Biology subjects

Bonanomi, M.

Publications and source records attributed to Bonanomi, M..

3 recordsLinked to original sources

bioRxiv2026

Disrupting Pregnane X Receptor Signaling Overcomes Temozolomide Resistance in Glioblastoma via Succisa pratensis-Derived Metabolites

Glioblastoma remains a highly aggressive and therapy-resistant brain tumor, with limited benefit from the current standard-of-care regimen combining surgery, radiotherapy, and temozolomide. Overcoming chemoresistance therefore represents a critical unmet clinical need. Here, we investigate the anticancer potential of Succisa pratensis and its ability to enhance TMZ efficacy in GBM models. Treatment with S. pratensis markedly reduced cell proliferation and migration while significantly increasing sensitivity to TMZ. Integrated multi-omics analyses revealed extensive metabolic rewiring, characterized by suppression of central carbon metabolism and activation of stress-adaptive pathways. Mechanistically, we identify the Pregnane X Receptor, a key regulator of drug metabolism and chemoresistance, as a central node affected by treatment. Although S. pratensis increased PXR expression, this was not accompanied by induction of canonical downstream targets, including MDR1 and ALDH1A1, indicating a functional impairment of PXR transcriptional activity. Consistently, pharmacological inhibition of PXR using the antagonist SPA70 further potentiated the cytotoxic effects of S. pratensis and TMZ. Docking analyses suggest that specific secondary metabolites, including apigenin-derived compounds, may interact with the PXR ligand-binding domain, providing a potential molecular basis for this effect. Collectively, our findings indicate that S. pratensis enhances TMZ efficacy by inducing metabolic vulnerability and functionally impairing PXR signaling. These results highlight the therapeutic potential of plant-derived metabolites as adjuvant strategies to overcome chemoresistance in glioblastoma. Article HighlightsO_LISuccisa pratensis enhances temozolomide efficacy in glioblastoma by reducing proliferation, migration, and clonogenic growth. C_LIO_LIIntegrated proteomic and metabolomic analyses reveal extensive metabolic rewiring, with suppression of central carbon metabolism and induction of stress-adaptive pathways. C_LIO_LIPregnane X Receptor (PXR), a key regulator of chemoresistance, is functionally impaired despite increased expression, resulting in reduced activation of drug-resistance genes. C_LIO_LIPharmacological inhibition of PXR further potentiates the antitumor effects of Succisa pratensis and temozolomide, promoting apoptotic cell death. C_LIO_LIApigenin-derived metabolites show high affinity for the PXR ligand-binding domain and emerge as promising candidates to overcome temozolomide resistance in glioblastoma. C_LI

cancer biology↗

bioRxiv2025

Integrated metabolomic and metagenomic profiling reveals distinct signatures in treatment naive multiple sclerosis patients.

Imbalances in gut microbiota composition and microbiota-associated metabolites have been linked with several neurological diseases, including multiple sclerosis (MS). However, a comprehensive multi-biofluid approach remains lacking. Most studies on MS include patients already receiving treatment and often neglect to account for sex-related differences, which could serve as potential confounding factors. Moreover, they mostly investigate only stools or plasma metabolomics. In this study, we recruited 18 treatment-naive neuroinflammatory patients at diagnosis and compared them with 20 healthy controls, matched for sex and age. We conducted multi-biofluids metabolomic analysis of urine, stool, serum, and cerebrospinal fluid, complemented by taxonomical and functional profiling of the gut microbiome using shotgun metagenomic sequencing. Our results show that MS patients exhibit distinct microbiome composition and urinary metabolomic profiles compared to healthy controls. Furthermore, neuroinflammation is associated with dysregulation of microbially-produced short-chain fatty acids, their intestinal absorption and systemic bioavailability, as illustrated by the altered plasma levels.

neuroscience↗

bioRxiv2021

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. First, metabolic regulation controls metabolic fluxes (i.e., the rate of individual metabolic reactions) through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. A second regulatory layer sets the maximal theoretical level for each enzyme-controlled reaction by controlling the expression level of the catalyzing enzyme. In isolation, high-throughput data, such as metabolomics and transcriptomics data do not allow for accurate characterization of the hierarchical regulation of metabolism outlined above. Hence, they must be integrated in order to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we proposes INTEGRATE, a computational pipeline that integrates metabolomics (intracellular and optionally extracellular) and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomic data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients. Author summaryThe study of metabolism and its regulation finds increasing application in various fields, including biotransformations, wellness, and health. Metabolism can be studied using post-genomic technologies, notably transcriptomics and metabolomics, that provide snapshots of transcripts and metabolites in specific physio-pathological conditions. In the health field, the transcriptome and, more recently, the metabolome have been broadly profiled at the pre-clinical and clinical levels. The informative power of single omic technologies is inadequate since metabolism regulation involves a complex interplay of regulatory steps. While gene expression regulates metabolism by setting the upper level of metabolic enzymes, the interaction of metabolites with metabolic enzymes directly auto-regulates metabolism. Therefore there is a need for methods that integrate multiple data sources. We present INTEGRATE, a computational pipeline that captures dynamic features from the static snapshots provided by transcriptomic and metabolomic data. Through integration in a steady-state metabolic model, the pipeline predicts which reactions are controlled purely by metabolic control rather than by gene expression or a combination of the two. This knowledge is crucial in a clinical setting to develop personalized therapies in patients of multifactorial diseases, such as cancer. Besides cancer, INTEGRATE can be applied to different fields in which metabolism plays a driving role.

systems biology↗