Barg, M. | bioRxiv Science

bioRxiv2019

Eomes and Brachyury control pluripotency exit and germ layer segregation by changes of chromatin state

The first lineage specification of pluripotent mouse epiblast segregates neuroectoderm (NE) from mesoderm and endoderm (ME) by currently poorly understood mechanisms. Here we demonstrate that the induction of any ME-gene programs critically relies on the T-box (Tbx) transcription factors Eomes and Brachyury that concomitantly repress pluripotency and NE gene programs. Tbx-deficient cells retain pluripotency and differentiate to NE lineages despite the presence of ME-inducing signals TGF{beta}/Nodal and WNT. Pluripotency and NE gene networks are additionally repressed by Tbx-induced ME factors, demonstrating a remarkable redundancy in program regulation to safeguard mutually exclusive lineage specification. Chromatin analyses revealed that accessibility of ME-gene enhancers depends on Tbx-binding, while NE-gene enhancers are accessible and activation-primed already at pluripotency state. This asymmetry of chromatin landscape thus explains the default differentiation of pluripotent cells to NE in the absence of ME-induction mediated through the activating and repressive functions of early Tbx factors Eomes and Brachyury.

developmental biology↗

bioRxiv2017

Extracting biological age from biomedical data via deep learning: too much of a good thing?

Aging-related physiological changes are systemic and, at least in humans, are linearly associated with age. Therefore, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. Aging acceleration, defined as the difference between the predicted and chronological age was found to be elevated in patients with major diseases and is predictive of mortality. In this work, we compare three increasingly accurate biological age models: metrics derived from unsupervised Principal Components Analysis (PCA), alongside two supervised biological age models; a multivariate linear regression and a state-of-the-art deep convolution neural network (CNN). All predictions were made using one-week long locomotor activity records from a 2003-2006 National Health and Nutrition Examination Survey (NHANES) dataset. We found that application of the supervised approaches improves the accuracy of the chronological age estimation at the expense of a loss of the association between the aging acceleration predicted by the model and all-cause mortality. Instead, we turned to the NHANES death register and introduced a novel way to train parametric proportional hazards models in a form suitable for out-of-the-box implementation with any modern machine learning software. Finally, we characterized a proof-of-concept example, a separate deep CNN trained to predict mortality risks that outperformed any of the biological age or simple linear proportional hazards models. Our findings demonstrate the emerging potential of combined wearable sensors and deep learning technologies for applications involving continuous health risk monitoring and real-time feedback to patients and care providers.

bioinformatics↗