Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Cis-regulatory architecture of a brain-signaling center predates the origin of chordates

ABSTRACT: Genomic approaches have predicted hundreds of thousands of tissue specific cis-regulatory sequences, but the determinants critical to their function and evolutionary history are mostly unknown1-4. Here, we systematically decode a set of brain enhancers active in the zona limitans intrathalamica (zli), a signaling center essential for vertebrate forebrain development via the secreted morphogen, Sonic hedgehog (Shh)5,6. We apply a de novo motif analysis tool to identify six position-independent sequence motifs together with their cognate transcription factors that are essential for zli enhancer activity and Shh expression in the mouse embryo. Using knowledge of this regulatory lexicon, we discover novel Shh zli enhancers in mice, and a functionally equivalent element in hemichordates, indicating an ancient origin of the Shh zli regulatory network that predates the chordate phylum. These findings establish a paradigm for delineating functionally conserved enhancers in the absence of overt sequence homologies, and over extensive evolutionary distances. Gene expression profiles from the mouse zona limitans intrathalamica (ZLI) region at E10.5

ORGANISM(S): Mus musculus

SUBMITTER: Ying-Tao Zhao

PROVIDER: E-GEOD-78005 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: identifying truly non-coding genes from de novo reconstructed transcriptomes, and prioritizing the hundreds of resulting putative lncRNAs for downstream experimental interrogation. We present slncky, a computational lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-sequencing data and further uses evolutionary constraint to prioritize lncRNAs that are likely to be functionally important. Our automated filtering pipeline is comparable to manual curation efforts and more sensitive than previously published computational approaches. Furthermore, we develop a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for analyzing sequence and transcript evolution. Our analysis reveals that evolutionary selection acts in several distinct patterns, and uncovers two notable classes of intergenic lncRNAs: one showing strong purifying selection on RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript. To study a comprehensive and comparable set of lncRNAs expressed in the pluripotent state, we analyzed RNA-Seq data from pluripotent cells derived from several strains and species, and grown in two physiological conditions. First we derived ânaÃ¯veâ ES cells (ESCs) from each of three different mice strains: 129SvEv, NOD, and Mus musculus castaneus (cast) mouse, a wild mouse subspecies originally from Thailand, as well as naÃ¯ve induced pluripotent stem (iPS) cells from rat and human. Next, to facilitate comparisons across pluripotent cells from different species, we also cultured mouse and rat cells in âprimedâ epiblast stem cell (EpiSC) culture conditions, since human iPS cells in culture are much more similar molecularly and functionally to mouse primed EpiSCs rather than to a ground state naÃ¯ve ESCs. We polyA selected RNA from each cell type and deeply sequenced on HiSeq2500

Dataset Information

Cis-regulatory architecture of a brain-signaling center predates the origin of chordates

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets