Pepe, D. | Search bioRxiv

bioRxiv2025

Translatome and translation dynamics analysis of a RiboCancer cell line panel reveals that leukemia-associated Rps15 mutations rewire translation through codon-specific tRNA accommodation defects.

Deletions and point mutations targeting ribosomal proteins (RPs) have been identified in cancer. Yet, their role in translational dysregulation remains poorly understood. We performed an integrated genome-wide translatome analysis (proteome, Ribo-seq and total RNA-seq) as well as RiboMethSeq on an isogenic cell line library modeling the most recurrent RP defects in cancer (Rpl5+/-, Rpl11+/-, Rpl22+/-, Rpl22-/-, Rpl10 R98S, Rps15 P131S and Rps15 H137Y). RP knock-out had minimal effects on translation, whereas RP point mutations induced a significant number of translation efficiency changes, affecting up to 10% of expressed genes in Rps15 mutants associated with Chronic Lymphocytic Leukemia (CLL). Cryo-electron microscopy and biochemical analyses revealed that the Rps15 mutations destabilize the C-terminal Rps15 domain, affecting the translation elongation cycle dynamics, and deregulating accommodation of aminoacylated tRNAs at the ribosomal A-site. Using Ribo-seq and translation reporter assays, we show that this accommodation defect shows codon specificity, explaining the reduced translation efficiency of genes enriched for these codons in Rps15 mutant cells, such as histones. Notably, genes with reduced translation efficiency in Rps15 mutated cells were enriched for transcriptional regulators such as transcription factor Runx3, resulting in downregulation of Runx3 target genes involved in immune regulation. Altogether, this study provides a comparative map of the translational rewiring driven by the most frequent somatic RP mutations. We provide unprecedented mechanistic insights in the translation defects induced by CLL-associated Rps15 mutations, and reveal an unappreciated cross-talk between translational and transcriptional dysregulation in these RP mutant cells. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/687986v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@cd2846org.highwire.dtl.DTLVardef@10f3c49org.highwire.dtl.DTLVardef@13f1595org.highwire.dtl.DTLVardef@a212df_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2020

Significant Shortest Paths For The Detection Of Putative Disease Modules

BackgroundThe characterization of diseases in terms of perturbated gene modules was recently introduced for the analysis of gene expression data. Some approaches were proposed in literature, but many times they are inductive approaches. This means that starting directly from data, they try to infer key gene networks potentially associated to the biological phenomenon studied. However they ignore the biological information already available to characterize the gene modules. Here we propose the detection of perturbed gene modules using the combination of data driven and hypothesis-driven approaches relying on biological metabolic pathways and significant shortest paths tested by structural equation modeling. The procedure was tested on microarray experiments relative to infliximab response in patients with inflammatory bowel disease. Starting from differentially expressed genes (DEGs) and pathway analysis, significant shortest paths between DEGs were found and merged together. The validation of the final disease module was principally done by the comparison of genes in the module with those already associated with the disease, using the Wang similarity semantic index, and enrichment analysis based on Disease Ontology. Finally a topological analysis of the module via centrality measures and the identification of the cut vertices, allowed to unveil important nodes in the network as the TNF gene, and other potential drug target genes as p65 and PTPN6. ConclusionsHere we propose a downstream method for the characterization of disease modules from gene expression data. The core of the method is rooted on the identification of significant shortest paths between DEGs by structural equation modeling. This allows to have a mix approach based on data and biological knowledge enclosed in biological pathways. Other methods here described as enrichment analysis and topological analysis were functional to the validation of the procedure. The results obtained were promising, considering the genes and their connections found in the putative disease modules.

bioinformatics↗