Evolution of gene expression

Slowly evolving cnidarians are useful models to study genome architecture. This study shows that sea anemones have a high degree of chromosomal macrosynteny, but poor microsynteny conservation. This is correlated with a small genome size and short distances of cis-regulatory elements to genes.

Regulatory and coding regions of genes are shaped by evolution to control expression levels. Here, the authors use deep learning to identify rules controlling gene expression levels and suggest that all parts of the gene regulatory structure interact in this.

Transcriptomic analyses in the offspring of Arctic charr morphs (Salvelinus alpinus) and their hybrids at two stages of cranial development suggest that gene expression variance can rapidly diverge in sympatry and is conditioned by dominance patterns.

Comparative analysis of human, macaque and mouse function and genetic heterogeneity in the brain reveals links between gene expression and orderly topography of functional networks.

In this Review, Hill et al. discuss how high-throughput methods for creating and characterizing mutations are providing insight into how regulatory variation is generated and evolves.

A framework for studying and engineering gene regulatory DNA sequences, based on deep neural sequence-to-expression models trained on large-scale libraries of random DNA, provides insight into the evolution, evolvability and fitness landscapes of regulatory DNA.

A transcriptomic analysis of 8 tissues across 20 bilaterian species reveals that ancestral gains of tissue-specific gene expression were closely associated with whole-genome duplications in vertebrates and the diversification of ancestral tissue types.

Analysis of single-cell RNA-sequencing data from seven amphioxus embryo stages informs the evolution of chordate cell type evolution and reveals three developmental origins for the vertebrate nervous system.