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Bettacchioli, E.

Publications and source records attributed to Bettacchioli, E..

2 recordsLinked to original sources

Assessing the Molecular Validity of Spontaneous Lupus Mouse Models and Its Implication for Human Studies

BackgroundSystemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by a loss of self-tolerance, causing inflammation and tissue damage in multiple organs. Animal models have advanced our understanding of SLEs molecular basis, but the FDAs recent elimination of animal testing requirements for drug approval has raised concerns about their validity, prompting a reevaluation of their role in basic research, especially for heterogeneous diseases like SLE. MethodsFour different spontaneous SLE mouse models were studied: MRLlpr/lpr, NZB/W, BXSB.Yaa, and Tlr7.Tg6. Transcriptome sequencing from blood, spleen, and kidney, flow cytometry from the spleen, and cytokines and autoantibody measurement in plasma were performed at four time points. Similar molecular data from human SLE patients was used for the integration. ResultsThe study identified specific molecular pathways driving the phenotype in each mouse model and established optimal time points for future experimental designs. By comparing these pathways to human SLE, the most similar ones and their relationship with disease activity were identified, providing crucial insight into translational relevance. Importantly, disease severity across models was linked to the extent and timing of molecular dysregulations. As expected, MRLlpr/lpr showed the most aggressive phenotype with early immune activation and apoptosis dysregulation, while Tlr7.Tg6 presented late-onset signatures associated with interferon and inflammation. Shared molecular features with human SLE included interferon responses, T and B cell depletion, and neutrophil activation. Integration analysis revealed distinct yet overlapping immune pathways between models and species, with some signatures such as age-associated B cells and double-negative memory T cells being model-specific but potentially relevant to early disease processes. ConclusionsThese findings build a valuable framework for future SLE research, reinforcing the utility of mouse models in studying specific molecular pathways related to human SLE pathogenesis and heterogeneity. The integration of longitudinal mouse data with human transcriptomes highlights the models that best recapitulate key aspects of human disease, offering guidance for the study of specific immunopathological mechanisms or therapeutic targets.

immunology↗

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↗