About the author Ana Garcia del Arco

Extracellular Vesicles: An Emerging Mechanism Governing the Secretion and Biological Roles of Tenascin-C

Lucas AlbaceteAlbaceteMiguel SánchezÁlvarez and Miguel Angel Del Pozo.

Abstract: ECM composition and architecture are tightly regulated for tissue homeostasis. Different disorders have been associated to alterations in the levels of proteins such as collagens, fibronectin (FN) or tenascin-C (TnC). TnC emerges as a key regulator of multiple inflammatory processes, both during physiological tissue repair as well as pathological conditions ranging from tumor progression to cardiovascular disease. Importantly, our current understanding as to how TnC and other non-collagen ECM components are secreted has remained elusive. Extracellular vesicles (EVs) are small membrane-bound particles released to the extracellular space by most cell types, playing a key role in cell-cell communication. A broad range of cellular components can be transported by EVs (e.g. nucleic acids, lipids, signalling molecules and proteins). These cargoes can be transferred to target cells, potentially modulating their function. Recently, several extracellular matrix (ECM) proteins have been characterized as bona fide EV cargoes, exosomal secretion being particularly critical for TnC. EV-dependent ECM secretion might underpin diseases where ECM integrity is altered, establishing novel concepts in the field such as ECM nucleation over long distances, and highlighting novel opportunities for diagnostics and therapeutic intervention. Here, we review recent findings and standing questions on the molecular mechanisms governing EV-dependent ECM secretion and its potential relevance for disease, with a focus on TnC.

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3ª Reunión científia del consorcio Tec4Bio

Hoy celebramos la tercera reunión científica y del Comité de Gestión de nuestro programa Tec4Bio. Durante el día de hoy realizaremos un balance y seguimiento del trabajo científico de los grupos y evaluaremos el progreso de las actividades del consorcio desde nuestra anterior reunión en octubre de 2020.

Bayesian inference of gene expression

Víctor Jiménez-Jiménez, Carlos Martí-Gómez, Miguel Ángel del Pozo, Enrique Lara-Pezzi and Fátima Sánchez-Cabo.


Omics techniques have changed the way we depict the molecular features of a cell. The integrative and quantitative analysis of omics data raises unprecedented expectations for understanding biological systems on a global scale. However, its inherently noisy nature, together with limited knowledge of potential sources of variation impacting health and disease, require the use of proper mathematical and computational methods for its analysis and integration. Bayesian inference of probabilistic models allows propagation of the uncertainty from the experimental data to our beliefs of the model parameters, allowing us to appropriately answer complex biological questions. In this chapter, we build probabilistic models of gene expression from RNA-seq data and make inference about their parameters using Bayesian methods. We present models of increasing complexity, from the quantification of a single gene expression to differential gene expression for a whole transcriptome, comparing them to the available tools for analysis of gene expression data. We provide Stan scripts that introduce the reader into the implementation of Bayesian statistics for omics data. The rationale that we apply for transcriptomics data may be easily extended to model the particularities of other omics data and to integrate the different regulatory layers.

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