Inference of the Xenopus tropicalis embryonic regulatory network and spatial gene expression patterns

Xiaohui Xie,William T Chiu,Qing Nie,Ken WY Cho,Zhenzhen Zheng,Ira L Blitz,Scott Christley

doi:10.1186/1752-0509-8-3

Abstract

BackgroundDuring embryogenesis, signaling molecules produced by one cell population direct gene regulatory changes in neighboring cells and influence their developmental fates and spatial organization. One of the earliest events in the development of the vertebrate embryo is the establishment of three germ layers, consisting of the ectoderm, mesoderm and endoderm. Attempts to measure gene expression in vivo in different germ layers and cell types are typically complicated by the heterogeneity of cell types within biological samples (i.e., embryos), as the responses of individual cell types are intermingled into an aggregate observation of heterogeneous cell types. Here, we propose a novel method to elucidate gene regulatory circuits from these aggregate measurements in embryos of the frog Xenopus tropicalis using gene network inference algorithms and then test the ability of the inferred networks to predict spatial gene expression patterns.ResultsWe use two inference models with different underlying assumptions that incorporate existing network information, an ODE model for steady-state data and a Markov model for time series data, and contrast the performance of the two models. We apply our method to both control and knockdown embryos at multiple time points to reconstruct the core mesoderm and endoderm regulatory circuits. Those inferred networks are then used in combination with known dorsal-ventral spatial expression patterns of a subset of genes to predict spatial expression patterns for other genes. Both models are able to predict spatial expression patterns for some of the core mesoderm and endoderm genes, but interestingly of different gene subsets, suggesting that neither model is sufficient to recapitulate all of the spatial patterns, yet they are complementary for the patterns that they do capture.ConclusionThe presented methodology of gene network inference combined with spatial pattern prediction provides an additional layer of validation to elucidate the regulatory circuits controlling the spatial-temporal dynamics in embryonic development.

Highlights

During embryogenesis, signaling molecules produced by one cell population direct gene regulatory changes in neighboring cells and influence their developmental fates and spatial organization
We applied LASSO in our linear steady-state ordinary differential equations (ODEs) model to produce a parsimonious regulatory network that is optimal as tested by crossvalidation, and we showed how the LASSO regularization operator could be extended to incorporate prior network information [17]
Simulation results We generated a set of time series simulation data to test the Markov model

Summary

Introduction

During embryogenesis, signaling molecules produced by one cell population direct gene regulatory changes in neighboring cells and influence their developmental fates and spatial organization. Detailed gene regulatory networks (GRNs) in a number of invertebrate species have provided an unprecedented global overview of the genetic program controlling development in sea urchin, Drosophila, and C. elegans [1,2,3,4] and have revealed a number of important and conserved regulatory cassettes employed in a diversity of developmental contexts [5] While generation of such networks will be . In the Xenopus blastula the presumptive germ layers are arranged along the vegetal-animal axis with endoderm arising from the vegetal cells, mesoderm in an equatorial ring and the ectoderm on the top overlying the blastocoel cavity This simple spatial arrangement in developing embryos, taken together with a low complexity in terms of numbers of different cell types and the ease in manipulating gene expression, makes the amphibian Xenopus ideally suited to study GRNs in early vertebrate development. We present a novel method to elucidate gene regulatory circuits from aggregate gene expression measurements in embryos of the frog Xenopus tropicalis using gene network inference algorithms and test the ability of the inferred networks to predict spatial gene expression patterns

Methods

Results

Conclusion