Biology subjects

Gomez-Cabrero, D.

Publications and source records attributed to Gomez-Cabrero, D..

33 records · Page 2Linked to original sources

bioRxiv2022

Origin of congenital coronary arterio-ventricular fistulae from anomalous epicardial and myocardial development

AimsIn this work we investigated the embryonic origin of coronary arterio-ventricular connections, known as coronary artery fistulas (CAF), a congenital heart disease associated to postnatal and adult changes in systemic hemodynamics that may cause cardiac ischemia. Methods and resultswe have used different animal models (mouse and avian embryos) to experimentally model CAF morphogenesis. Conditional Itga4 (alpha 4 integrin) epicardial deletion in mice and cryocauterisation of chick and quail embryonic hearts disrupted epicardial development and ventricular wall growth, two essential events in coronary embryogenesis. Additional transcriptomics and in vitro analyses were performed to better understand how arterio-ventricular connections are originated in the embryonic heart. Our results show myocardial discontinuities in the developing heart of mutant mice presenting epicardial defects and avian embryos submitted to a physical cryodamage of the ventricle. These ventricular discontinuities promote the formation of endocardial pouch-like structures resembling human CAF. The structure of these CAF-like anomalies was compared with histopathological data from a human CAF, showing histomorphological and immunochemical similarities. Both human and mutant mouse hearts showed similar anomalies in the compaction of the ventricular myocardium. In vitro experiments showed the abnormal contact between the epicardium and the endocardium promote the precocious differentiation of epicardial cells to smooth muscle. ConclusionOur work suggests that myocardial discontinuities in the embryonic ventricular wall are a causative of CAF. These discontinuities would promote the early contact of the endocardium with epicardial-derived coronary progenitors at the cardiac surface, leading to ventricular endocardial extrusion, precocious differentiation of coronary smooth muscle cells, and the formation of pouch-like aberrant coronary-like structures in direct connection with the ventricular lumen. Translational perspectiveCongenital coronary artery fistulas (CAFs) lead to complications such as myocardial hypertrophy, endocarditis, heart dilatation and failure. Unfortunately, and despite their clinical relevance, the origins these congenital anomalies remain unknown. In this work, we provide information on the developmental mechanisms involved in the formation of CAFs that is relevant for their early diagnosis and prevention.

developmental biology↗

bioRxiv2021

Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single cell resolution

Early hematopoiesis is a continuous process in which hematopoietic stem and progenitor cells (HSPCs) gradually differentiate toward specific lineages. Aging and myeloid malignant transformation are characterized by changes in the composition and regulation of HSPCs. In this study, we used single cell RNA sequencing (scRNAseq) to characterize an enriched population of human hematopoietic stem and progenitor cells (HSPC) obtained from young and elderly healthy individuals. Based on their transcriptional profile, we identified changes in the proportions of progenitor compartments during aging, and differences in their functionality, as evidenced by gene set enrichment analysis. Trajectory inference revealed that altered gene expression dynamics accompanied cell differentiation, which could explain age-associated changes in hematopoiesis. Next, we focused on key regulators of transcription by constructing gene regulatory networks and detected regulons that were specifically active in elderly individuals. Using previous findings in healthy cells as a reference, we analyzed scRNA-seq data obtained from patients with myelodysplastic syndrome (MDS) and detected specific alterations of the expression dynamics of genes involved in erythroid differentiation in all patients with MDS such as TRIB2. In addition, the comparison between transcriptional programs and GRN regulating normal HSPCs and MDS HSPCs allowed identification of regulons that were specifically active in MDS cases such as SMAD1, HOXA6, POU2F2 and RUNX1 suggesting a role of these TF in the pathogenesis of the disease. In summary, we demonstrate that the combination of single cell technologies with computational analysis tools enable the study of a variety of cellular mechanisms involved in early hematopoiesis and can be used to dissect perturbed differentiation trajectories associated with aging and malignant transformation. Furthermore, the identification of abnormal regulatory mechanisms associated with myeloid malignancies could be exploited for personalized therapeutic approaches in individual patients.

genomics↗

bioRxiv2021

Deep deconvolution of the hematopoietic stem cell regulatory microenvironment reveals a high degree of specialization and conservation between mouse and human.

Understanding the regulation of normal and malignant human hematopoiesis requires comprehensive cell atlas of the hematopoietic stem cell (HSC) regulatory microenvironment. Here, we develop a tailored bioinformatic pipeline to integrate public and proprietary single-cell RNA sequencing (scRNA-seq) datasets. As a result, we robustly identify for the first time 14 intermediate cell states and 11 stages of differentiation in the endothelial and mesenchymal BM compartments, respectively. Our data provide the most comprehensive description to date of the murine HSC-regulatory microenvironment and suggests a higher level of specialization of the cellular circuits than previously anticipated. Furthermore, this deep characterization allows to infer conserved features in human, suggesting that the layers of microenvironmental regulation of hematopoiesis may also be shared between species. Our resource and methodology are a steppingstone towards a comprehensive cell atlas of the BM microenvironment.

immunology↗

bioRxiv2021

Machine Translation between paired Single Cell Multi Omics Data

BackgroundSingle-cell multi-omics technologies allow the profiling of different data modalities from the same cell. However, while isolated modalities only capture one view of the total information of a biological cell, an integrative analysis capturing the different modalities is challenging. In response, bioinformatics and machine learning methodologies have been developed for multi-omics single-cell analysis. Nevertheless, it is unclear if current tools can address the dual aspect of modality integration and prediction across modalities without requiring extensive parameter finetuning. ResultsWe designed LIBRA, a Neural Network based framework, to learn a translation between paired multi-omics profiles such that a shared latent space is constructed. LIBRA is a state-of-the-art tool when evaluating the ability to increase cell-type (clustering) resolution in the latent space. When assessing the predictive power across data modalities, LIBRA outperforms existing tools. Finally, considering the importance of hyperparameters, we implemented an adaptative-tuning strategy, labelled aLIBRA, in the LIBRA package. As expected, adaptive parameter optimization significantly boosts the performance of learning predictive models from paired datasets. Additionally, aLIBRA provides parameter combinations balancing the integrative and predictive tasks. ConclusionsLIBRA is a versatile tool, uniquely targeting both integration and prediction tasks of Single-cell multi-omics data. LIBRA is a data-driven robust platform that includes an adaptive learning scheme. Furthermore, LIBRA is freely available as R and Python libraries (https://github.com/TranslationalBioinformaticsUnit/LIBRA).

bioinformatics↗

bioRxiv2020

scMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes

Recent progress in single-cell genomics has generated multiple tools for cell clustering, annotation, and trajectory inference; yet, inferring their associated regulatory mechanisms is unresolved. Here we present scMomentum, a model-based data-driven formulation to predict gene regulatory networks and energy landscapes from single-cell transcriptomic data without requiring temporal or perturbation experiments. scMomentum provides significant advantages over existing methods with respect to computational efficiency, scalability, network structure, and biological application. AvailabilityscMomentum is available as a Python package at https://github.com/larisa-msoto/scMomentum.git

systems biology↗

bioRxiv2020

STATegra: Multi-omics data integration - A conceptual scheme with a bioinformatics pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.

bioinformatics↗

bioRxiv2020

The human oral phageome is highly diverse and rich in jumbo phages

Until recently, the contribution of bacteriophages to the composition and function of the human microbiome has been largely overlooked. Recent developments in discovering novel bacteriophages from human metagenomes have been mostly focused on the gut. Here we profile and compare the phageome of 633 human oral sites and 221 paired gut phageomes acquired from countries worldwide. Phageome profiles are specific to oral sites and the gut across individuals, with the largest separation between the oral cavity and the gut. The differences in phage genotypes is bolstered by distinct phage host compositions between these sites. The greatest diversity of phages is found on the tongue and in dental plaque. Notably, 37 unique circular jumbo phage genomes were identified in oral sites, particularly on the dorsum of the tongue, whilst none were found in the gut. Oral sites provide conducive environments, such as robust biofilms, that can harbour genetically diverse phages.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2019

Abundance and diversity of resistomes differ between healthy human oral cavities and gut

The global threat of the \"antimicrobial resistance apocalypse\" that has arisen in recent years has driven the use of high-throughput sequencing techniques to monitor the profile of resistance genes, known as the \"resistome\", in microbial populations. The human oral cavity contains a poorly explored reservoir of these genes, and little is known about their abundance and diversity, or how their profile compares with antimicrobial resistance genes in the gut. Here we analyse the resistome profiles of 788 oral cavities worldwide and compare these profiles with paired stool samples from shotgun metagenomic data. We find country and body site-specific differences in the prevalence of antimicrobial resistance genes, classes and mechanisms in oral and stool samples. However, the abundance of these antimicrobial resistance classes do not correlate with antibiotic prescription rates. A greater similarity was found in interpersonal resistomes between the same body sites than intrapersonal resistomes across different body sites. Between individuals, the oral cavity contains the highest and lowest abundances of specific antimicrobial resistance genes, but a lower diversity of resistance genes compared to the gut, which is likely influenced by differences in microbial composition and exposure to antimicrobial selection pressures. Co-occurrence analysis shows contrasting ARG-species associations between saliva and stool samples. This is the first study to date that characterises the oral cavity resistome worldwide, identifying its distinctive signatures compared to the gut.Maintenance and persistence of antimicrobial resistance is likely to vary across different body sites. Thus, we highlight the importance of characterising the resistome across body sites to uncover the antimicrobial resistance potential in the human body.

microbiology↗

bioRxiv2019

Single-cell RNA-seq analysis reveals the crucial role of Collagen Triplex Helix Repeat Containing 1 (CTHRC1) cardiac fibroblasts for ventricular remodeling after myocardial infarction

Cardiac fibroblasts have a central role during the ventricular remodeling process associated with different types of cardiac injury. Recent studies have shown that fibroblasts do not respond homogeneously to heart damage, suggesting that the adult myocardium may contain specialized fibroblast subgroups with specific functions. Due to the limited set of bona fide fibroblast markers, a proper characterization of fibroblast population dynamics in response to cardiac damage is still missing. Using single-cell RNA-seq, we identified and characterized a fibroblast subpopulation that emerges in response to myocardial infarction (MI) in a murine model. These activated fibroblasts exhibit a clear pro-fibrotic signature, express high levels of the hormone CTHRC1 and of the immunomodulatory co-receptor CD200 and localize to the injured myocardium. Combining epigenomic profiling with functional assays, we show Sox9 and the non-canonical TGF-{beta} signaling as important regulators mediating their response to cardiac damage. We show that the absence of CTHRC1, in this activated fibroblast subpopulation, results in pronounced lethality due to ventricular rupture in a mouse model of myocardial infarction. Finally, we find evidence for the existence of similar mechanisms in a pig pre-clinical model of MI and establish a correlation between CTHRC1 levels and cardiac function after MI.

cell biology↗

bioRxiv2019

On the prediction of protein abundance from RNA

BackgroundProfiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. MethodsWe characterised time-series of primary human naive CD4+ T cells during early T-helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We then performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time-delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-Seq datasets to evaluate biomarker discovery in five T-cell associated diseases, validating the findings for multiple sclerosis (MS) and asthma. ResultsThe new models demonstrated median correlations of mRNA-to-protein abundance of 0.79-0.94, significantly out-performing models not including the usage of multiple splice variants and time-delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was clinically validated in MS using two independent cohorts, for treatment response and prognosis. ConclusionOur splice variant and time-delay models substantially improved the prediction of protein abundance from mRNA data in three immune cell-types. The models provided valuable biomarker candidates, which were validated in clinical studies of MS and asthma. We propose that our strategy is generally applicable for biomarker discovery.

bioinformatics↗

bioRxiv2019

STATegra: a comprehensive multi-omics dataset of B-cell differentiation in mouse

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

bioinformatics↗

bioRxiv2019

Gene regulatory network of human GM-CSF secreting T helper cells

GM-CSF produced by autoreactive CD4 positive T helper cells is involved in the pathogenesis of autoimmune diseases, such as Multiple Sclerosis. However, the molecular regulators that establish and maintain the features of GM-CSF positive CD4 T cells are unknown. In order to identify these regulators, we isolated human GM-CSF producing CD4 T cells from human peripheral blood by using a cytokine capture assay. We compared these cells to the corresponding GM-CSF negative fraction, and furthermore, we studied naive CD4 T cells, memory CD4 T cells and bulk CD4 T cells from the same individuals as additional control cell populations. As a result, we provide a rich resource of integrated chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) data from these primary human CD4 T cell subsets, and we show that the identified signatures are associated with human autoimmune disease, especially Multiple Sclerosis. By combining information about mRNA expression, DNA accessibility and predicted transcription factor binding, we reconstructed directed gene regulatory networks connecting transcription factors to their targets, which comprise putative key regulators of human GM-CSF positive CD4 T cells as well as memory CD4 T cells. Our results suggest potential therapeutic targets to be investigated in the future in human autoimmune disease.

bioinformatics↗

bioRxiv2018

Building gene regulatory networks from single-cell ATAC-seq and RNA-seq using Linked Self-Organizing Maps

Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq and scRNA-seq data that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of single-cells.

genomics↗

bioRxiv2018

The ultra-sensitive Nodewalk technique identifies stochastic from virtual, population-based enhancer hubs regulating MYC in 3D: Implications for the fitness of cancer cells

The relationship between stochastic transcriptional bursts and dynamic 3D chromatin states is not well understood due to poor sensitivity and/or resolution of current chromatin structure-based assays. Consequently, it is not well established if enhancers operate individually and/or in clusters to coordinate gene transcription. In the current study, we introduce Nodewalk, which uniquely combines high sensitivity with high resolution to enable the analysis of chromatin networks in minute input material. The >10,000-fold increase in sensitivity over other many-to-all competing methods uncovered that active chromatin hubs identified in large input material, corresponding to 10 000 cells, flanking the MYC locus are primarily virtual. Thus, the close agreement between chromatin interactomes generated from aliquots corresponding to less than 10 cells with randomly re-sampled interactomes, we find that numerous distal enhancers positioned within flanking topologically associating domains (TADs) converge on MYC in largely mutually exclusive manners. Moreover, when comparing with several enhancer baits, the assignment of the MYC locus as the node with the highest dynamic importance index, indicates that it is MYC targeting its enhancers, rather than vice versa. Dynamic changes in the configuration of the boundary between TADs flanking MYC underlie numerous stochastic encounters with a diverse set of enhancers to depict the plasticity of its transcriptional regulation. Such an arrangement might increase the fitness of the cancer cell by increasing the probability of MYC transcription in response to a wide range of environmental cues encountered by the cell during the neoplastic process.

genomics↗

bioRxiv2017

Software For The Integration Of Multi-Omics Experiments In Bioconductor

Multi-omics experiments are increasingly commonplace in biomedical research, and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multi-omics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide all of the multiple omics data for each cancer tissue in The Cancer Genome Atlas (TCGA) as ready-to-analyze MultiAssayExperiment objects, and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable and reproducible statistical analysis of multi-omics data and enhances data science applications of multiple omics datasets.

bioinformatics↗