Biology subjects

Bordron, A.

Publications and source records attributed to Bordron, A..

3 recordsLinked to original sources

bioRxiv2025

BiomiX-Driven Multi-Omics Integration of PRECISESADS Data Reveals Lysophosphatidic Acid and Metabolic Pathway Signatures in B Cells and Immune Macroenvironment in Sjogren's Disease

ObjectiveSjogrens disease (SjD) is a systemic autoimmune disorder characterized by lymphocytic infiltration of exocrine glands, resulting in xerostomia, keratoconjunctivitis sicca, fatigue, arthralgia, and systemic organ involvement. This study aimed to characterize the metabolic and immune dysregulation of SjD using a multi-omics approach, focusing on the metabolic environment and B-cell transcriptomic responses. MethodsTranscriptomic, methylomic, and metabolomic datasets from whole blood, plasma, and urine of 293 SjD patients and 508 controls were analyzed from the PRECISESADS study. B-cell transcriptomes were included to link systemic metabolic alterations to cell-intrinsic immune programs. Multi-omics factor analysis (MOFA) was used to integrate data and identify discriminant molecular drivers. ResultsMulti-omics integration revealed metabolic rewiring involving the urea cycle, glutamine/arginine metabolism, and NAD depletion linked to interferon signaling. Among the strongest contributors, plasma lysophosphatidic acids (LPA) emerged as key discriminants associated with interferon-driven activation. B-cell transcriptomes showed upregulation of LPA-related genes (CERS6, INPP1, TRIP6), and its receptor LPAR6. Importantly, in this study LPAR6 protein expression was confirmed in B cells for the first time. Secondary findings included alterations in sphingosine-1-phosphate (S1P) metabolism, suggesting a broader lysophospholipid signaling axis. ConclusionsThis study identifies the LPA-LPAR6 signaling axis as a potential metabolic driver of B-cell activation and interferon-associated inflammation in SjD, highlighting a previously unrecognized immunometabolic pathway. These findings highlight LPA-LPAR6 as a candidate target for therapeutic modulation in SjD, while also implicating S1P signaling as a complementary regulatory mechanism.

immunology↗

bioRxiv2025

Interferon-α-Driven Stratification of B Cell Reveals Metabolic Reprogramming of Double Negative, Naive and Transitional cell subsets and Refines Molecular Classification in Söjgren's Disease.

Sjogrens disease (SjD) is a chronic autoimmune condition marked by lymphocytic infiltration of exocrine glands and production of autoantibodies such as anti-SSA/Ro, anti-SSB/La, and rheumatoid factor. B lymphocytes play a central role in disease pathogenesis, driving autoantibody production and glandular damage, and contributing to lymphomagenesis. Despite promising therapies, no effective treatment is currently available, partly due to the biological and clinical heterogeneity of the disease. While interferon (IFN) signatures and B cell-related markers are used for patient stratification, their integration remains unexplored. This study analyzed B cell transcriptional and metabolic profiles using bulk transcriptomic, clinical, and flow cytometry data from the PRECISESADS consortium, alongside public single-cell RNA-sequencing datasets. A B cell-specific IFN- signature was established to stratify patients into IFN-positive and IFN-negative groups. Both showed reduced oxidative phosphorylation (OXPHOS) and translation in B cell subsets, suggesting a shared pre-metabolic state. IFN-positive patients, however, displayed additional features, including enhanced glycolysis, amino acid and lipid metabolism, autophagy, and NF-{kappa}B signaling. They also showed an expansion of IFN-activated naive (Naive IFN), Transitional, and double-negative (DN) B cells, particularly DN2 and DN2_CXCR3 subsets, which have been linked in the literature to autoreactivity and lymphoma development. The IFN signature in naive B cells and DN2 correlated with elevated anti-SSA/Ro and anti-SSB/La titers, while only naive B cells showed an association with increased histological focus scores. These findings support the relevance of a B cell-specific IFN signature in stratifying SjD patients and suggest new metabolic and transcriptional targets for disease monitoring and therapeutic development. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/672530v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@fd8a4eorg.highwire.dtl.DTLVardef@183241dorg.highwire.dtl.DTLVardef@b7f2d9org.highwire.dtl.DTLVardef@a89349_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

BiomiX, a User-Friendly Bioinformatic Tool for Automatized Multiomics Data Analysis and Integration

BiomiX addresses the data analysis bottleneck in high-throughput omics technologies, enabling the efficient, integrated analysis of multiomics data obtained from two cohorts. BiomiX incorporates diverse omics data. DESeq2/Limma packages analyze transcriptomics data, while statistical tests determine metabolomics peaks. The metabolomics annotation uses the mass-to-charge ratio in the CEU Mass Mediator database and fragmentation spectra in the TidyMass package while Methylomics analysis is performed using the ChAMP R package. Multiomics Factor Analysis (MOFA) integration and interpretation identifies common sources of variations among omics. BiomiX provides comprehensive outputs, including statistics and report figures, also integrating EnrichR and GSEA for biological process exploration. Subgroup analysis based on user gene panels enhances comparisons. BiomiX implements MOFA automatically, selecting the optimal MOFA model to discriminate the two cohorts being compared while providing interpretation tools for the discriminant MOFA factors. The interpretation relies on innovative bibliography research on Pubmed, which provides the articles most related to the discriminant factor contributors. The interpretation is also supported by clinical data correlation with the discriminant MOFA factors and pathways analyses of the top factor contributors. The integration of single and multi-omics analysis in a standalone tool, together with the implementation of MOFA and its interpretability by literature, constitute a step forward in the multi-omics landscape in line with the FAIR data principles. The wide parameter choice grants a personalized analysis at each level based on the user requirements. BiomiX is a user-friendly R-based tool compatible with various operating systems that aims to democratize multiomics analysis for bioinformatics non-experts. Key pointsO_LIBiomiX is the first user-friendly multiomics tool to perform single omics analysis for transcriptomics, metabolomics and methylomics and their data integration by MOFA in the same platform. C_LIO_LIMOFA algorithm was made accessible to non-bioinformaticians and improved to select the best model automatically, testing the MOFA factors performance in groups separation. C_LIO_LILarge improvement of MOFA factors interpretability by correlation, pathways analysis and innovative bibliography research. C_LIO_LIBiomiX is embedded in a network of other online tools as GSEA, metaboanalyst EnrichR etc, to provide a format compatible with further analyses in these tools. C_LIO_LIInterface and usage are intuitive and compatible with all the main operating systems, and rich parameters are set to grant personalized analysis based on the users needs. C_LI

bioinformatics↗