Biology subjects

Carlevaro-Fita, J.

Publications and source records attributed to Carlevaro-Fita, J..

9 recordsLinked to original sources

bioRxiv2026

Characterization of heterogeneous bioinformatics training needs in the AI era

Bioinformatics training providers serve heterogeneous audiences, a challenge sharpened by fast-evolving technologies such as generative artificial intelligence (AI). Analyzing training-needs surveys in aggregate can mask this heterogeneity. We present a network-based clustering protocol that groups respondents by the similarity of their Likert-type topic ratings: respondents form a Spearman correlation network, and community detection groups them into learner profiles with shared training needs, independent of demographics. We demonstrate the protocol on the 2026 SIB Swiss Institute of Bioinformatics (SIB) training needs survey (n=260), covering topics, barriers, and formats. We identified four interpretable learner profiles: AI and data, infectious disease genomics, functional genomics, and biochemistry, imaging & AI. Each had distinct topic priorities, barriers, and format preferences that an aggregate analysis hides. For example, LLM-based knowledge extraction ranked 2nd overall but only 16th in the infectious disease genomics profile, whereas phylogenetics ranked 17th overall but first there. Across profiles, AI/LLM skills and reproducible data practices were in high demand, whereas AI-assisted learning was, unexpectedly, the least preferred format. Because the protocol requires only a shared Likert scale, it is reusable for other training-needs assessments; the implementation is openly available. Profile-based curricula can target each group's topics, formats, and barriers.

bioinformatics↗

bioRxiv2026

Microbiota during early life windows orchestrates intestinal maturation and barrier formation in the offspring

There is a critical window in early life for immune development that prepares a young mammal for postnatal exposure to environmental, dietary and microbial antigens. Both the maternal microbiota and endogenous colonization have emerged as pivotal drivers in this process. Using gnotobiotic mouse models and reversible colonization to model the effects of the maternal microbiota during pregnancy compared with early life colonization on the small intestinal epithelium we show that both microbial metabolites originating from the maternal microbiota and the postnatal endogenous microbiota drive intestinal epithelial maturation by modulating epigenetic signatures. Microbiota-derived signals from both the pregnant mother and from endogenous postnatal colonisation drove the BLIMP1-guided epithelial neonatal-to-adult transition at weaning through epigenetic mechanisms and direct transcriptional effects. These microbial signals also shaped the developmental trajectory of barrier-forming tight junctional claudins in the neonatal intestine, increased aerobic respiration, and increased the gene expression of xenosensors. Thus, both maternal and endogenous microbiota derived drivers of the offspring's epigenetic and transcriptomic intestinal epithelial landscape fine-tuned the timing of epithelial maturation and augmented functional plasticity at weaning.

microbiology↗

bioRxiv2023

Deconvolution of ex-vivo drug screening data and bulk tissue expression predicts the abundance and viability of cancer cell subpopulations

Ex-vivo drug sensitivity screening allows the prediction of cancer treatment effectiveness in a personalized fashion. However, it only provides a readout on mixtures of cells, potentially occulting important information on clinically relevant cell subtypes. To address this shortcoming, we developed a machinelearning framework to decompose drug sensitivity recorded at the bulk level into cell subtype-specific drug sensitivity. We first determined that our method could decipher the cellular composition of bulk samples with top-ranking accuracy across five cancer types compared to state-of-the-art bulk deconvolution methods. We emphasize its effectiveness in the realm of Acute Myeloid Leukemia, where it appears to offer the most precise estimation of leukemic stem cell fractions across three test datasets and three patient cohorts. We then optimized an algorithm capable of estimating cell subtype- and single-cell-specific drug sensitivity, which we evaluated by performing in-vitro drug studies and in-depth simulations. We then applied our deconvolution strategy to the beatAML cohort dataset, currently the most extensive database of ex-vivo drug screening data. We developed a drug sensitivity profile tailored to specific cell subtypes, focusing on four therapeutic compounds predicted to target leukemic stem cells: the previously known midostaurin and A-674563, as well as SNS-032 and foretinib, which have not been previously linked to leukemic stem cells. Our work provides an attractive new computational tool for drug development and precision medicine.

bioinformatics↗

bioRxiv2023

SARS-CoV-2 shifts transcription of host gene to increase Spike acylation and boost infectivity

SARS-CoV-2 infection requires Spike protein mediating fusion between the viral and cellular membranes. The fusogenic activity of Spike requires its post-translational lipid modification by host S-acyltransferases, predominantly ZDHHC20. Previous observations indicate that SARS-CoV-2 infection augments the S-acylation of Spike when compared to transfection. Here, we find that SARS-CoV-2 infection triggers a change in the transcriptional start site of the zddhc20 gene, both in cells and in an in vivo infection model, resulting in a 67-amino-acid-long N-terminally extended protein with 37-times higher Spike acylating activity, leading to enhanced viral infectivity. Furthermore, we observed the same induced transcriptional change in response to other challenges, such as chemically induced colitis, indicating that SARS-CoV-2 hijacks an existing cell damage response pathway to generate more infectious viruses.

cell biology↗

bioRxiv2023

KRAB zinc finger proteins ZNF587/ZNF417 protect lymphoma cells from replicative stress-induced inflammation

Heterochromatin loss and genetic instability enhance cancer progression by favoring clonal diversity, yet uncontrolled replicative stress can lead to mitotic catastrophe and inflammatory responses promoting immune rejection. KRAB-containing zinc finger proteins (KZFPs) are epigenetic modulators, which for many control heterochromatin at transposable element (TE)-embedded regulatory sequences. We identified a cluster of 18 KZFPs associated with poor prognosis in diffuse large B cell lymphoma (DLBCL). We found their upregulation to correlate with increased copy number alterations and suppression of immune responses in tumor samples. Upon depleting two that target evolutionarily recent TEs, the primate-specific ZNF587 and ZNF417 paralogs, the proliferation of DLBCL cell lines was drastically impaired and replicative stress abruptly induced with marked alterations of the chromatin landscape and multiplication of DNA replication origins. Furthermore, ZNF587/417 knockdown upregulated interferon/inflammatory-related genes through activation of the cGAS-STING DNA sensing pathway, augmented the susceptibility of tumor cells to macrophage-mediated phagocytosis, and modified their immunogenicity through an increased surface expression of HLA-I and reshuffling of their immunopeptidome. ZNF587 and ZNF417 are thus pro-oncogenic factors allowing for higher degrees of genetic instability through attenuation of replicative stress and secondary inflammation, an influence that likely facilitates the clonal expansion, diversification, and immune evasion of cancer cells.

cancer biology↗

bioRxiv2017

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Discovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5UTRs of TP53, RFTN1, RNF34, and MTG2, in the 3UTRs of NFKBIZ and TOB1, and in the non-coding RNA RMRP. We provide evidence that the previously reported non-coding RNAs NEAT1 and MALAT1 may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyond TERT.

genomics↗

bioRxiv2017

Ancient exapted transposable elements drive nuclear localisation of lncRNAs

The sequence domains underlying long noncoding RNA (lncRNA) activities, including their characteristic nuclear enrichment, remain largely unknown. It has been proposed that these domains can originate from neofunctionalised fragments of transposable elements (TEs), otherwise known as RIDLs (Repeat Insertion Domains of Long Noncoding RNA), although just a handful have been identified. It is challenging to distinguish functional RIDL instances against a numerous genomic background of neutrally-evolving TEs. We here show evidence that a subset of TE types experience evolutionary selection in the context of lncRNA exons. Together these comprise an enrichment group of 5374 TE fragments in 3566 loci. Their host lncRNAs tend to be functionally validated and associated with disease. This RIDL group was used to explore the relationship between TEs and lncRNA subcellular localisation. Using global localisation data from ten human cell lines, we uncover a dose-dependent relationship between nuclear/cytoplasmic distribution, and evolutionarily-conserved L2b, MIRb and MIRc elements. This is observed in multiple cell types, and is unaffected by confounders of transcript length or expression. Experimental validation with engineered transgenes shows that these TEs drive nuclear enrichment in a natural sequence context. Together these data reveal a role for TEs in regulating the subcellular localisation of lncRNAs.

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↗