Periodontitis is a major public health problem worldwide as it causes teeth loss and worsens systemic diseases such as diabetes and rheumatoid arthritis. Severe forms corresponding to stage 3 and stage 4 periodontitis are characterized by severe alveolar bone destruction and immune dysregulation, with B-lineage cells and antimicrobial peptides (AMPs) playing pivotal roles. However, the spatial and functional interactions of these actors in gingival tissues remain poorly understood. In this study, B-lineage cells in stage 4 periodontitis were identified and their association with AMPs deciphered. Gingival tissues from 10 patients and 2 healthy controls were analyzed using imaging mass cytometry (IMC), confocal microscopy immunofluorescence, and spatial proximity assays. Immune cell densities, phenotypic profiles, and the presence of AMPs (HNP1-3, LL-37) were quantified. B-lineage cells, particularly antibody-secreting cells, were significantly enriched in stage 4 periodontitis tissues and colocalized with HNP1-3 + neutrophils. Intracellular HNP1-3 was detected in plasma cells, suggesting possible internalization and an influence on plasma cell activity. These findings highlight a novel interaction between B-lineage cells and AMPs, providing insights into the immunopathogenesis of periodontitis and potential therapeutic targets.

Sjögren’s 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-α 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-κB signaling. They also showed an expansion of IFN-activated naïve (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 naïve B cells and DN2 correlated with elevated anti-SSA/Ro and anti-SSB/La titers, while only naïve 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. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under Grant Agreement Number 115565, resources of which are composed of financial contributions from the European Union’s Seventh Framework Program (FP7/2007–2013) and EFPIA companies in-kind contributions. We would like to acknowledge 3TR and the PRECISESADS consortium, which guaranteed the availability of the data used in this article and the opportunity to analyse them. CI was funded by the Université de Brest and the Région Bretagne., 115565 [1]: pending:yes

Interpreting biological system changes requires interpreting vast amounts of multi-omics data. While user-friendly tools exist for single-omics analysis, integrating multiple omics still requires bioinformatics expertise, limiting accessibility for the broader scientific community. BiomiX tackles the bottleneck in high-throughput omics data analysis, enabling efficient and integrated analysis of multiomics data obtained from two cohorts. BiomiX incorporates diverse omics data, using DESeq2/Limma packages for transcriptomics, and quantifying metabolomics peak differences, evaluated via the Wilcoxon test with the False Discovery Rate correction. The metabolomics annotation for Liquid Chromatography-Mass Spectrometry untargeted metabolomics is additionally supported using the mass-to-charge ratio in the CEU Mass Mediator database and fragmentation spectra in the TidyMass package. Methylomics analysis is performed using the ChAMP R package. Finally, Multi-Omics Factor Analysis (MOFA) integration identifies shared sources of variation across omics data. BiomiX also generates statistics, report figures and integrates EnrichR and GSEA for biological process exploration and subgroup analysis based on user-defined gene panels enhancing condition subtyping. BiomiX fine-tunes MOFA models, to optimize factors number selection, distinguishing between cohorts and providing tools to interpret discriminative MOFA factors. The interpretation relies on innovative bibliography research on Pubmed, which provides the articles most related to the discriminant factor contributors. Furthermore, discriminant MOFA factors are correlated with clinical data, and the top contributing pathways are explored, all with the aim of guiding the user in factor interpretation. The analysis of single-omics and multi-omics integration in a standalone tool, along with MOFA implementation and its interpretability via literature, represents significant progress in the multi-omics field in line with the “Findable, Accessible, Interoperable, and Reusable” data principles. BiomiX offers a wide range of parameters and interactive data visualization, allowing for personalized analysis tailored to user needs. This R-based, user-friendly tool is compatible with multiple operating systems and aims to make multi-omics analysis accessible to non-experts in bioinformatics.

Objective: Sjogren's 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. Methods: Transcriptomic, 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. Results: Multi-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. Conclusions: This 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. ### Competing Interest Statement The authors have declared no competing interest. Innovative Medicines Initiative Joint Undertaking, 115565

OBJECTIVES:Systemic sclerosis (SSc) is a heterogeneous disease, complicating its management. Its complexity and the insufficiency of clinical manifestations alone to delineate homogeneous patient groups further challenge this task. However, autoantibodies could serve as relevant markers for the pathophysiological mechanisms driving the disease. Identifying specific immunological mechanisms based on patients' serological statuses might facilitate a deeper understanding of the diversity of the disease. METHODS:A cohort of 206 patients with SSc enrolled in the PRECISESADS cross-sectional study was examined. Patients were stratified based on their anti-centromere (ACA) and anti-SCL70 (SCL70) antibody statuses. Comprehensive omics analyses including transcriptomic, flow cytometric, cytokine and metabolomic data were analysed to characterise the differences between these patient groups. RESULTS:Patients with SCL70 antibodies showed severe clinical features such as diffuse cutaneous sclerosis and pulmonary fibrosis and were biologically distinguished by unique transcriptomic profiles. They exhibit a pro-inflammatory and fibrotic signature associated with impaired tissue remodelling and increased carnitine metabolism. Conversely, ACA-positive patients exhibited an immunomodulation and tissue homeostasis signature and increased phospholipid metabolism. CONCLUSIONS:Patients with SSc display varying biological profiles based on their serological status. The findings highlight the potential utility of serological status as a discriminating factor in disease severity and suggest its relevance in tailoring treatment strategies and future research directions.

ObjectiveTo link changes in the B-cell transcriptome from systemic lupus erythematosus (SLE) patients with those in their macroenvironment, including cellular and fluidic components.MethodsAnalysis was performed on 363 patients and 508 controls, encompassing transcriptomics, metabolomics, and clinical data. B-cell and whole-blood transcriptomes were analysed using DESeq and GSEA. Plasma and urine metabolomics peak changes were quantified and annotated using Ceu Mass Mediator database. Common sources of variation were identified using MOFA integration analysis.ResultsCellular macroenvironment was enriched in cytokines, stress responses, lipidic synthesis/mobility pathways and nucleotide degradation. B cells shared these pathways, except nucleotide degradation diverted to nucleotide salvage pathway, and distinct glycosylation, LPA receptors and Schlafen proteins.ConclusionsB cells showed metabolic changes shared with their macroenvironment and unique changes directly or indirectly induced by IFN-α signalling. This study underscores the importance of understanding the interplay between B cells and their macroenvironment in SLE pathology.

The conventional classification of mature B cells overlooks the diversity within IgD(+) CD27(-) na & iuml;ve B cells. Here, to identify distinct mature na & iuml;ve B cells, we categorized CD45RB(MEM55-) B cells (NA RB-) and CD45RB(MEM55+) B cells (NA RB+) and explore their function and localization in circulation and tissues under physiological and pathological conditions. NA RB+ B cells, found in secondary lymphoid organs, differentiate into plasmablasts and secrete IgM. In Sj & ouml;gren's disease, their numbers decrease, and they show over-activation and abnormal migration, suggesting an adaptive disease response. NA RB+ B cells also appear in inflamed salivary glands, indicating involvement in local immune responses. These findings highlight the distinct roles of NA RB+ B cells in health and Sj & ouml;gren's disease.

The recent emergence of imaging mass cytometry technology has led to the generation of an increasing amount of high-dimensional data and, with it, the need for suitable performant bioinformatics tools dedicated to specific multiparametric studies. The first and most important step in treating the acquired images is the ability to perform highly efficient cell segmentation for subsequent analyses. In this context, we developed YOUPI (Your Powerful and Intelligent tool) software. It combines advanced segmentation techniques based on deep learning algorithms with a friendly graphical user interface for non-bioinformatics users. In this article, we present the segmentation algorithm developed for YOUPI. We have set a benchmark with mathematics-based segmentation approaches to estimate its robustness in segmenting different tissue biopsies.

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.

BackgroundAnti-SSA/Ro autoantibodies are among the most frequently detected extractable nuclear antigen autoantibodies and have mainly been associated with primary Sjögren’s syndrome (pSS), systemic lupus erythematosus (SLE) and undifferentiated connective tissue disease (UCTD).ObjectivesIs there a common signature to all patients expressing anti-Ro60 autoantibodies regardless of their disease phenotype?MethodsUsing high-throughput multi-omics data collected within the cross-sectional cohort from the PRECISESADS IMI project [1] (genetic, epigenomic, transcriptomic, combined with flow cytometric data, multiplexed cytokines, classical serology and clinical data), we assessed by machine learning the integrated molecular profiling of 520 anti-Ro60-positive (anti-Ro60+) compared to 511 anti-Ro60-negative (anti-Ro60-) patients with pSS, SLE and UCTD, and 279 healthy controls (HCs).ResultsThe selected features for RNA-Seq, DNA methylation and GWAS data allowed a clear separation between anti-Ro60+ and anti-Ro60- patients. These results demonstrate that the different features selected by machine learning from the anti-Ro60+ patients constitute specific signatures when compared to anti-Ro60- patients and HCs. Remarkably, the gene transcript z-score of three genes (ATP10A, MX1 and PARP14), presenting an overexpression associated with a hypomethylation and genetic variation, and independently identified by the Boruta algorithm, was clearly higher in anti-Ro60+ patients compared to anti-Ro60- patients in all the diseases (Figure 1). Finally, we demonstrate that these signatures, enriched in interferon stimulated genes, were also found in anti-Ro60+ patients with rheumatoid arthritis and systemic sclerosis.Figure 1.Three genes common to RNA-Seq, DNA methylation and GWAS analysis characterize anti-Ro60+ patients. (A) ATP10/MX1/PARP14 z-score analyses were performed for 731 patients and 254 HCs according to anti-Ro60 expression. (B) ATP10/MX1/PARP14 z-score analyses were performed for 286 pSS, 351 SLE and 94 UCTD patients and 254 HCs. Two-tailed pairwise Wilcoxon-rank sum test results are shown. Plots show median, with error bars indicating ± interquartile range. (pSS: primary Sjögren’s syndrome, SLE: systemic lupus erythematosus, UCTD: undifferentiated connective tissue disease, HCs: healthy controls).ConclusionAnti-Ro60+ patients present a specific inflammatory signature regardless of their disease suggesting that a dual approach targeting both Ro-associated RNAs and anti-Ro60 autoantibodies should be considered.References[1]Barturen G, Babaei S, Català-Moll F, Martínez-Bueno M, Makowska Z, Martorell-Marugán J, et al. Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases. Arthritis Rheumatol Hoboken NJ. 2021 Jun;73(6):1073–85FundingThe research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under the Grant Agreement Number 115565 (PRECISESADS project), resources of which are composed of financial contribution from the European Union’s Seventh Framework Program (FP7/2007–2013) and EFPIA companies’ in-kind contribution.AcknowledgementsThe study has been conducted thank to the contribution of the PRECISESADS clinical consortium and the PRECISESADS flow cytometry consortium.Disclosure of InterestsNone declared.

Systemic lupus erythematosus (SLE) and primary Sjogren's syndrome (pSS) are two autoimmune diseases characterised by the production of pathogenic autoreactive antibodies. Their aetiology is poorly understood. Nevertheless, they have been shown to involve several factors, such as infections and epigenetic mechanisms. They also likely involve a physiological process known as glycosylation. Both SLE T cell markers and pSS-associated autoantibodies exhibit abnormal glycosylation. Such dysregulation suggests that defective glycosylation may also occur in B cells, thereby modifying their behaviour and reactivity. This study aimed to investigate B cell subset glycosylation in SLE, pSS and healthy donors and to extend the glycan profile to serum proteins and immunoglobulins. We used optimised lectin-based tests to demonstrate specific glycosylation profiles on B cell subsets that were specifically altered in both diseases. Compared to the healthy donor B cells, the SLE B cells exhibited hypofucosylation, whereas only the pSS B cells exhibited hyposialylation. Additionally, the SLE B lymphocytes had more galactose linked to N-acetylglucosamine or N-acetylgalactosamine (Gal-GlcNAc/Gal-GalNAc) residues on their cell surface markers. Interestingly, some similar alterations were observed in serum proteins, including immunoglobulins. These findings indicate that any perturbation of the natural glycosylation process in B cells could result in the development of pathogenic autoantibodies. The B cell glycoprofile can be established as a preferred biomarker for characterising pathologies and adapted therapeutics can be used for patients if there is a correlation between the extent of these alterations and the severity of the autoimmune diseases.

Le lupus érythémateux systémique (LES) et le syndrome de Sjögren primaire (SSP) sont deux maladies auto-immunes caractérisées par la production d’anticorps autoréactifs pathogènes. Leur étiologie est mal connue. Néanmoins, il a été démontré l’implication de plusieurs facteurs, tels que des infections et des mécanismes épigénétiques mais également d’un processus physiologique connu sous le nom de glycosylation. En effet, les cellules T du LED et les auto-anticorps associés au SSP présentent tous deux une glycosylation anormale. Une telle dérégulation suggère qu’une glycosylation défectueuse peut également se produire dans les cellules B, modifiant ainsi leur comportement et leur réactivité. Cette étude vise à étudier la glycosylation des sous-populations de cellules B dans le LED, le SSP et chez les donneurs sains et à étendre les profil de glycosylation aux protéines sériques et aux immunoglobulines (Igs). Grâce à une cohorte de 30 donneurs sains, 13 SSP et 17 LED, nous avons analysé les profils de glycosylation protéiques des sous-populations de lymphocytes B (LB), des sérums et des immunoglobulines. Pour cela, nous avons optimisé différentes techniques en cytométrie en flux et en ELISA basées sur l’utilisation de lectines reconnaissant des structures sucrées particulières. Par rapport aux LB de donneurs sains, les cellules B du LED présentent une hypofucosylation, tandis que seules les cellules B du SSP présentent une hyposialylation. En outre, les LB du LED présentent davantage de galactose lié à des résidus de N-acétylglucosamine ou de N-acétylgalactosamine (Gal-GlcNAc/Gal-GalNAc) à leur surface. De plus, il est intéressant de noter que des altérations similaires ont été observées dans les protéines sériques et notamment les Igs. Nous pouvons établir un glycoprofil des cellules B spécifiques de chaque pathologie. Les anomalies observées (hypofucosylation et hyposialylation notamment) sont en accord avec le développement du contexte inflammatoire et de la pathogénicité des auto-anticorps. Aussi, l’identification des voies altérées de glycosylation et la correction de ces anomalies doit permettre de développer des thérapeutiques adaptées avec la production de LB et d’Igs non autoréactifs. Cette étude indique que toute perturbation de la glycosylation des LB peut entraîner le développement et la sécrétion d’auto-anticorps pathogènes et doit amener au développement de nouvelles thérapeutiques.

Metabolic pathways have been studied for a while in eukaryotic cells. During glycolysis, glucose enters into the cells through the Glut1 transporter to be phosphorylated and metabolized generating ATP molecules. Immune cells can use additional pathways to adapt their energetic needs. The pentose phosphate pathway, the glutaminolysis, the fatty acid oxidation and the oxidative phosphorylation generate additional metabolites to respond to the physiological requirements. Specifically, in B lymphocytes, these pathways are activated to meet energetic demands in relation to their maturation status and their functional orientation (tolerance, effector or regulatory activities). These metabolic programs are differentially involved depending on the receptors and the co-activation molecules stimulated. Their induction may also vary according to the influence of the microenvironment, i.e. the presence of T cells, cytokines … promoting the expression of particular transcription factors that direct the energetic program and modulate the number of ATP molecule produced. The current review provides recent advances showing the underestimated influence of the metabolic pathways in the control of the B cell physiology, with a particular focus on the regulatory B cells, but also in the oncogenic and autoimmune evolution of the B cells.

Abatacept mimics natural CD152 and competes with CD28 for binding to CD80/CD86 on APC, such as B cells, thereby preventing T cell activation. However, its potential impact on B cells has not been identified. The aim of this study was to assess whether abatacept can potentiate the immunoregulatory properties of B cells in vitro and in patients with rheumatoid arthritis (RA). T and B cells from healthy controls were purified. The suppressor properties of B cells in the presence of abatacept or control IgG1 were evaluated based on the ability of these cells to inhibit the polyclonal expansion (anti-CD3/CD28 stimulation) of T cells or their differentiation into Th1 or Th17 cells. Similar analyses were also performed with cells from RA patients before and 3 mo after abatacept initiation. Abatacept significantly potentiated regulatory B cell regulatory functions by enhancing their ability to produce IL-10 and TGF-β, resulting in the increased generation of regulatory T cells and limited T cell proliferation and differentiation into Th1 and Th17 cells. Interestingly, B cells isolated from patients that received a 3-mo treatment with abatacept had an increased ability to reduce T cell functions, confirming the above observations. Abatacept binding to CD80/CD86 induces and promotes regulatory B cell functions by enhancing the ability of these cells to produce IL-10 and TGF-β in vitro and in RA patients.

Autoimmune disease development depends on multiple factors, including genetic and environmental. Abnormalities such as sialylation levels and/or quality have been recently highlighted. The adjunction of sialic acid at the terminal end of glycoproteins and glycolipids is essential for distinguishing between self and non-self-antigens and the control of pro- or anti-inflammatory immune reactions. In autoimmunity, hyposialylation is responsible for chronic inflammation, the anarchic activation of the immune system and organ lesions. A detailed characterization of this mechanism is a key element for improving the understanding of these diseases and the development of innovative therapies. This review focuses on the impact of sialylation in autoimmunity in order to determine future treatments based on the regulation of hyposialylation.