Bruns, Y. | Search bioRxiv

bioRxiv2026

Transcriptome-wide analysis of alternative splicing in women with fibromyalgia highlights GNLY as an immune candidate

Background Fibromyalgia syndrome (FMS) affects 1 to 2 percent of the general population, with higher prevalence estimates in women. Recent evidence supports peripheral immune involvement in disease pathophysiology. Alternative splicing (AS) regulates immune cell function independently of transcript abundance and carries disease-relevant signal in related autoimmune conditions. No study has performed transcriptome-wide differential AS analysis directly in RNA-sequencing data from individuals with FMS. Methods We performed a secondary analysis of GSE221921, restricted to female participants (n = 91 FMS, n = 41 controls). Peripheral immune cell composition was estimated with ABIS, a deconvolution method specifically trained on peripheral blood mononuclear cell signatures. Differential AS was analysed with LeafCutter incorporating cell composition principal components as covariates. Differential gene expression was performed with DESeq2 under the same adjustment framework. Results FMS samples showed lower ABIS-inferred conventional monocyte estimates (Cliff's {delta} = -0.38, FDR = 0.008) and higher memory B-cell estimates (Cliff's {delta} = +0.32, FDR = 0.030). Composition-adjusted LeafCutter analysis identified 14 significant intron clusters, of which 9 were retained for biological interpretation. GNLY (granulysin) was the primary candidate, with a composition-adjusted 5' junction usage shift illustrated by pooled Sashimi visualisation. Composition adjustment substantially reduced significant differentially expressed genes from 10,803 to 3,186 (baseMean [≥] 10). Conclusions This first transcriptome-wide differential AS analysis in FMS identifies GNLY, encoding the cytolytic lymphocyte effector granulysin, as a splicing-specific candidate for functional follow-up. AS captures FMS-associated signal not detectable by gene-level expression analysis, and composition adjustment substantially altered the differential expression landscape.

genomics↗

bioRxiv2025

Modanovo: A Unified Model for Post-Translational Modification-Aware de Novo Sequencing Using Experimental Spectra from In Vivo and Synthetic Peptides

Post-translational modifications (PTMs) play a central role in cellular regulation and are implicated in numerous diseases. Database searching remains the standard for identifying modified peptides from tandem mass spectra, but is hindered by the combinatorial expansion of modification types and sites. De novo peptide sequencing offers an attractive alternative, yet existing methods remain limited to unmodified peptides or a narrow set of PTMs. Here, we curated a large dataset of spectra from endogenous and synthetic peptides from ProteomeTools spanning 19 biologically relevant amino acid-PTM combinations, covering phosphorylation, acetylation, and ubiquitination. We used this dataset to develop Modanovo, an extension of the Casanovo transformer architecture for de novo peptide sequencing. Modanovo achieved robust performance across these amino acid-PTM combinations (median area under the precision-coverage curve 0.92), while maintaining performance on unmodified peptides (0.93), nearly identical to Casanovo (0.94). The model outperformed {pi}-PrimeNovo-PTM and showed increased precision and complementarity to the database search tool MSFragger. Robustness was confirmed across independent datasets, particularly at peptide lengths frequently represented in the curated dataset. Applied to a phosphoproteomics dataset from monkeypox virus-infected cells, Modanovo recovered numerous confident peptides not reported by database search, including new viral phosphosites supported by spectral evidence, thereby demonstrating its complementarity to database-driven identification approaches. These results establish Modanovo as a broadly applicable model for comprehensive de novo sequencing of both modified and unmodified peptides.

bioinformatics↗