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Leclercq, M.

Publications and source records attributed to Leclercq, M..

10 recordsLinked to original sources

mTORC2 stabilizes HIF-1β to coordinate metabolic adaptation in lung cancer

Despite extensive genetic heterogeneity, lung tumors frequently converge on shared signaling dependencies that remain therapeutically underexploited. Here, we identify mTORC2 signaling as a convergent dependency across genetically distinct lung cancer subtypes and uncover HIF-1{beta} as a selective metabolic effector downstream of mTORC2 that promotes lung tumor progression. Elevated mTORC2 signaling in lung adenocarcinoma was associated with poor overall survival, metastatic dissemination and metabolic rewiring. Using complementary genetically engineered mouse models of Rictor deletion or overexpression in Kras-driven lung tumors, we show that mTORC2 activity is dispensable for normal lung homeostasis but required for tumor progression and metabolic adaptation in vivo. Mechanistically, mTORC2 stabilized HIF-1{beta} by preventing its ubiquitin-independent proteasomal degradation through a non-canonical PKC-CK2 signaling axis, independently of AKT. Integrated multi-omics analyses identified extensive metabolic rewiring downstream of the mTORC2-HIF-1{beta} axis, with sphingolipid metabolism emerging as a prominent and therapeutically exploitable vulnerability. Accordingly, pharmacological targeting of sphingolipid metabolism markedly impaired the growth of mTORC2-driven lung tumors in vivo. Together, our findings establish a non-canonical mTORC2-HIF-1{beta} signaling axis that couples oncogenic signaling to metabolic adaptation and defines therapeutically actionable metabolic vulnerabilities in lung cancer.

cancer biology↗

PeptiDIA: A Machine Learning Framework for Enhanced Peptide Identification in Fast-Gradient Data-Independent Acquisition Proteomics

Data-independent acquisition (DIA) mass spectrometry has become increasingly prevalent in proteomics as advances in instrumentation, chromatography, and computational analysis have enabled robust proteome identification across complex biological samples. However, analytical depth achieved with fast chromatographic gradients remains lower than that obtained using long-gradients, reflecting a throughput-depth trade-off. Here, we present PeptiDIA, a machine learning framework that enhances peptide identification in fast-gradient DIA data by leveraging paired fast and long-gradient acquisitions from identical samples. PeptiDIA processes DIA-NN outputs generated at relaxed false discovery rate thresholds to obtain expanded candidate peptide pools and trains gradient-boosted decision tree models using long-gradient identifications as reference labels. The model integrates DIA-NN features with engineered peptide descriptors and applies isotonic regression to calibrate probabilities, enabling controlled peptide recovery relative to the long-gradient reference. Applied to human and murine datasets spanning six tissues acquired on an Orbitrap Exploris 480, PeptiDIA increased peptide identifications by 25-34% at 1% target reference-discordance rate (RDR) and increased the number of protein groups containing at least one rescued peptide by 15-17%. Overall, PeptiDIA improves the identification depth of fast-gradient DIA-NN workflows without altering acquisition strategies. The framework is available as a web application and command-line tool at https://github.com/Jordano700/PeptiDIA.

bioinformatics↗

Pairing Data Independent Acquisition and High-Resolution Full Scan for Fast Urinary Tract Infection Diagnosis

BackgroundRapid and accurate identification of urinary tract infection (UTI) pathogens is critical for effective treatment and combating antimicrobial resistance. Conventional culture-based diagnostics are slow, and standard tandem mass spectrometry workflows are resource-intensive. MethodsWe present a proof-of-concept workflow that integrates high-resolution data-independent acquisition (DIA) MS/MS on the Thermo Scientific Orbitrap Astral with MS1-only spectra from the Orbitrap Exploris 480. DIA data establish a reference panel of pathogen-specific peptides, which are then identified in MS1 spectra from urine samples. Machine learning models trained on these matched MS1 features were used to classify eight common uropathogens and non-infected controls across synthetic inoculations, pure cultures, and clinical patient samples. Model development employed a one-vs-all Random Forest (Ranger) framework with nested cross-validation for feature selection and hyperparameter tuning, followed by evaluation on an independent held-out external patient cohort. ResultsThe approach accurately distinguished bacterial species in both controlled inoculated samples and clinical patient samples. Using repeated nested cross-validation, the model achieved a mean Matthews Correlation Coefficient (MCC) of 0.88, indicating robust classification performance across resampled training partitions. Performance generalized to an independent patient cohort, achieving an MCC of 0.822, confirming the models ability to maintain predictive accuracy under external validation. ConclusionsThis proof-of-concept demonstrates that pairing DIA-derived peptide panels with MS1-only data acquired on a cost-effective instrument suitable for routine analysis, enables rapid, culture-free identification of UTI pathogens. The method provides a scalable, high-throughput platform suitable for clinical applications and establishes a foundation for broader biomarker discovery and potential quantitative workflows. Key Points- DIA-derived peptide panels enable pathogen detection using MS1-only measurements. - Machine learning classifies eight common uropathogens covering 84% of infections. - MS1-only workflows enable culture-free pathogen identification of 300 samples/day.

bioinformatics↗

Translatome and translation dynamics analysis of a RiboCancer cell line panel reveals that leukemia-associated Rps15 mutations rewire translation through codon-specific tRNA accommodation defects.

Deletions and point mutations targeting ribosomal proteins (RPs) have been identified in cancer. Yet, their role in translational dysregulation remains poorly understood. We performed an integrated genome-wide translatome analysis (proteome, Ribo-seq and total RNA-seq) as well as RiboMethSeq on an isogenic cell line library modeling the most recurrent RP defects in cancer (Rpl5+/-, Rpl11+/-, Rpl22+/-, Rpl22-/-, Rpl10 R98S, Rps15 P131S and Rps15 H137Y). RP knock-out had minimal effects on translation, whereas RP point mutations induced a significant number of translation efficiency changes, affecting up to 10% of expressed genes in Rps15 mutants associated with Chronic Lymphocytic Leukemia (CLL). Cryo-electron microscopy and biochemical analyses revealed that the Rps15 mutations destabilize the C-terminal Rps15 domain, affecting the translation elongation cycle dynamics, and deregulating accommodation of aminoacylated tRNAs at the ribosomal A-site. Using Ribo-seq and translation reporter assays, we show that this accommodation defect shows codon specificity, explaining the reduced translation efficiency of genes enriched for these codons in Rps15 mutant cells, such as histones. Notably, genes with reduced translation efficiency in Rps15 mutated cells were enriched for transcriptional regulators such as transcription factor Runx3, resulting in downregulation of Runx3 target genes involved in immune regulation. Altogether, this study provides a comparative map of the translational rewiring driven by the most frequent somatic RP mutations. We provide unprecedented mechanistic insights in the translation defects induced by CLL-associated Rps15 mutations, and reveal an unappreciated cross-talk between translational and transcriptional dysregulation in these RP mutant cells. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/687986v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@cd2846org.highwire.dtl.DTLVardef@10f3c49org.highwire.dtl.DTLVardef@13f1595org.highwire.dtl.DTLVardef@a212df_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

LC-SRM combined with machine learning enables fast identification and quantification of bacterial pathogens in urinary tract infections

Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate antibiotherapy to patients and to avoid the emergence of drug-resistant pathogens. While the gold standard requires 24h to 48h of bacteria culture prior MALDI-TOF species identification, we propose a culture-free workflow, enabling a bacterial identification and quantification in less than 4 hours using 1mL of urine. After a rapid and automatable sample preparation, a signature of 82 bacterial peptides, defined by machine learning, was monitored in LC-MS, to distinguish the 15 species causing 84% of the UTIs. The combination of the sensitivity of the SRM mode on a triple quadrupole TSQ Altis instrument and the robustness of capillary flow enabled us to analyze up to 75 samples per day, with 99.2% accuracy on bacterial inoculations of healthy urines. We have also shown our method can be used to quantify the spread of the infection, from 8x104 to 3x107 CFU/mL. Finally, the workflow was validated on 45 inoculated urines and on 84 UTI-positive urine from patients, with respectively 93.3% and 87.1% of agreement with the culture-MALDI procedure at a level above 1x105 CFU/mL corresponding to an infection requiring antibiotherapy. HIGHLIGHTS- LC-MS-SRM and machine learning to identify and quantify bacterial species of UTI - Fast sample preparation without bacterial culture and high-throughput MS analysis - Accurate quantification through calibration curves for 15 species of UTIs - Validation on inoculations (93% accuracy) and on patients specimens (87% accuracy)

microbiology↗

BioDiscViz : a visualization support and consensus signature selector for BioDiscML results

Machine learning (ML) algorithms are powerful tools to find complex patterns and biomarker signatures where conventional statistical methods may fail to identify them. While the ML field made significant progress, state of the art methodologies to build efficient and non-overfitting models are not always applied in the litterature. To this purpose, automatic programs, such as BioDiscML, have been designed to identify biomarker signatures and correlated features while escaping overfitting using multiple evaluation strategies, such as cross validation, bootstrapping and repeated holdout. To further improve BioDiscML and reach a broader audience, better visualization support and flexibility in choosing the best models and signatures are needed. Thus, to provide researchers with an easily accessible and usable tool for in depth investigation of the results from BioDiscML outputs, we developed a visual interaction tool called BioDiscViz. This tool provides summaries, tables and graphics, in the form of Principal Component Analysis (PCA) plots, heatmaps and boxplots for the best model and the correlated features. Furthermore, this tool also provides visual support to extract a consensus signature from BioDiscML models using a combination of filters. BioDiscViz will be a great visual support for research implying machine learning, hence new opportunities in this field by opening it to a broader community.

bioinformatics↗

Antifungal activity of Cinnamomum cassia and Origanum compactum essential oils against Aspergillus oryzae

Aspergillosis is a nosocomial disease that usually affects the respiratory system. While most patients develop mild symptoms, aspergillosis can become a serious health threat in immunocompromised populations and patients with pre-existing respiratory conditions. Aspergillosis is caused by fungi of the Aspergillus genus. Existing treatments include drugs such as amphotericin B, with strong side effects. Numerous studies have shown the antifungal properties of essential oils (EOs), which represent potential alternative treatments against fungal infections. Here, we screened the antifungal properties of five EOs against Aspergillus oryzae: Melaleuca alternifolia, Mentha x piperita, Thymus zygis, Origanum compactum, and Cinnamomum cassia. Of the five EOs, two demonstrated antifungal activity: Origanum compactum acted as a fungistatic, while Cinnamomum cassia showed both fungistatic and fungicidal effects against A. oryzae. Therefore, both EOs represent potential alternative treatments against Aspergillosis.

microbiology↗

Extensive and accurate benchmarking of DIA acquisition methods and software tools using a complex proteomic standard

Over the past decade, the data-independent acquisition mode has gained popularity for broad coverage of complex proteomes by LC-MS/MS and quantification of low-abundance proteins. However, there is no consensus in the literature on the best data acquisition parameters and processing tools to use for this specific application. Here, we present the most comprehensive comparison of DIA workflows on Orbitrap instruments published so far in the field of proteomics. Using a standard human 48 proteins mixture (UPS1 - Sigma) at 8 different concentrations in an E. coli proteome background, we tested 36 workflows including 4 different DIA window acquisition schemes and 6 different software tools (DIA-NN, DIA-Umpire, OpenSWATH, ScaffoldDIA, Skyline and Spectronaut) with or without the use of a DDA spectral library. Based on the number of proteins identified, quantification linearity and reproducibility, as well as sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations, we summarize the major considerations and propose guidelines for choosing the DIA workflow best suited for LC-MS/MS proteomic analyses. Our 96 DIA raw files and software outputs have been deposited on ProteomeXchange for testing or developing new DIA processing tools.

bioinformatics↗

Male sex exacerbates the plasticity and pathogenicity of Th17 cells in a chronic model of CNS autoimmunity.

Sex differences in the incidence and severity of multiple sclerosis (MS) have long been recognized. However, the underlying cellular and molecular mechanisms for why male sex is associated with more aggressive and debilitating disease remain poorly defined. Using an T cell adoptive transfer model of chronic EAE, we find that male Th17 cells induced disease of increased severity relative to female Th17 cells, irrespective of whether transferred to male or female recipients. Throughout the disease course, a greater frequency of male Th17 cells produced the heterodox cytokine IFN{gamma}, a hallmark of pathogenic Th17 responses. Intriguingly, sex chromosomal complement, and not hormones, were responsible for the increased pathogenicity of male Th17 cells and an X-linked immune regulator, Jarid1c, was downregulated in both pathogenic male Th17 and CD4+ T cells from men with MS. Together, our data indicate that male sex critical regulates Th17 cell plasticity and pathogenicity via sex chromosomal complement.

immunology↗

Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning

The identification of microbial species in biological samples is essential to many applications in health, food safety and environment. MALDI-TOF MS technology has become a tool of choice for microbial identification but it has several drawbacks including: it requires a long step of bacterial culture prior to analysis (24h), it has a low specificity and is not quantitative. We have developed a new strategy for identifying bacterial species in biological samples using specific LC-MS/MS peptidic signatures. In the first training step, deep proteome coverage of bacteria of interest is obtained in Data Independent Acquisition (DIA) mode, followed by the use of machine learning to define the peptides the most susceptible to distinguish each bacterial species from the others. Then, in the second step, this peptidic signature is monitored in biological samples using targeted proteomics. This method, which allows the bacterial identification from clinical specimens in less than 4h, has been applied to fifteen species representing 84% of all Urinary Tract Infections (UTI). More than 31000 peptides in 200 samples have been quantified by DIA and analyzed by machine learning to determine an 82 peptides signature and build prediction models able to classify the fifteen bacterial species. This peptidic signature was validated for its use in routine conditions using Parallel Reaction Monitoring on a capillary flow chromatography coupled to a Thermo Scientific Q Exactive HF-X instrument. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the 1x105 CFU/mL threshold commonly used by clinical laboratories. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.

microbiology↗