Biology subjects

Wegrzyn, A. B.

Publications and source records attributed to Wegrzyn, A. B..

7 recordsLinked to original sources

bioRxiv2026

Multi-omics characterization of breast cancer metabolism identifies new metabolic targets

Breast cancer cells undergo metabolic reprogramming to support proliferation and metastasis, but these alterations are heterogeneous. To systematically assess this heterogeneity under controlled conditions, we profiled 31 intracellular polar metabolites and 50 amines, together with exchange rates of 57 amines, in a panel of 51 breast cancer cell lines spanning diverse phenotypes. These data were integrated with lipidomics and transcriptomics. Multi-omics factor analysis identified metabolic signatures linked to proliferation and to differences between luminal, HER2-positive, basal A, and basal B cell lines, overlapping with an epithelial-to-mesenchymal transition phenotype. Fast-proliferating, aggressive cell lines showed increased uptake of essential amino acids and altered nucleotide levels, while heterogeneity in glutamine metabolism was mainly driven the subtype. This was partially associated with heterogeneous expression of metabolite transporters. To test the functional relevance, 67 metabolic genes were silenced in Hs578T using siRNA, and effects on proliferation and migration were measured by a sulforhodamine B assay, live-cell imaging, and a random cell migration assay. This resulted in 34 knockdowns that reduced proliferation and 20 reduced migration, with strong effects for the glycosyltransferases EXT1 and EXT2, nucleotide metabolism genes GART and HPRT1, and metabolite transporters SLC7A1, SLC7A11, and SLC16A3. These findings highlight phenotype-specific metabolic dependencies and identify candidate drug targets in aggressive breast cancer.

cancer biology↗

bioRxiv2025

mzQuality: A tool for quality monitoring and reporting of targeted mass spectrometry measurements

Analyzing metabolites using mass spectrometry can offer valuable insight into an individuals health or disease status. However, various sources of experimental variation can affect the data, making robust quality control essential. In this context, we introduce mzQuality, a user-friendly software tool designed to evaluate and correct technical variations in mass spectrometry-based metabolomics data. MzQuality offers key quality control features, such as batch correction, outlier identification, and analysis of signal-to-noise ratios. It supports any peak-integrated processed data independent of vendor software and does not require the user to have any programming skills. We demonstrate the functionality of mzQuality with a data set of 419 samples measured across six batches, in which mzQuality effectively minimized experimental variation, ensuring the datas readiness for statistical analysis and biological interpretation. With customizable settings, mzQuality can be seamlessly integrated into research workflows to produce more accurate and reproducible metabolomics data.

bioinformatics↗

bioRxiv2024

fluxTrAM: Integration of tracer-based metabolomics data into atomically resolved genome-scale metabolic networks for metabolic flux analysis

Quantitative inference of intracellular reaction rates is essential for characterising metabolic phenotypes. The classical experimental method for measuring metabolic fluxes makes use of stable-isotope tracing of metabolites through the metabolic network, followed by mass spectrometry analysis. The most common 13C-based metabolic flux analysis requires multidisciplinary knowledge in analytical chemistry, cell biology, and mathematical modelling, as well as the use of multiple independent tools for handling mass spectrometry data. Besides, flux analysis is usually carried out within a small network to validate a specific biological hypothesis. To overcome interdisciplinary barriers and extend flux interpretation towards a genome-scale level, we developed fluxTrAM, a semi-automated pipeline for processing tracer- based metabolomics data and integrating it with atomically resolved genome-scale metabolic networks to enable flux predictions at genome-scale. fluxTrAM integrates different software packages inside and outside of the COBRA Toolbox v3.4 for the generation of metabolite structure and reaction databases for a genome-scale model, labelled mass spectrometry data processing into standardised mass isotopologue distribution data (MID), and metabolic flux analysis. To demonstrate the utility of this pipeline, we generated 13C-labeled metabolomics data on an in vitro human induced pluripotent stem cell (iPSC)-derived dopaminergic neuronal culture and processed 13C-labeled MID datasets. In parallel, we generated a cheminformatic database of standardised and context-specific metabolite structures, and atom-mapped reactions for a genome-scale dopaminergic neuronal metabolic model. MID data could be exported into established flux inference software for conventional flux inference on a core model scale. It could also be integrated into the atomically resolved metabolic model for flux inference at genome-scale using moiety fluxomics method. The core model flux solution and moiety flux solution were then compared to two additional flux solutions predicted via flux balance analysis and entropic flux balance analysis. The extensive computational flux analysis and comparison helped to better evaluate the obtained flux feasibility of the neuron-specific genome-scale model and suggested new tracer-based metabolomics experiments with novel labeling configurations, such as labelling a moiety within the thymidine metabolite. Overall, fluxTrAM enables the automation of labelled liquid chromatography (LC)-mass spectrometry (MS) data processing into MID datasets and atom mapping for any given genome-scale metabolic model. It contributes to the standardisation and high throughput of metabolic flux analysis at genome- scale.

systems biology↗

bioRxiv2024

Phenylketonuria: modelling cerebral amino acid and neurotransmitter metabolism

ObjectivePhenylketonuria (PKU) is a metabolic disorder characterised by deficient hepatic phenylalanine hydroxylase activity, leading to elevated phenylalanine levels. Despite adherence to a phenylalanine-restricted diet, many adult PKU patients continue to experience executive function deficits, likely linked to high cerebral phenylalanine concentrations and deficiencies in monoaminergic neurotransmitters. Given the complexity of the interaction between diet and brain neurotransmitter metabolism, we employed computational metabolic modelling alongside experimental data from dietary intervention studies in PKU mice to identify key metabolic drivers underlying these deficits. Our goal was to provide a mechanistic, model-based foundation to support and optimise dietary therapies in PKU. MethodWe developed a computational model simulating large neutral amino acid (LNAA) transport across the blood-brain barrier and the subsequent metabolism of cerebral amino acids and monoaminergic neurotransmitters. The model was validated using direct measurements of brain amino acid concentrations in PKU mice subjected to various dietary regimens. ResultsThe model predicts that cerebral amino acid levels are primarily influenced by their plasma concentrations and, to a lesser extent, by competition among LNAAs for transport mechanisms. Notably, it suggests that cerebral monoaminergic neurotransmitter levels are more significantly affected by elevated phenylalanine levels, likely through non-competitive inhibition of hydroxylase enzymes, than by the availability of precursor amino acids. Consequently, the model indicates that reducing phenylalanine levels, in conjunction with supplementing tyrosine and tryptophan, is more effective in restoring neurotransmitter levels than precursor supplementation alone. ConclusionThis study presents the first comprehensive model integrating LNAA transport and cerebral neurotransmitter metabolism in PKU. The model enhances our understanding of the diseases pathophysiology and highlights the importance of combined therapeutic strategies that target both phenylalanine reduction and precursor amino acid supplementation. Furthermore, it identifies knowledge gaps in LNAA transport mechanisms and offers a framework applicable to other neurological disorders involving diet-gene-neurotransmitter interactions.

systems biology↗

bioRxiv2021

XomicsToModel: Multiomics data integration and generation of thermodynamically consistent metabolic models

Constraint-based modelling can mechanistically simulate the behaviour of a biochemical system, permitting hypotheses generation, experimental design and interpretation of experimental data, with numerous applications, especially modelling of metabolism. Given a generic model, several methods have been developed to extract a context-specific, genome-scale metabolic model by incorporating information used to identify metabolic processes and gene activities in each context. However, existing model extraction algorithms are unable to ensure that a context-specific model is thermodynamically flux consistent. This protocol introduces XomicsToModel, a semiautomated pipeline that integrates bibliomic, transcriptomic, proteomic, and metabolomic data with a generic genome-scale metabolic reconstruction, or model, to extract a context-specific, genome-scale metabolic model that is stoichiometrically, thermodynamically and flux consistent. One of the key advantages of the XomicsToModel pipeline is its ability to seamlessly incorporate omics data into metabolic reconstructions, ensuring not only mechanistic accuracy but also physicochemical consistency. This functionality allows for more accurate metabolic simulations and predictions across different biological contexts, enhancing its utility in diverse research fields, including systems biology, drug development, and personalised medicine. The XomicsToModel pipeline is exemplified for extraction of a specific metabolic model from a generic metabolic model; it enables omics data integration and extraction of physicochemically consistent mechanistic models from any generic biochemical network. It can be implemented by anyone who has basic MATLAB programming skills and the fundamentals of constraint-based modelling. Key pointsXomicsToModel is a semi-automated pipeline that integrates bibliomic, transcriptomic, proteomic, and metabolomic data with a generic genome-scale metabolic reconstruction or model. It enables the seamless incorporation of multi-omics datasets into metabolic reconstructions, ensuring mechanistic accuracy and physicochemical consistency.

systems biology↗

bioRxiv2019

Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease

Refsum disease is an inborn error of metabolism that is characterised by a defect in peroxisomal -oxidation of the branched-chain fatty acid phytanic acid. The disorder presents with late-onset progressive retinitis pigmentosa and polyneuropathy and can be diagnosed biochemically by elevated levels of phytanic acid in plasma and tissues of patients. To date, no cure exists for Refsum disease, but phytanic acid levels in patients can be reduced by plasmapheresis and a strict diet.\n\nIn this study, we reconstructed a fibroblast-specific genome-scale model based on the recently published, FAD-curated model, based on Recon3D reconstruction. We used transcriptomics (available via GEO database with identifier GSE138379), metabolomics, and proteomics data (available via ProteomeXchange with identifier PXD015518), which we obtained from healthy controls and Refsum disease patient fibroblasts incubated with phytol, a precursor of phytanic acid.\n\nOur model correctly represents the metabolism of phytanic acid and displays fibroblast-specific metabolic functions. Using this model, we investigated the metabolic phenotype of Refsum disease at the genome-scale, and we studied the effect of phytanic acid on cell metabolism. We identified 53 metabolites that were predicted to discriminate between Healthy and Refsum disease patients, several of which with a link to amino acid metabolism. Ultimately, these insights in metabolic changes may provide leads for pathophysiology and therapy.

systems biology↗

bioRxiv2018

Cofactors revisited - predicting the impact of flavoprotein-related diseases on a genome scale

Flavin adenine dinucleotide (FAD) and its precursor flavin mononucleotide (FMN) are redox cofactors that are required for the activity of more than hundred human enzymes. Mutations in the genes encoding these proteins cause severe phenotypes, including a lack of energy supply and accumulation of toxic intermediates. Ideally, patients should be diagnosed before they show symptoms so that treatment and/or preventive care can start immediately. This can be achieved by standardized newborn screening tests. However, many of the flavin-related diseases lack appropriate biomarker profiles. Genome-scale metabolic models can aid in biomarker research by predicting altered profiles of potential biomarkers. Unfortunately, current models, including the most recent human metabolic reconstructions Recon and HMR, typically treat enzyme-bound flavins incorrectly as free metabolites. This in turn leads to artificial degrees of freedom in pathways that are strictly coupled. Here, we present a reconstruction of human metabolism with a curated and extended flavoproteome. To illustrate the functional consequences, we show that simulations with the curated model - unlike simulations with earlier Recon versions - correctly predict the metabolic impact of multiple-acyl-CoA-dehydrogenase deficiency as well as of systemic flavin-depletion. Moreover, simulations with the new model allowed us to identify a larger number of biomarkers in flavoproteome-related diseases, without loss of accuracy. We conclude that adequate inclusion of cofactors in constraint-based modelling contributes to higher precision in computational predictions.

systems biology↗