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Genome-wide detection of Tbrain binding sites during early development in both sea urchin (S. purpuratus) and sea star (P. miniata)

ABSTRACT: Sea stars and sea urchins are model systems for interrogating the types of deep evolutionary changes that have restructured developmental gene regulatory networks (GRNs). While cis regulatory DNA evolution is likely the predominant mechanism of change, it was recently shown that Tbrain, a Tbox transcription factor protein, has evolved a changed preference for a low affinity, secondary binding motif, although the primary, high affinity motif is conserved. To date, however, no genome-wide comparisons have been performed in order to provide an unbiased assessment of the evolution of GRNs between these taxa; and no study has attempted to determine the interplay between transcription factor binding motif evolution and GRN topology. The study here measures genome-wide binding of Tbrain orthologs using ChIP-seq, and associates these with putative target genes to assess global function. Targets of both factors are enriched for other regulatory genes, although non-overlapping sets of functional enrichments in the two datasets suggest a much diverged function. The number of low affinity binding motifs are significantly depressed in sea urchins compared to sea star, but both motifs types are associated with genes from a range of functional categories. Only a small fraction (~10%) of genes are predicted to be orthologous targets. Collectively these data indicate that Tbr has evolved significantly different developmental roles in these echinoderms, and that the maintained and unique targets, and their associated binding motifs are dispersed throughout the hierarchy of the GRN, rather than being biased towards terminal process or discrete functional blocks suggesting extensive evolutionary tinkering.

ORGANISM(S): Strongylocentrotus purpuratus Patiria miniata

PROVIDER: GSE89862 | GEO | 2016/12/01

SECONDARY ACCESSION(S): PRJNA354530

REPOSITORIES: GEO

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Project description:Purpose: The Tbrain transcription factor has demonstrated an evolved preference for low-affinity, secondary site binding motifs between the sea star and sea urchin orthologs. We sought to identify targets of sea urchin and sea star orthologs of Tbr. Because less is known about the function of Tbr during sea star development, we used RNA-seq in conjuction with ChIP-seq studies (GEO:xxxx) to determine the targets of sea star Tbr in early development. Methods: Sea star (Patiria miniata) embryos were injected with translation-blocking morpholino antisense oligonucleotides to knock-down PmTbr expression, as described previously. Control morpholinos were injected into sibling embryos. Embryos were allowed to develop until hatching (30-36 hpf) at which point injected embryos were collected and RNA was extracted. RNA-seq libraries were prepared, sequenced, and analyzed using standard protocols. Results: There are 2,562 genes that are significantly differentially expressed relative to control morpholino inected embryos (FDR < 0.05). There are roughly equivalent numbers of genes down-regulated (1,041) and up-regulated (1,521) by Pm-tbr knockdown, suggesting that PmTbr may act as both a transcriptional activator and repressor. 1,165 differentially expressed genes are located within 75 kb of a PmTbr binding site determined using ChIP-seq, and this set is used as a basis for comparison between sea star and sea urchin binding sites. Conclusions: 1,165 targets of the PmTbr transcription factor were identified based on differential expression following knockdown and the presence of transcription factor binding sites proximal to differentially expressed genes. There are an equal number of up- and down-regulated targets, suggesting Tbr may function as a transcriptional activator and repressor, depending on context and target gene. There was no clear association of motif utilization with either the direction of differential expression or ontological category of the target gene. There are only a small fraction of target genes (approximately 10%) that are in common between the sea star and sea urchin sets.

Project description:This a model from the article: Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network. Kühn C, Wierling C, Kühn A, Klipp E, Panopoulou G, Lehrach H, Poustka AJ. BMC Syst Biol.2009 Aug 23;3:83. 19698179, Abstract: BACKGROUND: Gene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs. RESULTS: We developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data. CONCLUSION: The method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can be used to assess a completeness grade of any GRN. This could be especially useful for GRNs involved in human diseases, where often the amount of connectivity is unknown and/or many genes/interactions are missing. The paper describes several models, Mi, i=1...n, where M0 correspond to the unperturbed model and all the others correspond to the perturbed model. This model is the unperturbed model. The model reproduces figure 5 of the reference publication. The figures were generated by running 1 simulation, whereas in the paper the plotted values are the means of 800 simulations using randomly samples parameter sets. Additional information from the Author: The parameter that were randomly samples are the transcription parameters c_Proteins... and k_Proteins. The parameter were sampled from a lognormal distribution with sigma = 1.5 and mu = 0.5 This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Genome-wide detection of Tbrain binding sites during early development in both sea urchin (S. purpuratus) and sea star (P. miniata)

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