An adaptive path through jungle DNA

Predicting gene expression responses to environment in Arabidopsis thaliana using natural variation in DNA sequence

The evolution of gene expression responses are a critical component of adaptation to variable environments. Predicting how DNA sequence influences expression is challenging because the genotype to phenotype map is not well resolved for cis regulatory elements, transcription factor binding, regulatory interactions, and epigenetic features, not to mention how these factors respond to environment. We tested if flexible machine learning models could learn some of the underlying cis- regulatory genotype to phenotype map. We tested this approach using cold-responsive transcriptome profiles in 5 diverse Arabidopsis thaliana accessions. We first tested for evidence that cis regulation plays a role in environmental response, finding 14 and 15 motifs that were significantly enriched within the up- and down-stream regions of cold-responsive differentially regulated genes (DEGs). We next applied convolutional neural networks (CNNs), which learn de novo cis- regulatory motifs in DNA sequences to predict expression response to environment. We found that CNNs predicted differential expression with moderate accuracy, with evidence that predictions were hindered by biological complexity of regulation and the large potential regulatory code. Overall, DEGs between specific environments can be predicted based on variation in cis- regulatory sequences, although more information needs to be incorporated and better models may be required.

The quest for adaptive evolution: a theoretical challenge in a maze of data

Advances in sequencing technology have brought opportunities to refine our searches for adaptive evolution and to address and identify new questions regarding how adaptive evolution has shaped genomic diversity. Recent theoretical developments incorporate demographic and complex selective histories into tests of non-neutral evolution, thereby significantly improving our power to detect selection. These analyses combined with large data sets promise to identify targets of selection for which there was no a priori expectation. Moreover, they contribute to elucidate the role selection has played in shaping diversity in transposable elements, conserved noncoding DNA, gene family size, and other multicopy features of genomes.