ArticleGenome biology2026
OTTR-seq profiling reveals dynamic tRNA modification landscapes across diverse archaeal species.
Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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Who cites it
1 citing paper in PubMed.
- OTTR-seq profiling reveals dynamic tRNA modification landscapes across diverse archaeal species.Genome biology · 2026Article
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4 authors.
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Abstract
backgroundTransfer RNA (tRNA) modifications are essential for structural integrity, decoding fidelity, and stress adaptation, yet their evolutionary dynamics remain poorly characterized in archaea. Here, we apply Ordered Two-Template Relay sequencing (OTTR-seq), a high-throughput approach that captures full-length tRNAs and modification-sensitive reverse transcription signatures, to systematically profile tRNA modification landscapes across diverse archaeal species.
resultsAcross nine archaeal species spanning thermophilic, acidophilic, halophilic, and mesophilic environments, we identify position-specific and clade-dependent patterns of tRNA modifications. We detect coordinated and mutually exclusive methylation at acceptor stem positions 6 and 67 in hyperthermophiles, as well as clade-specific co-modification at positions 10 and 26, which are typically known as tRNA modification anti-determinants. Comparative analyses also reveal lineage-specific divergence in the domain architectures of tRNA methyltransferases, including Trm1, Trm10, Trm11, and Trm14, linking enzymatic evolution to substrate specificity. We further refine known identity elements, such as the G10oU25 pairing, and identify novel structural features that may facilitate or prevent modification.
conclusionsThese findings exemplify the co-evolution of tRNAs and their modifying enzymes, providing new insights into how archaea may fine-tune translation in extreme environments. The broad scope of data and comparative analyses establishes a multispecies framework for future biochemical, mechanistic, and predictive modeling efforts.
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