Biology subjects

Burri, O.

Publications and source records attributed to Burri, O..

3 recordsLinked to original sources

bioRxiv2025

Intestinal stem cell renewal controlled by capillary morphogenesis gene 2 following injury

Patients with the rare genetic disorder Hyaline Fibromatosis Syndrome (HFS) often succumb before 18 months of age due to severe diarrhea and protein-losing enteropathy. As HFS is caused by loss-of-function mutations in the gene encoding capillary morphogenesis gene 2 (CMG2), also known as Anthrax Toxin Receptor 2, these symptoms highlight a critical yet unclear role for CMG2 in the gut. Here, we demonstrate that CMG2 knockout mice exhibit normal colon morphology and no signs of inflammation until the chemical induction of colitis. In these conditions, the colons of knockout mice do not regenerate despite previously experiencing similarly severe colitis, due to an inability to replenish their intestinal stem cell pool. Specifically, CMG2 knockout impairs the transition from fetal-like to Lgr5+ adult stem cells, which is associated with a defect in {beta}-catenin nuclear translocation. Our findings suggest that CMG2 functions as a context-specific modulator of Wnt signaling, essential for replenishing the pool of intestinal stem cells following injury. This study provides new insights into the molecular mechanisms underlying protein-losing enteropathy in HFS and offers a broader understanding of fetal-like regenerative responses. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/631493v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1a38f2corg.highwire.dtl.DTLVardef@10ca815org.highwire.dtl.DTLVardef@1ef3bddorg.highwire.dtl.DTLVardef@1433671_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

Inhibition of CERS1 in skeletal muscle exacerbates age-related muscle dysfunction

Age-related muscle wasting and dysfunction render the elderly population vulnerable and incapacitated, while underlying mechanisms are poorly understood. Here, we implicate the CERS1 enzyme of the de novo sphingolipid synthesis pathway in the pathogenesis of age-related skeletal muscle impairment. In humans, CERS1 abundance declines with aging in skeletal muscle cells and, correlates with biological pathways involved in muscle function and myogenesis. Furthermore, CERS1 is upregulated during myogenic differentiation. Pharmacological or genetic inhibition of CERS1 in aged mice blunts myogenesis and deteriorates aged skeletal muscle mass and function, which is associated with the occurrence of morphological features typical of inflammation and fibrosis. Ablation of the CERS1 orthologue lagr-1 in C. elegans similarly exacerbates the age-associated decline in muscle function and integrity. We discover genetic variants reducing CERS1 expression in human skeletal muscle and Mendelian randomization analysis in the UK biobank cohort shows that these variants reduce muscle grip strength and overall health. In summary, our findings link age-related impairments in muscle function to a reduction in CERS1, thereby underlining the importance of the sphingolipid biosynthesis pathway in age-related muscle homeostasis.

physiology↗

bioRxiv2020

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cells contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier. Author summaryBreast cancer is the most commonly diagnosed tumor in women worldwide and its incidence in the population is increasing over time. Because our understanding of such disease has been hampered by the lack of adequate human preclinical model, efforts have been made in order to develop better approaches to model the human complexity. Recent advances in this regard were achieved with Patient-Derived Xenografts (PDXs), which entail the implantation of human-derived specimens to recipient immunosuppressed mice and are, thus far, the preclinical system best recapitulating the heterogeneity of both normal and malignant human tissues. However, histological analyses of the resulting tissues are usually confounded by the presence of cells of different species. To circumvent this hurdle and to facilitate the discrimination of human and murine cells in xenografted samples, we developed Single Cell Classifier (SCC), a deep learning-based open-source software, available as a plugin in ImageJ/Fiji, performing automated species classification of individual cells in H&E stained sections. We show that SCC can reach up to 96% classification accuracy to classify cells of different species mainly leveraging on their contextual features in both normal and tumor PDXs. SCC will improve and automate histological analyses of human-in-mouse xenografts and is open to new in-house built models for further classification tasks and applications in image analysis.

systems biology↗