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Lemoine, J.

Publications and source records attributed to Lemoine, J..

8 recordsLinked to original sources

Importance of taking Single Amino Acid Variant and accessory proteome variability into account in Data Independent Acquisition Proteomics: illustrated with Legionella pneumophila analysis

Understanding allelic variability is crucial for elucidating intrinsic bacterial mechanisms and distinguishing phenotypic profiles. However, such variability poses a major challenge for the reliable identification of proteins in data-independent acquisition (DIA) proteomics. To address this, we developed an analytical workflow that integrates protein sequence variability to enhance proteome coverage. Fifteen Legionella pneumophila isolates were analyzed using DIA-NN, with spectral libraries generated either from a reference proteome or incorporating allelic variability. Our workflow includes protein clustering and subsequent protein inference from these clusters, allowing the accurate assignment of shared and variant-specific peptides. Integration of variability enabled the identification of a comparable number of proteins as the reference proteome while capturing between 28 and 77 % of variant-specific sequences in each isolate, all while maintaining a low false positive rate. These findings demonstrate that accounting for allelic variability substantially improves proteomic coverage and identification confidence, providing a more comprehensive view of the proteome. This approach facilitates a deeper understanding of biological mechanisms and enables precise bacterial proteotyping of Legionella pneumophila isolates.

bioinformatics↗

Harnessing the CD2 axis to broaden and enhance the efficacy of CAR T cell therapies

Patients with T-cell lymphomas and leukemias have overall poor outcomes due to the lack of targeted and effective treatments, particularly in the relapsed and refractory settings. Development of chimeric antigen receptor (CAR) T-cells against T-cell neoplasms is limited by a lack of discriminating T-cell antigens that allow for effective anti-tumor responses while preventing CAR T-cell fratricide. We hypothesized that targeting CD2, a pan-T-cell antigen, using anti-CD2 CAR T-cells engineered without CD2 expression (CART2), would support CAR T-cell manufacturability and preclinical efficacy. Optimized CD2-knockout CART2, generated using CRISPR-Cas9, eradicated primary patient-derived CD2+ hematological neoplasms in vitro and in vivo, secreted effector cytokines, and exhibited adequate proliferative capacity. Nevertheless, CD2 has a key costimulatory function, and its deletion could lead to CAR T-cell dysfunction. Therefore, we tested the role of the CD2:CD58 axis in CAR T-cells, using the anti-CD19 CART models. We demonstrate that CD2 loss attenuates CART19 efficacy by reducing avidity for tumor antigen, co-stimulation, and ultimately in vivo activity. Analogously, we show that tumor CD58 loss reduces CART19 efficacy. To overcome this issue, we developed a novel PD-1:CD2 switch receptor that rescues intracellular CD2 signaling, particularly when PD-L1 is engaged, resulting in improved in vivo outcomes. Collectively, we studied the role of CD2 both as a target for CAR T cell therapy and as a critical costimulatory protein, whose signaling can be rescued using the PD-1:CD2 switch receptor. This receptor can be incorporated into CAR T-cells and provides an effective strategy to overcome CD2-signaling deficiencies.

cancer biology↗

Concomitant investigation of crustacean amphipods lipidome and metabolome during molting stage by Zeno SWATH Data-Independent Acquisition coupled with Electron Activated Dissociation and machine learning

BackgroundDIA (Data-Independent Acquisition) is a powerful technique in Liquid Chromatography coupled with high-resolution tandem Mass Spectrometry (LC-MS/MS) in metabolomics and lipidomics, for comprehensive profiling of molecules in biological samples. It provides unbiased coverage, improved reproducibility, and quantitative accuracy compared to Data-Dependent Acquisition (DDA). Combined with the Zeno trap and Electron-Activated Dissociation (EAD), DIA enhances data quality and structural elucidation compared to conventional fragmentation under CID. These tools were applied to study the lipidome and metabolome of the freshwater amphipod Gammarus fossarum, successfully discriminating stages and highlighting significant biological features. Despite being underused, DIA, along with the Zeno trap and EAD, holds great potential for advancing research in the omics field. ResultsDIA combined with the Zeno trap enhances detection reproducibility compared to conventional DDA, improving fragmentation spectra quality and putative identifications. LC coupled with Zeno-SWATH-DIA methods were used to study molecular changes in reproductive cycle of female gammarids. Multivariate data analysis including Principal Component Analysis and Partial Least Square Discriminant Analysis successfully identified statistically significant features responsible for group segregation. EAD fragmentation helped to identify unknown features and to confirm their molecular structure using fragmentation spectra DB annotation or machine learning. EAD database matching accurately annotated five glycerophospholipids, including the position of double bonds on fatty acid chain moieties. SIRIUS database predicted structures of unknown features based on experimental fragmentation spectra to compensate for database incompleteness. SignificanceReproducible detection of features and confident identification of putative compounds are pivotal stages within analytical pipelines. The DIA approach combined with Zeno pulsing and EAD synergistically enhances detection sensitivity while providing orthogonal fragmentation information, thereby facilitating a comprehensive and insightful exploration of pertinent biological molecules associated with the reproductive cycle of gammarids. The developed methodology holds great promises for identifying biomarkers of significance in the field of environmental assessment. HighlightsO_LIAdvancements in Data-Independent-Acquisition methods for lipidomics and metabolomics. C_LIO_LIZeno pulsing improves sensitivity in fragment detection and compound identification. C_LIO_LIComprehensive structure elucidation of lipids and metabolites using EAD fragmentation. C_LI

biochemistry↗

Proteomic assay for rapid characterization of Staphylococcus aureus antimicrobial resistance directly from blood cultures

An efficient management of bloodstream infections requires a fast identification of the pathogen and a determination of its antimicrobial resistance profile. Staphylococcus aureus is among the most common pathogen causing bloodstream infection. A prompt characterization of methicillin-resistant Staphylococcus aureus (MRSA) and their aminoglycoside resistance profile is therefore crucial to quickly adapt the treatment and improve clinical outcomes. Among analytical technologies, targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) has emerged as a promising tool to detect resistance mechanisms in clinical samples. Herein we present a rapid proteomic workflow to detect and quantify the most clinically relevant antimicrobial resistance effectors in S. aureus: PBP2a, PBP2c, APH(3)-III, ANT(4)-I, and AAC(6)-APH(2), directly from positive blood cultures and in less than 70 minutes. This approach provided 99% sensitivity for PBP2a (n=98/99 strains) detection. Sensitivity was 100% for PBP2c (n=5/5), APH(3)-III (n=16/16) and ANT(4)-I (n=20/20), and 94% for AAC(6)-APH(2) (n=16/17). Across the entire collection, 100% specificity was reported for each of the 5 resistance proteins. Additionally, relative quantification of ANT(4)-I expression allowed to discriminate kanamycin-susceptible and -resistant strains, in strains all harboring the ant(4)-Ia gene. The LC-MS/MS method presented herein demonstrates its ability to provide a reliable and in-depth profiling of S. aureus resistance, directly from positive blood culture and in a short turnaround time, as required in clinical laboratories.

microbiology↗

Staphylococcus aureus virulence factor expression matters: input from targeted proteomics shows Panton-Valentine leucocidin impact on mortality

In the case of commensal bacteria such as Staphylococcus aureus, the transition from commensalism to invasion and disease as well as disease severity in the course of an infection remain poorly predictable on the sole basis of virulence gene content. To determine whether variations in the levels of expression of the numerous S. aureus virulence factors could affect disease occurrence and/or severity, we developed a targeted proteomic approach that monitored 149 peptide surrogates targeting 44 proteins. Semi-quantification was achieved by normalization on the signal of ribosomal proteins. We then evaluated this approach on a series of S.aureus strains from 136 patients presenting a severe community-acquired pneumonia, all admitted to an intensive care unit. After adjusting to the Charlson Comorbidity Index score the multivariate analysis of severity parameters found that HlgB, Nuc, and Tsst-1 were positively associated while BlaI and HlgC were negatively associated with leucopenia. BlaZ and HlgB were positively associated with hemoptysis and HlgC was negatively associated with hemoptysis. Regarding mortality, both the multivariate (1.28; 95%CI[1.02;1.60]) and survival (1.15; 95%CI[1.016;1.302]) analyses showed that only PVL was associated with death in a dose-dependent manner. Beyond highlighting the decisive role of PVL in community-acquired pneumonia severity, this study brings the proof of concept that "expression matters" and proposes a method that can be routinely implemented in laboratories, for any Staphylococcal disease, and which could be developed for other commensal bacteria. One Sentence SummaryA highly multiplexed semi-quantitative mass spectrometry method was developed for 44 Staphylococcus aureus virulence factors; applied to a 136-strain collection from severe community-acquired pneumonia patients, it showed that Panton-Valentine leucocidin was the only factor to impact mortality in a dose-dependent manner.

microbiology↗

Shotgun lipidomics and mass spectrometry imaging unveil diversity and dynamics in lipid composition in Gammarus fossarum

Sentinel species are playing an indispensable role in monitoring environmental pollution in aquatic ecosystems. Many pollutants found in water prove to be endocrine disrupting chemicals that could cause disruptions in lipid homeostasis in aquatic species. A comprehensive profiling of the lipidome of these species is thus an essential step towards understanding the mechanism of toxicity induced by pollutants. We here extensively examined both the composition and spatial distribution of lipids in freshwater crustacean Gammarus fossarum. The baseline lipidome of gammarids of different gender and reproductive stage was established by high throughput shotgun lipidomics. Spatial lipid mapping by high resolution mass spectrometry imaging led to the discovery of sulfate-based lipids in hepatopancreas and their accumulation in mature oocytes. We uncovered in G. fossarum a diverse and dynamic lipid composition that deepens our understanding of the biochemical changes during development and which could serve as a reference for future ecotoxicological studies.

biochemistry↗

Large-scale transcriptomics to dissect two years of the life of a fungal phytopathogen interacting with its host plant

The fungus Leptosphaeria maculans has an exceptionally long and complex relationship with its host plant, Brassica napus, during which it switches between different lifestyles, including asymptomatic, biotrophic, necrotrophic, and saprotrophic stages. The fungus is also exemplary of "two-speed" genome organisms in which gene-rich and repeat-rich regions alternate. Except for a few stages of plant infection under controlled conditions, nothing is known about the genes mobilized by the fungus throughout its life cycle, which may last several years in the field. We show here that about 9% of the genes of this fungus are highly expressed during its interactions with its host plant. These genes are distributed into eight well-defined expression clusters, corresponding to specific infection lifestyles or to tissue-specific genes. All expression clusters are enriched in effector genes, and one cluster is specific to the saprophytic lifestyle on plant residues. One cluster, including genes known to be involved in the first phase of asymptomatic fungal growth in leaves, is re-used at each asymptomatic growth stage, regardless of the type of organ infected. The expression of the genes of this cluster is repeatedly turned on and off during infection. Whatever their expression profile, the genes of these clusters are located in regions enriched in heterochromatin, either constitutive or facultative. These findings provide support for the hypothesis that fungal genes involved in niche adaptation are located in heterochromatic regions of the genome, conferring an extreme plasticity of expression. This work opens up new avenues for plant disease control, by identifying stage-specific effectors that could be used as targets for the identification of novel durable disease resistance genes, or for the in-depth analysis of chromatin remodeling during plant infection, which could be manipulated to interfere with the global expression of effector genes at crucial stages of plant infection. Author SummaryFungi are extremely important organisms in the global ecosystem. Some are damaging plant pathogens that threaten global food security. A knowledge of their biology and pathogenic cycle is vital for the design of environmentally-friendly control strategies. Unfortunately, many parts of their life cycle remain unknown, due to the complexity of their life-cycles and technical limitations. Here, we use a rapeseed pathogen, Leptosphaeria maculans, which has a particularly complex life-cycle, to show that large-scale RNA-Seq analyses of fungal gene expression can decipher all stages of the fungal cycle over two years of interaction with living or dead hosts, in laboratory and agricultural conditions. We found that the fungus uses about 9% of the genes of its genome specifically during interactions with the plant, and observed waves of extremely tight, complex regulation during the colonization of specific tissues and specific parts of the life-cycle. Our findings highlight the importance of genes encoding effectors, small secreted proteins manipulating the host. This work opens up new avenues for plant disease control through the identification of stage-specific effectors leading to the discovery of novel durable disease resistance genes, or the analysis of epigenetic regulation, which could be manipulated to interfere with effector gene expression.

pathology↗

Natural epiallelic variation is associated with quantitative resistance to the pathogen Plasmodiophora brassicae

Clubroot caused by the protist Plasmodiophora brassicae is a major disease affecting cultivated Brassicaceae. Here, we uncover the existence of a natural epigenetic variation that is associated with partial resistance to clubroot in Arabidopsis, by using QTL fine mapping followed by extensive DNA sequence and methylation analyses. We show that at QTL Pb-At5.2, DNA methylation variation is extensive across accessions and strictly correlates with expression variation of the two neighboring genes At5g47260 and At5g47280, which encode NLR-immune receptors. Moreover, these natural variants are stably inherited and are not consistently associated with any nucleotide variation. These findings suggest a direct role for epigenetic variation in quantitative resistance of plants to pathogen attacks.

genetics↗