Biology subjects

Ferrari, E.

Publications and source records attributed to Ferrari, E..

7 recordsLinked to original sources

bioRxiv2026

Microfluidic System for On-Demand Imaging of Organotypic Slices

Organotypic brain slice cultures (OSCs) provide a physiologically relevant model for recapitulating complex tissues in vitro. However, the longitudinal monitoring of dynamic processes in OSCs through high-resolution imaging is limited in standard culturing methods, as they rely on slice placement on porous membranes at the air-liquid interface to preserve tissue viability. Here, a new experimental paradigm based on a modular slice culture insert is introduced, which is compatible with static and perfused culturing, and facilitates longitudinal observation of cellular and tissue dynamics at high spatiotemporal resolution. Besides compatibility with conventional culturing in well plates, the insert is integrated into an automated microfluidic instrument to provide user-friendly operation while enabling precise temporal fluid control and compound delivery. The system is applied to monitor calcium responses and cellular morphology of virally labeled cerebellar astrocytes and Purkinje cells exposed to different concentrations of glutamate. Cellular responses were evaluated at baseline, during glutamate exposure, and 48 h and 96 h after stimulation to identify acute and long-term excitotoxic effects. Our results indicate that the developed system enables high-resolution monitoring of cellular morphology and dynamic events of the same intact tissue over multiple days, which provides a robust foundation for mechanistic investigations of neural signaling.

bioengineering↗

bioRxiv2025

Tankyrase inhibition demonstrates anti-fibrotic effects in preclinical pulmonary fibrosis models

BackgroundIdiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease with limited treatment options. Although transforming growth factor beta 1 (TGFB1, TGF{beta}) is a key driver of fibrosis, additional signaling pathways, including wingless-type mammary tumor virus integration site (WNT)/{beta}-catenin and yes-associated protein 1 (YAP), contribute to IPF pathogenesis. Clinical data indicate that inhibition of TGF{beta} alone provides limited efficacy or is associated with toxicity, underscoring the need for alternative therapeutic approaches. Tankyrase (TNKS) 1 and 2 are post-translational regulators of WNT/{beta}-catenin and YAP signaling and therefore represent promising antifibrotic targets. OM-153, a potent and selective TNKS inhibitor, exhibits pharmacological properties suitable for preclinical development in IPF. MethodsPrimary normal human lung fibroblasts (NHLF), Scar-in-a-Jar assays, lung-on-a-chip models, and precision-cut lung slices (PCLS) from non-pulmonary fibrosis (non-PF) tissue were stimulated with an IPF-relevant cytokine cocktail (IPF-RC) designed to accurately recapitulate the pro-fibrotic environment and compared to TGF{beta}. These models, with bleomycin-challenged mice and PCLS from end-stage pulmonary fibrosis (PF) patients, were treated with OM-153. Fibrosis markers, extracellular matrix (ECM) components, and signaling pathway-specific gene expression or protein markers were assessed by real-time qRT-PCR, RNA sequencing, immunoblotting, ELISA, and immunofluorescence. ResultsOM-153 stabilized the direct TNKS targets axin 1 (AXIN1) and angiomotin-like 1 (AMOTL1), suppressed WNT/{beta}-catenin and YAP signaling. In parallel, it reduced profibrotic ECM expression across in vitro, in vivo, and ex vivo IPF models. ConclusionsSelective TNKS inhibition by OM-153 demonstrates broad antifibrotic activity in multiple preclinical models, supporting further development as a potential disease-modifying strategy for IPF. Shareable abstractOur findings show that the potent and selective TNKS inhibitor OM-153 suppresses WNT/{beta}-catenin and YAP signaling, reducing pro-fibrotic ECM expression in preclinical IPF models, supporting TNKS inhibition as a novel antifibrotic strategy.

cell biology↗

bioRxiv2024

microGalaxy: A gateway to tools, workflows, and training for reproducible and FAIR analysis of microbial data

The explosion of microbial omics data has outpaced the ability of many researchers to analyze it, with complex tools and limited computational resources creating barriers to discovery. To address this gap, we present the Microbiology Galaxy Lab: a free, globally accessible, community-supported platform that combines state-of-the-art analytical power with user-friendly accessibility. Supported by the Galaxy and global microbiology communities, this platform integrates over 315 tool suites and 115 curated workflows, enabling comprehensive metabarcoding, (meta)genomic, (meta)transcriptomic, and (meta)proteomic data analysis within a FAIR-aligned environment. It also supports research in the health and infectious disease sectors, as well as in environmental microbiology. The platforms utility is exemplified through various use cases, including antimicrobial resistance tracking, biomarker prediction, microbiome classification, and functional annotation of key microbes. Built on reproducibility and community engagement, it supports creation, sharing, and updating of best-practice workflows. Over 35 tutorials and learning paths empower scientists, fostering an ecosystem that keeps resources at the forefront of microbial science. The Microbiology Galaxy Lab enables collective analysis, democratising research, thereby accelerating discovery across the global microbiology community (microbiology.usegalaxy.org, microbiology.usegalaxy.eu, microbiology.usegalaxy.org.au, microbiology.usegalaxy.fr).

bioinformatics↗

bioRxiv2024

Exploiting pathogen defence trade-offs to manage risks of crop pests evolving resistance to biocontrol.

Pathogens often exert strong selection, yet host populations maintain considerable genetic variation for resistance, possibly due to environmental heterogeneity causing fitness trade-offs. These trade-offs could help explain genetic variation for pathogen defence, and also constrain resistance evolution to microbial pesticides (an underappreciated risk). However, the presence and strength of such trade-offs remain unclear. We investigate whether pathogen identity or host diet has a stronger effect on the fitness of resistance alleles. We quantify genetic variation and covariation for pathogen resistance in an insect pest across distinct pathogen and plant diet combinations. We demonstrate substantial heritability, indicating considerable risks of biopesticide resistance. Contrary to conventional thinking in host-pathogen biology, we found no strong genetic trade-offs for resisting two fungal pathogen species, but changes in plant diet dramatically altered selection for resistance, revealing diet-mediated genetic trade-offs affecting pest survival. Our data suggest that trade-offs not strictly related to immunocompetence could nevertheless maintain genetic variation for pathogen defence in natural and agricultural landscapes.

evolutionary biology↗

bioRxiv2022

A deep neural network provides an ultraprecise transcriptomic clock of the short-lived fish Nothobranchius furzeri and identifies predictive genes translatable to human aging.

A key and unresolved question in aging research is how to quantify aging at the individual level that led to development of "aging clocks", machine learning algorhythms trained to predict individual age from high-dimensional molecular data under the the assumption that individual deviations of the predicted age from the chronological age contain information on the individual condition (often referred to as "biological age"). A full validation of such clocks as biomarkers for clinical studies of ageing would require a comparison of their predictions with information on actual lifespan and long-term health. Such studies take decades in humans, but could be conducted in a much shorter time-frame in animal models. We developed a transcriptomic clock in the turquoise killifish Nothobranchius furzeri. This species is the shortest-lived vertebrate that can be cultured in captivity and is an emerging model organism for genetic and experimental studies on aging. We developed a proprietary deep learning architecture that autonomously selects a customizable number of input genes to use for its predictions in order to reduce overfitting and increase interpretability, and adopts an adversarial learning framework to identify tissue-independent transcriptional patterns. We called this architecture the Selective Adversarial Deep Neural Network (SA-DNN) and trained it on a multi-tissue transcriptomic dataset of N. furzeri. This SA-DNN predicted age of the test set with an accuracy of 1 day, i.e. less than 1% of the total species lifespan and detected genetic, pharmacological and environmental interventions that are known to influence lifespan in this species. Finally, a human transcriptomic multi-tissue clock that uses as input the orthologs of the genes selected by our SA-DNN in N. furzeri reaches an average error of[~] 3 years rivalling epigenetic clocks. Our SA-DNN represents the prototype of a new class of aging clocks that provide biomarkers applicable to intervention studies in model organisms and humans.

genomics↗

bioRxiv2022

A multi-enhancer hub at the Ets1 locus controls T cell differentiation and allergic inflammation through 3D genome topology

Multi-enhancer hubs are spatial clusters of enhancers which have been recently characterized across numerous developmental programs. Yet, the functional relevance of these three-dimensional (3D) structures is poorly understood. Here we show that the multiplicity of enhancers interacting with the transcription factor Ets1 is essential to control the precise expression level of this gene in response to cellular cues, and the failure to do so can lead to allergic diseases. Focusing on T cells as a model, we identified a highly connected multi-enhancer hub at the Ets1 locus, comprising a noncoding regulatory element that is a hotspot for sequence variation associated with allergic diseases. We deleted this hotspot and found that the multi-enhancer connectivity is dispensable for T cell development but required for CD4+ T helper (Th1) differentiation in response to changes in the cytokine milieu. Mice lacking this hotspot are thus protected from Th1-mediated colitis but demonstrate an overt allergic response to house dust mites, a T cell-mediated response which is dampened by Th1 cells. Mechanistically, the multi-enhancer hub controls the expression level of Ets1 that is dispensable for the active enhancer landscape but required for the Th1-specific genome topology through recruitment of CTCF. Together, we establish a paradigm for the functional and mechanistic relevance of multi-enhancer hubs controlling cellular competence to respond specifically to an inductive cue.

genomics↗

bioRxiv2019

Impaired cholesterol metabolism in the mice model of cystic fibrosis. A vicious circle?

Patients with cystic fibrosis (CF) have low cholesterol absorption and, despite enhanced endogenous biosynthesis, low serum cholesterol. Herein, we investigated cholesterol metabolism in a murine CF model in comparison to wild type (WT) testing serum and liver surrogate biomarkers together with the hepatic expression of genes involved in cholesterol metabolism. CF mice display lower sterols absorption and increased endogenous biosynthesis. Subsequently, we evaluated the effects of a cholesterol-supplemented diet on cholesterol metabolism in CF and WT mice. The supplementation in WT mice determines biochemical changes similar to humans. Instead, CF mice with supplementation did not show significant changes, except for serum phytosterols (-50%), liver cholesterol (+35%) and TNF mRNA expression, that resulted 5-fold higher than in CF without supplementation. However, liver cholesterol in CF mice with supplementation resulted significantly lower compared to WT supplemented mice. This study shows that in CF mice there is a vicious circle in which the altered bile salts synthesis/secretion contribute to reduce cholesterol digestion/absorption. The consequence is the enhanced liver cholesterol biosynthesis that accumulates in the cell triggering inflammation.

biochemistry↗