Genome Biology

GlycoRNA, newly identified RNA molecules bearing glycan modifications on cell membranes, is implicated in cell communication and immune regulation. However, current technological limitations impede a thorough elucidation of their biological roles and clinical significance. Here, we developed Nucleotides Hybridization and Aptamer-based Proximity Ligation (NHAPL), a homogeneous assay enabling sensitive and quantitative glycoRNA analysis from 160pg total cell RNA and 1{micro}l serum. NHAPL integrates dual recognition by a sialic acid aptamer and RNA binding probe, followed by ligation and qPCR amplification. We further established multiplexed NHAPL for simultaneous detection of multiple glycoRNA. Using NHAPL, we uncover for the first time that protein-coding mRNAs, specifically 3' untranslated region (3'UTR) fragments of FNDC3B and CTSS, undergo sialic acid-containing N-glycosylation on the cell surface. These glycoRNAs functionally promote monocyte adhesion to endothelial cells and hepatoma cell migration, revealing a direct role in cell-cell interactions and cancer-related phenotypes. Applying multiplexed NHAPL to human serum, we identify glycoRNA signatures highly specific to systemic lupus erythematosus (SLE). In particular, glycoY5 and glycoU1 achieve near-complete discrimination between patients and healthy controls (area under the curve (AUC) = 1.00 and 0.9977), whereas conventional total RNA analysis fails to capture these differences, highlighting RNA glycosylation modification as a distinct regulatory layer. Its simplicity and flexibility make it well suited for clinical glycoRNA profiling and biomarker discovery. Overall, NHAPL represents a robust and versatile platform for advancing glycoRNA research and diagnostic development.

Methods that analyze single-cell RNA-seq+ATAC-seq multiome data have shown promise in linking enhancers to target genes by correlating chromatin accessibility with gene expression across cells. However, correlations among ATAC-seq peaks may induce non-causal tagging peak-gene links (analogous to tagging associations in GWAS); indeed, we confirm that tagging effects induced by peak co-accessibility are pervasive in peak-gene linking. We defined two scores for each ATAC-seq peak: co-accessibility score, the sum of squared correlations with each nearby peak; and co-activity score, the sum of squared correlations with each nearby gene. We compared these scores in 4 multiome data sets (spanning 86k cells and 6 immune/blood cell types) and determined that co-accessibility score and co-activity score were strongly correlated across peaks (r = 0.57-0.73); these correlations were not explained by read depth, cell subtypes, or measurement noise, but are consistent with tagging. Indeed, non-causal peak-gene correlations were strongly correlated to a peaks tagging correlation with a causal peak in CRISPRi data (r = 0.92). We further determined that causal peak-gene associations are concentrated in specific functional categories of peaks, by regressing co-activity scores on stratified co-accessibility scores (S-CASC): e.g. 2.91x (s.e. 0.67) enrichment for peaks closest to a genes TSS and 1.41x (s.e. 0.11) enrichment for peaks overlapping H3K27ac marks. Co-accessibility scores were substantially driven by the number of transcription factor binding sites (TFBS) within a peak, and peak-peak correlations were substantially driven by the number of TFBS pairs within the two peaks with a shared TF. These effects were concentrated in a small number of pioneer TFs, which activate repressed chromatin regions. Consistent with widespread tagging, peak-gene links that we fine-mapped using SuSiE significantly outperformed marginal peak-gene links in evaluation sets derived from CRISPRi and eQTL data. We provide examples demonstrating the impact of tagging effects at specific peaks and genes implicated in GWAS of blood cell traits. Our findings underscore the importance of accounting for tagging effects when linking enhancers to target genes.

Gout is driven by an interleukin-1{beta}-mediated intense innate immune reaction to monosodium urate (MSU) crystals (MSUc). In cell culture models of inflammatory gout there is a synergistic effect of phagocytosis of MSUc and TLR2 and TLR4 activation by agonists such as free fatty acid and lipopolysaccharide (LPS) in NLRP3-inflammasome activation and IL-1{beta} secretion. A substantial number of gout patients do not report a dietary trigger, and observational studies associate airborne particulate matter with incident gout and flares. Airborne particulate matter contains LPS and airborne-derived particulate matter stimulates IL-1{beta} secretion in cell culture. We hypothesized that air-borne particulate matter could co-stimulate, with MSUc, IL-1{beta} secretion and inflammation. We tested the hypothesis using MSUc with extracted airborne PM4 in human cells (the THP-1 monocyte cell line, primary human monocytes and PBMCs) or carbon black particles with ozone (CB+O3) in a murine foot-pad injection model of gout. There was strong NLRP3-inflammasome-dependent co-stimulation of IL-1{beta} secretion in THP-1 cells with PM4+MSUc and a moderate additive effect in primary human PBMCs. However, there was no added effect on IL-1{beta} secretion of PM4 in isolated primary human monocytes. Inhalation of CB+O3 persistently exacerbated MSUc-induced murine paw inflammation, with an increase of alveolar/lavage macrophages that contained CB+O3 particles and increased lavage expression of IL-1{beta}. In conclusion, airborne-derived PM4 particulate matter enhanced MSUc-induced IL-1{beta} secretion in THP-1 cells and PBMCs. Combined with exacerbation of MSUc-induced inflammation by fine particulate matter in in vivo experiments, these data provide evidence that exposure to fine particulate matter may play a role in the etiology of gout.

BackgroundOsteoarthritis (OA) is a leading cause of disability worldwide, yet no licensed therapies can prevent or slow its progression. We aimed to identify potential targets for disease-modifying OA drugs (DMOADs) by integrating genetic and differential protein expression (DPE) evidence. MethodsWe evaluated genetically predicted perturbations of plasma protein levels using cis-protein quantitative trait loci (cis-pQTLs) across three large European cohorts (UK Biobank Pharma Proteomics Project, deCODE, and Fenland) and outcome data from the Genetics of Osteoarthritis Consortium, covering 11 OA phenotypes. DPE analyses were performed in 44,789 UKB participants, comparing 2,920 protein measurements between OA cases and controls, supported by sensitivity analyses. Proteins identified through genetic and/or DPE approaches were further assessed in downstream analyses. FindingsIn total, 305 proteins showed evidence of association with OA through genetically predicted perturbations, with 81 supported by colocalisation across datasets. DPE analyses identified 605 proteins associated with at least one OA phenotype, of which 450 (74{middle dot}4%) remained robust after sensitivity testing. Several novel targets were identified, including PPP1R9B, PCSK7, and ITIH4. Integration of both approaches prioritised 5 proteins, 4 of which demonstrated druggable potential, including 3 high-confidence candidates DLK1, TNFRSF9, and OGN. Downstream analyses highlighted key biological pathways and candidate compounds with potential for repurposing. InterpretationThis large-scale study combines genetic and DPE evidence to prioritise candidate DMOAD targets. Findings reinforce established biology while revealing novel proteins and pathways, providing a foundation for therapeutic development in OA. FundingWL is supported by the Guangzhou Elite Project (project no. JY202314). SSZ is supported by The University of Manchester Deans Prize, Arthritis UK Career Development Fellowship (grant no. 23258). This work is supported by the NIHR Manchester Biomedical Research Centre (NIHR203308). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSCirculating proteins have been linked to osteoarthritis (OA) in observational studies, supporting their potential as biomarkers and drug targets. However, differential protein expression analyses are vulnerable to confounding and reverse causation. Mendelian randomisation (MR) studies using proteomic GWAS instruments have suggested causal roles for several circulating proteins in OA-related traits and highlighted druggable candidates. However, many analyses relied on earlier OA GWAS data (e.g., Genetics of Osteoarthritis Consortium 1{middle dot}0) and smaller proteomic GWAS datasets, and typically did not integrate MR findings with large-scale differential protein expression. As a result, it remains unclear how well genetically predicted protein effects align with observed protein expression in OA, and how robust prioritised targets are when replicated across proteomic data from multiple cohorts. Added value of this studyThis study integrates large-scale proteomic MR and differential protein expression (DPE) analyses across multiple OA phenotypes using the largest datasets to date. By combining genetic evidence with observed protein dysregulation in population-based cohorts, we strengthen causal inference and improve robustness of target prioritisation. This approach allows us to distinguish proteins that are likely to play a causal role in OA from those that reflect downstream disease processes, and to highlight targets with greater translational relevance than identified by either method alone. Implications of all the available evidenceTaken together, our findings support a causal role for a subset of circulating proteins in OA and demonstrates the value of integrating genetic and observational proteomic data for target prioritisation. Proteins supported by both MR and DPE are more likely to represent biologically relevant drivers of disease and actionable therapeutic targets. This integrated framework reduces false positives arising from confounding or reverse causation and provides a more reliable basis for drug development, biomarker discovery, and patient stratification in OA.

Genome-wide association studies have identified genetic polymorphisms at 11p15 associated with Systemic Lupus Erythematosus (lupus). Statistical fine mapping prioritizes a highly prevalent coding haplotype within the IRF7 gene. Analysis of ancient DNA confirms that this haplotype has persisted at high frequencies in the global population for millennia. The IRF7 risk haplotype is sufficient to increase nuclear localization of IRF7 and transcriptional activity downstream of pattern recognition receptor pathways. This risk haplotype increases IRF7 DNA binding strength and alters IRF7 DNA sequence specificity, resulting in genotype-dependent increases in IFN- production in numerous biological systems, including monocytes and airway epithelial cells. CRISPR engineering of a homologous risk variant in mouse Irf7 results in both enhanced innate control of virus infection and increased autoantibody titers in a model of autoimmunity. Altogether, we establish a persistent and prominent genetic IRF7 haplotype that amplifies IRF7 activity in a manner that has immunological risks and benefits. HIGHLIGHTS[bullet] Genetic analysis using modern and evolutionary datasets identifies a persistent and highly prevalent lupus-associated coding haplotype in IRF7 at 11p15 [bullet]The IRF7 lupus risk haplotype increases IFN- production by monocytes and airway epithelial cells [bullet]The IRF7 lupus risk haplotype increases IRF7 DNA binding strength and alters DNA sequence specificity [bullet]A homologous lupus risk variant in mouse Irf7 enhances control of vesicular stomatitis virus and exacerbates autoantibody production

BackgroundThe hyperpolymorphic nature and structural complexity of the human leukocyte antigen (HLA) genomic region present challenges for accurate and scalable typing across diverse sample types. While wholegenome sequencing (WGS) offers the opportunity to infer HLA genotypes without targeted enrichment, systematic benchmarks across sequencing platforms, biospecimens and coverage levels remain limited. ResultsWe assembled a multi-platform resource of WGS datasets derived from short-read (Illumina, MGI) and long-read (Oxford Nanopore Technologies R9 and R10) sequencing, spanning 29 biospecimens including cell lines, blood, buccal swab and saliva. We evaluated the performance of the HLA caller HLA*LA across 13 HLA genes, using a clinically validated assay as reference. WGSbased HLA genotyping achieved [~]95% accuracy across sequencing platforms, with Class I loci exhibiting higher accuracy than Class II. Crossplatform concordance was high, and performance remained consistent across Illumina, MGI and Oxford Nanopore chemistries. Analysis of blood, buccal swab and saliva samples showed that blood and buccal swabs supported accurate HLA inference, whereas saliva yielded reduced concordance. Downsampling experiments demonstrated that 15x coverage was sufficient to retain >95% accuracy at twofield resolution, with lower depths supporting lower-resolution typing. ConclusionsOur results demonstrate that WGS provides a robust, platformagnostic framework for accurate HLA genotyping across sample types and coverage levels. These benchmarks establish practical conditions for reliable HLA inference and underscore the utility of WGS for populationscale HLA analyses and future clinical applications.

BackgroundKnee osteoarthritis (OA) is a prevalent painful degenerative disease without effective disease-modifying drugs. The rising prevalence of comorbid obesity and knee OA underscores the urgent need for effective management to delay or prevent disease progression. In a recently completed randomized, placebo-controlled trial in adults with comorbid obesity (BMI >30 kg/m{superscript 2}) and unilateral or bilateral knee OA (Kellgren-Lawrence grade II-III), we were the first to demonstrate that a 6-month prebiotic intervention (16 g/day oligofructose-enriched inulin) significantly improved physical function and metabolic health. MethodsTo elucidate the underlying mechanisms, we incorporated metagenomics, metabolomics, and machine-learning-based multi-omics integration in 30 participants who completed baseline and at least one follow-up assessment and sample collection at months 3 and 6. ResultsPrebiotic supplementation reshaped gut microbial composition and function, increasing diet-derived carbohydrate availability, mitigating excessive host-glycan degradation and mucosal barrier disruption, reducing systemic inflammation and metabolic dysregulation, and ultimately improved physical performance and metabolic health. In a diet-induced obese rat model, prebiotic treatment reduced tibial cartilage degeneration and synovial membrane thickening, providing protection against OA onset and progression through a shared inflammatory pathway. ConclusionsOur findings provide mechanistic evidence supporting the therapeutic potential of prebiotic supplementation as a conservative management in humans and as a preventive approach for obesity-related knee OA in a preclinical rat model, mediated through the gut-joint axis. Trial registrationClinicaltrials.govNCT04172688

Mitochondria are semi-autonomous organelles whose generation and maintenance demand precise expression, processing, and assembly of >1,000 proteins encoded across two genomes. To explore this cooperativity, we performed multiomic analyses on >200 cell lines harboring mitochondrial gene perturbations, generating >26M molecular measurements. Our data reveal that mitochondrial proteome homeostasis is heavily influenced by post-transcriptional processes. Through nearest neighbor analyses, we reveal diverse protein activities undergirding this regulation, including MDH2s regulation of MT-ND3 transcription via FASTKD1 binding and CLPPs processing of the mitoribosomal assembly factor MALSU1, which we establish as a disease gene. Through entropy analysis, we reveal unexpectedly heterogeneous protein-level variability across complexes and use complexome profiling to identify new complex-specific membership, including C15orf61s association with complex V. We further observe substantial mtDNA copy number variation, notably upon disruption of the disease-related cobalamin biosynthesis protein MMADHC. Together, we establish new protein functions and provide a multilayered view into mitochondrial proteome regulation. HighlightsO_LIMultiomic signatures across perturbations reveal extensive post-transcriptional regulation C_LIO_LIThe TCA cycle enzyme MDH2 binds FASTKD1 to modulate MT-ND3 transcript levels C_LIO_LIMALSU1 is a CLPP protease substrate whose deficiency causes a mitochondrial disease C_LIO_LIC15orf61 binds ATP synthase and negatively regulates its higher order assembly C_LIO_LIMMADHC inversely affects mtDNA levels potentially mediated through LONP1 C_LI

GWAS have identified multiple genetic regions that confer risk for juvenile idiopathic arthritis (JIA). However, identifying the single nucleotide polymorphisms (SNPs) that drive disease risk has been impeded by the fact that the SNPs used to identify risk loci are in linkage disequilibrium (LD) with hundreds of other SNPs. Since the causal SNPs remain unknown, it is difficult to identify target genes and thus use genetic information to elucidate disease biology and inform patient care. We next used existing genotyping data from 3,939 children with JIA and 14,412 healthy controls to identify SNPs on JIA risk haplotypes that: present within open chromatin in multiple immune cell types and more common in children with JIA than the controls (p<0.05) in the genotyping data sets. We identified SNPs within cis-regulatory regions (CREs) using precision run-on sequencing data, and identified likely target genes using MicroC in both resting and activated CD4+ T cells. We identified 138 SNPs within the PROseq-identified CREs, and n=41 genes with which these CREs physically interacted. Data from GTEx corroborated these analyses by showing allelic effects for SNPs within the CREs in the ERAP2 and IRF1 risk loci. We further corroborated IRF1 allelic effects using a luciferase reporter assay. Our findings significantly reduce the genomic search space for risk-driving variants and target genes and support the roles of IRF1, ERAP2 and LNPEP in driving risk for JIA.

A common signature of cancer genomes is a complex, rearranged karyotype, characterized by acquired gains or losses of chromosomal material, referred to as somatic copy number alterations (CNAs). Identification of haplotype-specific CNAs from bulk sequencing data is a key step in many short-read cancer genomic workflows; however, short reads have a limited phasing range. In contrast, long reads can directly phase genomic variants into contiguous haplotypes. Here, we present Wakhan, a long-read method for haplotype-specific CNA calling that can reconstruct longer, up to chromosome-scale CNA profiles of rearranged cancer genomes. Using multi-technology sequencing of a cell line panel, combined with high-quality de novo assemblies, we show that Wakhan CNA profiles have better consistency with sequencing data, as compared to the other popular short- and long-read CNA callers. Further, we show that in combination with accurate somatic SV calls, Wakhan CNA profiles provide additional insights into mutational processes in various breast cancer genomes. Finally, we apply Wakhan to multiple pediatric cancer samples and illustrate the high consistency with standard clinical genetic testing.

Individuals who have serum elevations of anti-cyclic citrullinated protein (anti-CCP) antibodies are at risk for developing rheumatoid arthritis (RA), yet immunologic factors that lead to a transition from pre- to clinical RA remain unclear. Here, we used materials from anti-CCP antibody-positive individuals enrolled in a clinical trial that evaluated the efficacy of hydroxychloroquine to prevent clinical RA, and performed multi-modal single-cell profiling (transcriptome, surface proteins, T/B-cell receptor sequencing, and chromatin accessibility) on samples obtained at baseline and at RA onset in those who developed clinical RA (Converters) or follow-up point in matched Nonconverters. At both baseline and follow-up, Converters had expansions of peripheral helper T (Tph) cells and CD8+ T cells expressing GZMK and GZMB, along with elevated potentially autoreactive T-cell receptors in CD4+ T cells compared to Nonconverters. Induction of age-associated B cell signatures was observed in B cells of Converters prior to RA onset. Epigenetic profiling further identified chromatin accessibility changes in Converters over time, particularly within myeloid and NK cells. Lastly, predictive modeling using baseline immune features, including Tph cells, GZMK+XCL1+ CD8+, and GZMB+CD57+ CD8+ T cells, together with clinical features such as anti-CCP3 levels, RF-positivity, and HLA shared epitope status, stratified RA risk and predicted time to onset. These findings define immune endotypes in pre-RA that could serve as targets for future preventive interventions and be used to stratify the risk of developing clinical RA in anti-CCP antibody-positive individuals.

Anti-{beta}2-glycoprotein I (anti-{beta}2GPI) antibodies are central to the pathogenesis of antiphospholipid syndrome (APS), an autoimmune disease characterized by a strong predisposition to venous thromboembolism (VTE). In this study, we conducted a multi-ancestry genome-wide association study (GWAS) of quantitative total anti-{beta}2GPI levels in 5,969 participants enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA) and identified a genome-wide significant association at the APOH locus. Paradoxically, genetically determined increases in anti-{beta}2GPI levels at this locus were associated with lower VTE risk. Fine-mapping and functional genomics prioritized the missense variant rs1801690 (W335S) in {beta}2GPI (apolipoprotein H, [APOH]) as the most likely causal variant. This variant has an allele frequency of 5-6% in European and East Asian ancestries but only 1% in African ancestries. Integrating prior experimental studies, molecular dynamics simulations and structure-based epitope prediction, we propose a dual-effect mechanism whereby W335S reduces thrombotic risk by disrupting phospholipid binding in Domain V, yet increases autoantibody production through conformational changes that enhance epitope exposure in Domains I and II. These findings mechanistically uncouple autoantibody formation from thrombotic risk in carriers of the W335S variant, and suggest that APOH genotype may represent a clinically relevant genetic biomarker with potential utility for thrombotic risk stratification in anti-{beta}2GPI-positive individuals.

Detecting low variant allele fraction (VAF) mosaic variants without matching controls remains a major challenge in genomics, limited by technical noise, lack of benchmarks, and computational scalability. We present the DRAGEN mosaic caller, a hardware-accelerated approach identifying variants down to [~]1-2% VAF with low false-positive rates and hour-scale runtimes for mosaic SNV/indel detection from bulk sequencing. To support evaluation, we introduce a genome-wide low-VAF benchmark for variants between 1-10% VAF. Application to blood, sperm, and brain tissues revealed patterns, including mosaic hotspots and mutational signatures. The first analysis of HG002 blood showed that many "mosaic" variants defined from HG002 cell lines are likely culture-derived and not in vivo mutations. Importantly, DRAGEN also enables personalized assembly pangenome references to improve alignment and mosaic variant detection in complex regions. Together, this development makes routine low-VAF discovery feasible, opening new opportunities to study mosaic mutations in healthy and disease individuals.

We present a streamlined, solid-phase workflow for Oxford Nanopore sequencing that integrates DNA extraction, purification, and library preparation within a single microfluidic cartridge. By eliminating tube transfers and performing all enzymatic steps directly on captured DNA, the method minimizes sample loss, reduces hands-on time, and simplifies library generation for long-read sequencing. Starting from volumes as small as a single drop of blood, this integrated approach produces high-quality sequencing libraries from cell lines, whole blood, and tissue. The workflow achieves robust recovery of high-molecular-weight DNA and high pore occupancy, enabling rapid, low-complexity sample preparation suitable for clinical, field, and decentralized sequencing applications.

Recent studies showed that expression QTLs, even from trait-related tissues, explained a small fraction of complex trait heritability. A natural strategy to close this gap is to incorporate molecular QTLs (molQTLs) beyond gene expression, across diverse tissue/cellular contexts. Yet, integrating such QTL data presents analytical challenges. Molecular traits often share QTLs or have QTLs in high LD, complicating the attribution of GWAS signals to specific molecular traits. Our simulations showed that commonly used colocalization and TWAS methods have highly inflated false positive rates in such settings. Building on our earlier work, we developed multi-group causal TWAS (M-cTWAS), for integrating QTLs of different modalities and contexts. M-cTWAS is able to estimate the contribution of each group of molQTLs to the trait heritability, and using such information, identifies the causal molecular traits, informing the modalities and contexts through which genetic variations act on the phenotype. M-cTWAS showed improved control of false discoveries than commonly used methods. Using M-cTWAS, we found that QTLs of multiple modalities greatly increased the explained heritability compared to using eQTLs alone, and enabled the discovery of many more risk genes of a range of complex traits. In conclusion, M-cTWAS effectively integrates diverse molecular QTLs with GWAS to enable causal gene discovery.

The growing number of genomic discoveries in complex human traits has highlighted the need for advanced functional genomics tools that parse their polygenic and pleiotropic genetic architecture to provide biological insights. We present bivariate GSA-MiXeR, a novel tool that models the partitioned heritability and covariance of two traits within a genomic region of interest (ROI) and estimates the (i) trait-specific fold enrichment, (ii) local genetic correlation, and (iii) local genetic omnibus statistic for ranking genomic ROIs. Unlike previous methods, our tool estimates local genetic correlations both in continuous and disjoint genomic ROIs, expanding the ability to assess local genetic overlap among complex traits. We perform simulations to validate our tool and illustrate its utility in applied analyses that implicate diverse neural cell types for psychiatric and neurodegenerative disorders using single cell RNA sequencing data. Bivariate GSA-MiXeR provides new analytical avenues that facilitate a transition from genetic discovery to mechanistic insights. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/25342384v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@5e60f2org.highwire.dtl.DTLVardef@2ed1e8org.highwire.dtl.DTLVardef@1d6f84aorg.highwire.dtl.DTLVardef@46ca65_HPS_FORMAT_FIGEXP M_FIG C_FIG

ObjectivesCardiovascular risk excess in rheumatoid arthritis (RA) cannot be explained by traditional risk factors alone. Recent experimental data have identified ALDH4A1 as a mitochondrial self-antigen implicated in atherosclerosis, yet its clinical significance in human autoimmunity remains unexplored. We aimed to characterize ALDH4A1 and anti-ALDH4A1 antibody levels in early RA, and evaluate their associations with atherosclerosis burden and lipoprotein traits. MethodsALDH4A1 and anti-ALDH4A1 antibodies (IgM, IgG, IgA, and IgG subclasses) were measured in early RA (n=82), clinically suspect arthralgia (n=14), healthy controls (n=70), and a validation cohort of established RA. A prospective cohort (n=13) explored therapeutic modulation under TNF blockade. Associations with atherosclerosis burden, lipid/lipoprotein profiles, oxylipin signatures, proteomics, and cell-free DNA were assessed. ResultsALDH4A1 serum levels were associated with apoptotic-related proteomic pathways, cell-free DNA and lipidomic signatures in early RA. Reduced anti-ALDH4A1 antibodies were found, although divergent patters were noted across isotypes. These differences were confirmed in a validation cohort. IgG (predominantly IgG3) anti-ALDH4A1 correlated with favourable lipoprotein traits and cardiometabolic risk factors. Increased ALDH4A1 and reduced IgM/IgG anti-ALDH4A1 antibodies independently predicted atherosclerosis and improved risk stratification beyond mSCORE, most notably for IgG. ALDH4A1 tracked with TNF dynamics under TNF blockade, whereas increases in IgG antibodies occurred in responders and paralleled changes in lipoprotein features. ConclusionsThe ALDH4A1/anti-ALDH4A1 axis emerges as a novel player bridging lipid disturbances and atherosclerosis along the RA spectrum, hence highlighting the involvement of mitochondrial targets. These components hold promise as functional players, clinical tools and therapeutic targets.

MotivationFanconi anemia (FA) is a rare disease mainly caused by biallelic pathogenic variants, including structural variants such as large deletions and insertions in FA genes. Currently, variant detection is based on short-read sequencing and probe-based approaches. However, determining the exact genomic breakpoint or achieving allelic discrimination remains challenging. Nanopore-based long-read sequencing enables a comprehensive detection of FA variants, but a unified bioinformatic analysis platform for these data is missing. ResultsWe present FA-NIVA (Fanconi anemia - Nanopore Indel and Variant Analysis), an automated and adaptable analysis workflow tailored for Nanopore-based long-read sequencing data in FA genetic analysis. FA-NIVA integrates state-of-the-art tools to comprehensively detect both single nucleotide variants (SNVs) and structural variants (SVs). Our analysis platform enhances genotyping accuracy for biallelic variants by a joint SNV-SV based phasing in FA associated genes. Built within the Nextflow ecosystem and powered by containerized Docker images, FA-NIVA ensures reproducibility, flexibility, scalability and transparency across different computing environments. Together, FA-NIVA provides a robust end-to-end solution for the automated analysis of SVs and SNVs and high-resolution phasing analysis in FA genes, enabling an accurate and efficient pipeline for genetic analysis. AvailabilityFA-NIVA is available on GitHub at: https://github.com/UKWgenommedizin/FA-NIVA.

Genetic studies have largely focused on homogeneous populations, limiting our understanding of the genetic architecture of complex traits in admixed individuals. The advent of diverse biobanks like the All of Us Research Program (AoU) and computationally efficient local ancestry inference (LAI) methods now enable admixture mapping (ADM) at biobank scale. Here, we used two orthogonal LAI methods (GNOMIX and FLARE) to characterize local ancestry in the entire AoU v7.1 cohort (n=230,019). We then used GNOMIX labels to identify associations between African (AFR) and Native American (NAT) local ancestry with 29 quantitative traits. We first analyzed All of Us v7.1 data across African (n=49,797) and Admixed American (n=40,327) cohorts, which identified 97 significant local ancestry associations (65 AFR, 32 NAT). These include strong known signals, such as an association between AFR ancestry at the DARC locus and white blood cell traits and between NAT ancestry at the BUD13/APOE5/ZPR1 locus and triglycerides, as well as additional signals not previously associated with local ancestry. We observed that trait associations with AFR local ancestry are largely consistent across the African and Admixed American cohorts, but that several AFR signals reach genome-wide significance exclusively in Admixed Americans. Grouping associations by trait category revealed distinct ancestral patterns: all endocrine, renal, and 75.0% of liver signals were driven by associations with NAT ancestry, whereas white blood cell (90.9%), red blood cell (65.1%), and lipid (66.7%) signals were largely associated with AFR ancestry, possibly reflecting different population-driven environmental exposures throughout history. Finally, we performed a second round of analysis, comprising the largest ADM study to date, on the entire AoU v7.1 cohort in which we pooled individuals from all ancestries. Despite evidence of confounding due to population structure, summary statistics for pooled results showed strong correlation (r>0.98) with those from single ancestry analysis and detected 2.7x fold more signals, most of which passed at least nominal significance and all of which showed consistent effect directions in the single ancestry cohorts. Overall, these results demonstrate the power of using large, admixed cohorts to gain new insights into the relationship between local ancestry and the genetic architecture of medically relevant complex traits.

Regular physical activity delays biological ageing, yet how transient exercise-induced molecular responses are translated into stable ageing signatures in humans remains unclear. Here, we introduce the first in silico perturbation framework for human exercise biology that integrates genetically anchored gene prioritisation, graph-based network modelling, and human experimental validation. Genes causally associated with habitual vigorous physical activity (VPA) were prioritised using Mendelian randomisation (MR) across proteomic, epigenomic, glycomic, and single-cell transcriptomic layers and represented using self-supervised graph learning. Targeted acute in silico perturbations were then propagated via network diffusion within a network shaped by genetically proxied habitual VPA to forecast downstream alignment with epigenetic and proteomic ageing clocks. Perturbation of validated glycosylation enzymes consistently yielded diffusion neighbourhoods enriched for clock-associated genes (empirical p < 0.01), whereas perturbation of canonical DNA repair and stress response genes did not consistently align with ageing clock architecture. Acute high-intensity exercise validation demonstrated rapid modulation of plasma glycosylation alongside activation of DNA repair programmes, providing biological context for distinct network behaviours. Together, these findings reveal a hierarchical organisation in which DNA repair pathways act as adaptive buffers of acute physiological stress but do not directly encode biological ageing state, while downstream glycosylation networks occupy a more proximal, integrative position, predictively encoding stable molecular states captured by human ageing clocks. By resolving stress buffering from ageing state encoding at the network level, this work refines damage-centric models of ageing and establishes in silico perturbation as a principled approach to forecast how habitual physical activity shapes long-term biological ageing trajectories. O_FIG O_LINKSMALLFIG WIDTH=173 HEIGHT=200 SRC="FIGDIR/small/26345311v3_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@1fc6345org.highwire.dtl.DTLVardef@d9783eorg.highwire.dtl.DTLVardef@1654886org.highwire.dtl.DTLVardef@7c8865_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO In silico perturbation framework linking habitual physical activity to ageing-related molecular architecture. Genetically proxied habitual vigorous physical activity (VPA) is integrated with multi-omic Mendelian randomisation (MR) across epigenomic, transcriptomic, proteomic, and glycomic layers to construct a causal, exercise-adapted molecular network. Within this network, targeted acute in silico gene perturbations are propagated by network diffusion and evaluated for alignment with epigenetic and proteomic ageing clocks as external annotations. A controlled human high-intensity exercise intervention provides experimental validation, demonstrating acute activation of DNA repair programmes alongside rapid modulation of plasma glycosylation. C_FIG