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Genetics has been an important tool for discovering new aspects of biology across life. In humans, there is growing momentum behind the application of this knowledge to drive innovation in clinical care, most notably through developments in precision medicine. Nowhere has the impact of genetics on clinical practice been more striking than in the field of rare disorders. For most of these conditions, individual disease susceptibility is influenced by DNA sequence variation in a single or a small number of genes. In contrast, most common disorders are multifactorial and are caused by a complex interplay of multiple genetic, environmental and stochastic factors. The longstanding division of human disease genetics into rare and common components has obscured the continuum of human traits and echoes aspects of the century-old debate between the Mendelian and biometric views of human genetics. In this article, we discuss the differences in data and concepts between rare and common disease genetics. Opportunities to unify these two areas are noted and the importance of adopting a holistic perspective that integrates diverse genetic and environmental factors is discussed.

Multi‐omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi‐Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi‐omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy‐chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single‐cell multi‐omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation. Synopsis Multi‐Omics Factor Analysis (MOFA) is a computational framework for unsupervised discovery of the principal axes of biological and technical variation when multiple omics assays are applied to the same samples. MOFA is a broadly applicable approach for multi‐omics data integration. The inferred latent factors represent the underlying principal axes of heterogeneity across the samples. Factors can be shared by multiple data modalities or can be data‐type specific. The model flexibly handles missing values and different data types. In an application to Chronic Lymphocytic Leukaemia, MOFA discovers a low dimensional space spanned by known clinical markers and underappreciated axes of variation such as oxidative stress. In an application to multi‐omics profiles from single‐cells, MOFA recovers differentiation trajectories and identifies coordinated variation between the transcriptome and the epigenome. Graphical Abstract Multi‐Omics Factor Analysis (MOFA) is a computational framework for unsupervised discovery of the principal axes of biological and technical variation when multiple omics assays are applied to the same samples. MOFA is a broadly applicable approach for multi‐omics data integration.

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Background:The diagnosis and management of primary Sjögren’s syndrome (pSS) remains a challenge, and treatments preventing progression of the disease are yet to be discovered. Proximity extension assay (PEA) allows for highly sensitive and specific detection of aberrances in the serum and tissue proteome. Variabilities of proteomic profiles will allow for stratification of patient subsets and may aid identification of patients at risk for extraglandular manifestations, monitoring of the disease course, as well as evaluation of immunomodulatory effects of novel treatments.Objectives:To obtain an in-depth understanding of the salivary gland and serum proteome in patients with pSS and to relate proteomic variations to clinical, serological, and genetic parameters.Methods:Paired lysates of salivary gland biopsies and serum from patients with pSS (n=80) and sicca controls (n=19), as well as serum samples from patients with pSS and healthy controls from a Swedish (n=456 and n=141) and a Norwegian (n=233 and n=137) cohort were analyzed by Olink PEA technology. Patients were stratified based on occurrence of extraglandular manifestations and the presence or absence of ANA, anti-Ro/SSA, and anti-La/SSB autoantibodies. Risk-associated HLA alleles were imputed. Proteins detected in >80% of samples were analyzed, and all analyses were adjusted for age and sex. Cut-off for significance was set at 5% FDR and ± 0.5 log2 FC.Results:Initial screening of salivary gland lysates and serum using six different PEA panels encompassing 512 unique protein markers determined the Immuno-Oncology panel to be the most relevant for further use in the larger cohorts. In the Swedish discovery cohort, 28 serum proteins significantly associated with pSS compared to healthy controls, out of which 16 were successfully validated in the Norwegian replication cohort. The most significant proteins comparing patients and healthy controls in the discovery cohort which could also be validated were galectin-9, CCL19, TNF, and soluble PD-1. Pulmonary involvement was present in 23 patients and associated with a striking up-regulation of 22 inflammatory proteins. Differences in distinct protein levels were also noted for cutaneous, lymphadenopathy, renal, and biological manifestations with CXCL10 being the most frequently detected marker. Notably, levels of the 16 validated protein biomarkers were found to gradually increase according to autoantibody profiles, with the lowest levels in ANA negative patients and highest levels in patients positive for both anti-Ro/SSA and anti-La/SSB. Statistical analysis of trend among these patient subgroups revealed that the protein level gradients were significant for all 16 proteins (p<0.0001). Correspondingly, higher inflammatory protein levels as well as a higher protein type I interferon (pIFN) score associated with frequency of the risk-associated HLA-DRB1*03 and DRB1*15.Conclusion:These data increase our understanding of the dysregulated proteome in patients with pSS and indicate how such aberrances relate to the presence of autoantibody specificities, risk-associated HLA as well as extraglandular manifestations.REFERENCES:NIL.Acknowledgements:This study was partly funded by the NECESSITY grant of EU Innovative Medicines Initiative 2.Disclosure of Interests:Albin Björk: None declared, Johannes Mofors: None declared, Lauro Meneghel: None declared, Marika Kvarnström: None declared, Roland Jonsson: None declared, Roald Omdal: None declared, Helena Forsblad-d’Elia: None declared, Sara Magnusson Bucher: None declared, Per Eriksson: None declared, Thomas Mandl Working as medical lead at UCB., Peter Olsson: None declared, Juliana Imgenberg-Kreuz: None declared, Gunnel Nordmark: None declared, Marie Wahren-Herlenius: None declared.

Recent breakthroughs in spatially resolved multi‐omics have unlocked the ability to simultaneously profile multiple molecular layers within tissues, offering unprecedented insights into their coordinated roles in development and disease. Despite these advancements, integrative analysis of multi‐omics data remains a formidable challenge due to inherent biological and technical discrepancies across assays, often leading to gradient conflicts during joint learning. These conflicts arise as optimization trajectories from different omics compete or contradict, thereby constraining integration performance. To overcome this challenge, SpaBalance, a unified computational framework designed to harmonize cross‐omics learning via gradient coordination and adaptive feature decomposition, is proposed. SpaBalance introduces a novel gradient equilibrium mechanism that dynamically balances inter‐omics contributions during backpropagation, resolving conflicts through task‐specific prioritization without requiring manual weighting. Concurrently, SpaBalance leverages a dual‐stream architecture to simultaneously learn shared representations and preserve omics‐specific features. Extensive evaluations across a variety of spatial omics datasets, including paired epigenome‐transcriptome and proteome‐transcriptome data from human tumors and brain tissues, demonstrate SpaBalance's superior ability to delineate complex spatial domains and uncover previously hidden multi‐omics regulatory hubs, significantly improving clustering accuracy and biological interpretability. Moreover, SpaBalance flexibly scales to integrate multiple omics, bridging data integration with biological discovery and advancing spatially resolved systems biology. SpaBalance is a computational framework that harmonizes multi‐omics learning via gradient equilibrium and dual‐stream feature decomposition, achieving superior clustering accuracy, biological interpretability, and scalable integration of three or more spatial omics modalities.

Background:Juvenile localised scleroderma (jLS) is a rare rheumatic disease in children characterized by inflammation and fibrosis in the skin [1, 2]. The cause and pathogenesis of jLS remain unclear, and both skin lesions and possible extracutaneous involvement may result in functional impairment and growth disturbances [2]. The treatment options to cure jLS are limited [3].In recent years, omics technologies have been used to identify novel biomarkers in different diseases [4, 5]. Among the different omics technologies, metabolomics and lipidomics provide snapshots of the metabolic network.Objectives:We aim to identify biomarkers and treatment targets for jLS using metabolomics and lipidomics.Methods:Children with jLS and age-matched controls were recruited at Bambino Gesù Children’s Hospital, Roma, Italy. The characteristics of the participants are shown in Table 1.Plasma samples from 9 controls and 12 patients with jLS (before treatment initiation and 17 months after treatment) were sent to Swedish Metabolomics Center, where liquid chromatography–mass spectrometry and gas chromatography–mass spectrometry were performed (Figure 1A). Peak intensities were recorded, and the data analysis was performed using Metaboanalyst 5.0 and Graphpad Prism 10 software. Pathway enrichment bubble plots were generated using SRplot [6]. Mann-Whitney test was used to compare healthy control and baseline patient groups, and Wilcoxon test was used to compare differences between baseline and treated patients.Results:In total, 250 metabolites and 194 putative lipids were annotated (Figure 1A). Patients at baseline had significantly lower peak intensities of lenticin, 3-hydroxybutyrylcarnitine, 1-dodecanoyllysophosphatidylcholine, phosphatidylcholine (PC) 38:6 and 40:9, and phosphatidylserine (PS) 38:1 as well as significantly higher peak intensities of L-tyrosine, phenylpyruvic acid, (3-hydroxyphenyl)hydracrylate, and cortisol compared to controls (Figure 2B). After treatment, peak intensities of adenosine monophosphate, hypoxanthine, 3-phosphoglyceric acid, lysophosphatidylcholine (LPC) 18:2, Cholesteryl Octanoate (CE 8:0), and 2-Hydroxylauroylcarnitine (CAR 12:0) were decreased, whereas peak intensities of L-octanoylcarnitine and eleven molecular species of triacylglycerols were increased compared to baseline patients (Figure 2D). The top enriched pathways are shown in Figure 2C and 2E.Conclusion:We have described the metabolic profile in blood of children with jLS for the first time. Children with jLS show a distinct metabolic profile compared to healthy children, especially in tyrosine-related pathways. Compared to baseline levels, the metabolism of several amino acids was altered after treatment, and the energy storage function might be modified as eleven molecular species of triacylglycerols were found decreased.REFERENCES:[1] Zulian, F., et al., Consensus-based recommendations for the management of juvenile localised scleroderma. Ann Rheum Dis, 2019. 78(8): p. 1019-1024.[2] Li, S.C. and R.J. Zheng, Overview of Juvenile localized scleroderma and its management. World J Pediatr, 2020. 16(1): p. 5-18.[3] Li, S.C., Treatment of juvenile localized scleroderma: current recommendations, response factors, and potential alternative treatments. Current Opinion in Rheumatology, 2022. 34(5): p. 245-254.[4] Puentes-Osorio, Y., et al., Potential clinical biomarkers in rheumatoid arthritis with an omic approach. Autoimmunity Highlights, 2021. 12(1).[5] Xiao, Y.A., et al., Multi-omics approaches for biomarker discovery in early ovarian cancer diagnosis. Ebiomedicine, 2022. 79.[6] Tang, D., et al., SRplot: A free online platform for data visualization and graphing. PLoS One, 2023. 18(11): p. e0294236.Table 1.Characteristics of the participantsCharacteristicsPatientsControlsN129Age, years10±410±4Females, n (%)6 (50)8 (89)Follow-up, months17±3Treatment:Prednisolone, n (%)10 (83)Methotrexate, n (%)12 (100)Tocilizumab, n (%)2 (17)Figure 1.Study design (A), volcano plot of all the annotated metabolites and lipids (B, D), metabolic pathway analysis with the most changed metabolites (C, E).Acknowledgements:Participants who donated the blood samplesDisclosure of Interests:Yuan Zhang: None declared, Angela Aquilani: None declared, Rebecca Nicolai: None declared, Fabrizio De Benedetti Speaker for Novartis and SOBI, Emiliano Marasco: None declared, Cristina Maglio: None declared.

Background:Lupus nephritis (LN) leads to end-stage kidney disease (ESKD) in >20% of patients despite optimal treatment. Up to 30% of LN patients have membranous LN which is characterized by subepithelial immune deposits without immune infiltration in the glomeruli. Despite its association with ESKD, membranous LN is considered a milder type of LN with no consensus on the optimal use of immunosuppression.Objectives:To develop mechanistic hypotheses of disease, we analyzed kidney samples from patients with LN using a whole slide spatially resolved proteomic approach as part of the Accelerating Medicines Partnership in RA/SLE. We report here the initial analysis.Methods:We developed a serial immunohistochemistry (sIHC) staining workflow to stain for 18 antibodies, DNA, and PAS to be visualized on a single section via a cycle of staining, imaging, and destaining. This included incubation of FFPE slides with primary antibody, secondary HRP reagents, AEC-Red Chromogen, and Hematoxylin. Image processing was performed using HALO (Indica Labs) and included deconvolution of single-color channels, registration, fusion, cell segmentation, and automated tissue classification. To minimize batch effect, the analytical pipeline included within sample CLR-normalization and scaling, followed by harmonization (Harmony).Results:In this initial analysis, we included 29 clinically indicated kidney biopsies classified as LN: 13 pure proliferative (ISN class III or IV), 10 pure membranous (ISN class V), 5 mixed, and 1 ISN class II). Patients were 79% female, 34% White, 31% Black, 10% Asian, and 24% identified with Other race/ethnicity. We detected 182,783 CD45+ cells out of 1,913,845 cell objects. Our analysis identified 10 immune cell clusters at low resolution (Figure 1A). Figure 1B displays the tissue distribution of each cell subset. B and T lymphocytes dominated the tubulointerstitium. The CD68+ myeloid subsets were the predominant cell type in the glomeruli (Figure 2). More than half of CD68+ cells expressed Ki67 indicating active proliferation. Surprisingly, we identified intraglomerular CD68+ cells (including endocapillary) also in patients with pure membranous LN, but at a lower tissue density than proliferative LN (Figure 2B). Figure 2C demonstrates intraglomerular CD68+ cells in a biopsy classified as pure membranous LN by two experienced renal pathologists.Conclusion:sIHC can be successfully employed to perform multiplexed whole slide analysis harnessing both the subcellular resolution (brightfield) and the reliability of IHC. Our analysis revealed intraglomerular CD68+ myeloid cells in pure membranous LN. By traditional clinical pathology, intraglomerular/ endocapillary immune cells characterize proliferative LN and are not consistent with pure membranous LN. These findings implicate macrophages/monocytes in the glomerular disease in membranous LN with therapeutic implications. The analysis of 90 additional biopsies and a myeloid-focused panel is underway to validate and extend these findings.Figure 1.Phenotype and spatial distribution of intrarenal immune cells. (A) UMAP of CD45+ cells (n=182,783, 29 patients) indicating the low-resolution cluster annotation. (B) Digital reproduction of a representative biopsy displaying the distribution of the cells clusters (colors matching panel A). ISN class III, NIH Activity Index 6, NIH Chronicity Index 3.Figure 2.Myeloid cells dominate intraglomerular inflammation, including membranous LN. Box plots displaying the density (cells/ area) of the immune cell clusters in the tubulointerstitium (A) and glomeruli (B) according to ISN class. CD68+ myeloid cell clusters showed a statistically significant higher intraglomerular density compared to all other clusters in proliferative and membranous LN (p<0.05, Wilcoxon). Proliferative LN (n=18); membranous LN (n=10). (C) Immunohistochemistry images displaying the expression of CD45 and CD68 in a glomerulus from a patient with proliferative LN and one with pure membranous LN. The yellow arrows indicate intraglomerular CD68+ cells in membranous LN.REFERENCES:NIL.Acknowledgements:NIL.Disclosure of Interests:Andrea Fava Sanofi, AnnexonBio, AstraZeneca, UCB., Chen-Yu Lee: None declared, Matthew Caleb Marlin: None declared, Xiaoping Yang: None declared, Tayte Stephens: None declared, Alessandra Ida Celia: None declared, Jeffrey Hodgin AstraZeneca, Eli Lilly, Gilead, Janssen, Moderna, Novo Nordisk, Regeneron, Dawit Demeke: None declared, Peter Izmirly: None declared, Jill Buyon BMS, GSK, Related Sciences, Ventus, Artiva, Equillium, Chaim Putterman Equillium, KidneyCure, Progentec, Judith A. James Bristol-Myers Squibb(BMS), GlaxoSmithKlein(GSK), Novartis, Progentec Biosciences., Michelle Petri Arthros-FocusMedEd, Aurinia, Amgen, AnaptysBio, Annexon Bio, Argenx, AstraZeneca, Axdev, Boxer Capital, Cabaletto Bio, Caribou Biosciences Inc, CVS Health, Escient Pharmaceuticals, Exo Therapeutics, Gentibio, GSK, Horizon Therapeutics, iCell Gene Therapeutics, Idorsia Pharmaceuticals, Kira Pharmaceuticals, Eli Lilly, MedShr, Momenta Pharmaceuticals, Nexstone Immunology, Nimbus Lakshmi, Proviant, Regeneron Pharmaceuticals, Sanofi, Seismic Therapeutic, Sinomab Biosciences, Takeda, Tenet Medicines Inc, TG Therapeutics, UCB, Zydus. DSMB: CTI Clinical Trial and Consulting Services, Emergent Biosolutions, IQVIA, Merck EMD Serono., Aurinia, Eli Lilly, Exagen, GSK, Janssen, AstraZeneca, Joel M Guthridge: None declared, Avi Rosenberg: None declared.

Tissue-resident fibroblasts have emerged as a key cell type controlling the local tissue microenvironment in chronic and inflammatory diseases. The specific contribution of fibroblasts in the pathogenesis of Sjögren’s syndrome (SjS) remains incompletely understood. To characterize the inflammatory response of cultured human salivary gland-derived fibroblasts (SGF) from patients with SjS. SGF were cultured form enzymatically digested minor salivary gland biopsies of patients with suspected SjS, including samples of patients with SjS and controls (sicca symptoms) that do not fulfil the SjS classification criteria. SGF were characterized by FACS analysis for the presence of fibroblast markers (CD90, PDPN) and the absence of epithelial (EPCAM) and lymphocyte (CD45) markers. SGF were treated with IL-1 (1 ng/µl) for 24h. Transcriptomes were analysed by RNA-sequencing (n=3/ group; Illumina NovaSeq 6000). Pathway enrichment analysis was conducted using Gene Onthology and Gene Set Enrichment Analysis (GSEA). Levels of cytokines and chemokines (BAFF, CLL2, CCL5, CX3CL1, CXCL10, IL6, IL8) in cell cultured supernatants were measured by ELISA. Proliferation of SGF was assessed by BrdU assay. SGF cultures (n=6) were positive for CD90 and PDPN and negative for EPCAM and CD45. SGF from patients with SjS (n=7) exhibited increased proliferation rates compared to SGF from sicca controls (n=5; p<0.048). We detected 81 differentially expressed genes (DEG; ± fold change > 1.5, FDR< 0.05) between SjS and sicca controls in IL1-stimulated SGF. Upregulated DEG in SjS were enriched in the biological processes (GO BP) “Signal transduction” and “Regulation of cell migration” “Positive chemotaxis” and “Mitotic cytokinesis”. Downregulated DEG in SjS were enriched in the GO BPs “Cell-cell signaling”, “Cell differentiation”, “Oxidative phosphorylation” and “Mesenchymal cell proliferation”. GSEA revealed an increased enrichment of “MYC targets” (FLB, SNRPD2), “oxidative phosphorylation” (UQCRH, UQCR10, ATP5MC2), “glycolysis”, “E2F targets”, “G2M checkpoint” (JPT1, HMGA1, SMARCC1), and “MTOR signaling” (CD9, NHERF1) in SjS SGF. We observed low levels of BAFF, CCL5, IL6 and IL8 in unstimulated SGF supernatants, with no difference between SjS and sicca controls; the levels of CCL2, CX3CL1 and CXCL10 were not detectable. Upon IL-1 stimulation, SGF exhibited a significant increase in the secretion of CCL2, IL6, and IL8 (all with p<0.0001), both in samples derived from patients with SjS (n=6) and sicca controls (n=5). Our data point to an intrinsic activation of SjS SGF compared to control SGF, characterized by increased proliferation and differences in their transcriptomes in response to stimulation with IL-1. Local SGF activity in SjS may promote the vicious circle of chronic inflammation in salivary glands. NIL. NIL. Kerstin Klein: None declared, Ondrej Pastva: None declared, Matthias Brunner: None declared, Daniel Guggisberg: None declared, Marco Kreuzer: None declared, Larissa Moser: None declared, Marco Sprecher: None declared, Muriel Elhai: None declared, Rémy Bruggmann: None declared, Britta Maurer Boehringer-Ingelheim, GSK, Novartis, Otsuka, MSD, Novartis, Boehringer Ingelheim, Jannsen-Cilag, GSK, Novartis.

Background Omics data, driven by rapid advances in laboratory techniques, have been generated very quickly during the COVID-19 pandemic. Our aim is to use omics data to highlight the involvement of specific pathways, as well as that of cell types and organs, in the pathophysiology of COVID-19, and to highlight their links with clinical phenotypes of SARS-CoV-2 infection. Methods The analysis was based on the domain model, where for domain it is intended a conceptual repository, useful to summarize multiple biological pathways involved at different levels. The relevant domains considered in the analysis were: virus, pathways and phenotypes. An interdisciplinary expert working group was defined for each domain, to carry out an independent literature scoping review. Results The analysis revealed that dysregulated pathways of innate immune responses, (i.e., complement activation, inflammatory responses, neutrophil activation and degranulation, platelet degranulation) can affect COVID-19 progression and outcomes. These results are consistent with several clinical studies. Conclusions Multi-omics approach may help to further investigate unknown aspects of the disease. However, the disease mechanisms are too complex to be explained by a single molecular signature and it is necessary to consider an integrated approach to identify hallmarks of severity.