Biology subjects

Diez-Sanchez, A.

Publications and source records attributed to Diez-Sanchez, A..

6 recordsLinked to original sources

bioRxiv2026

Prostaglandin E2 facilitates reciprocal crosstalk between intestinal smooth muscle tissue and epithelium in tumor development

Colorectal cancer arises from intestinal epithelial cells and is driven by the accumulation of aberrant signaling events that disrupt intestinal stem cell (ISC) homeostasis. While stromal regulation of the ISC niche is increasingly recognized, the contribution of the underlying/adjacent smooth muscle tissue (SMT) remains poorly characterized. In particular, the potential crosstalk between normal or tumoral epithelium and the SMT is largely unexplored. Here, we investigate the ability of the SMT in modulating normal and tumorigenic epithelial biology, and the impact of the tumoral epithelium on the underlying SMT. Mechanistically, by using imaging analyses, transcriptomics, and mass spectrometry, we identify the SMT-derived factor Prostaglandin E2 (PGE2) as the key driver of epithelial dedifferentiation, promoting YAP transcriptional activation, organoid enlargement, and spheroid morphology in intestinal epithelial organoids following SMT supernatant exposure. Conversely, we show that non-invasive tumoral epithelium induces significant changes in the underlying SMT. Tumor-associated SMT shows structural remodeling, inflammation, and reduced muscle fitness. Furthermore, we detected increased prostaglandin signaling, including upregulation of PGE2 synthase Ptgs1 (COX1), in the SMT underneath the epithelial tumor. Our results reveal a communication between the tumoral epithelium and the underlying SMT prior to metastasis, which could be a facilitating step for tumoral extramural progression. Taken together, these findings highlight a novel tumor-stroma interaction.

Molecular Biology↗

bioRxiv2023

Postnatal intestinal epithelial maturation by LSD1 controls the small intestinal immune cell composition independently from the microbiota

Postnatal development of the gastrointestinal tract involves the establishment of the commensal microbiota, maturation of the intestinal epithelium, and the acquisition of immune tolerance via a balanced immune cell composition. While studies have uncovered an interplay between the commensal microbiota and immune system development, less is known about the role of the maturing epithelium. Here, we comprehensively show that intestinal-epithelial intrinsic expression of lysine-specific demethylase 1A (LSD1) is necessary for the postnatal maturation of intestinal epithelium as well as maintaining this developed epithelial state in adulthood. Although the stool microbiome was altered in animals with an intestinal-epithelial specific deletion of Lsd1, by depleting the microbial component using antibiotics, we found that the cellular state and number of certain immune cell types were dependent on maturation of the epithelium. We found plasma cells, innate lymphoid cells (ILCs), and a specific myeloid population to be depending on epithelial LSD1 expression. We propose that LSD1 controls the expression of epithelial-derived chemokines, such as Cxcl16, and this is a mode of action for this epithelial-immune cell interplay. For example, we show that LSD1-mediated epithelial-intrinsic CXCL16 controls the number of local ILC2s but not ILC3s. Together, our findings suggest that the maturing epithelium plays a dominant role in regulating the local immune cell composition, thereby contributing to gut homeostasis.

immunology↗

bioRxiv2020

A semi-automated intestinal organoid screening method demonstrates epigenetic control of epithelial maturation

Intestinal organoids are an excellent model to study epithelial biology. Yet, the selection of analytical tools to accurately quantify heterogeneous organoid cultures remains limited. Here, we developed a semi-automated organoid screening method, which we applied to a library of highly specific chemical probes to identify epigenetic regulators of intestinal epithelial biology. The role of epigenetic modifiers in adult stem cell systems, such as the intestinal epithelium, is still undefined. Based on this resource data, we identified several targets that affected epithelial cell differentiation, including HDACs, EP300/CREBBP, LSD1, and type I PRMTs, which were verified by complementary methods. For example, we show that inhibiting type I PRMTs, which leads enhanced epithelial differentiation, blocks the growth of adenoma but not normal organoid cultures. Thus, epigenetic probes are powerful tools to study intestinal epithelial biology and may have therapeutic potential.

cell biology↗

bioRxiv2020

Developmental pathways regulate cytokine-driven effector and feedback responses in the intestinal epithelium

The intestinal tract is a common site for infection, and it relies on an appropriate immune response to defend against pathogens. The intestinal epithelium has an important role in effector responses, which is coordinated by immune-type specific cytokines. It is incompletely understood how cytokines drive epithelial responses. Here, using organoid-cytokine co-cultures, we provide a comprehensive analysis of how key cytokines affect the intestinal epithelium, and relate this to in vivo infection models. We use imaging, based on a convolutional neural network, and transcriptomic analysis to reveal that cytokines use developmental pathways to define intestinal epithelial effector responses. For example, we find IL-22 and IL-13 dichotomously induce goblet cells, in which only IL-13 driven goblet cells are associated with NOTCH signaling. We further show that IL-13 induces BMP signalling to act as a negative feedback loop in IL-13 induced tuft cell hyperplasia, an important aspect of type 2 immunity. Together, we show that targeting developmental pathways may be a useful tool to tailor epithelial effector responses that are necessary for immunity to infection.

immunology↗

bioRxiv2020

LSD1 represses a neonatal/reparative gene program in adult intestinal epithelium

Intestinal epithelial homeostasis is maintained by adult intestinal stem cells, which, alongside Paneth cells, appear after birth in the neonatal period. We aimed to identify new regulators of neonatal intestinal epithelial development by testing a small library of epigenetic modifier inhibitors in Paneth cell-skewed organoid cultures. We found that Lysine-specific demethylase 1A (Kdm1a/Lsd1) is absolutely required for Paneth cell differentiation. Lsd1-deficient crypts, devoid of Paneth cells, are still able to form organoids without a requirement of exogenous or endogenous Wnt. Mechanistically, we find that LSD1 represses genes that are normally expressed in fetal and neonatal epithelium. This gene profile is similar to what is seen in repairing epithelium, and indeed, we find that Lsd1-deficient epithelium has superior regenerative capacities after irradiation injury. In summary, we found an important regulator of neonatal intestinal development and identified a druggable target to reprogram intestinal epithelium towards a reparative state.

developmental 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↗