bioRxivNeurosciencePreprint
Transcript architecture predetermines m6A remodeling and sensory neuron vulnerability in chemotherapy-induced peripheral neuropathy
Abstract
Whether individual transcripts carry intrinsic features that predetermine their response to perturbations is unknown. Here we used nanopore direct RNA sequencing of male mouse dorsal root ganglia (DRG) to simultaneously profile N6-methyladenosine (m6A) modifications, poly(A) tail dynamics, and full-length isoform identity from mice treated with bortezomib, a proteasome inhibitor that causes painful peripheral neuropathy. Machine learning revealed that transcript-intrinsic features predetermine the magnitude of perturbation-induced m6A loss (R{superscript 2} = 0.983). Expression level contributed just 2.6% of predictive importance. Bortezomib removed a fixed [~]73.5% fraction of m6A marks, meaning absolute loss scaled linearly with baseline density and a transcripts epitranscriptomic fate was encoded in its architecture before drug exposure. Unsupervised clustering identified four response programs where the dominant m6A erosion cluster enriched for oxidative phosphorylation (OXPHOS, p = 1.0 x 10-{superscript 1}) and proteasome (p = 2.8 x 10-{superscript 1}) genes, recapitulating bortezomibs established mechanisms without prior biological knowledge. Isoform-resolved analysis uncovered m6A remodeling patterns suggesting post-transcriptional regulation of glycolytic and OXPHOS genes, and Western blot confirmed protein-level suppression of OXPHOS components. Integration with single-nuclei sequencing showed sensory neurons carried 2.2-fold greater m6A loss burden than non-neuronal cells, a direct consequence of architectural determinism applied to cell-type-specific transcriptomes. These findings establish that epitranscriptomic bortezomib response is predetermined by transcript architecture, with pathway specificity and cell-type vulnerability emerging as downstream consequences of intrinsic RNA structure.
- organism: mouse
- assay: long-read sequencing
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: machine learning (classical)
- Citations
- 0
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