Biology subjects

Le Gouellec, A.

Publications and source records attributed to Le Gouellec, A..

9 recordsLinked to original sources

bioRxiv2026

Engineering growth-coupled metabolic biosensors for disease prognosis and diagnosis using full growth trajectories

Although metabolomics has shown considerable promise for biomarker discovery, and the development of diagnostic and prognostic applications, its translation into routine clinical practice remains limited by analytical complexity, cost, throughput, and standardization challenges. These limitations underscore the need for complementary tools, particularly in resource-limited settings. In this study, we developed a workflow for the engineering and characterization of growth-coupled metabolic sensors capable of disease detection (healthy vs. infected) and outcome prediction (mild vs. severe), which we illustrated using COVID-19 as a proof-of-concept application. We first generated a biomarker-guided library of 34 candidate sensors leveraging both auxotrophic phenotypes and less stringent metabolic dependencies. We then screened the library against patient plasma pools, identifying 19 sensor candidates with diagnostic and/or prognostic potential, including 14 with prognostic potential. Lastly, a selected subset of candidates was further evaluated on a patient cohort using two newly developed analytical frameworks designed to extract additional information from bacterial growth curves. The best-performing sensors achieved a balanced accuracy of 0.88{+/-} 0.06 for prognostic prediction (outer-test AUC = 0.89, 5-fold cross-validation, n = 37) and 1.00 for diagnostic classification (outer-test AUC = 1.00, 5-fold cross-validation, n = 56). Collectively, these findings establish a proof of concept for translating disease-associated plasmatic metabolic signatures into low-cost, growth-coupled biosensors with diagnostic and prognostic capabilities.

synthetic biology↗

bioRxiv2026

Isoprenoid quinone profiling in complex biological samples using a novel semi-quantitative HPLC-MS/MS method

Isoprenoid quinones are ubiquitous redox lipids that mediate electron transfer in various cellular processes across all domains of life. These molecules also serve as taxonomic and metabolic markers, facilitating the characterisation of microbial communities. However, their structural diversity and extreme hydrophobicity are challenging for comprehensive detection and quantification in complex biological matrices. In this study, we present a semi-quantitative HPLC-MS/MS method that enables the sensitive analysis of the widest range of quinones reported to date. Using a 16-quinone standard mixture, we optimised separation within a 14-minute HPLC gradient and achieved femtomole-level sensitivity in targeted analyses. When applied to sewage sludges sampled weekly over three weeks, our method detected 57 distinct quinones, revealing stage-specific quinone profiles that reflect shifts in bacterial communities during wastewater treatment. This rapid and sensitive workflow provides a robust tool for accurate quinone profiling in complex samples, opening avenues for the discovery of novel quinones through untargeted approaches. By pushing the boundaries of quinone profiling, our method holds significant promise for advancing microbial ecology, environmental monitoring, and biotechnological applications. HighlightsO_LIuHPLC-Orbitrap method for the semi-quantitative profiling of isoprenoid quinones C_LIO_LIAnalysis of the widest range of isoprenoid quinones to date C_LIO_LIFemtomole-level sensitivity in just 14 minutes of chromatographic separation C_LIO_LIDetection of 57 quinones in complex wastewater sludge matrices C_LIO_LIMost comprehensive set of quinone standards including purified microbial quinones C_LI

biochemistry↗

bioRxiv2025

Dynamics of interactions between three major respiratory pathogens in reconstituted human epithelium

Respiratory viral coinfections pose a substantial global health burden, yet the underlying virus-virus interactions remain incompletely understood. Here, we systematically examined the interplay among influenza A virus (IAV), SARS-CoV-2, and respiratory syncytial virus (RSV) using a reconstituted human airway epithelium model. We monitored viral replication dynamics and host transcriptional responses under both simultaneous and sequential infection conditions. Our results reveal complex, asymmetric interactions strongly influenced by infection timing and sequence. IAV exerted a pronounced inhibitory effect on SARS-CoV-2, mediated mainly by a robust type III interferon response. However, transient early enhancement of SARS-CoV-2 by IAV and subsequent bidirectional inhibition were also observed. Transcriptomic profiling identified coinfection-specific gene expression signatures enriched for metabolic and cell death pathways. In IAV-RSV coinfections, IAV generally suppressed RSV replication; strikingly, prior RSV infection significantly enhanced subsequent IAV replication, potentially through RSV-induced cellular remodeling or syncytia formation. Overall, host responses to coinfection were integrated and non-additive, with distinct transcriptional programs suggesting activation of unique cellular pathways beyond canonical antiviral responses. Together, these findings highlight the pivotal role of innate immunity and the order of infection in determining the outcome of respiratory viral coinfections, providing mechanistic insights with implications for clinical management and epidemic modeling. Author SummaryRespiratory viruses often circulate simultaneously; however, their interactions within the same host remain poorly understood. We used a reconstituted human airway epithelium model to examine coinfections involving influenza A virus (IAV), SARS-CoV-2, and respiratory syncytial virus (RSV). Our findings reveal that these viruses do not simply compete or coexist: their interactions are dynamic, asymmetric, and highly dependent on the order in which infections occur. IAV generally suppresses SARS-CoV-2 replication through a strong type III interferon response, although brief early enhancement and later mutual inhibition can occur. Transcriptomic profiling reveals that coinfection triggers a complex array of host responses, including alterations in metabolic and cell-death pathways. In IAV/RSV coinfections, IAV consistently inhibits RSV; however, prior RSV infection markedly boosts subsequent IAV replication--likely due to RSV-induced cellular remodeling, such as syncytia formation. These transcriptional signatures point to altered cell adhesion and motility as key features of such interactions. Overall, our results demonstrate that innate immunity and the sequence of infection shape coinfection outcomes in non-additive ways, driving distinct cellular responses beyond classical antiviral pathways. Understanding these mechanisms is essential for improving clinical management and guiding the development of targeted antiviral strategies.

microbiology↗

bioRxiv2025

MS/MS Mass Spectrometry Filtering Tree for Bile Acid Isomer Annotation.

Bile acids are essential steroids regulating immunity, nutrient absorption, insulin, appetite, and body temperature. Their structural diversity is vast, but due to spectral similarities, MS/MS spectral matching often fails to resolve isomers. This study introduces a proof-of-concept workflow using a mass spectrometry query language filtering tree that distinguishes isomeric bile acids in untargeted LC-MS/MS data. Its application revealed a deoxycholyl-2-aminophenol amidate linked to whole grain consumption.

bioinformatics↗

bioRxiv2023

Cytosolic acetyl-CoA synthetase (ACSS2) does not generate butyryl- and crotonyl-CoA

Acetyl and other acyl groups from different short-chain fatty acids (SCFA) competitively modify histones at various lysine sites. To fully understand the functional significance of such histone acylation, a key epigenetic mechanism, it is crucial to characterize the cellular sources of the corresponding acyl-CoA molecules required for the lysine modification. Like acetate, SCFAs such as propionate, butyrate and crotonate are thought to be the substrates used to generate the corresponding acyl-CoAs by enzymes known as acyl-CoA synthetases. The acetyl-CoA synthetase, ACSS2, which produces acetyl-CoA from acetate in the nucleocytoplasmic compartment, has been proposed to also mediate the synthesis of acyl-CoAs such as butyryl- and crotonyl-CoA from the corresponding SCFAs. This idea is now widely accepted and is sparking new research projects. However, based on our direct in vitro experiments with purified or recombinant enzymes and structural considerations, we demonstrate that ACSS2 is unable to mediate the generation of non-acetyl acyl-CoAs like butyryl- and crotonyl-CoA. It is therefore essential to re-examine published data and corresponding discussions in the light of this new finding.

biochemistry↗

bioRxiv2023

Multi-method quantification of acetyl-CoA and further acyl-CoA species in normal and ischemic rat liver

Thioesters of coenzyme A (CoA) carrying different acyl chains (acyl-CoAs) are central intermediates of many metabolic pathways and donor molecules for protein lysine acylation. Acyl-CoA species largely differ in cellular concentrations and physico-chemical properties, rendering their analysis challenging. Here we compare several approaches to quantify cellular acyl-CoAs concentrations in normal and ischemic rat liver, using HPLC and LC-MS/MS for multi-acyl-CoA analysis, as well as NMR, fluorimetric and spectrophotometric techniques for quantification of acetyl-CoA. In particular, we describe a simple LC-MS/MS protocol that is suitable for the relative quantification of short and medium chain acyl-CoAs species. We show that ischemia induces specific changes in the short chain acyl-CoA relative concentrations, while mild ischemia (1-2 min), although reducing succinyl-CoA, has little effects on acetyl-CoA, and even increases some acyl-CoAs species upstream of the tricarboxylic acid cycle. In contrast, advanced ischemia (5-6 min) also reduces acetyl-CoA levels. Our approach provides the keys to accessing the acyl-CoA metabolome for a more in-depth analysis of metabolism, protein acylation and epigenetics.

biochemistry↗

bioRxiv2020

Ion Identity Molecular Networking in the GNPS Environment

Molecular networking connects tandem mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. To connect ion species of the same molecule, we developed Ion Identity Molecular Networking. These new relationships improve network connectivity, are shown to reveal novel ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral libraries.

bioinformatics↗

bioRxiv2019

Feature-based Molecular Networking in the GNPS Analysis Environment

Molecular networking has become a key method used to visualize and annotate the chemical space in non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and quantitative interpretation are currently limited. Therefore, we created Feature-based Molecular Networking (FBMN) as a new analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN leverages feature detection and alignment tools to enhance quantitative analyses and isomer distinction, including from ion-mobility spectrometry experiments, in molecular networks.

bioinformatics↗

bioRxiv2019

Repository-scale Co- and Re-analysis of Tandem Mass Spectrometry Data

Metabolomics data are difficult to find and reuse, even in public repositories. We, therefore, developed the Reanalysis of Data User (ReDU) interface (https://redu.ucsd.edu/), a community- and data-driven approach that solves this problem at the repository scale. ReDU enables public data discovery and co- or re-analysis via uniformly formatted, publicly available MS/MS data and metadata in the Global Natural Product Social Molecular Networking Platform (GNPS), consistent with findable, accessible, interoperable, and reusable (FAIR) principles.1

bioinformatics↗