Biology subjects

Pasquali, S.

Publications and source records attributed to Pasquali, S..

11 recordsLinked to original sources

bioRxiv2026

Rewiring of Integrin Signaling and Cell-cycle Deregulation Drive SMARCB1-Deficient Epithelioid Sarcoma

Epithelioid sarcoma (EPS) is an aggressive soft-tissue sarcoma characterized by loss of the chromatin-remodeling subunit SMARCB1. The oncogenic programs driving EPS remain poorly understood. Through CRISPR loss-of-function screens, we identified conserved dependencies on integrin signaling components and cyclin-dependent kinases (CDKs). Genetic disruption of integrin subunit alpha V (ITGAV)-centered signaling impaired epithelioid cluster formation and reduced MYC expression. SMARCB1 re-expression phenocopied these effects and revealed that SMARCB1 loss selectively represses context-dependent integrin subunits while preserving an ITGAV-centered pro-survival axis, associated with altered BAF complex occupancy. Analysis of EPS cell lines and primary tumors revealed frequent genetic or epigenetic inactivation of CDKN2A/p16, indicating that loss of cell-cycle control is a key cooperating event in EPS development and providing a mechanistic rationale for targeting CDK4/6. Together, these findings establish integrin-driven oncogenic signaling coupled with disruption of cell-cycle control as a central oncogenic program in EPS and identify actionable therapeutic vulnerabilities.

cancer biology↗

bioRxiv2026

Histone deacetylase activity limits the response to EZH2 inhibition-based therapy in epithelioid sarcoma and is targetable by epigenetic combination

Epithelioid sarcoma (EpS) is an ultra-rare, aggressive soft tissue sarcoma (STS) driven by INI1 loss and consequent hyperactivation of the chromatin-modifying enzyme EZH2. Although the EZH2 inhibitor tazemetostat has shown clinical activity, responses remain limited, highlighting the need for improved treatment strategies. Here, using two in-house generated patient-derived xenograft models and matched cell lines derived from INI-1 deficient EpS, we investigated EZH2 inhibition in combination with doxorubicin, the first-line standard for advanced STSs, identifying distinct patterns of response and resistance. Integrated transcriptomic and functional analyses revealed that response to EZH2 inhibition-based therapy was associated with chromatin remodeling, characterized by increased H3K27 acetylation and downregulation of histone deacetylase (HDAC)-related transcriptional programs. Conversely, the intrinsically resistant model failed to undergo this epigenetic transition despite EZH2 inhibition. Pharmacological HDAC inhibition restored H3K27 acetylation, promoted apoptosis, and enhanced the activity of EZH2 inhibition-based therapy. These findings identify failure to accumulate H3K27 acetylation as a hallmark of resistance to EZH2 inhibition-based treatment, and show that pharmacological HDAC inhibition can restore this chromatin transition and re-sensitize resistant tumors, providing a rationale for combined epigenetic targeting strategies in INI1-deficient malignancies. Translational relevanceProspective trials are challenging in rare tumors such as epithelioid sarcoma (EpS), limiting the level of evidence for existing therapies and the development of new agents. This is particularly relevant for EpS, where drug regimens are those used for all soft tissue sarcomas (STSs), and the specific mechanisms of drug response remain poorly understood. This preclinical study of tazemetostat in combination with doxorubicin shows differential outcomes in two INI1-deficient proximal-type EpS models, providing evidence of the heterogeneity that exists even within the same tumor subtype and fostering the need to better understand the molecular mechanisms driving drug sensitivity/resistance in this disease. In addition, we demonstrated the potential to treat EpS models through modulation of epigenetic mechanisms, showing that HDAC inhibition may restore sensitivity to EZH2-targeted therapy. These findings support the rationale for developing combination strategies incorporating epigenetic modulators and provide a preclinical framework for overcoming resistance to current therapies in EpS.

cancer biology↗

bioRxiv2026

Active Surveillance Reveals a Systemic Pro-Resolving Th2 Immune Program Linked to 1 Desmoid Tumor Regression

Desmoid fibromatosis (DF) is a rare mesenchymal neoplasm with an unpredictable clinical course, where spontaneous regression or progression occurs in a significant subset of patients through largely undefined mechanisms. The use of active surveillance (AS) offers the opportunity to investigate whether tumor- or host-driven systemic and local immune features may explain these divergent outcomes, improving patient management. A prospective observational study enrolled 55 patients with primary sporadic DF managed with AS. Clinical evolution was categorized as progression, regression, or stable disease according to RECIST 1.1. Immunomonitoring with multicolor flow cytometry identified distinct systemic T-helper polarization states stratifying clinical trajectories: regressors showed a Th2-skewed profile, while progressors displayed activated T-helper cells and Th1/Th9/Th17 subsets. Higher baseline Th2 levels associated with regression and longer progression-free survival. Plasma proteomic and whole-blood transcriptomic analyses confirmed coordinated IL-4/IL-13-linked pro-resolving programs in regressors and inflammatory, early T-cell activation signatures in progressors. Tumor transcriptomics revealed adaptive, antigen-presentation and restrained immune programs in regressing lesions versus innate inflammatory, interferon and TGF-{beta}-driven fibrotic pathways in progressing tumors. These findings identify systemic T-helper polarization as a biomarker of DF behavior and highlight coordinated systemic-tumoral immune programs underlying clinical outcomes, supporting more precise clinical management.

immunology↗

bioRxiv2026

Exploring RNA conformational ensembles in silico: progress and challenges

RNA function is intrinsically linked to its structural polymorphism, with molecules exploring the heterogeneous conformational ensembles resulting from complex energy landscapes. These landscapes arise from competing interactions, small energetic separations between microstates, and strong coupling to the environment, posing significant challenges for both experimental characterization and molecular simulation. In this chapter, we review current computational strategies that aim to explore RNA conformational ensembles in silico, with a specific focus on energy landscape-based approaches and atomistic simulations. We discuss key limitations related to sampling efficiency, force-field accuracy, and ensemble analysis, and illustrate their impact through case studies on a self-cleaving ribozyme and an H-type pseudoknot. Finally, we highlight emerging directions, including closer integration with experimental data and the growing role of machine learning, which will probably reinforce the predictive power of in silico RNA energy landscape exploration.

molecular biology↗

bioRxiv2025

ARNy Plotter: A Comprehensive Web Server for RNA Ensemble Structural Analysis and Visualization

Analyzing RNA structural ensembles, whether derived from molecular dynamics, enhanced sampling techniques, or experimental data, poses significant challenges due to the intrinsic flexibility and diverse conformational landscapes of RNA. We present ARNy Plotter, a web-based platform designed for comprehensive analysis and visualization of RNA structural ensembles. ARNy Plotter integrates multiple state-of-the-art methods into a unified, user-friendly environment, enabling intuitive examination of RMSD and eRMSD distributions, torsion-angle variability, dynamic secondary-structure patterns, contact-frequency networks, and multidimensional conformational landscapes. By leveraging MDAnalysis, Barnaba, and foRNA, the platform supports all major trajectory and ensemble formats while offering real-time 3D visualization via MolStar. Unlike existing tools that focus primarily on static structures or provide limited dynamic analysis, ARNy Plotter allows in-depth exploration of conformational transitions and population distributions within RNA ensembles, and facilitates result sharing without distributing full datasets. This platform addresses the growing need for accessible, comprehensive, and interactive tools for RNA structural biology. ARNy Plotter is freely available at https://arny-plotter.rpbs.univ-paris-diderot.fr/, requires no installation or registration, and can also be run offline with a local webserver, including an API for automated figure generation.

bioinformatics↗

bioRxiv2025

VCAN is essential for ERK5-driven tumorigenesis in soft tissue sarcoma

The ERK5 signaling pathway has recently emerged as a critical regulator of soft tissue sarcoma (STS) biology, contributing to tumor initiation, progression, and maintenance. In this study, we identify VCAN, a chondroitin sulfate proteoglycan, as a novel transcriptional target of ERK5 and a central mediator of ERK5-related oncogenesis. Through a combination of genetic (silencing, overexpression) and pharmacological approaches, applied in both a chemically induced murine sarcoma model and several human STS cell lines, we demonstrate that ERK5 positively regulates VCAN expression. Functionally, VCAN silencing (by shRNAs) recapitulates the phenotypes of ERK5 silencing, including impaired migration, adhesion, proliferation, and tumorigenesis. Conversely, VCAN overexpression rescues these effects, confirming its essential role in ERK5-mediated oncogenesis. Furthermore, transcriptomic profiling reveals that VCAN accounts for a substantial portion of ERK5-regulated gene expression program. Analyses of human STS patient samples reveal significantly elevated mRNA levels of both VCAN and ERK5 compared to normal tissues. Notably, a strong correlation between VCAN and ERK5 expression, both at mRNA and protein levels, emerged in biopsies from leiomyosarcomas and undifferentiated pleomorphic sarcomas. Together, these findings uncover VCAN as a key effector in ERK5-driven tumorigenesis and highlight the ERK5/VCAN signaling axis as a promising therapeutic target in soft tissue sarcomas.

cancer biology↗

bioRxiv2025

Statistical Molecular Interaction Fields: A Fast andInformative Tool for Characterizing RNA and ProteinBinding Pockets

Developing a physical understanding of the interactions between a macro-molecular target and its ligands is a crucial step in structure-based drug design. Although many tools exist to characterize protein-binding pockets in silico, this is not yet the case for RNA, which has only recently been recognized as a suitable target for small ligands. Molecular Interaction Fields (MIF) are a useful tool to characterize the interactions of a given binding pocket. However, classical MIFs heavily rely on the use of probes, which makes their calculation accurate but very specific to the binding partners in question. We develop here a simple version of MIF, that we call Statistical Molecular Interaction Fields (SMIF), based on functional forms inspired by coarse-grained models and parametrized based on PDB structures and previous statistical analysis of the main form of interactions typical of macromolecules, namely hydrogen bonding, stacking, and hydrophobic interactions. We show that these fields, despite their simplicity, are very informative and overall in agreement with pharmacophoric models. Thanks to a carefully optimized code, our calculations are fast and can be performed in bulk on a large set of binding pockets or even on a full macromolecule. As shown in a few representative examples, the latter possibility opens the way to the analysis of systems as large as 20000 to 80000 atoms in relation to the surrounding environment, i.e., a lipidic membrane, a small ligand, or another macromolecular partner, allowing for a detailed visualization of the possible interactions. The complete software and its documentation are available here: https://smiffer.mol3d.tech/

biophysics↗

bioRxiv2024

Molecular modeling of Proteinase-Activated Receptor 1 in complex with Thrombin Receptor Activator Peptide 6.

The protease-activated receptor 1 (PAR1) and its activator thrombin re-ceptor activator peptide 6 (TRAP6) play crucial roles in various physiologi-cal and pathological processes, including hemostasis, thrombosis, and cancer progression. Although the interaction between PAR1 and TRAP6 has been heavily studied using experimental technique such as mutagenesis, structural data remains scarce due to the technical hardship of studying membrane pro-teins such as PAR1. In this study, we employed an integrative modeling approach to elucidate the structure of the PAR1-TRAP6 complex. Leveraging state-of-the-art AI-based protein modeling tools, including AlphaFold2 and ESMFOLD, we in-tegrated HADDOCK, a physics-based method to refine predictions. Overall, the predicted structures are in good agreement with the experimental data available in the literature. Our model unveiled a new T-shaped pi-stacking interaction between TRAP6s F2 and PAR1s Y360. The integrative modeling approach combining the predictions of the deep learning model with a physics-based method proves to be an interesting strat-egy for solving challenging membrane protein structures with high confidence. Our model of the PAR1-TRAP6 complex will be an interesting starting point for further investigation of the activation of PAR1 by TRAP6.

bioinformatics↗

bioRxiv2023

Proteomic characterisation of Sarculator nomogram-defined risk groups in soft tissue sarcomas of the extremities and trunk wall

BackgroundHigh-risk soft tissue sarcomas of the extremities and trunk wall (eSTS), as defined by the Sarculator nomogram, are more likely to benefit from (neo)adjuvant anthracycline-based therapy compared to low/intermediate-risk patients. The biology underpinning these differential treatment outcomes remain unknown. MethodsWe analysed proteomic profiles and clinical outcomes of 123 eSTS patients. A Cox model for overall survival including the Sarculator was fitted to individual data to define 4 risk groups. A DNA replication protein signature - Sarcoma Proteomic Module 6 (SPM6) was evaluated for association with clinicopathological factors and risk groups. SPM6 was added as a covariate together with Sarculator in a multivariable Cox model to assess improvement in prognostic risk stratification. ResultsDNA replication and cell cycle proteins were upregulated in high risk versus very low risk patients. Evaluation of the functional effects of CRISPR-Cas9 gene knockdown of proteins enriched in high risk patients identified candidate drug targets. SPM6 was significantly associated with tumour malignancy grade (p = 1.6e-06), histology (p = 1.4e-05) and risk groups (p = 2.6e-06). Cox model analysis showed that SPM6 substantially contributed to a better calibration of the Sarculator nomogram (Index of Prediction Accuracy =0.109 for Sarculator alone versus 0.165 for Sarculator + SPM6). ConclusionsRisk stratification of patient with STS is defined by distinct biological pathways across a range of cancer hallmarks. Incorporation of SPM6 protein signature improves prognostic risk stratification of the Sarculator nomogram. This study highlights the utility of integrating protein signatures for the development of next-generation nomograms.

cancer biology↗

bioRxiv2021

Investigating the structural changes due to adenosine methylation of the Kaposi's sarcoma-associated herpes virus ORF50 transcript

Kaposis sarcoma-associated herpes virus (KSHV) is a human oncovirus. KSHV relies on manipulating the host cell N6-methyl adenosine (m6A) RNA modification pathway. Methylation within a RNA stem loop of the open reading frame 50 (ORF50) enhances transcript stability via the recruitment of the m6A reader, SND1. In this contribution we explore the energy landscapes of the unmethylated and methylated RNA stem loops of ORF50 to investigate the effect of methylation on the structure of the stem loop. We observe a significant shift upon methylation between an open and closed configuration of the top of the stem loop. In the unmethylated stem loop the closed configuration is much lower in energy, and, as a result, exhibits higher occupancy.

biophysics↗

bioRxiv2021

Biasing RNA coarse-grained folding simulations with Small--Angle X--ray Scattering (SAXS) data

RNA molecules can easily adopt alternative structures in response to different environmental conditions. As a result, a molecules energy landscape is rough and can exhibits a multitude of deep basins. In the absence of a high-resolution structure, Small Angle X-ray Scattering data (SAXS) can narrow down the conformational space available to the molecule and be used in conjunction with physical modeling to obtain high-resolution putative structures to be further tested by experiments. Because of the low-resolution of this data, it is natural to implement the integration of SAXS data into simulations using a coarse-grained representation of the molecule, allowing for much wider searches and faster evaluation of SAXS theoretical intensity curves than with atomistic models. We present here the theoretical framework and the implementation of a simulation approach based on our coarse-grained model HiRE-RNA combined with SAXS evaluations "on-the-fly" leading the simulation toward conformations agreeing with the scattering data, starting from partially folded structures as the ones that can easily be obtained from secondary structures predictions based tools. We show on three benchmark systems how our approach can successfully achieve high-resolution structures with remarkable similarity with the native structure recovering not only the overall shape, as imposed by SAXS data, but also the details of initially missing base pairs.

biophysics↗