expression profiles for each stage. Based on the correlated clusters of spatial gene expression and detailed descriptions of gene expression domains, various mechanisms for developmental gene expression are proposed.

Results

In the blastula and gastrula stages of development in N. vectensis, its continuous sheet of cells is partitioned into correlating gene expression domains. During progressing development, these regions likely correspond to different fates. A statistical analysis shows that genes generally remain expressed during the planula stages in those major regions that they occupy at the end of gastrulation.

Discussion

Observed shifts in gene expression domain boundaries suggest that elongation in the planula stage mainly occurs in the vegetal ring under the influence of the gene Rx. The secondary body axis in N. vectensis is proposed to be determined at the mid blastula transition.

Conclusions

Early gene expression domains in N. vectensis appear to maintain a positional order along the primary body axis. Early determination in N. vectensis occurs in two stages: expression in broad circles and rings in the blastula is consolidated during gastrulation, and more complex expression patterns appear in the planula within these broad regions. Quantification and comparison of gene expression patterns across a database can generate hypotheses about collective cell movements before these movements are measured directly.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0209-4) contains supplementary material, which is available to authorized users.

Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration

Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form.

Developmental and regenerative biology experiments are producing a huge number of morphological phenotypes from functional perturbation experiments. However, existing pathway models do not generally explain the dynamic regulation of anatomical shape due to the difficulty of inferring and testing non-linear regulatory networks responsible for appropriate form, shape, and pattern. We present a method that automates the discovery and testing of regulatory networks explaining morphological outcomes directly from the resultant phenotypes, producing network models as testable hypotheses explaining regeneration data. Our system integrates a formalization of the published results in planarian regeneration, an in silico simulator in which the patterning properties of regulatory networks can be quantitatively tested in a regeneration assay, and a machine learning module that evolves networks whose behavior in this assay optimally matches the database of planarian results. We applied our method to explain the key experiments in planarian regeneration, and discovered the first comprehensive model of anterior-posterior patterning in planaria under surgical, pharmacological, and genetic manipulations. Beyond the planarian data, our approach is readily generalizable to facilitate the discovery of testable regulatory networks in developmental biology and biomedicine, and represents the first developmental model discovered de novo from morphological outcomes by an automated system.