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Omgba, P. A.

Publications and source records attributed to Omgba, P. A..

2 recordsLinked to original sources

An advanced head-to-tail mouse embryo model with hypoxia-mediated neural patterning

The developing mammalian embryo is guided by the continuously changing signals that it receives from maternal tissues and its microenvironment. The dynamic cell-cell and cell-environment interactions that together shape the embryo largely remained unexplorable until the advance of stem cell-based embryo models. These revealed the self-organizing properties of cells in response to endogenous and exogenous cues. Among the latter, restricted oxygen (hypoxia) emerged as a critical microenvironmental regulator that influences cell type diversification in multicellular systems. Here we built a modular ESC-based head-to-tail model of mouse embryogenesis by developing an antero-posterior (AP) assembly strategy under hypoxia. These structures called HAP-gastruloids feature stage-appropriate anterior neural tissues that recapitulate the morphological organization and transcriptional identity of fore- and midbrain including spatial organizer regions such as the midbrain-hindbrain boundary. These anterior tissues develop in synchrony with posterior tissues such as the spinal cord, somites, and gut endoderm derivatives, ultimately yielding a unified structure. We show via genetic, environmental, and pharmacological perturbations that timed hypoxia is essential to boost anterior neural cell identities and their patterning through HIF1a and in part by modulating TGF{beta} activity. These results underline the key beneficial role of hypoxia in early development and offer a uniquely modular system to investigate antero-posterior phenotypes for basic discovery and translation.

developmental biology↗

CENTRE: A gradient boosting algorithm for Cell-type-specific ENhancer-Target pREdiction

MotivationIdentifying target promoters of active enhancers is a crucial step for realizing gene regulation and deciphering phenotypes and diseases. Up to now, several computational methods were developed to predict enhancer gene interactions but they require either many epigenomic and transcriptomic experimental assays to generate cell-type-specific predictions or a single experiment applied to a large cohort of cell types to extract correlations between activities of regulatory elements. Thus, inferring cell-type-specific enhancer gene interactions in unstudied or poorly annotated cell types becomes a laborious and costly task. ResultsHere, we aim to infer cell-type-specific enhancer target interactions, using minimal experimental input. We introduce CENTRE, a machine learning framework that predicts enhancer target interactions in a cell-type-specific manner, using only gene expression and ChIP-seq data for three histone modifications for the cell type of interest. CENTRE exploits the wealth of available datasets and extracts cell-type agnostic statistics to complement the cell-type specific information. CENTRE is thoroughly tested across many datasets and cell types and achieves equivalent or superior performance than existing algorithms that require massive experimental data. AvailabilityCENTREs open source code is available at GitHub via https://github.com/slrvv/CENTRE

bioinformatics↗