Back

Functional class 2 CCA-adding enzymes in Asgard archaea and their implications for eukaryogenesis

Cassidy, E.; Doktor, C.; Betat, H.; Moerl, M.; Prohaska, S. J.

2026-05-31 evolutionary biology
10.64898/2026.05.29.728689 bioRxiv
Show abstract

CCA-adding enzymes are essential for transfer RNA (tRNA) maturation and translation among all domains of life. A long-standing dogma holds that archaea encode exclusively class one CCA-adding enzymes (CCA1), whereas bacteria and eukaryotes encode class two enzymes (CCA2), creating a perceived strict evolutionary separation between archaeal and eukaryotic CCA-adding enzymes. Here, we show that this view is incorrect. We identify widespread CCA2 enzymes across multiple archaeal superphyla, including Asgard archaea, and demonstrate that these enzymes are functional enzymes and can replace their archaeal CCA1 counterparts. Within Heimdallarchaeia, the proposed closest archaeal relatives of eukaryotes, we identify distinct clades with different CCA-adding enzyme repertoires that trace back to independent bacterial sources. One Heimdallarchaeia lineage exhibits strongest similarity to Bacteroidetes/Chlorobi, a bacterial superphylum previously implicated in early gene contributions to eukaryogenesis. CCA-adding enzymes from early-branching eukaryotes share this deep signature, indicating that these enzymes had already been long-established and functionally adapted within the ancestral Asgardian gene pool. Together, these findings challenge a central paradigm of CCA-adding enzyme evolution and provide molecular-level support for the ancestral integration and subsequent transmission of bacterial functions prior to or during eukaryogenesis, aligning with current evolutionary frameworks such as the Heimdall nucleation-decentralized innovation-hierarchical import (HDH) model.

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.