Santos Rosa, H. | bioRxiv Science

bioRxiv2025

Compensatory tRNA Modification by DUS3L Confers Resistance to METTL1 Loss in Oesophageal Cancer

tRNA N-methylguanosine (mG46), installed by the METTL1/WDR4 methyltransferase, stabilizes the tRNA pool supporting high proliferative rates. Accordingly, many cancer cell lines are vulnerable to METTL1 loss, underscoring its potential as a therapeutic target. We identify a subset of cell lines insensitive to METTL1 depletion and use oesophageal lines as a model to explore the mechanism. The METTL1-resistant adenocarcinoma line OE33 preserves tRNA balance upon mG46 deficiency by elevating dihydrouridylation at position 47 (D47). Depletion of DUS-L enzymes that catalyse D16/D17/D20/D47 on tRNAs sensitises OE33 to METTL1 loss. Concomitant loss of dihydrouridylation and mG46 destabilizes the canonical L-shaped tRNA structure, disrupts coaxial helix stacking and anticodon-loop presentation, and reduces cellular fitness. Notably, METTL1-sensitive cell lines lack this compensatory pathway due to the deleterious effects of increasing their intrinsically low DUS3L levels. These findings reveal a DUS-L-mediated buffering mechanism that supports tRNA homeostasis in METTL1-resistant cancers and highlight DUS3L abundance as a potential biomarker for predicting METTL1 sensitivity. Moreover, they suggest that combined targeting of DUS-L and METTL1 may provide a therapeutic strategy for overcoming resistance in cancers, exemplified in cell lines such as OE33. HighlightsO_LIThe oesophagus cancer line OE33 shows no proliferative defect upon METTL1/WDR4 loss, despite complete lack of m7G46 on its tRNA. C_LIO_LIOE33 coordinates codon usage and abundance of cognate tRNAs in the absence of METTL1/WDR4; the METTL1/WDR4 sensitive line, OE21, fails to do so. C_LIO_LIDepleting dihydrouridine synthases (DUS-L) renders OE33 sensitive to METTL1/WDR4 loss. C_LIO_LIOE33 responds to lack of METTL1/WDR4 activity by upregulating DUS3L and its activity (D47) on tRNA; the sensitive line OE21 fails to do so. C_LIO_LIMETTL1-sensitive lines have intrinsically low DUS3L and cannot sustain higher levels. C_LIO_LIDUS3L reveals as a potential biomarker for METTL1 sensitivity. C_LI

biochemistry↗

bioRxiv2021

Predicting genes associated with RNA methylation pathways using machine learning

RNA methylation plays an important role in functional regulation of RNAs, and has thus attracted an increasing interest in biology and drug discovery. Here, we collected and collated transcriptomic, proteomic, structural and physical interaction data from the Harmonizome database, and applied supervised machine learning to predict novel genes associated with RNA methylation pathways in human. We selected five types of classifiers, which we trained and evaluated using cross-validation on multiple training sets. The best models reached 88% accuracy based on cross-validation, and an average 91% accuracy on the test set. Using protein-protein interaction data, we propose six molecular sub-networks linking model predictions to previously known RNA methylation genes, with roles in mRNA methylation, tRNA processing, rRNA processing, but also protein and chromatin modifications. Our study exemplifies how access to large omics datasets joined by machine learning methods can be used to predict gene function.

bioinformatics↗