Biology subjects

Vanoni, M.

Publications and source records attributed to Vanoni, M..

4 recordsLinked to original sources

bioRxiv2025

A Computational Approach for Threonine Accumulation in Escherichia coli and Its Integraton as a Platform for Biosynthesis of High-Value Fine Chemicals

Using genome-wide metabolic models and Flux Balance Analysis, we are devising an Escherichia coli strain to produce L-threonine through previously undescribed routes. In silico simulations and preliminary in vivo experiments show that diverting carbon flux away from serine metabolism may be beneficial to enhance threonine production. This system may be further explored not only to produce threonine itself, but also to develop an Escherichia coli platform capable of producing high value-added products which require threonine as a starting substrate. In order to do this we took advantage of the increased threonine production to sustain the production of the high value-added chemical 2,5-dimethylpyrazine (DMP) from glucose.

bioengineering↗

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↗

bioRxiv2023

A modular model integrating metabolism, growth, and cell cycle predicts that fermentation is required to modulate cell size in yeast populations

For unicellular organisms, the reproduction rate and growth are crucial determinants of fitness and, therefore, essential functional manifestations of the organism genotype. Using the budding yeast Saccharomyces cerevisiae as a model organism, we integrated metabolism, which provides energy and building blocks for growth, with cell mass growth and cell cycle progression into a low-granularity, multiscale (from cell to population) computational model. This model predicted that cells with constitutive respiration do not modulate cell size according to the growth conditions. We experimentally validated the model predictions using mutants with defects in the upper part of glycolysis or glucose transport. Plugging in molecular details of cellular subsystems allowed us to refine predictions from the cellular to the molecular level. Our hybrid multiscale modeling approach provides a framework for structuring molecular knowledge and predicting cell phenotypes under various genetic and environmental conditions.

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↗