Spatial regulation by multiple Gremlin1 enhancers provides digit development with cis-regulatory robustness and evolutionary plasticity.

Precise cis-regulatory control of gene expression is essential for normal embryogenesis and tissue development. The BMP antagonist Gremlin1 (Grem1) is a key node in the signalling system that coordinately controls limb bud development. Here, we use mouse reverse genetics to identify the enhancers in the Grem1 genomic landscape and the underlying cis-regulatory logics that orchestrate the spatio-temporal Grem1 expression dynamics during limb bud development. We establish that transcript levels are controlled in an additive manner while spatial regulation requires synergistic interactions among multiple enhancers. Disrupting these interactions shows that altered spatial regulation rather than reduced Grem1 transcript levels prefigures digit fusions and loss. Two of the enhancers are evolutionary ancient and highly conserved from basal fishes to mammals. Analysing these enhancers from different species reveal the substantial spatial plasticity in Grem1 regulation in tetrapods and basal fishes, which provides insights into the fin-to-limb transition and evolutionary diversification of pentadactyl limbs

Heterochronic Shift in Hox-Mediated Activation of Sonic hedgehog Leads to Morphological Changes during Fin Development

We explored the molecular mechanisms of morphological transformations of vertebrate paired fin/limb evolution by comparative gene expression profiling and functional analyses. In this study, we focused on the temporal differences of the onset of Sonic hedgehog (Shh) expression in paired appendages among different vertebrates. In limb buds of chick and mouse, Shh expression is activated as soon as there is a morphological bud, concomitant with Hoxd10 expression. In dogfish (Scyliorhinus canicula), however, we found that Shh was transcribed late in fin development, concomitant with Hoxd13 expression. We utilized zebrafish as a model to determine whether quantitative changes in hox expression alter the timing of shh expression in pectoral fins of zebrafish embryos. We found that the temporal shift of Shh activity altered the size of endoskeletal elements in paired fins of zebrafish and dogfish. Thus, a threshold level of hox expression determines the onset of shh expression, and the subsequent heterochronic shift of Shh activity can affect the size of the fin endoskeleton. This process may have facilitated major morphological changes in paired appendages during vertebrate limb evolution

DeepGMAP-data-light.tar.gz

A lighter version of data set to try DeepGMA

DeepGMAP-data-light.tar.gz

A light version of data for DeepGMAP. It contains mouse genome files, a subset of DNase-seq data for training, and trained variables

Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information.

With advances in sequencing technology, a vast amount of genomic sequence information has become available. However, annotating biological functions particularly of non-protein-coding regions in genome sequences without experiments is still a challenging task. Recently deep learning-based methods were shown to have the ability to predict gene regulatory regions from genome sequences, promising to aid the interpretation of genomic sequence data. Here, we report an improvement of the prediction accuracy for gene regulatory regions by using the design of convolution layers that efficiently process genomic sequence information, and developed a software, DeepGMAP, to train and compare different deep learning-based models (https://github.com/koonimaru/DeepGMAP). First, we demonstrate that our convolution layers, termed forward- and reverse-sequence scan (FRSS) layers, integrate both forward and reverse strand information, and enhance the power to predict gene regulatory regions. Second, we assessed previous studies and identified problems associated with data structures that caused overfitting. Finally, we introduce visualization methods to examine what the program learned. Together, our FRSS layers improve the prediction accuracy for gene regulatory regions