Biology subjects

Ruangroengkulrith, S.

Publications and source records attributed to Ruangroengkulrith, S..

2 recordsLinked to original sources

bioRxiv2022

Zygotic genome activation by the totipotency pioneer factor Nr5a2

Life begins with a switch in genetic control from the maternal to the embryonic genome during zygotic genome activation (ZGA) in totipotent embryos. Despite its importance, the essential regulators of ZGA remain largely unknown in mammals. Based on de novo motif searches, we identified the orphan nuclear receptor Nr5a2 as a key activator of major ZGA in mouse embryos. Nr5a2 binds to its motif within a subtype of SINE B1/Alu transposable elements found in cis-regulatory regions of ZGA genes. Chemical inhibition suggests that 72% of ZGA genes are regulated by Nr5a2 and potentially other orphan nuclear family receptors. Consistent with a role in ZGA, Nr5a2 is required for progression beyond the 2-cell stage. Nr5a2 promotes chromatin accessibility during ZGA and binds to entry/exit sites of nucleosomal DNA in vitro. We conclude that Nr5a2 is an essential pioneer factor that distinctly regulates totipotency and pluripotency during mammalian development. One-Sentence SummaryNr5a2 is an essential pioneer transcription factor that activates expression of zygotic genes in mouse embryos.

developmental biology↗

bioRxiv2020

Predicting the impact of sequence motifs on gene regulation using single-cell data

BackgroundBinding of transcription factors (TFs) at proximal promoters and distal enhancers is central to gene regulation. Yet, identification of TF binding sites, also known as regulatory motifs, and quantification of their impact on gene expression remains challenging. ResultsHere we infer putative regulatory motifs along with their cell type-specific importance using a convolutional neural network trained on single-cell data. Comparison of the importance score to expression levels across cells allows us to identify the TFs most likely to be binding at a given motif. Using multiple mouse tissues we obtain a model with cell type resolution which explains 29% of the variance in gene expression. Finally, by applying scover to distal enhancers identified using scATAC-seq from the mouse cerebral cortex we characterize changes in distal regulatory motifs during development. ConclusionsIt is possible to identify regulatory motifs as well as their importance from single-cell data using a neural network model where all of the parameters and outputs are easily interpretable to the user.

bioinformatics↗