Biology subjects

Galuzzi, B. G.

Publications and source records attributed to Galuzzi, B. G..

4 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

Mechanistically Informed Machine Learning Links Non-Canonical TCA Cycle Activity to Warburg Metabolism and Hallmarks of Malignancy

Cancer cells undergo extensive metabolic rewiring to support growth, survival, and phenotypic plasticity. A non-canonical variant of the tricarboxylic acid (TCA) cycle, characterized by mitochondrial-to-cytosolic citrate export, has emerged as critical for embryonic stem cell differentiation. However, its role in cancer remains poorly understood. Here, we present a two-step computational framework to systematically analyze the activity of this non-canonical TCA cycle across over 500 cancer cell lines and investigate its role in shaping hallmarks of malignancy. First, we applied constraint-based modeling to infer cycle activity, defining two complementary metrics: Cycle Propensity, measuring the likelihood of its engagement in each cell line, and Cycle Flux Intensity, quantifying average flux through the reaction identified as rate-limiting. We identified distinct tumor-specific patterns of pathway utilization. Notably, cells with high Cycle Propensity preferentially rerouted cytosolic citrate via aconitase 1 (ACO1) and isocitrate dehydrogenase 1 (IDH1), promoting -ketoglutarate (KG) and NADPH production. Elevated engagement of this cycle strongly correlated with Warburg-like metabolic shifts, including decreased oxygen consumption and increased lactate secretion. In the second step, to uncover non-metabolic transcriptional signatures associated with non-canonical TCA cycle activity, we performed machine learning-based feature selection using ElasticNet and Random Forest, identifying robust gene signatures predictive of cycle activity. Over-representation analysis revealed enrichment in genes involved in invasiveness, angiogenesis, stemness, and key oncogenic pathways. Analysis of DepMap gene dependency data revealed that TCA cycle activity correlates with differential vulnerability to perturbation of these oncogenic pathways, reinforcing the functional relevance of identified transcriptional signatures. To further interpret the predictive models, SHapley Additive exPlanations (SHAP) was applied to prioritize genes contributing most to non-canonical cycle activity, suggesting novel candidates for experimental investigation. Overall, our framework enables comprehensive analysis of non-canonical TCA cycle dynamics and uncovers potential links between metabolic plasticity and malignant phenotypes.

cancer biology↗

bioRxiv2024

Spatial Flux Balance Analysis reveals tissue-of-origin and spatially dependent metabolic rewiring in renal and colorectal cancer

1To fully understand how cancer metabolism differs between primary tumors and metastases, resolving cell metabolism with spatial precision is essential. Yet, spatial fluxomics lags behind advancements in spatial transcriptomics. To address this gap, we generated high-resolution spatial transcriptomics datasets from paired primary colorectal tumors and liver metastases, designed to capture metabolic adaptations across distinct tumor sites. Concurrently, we developed the Spatial Flux Balance Analysis (spFBA) computational framework to leverage them. Since broad metabolic differences between tumors and healthy tissues are established, we first validated spFBA on a publicly available renal cancer dataset, including tumor-normal interface samples. spFBA detected cancer metabolic hallmarks, like enhanced glucose uptake and metabolic growth, but with unprecedented resolution, revealing lactate production with sustained oxygen consumption at the tumor interface and with reduced respiration in the core. Next, applying spFBA to our colorectal cancer dataset, we provided biological insights, confirming that metastases mimic the metabolic traits of their tissue of origin. Additionally, our approach uncovered the first in vivo evidence of lactate-consuming cancer cells, marking a significant advance in understanding cancer metabolism. spFBA stands out as a powerful approach to unravel the spatial metabolic complexity of cancer and beyond, leveraging the expanding landscape of spatial transcriptomics datasets.

systems biology↗

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↗