Biology subjects

Valentini, S.

Publications and source records attributed to Valentini, S..

4 recordsLinked to original sources

bioRxiv2025

A TranSNP in the DDIT4 mRNA can impact its translation efficiency and modulate p53-dependent responses in cancer cells

Relatively few studies have examined the link between SNPs and mRNA translation, despite the established importance of translational regulation in shaping cell phenotypes. We developed a pipeline analyzing the allelic imbalance in total and polysome-bound mRNAs from paired RNA-seq data of HCT116 cells and identified 40 candidate tranSNPs, i.e. SNPs associated with allele-specific translation. Among them, the SNP rs1053639 (T/A) on DNA damage-inducible transcript 4 (DDIT4) 3UTR was identified, with the reference T allele showing a higher polysome association. rs1053639 TT clones generated by genome editing exhibited significantly higher DDIT4 protein levels than AA ones. The difference in DDIT4 proteins was even greater when cells were treated with Thapsigargin or Nutlin, two perturbations that induce DDIT4 transcription. The RNA-binding protein RBMX influenced these allele-dependent differences in DDIT4 protein expression, as shown by RNA-EMSA, RIP, and smiFISH assays. RBMX depletion reduced DDIT4 protein in TT clones to the AA levels. Functionally, TT clones more effectively repressed mTORC1 under ER stress, while AA clones outcompeted TT clones in vitro or when injected in zebrafish embryos. RBMX depletion increased the fitness of TT cells in co-culture experiments. The rs1053639 AA genotype, under a recessive model, correlates with poor prognosis in TCGA cancer data. Key points- Translatome analysis in HCT116 cells revealed allele-specific mRNA translation for 40 SNPs - rs1053639 (T/A) in DDIT4 3UTR showed allelic differences in mRNA localization & protein expression - AA cells showed weaker mTOR inhibition & higher proliferation; AA individuals had poorer prognosis

molecular biology↗

bioRxiv2025

NGSTroubleFinder: A tool for detection and quantification of contamination and kinship across human NGS data

SummaryQuality control is a fundamental but often neglected step in any NGS pipeline. Detecting issues like cross-sample contamination and sample swaps is essential to control the data integrity. Here, we present NGSTroubleFinder, a novel python tool to detect cross-sample contamination in human Whole-Genome and Whole-Transcriptome Sequencing data, sample swaps and mismatches between the reported and the inferred genetic and transcriptomic sexes. NGSTroubleFinder is implemented in Python and incorporates a custom-built parallelized pileup engine written in C. The tool reports extensive information on the samples both in textual and HTML format including key plots for easy interpretation of the results. Availability and ImplementationNGSTroubleFinder is written in Python and C, and it can be easily installed with pip. The tool source code and the models are freely available on github (https://github.com/STALICLA-RnD/NGSTroubleFinder) and a containerized version is available on dockerhub (https://hub.docker.com/r/staliclarnd/ngstroublefinder).

bioinformatics↗

bioRxiv2023

3Dmapper: A Command Line Tool For BioBank-scale Mapping Of Variants To Protein Structures

The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis. To address this issue, we introduce 3Dmapper, a standalone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable. 3Dmapper is freely available on GitHub at https://github.com/vicruiser/3Dmapper.

bioinformatics↗

bioRxiv2022

Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities

The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at https://cellmodelpassports.sanger.ac.uk, is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.

systems biology↗