Biology subjects

Mas-Ponte, D.

Publications and source records attributed to Mas-Ponte, D..

4 recordsLinked to original sources

bioRxiv2026

Single genome analysis of normal cells identifies the early steps of kidney cancer.

Whole genome analyses have shown that every cell of the body accumulates thousands of genetic changes during a lifetime, with implications for cancer initiation. We have analyzed normal kidney cells from both healthy individuals and patients with the Von Hippel Lindau (VHL) syndrome, which predisposes to kidney cancer. Using a clonal-expansion method that enables the collection of high confidence genetic and gene expression data from the same cell, we define a new mutational signature that marks a specific subset of damage-activated proximal tubule cells, as well as 96% of clear cell renal cell carcinomas. The underlying mutational process is specifically active in highly transcribed DNA and impacts the occurrence of cancer driver variants in the VHL gene. Our data from normal cells, supported by in vitro experiments, point to perturbations of oxygen availability as a causative factor. These findings link hypoxia, a frequent endogenous stress in the normal renal tissue, to excessive mutation and cancer initiation.

cancer biology↗

bioRxiv2023

Mutation rate heterogeneity at the sub-gene scale due to local DNA hypomethylation

Local mutation rates are highly heterogeneous across the human genome. This variability was better studied at the scale of megabase-sized chromosomal domains on the one extreme, and at the scale of oligonucleotides at the other extreme. The intermediate, kilobase-scale heterogeneity in mutation risk was less studied. Here, by analyzing thousands of somatic genomes, we considered the hypothesis there are mutation risk gradients along gene bodies, representing a genomic scale spanning roughly 1 kb - 10 kb, and that different mutational mechanisms are differently distributed across gene segments. The main intragenic heterogeneity concerns several kilobases at the transcription start site and further into 5 ends of gene bodies, which are commonly hypomutated with respect to several mutational signatures, most prominently the ubiquitous mutational signature of C>T changes at CpG dinucleotides. Width and shape of this mutational coldspot at 5 gene ends is variable across genes, and corresponds to variable interval of lowered DNA methylation across genes. These hypomutated genic intervals correspond to hypomethylation that can originate from various causes, including intragenic enhancers, Polycomb-marked regions, or chromatin loop anchor points. Tissue-specific DNA hypomethylation begets tissue-specific local hypomutation. However, direction of mutation rate effect is inverted for some mutational processes, where signatures of AID/APOBEC3 cytosine deaminase activity are actually increased in hypomethylated regions. Overall, local DNA methylation determines mutation rate heterogeneity at the sub-gene level, and can generate either mutational coldspots or hotspots, depending on the mutagen exposure history of a cell.

genomics↗

bioRxiv2017

Unique genomic features and deeply-conserved functions of long non-coding RNAs in the Cancer LncRNA Census (CLC)

Long non-coding RNAs (lncRNAs) that drive tumorigenesis are a growing focus of cancer genomics studies. To facilitate further discovery, we have created the \"Cancer LncRNA Census\" (CLC), a manually-curated and strictly-defined compilation of lncRNAs with causative roles in cancer. CLC has two principle applications: first, as a resource for training and benchmarking de novo identification methods; and second, as a dataset for studying the fundamental properties of these genes.\n\nCLC Version 1 comprises 122 lncRNAs implicated in 29 distinct cancers. LncRNAs are included based on functional or genetic evidence for causative roles in cancer progression. All belong to the GENCODE reference annotation, to enable integration across projects and datasets. For each entry, the evidence type, biological activity (oncogene or tumour suppressor), source reference and cancer type are recorded. Supporting its usefulness, CLC genes are significantly enriched amongst de novo predicted driver genes from PCAWG. CLC genes are distinguished from other lncRNAs by a series of features consistent with biological function, including gene length, high expression and sequence conservation of both exons and promoters. We identify a trend for CLC genes to be co-localised with known protein-coding cancer genes along the human genome. Finally, by integrating data from transposon-mutagenesis functional screens, we show that mouse orthologues of CLC genes tend also to be cancer genes.\n\nThus CLC represents a valuable resource for research into long non-coding RNAs in cancer. Their evolutionary and genomic properties have implications for understanding disease mechanisms and point to conserved functions across ~80 million years of evolution.

bioinformatics↗

bioRxiv2017

LncATLAS database for subcellular localisation of long noncoding RNAs

BackgroundThe subcellular localisation of long noncoding RNAs (lncRNAs) holds valuable clues to their molecular function. However, measuring localisation of newly-discovered lncRNAs involves time-consuming and costly experimental methods.\n\nResultsWe have created \"LncATLAS\", a comprehensive resource of lncRNA localisation in human cells based on RNA-sequencing datasets. Altogether, 6768 GENCODE-annotated lncRNAs are represented across various compartments of 15 cell lines. We introduce \"Relative concentration index\" (RCI) as a useful measure of localisation derived from ensemble RNAseq measurements. LncATLAS is accessible through an intuitive and informative webserver, from which lncRNAs of interest are accessed using identifiers or names. Localisation is presented across cell types and organelles, and may be compared to the distribution of all other genes. Publication-quality figures and raw data tables are automatically generated with each query, and the entire dataset is also available to download.\n\nConclusionsLncATLAS makes lncRNA subcellular localisation data available to the widest possible number of researchers. It is available at lncATLAS.crg.eu.

bioinformatics↗