Biology subjects

Fontaine, F.

Publications and source records attributed to Fontaine, F..

8 recordsLinked to original sources

bioRxiv2026

LFQ Benchmark Dataset - Generation Beta: Assessing Modern Proteomics Instruments and Acquisition Workflows with High-Throughput LC Gradients

Recent advances in liquid chromatography-mass spectrometry (LC-MS) have accelerated the adoption of high-throughput workflows that deliver deep proteome coverage using minimal sample amounts. This trend is largely driven by clinical and single-cell proteomics, where sensitivity and reproducibility are essential. Here, we extend our previous benchmark dataset (PXD028735) using next-generation LC-MS platforms optimized for rapid proteome analysis. We generated an extensive DDA/DIA dataset using a human-yeast-E. coli hybrid proteome. The proteome sample was distributed across multiple laboratories together with standardized analytical protocols specifying two short LC gradients (5 and 15 min) and low sample input amounts. This dataset includes data acquired on four different platforms, and features new scanning quadrupole-based implementations, extending coverage across different instruments and acquisition strategies. Our comprehensive evaluation highlights how technological advances and reduced LC gradients may affect proteome depth, quantitative precision, and cross-instrument consistency. The release of this benchmark dataset via ProteomeXchange (PXD070049 and PXD071205), allows for the acceleration of cross-platform algorithm development, enhance data mining strategies, and supports standardization of short-gradient, high-throughput LC-MS-based proteomics.

bioinformatics↗

bioRxiv2025

An automated software-assisted approach for exploring metabolic susceptibility and degradation products in macromolecules using High-Resolution Mass Spectrometry

A comprehensive understanding of drug metabolism is crucial for advancements in drug development. Automation has improved various stages of this process, from compound procurement to data analysis, supporting small molecules, peptides, and oligonucleotides. However, challenges remain, particularly in the time-consuming analysis of samples for metabolite identification. This article introduces new algorithms for automated Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) data applicable to both small and macromolecules. While methodologies for small molecules are well established, adapting them for macromolecules presents challenges, including computational demands, peak detection complexities, and visualization issues. A data analysis employing diverse algorithms in the data preprocessing step was conducted across six datasets, ranging from small/medium linear or macrocyclic peptides to oligonucleotides with natural and unnatural monomers. Two peak detection approaches were evaluated: using the monoisotopic mass versus the most abundant isotope for mass calculation. Additionally, an exploration of two distinct structure visualization options was conducted for one of the datasets. Furthermore, data obtained through two different acquisition modes was processed. The computational time required for data processing was recorded throughout, ranging from 5 minutes to 2 hours per experiment. The results have been compared against prior studies, revealing substantial reductions in processing time, consistent identification of degradation products, and improved visualization techniques, thereby enhancing result interpretation. A comprehensive identification of 970 metabolites was achieved under varied incubation conditions across the six datasets, showcasing the workflows efficiency in managing experimental data within a molecular range from 700 to 7630 Daltons (Da). Particularly in larger molecules, the most abundant mass algorithm demonstrated higher scores and a greater number of matches, instilling greater confidence in the accurate prediction of metabolite structures. It has been illustrated how the visualization algorithm for macromolecules allows the combination of monomer and atom/bond notation, facilitating a clear depiction of metabolic changes in the molecular structure.

bioinformatics↗

bioRxiv2024

Nuclear metabolism oscillation during the cell cycle reveals a link between the phosphatidylinositol pathway and histone methylation.

The progression of the cell cycle is regulated by the expression of specific genes and fluctuations in cellular metabolic states. Previous research has employed cell cycle-based transcriptomics, proteomics, and metabolomics analyses to identify cell cycle-dependent changes at the gene expression, protein, and metabolic levels. However, the role of protein compartmentalization in regulating protein function, coupled with evidence that metabolic enzymes can localize to the nucleus and influence chromatin states, suggests that fluctuations in nuclear metabolism may play a role in regulating cell cycle progression. In this study, we developed an approach to resolve chromatin and nuclear changes during the cell cycle in an unbiased and systematic manner. This was achieved by integrating cell cycle fluorescent reporters with chromatin mass spectrometry and cellular imaging. Our investigation focused on metabolic enzymes and revealed that phosphatidylinositol metabolism localizes to the nucleus in a cell cycle-dependent manner. Moreover, disruption of phosphatidylinositol metabolism affects the nuclear distribution of phosphatidylinositol 4,5-bisphosphate, alters the number and morphology of nucleoli, and influences the maintenance of distinct heterochromatin states throughout the cell cycle. Finally, given the established link between phosphatidylinositol metabolism and methionine synthesis, as well as the differential impact observed on distinct histone marks when phosphatidylinositol metabolism is perturbed, we proposed that distinct pools of methionine may be involved in the maintenance of histone marks that decorate heterochromatin in a cell cycle-dependent manner.

cell biology↗

bioRxiv2024

Dual action of sphinganine in the plant disease resistance to bacteria.

Sphingolipids are ubiquitous, highly diverse molecules constituting at least 40% of plant plasma membranes. Initially known as modulators of membrane integrity, they now emerge as important players in plant responses to (a)biotic stresses. The interaction between Arabidopsis thaliana and the bacterium Pseudomonas syringae pv. tomato DC3000 AvrRpm1 (Pst AvrRpm1) culminates in the activation of a programmed cell death known as the hypersensitive response, which is part of the plant immune response. In this study, we showed that the co-infiltration of Pst AvrRpm1 and sphinganine (d18:0) in Arabidopsis leaves suppress the hypersensitive response. This suppression phenotype is also observed with bacteria carrying the effectors AvrB and AvrPphB but not with the ones carrying AvrRpt2 and AvrRps4. Sphingolipid-induced hypersensitive response suppression by Pst AvrRpm1 is correlated with the down-regulation of the gene AtNMT1 encoding a N-myristoyltransferase. d18:0 does not have a direct antibacterial effect and its co-infiltration in plants does not display typical signs of immune response such as activation of salicylic acid signaling pathway and extracellular reactive oxygen species production. Biophysical studies showed that d18:0 interacts with plant plasma membrane lipids. More specifically, d18:0 disturbs plant plasma membrane organization and mechanical properties. Our results demonstrate that sphingolipids play an important role in plant resistance, especially by interfering with the plasma membrane organization and effector localization and thus disturbing their function and subsequent immune responses.

plant biology↗

bioRxiv2023

Comprehensive chromatome profiling identifies metabolic enzymes on chromatin in healthy and cancer cells

Metabolic and epigenetic rewiring are widely considered hallmarks of cancer, with emerging evidence of crosstalk between them. Anecdotal evidence of metabolic enzymes moonlighting in the chromatin environment has suggested how this crosstalk might be facilitated, but the extent of nuclear relocalization of metabolic enzymes remains elusive. Here, we provide a comprehensive chromatin proteomics resource across cancer lineages as well as healthy samples and demonstrate that metabolic enzyme moonlighting on chromatin is widespread across tissues and pathways. We show that the abundance of metabolic enzymes on chromatin is tissue-specific, with oxidative phosphorylation proteins depleted in lung cancer samples, perhaps suggesting an interplay between cell identity and nuclear metabolism. Finally, we explore metabolic functions in the chromatin environment and show that one-carbon folate enzymes are associated with DNA damage and repair processes, providing an approach to explore non-canonical functions of metabolic enzymes.

cancer biology↗

bioRxiv2022

A metabolic map of the DNA damage response identifies PRDX1 in nuclear ROS scavenging and aspartate synthesis

While cellular metabolism impacts the DNA damage response, a systematic understanding of the metabolic requirements that are crucial for DNA damage repair has yet to be reported. Here, we investigate the metabolic enzymes and processes that are essential when cells are exposed to DNA damage. By integrating functional genomics with chromatin proteomics and metabolomics, we provide a detailed description of the interplay between cellular metabolism and the DNA damage response. Subsequent analysis identified Peroxiredoxin 1, PRDX1, as fundamental for DNA damage repair. During the DNA damage response, PRDX1 translocates to the nucleus where it is required to reduce DNA damage-induced nuclear reactive oxygen species. Moreover, PRDX1 regulates aspartate availability, which is required for the DNA damage-induced upregulation of de novo nucleotide synthesis. Loss of PRDX1 leads to an impairment in the clearance of DNA damage, accumulation of replicative stress and cell proliferation defects, thus revealing a crucial role for PRDX1 as a DNA damage surveillance factor.

molecular biology↗

bioRxiv2020

A dominant-negative SOX18 mutant disrupts multiple regulatory layers essential to transcription factor activity.

Few genetically dominant mutations involved in human disease have been fully explained at the molecular level. In cases where the mutant gene encodes a transcription factor, the dominant-negative mode of action of the mutant protein is particularly poorly understood. Here, we studied the genome-wide mechanism underlying a dominant-negative form of the SOX18 transcription factor (SOX18RaOp) responsible for both the classical mouse mutant Ragged opossum and the human genetic disorder Hypotrichosis-Lymphedema-Telangiectasia-Renal Syndrome. Combing three single-molecule imaging assays in living cells, we found that SOX18RaOp disrupts the system through an accumulation of molecular interferences which impair several functional properties of the wild-type SOX18 protein, including its chromatin-binding dynamics. The dominant-negative effect is further amplified by recruiting the interactome of its wild-type counterpart, which perturbs regulatory nodes such as SOX7 and MEF2C. Our findings explain in unprecedented detail the multi-layered process that underpins the molecular etiology of dominant-negative transcription factor function.

cell biology↗

bioRxiv2018

Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery.

Peptide drugs have been used in the treatment of multiple pathologies. During peptide discovery, it is crucially important to be able to map the potential sites of cleavages of the proteases. This knowledge is used to later chemically modify the peptide drug to adapt it for the therapeutic use, making peptide stable against individual proteases or in complex medias. In some other cases it needed to make it specifically unstable for some proteases, as peptides could be used as a system to target delivery drugs on specific tissues or cells. The information about proteases, their sites of cleavages and substrates are widely spread across publications and collected in databases such as MEROPS. Therefore, it is possible to develop models to improve the understanding of the potential peptide drug proteolysis. We propose a new workflow to derive protease specificity rules and predict the potential scissile bonds in peptides for individual proteases. WebMetabase stores the information from experimental or external sources in a chemically aware database where each peptide and site of cleavage is represented as a sequence of structural blocks connected by amide bonds and characterized by its physicochemical properties described by Volsurf descriptors. Thus, this methodology could be applied in the case of non-standard amino acid. A frequency analysis can be performed in WebMetabase to discover the most frequent cleavage sites. These results were used to train several models using logistic regression, support vector machine and ensemble tree classifiers to map cleavage sites for several human proteases from four different families (serine, cysteine, aspartic and matrix metalloproteases). Finally, we compared the predictive performance of the developed models with other available public tools PROSPERous and SitePrediction.

bioinformatics↗