Biology subjects

Ehrenhofer-Murray, A. E.

Publications and source records attributed to Ehrenhofer-Murray, A. E..

3 recordsLinked to original sources

bioRxiv2026

The gut microbiota-derived metabolite queuosine regulates neuronal development and network function through tRNA modification

Queuosine (Q) modification is a hypermodified nucleoside derived from guanine on tRNAs that enhances the decoding of codons and equilibrates translational speed. Q is biosynthesized in bacteria, and eukaryotes salvage Q and the nucleobase queuine from the diet and the gut microbiome. In animals, Q deficiency causes impaired proteostasis, mitochondrial dysfunction, and neurological phenotypes, possibly due to the longevity and high metabolic demand of neurons. Yet, how Q affects isolated neurons has not been explored yet. Here, primary rat cortical neurons were cultured in Q-free synthetic medium to directly modulate Q modification levels independently of genetic perturbation, enabling assessment of its effects on neuronal development, survival, morphology, synaptic organization, and activity. Importantly, we found that the presence of Q modification facilitated neuronal arborization, decreased inhibitory synaptic density, and increased the frequency of spontaneous calcium transients, showing that tRNA Q modification enhances neuronal structural maturation and synaptic activity. Thus, the fine-tuning of neuronal translation programs by Q-tRNAs is required for proper network development and may influence neuronal resilience and synaptic function.

neuroscience↗

bioRxiv2025

Evaluation of Dorado v5.2.0 de novo basecalling models for the detection of tRNA modifications using RNA004 chemistry

Direct RNA sequencing with Oxford Nanopore Technologies (ONT) captures nucleotide-specific current signals that reflect both sequence and chemical modifications, offering the potential to detect RNA modifications directly from native RNA molecules. To interpret such signals, ONT provides modification-aware basecalling models that estimate the probability of selected modifications at each nucleotide. In May 2025, ONT released updated modification-calling models (Dorado v5.2.0) for pseudouridine ({Psi}), inosine, m6A and m5C, alongside new models for 2'O-ribose-methylations, necessitating independent validation. Here, we benchmark Dorado v5.2.0 against v5.1.0 using ex cellulo tRNAs from Schizosaccharomyces pombe, leveraging their well-defined modification landscape. We generated modification probability profiles at single-nucleotide resolution and quantified model performance using curated sets of annotated and validated modification sites. Our results reveal that, despite notable improvements in {Psi} detection, most modification callers remain challenged by the dense and heterogeneous modification environments of tRNAs. This work provides the first comprehensive evaluation of Dorado v5.2.0 on native tRNAs and establishes a methodological framework for benchmarking future ONT modification models in complex RNA modification contexts.

molecular biology↗

bioRxiv2025

MoDorado: Enhanced detection of tRNA modifications in nanopore sequencing by off-label use of modification callers

Rapid and accurate identification of tRNA modifications is crucial for understanding their role in protein translation and disease. However, their detection on tRNAs is challenging due to their high modification density. Recently, modification calling models for nanopore direct RNA sequencing became available for pseudouridine ({Psi}), m6A, inosine and m5C, as part of the Dorado basecaller. Applying the {Psi} model to tRNAs, we have mapped both known and novel {Psi} sites in Schizosaccharomyces pombe and assigned the responsible pseudouridine synthetases. Furthermore, we have developed MoDorado, an algorithm to detect modifications beyond those used in model training ("off-label use") by measuring prediction differences of pre-trained machine learning models. By leveraging the {Psi}/m6A/inosine/m5C models, MoDorado detected seven additional modifications (ncm5U, mcm5U, mcm5s2U, m7G, queuosine, m1A, and i6A), thus generating a tRNA modification map of S. pombe. This work demonstrates the potential of pre-trained models in determining the intricate landscape of tRNA modifications.

molecular biology↗