Biology subjects

Madrigal, P.

Publications and source records attributed to Madrigal, P..

10 recordsLinked to original sources

bioRxiv2026

Viral non-coding RNA structure annotation and API-based data retrieval with Rfam and R2DT

Rfam is a comprehensive database of non-coding RNA (ncRNA) families providing curated sequence alignments, consensus secondary structures, and covariance models for thousands of RNA families. The database is essential for identifying structured non-coding RNAs in newly sequenced genomes and understanding RNA structure-function relationships. Here we present computational protocols for automated ncRNA annotation of viral genomes, and for programmatic interaction with Rfam through its RESTful API. We showcase genome-wide RNA structure visualization from a genome sequence and from a multiple sequence alignment by generating comprehensive 2D structure diagrams using newly developed features in R2DT. We also present practical examples for retrieving family metadata, downloading alignments, accessing secondary structures, and searching user sequences from the Rfam API. These methods enable researchers in virology and RNA biology to integrate Rfam data into custom bioinformatics pipelines, comparative analyses, and machine learning workflows.

bioinformatics↗

bioRxiv2025

RNAcentral in 2026: Genes and literature integration

RNAcentral was founded in 2014 to serve as a comprehensive database of non-coding RNA sequences. It began by providing a single unified interface to more specialised resources, and now contains 45 million sequences. It has grown beyond providing a single interface to many specialised resources and now provides several services and analyses. These include secondary structure prediction with R2DT, sequence search, and analysis with Rfam. Since its last publication in 2021, RNAcentral has developed two major features. First, literature integration with the development of LitScan and LitSumm. LitScan automatically identifies and links relevant publications to RNA entries, while LitSumm uses natural language processing to generate functional summaries from the literature. Together, these tools address the critical challenge of connecting sequence data with scattered functional knowledge across thousands of publications. Secondly, RNAcentral has created gene level entries. Gene level entries represent a large structural change to RNAcentral. While RNAcentral previously organized data exclusively at the sequence level, we now group related transcripts into gene-centric views. This allows researchers to explore all isoforms, splice variants, and related sequences for a gene in a unified interface, better reflecting biological organization and facilitating comparative analyses. RNAcentral is freely available at: https://rnacentral.org.

bioinformatics↗

bioRxiv2025

Delayed Trp53 activation protects Dnmt3a-mutant hematopoietic stem cells from inflammatory attrition.

Hematopoietic stem cells (HSCs) accumulate somatic mutations over time, some conferring a fitness advantage that can lead to clonal hematopoiesis (CH). Mutations in DNMT3A, particularly at hotspot R882, are the most prevalent in CH and carry an increased risk of acute myeloid leukemia (AML). Although DNMT3A R882 mutations are linked to global DNA hypomethylation, the mechanisms underlying their selective advantage remain unclear. Here, we show that Dnmt3a-R882H mutant HSCs exhibit resilience under inflammatory and genotoxic stress. During IL-1{beta}-induced emergency granulopoiesis, Dnmt3a R882H/+ HSCs uncouple increased proliferation from stem cell exhaustion. In contrast, wild-type HSCs rapidly progress to terminal differentiation. We link this phenotype to a delayed activation of the p53-p21-DREAM axis, that allows mutant HSCs to avoid attrition, despite increased replication stress. Similarly, mutant HSCs exhibit delayed Trp53 activation following irradiation, but eventually recover a physiological Trp53 response. Analysis of patient data reveals shared phenotypic features between DNMT3A and monoallelic TP53 mutations in CH and myeloid neoplasms, highlighting potential functional similarities. Collectively, these findings suggest that the expansion of DNMT3A-mutant clones is affected by impaired TP53 signaling, which confers resilience against stressors. Therapeutic strategies targeting inflammatory pathways or the p53-p21-DREAM axis may reduce DNMT3A-CH expansion and/or progression and its associated risks.

molecular biology↗

bioRxiv2024

Mutational synergy with CREBBP loss in lymphomagenesis identified through forward insertional mutagenesis in a new DLBCL mouse model

Loss-of-function mutations in the gene encoding the acetyltransferase CREBBP have been reported in numerous cancers but are particularly frequent in lymphoid malignancies. However, the functional significance of CREBBP loss in transformation and disease progression, most likely through cooperation with secondary genetic hits, has not yet been fully unravelled. Similarly, the contribution of the initial cell population sustaining CREBBP loss in the course of disease remains elusive. Here, we developed a new lymphoma mouse model integrating Crebbp loss at various stages of B cell development with a transposon-based insertional mutagenesis system. We demonstrated that Crebbp loss from the HSPC compartment resulted in an aggressive DLBCL-like disease, recapitulating well-characterised histological and molecular features of the human disease, as well as the recently described enhanced CD24 expression. More importantly, we identified candidate genes functionally equivalent to patient mutated genes. Those genes, mainly related to B cell development and cellular signalling, may represent novel therapeutic targets. Overall, this new model provides a powerful resource in which to conduct future mechanistic and therapeutic studies.

cancer biology↗

bioRxiv2023

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data

A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of featureobservation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple listlike schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.

bioinformatics↗

bioRxiv2022

Mannose metabolism inhibition sensitizes acute myeloid leukemia cells to cytarabine and FLT3 inhibitor therapy by modulating fatty acid metabolism to drive ferroptotic cell death.

Resistance to standard and novel therapies remains the main obstacle to cure in acute myeloid leukemia (AML) and is often driven by metabolic adaptations which are therapeutically actionable. Here we identify inhibition of mannose-6-phosphate isomerase (MPI), the first enzyme in the mannose metabolism pathway, as a sensitizer to both cytarabine and FLT3 inhibitors across multiple AML models. Mechanistically, we identify a connection between mannose metabolism and fatty acid metabolism, that is mediated via preferential activation of the ATF6 arm of the unfolded protein response (UPR). This in turn leads to cellular accumulation of polyunsaturated fatty acids, lipid peroxidation and ferroptotic cell death in AML cells. Our findings provide further support to the role of rewired metabolism in AML therapy resistance, unveil a novel connection between two apparently independent metabolic pathways and support further efforts to achieve eradication of therapy-resistant AML cells by sensitizing them to ferroptotic cell death.

cancer biology↗

bioRxiv2022

Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome

BackgroundAntimicrobial Resistance (AMR) has a detrimental impact on human health on Earth and it is equally concerning in other environments such as space due to microgravity, radiation and confinement, especially for long-distance space travel. The International Space Station (ISS) is ideal for investigating microbial diversity and virulence. The shotgun metagenomics data of the ISS generated during the Microbial Tracking - 1 (MT-1) project and resulting metagenome-assembled genomes (MAGs) across three flights in eight different locations during 12 months were used in this study. The objective of this study was to identify the AMR genes associated with whole genomes of 227 cultivable strains, 21 shotgun metagenome sequences, and 24 MAGs retrieved from the ISS environmental samples that were treated with propidium monoazide (PMA; viable microbes). ResultsWe have analyzed the data using a deep learning model, allowing us to go beyond traditional cut-offs based only on high DNA sequence similarity and extending the catalog of AMR genes. Our results in PMA treated samples revealed AMR dominance in the last flight for Kalamiella piersonii, a bacteria related to urinary tract infection in humans. The analysis of 227 pure strains isolated from the MT-1 project revealed hundreds of antibiotic resistance genes from many isolates, including two top-ranking species that corresponded to strains of Enterobacter bugandensis and Bacillus cereus. Computational predictions were experimentally validated by antibiotic resistance profiles in these two species, showing a high degree of concordance. Specifically, disc assay data confirmed the high resistance of these two pathogens to various beta-lactam antibiotics. ConclusionOverall, our computational predictions and validation analyses demonstrate the advantages of machine learning to uncover concealed AMR determinants in metagenomics datasets, expanding the understanding of the ISS environmental microbiomes and their pathogenic potential in humans.

bioinformatics↗

bioRxiv2020

Epigenetic regulations follow cell cycle progression during differentiation of human pluripotent stem cells.

Most mammalian stem cells undergo cellular division during their differentiation to produce daughter cells with a new cellular identity. However, the cascade of epigenetic events and molecular mechanisms occurring between successive cell divisions upon differentiation have not yet been described in detail due to technical limitations. Here, we address this question by taking advantage of the Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) reporter to develop a culture system allowing the differentiation of human Embryonic Stem Cells (hESCs) synchronised for their cell cycle. Using this approach, we have assessed the epigenome and transcriptome dynamics during the first two divisions leading to definitive endoderm. We first observed that transcription of key markers of differentiation occurs before division suggesting that differentiation is initiated during the progression of cell cycle. Furthermore, ATAC-seq shows a major decrease in chromatin accessibility after pluripotency exit indicating that the first event of differentiation is the inhibition of alternative cell fate. In addition, using digital genomic footprinting we identified novel cell cycle-specific transcription factors with regulatory potential in endoderm specification. Of particular interest, Activator protein 1 (AP-1) controlled p38/MAPK signalling seems to be necessary for blocking endoderm shifting cell fate toward mesoderm lineage. Finally, histone modifications analyses suggest a temporal order between different marks. We can also conclude that enhancers are dynamically and rapidly established / decommissioned between different cell cycle upon differentiation. Overall, these data not only reveal key the successive interplays between epigenetic modifications during differentiation but also provide a valuable resource to investigate novel mechanisms in germ layer specification.

cell biology↗

bioRxiv2019

Single-cell RNA-seq of differentiating iPS cells reveals dynamic genetic effects on gene expression

Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts.

genomics↗

bioRxiv2018

Sparse functional data analysis accounts for missing information in single-cell epigenomics

Single-cell epigenome assays produce sparsely sampled data, leading to coverage pooling across cells to increase resolution. Imputation of missing data using deep learning is available but requires intensive computation, and it has been applied only to DNA methylation obtained by single cell bisulfite sequencing. Here, sparsity in chromatin accessibility obtained by scNMT-seq is addressed using functional data analysis to fit sparsely sampled GpC coverage profiles of individual cells taking into account all the cells of the same cell-type or condition. For that, sparse functional principal component analysis (S-FPCA) is applied, and the principal components are used to estimate chromatin accessibility coverage in individual cells. This methodology can potentially be used with other single-cell assays with missing data such as scBS-seq, scNOME-seq, or scATAC-seq. The R package fdapace is available in CRAN, and R code used in this manuscript can be found at: http://github.com/pmb59/sparseSingleCell.

bioinformatics↗