Biology subjects

Pagano, L.

Publications and source records attributed to Pagano, L..

4 recordsLinked to original sources

bioRxiv2026

Robust Myocardial Regeneration After Selective Cardiomyocyte Loss Is Driven by Cardiac Stem Cell Activation Through the miR-221-p57 Axis

A central unresolved and highly contested question in cardiac biology is whether the adult mammalian heart, believed to have a very limited endogenous cardiomyocyte (CM) regenerative capacity, can be coaxed into an effective regenerative response after acute CM loss. Using TgMyh6MCM:R26stop-DTA mice, we show that selective diffuse ablation of [~]15% of left ventricular CMs causes acute heart failure but is followed by complete structural and functional recovery within 28 days. Recovery is accomplished by robust generation of new mononucleated CMs, replacing [~]1/10 of the left ventricular CM compartment. This CM regeneration is produced by the activation of resident cardiac stem cells (CSCs), which exit quiescence, proliferate, produce new CMs, and subsequently return to quiescence. Depletion of the putative CSCs blocks repair, whereas transplantation of either clonogenic or primary CSCs through the systemic circulation fully restores myocardial regeneration and function, establishing that the CSCs home, nest and differentiate in the damaged myocardium and, therefore, are the main effectors of regeneration in this setting. Mechanistically, we show that miR-221-dependent repression of p57 governs the transition from quiescence--to activation--to differentiation--to quiescence of the CSCs, defining a reversible regulatory program which, under the proper conditions, endows the adult myocardium with robust CM regenerative competence.

cell biology↗

bioRxiv2026

An Atlas of Short Linear Motif-Mediated Human Protein-Protein Interactions

Short linear motifs (SLiMs) within intrinsically disordered protein regions mediate transient interactions crucial for cell physiology1. However, the global interaction landscape of human SLiMs remains largely uncharted. Here we present the Atlas of SLiM-mediated Human protein-protein Interactions (ASHI), which maps more than 20,000 interactions by screening over 800 human protein domains against a library of one million peptides tiling the human disordered proteome. ASHI expands the SLiM interactome, uncovers novel binding modes for known peptide-binding domains, and reveals unexpected peptide-binding activities in enzymes, chaperones, RNA-binding proteins, and modification-reader domains. Furthermore, intrinsically disordered regions emerge as densely encoded interaction platforms where interaction specificity is governed by diverse mechanisms, including key motif determinants, flanking residues, competition, and multivalency. These data provide an unprecedented foundation for modeling dynamic interaction networks, interpreting disease-associated variants, and decoding the dark proteome.

biochemistry↗

bioRxiv2023

Towards Generalizability and Robustness in Biological Object Detection in Electron Microscopy Images

Machine learning approaches have the potential for meaningful impact in the biomedical field. However, there are often challenges unique to biomedical data that prohibits the adoption of these innovations. For example, limited data, data volatility, and data shifts all compromise model robustness and generalizability. Without proper tuning and data management, deploying machine learning models in the presence of unaccounted for corruptions leads to reduced or misleading performance. This study explores techniques to enhance model generalizability through iterative adjustments. Specifically, we investigate a detection tasks using electron microscopy images and compare models trained with different normalization and augmentation techniques. We found that models trained with Group Normalization or texture data augmentation outperform other normalization techniques and classical data augmentation, enabling them to learn more generalized features. These improvements persist even when models are trained and tested on disjoint datasets acquired through diverse data acquisition protocols. Results hold true for transformerand convolution-based detection architectures. The experiments show an impressive 29% boost in average precision, indicating significant enhancements in the models generalizibality. This underscores the models capacity to effectively adapt to diverse datasets and demonstrates their increased resilience in real-world applications.

bioengineering↗

bioRxiv2023

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations

Electron microscopy (EM) enables imaging at nanometer resolution and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task; however, analyzing them is now the bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, semi-supervised learning as well as next steps for the mitigation of the manual segmentation bottleneck.

bioengineering↗