Dataset Information

A developmental gene regulatory network for C. elegans anchor cell invasion.

ABSTRACT: Cellular invasion is a key part of development, immunity and disease. Using an in vivo model of Caenorhabditis elegans anchor cell invasion, we characterize the gene regulatory network that promotes cell invasion. The anchor cell is initially specified in a stochastic cell fate decision mediated by Notch signaling. Previous research has identified four conserved transcription factors, fos-1 (Fos), egl-43 (EVI1/MEL), hlh-2 (E/Daughterless) and nhr-67 (NR2E1/TLX), that mediate anchor cell specification and/or invasive behavior. Connections between these transcription factors and the underlying cell biology that they regulate are poorly understood. Here, using genome editing and RNA interference, we examine transcription factor interactions before and after anchor cell specification. Initially, these transcription factors function independently of one another to regulate LIN-12 (Notch) activity. Following anchor cell specification, egl-43, hlh-2 and nhr-67 function largely parallel to fos-1 in a type I coherent feed-forward loop with positive feedback to promote invasion. Together, these results demonstrate that the same transcription factors can function in cell fate specification and differentiated cell behavior, and that a gene regulatory network can be rapidly assembled to reinforce a post-mitotic, pro-invasive state.

SUBMITTER: Medwig-Kinney TN

PROVIDER: S-EPMC6983719 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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A developmental gene regulatory network for C. elegans anchor cell invasion.

Medwig-Kinney Taylor N TN Smith Jayson J JJ Palmisano Nicholas J NJ Tank Sujata S Zhang Wan W Matus David Q DQ

Development (Cambridge, England) 20200102 1

Cellular invasion is a key part of development, immunity and disease. Using an in vivo model of Caenorhabditis elegans anchor cell invasion, we characterize the gene regulatory network that promotes cell invasion. The anchor cell is initially specified in a stochastic cell fate decision mediated by Notch signaling. Previous research has identified four conserved transcription factors, fos-1 (Fos), egl-43 (EVI1/MEL), hlh-2 (E/Daughterless) and nhr-67 (NR2 ...[more]

PMID: 31806663

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Project description:Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.

Dataset Information

A developmental gene regulatory network for C. elegans anchor cell invasion.

Publications

A developmental gene regulatory network for <i>C. elegans</i> anchor cell invasion.

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