Back

Regulatory innovation and transcriptome turnover drive the evolution of multicellularity in brown algae

Lotharukpong, J.; Zheng, M.; Luthringer, R.; Liesner, D.; Bogaert, K. A.; Hoshino, M.; Lopez, E.; Fernandez-Pozo, N.; Haas, F. B.; Coelho, S. M.

2026-04-29 evolutionary biology
10.64898/2026.04.27.720984 bioRxiv
Show abstract

How complex multicellularity evolved repeatedly across eukaryotes remains a central unresolved question in biology. Whether principles inferred largely from animals and plants reflect universal features of multicellular evolution or lineage-specific outcomes is unknown. Here, using a stage- and tissue-resolved transcriptomic atlas spanning the full life cycles of 15 species across the brown algal radiation, comprising 639 RNA-seq libraries, we uncover general principles governing the evolution of developmental programs in an independently evolved multicellular lineage. Despite broad conservation of genome architecture and synteny, developmental and morphological diversification is accompanied by pervasive rewiring of gene expression and rapid turnover of co-expression networks, identifying regulatory evolution as a major driver of multicellular innovation. We show that evolutionarily young genes, including genes of giant viral origin, are repeatedly recruited into developmental programs, particularly in motile unicellular stages, revealing a broader role for these stages as hotspots of evolutionary novelty. We further identify lineage-specific cis-regulatory innovation linked to active chromatin and large-scale transcriptional rewiring, providing a mechanistic basis for transcriptome turnover. Together, these findings show that brown algae reinvented complex multicellularity through distinct molecular routes shaped by rapid regulatory innovation, while revealing organizational principles conserved across independent multicellular lineages.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.