A large-effect fitness trade-off across environments is explained by a single mutation affecting cold acclimation

Low frequency of the wild-type freezing-tolerance LsCBF7 allele among lettuce population suggests a negative selection during domestication and breeding

KEY MESSAGE

Sustainable winter production in lettuce requires freezing tolerant varieties. This study identified a wild-type allele of LsCBF7 that could contribute to freezing tolerance improvement in lettuce. Lettuce is one of the most consumed vegetables globally. While ideally grown in 13-21 °C, its cultivation extends into winter in milder climates. However, occasional freezing temperatures can significantly reduce yields. Therefore, the development of freezing-tolerant lettuce varieties has become a long-term goal of lettuce breeding programs. Despite its significance, our understanding of freezing tolerance in lettuce remains limited. Plants have evolved a coping mechanism against freezing, known as cold acclimation, whereby they can increase freezing tolerance when pre-exposed to low nonfreezing temperatures. The CBF pathway is well-known for its central role in cold acclimation. Previously, we identified 14 CBF genes in lettuce and discovered that one of them, LsCBF7, had a loss-of-function mutation. In this study, we uncovered that accessions from colder regions carried the wild-type allele of LsCBF7 and this allele likely contributed to increased freezing tolerance, with 14% of the lettuce population carrying this allele. Interestingly, in wild lettuce (L. serriola) that is considered a progenitor of cultivated lettuce, this wild-type allele was much more common, with a frequency of 90%. This finding suggests that this wild-type allele may have undergone negative selection during the domestication or breeding of lettuce. Our data strongly indicate that this allele could be linked to early bolting, an undesirable trait in lettuce, which may have driven the negative selection. While this wild-type allele shows promise for improving freezing tolerance in lettuce, it is crucial to decouple it from the early bolting trait to fully harness its potential in lettuce breeding.

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.