Biology subjects

Boccard, J.

Publications and source records attributed to Boccard, J..

3 recordsLinked to original sources

bioRxiv2026

Breast cancer metabolism and responsiveness to dichloroacetate: relationships with 15N and 13C natural abundance

BackgroundMetabolic reprogramming is a hallmark of breast cancer (BrCa), with alterations in glycolysis, glutamine metabolism, and the urea cycle contributing to tumour progression. Dichloroacetate (DCA), a pyruvate dehydrogenase kinase (PDK) inhibitor, shifts metabolism toward oxidative phosphorylation and has been proposed as a therapeutic agent. While isotope tracing is well-established, natural isotope abundance ({delta}{superscript 1}3C, {delta}{superscript 1}N) is emerging as a biomarker of metabolic alterations in cancer. MethodsWe investigated the relationship between isotope composition and metabolism in BrCa using two BALB/c mouse mammary tumour models (V14 and 4T1) and assessed the effects of DCA treatment using metabolomics, lipidomics and isotopomics. ResultsV14 and 4T1 tumours exhibited isotopic patterns similar to human tumours, with {delta}{superscript 1}3C enrichment and {delta}{superscript 1}N depletion relative to non-cancerous mammary tissue. V14 tumours were more {delta}{superscript 1}N-depleted than 4T1, reflecting differences in nitrogen metabolism. Multivariate analysis integrating isotopic, metabolomic, and lipidomic data revealed isotopic features as key discriminators between tumours and normal tissues. Compared to V14, 4T1 tumours were enriched in TCA intermediates, sphingolipids, and amino acids, whereas V14 tumours showed elevated glutaminolytic and nitrogenous metabolites. DCA treatment differentially affected tumour growth, with V14 tumours more sensitive than 4T1. DCA altered nitrogen metabolism, increasing the arginine-to-ornithine ratio, and modulating {delta}{superscript 1}N values in a tumour-specific manner increasing V14 and decreasing 4T1 {delta}{superscript 1}N values. DCA had little effect on {delta}{superscript 1}3C. {delta}{superscript 1}3C values were primarily determined by the balance between lipid and TCA cycle metabolites, rather than glycolytic flux. {delta}{superscript 1}N variation was linked to nitrogen metabolism, including urea cycle intermediates and sphingolipid composition, with a potential role for choline-related fractionation in {delta}{superscript 1}N depletion. Altered gene expression of Hacd2 and Acot12 in V14 tumours after DCA treatment was reflected in shorter fatty acid tails in phosphatidyl cholines, supporting the lipidomics data. ConclusionsThese findings support the hypothesis that cancer-associated metabolic reprogramming influences natural isotope abundance. Correlations between isotope shifts and metabolic signatures highlight the potential of lipid-derived {delta}{superscript 1}N as a biomarker of tumour metabolic state, with implications for noninvasive metabolic profiling in BrCa. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/710495v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1589d0eorg.highwire.dtl.DTLVardef@af2ad4org.highwire.dtl.DTLVardef@24e67forg.highwire.dtl.DTLVardef@98da7f_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

A benchmarking workflow for assessing the reliability of batch correction methods

One of the most pervasive challenges in large-scale untargeted metabolomics is short and long-term analytical variability introducing the necessity of batch effect correction. In this context, several strategies and methods have been developed to limit those effects, either by monitoring the data generation process to maximize reproducibility or by applying post-analysis data correction. Different evaluation frameworks, either assessing the degree of bias in the data through visual tools or quantitative indicators, or evaluating the prediction performance of known biomarkers, were also proposed. However, there is currently no clear consensus on how to evaluate batch correction methods. This work offers a strategy to assess multiple dimensions of batch correction efficiency within a comprehensive and reliable framework, designed to assess the effectiveness and reliability of batch correction methods. Based on Mahalanobis Conformity Index (MCI), it provides a multivariate and covariance-aware metric to quantify within- and between-batch variability. Additionally, it combines visualization techniques (Principal Component Analysis (PCA) and Multivariate INTegrative (MINT) PCA) with numerical indicators (batch dispersion, Coefficient of Variation), supporting both multidimensional and metabolite-specific evaluations. Lastly, this novel approach integrates statistical tools alongside chemistry-based metrics for method overfitting and overcorrection assessment. Applied within a use case for comparing LOESS-based and ComBat correction methods, the present workflow provided a structured approach to systematically assess the reliability of batch corrections, ensuring both data intercomparability and biological relevance in metabolomics studies. Author summaryThe assessment of batch correction is a challenge in metabolomics, where there is no consensus for a define strategy, making it a complex task. However, knowing the impact of batch correction on the datasets and consecutive possible impact on downstream statistical analyses, providing a reliable framework for its assessment is a cornerstone for reproducible results. The objective of the present work was to provide a framework and a set of interpretation tools combining numerical indicators, as well as diagnostic plots, for assessing the reliability of batch correction methods. We introduced a robust evaluation framework centered on the Mahalanobis Conformity Index, providing a multivariate and covariance-aware metric to quantify within- and between-batch variability. By coupling this index with visual tools (based on Principal Component Analysis (PCA) and Multivariate INTegrative (MINT) PCA), as well as compound-level diagnostics, we enabled a fine-grained and interpretable comparison of correction strategies, highlighting their strengths and potential pitfalls.

bioinformatics↗

bioRxiv2025

Neurotoxicity of Propylene Glycol Butyl Ether: Multiomic Evidence from Human BrainSpheres

Exposure to solvents may contribute to the development of neurodevelopmental and neurodegenerative diseases. Glycol ethers consist in a widely used class of organic solvents leading to workers and consumers exposure via many different applications. Ethylene glycol ethers are gradually being replaced by propylene glycol ethers thought to be less toxic. However, their neurotoxicity is not systematically assessed prior to placing them on the market. Therefore, this study investigated the potential neurotoxicity of propylene glycol butyl ether (PGBE) for which no official occupational limit has been established. To this aim, new approach methodologies have been used. Human induced pluripotent stem cells-derived BrainSpheres model was exposed to PGBE and to its main metabolite, 2-butoxypropanoic acid (2BPA). An integrative multiomic approach (transcriptomics, proteomics, metabolomics and lipidomics) was adopted to assess molecular alterations, derive benchmark concentrations and define potential mechanisms of action. PGBE was neurotoxic at occupationally relevant exposure concentrations. This was shown for the first time in human cells. And, although PGBE was more cytotoxic than 2BPA, both compounds showed very similar neurotoxicity. PGBE and 2BPA strongly affected the cell cycle, induced oxidative stress and perturbed energy and lipid metabolism. They also targeted specific nervous system processes, such as axon guidance and synapse organization. Finally, 2BPA may trigger ferroptosis by increased iron uptake. Our results show an urgent need for public health authorities to carefully assess the risk glycol ethers pose to humans, to properly protect the workers as well as individuals in the general population unknowingly exposed from indoor air contaminations.

pharmacology and toxicology↗