An hourglass pattern of inter-embryo gene expression variability and of histone regulation in fly embryogenesis
Liu, J.; Frochaux, M.; Gardeux, V.; Deplancke, B.; Robinson-Rechavi, M.
Show abstract
The evolution of embryological development has long been characterized by deep conservation. Both morphological and transcriptomic surveys have proposed a \"hourglass\" model of Evo-Devo1,2. A stage in mid-embryonic development, the phylotypic stage, is highly conserved among species within the same phylum3-7. However, the reason for this phylotypic stage is still elusive. Here we hypothesize that the phylotypic stage might be characterized by selection for robustness to noise and environmental perturbations. This could lead to mutational robustness, thus evolutionary conservation of expression and the hourglass pattern. To test this, we quantified expression variability of single embryo transcriptomes throughout fly Drosophila melanogaster embryogenesis. We found that indeed expression variability is lower at extended germband, the phylotypic stage. We explain this pattern by stronger histone modification mediated transcriptional noise control at this stage. In addition, we find evidence that histone modifications can also contribute to mutational robustness in regulatory elements. Thus, the robustness to noise does indeed contributes to robustness of gene expression to genetic variations, and to the conserved phylotypic stage.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Evolution of chemosensory tissues and cells across ecologically diverse Drosophilids 96%
- Transcription factor paralogs orchestrate alternative gene regulatory networks by context-dependent cooperation with multiple cofactors 95%
- Satb2 acts as a gatekeeper for major developmental transitions during early vertebrate embryogenesis 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.