Biology subjects

Iperi, C.

Publications and source records attributed to Iperi, C..

8 recordsLinked to original sources

bioRxiv2026

Development of a multi-organ single-cell atlas of systemic sclerosis reveals shared and tissue-specific pathogenic pathways.

Objectives: Systemic sclerosis (SSc) is a multi-organ autoimmune disease for which no broadly effective disease-modifying treatment exists. We developed an integrated multi-organ single-cell atlas of SSc to identify shared and organ specific disease-associated transcriptional programmes and potential therapeutic targets. Methods: We integrated single-cell RNA-sequencing data from skin, lung, synovium, and peripheral blood mononuclear cells (six studies, 401,902 cells, 90 individuals) using Harmony. Pseudobulk differential expression and gene set enrichment analysis were performed at the tissue cell-compartment, cell-type, and subpopulation levels, including independent reclustering of lymphoid, myeloid and fibroblast populations. Results: Disease-associated transcriptional programmes showed marked tissue dependence, even within the same cell lineage, highlighting the importance of organ-specific mechanisms in SSc. Nevertheless, recurrent extracellular matrix (ECM) remodelling programmes were identified in lung, skin and synovium, and interferon signalling programmes in skin, synovium and peripheral blood mononuclear cells, across immune and fibroblast cell populations. Although no subpopulation was exclusive to SSc, a shared myofibroblast subpopulation expressing SFRP2, COL1A1, POSTN, ACTA2, and COMP was identified across lung, skin, and synovium. In addition, perivascular and mitotic fibroblast subpopulations exhibited both profibrotic and inflammatory signatures, including interferon- and IL-2-related signalling programmes. Conclusions: SSc is characterised by two complementary axes: a tissue-dependent cellular programme and a recurrent fibrotic and interferon-associated signature extending across multiple organs and cell populations. These findings are consistent with current organ-based therapeutic approaches while highlighting interferon signalling as a potential multi-organ therapeutic target. The resulting interactive single-cell atlas provides a resource for investigating shared and organ-specific disease mechanisms and guiding future therapeutic research.

immunology↗

bioRxiv2026

BiomiX 3.0: A user-friendly platform for democratized multi-omics integration with graph-based learning.

Background Multi-omics integration has emerged as a powerful strategy to decode the molecular complexity of biological systems. However, the diversity of available methods, each designed with distinct assumptions, objectives, and computational requirements, makes method selection, usage and interpretation challenging for nonexpert users. Here we present BiomiX 3.0, an updated version of the BiomiX platform that extends its integration capabilities with four additional methods: Similarity Network Fusion (SNF), NEighborhood-based Multi-Omics clustering (NEMO), Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO), and PRAMIGO (Phenotyping netwoRk Application for Multi-omics InteGratiOn), a novel supervised heterogeneous graph transformer (HGT) introduced in this work. Results We benchmarked all five methods, MOFA, DIABLO, SNF, NEMO, and PRAMIGO, on two independent multiomics datasets derived from a Chronic Lymphocytic Leukemia (CLL) cohort comparing IGHV-mutated and unmutated patients, and a pulmonary tuberculosis (PTB) cohort versus healthy controls. Supervised methods (DIABLO, PRAMIGO) consistently achieved higher condition-specific discrimination as measured by the Adjusted Rand Index (ARI) and the Adjusted Mutual Information (AMI). In contrast, unsupervised methods (SNF, NEMO) revealed alternative patient stratifications driven by independent sources of biological variance while MOFA performed in a semi-supervised way occupies an intermediate position, capturing latent factors that explain both disease-associated and orthogonal sources of variance. Systematic gene-centric analysis of the top-ranked features prioritized by each method was supported by manual biological annotation of shared and method-specific signals. Across cohorts, we annotated 154 shared features (116 genes, 38 metabolites) and 60 method-unique features per cohort, demonstrating that no single integration strategy captures the full landscape of biologically relevant signals. In the CLL cohort, shared features spanned B-cell receptor biology, innate immune signaling, RAS/MAPK activation, and epigenetic regulation, while methodunique features revealed supervised-method-specific insights into vesicle trafficking (DIABLO), immune checkpoints (MOFA), and ncRNA regulation (PRAMIGO). In the PTB cohort, a convergent interferon/innate immune signature dominated shared features across all methods. Still, method-unique analysis uncovered DIABLO-specific acylcarnitine metabolic reprogramming, MOFA-specific restoration of lysosomal trafficking, and PRAMIGO-specific {gamma}{delta} T-cell and immunoglobulin repertoire diversity. Across both cohorts, SNF and NEMO proved useful for detecting biological and technical sources of variation that were orthogonal to the primary condition of interest. Ultimately, PRAMIGO uniquely enables the construction of heterogeneous graphs modeling cross-modal molecular interactions, uncovering epigenetic co-regulation programs in CLL and multi-omics inflammatory modules in PTB that are difficult to identify using conventional integration approaches. Conclusions BiomiX 3.0 provides a graphical user interface (GUI) multi-method integration environment that democratizes access to state-of-the-art multi-omics analysis. By combining both unsupervised and supervised integration strategies within a unified platform and introducing graph-based learning through PRAMIGO, BiomiX 3.0 enables researchers across disciplines with complementary tools to interrogate the biological sources of variation in their data, without requiring bioinformatics expertise.

bioinformatics↗

bioRxiv2026

Compartmental Profiling of PDE4B in Systemic Sclerosis

ObjectivesThe preferential phosphodiesterase 4B (PDE4B) inhibitor nerandomilast was recently approved for treatment of idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis. Its proposed immunomodulatory, anti-fibrotic, and endothelial-stabilising actions target all three cardinal features of SSc, yet PDE4B expression has not been systematically characterised in SSc tissue. We aimed to define PDE4B expression across fibrotic organs and cellular compartments in SSc. MethodsPDE4B expression was profiled in SSc lung, peripheral blood mononuclear cells (PBMCs) and skin on the transcript level using single-cell RNA sequencing data and on the protein level using immunohistochemistry, immunofluorescence and multiplexed immunofluorescent stainings. ResultsPDE4B was consistently dysregulated in immune cells across SSc tissue and PBMCs, with compartment-specific direction and distribution. In SSc-ILD lung, expression was increased in CD8 and CD4 memory T-cells. In PBMCs, expression was increased in B cells, monocytes, and CD8 T-cells, and stratified patients into three endotypes (PDE4B//hi) not distinguishable by clinical variables. In skin, bulk RNA-seq showed a significant global increase, which localized to myeloid cells in scRNA-seq data. Approximately 90% of FAP activated fibroblasts co-expressed PDE4B at the protein level in SSc skin, identifying the activated fibroblast compartment as a candidate target for PDE4B inhibition. No PDE4B dysregulation was detected in vascular cell types. ConclusionsThis first cell-type-resolved characterisation of PDE4B in SSc demonstrates consistent immune-cell dysregulation across tissues and protein-level enrichment in activated fibroblasts. This provides a human-tissue rationale for the immunomodulatory and anti-fibrotic effects of PDE4B inhibition and supporting PDE4B as a disease-relevant therapeutic target in SSc. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LINerandomilast (BI 1015550), a PDE4B-preferential inhibitor, was approved for idiopathic pulmonary fibrosis and progressive pulmonary fibrosis. C_LIO_LIPre-clinical studies indicate that PDE4B inhibition may act on all cardinal features of SSc. C_LI What this study addsO_LIFirst cell-type-resolved characterization of PDE4B expression across SSc-affected lung, PBMCs, and skin. C_LIO_LIPBMC PDE4B expression is heterogeneous, stratifying patients into PDE4B// endotypes independent of standard clinical variables. C_LIO_LIscRNA-seq shows increased myeloid PDE4B expression in SSc skin, while [~]90% of FAP activated fibroblasts in SSc skin express PDE4B protein. C_LI How this study might affect research, practice or policyO_LIThe study strengthens the human-level evidence underpinning the target rationale for PDE4B inhibition in SSc. C_LI

molecular biology↗

bioRxiv2026

Synovitis in systemic sclerosis is an interferon-driven stromal condition distinct from rheumatoid arthritis

Joint involvement is a major driver of disability in systemic sclerosis (SSc), yet its pathophysiology remains poorly understood. In the absence of specific evidence, SSc synovitis is treated by analogy with rheumatoid arthritis (RA). Here, we present the first comprehensive molecular characterization of SSc synovitis, integrating histology, single-cell RNA sequencing, and spatial multi-omics of synovial biopsies from SSc patients, RA patients, and non-inflammatory controls with in vitro validation. We show that SSc synovitis is characterized by distinct pathomechanisms from RA. Histologically, most SSc biopsies displayed a pauci-immune pathotype with sparse immune infiltrates and predominant stromal cells. At molecular level, synovial fibroblasts in SSc were characterized by a disease-specific type I interferon (IFN) response program, in contrast to the TNF-dominant profile of RA, accompanied by dysregulation of the complement cascade. This IFN program extended across multiple synovial cell types, including monocyte-derived macrophages and endothelial cells, and was spatially organized into focal myeloid niches and a diffuse stromal program. Systemically, elevated serum IFN-2a levels were associated with the presence of clinical synovitis in an independent cohort of SSc patients. We furthermore show that similar IFN-driven programs are shared between skin and synovium in SSc. Genes downregulated by IFNAR1 blockade in SSc skin were enriched in SSc synovium, supporting IFN receptor blockade as a multi-organ target therapeutic strategy. These findings reframe SSc synovitis as a less destructive, IFN-driven stromal condition distinct from RA and provide a mechanistic basis for dedicated clinical trials for joint inflammation in SSc. One Sentence SummarySSc synovitis is a pauci-immune, IFN-driven stromal condition distinct from RA, supporting IFNAR1 blockade as a therapeutic strategy. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/733140v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@d85bb5org.highwire.dtl.DTLVardef@6cffe6org.highwire.dtl.DTLVardef@148eb4org.highwire.dtl.DTLVardef@1a4cd83_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2026

Metabolic-secretory decoupling defines a disease-intrinsic state in rheumatoid arthritis monocytes

ObjectivesCirculating monocytes from rheumatoid arthritis (RA) patients are pre-primed for inflammatory activation, but their disease-intrinsic features have not been systematically characterized. Given the important role of metabolism in shaping immune cell function, we aimed to determine how this pre-primed state is underpinned metabolically and whether these changes persist across different activation states, using an unbiased multi-omics approach. MethodsPeripheral blood CD14 monocytes from RA patients and matched healthy donors were analyzed in an undifferentiated state (M0) and after differentiation into classically activated M(IFN{gamma}+LPS) and alternatively activated M(IL-4) macrophages, followed by acute lipopolysaccharide (LPS) stimulation. Metabolomic (untargeted LC-MS/MS), transcriptomic (RNA-seq), and proteomic (label-free mass LC-MS/MS) profiling were performed. Data was comprehensively analyzed by weighted gene correlation network analysis, differential analysis, gene set enrichment analysis, multi-omics factor analysis and metabolic flux modeling. ResultsRA monocytes exhibited a stable disease-driven signature across activation states. Integration of metabolomic, transcriptomic and proteomic data revealed an unexpected convergence on metabolic-secretory coupling, with depletion of nucleotide and redox metabolites, downregulation of mitochondrial and translational pathways, and remodeling of the secretory apparatus, including loss of cis-Golgi components. Consistently, metabolic modeling predicted reduced glycosylation fluxes, connecting metabolic changes to altered secretory capacity. ConclusionsRA monocytes adopt a stable, disease-intrinsic state that persists across activation conditions. Multi-omics data identify a linked metabolic and secretory defect, with reduced glycosylation capacity as a potential functional consequence. This metabolic-secretory coupling represents a defining feature of RA monocyte dysfunction and a potential therapeutic target.

immunology↗

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↗