Biology subjects

del Pozo, M. A.

Publications and source records attributed to del Pozo, M. A..

7 recordsLinked to original sources

bioRxiv2025

Loss of Gαq reshapes key fibroblast traits and drives matrix remodeling and aggressive progression of oral cancer tumors

Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive cancer, with limited therapeutic options and a high mortality rate, primarily due to metastasis and recurrence. Tumor-stroma interactions, and namely cancer-associated fibroblasts (CAFs), are pivotal in shaping HNSCC progression. CAFs remodel the extracellular matrix (ECM) and secrete factors and vesicles that promote tumor growth and metastasis. The interplay between autophagy and endosomal/exosomal pathways has been suggested to regulate cellular secretory functions, but their potential involvement in HNSCC progression remains poorly understood. Since we have recently uncovered Gq as a key modulator of autophagy, we have investigated the impact of Gq loss on fibroblast functionality and on its crosstalk with oral HNSCC cells. We report that the absence of Gq rewires murine embryonic fibroblasts towards CAF-like traits, leading to an increased pro-tumorigenic capacity of co-cultured human oral cancer cells through enhanced collagen I deposition and ECM remodeling. Strikingly, fibroblasts lacking Gq display a shift in the balance of intracellular trafficking, degradative and secretory pathways. Exosomes released from Gq-deficient fibroblasts show a marked enrichment in tumor-growth factor receptors and can facilitate aberrant tumor growth of HNSCC cells. Gq-silenced fibroblasts promote the formation of "railroad-tracks" structures around HNSCC cells, enhancing their migratory and invasive capabilities both in vitro and in vivo, and reduced Gq expression in human HNSCC CAFs correlates with enhanced tumor progression. Overall, our data put forward Gq as a key regulator of the HNSCC tumor microenvironment by modulating fibroblast plasticity and functionality.

cancer biology↗

bioRxiv2022

Cell response to extracellular matrix energy dissipation outweighs rigidity sensing

The mechanical properties of the extracellular matrix (ECM) determine cell differentiation, proliferation and migration through mechanoresponsive proteins including YAP. However, how different mechanical signals cooperate, synergize or compete to steer cell behavior remains poorly understood. Here, we have examined competition between the two major ECM mechanical cues, i.e. rigidity, which activates cell mechanosensing, and viscous energy dissipation, which reduces stiffness blunting cell mechanotransduction. To trigger competition, we have engineered protein hydrogels allowing concomitant modulation of stiffness and viscosity by mechanisms characteristic of native ECM. Culturing cells on these hydrogels, we have found that substrate energy dissipation attenuates YAP mechanosensing prevailing over stiffness cues. Hampered YAP activation on more dissipative substrates correlates with faster actin flow and smaller focal adhesions. Mechanistically, inhibition of actomyosin contractility reverses the outcome of the competition between rigidity and energy dissipation. Our results highlight the dominating contribution of substrate viscosity to the biology of the cell.

bioengineering↗

bioRxiv2021

De novo identification of universal cell mechanics regulators

Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way towards engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.

biophysics↗

bioRxiv2021

PERK-dependent reciprocal crosstalk between ER and non-centrosomal microtubules coordinates ER architecture and cell shape

The architecture of the endoplasmic reticulum (ER) is tightly controlled as a key determinant of its function. Its dynamics are linked to those of the cytoskeleton, but our understanding of how this coordination occurs and what its functional relevance is, is limited. We found the Unfolded Protein Response (UPR) transducer EIF2AK3/PERK is essential for acute stress-induced peripheral redistribution and remodeling of the ER, through eIF2a phosphorylation and translation initiation shutdown. PERK-mediated eIF2a phosphorylation can be bypassed by blocking ribosome activity; by depleting microtubule-anchoring ER proteins such as REEP4, p180/RRBP1 and Climp63/CKAP4; or by disrupting the microtubule cytoskeleton. Notably, specific disruption of non-centrosomal microtubules, but not centrosome depletion, relieved blockade of ER redistribution in PERK-deficient cells. Conversely, PERK deficiency stabilized non-centrosomal microtubules, promoting polarized protrusiveness in epithelial cells and neuroblasts. We propose that PERK coordinates ER architecture and homeostasis with cell morphogenesis by coupling ER remodeling and non-centrosomal MT dynamics.

cell biology↗

bioRxiv2018

ECM deposition is driven by caveolin1-dependent regulation of exosomal biogenesis and cargo sorting

The composition and physical properties of the extracellular matrix (ECM) critically influence tumour cell behaviour, and ECM deposition and remodelling by stromal fibroblast populations is therefore pivotal for tumour progression. The molecular mechanisms by which stromal and tumour cell populations regulate ECM layering are poorly understood. Tumour-stroma interaction is critically dependent on cell-cell communication mediated by exosomes, small vesicles secreted by most cell types and generated within multivesicular bodies (MVBs). Here, we show that caveolin-1 (Cav1), an essential regulator of stromal remodelling and tumour cell fate, plays a central role in modulating both exosome biogenesis and exosomal protein cargo sorting through cholesterol-dependent mechanisms. Quantitative proteomics profiling revealed that a major share of Cav1-dependent exosomal cargoes are compsed of ECM proteins, one of the most important components being tenascin-C (TnC). Comparative functional assays demonstrated that Cav1 is required for fibroblast-derived exosomes to depose ECM and promote tumour cell invasiveness. Exosomes purified from Cav1WT cells, but not those from Cav1-null cells, were able to nucleate distant stromal niches in different organs in vivo. These findings suggest a key role for Cav1 as a cholesterol rheostat in MVBs, and seems to determine ECM deposition by eliciting ECM component sorting into specific exosome pools. These results, together with previous work, support a model in which Cav1 is a central regulatory hub for tumour-stroma interactions through a novel exosome-dependent ECM deposition mechanism.

cell biology↗

bioRxiv2018

Tox_(R)CNN: Deep Learning-Based Nuclei Profiling tool For Drug Toxicity Screening

Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction of toxicity based on nucleus pattern recognition. Deep learning algorithms obtain abstract representations of images through an automated process, allowing them to efficiently classify complex patterns, and have become the state-of-the art in machine learning for computer vision. Here, deep convolutional neural networks (CNN) were trained to predict toxicity from images of DAPI-stained cells pre-treated with a set of drugs with differing toxicity mechanisms. Different cropping strategies were used for training CNN models, the nuclei-cropping-based Tox-CNN model outperformed other models classifying cells according to health status. Tox-CNN allowed automated extraction of feature maps that clustered compounds according to mechanism of action. Moreover, fully automated region-based CNNs (RCNN) were implemented to detect and classify nuclei, providing per-cell toxicity prediction from raw screening images. We validated both Tox-(R)CNN models for detection of pre-lethal toxicity from nuclei images, which proved to be more sensitive and have broader specificity than established toxicity readouts. These models predicted toxicity of drugs with mechanisms of action other than those they had been trained for and were successfully transferred to other cell assays. The Tox-(R)CNN models thus provide robust, sensitive, and cost-effective tools for in vitro screening of drug-induced toxicity. These models can be adopted for compound prioritization in drug screening campaigns, and could thereby increase the efficiency of drug discovery.\n\nAuthor summaryVisualization of nuclei using different microscopic approaches has for decades allowed the identification of cells undergoing cell death, based on changes in morphology, nuclear density, etc. However, this human-based visual analysis has not been traslated into quantitative tools able to objectively measure cytotoxicity in drug-exposed cells. We asked ourselves if it would be possible to train machines to detect cytotoxicity from microscopy images of fluorescently stained nuclei, without using specific toxicity labeling. Deep learning is the most powerful supervised machine learning methodology available, with exceptional abilities to solve computer vision tasks, and was thus selected for the development of a toxicity quantification tool. Two convolutional neural networks (CNN) were developed to classify cells based on health status: Tox-CNN, relying on prior cell segmentation and cropping of nuclei images, and Tox-RCNN which carries out fully-automated cell detection and classification. Both Tox-(R)CNN classification outputs provided sensitive screening readouts that detected pre-lethal toxicity and were validated for a broad array of toxicity pathways and cell assays. Tox-(R)CNN approaches excel in affordability and applicability to other in vitro toxicity readouts and constitute a robust screening tool for drug discovery.

pharmacology and toxicology↗

bioRxiv2017

Mechanochemical feedback and control of endocytosis and membrane tension

Plasma membrane tension is an important factor that regulates many key cellular processes. Membrane trafficking is tightly coupled to membrane tension and can modulate the latter by addition or removal of the membrane. However, the cellular pathway(s) involved in these processes are poorly understood. Here we find that, among a number of endocytic processes operating simultaneously at the cell surface, a dynamin and clathrin-independent pathway, the CLIC/GEEC (CG) pathway, is rapidly and specifically upregulated upon reduction of tension. On the other hand, inhibition of the CG pathway results in lower membrane tension, while up regulation significantly enhances membrane tension. We find that vinculin, a well-studied mechanotransducer, mediates the tension-dependent regulation of the CG pathway. Vinculin negatively regulates a key CG pathway regulator, GBF1, at the plasma membrane in a tension dependent manner. Thus, the CG pathway operates in a negative feedback loop with membrane tension which leads to a homeostatic regulation of membrane tension.

cell biology↗