Audrey Le Gouellec

Untargeted LC-MS/MS metabolomics routinely produces complex annotation landscapes in which individual spectra can yield multiple candidate metabolite annotations. Although spectral similarity provides an essential foundation for library-based annotation, determining whether a candidate annotation is consistent with the available evidence and whether alternative explanations remain plausible requires integration of multiple lines of evidence. In current practice, this evaluation is largely embedded in expert judgment, making annotation review difficult to teach, standardize, and reproduce. Here, we introduce an annotation consistency checker that aids this expert review process by generating a user-inspectable report for spectral library matches. The framework integrates complementary evidence dimensions, including spectral agreement, structural relationships among candidate annotations, and competing library matches to highlight annotations that are supported by, or inconsistent with, the available data. Biological context is provided separately to aid interpretation of metabolite occurrence across organisms, tissues, and sample types but is not incorporated into the annotation score. By enabling systematic evaluation of multiple evidence streams, this approach provides a transparent workflow that helps users critically assess metabolite annotations, reduce overinterpretation of spectral matches, and make informed decisions beyond only accepting the top-ranked library match.

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 optimized separation within a 14-minute HPLC gradient and achieved a sensitivity ranging from 55.2 to 0.3 femtomoles in targeted analyses. When applied to sewage sludges sampled weekly over three weeks, our method detected 57 quinones, revealing distinct quinone profiles at various wastewater treatment stages. This rapid and sensitive workflow enables quinone profiling in complex samples, and holds significant promise for advancing microbial ecology, environmental monitoring, and biotechnological applications.

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.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has challenged health care systems worldwide, in certain areas exceeding hospital capacities and human resources. This has underscored the importance of having better tools to predict the outcome of potentially severe respiratory infections such as SARS-CoV-2. Predicting COVID-19 severity may allow physicians to better manage ICU beds and increase the chances of patient survival through appropriate management. During the toughest months of the pandemic, most physicians tried to identify patients that might develop severe forms based primarily on clinical features on admission (e.g., BMI, age). In this context, significant research has focused on identifying comorbidities, clinical manifestations, and routine blood biomarkers to predict disease severity. However, despite the demonstrated value of untargeted metabolomics in assessing severity, limited data exist on its use for identifying novel metabolite biomarkers that could improve both the sensitivity and specificity of outcome prediction. Our goal is to identify metabolite biomarkers that could enhance the predictive accuracy of standard medical biology data and clinical parameters. Methods and analysis: This is a retrospective, observational, monocentric cohort study conducted at the Centre Hospitalier Universitaire Grenoble Alpes (CHUGA). The maximum number of eligible patients admitted for PCR-confirmed COVID-19 between March and December 2020 will be included. Severity outcome is defined using the WHO 10-category ordinal scale (mild: categories 4–5; severe: >5). Blood samples were collected within 48 hours of admission and analyzed for 62 routine blood tests and untargeted multiplatform LC-MS/MS metabolomics across four national platforms. Statistical analysis will include logistic regression with variable selection for the primary aim, and multi-block chemometric integration of clinical, biological, and metabolomics data as a secondary aim. Ethics and dissemination: A study steering committee has been formed to ensure the accuracy of the collected data by thoroughly reviewing it prior to the data lock. All aspects of the study comply with ethical standards, including approval by the CHUGA institutional review board and adherence to CNIL Reference Methodology MR004 for the protection of participants’ rights, privacy, and confidentiality. This study is registered on the French Health Data Hub (number F20210218154851). Results will be disseminated through peer-reviewed publications, presentations at national and international scientific and clinical conferences, and reports shared with key healthcare system stakeholders. ARTICLE SUMMARY: Strengths and limitations of this study Blood samples were collected within 48 hours of admission and before any severe symptom onset, aliquoted within 4 hours of collection and stored at −80°C, ensuring high pre-analytical quality. This study uses a multiplatform untargeted LC-MS/MS metabolomics approach across four complementary analytical platforms (CEA Saclay, INRAE Clermont, MetaToul, GEMELI), providing broad metabolome coverage. The integration of three heterogeneous data blocks (Metabolome, Biologicome, Clinicome) via multi-block chemometrics enables a systems-level view of COVID-19 severity. The sample size is limited by the complexity and cost of metabolomics analyses, and no formal sample size calculation was performed; findings should be considered exploratory and hypothesis-generating. As a monocentric, retrospective study conducted during a single epidemic wave (March–December 2020), generalizability may be limited by incomplete data across some routine blood tests and by differences in settings, subsequent variants, or treated populations.

Background SARS-CoV-2 and Influenza virus are two major respiratory viruses, responsible for the COVID-19 and the flu respectively. To achieve their replication, these viruses do not merely hijack host cells; they reprogram them, and the host’s metabolism is no exception. Aim of the review This review explores how two major respiratory pathogens, SARS-CoV-2 and influenza virus, manipulate host metabolism to fuel their replication and drive disease. Key scientific concepts of the review Despite differences in their genome organization and replication strategies, both viruses disrupt central metabolic pathways, including glucose processing, the Krebs cycle, amino acid and nucleotide synthesis. Common effects include enhanced anaerobic glycolysis, depletion of key amino acids, activation of the kynurenine pathway, and disruption of purine signaling. However, each virus leaves a distinct metabolic fingerprint. SARS-CoV-2 infection leads to glucose accumulation, alters late-stage Krebs cycle metabolites, and significantly disrupts nucleotide production. In contrast, influenza viral infection depletes glucose and dampens Krebs cycle activity, with a less consistent impact on nucleotide pathways. Viral proteins, such as SARS-CoV-2 ORF3a and influenza virus NS1, play a crucial role in these metabolic shifts. These changes not only reflect viral strategies but also correlate with disease severity. As metabolomics technologies advance, understanding these virus-specific and shared metabolic changes could unlock new diagnostic tools, prognostic markers, and treatment strategies.

: Parkinson’s disease (PD) diagnosis remains delayed and suboptimally accurate, largely due to clinical overlap with atypical parkinsonian syndromes and the lack of reliable biomarkers. In this context, we recently patented a 6-metabolites blood biomarker (6M-BB) for PD diagnosis versus healthy controls (HC), using high field nuclear magnetic resonance (NMR). Here, we evaluated its performance for the differential diagnosis of PD and its translation to clinical In-Vitro Diagnosis (IVDr) NMR platform. Patient serum samples from de novo PD (n=30), multiple system atrophy (MSA, n=30), progressive supranuclear palsy (PSP, n=30), Alzheimer’s disease (AD, n=33), and healthy individuals (n=29), were profiled by proton NMR both at 950 MHz and on an IVDr 600 MHz system. First, we reproduced the 6M-BB performance for PD vs. HC with 87.9% accuracy, but above all, we obtained an overall accuracy of 82.6% across all groups including MSA/PSP. Secondly, the 6M-BB refit using IVDr data achieved an overall accuracy of 77%. Adding VLDL-5 free cholesterol (V5FC) and citrate markedly increased accuracy to 95% for PD vs. HC and 85% vs. MSA/PSP. To conclude, in addition to independently validate the 6M-BB with a new PD/HC cohort, we demonstrated here its ability to efficiently distinguish PD from other confounding parkinsonian syndromes at de novo stage, thereby addressing a major challenge in the early clinical diagnosis of the disease. Its successful transfer to a fully automated, standardized IVDr system, with gains from V5FC and citrate, supports the feasibility and promising potential for clinical implementation, justifying future prospective multicenter studies. One Sentence Summary: We offer a clinically deployable blood biomarker that addresses the major challenge of distinguishing Parkinson’s disease from other parkinsonism.

Introduction The gut microbiome is a complex ecosystem stratified that varies along different sections of the gut. It comprises a wide array of metabolites originating from both food, host, and microbes. Microbially-derived metabolites, such as bile acids, short-chain fatty acids, and indole derivatives, are of significant interest due to their direct interactions with host physiology and regulating function. Most current studies on the gut microbiome focus on fecal samples, which do not fully represent the upper parts of the gut due to its stratification. To collect microbiome samples from the proximal gut microbiome, endoscopic methods or new non-invasive medical devices can be used. Objectives To enable comprehensive profiling of the gut metabolome and analyze key metabolites, we developed a combined approach combining untargeted and semi-targeted metabolomics using a Q-Exactive Plus Orbitrap mass spectrometer. Methods Initially, we selected 49 metabolites of interest for the gut metabolome based on four distinct criteria. We validated these metabolites through repeatability and linearity tests and created a compound database using the software TraceFinder (ThermoFisher Scientific). For untargeted metabolomics, we established a workflow for the annotation and discovery of molecules. Results Finally, 37 metabolites were validated for semi-targeted metabolomics, and we conducted a proof of concept on small intestinal and fecal samples form a clinical trial (NCT05477069). Conclusion Our combined approach, facilitated by molecular networking, demonstrated the potential to discover new metabolites.

The invasive nature of sample collection for studying the small intestinal (SI) microbiome often results in its poor characterization. This study evaluated a novel ingestible medical device (MD) for SI luminal sample collection. A monocentric interventional trial ( NCT05477069 ) was conducted on 15 healthy subjects. Metagenomics, metabolomics and culturomics assessed the MD’s effectiveness in characterizing the healthy SI microbiome and identifying potential biomarkers. The SI microbiota differed significantly from the fecal microbiota, displaying high inter-individual variability, lower species richness, and reduced alpha diversity. A combined untargeted and semi-targeted LC-MS/MS metabolomics approach identified a distinct SI metabolic footprint, with bile acids and amino acids being the most abundant classes of metabolites. Host and host/microbe-derived bile acids were particularly abundant in SI samples. The application of a fast culturomics approach to two SI samples enabled species-level characterization, resulting in the identification of 90 bacterial species, including five potential novel species. The present study demonstrates the efficacy of our novel sampling MD in enabling comprehensive SI microbiome analysis through an integrative multi-omics approach, allowing the identification of distinct microbiome signatures between SI and fecal samples.

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.