Biology subjects

Grecco, H. E.

Publications and source records attributed to Grecco, H. E..

10 recordsLinked to original sources

bioRxiv2026

Oncomimetic β-catenin activity onset, duration and region defines aberrant intestinal development

In early intestinal carcinogenesis, adenoma formation is commonly initiated by loss-of-function mutations in a tumor suppressor that lead to oncoprotein gain-of-function, like in the tumor suppressor-oncoprotein pair APC-{beta}-catenin. Small intestinal organoids provide an in vitro system to study consequences of such mutations on tissue organization. However, conventional genetic manipulations do not allow precise control over the onset and duration of oncoprotein activity to study their influence on tissue transformation. Furthermore, homogenous tissues of clonal genetic models do not readily capture cellular interaction among mutated and neighboring wildtype tissue during early transformation. To mimic oncogenic activation of {beta}-catenin, we established a chemical-(opto)genetic approach to gain bio-orthogonal acute, spatial and temporal control over {beta}-catenin oncoprotein stability and relate oncoprotein levels to morphological development of mouse small intestinal organoids. We identified aberrant phenotypes that result from bio-orthogonally induced oncoprotein activity but persist even after oncoprotein depletion. Furthermore, local activation of oncomimetic {beta}-catenin activity within the stem cell niche leads to aberrant differentiation during homeorhesis and homeostasis, recapitulating early events of tissue transformation.

cancer biology↗

bioRxiv2025

Differential contribution of the RNA recognition motifs to SRSF1 protein interaction network

The protein SRSF1, initially identified as a splicing factor, is currently known to participate in a variety of cellular processes. However, its role and regulatory mechanisms in these processes remain incompletely understood. Using a SILAC proteomic approach, we have characterised the SRSF1 interactome, thereby identifying multiple diverse protein interaction partners of SRSF1 and the molecular complexes with which it is associated. Given that the RNA-binding ability of SRSF1 is necessary for many of its activities, we extended the analysis of the SRSF1 interactome to compare the wild type protein against two site-specific SRSF1 variants, bearing mutations in each of its two RNA recognition motifs (RRMs). The first mutation disrupted the canonical RRM (RRM1), while the second affected the pseudo-RRM (RRM2). Interestingly, the data show clear differences between the SRSF1 interacting proteins, depending on the functional status of each RRM domain. In particular, a functional RRM1 was critical for maintaining interactions with multiple partners, especially those involved in translation-related processes, showing a far greater loss of interactions upon mutation than RRM2. This study provides valuable new insights into the broader cellular roles and interaction landscape of SRSF1.

molecular biology↗

bioRxiv2024

Poincare and SimBio: a versatile and extensible Python ecosystem for modeling systems.

Chemical Reaction Networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level modelling of CRNs enables various simulation approaches, including deterministic and stochastic methods. However, existing Python tools for CRN modelling typically wrap external C/C++ libraries for modelling and simulation, limiting their extensibility and integration with the broader Python ecosystem. In response, we developed Poincare and SimBio, two novel Python packages for the definition and simulation of dynamical systems and CRNs. Poincare serves as a foundation for dynamical system modelling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincare and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be Just-In-Time compiled with Numba. Our benchmark tests using curated models from the BioModels repository, demonstrate that these tools may provide a potentially superior performance advantage, compared to other existing tools. Additionally, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with Integrated Development Environments (IDEs). Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincare and SimBio as valuable tools for the modelling community.

bioinformatics↗

bioRxiv2021

Robust and unbiased estimation of the background distribution for automated quantitative imaging

Quantitative analysis of high-throughput microscopy images requires robust automated algorithms. Background estimation is usually the first step and has an impact on all subsequent analysis, in particular for foreground detection and calculation of ratiometric quantities. Most methods recover only a single background value, such as the median. Those that aim to retrieve a background distribution by dividing the intensity histogram yield a biased estimation in images in non-trivial cases. In this work, we present the first method to recover an unbiased estimation of the background distribution directly from an image and without any additional input. Through a robust statistical test, our method leverages the lack of local spatial correlation in background pixels to select a subset of pixels that accurately represent the background distribution. This method is both fast and simple to implement, as it only uses standard mathematical operations and an averaging filter. Additionally, the only parameter, the size of the averaging filter, does not require fine tuning. The obtained background distribution can be used to test for foreground membership of individual pixels, or to estimate confidence intervals in derived quantities. We expect that the concepts described in this work can help to develop a novel family of robust segmentation methods.

cell biology↗

bioRxiv2021

Automatic inference of cell neighborhood in 2D and 3D using nuclear markers

SignificanceEstimating neighboring cells by using only nuclear markers is crucial in many biological applications. Although several strategies have been used for this purpose, most published methods lack a rigorous characterization of their efficiencies. Remarkably, previously described methods are not automatic and depend only on cell-cell distance, neglecting the importance of pair-neighborhood interaction. AimTo develop a robust and automatic method for assessing cell local neighborhood, while analyzing the impact of the physical variables involved in this task. ApproachWe inferred neighbors from images with nuclei labeling by approximating the cell-cell interaction graph by the Delaunay triangulation of nuclei centroids. Each edge of this graph was filtered by thresholding in cell-cell distance and the maximum angle that each pair subtends with shared neighbors (pair-neighborhood interaction). Thresholds were calculated by maximizing a new robust statistic that measures the communicability efficiency of the cell graph. Using a variety of images of diverse tissues with additional membrane labeling to find the ground truth, we characterized the assessment performance. ResultsOn average, our method detected 95% of true neighbors, with only 6% of false discoveries. Even though our methods performance and tissue regularity are correlated, it works with performance metrics over 86% in very different organisms, including Drosophila melanogaster, Tribolium castaneum, Arabidopsis thaliana and C. elegans. ConclusionsWe automatically estimated neighboring relationships between cells in 2D and 3D using only nuclear markers. To achieve this goal, we filtered the Delaunay triangulation of nuclei centroids with a new measure of graph communicability efficiency. In addition, we found that taking pair-neighborhood interactions into account, in contrast to considering only cell-cell distances, leads to significant performance improvements. This becomes more notorious when the number of cells is low or the geometry of the cell graph is highly complex.

developmental biology↗

bioRxiv2021

An Integrative Apoptotic Reaction Model for extrinsic and intrinsic stimuli

Apoptosis, a form of programmed cell death central to all multicellular organisms, plays a key role during organism development and is often misregulated in cancer. Devising a single model applicable to distinct stimuli and conditions has been limited by lack of robust observables. Indeed, previous numerical models have been tailored to fit experimental datasets in restricted scenarios, failing to predict response to different stimuli. We quantified the activity of three caspases simultaneously upon intrinsic or extrinsic stimulation to assemble a comprehensive dataset. We measured and modeled the time between maximum activity of intrinsic, extrinsic and effector caspases, a robust observable of network dynamics, to create the first integrated Apoptotic Reaction Model (ARM). Observing how effector caspases reach maximum activity first irrespective of stimuli used, led us to identify and incorporate a missing feedback into a successful model for extrinsic stimulation. By simulating different recently performed experiments, we corroborated that ARM adequately describes them. This integrated model provides further insight into the indispensable feedback from effector caspase to initiator caspases.

systems biology↗

bioRxiv2021

CASPAM: a triple modality biosensor for multiplexed imaging of caspase network activity

Understanding signal propagation across biological networks requires to simultaneously monitor the dynamics of several nodes to uncover correlations masked by inherent intercellular variability. To monitor the enzymatic activity of more than two components over short time scales has proven challenging. Exploiting the narrow spectral width of homoFRET-based biosensors, up to three activities can be imaged through fluorescence polarization anisotropy microscopy. We introduce CASPAM (Caspase Activity Sensor by Polarization Anisotropy Multiplexing) a single-plasmid triple-modality-reporter of key nodes of the apoptotic network. Apoptosis provides an ideal molecular framework to study interactions between its three composing pathways (intrinsic, extrinsic and effector). We characterized the biosensor performance and demonstrated the advantages that equimolar expression has both in simplifying experimental procedure and reducing observable variation, thus enabling robust data-driven modelling. Tools like CASPAM become essential to analyze molecular pathways where multiple nodes need to be simultaneously monitored.

cell biology↗

bioRxiv2020

Smaug membraneless organelles regulate mitochondrial function

Smaug is a conserved translational repressor that recognizes specific RNA motifs in a large number of mRNAs, including nuclear transcripts that encode mitochondrial enzymes. Smaug orthologs have been shown to form membraneless organelles (MLOs) in several organisms and cell types. Using single-molecule FISH we show here that SDHB and UQCRC1 mRNAs associate with Smaug1 MLOs in the human cell line U2OS. Simultaneous loss of function of Smaug1 and Smaug2 affects both mitochondrial respiration and mitochondrial network morphology. Deletion of specific Smaug1 protein regions resulted in impaired MLO formation that correlates with mitochondrial defects. In addition, rotenone but not the respiratory chain uncoupling agent CCCP rapidly induces Smaug1 MLO dissolution. Finally, metformin elicits a similar effect on Smaug1 MLOs and provokes the release of bounded mRNAs. We propose that mitochondrial activity affects Smaug1 MLO dynamics, thus allowing for regulation of nuclear mRNAs that encode key mitochondrial proteins.

cell biology↗

bioRxiv2020

Furry is required for cell movements during gastrulation and functionally interacts with NDR1

Gastrulation is a key event in animal embryogenesis during which the germ layers precursors are rearranged and the embryonic axes are established. Cell polarization is essential during gastrulation driving asymmetric cell division, cell movements and cell shape changes. Furry (Fry) gene encodes an evolutionarily conserved protein with a wide variety of cellular functions mostly related to cell polarization and morphogenesis in invertebrates. However, little is known about its function in vertebrate development. Here we show that in Xenopus, Fry participates in the regulation of morphogenetic processes during gastrulation. Using morpholino knock-down, we demonstrate a role of Fry in blastopore closure and dorsal axis elongation. Loss of Fry function drastically affects the movement and morphological polarization of cells during gastrulation, in addition to dorsal mesoderm convergent extension, responsible for head-to-tail elongation. Finally, we demonstrate a functional interaction between Fry and NDR1 kinase, providing evidence of an evolutionarily conserved complex required for morphogenesis.

developmental biology↗

bioRxiv2018

Interdependence of EGFR with PTPs on juxtaposed membranes generates a growth factor sensing and responding network

The proto-oncogenic epidermal growth factor receptor (EGFR) is a tyrosine kinase whose sensitivity to growth factors (GFs) and signal duration determines cellular behavior. We resolve how EGFRs response to epidermal growth factor (EGF) originates from dynamically established recursive interactions with spatially organized protein tyrosine phosphatases (PTPs). Opposed genetic PTP perturbations enabled identification of receptor-like PTPRG/J at the plasma membrane (PM) and endoplasmic reticulum (ER) associated PTPN2 as the major EGFR dephosphorylating activities. Imaging spatial-temporal PTP reactivity then revealed that vesicular trafficking establishes a spatially-distributed negative feedback with PTPN2 that determines signal duration, whereas single cell dose-response analysis uncovered a ROS-mediated toggle switch between autocatalytically activated monomeric EGFR and the tumor suppressor PTPRG that governs EGFRs sensitivity to EGF. Vesicular recycling of monomeric EGFR unifies the interactions with these PTPs on juxtaposed membranes, dynamically generating a network architecture that can sense and respond to time varying growth factor signals.

systems biology↗