Biology subjects

Lopez-Perez, A. R.

Publications and source records attributed to Lopez-Perez, A. R..

4 recordsLinked to original sources

bioRxiv2024

GeneSetCluster 2.0: a comprehensive toolset for summarizing and integrating gene-sets analysis

BackgroundGene-Set Analysis (GSA) is commonly used to analyze high-throughput experiments. However, GSA cannot readily disentangle clusters or pathways due to redundancies in upstream knowledge bases, which hinders comprehensive exploration and interpretation of biological findings. To address this challenge, we developed GeneSetCluster, an R package designed to summarize and integrate GSA results. Over time, we and users as well identified limitations in the original version, such as difficulties in managing redundancies across multiple gene-sets, large computational times, and its lack of accessibility for users without programming expertise. ResultsWe present GeneSetCluster 2.0, a comprehensive upgrade that delivers methodological, computational, interpretative, and user-experience enhancements. Methodologically, GeneSetCluster 2.0 introduces a novel approach to address duplicated gene-sets and implements a seriation-based clustering algorithm that reorders results, aiding pattern identification. Computationally, the package is optimized for parallel processing, significantly reducing execution time. GeneSetCluster 2.0 enhances cluster annotations by associating clusters with relevant tissues and biological processes to improve biological interpretation, particularly for human and mouse data. To broaden accessibility, we have developed a user-friendly web application enabling non-programmers to use it. This version also ensures seamless integration between the R package, catering to users with programming expertise, and the web application for broader audiences. We evaluated the updates in a single-cell RNA public dataset. ConclusionGeneSetCluster 2.0 offers substantial improvements over its predecessor. Furthermore, by bridging the gap between bioinformaticians and clinicians in multidisciplinary teams, GeneSetCluster 2.0 facilitates collaborative research. The R package and web application, along with detailed installation and usage guides, are available on GitHub (https://github.com/TranslationalBioinformaticsUnit/GeneSetCluster2.0), and the web application can be accessed at https://translationalbio.shinyapps.io/genesetcluster/.

bioinformatics↗

bioRxiv2024

Spatial Transcriptomics Reveals a Myeloma Cell Architecture with Dysfunctional T-Cell Distribution, Neutrophil Traps, and Inflammatory Signaling

The bone marrow (BM) is a complex tissue where spatial relationships influence cell behavior, signaling, and function. Consequently, understanding the whole dynamics of cellular interactions requires complementary spatial techniques that preserve and map the architecture of cell populations in situ. We successfully conducted spatial transcriptional profiling using Visium Spatial Gene Expression in formalin-fixed paraffin-embedded (FFPE) BM samples obtained from healthy and Multiple Myeloma (MM) mouse models and patients, addressing the technical challenges of applying spatial technology to long bone samples. A custom data-analysis framework that combines spatial with single-cell transcriptomic profiles identified both the BM cellular composition and the existing cell relations. This allowed us to visualize the spatial distribution of transcriptionally heterogeneous MM plasma cells (MM-PC). We spatially delineated transcriptional programs associated with MM, including NETosis and IL-17-driven inflammatory signaling, which were inversely correlated to malignant PC-enriched regions. Furthermore, a gradient of MM-PC density spatially correlated with a shift from effector-to-exhausted T cell phenotypes. The translational relevance of our findings was confirmed using FFPE BM biopsies from MM patients with varying levels of malignant PC infiltration. In summary, we provide the first spatial transcriptomics analysis applied to a mouse and human mineralized bone tissue and illustrate the BM cellular architecture of MM, revealing deregulated mechanisms underlying MM intercellular communication.

cancer biology↗

bioRxiv2023

Diet-induced rewiring of the Wnt gene regulatory network connects aberrant splicing to fatty liver and liver cancer in DIAMOND mice

Background & AimsThis study aimed to provide a comprehensive understanding of the regulatory and transcriptional landscape in liver tumours from DIAMOND mice, a mouse model that mimics human hepatocellular carcinoma (HCC) in the context of metabolic associated fatty liver disease (MAFLD). MethodsRNA-sequencing and ChIP-sequencing were used to study the gene expression and regulatory changes in DIAMOND liver tumours. RNA in situ hybridisation splice variant analysis was used to study {beta}-catenin exon 3 exclusion in tumours at cellular resolution. Sequencing data on {beta}-catenin exon 3 splicing in DIAMOND tumours was compared to data from human patients and cell lines. ResultsThe study found an increase in Wnt/{beta}-catenin-signalling accompanied by rewiring of the Wnt/{beta}-catenin regulatory network in DIAMOND tumours. Changes include switching in the expression of the canonical TCF/LEF downstream effectors and associated chromatin remodelling. In addition, a large subset of DIAMOND tumours showed aberrant splicing of {beta}-catenin, which generate an mRNA isoform that encodes an oncogenic protein. Similar splicing events were found in a fraction of human HCC and hepatoblastoma samples. ConclusionsThis study provides evidence that western diet induces aberrant genome-wide splicing in DIAMOND livers, and in particular of the {beta}-catenin gene in a subset of DIAMOND liver tumours. This mechanism is distinct from previously reported activation of {beta}-catenin in HCC and mouse models, since it is independent on mutations in the locus. Our data suggests that metabolic input modulates gene regulatory network responses to active Wnt-signalling, which will be an important consideration also in the human setting. Lay summarySequencing data generated in this study highlights the effect of diet in modulating oncogenic gene expression and underscores an alternative mutation-independent mechanism leading to constitutive activation of {beta}-catenin, a well-known driver of liver cancer.

cancer biology↗

bioRxiv2020

Gata6 and Hnf1ba are the major downstream effectors of retinoic acid for the specification of zebrafish pancreas

Retinoic acid (RA) is a key signal for the specification of the pancreas. Still, the gene regulatory cascade triggered by RA in the endoderm remains poorly characterized. In this study, we investigated this regulatory network in zebrafish by combining RNA-seq, RAR ChIP-seq and ATAC-seq assays. By analysing the effect of RA and of the RA receptor (RAR) antagonist BMS439 on the transcriptome and on the chromatin accessibility of endodermal cells, we identified a large set of genes and regulatory regions regulated by RA signalling. RAR ChIP-seq further defined the direct RAR target genes including the known hox genes as well as several pancreatic regulators like mnx1, insm1b, hnf1ba and gata6. Comparison of our zebrafish data with available murine RAR ChIP-seq data highlighted conserved direct target genes and revealed that some RAR sites are under strong evolutionary constraints. Among them, a novel highly conserved RAR-induced enhancer was identified downstream of the HoxB locus and driving expression in the nervous system and in the gut in a RA-dependant manner. Finally, ATAC-seq data unveiled the role of the RAR-direct targets Hnf1ba and Gata6 in opening chromatin at many regulatory loci upon RA treatment. Summary statementCombination of RNA-seq, ChIP-seq and ATAC-seq assays identifies genes directly and indirectly regulated by RA signalling in zebrafish endoderm. Comparison with murine data highlights RAR binding sites conserved among vertebrates.

genomics↗