Extensive modulation of a conserved cis-regulatory code across 589 grass species

Authors: HALE, CHARLES - Cornell University; HSU, SHEN-KAI - Cornell University; ZHAI, JINGJING - Cornell University; SCHULZ, AIMEE - Cornell University; AUBUCHON-ELDER, TAYLOR - Donald Danforth Plant Science Center; COSTA-NETO, GERMANO - Cornell University; GELFORD, ALLEN - Cornell University; EL-WALID, MOHAMED - Cornell University; HUFFORD, MATTHEW - Iowa State University; KELLOG, ELIZABETH - Donald Danforth Plant Science Center; LA, THUY - Cornell University; MARAND, ALEXANDRE - University Of Michigan; SEETHARAM, ARUN - Iowa State University; SCHEBEN, ARMIN - Northwestern University; STITZER, MICHELLE - Cornell University; WRIGHTSMAN, TRAVIS - Cornell University; ROMAY, M. CINTA - Cornell University; Buckler Iv, Edward

Submitted to: bioRxiv
Publication Type: Pre-print Publication
Publication Acceptance Date: 4/28/2025
Publication Date: 4/28/2025
Citation: Hale, C.O., Hsu, S., Zhai, J., Schulz, A.J., Aubuchon-Elder, T., Costa-Neto, G., Gelford, A., El-Walid, M., Hufford, M., Kellog, E.A., La, T., Marand, A.P., Seetharam, A.S., Scheben, A., Stitzer, M., Wrightsman, T., Romay, M., Buckler Iv, E.S. 2025. Extensive modulation of a conserved cis-regulatory code across 589 grass species. bioRxiv. https://doi.org/10.1101/2025.04.23.650228.
DOI: https://doi.org/10.1101/2025.04.23.650228

Interpretive Summary: Understanding how crops adapt to new environments requires identifying the genetic elements that regulate gene activity. In this study, university and ARS scientists examined how short regulatory DNA sequences (motifs) evolved across nearly 600 grass species. These short sequences act like switches that control gene expression. The research found that the motifs were used across all grasses; however, there were tremendous changes in how the motifs regulated any given gene. Using advanced models, the team found that changes in these motifs were often associated with shifts in climate and habitat. Notably, some stress-response motifs repeatedly appeared in genes tied to survival in temperate environments. This work supports a "stable code, variable sites" model—suggesting the regulatory logic remains consistent, even as specific DNA switches evolve. These findings offer new insights into how grasses adapt and may aid in improving and designing crop resilience.

Technical Abstract: The growing availability of genomes from non-model organisms offers new opportunities to identify functional loci underlying trait variation through comparative genomics. While cis-regulatory regions drive much of phenotypic evolution, linking them to specific functions remains challenging. We identified 514 cis-regulatory motifs enriched in regulatory regions of five diverse grass species, with 73% consistently enriched across all, suggesting a deeply conserved regulatory code. We then quantified conservation of specific motif instances across 589 grass species, revealing widespread gain and loss over evolutionary time. Conservation declined rapidly over the first few million years of divergence, yet ~50% of motif instances were conserved back to the origin of grasses ~100 million years ago. Conservation patterns varied by gene class, with modestly higher conservation at transcription factor genes. To test for adaptive cis-regulatory changes, we used phylogenetic mixed models to identify motif gains and losses associated with ecological niche transitions. Our models revealed polygenic adaptation across 810 motif-orthogroup combinations, including convergent gains of HSF/GARP motifs at an Alpha-N-acetylglucosaminidase gene associated with adaptation to temperate environments. Our results support a “stable code, variable sites” model in which cis-regulatory evolution involves extensive turnover of individual binding site instances while largely preserving transcription factors’ binding preferences. Cis-regulatory changes at hundreds to thousands of genes appear to contribute to environmental adaptation. Our results highlight the potential of comparative genomics and phylogenetic mixed models to reveal the genetic basis of complex traits.