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PubMed2026

Do Multi-Omics Approaches Improve the Diagnosis of Microbial Overgrowth Syndromes?

PURPOSE OF REVIEW: This review investigates how advances in breath testing (BT), small bowel (SB) culture, metagenomics, metatranscriptomics, transcriptomics and proteomics are reshaping the definition and diagnosis of small intestinal bacterial overgrowth (SIBO). It also discusses whether SIBO should be redefined as part of a larger group of microbial overgrowth syndromes. RECENT FINDINGS: Recent studies identify distinct hydrogen-, methane-, and hydrogen sulfide-associated overgrowth phenotypes, termed SIBO, intestinal methanogen overgrowth (IMO), and intestinal sulfide overproduction (ISO). SB sampling shows that these conditions involve different microbial patterns and functional activity, symptoms, and host responses. Quantitative shotgun metagenomics provides greater taxonomic and functional resolution than culture, while metatranscriptomics reveals active microbial pathways. On top of that, host transcriptomics and proteomics contribute to the better understanding of the predominant microbial effects in host cellular mechanisms in each of the distinct small bowel overgrowth types. SIBO has been increasingly identified as a disorder of microbial ecology and function rather than bacterial quantity alone. Integrating BT with SB sampling and multi-omics approaches may improve classification, clarify symptom mechanisms, and support a more individualized treatment, although standardized methods and further clinical validation remain necessary.

Humans↗
PubMed2026

Modeling meningioma in vitro in the omics era.

Meningioma biology has been substantially clarified by recent omics-based studies, which have identified recurrent mutations, copy-number alterations, and distinct molecular subgroups. However, although these approaches have provided a valuable framework, they are inherently limited in their ability to establish direct causal relationships. The mechanistic studies are therefore indispensable for translating these molecular observations into biological understanding. Nevertheless, the mechanistic literature has often evolved in a fragmented manner, with individual pathways and model systems studied in relative isolation from the broader multi-omic landscape. In this review, we synthesize these complementary bodies of work into an integrated framework and outline a clear roadmap for future studies. We first review the historical development of established meningioma cell lines, their current molecular characterization, and the recent emergence of 3D models and organoids. Intrinsic challenges in modeling meningioma in vitro are discussed, including the difficulty of establishing immortalized cell lines from predominantly benign tumors, genetic alterations introduced during immortalization, and drift under culture conditions that differ substantially from those of the parental tumors. Next, insights from functional studies centered on these models are integrated within the molecular framework established by large-scale omics analyses. To avoid fragmentation and overemphasis on isolated findings, prior studies are organized into six categories based on major signaling pathways: Hippo, PI3K/Akt/mTOR, MAPK, Wnt/β-catenin, FOXM1, and Notch. Finally, lessons from other cancer models, including experimental approaches to chromosome-scale genomic disturbances, are considered to provide a more integrated view of meningioma biology and to highlight directions for future research.

Meningioma↗
PubMed2026

Longitudinal dynamics of gene expression and metabolomics in an aging population cohort.

Multiomic profiling provides a comprehensive physiological overview at the molecular level, but understanding of its spatiotemporal dynamics remains limited in human populations. We profiled longitudinal whole-blood gene expression and metabolite levels in 335 females over 8 years. Levels of 5061 genes and 181 metabolites changed over time, with individual trajectories often diverging from population-level trends. Longitudinally variable genes showed cell type specificity and enrichment for aging-relevant pathways, including cardiometabolic and neurodegenerative disorders. Longitudinal trajectories were further shaped by genetics, circadian rhythm, seasonality, and environmental pollutant exposures. Integrative analyses revealed extensive static and time-variable cross-omic connectivity. Longitudinal profiling offers insight into the temporal evolution of age-related conditions at the molecular level, and understanding individual variation within these longitudinal patterns will be essential for future precision medicine approaches.

Female↗
PubMed2026

Urine-Derived Cells in Kidney Transplantation: Linking Cellular Phenotypes, Secretome Signatures and Multi-Omic Technologies.

Kidney transplant monitoring and early identification of graft dysfunction are central needs for long-term graft survival. Although kidney biopsy represents the gold standard in diagnostic procedures, its invasiveness may limit frequent longitudinal evaluation, highlighting the necessity of non-invasive diagnostic tools. Urine has emerged over the years as an easily obtainable source of cellular and molecular components derived from transplanted kidneys. Particularly, exfoliated cells and extracellular vesicles (EVs) represent complementary and interconnected markers able to reflect tissue injury, inflammatory signals, immune cell infiltration, and regenerative processes occurring in the graft. Also, recent advances in transcriptomics, proteomics, metabolomics, and single-cell technologies have expanded the diagnostic and mechanistic value of urinary liquid biopsy, enabling the identification of disease-specific molecular signatures associated with rejection, delayed graft function, fibrosis, and graft loss. This review discusses the emerging concept of an integrated urinary cell-EV ecosystem and highlights how integrated multi-omic approaches may transform non-invasive graft surveillance and advance precision medicine in kidney transplantation.

Humans↗
PubMed2026

Multi-omics panorama of glaucoma: Pathogenesis, biomarkers, and novel therapeutic strategies.

Glaucoma is a group of irreversible, blinding eye diseases characterized by progressive loss of retinal ganglion cells, leading to gradual visual field defects that severely impact patients' quality of life. Its complex pathophysiological mechanisms remain incompletely understood, limiting the development of early diagnostic and effective therapeutic strategies. Advances in omics technologies have provided new insights into elucidating the pathophysiology of glaucoma. We summarize specific alterations in genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics associated with glaucoma. We emphasize the systematic analysis of disease mechanisms, identification of clinically applicable biomarkers, and discovery of novel therapeutic targets through the integration of these data. This approach paves new pathways for glaucoma subtype diagnosis and personalized treatment, while also outlining future research directions and challenges.

Humans↗
PubMed2026

Proteomics-based approaches to neutrophil biology.

INTRODUCTION: Neutrophils are central effectors of innate immunity and key contributors to inflammation, host defense, and tissue injury across a wide range of physiological and pathological contexts. Due to their short lifespan, rapid activation, and extensive post-translational regulation, comprehensive molecular characterization of neutrophil function requires approaches that go beyond transcriptomics or marker-based analyses. AREAS COVERED: This review summarizes how proteomic technologies have advanced the understanding of neutrophil biology by enabling unbiased, system-wide profiling of protein abundance, subcellular organization, post-translational modifications, and functional heterogeneity. We discuss global and subcellular proteomics, PTM-centric analyses, and emerging low-input and single-cell proteomic strategies, highlighting recent studies of infection, cancer, metabolic disorders, aging, autoimmune disease, and inflammation. The literature covered includes current large-scale quantitative proteomics, targeted PTMs, and integrative multi-omics studies in both human samples and relevant experimental models. EXPERT OPINION: Proteomics has established neutrophils as highly plastic and context-dependent cells whose functions are governed by coordinated remodeling of signaling, metabolism, and effector pathways. Future progress will depend on expanding neutrophil-specific PTM maps, improving low-input workflows, and integrating single-cell and spatial proteomics. Together, these advances are expected to redefine neutrophil functional states and accelerate translation toward clinically meaningful biomarkers and therapeutic strategies.

Humans↗
PubMed2026

Bridging Organ-on-a-Chip and Omics: A Multi-Dimensional Frontier in Biomedical Research.

Organ-on-a-Chip (OOC) technology offers a powerful platform for replicating human tissue-specific microenvironments, thereby narrowing the translational gap between conventional biomedical models and actual human physiology. Concurrently, omics technologies deliver comprehensive molecular-level insights into biological systems. This review highlights the transformative potential of integrating OOC platforms with high-throughput omics methodologies. We systematically examine the classification, structural configurations, and engineering principles underlying OOC systems, alongside the defining attributes of key omics domains-genomics, transcriptomics, proteomics, and metabolomics. The convergence of dynamic OOC models with advanced omics technologies enables high-resolution, multi-dimensional analyses across numerous biomedical applications, including drug metabolism, disease mechanisms, environmental toxicity assessments, and host-microbiome interactions. This interdisciplinary integration is driving a paradigm shift in precision and translational medicine. However, several challenges remain to be addressed, such as the development of whole-organ mimetics, adaptation of sample collection techniques, and real-time artificial intelligence-based integration of biosensor data with multi-omics datasets. Addressing these hurdles will be vital for unlocking the full potential of this technological synergy in biomedical science.

Multiomics↗
PubMed2025

Pharmacogenomic insights into angiotensin converting enzyme inhibitors and calcium channel blockers for personalized hypertension treatment.

Arterial hypertension is a complex disorder influenced by extensive genetic variability, which contributes to interindividual differences in drug response by altering metabolism, transport, and receptor interaction. Current antihypertensive therapies effectively control arterial hypertension in only about half of patients, emphasizing the need for precise strategies. Genetic variation plays a crucial role in modulating drug response, and integrating this knowledge into clinical practice could significantly transform the management of hypertension through personalized medicine. This review examines the impact of genetic factors on the efficacy of antihypertensive drug classes, including angiotensin converting enzyme inhibitors and calcium channel blockers. It also examines advances in pharmacogenomic research that can aid in tailoring drug selection and dose adjustment based on genetic profiles. Beyond genomics, this review also highlights the impact of multiomics approaches, such as proteomics, metabolomics, and microbiomics, in advancing precision medicine and enabling a comprehensive, personalized approach to hypertension management. Pharmacogenomics can help refine hypertension care, improve patient outcomes, and reduce the burden of the disease. The future of hypertension treatment lies in precision medicine, where therapy is tailored to individual needs for effective and personalized management.

Humans↗
PubMed2025

The application of AI-driven and engineered intratumoral microbes in cancer therapy.

BACKGROUND: Although investigations of the intratumoral microbiota date back thousands of years, breakthrough transformations have only recently been achieved through high-throughput sequencing and multiomic technologies. These advances have revealed diverse and tumor type-specific microbial communities that drive carcinogenesis via immunomodulation, metabolic reprogramming, and genomic instability. Current cornerstones of cancer therapies-including chemotherapy, radiotherapy, immunotherapy, and targeted therapy-are limited by systemic toxicity, localized tissue damage, drug resistance, and low patient response rates. These constraints underscore the urgent need for more effective and precise therapeutic strategies. MAIN BODY: This review comprehensively integrates artificial intelligence (AI) technologies into the characterization of the intratumoral microbiota, facilitating the development of novel computational pipelines for mapping microbe-host crosstalk. We systematically summarize recent advances in engineered microbial therapeutics, including bacteria designed for targeted antitumor activity and engineered microorganisms that enable the localized delivery of therapeutic agents. Furthermore, this review critically evaluates the safety profiles of microbiota-based interventions and discusses key challenges in clinical translation. CONCLUSIONS: By combining cutting-edge computational technologies, biological research, and clinical insights, this review aims to bridge the gap between microbiome science and oncological practice, pioneering innovative strategies for microbiota-guided diagnostics and personalized cancer therapy.

Humans↗
PubMed2026

Fungi to the rescue: recent advances, mechanistic insights and omics-based perspectives in heavy metal mycoremediation.

Heavy metal (HM) contamination arising from rapid industrialization poses critical threats to global ecosystem integrity and public health. Conventional physicochemical approaches are limited by high costs, incomplete removal, and toxic waste generation, necessitating sustainable alternatives. Mycoremediation, which harnesses the remarkable, diverse capacities of fungi to tolerate and mitigate HM stress through sophisticated biological mechanisms, has emerged as a promising and sustainable approach to address HM pollution. This review examines the sources and ecotoxicological impacts of HM pollution, alongside the intracellular and extracellular mechanisms underlying fungal tolerance and removal, including biosorption, precipitation, membrane transport, antioxidant defense, chelation, bioaccumulation, and biotransformation. It further synthesizes fungal-based bioremediation strategies, while examining how metagenomic, metatranscriptomic, transcriptomic, proteomic, and metabolomic approaches are advancing understanding of fungal community structure and active detoxification pathways. This work uniquely integrates community- and isolate-level multi-omics data, explicitly bridges mechanistic understanding with omics-driven insights, and extends this into translational roadmap for applied bioremediation.

Biodegradation, Environmental↗
PubMed2026

A One Health approach to Antimicrobial Resistance: Concepts, challenges, and advances in omics.

Antimicrobial resistance (AMR) is a global threat driven by the interplay between microbial evolution and human activity. Antimicrobial use in human and veterinary medicine, as well as in agriculture, accelerates the selection and dissemination of resistant bacteria and genes across interconnected human, animal, and environmental reservoirs. These dynamic exchanges render single-sector interventions ineffective. A One Health approach integrating human, animal, and environmental health is therefore essential to understand and mitigate the emergence and spread of AMR. This chapter focuses on bacterial antimicrobial resistance, addressing key concepts, major challenges, and emerging technologies within a One Health framework. Advances in next-generation sequencing and omics technologies have transformed our capacity to resolve AMR at unprecedented scale and resolution. These tools enable the tracking of resistance genes and high-risk clones across ecosystems, uncover transmission pathways, and identify key drivers of dissemination. Such insights support real-time epidemiological surveillance, outbreak detection, and targeted interventions. However, translating these advances into routine practice remains a major challenge, requiring harmonized methodologies, data integration, and cross-sector coordination. Addressing AMR demands sustained collaboration across disciplines and stakeholders, including clinicians, veterinarians, farmers, researchers, policymakers, industry, and the public. And framing AMR as a shared ecological and societal responsibility underscores the urgency of coordinated global action. We call for the urgent integration of One Health principles into surveillance, policy, and innovation to preserve antimicrobial effectiveness and safeguard future health.

Humans↗
PubMed2026

Discovering hidden candidate plastic-degrading enzymes: Combined multi-omics and machine learning strategy.

Plastic pollution poses a major threat to the stability of natural ecosystems as well as human health. Microbial enzymes have long been considered a potential resource for targeted biodegradation but, except for a few successful cases, the discovery of efficient enzymes has proved challenging. Aiming to accelerate the process, we propose an approach combining metagenomics, metatranscriptomics and semi-supervised learning that selects promising plastic-degrading candidate enzymes from the proteome of relevant microorganisms. Tested on a dataset of over 10,000 microbial proteins, ranking models consistently prioritize known plastic-degrading enzymes, achieving an area under the cumulative distribution function curve above 0.96, with leave-one-family-out cross-validation indicating that performance is largely retained across protein families. As a case study, this work focuses on mixed microbial cultures exposed for extended periods to polyethylene, polyethylene terephthalate, and polyurethane substrates. The prevalent species after selective enrichment were functionally characterized, finding Rhodococcus aetherivorans as the most relevant species in two of the five cultures under investigation. Among the top-ranked proteins, several have high structural similarity with known enzymes despite not being identified by sequence similarity search. Moreover, according to metatranscriptomics results, several of these enzymes were found to be expressed at the same level or above that of annotated enzymes, suggesting that they may have functional relevance. Overall, this work highlights the potential of integrating multi-omics with data-driven methods for enzyme discovery and for accelerating the development of biotechnological solutions to plastic pollution.

Biodegradation, Environmental↗
PubMed2026

Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

Multiomics↗
PubMed2026

Integrated multi-omics analysis of metabolomics and proteomics uncovers dysregulated amino acid metabolism in HCC metastasis.

BACKGROUND: Metastasis is the primary cause of treatment failure and adverse prognosis in hepatocellular carcinoma (HCC), and the molecular basis of HCC metastasis remains poorly defined. This work investigated the potential mechanisms underlying HCC metastasis through integrated multi-omics analysis of metabolomics and proteomics. METHOD: This retrospective study included 105 individuals with HCC, with comparative analysis between metastatic and non-metastatic cases. We further evaluated the effects of metastasis on serum metabolomics and proteomics in HCC patients. RESULT: Widespread disturbances in amino acid metabolism were identified via untargeted metabolomics in HCC patients with metastasis, closely governing inflammation-related metabolic remodeling and oxidative stress responses. Specifically, we identified 91 and 59 distinct differential metabolites capable of indicating HCC metastasis, with the screening criteria set as log2 fold change > 1.5, adjusted P value < 0.05, and VIP > 1.5 in positive and negative modes, respectively. The alanine, aspartate and glutamate metabolism pathway correlated with HCC-associated lung metastasis, while the gluconeogenesis pathway was linked to HCC-associated bone metastasis. Compared with HCC (non-metastatic hepatocellular carcinoma), the key molecular alterations in the multi-omics network of HCC_M (HCC with metastasis) are implicated in inflammatory metabolic reprogramming, oxidative stress response, gluconeogenesis, glycolysis, and the tricarboxylic acid (TCA) cycle. Twenty-five proteins, including PKM2, PERCK, ALDH2, CPS1, GLS1, GLUD1, GOT1, and SLC38A2, were identified as potential biomarkers for HCC metastasis. CONCLUSION: By integrating untargeted metabolomic and proteomic profiling, we identified distinct metabolic and proteomic changes linked to HCC metastasis. This work also characterized the pathological characteristics and core pathways underlying HCC metastasis, while identifying potential therapeutic candidates.

Humans↗
PubMed2026

Project ODIN: advancing environmental genomic surveillance for public health across sub-Saharan Africa.

Persistent SARS-CoV-2 transmission, ongoing mpox outbreaks, and the continued spread of endemic diseases such as typhoid fever and cholera underscore the urgent need for global, multiomics surveillance. In this Personal View, we present Project ODIN, a consortium of European and African partners launched in 2023 that aims to meet this challenge by deploying innovative systems for near real-time pathogen detection and actionable public health insights. The project is a collaboration between high-income and low-income countries in northern Europe and sub-Saharan Africa. Focusing on low-income and middle-income countries, ODIN integrates metagenomics with mobile laboratory systems for comprehensive pathogen monitoring across diverse environments. ODIN emphasises standardised sampling, bioinformatics pipelines, and data-sharing protocols to ensure reliable, interoperable results while addressing infrastructure and resource limitations. By bridging gaps in genomic surveillance, these initiatives seek to strengthen outbreak preparedness, improve pathogen detection, monitor antimicrobial resistance, and provide a holistic approach to One Health challenges. Together, these innovations could advance global surveillance capacity-particularly in under-resourced regions-paving the way for effective disease control and evidence-based policy making.

Humans↗
PubMed2026

Is impulsivity simply a failure of self-control? Evidence based on multi-omics analyses of genomics, metabolomics and brain imaging.

High impulsivity-a hallmark of various adverse life outcomes such as substance abuse, impulsive buying, violence, and crime-has typically been considered as a failure of self-control. However, is impulsivity simply a failure of self-control? To address this issue, we employed multi-omics combined with brain imaging approach in a large-scale sample (Nbrain imaging=1524, Ngenomics=835, Nmetabolomics=946) to elucidate the relationship between impulsivity and self-control. Mendelian randomization showed a bidirectional association between impulsivity and self-control, suggesting that they influenced each other. Partial least squares analysis highlighted that self-control primarily implicates the frontal lobe regions (e.g., superior frontal gyrus), whereas impulsivity involves the amygdala, insula, and basal ganglia. The cerebellum, superior frontal gyrus, and middle frontal gyrus were identified as shared areas in impulsivity and self-control. Furthermore, gene-based association analysis identified heterochromatin protein 1 binding protein 3 as specifically related to impulsivity, while pathway enrichment analysis demonstrated that arginine and proline metabolism was a common metabolic pathway associated with both impulsivity and self-control. Overall findings demonstrate that impulsivity and self-control involve both shared and distinct brain regions, genetic and metabolic foundations. The brain imaging results suggest that impulsivity is related not only to self-control-related processes but also to the motivation to pursue rewards. Together, this large-scale integrative study firstly provides a side-by-side map of genomic, metabolic, and limbic-network signatures of impulsivity distinct from self-control, offering a foundation for mechanism-driven biomarker and intervention research in maladaptive impulsivity.

Impulsive Behavior↗
PubMed2026

Inclusion of Multi-Omic Biomarkers Improves Prediction Accuracy of Response, Relapse, and Overall Survival in Acute Myeloid Leukemia Patients Receiving High-Intensity Induction Chemotherapy.

BACKGROUND: Despite advancements in genetic markers for acute myeloid leukemia (AML) risk stratification, outcome prediction remains challenging due to disease heterogeneity and dynamic genetic changes, highlighting the need for reliable biomarkers to improve AML treatment strategies and patient outcomes. To refine outcome predictions, we investigated the use of microbial-derived biomarkers to predict composite complete remission (CRc), relapse, and survival for patients on high- and low-intensity regimens, and to integrate those variables into the widely clinically utilized European Leukemia Network (ELN-2022) genetic risk classification model for high-intensity-treated patients. METHODS: We first developed machine learning models that integrate baseline fecal metabolomics, 16S rRNA-based stool microbiome features, and clinical metadata (sex, antibiotic administration, AML somatic mutations, and cytogenetics) from two cohorts of AML patients (n&#x2009;=&#x2009;83) undergoing remission induction chemotherapy. Univariate tests and sparse canonical correlation analysis were employed for variable selection and to explore fecal metabolite-microbe relationships. A robust machine learning approach using XGBoost was employed, with 100 stratified data splits (80% training, 20% testing) and coarse-to-fine hyperparameter optimization. Variable importance was aggregated across all models to select key predictors. RESULTS: For high-intensity-treated patients, XGBoost models achieved aggregated AUROC scores of 0.719, 0.729, and 0.65 for CRc, relapse, and overall survival, respectively. For low-intensity-treated patients, these models achieved aggregate AUROC scores of 0.945, 0.724, and 0.768 for these same outcomes, respectively. Integrating the biomarkers identified in the high-intensity machine-learning models with the current ELN-2022 AML risk stratification system effectively stratified patients into risk categories, which obtained higher concordance indices and likelihood ratios, demonstrating improved prognostic accuracy for each outcome compared to ELN-2022 alone. CONCLUSIONS: The inclusion of microbial-derived biomarkers serves as a robust prognostic tool to improve outcome prediction in AML patients, highlighting the potential of its integration into AML risk assessment and paving the way for personalized treatment strategies and improved patient outcomes.

Humans↗
PubMed2026

Integrative TWAS and multi-omics analyses prioritize HSPE1 as a candidate risk gene for bipolar disorder with immune cell-specific regulatory evidence.

BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder associated with substantial disability. Although genome-wide association studies have identified multiple BD-associated loci, the underlying genes and mechanisms remain incompletely understood. METHODS: We integrated a European-ancestry BD genome-wide association dataset with cross-tissue and tissue-specific transcriptome-wide association studies (TWAS) and complementary gene-based analysis. Candidate genes were further evaluated using differential expression analysis, consensus clustering, immune infiltration analysis, machine learning, summary-data-based Mendelian randomization, Mendelian randomization using single-cell expression quantitative trait locus data, single-nucleus transcriptomics, phenome-wide association analysis, and virtual screening. RESULTS: The integrative analyses prioritized 37 candidate genes. Peripheral-blood differential-expression analysis identified 14 genes that remained significant after FDR correction, and their expression profiles separated BD samples into two expression-defined clusters. Machine-learning analysis selected UNC50, LMAN2L, LYG2, HSPE1, and KANSL3 for an exploratory classification nomogram. SMR associated genetically predicted higher HSPE1 expression with increased BD risk in two blood eQTL datasets. Cell-type-specific analyses indicated HSPE1-related associations in T-cell and natural killer cell subsets, while single-nucleus analysis descriptively showed higher HSPE1 expression in medial thalamic T cells from BD samples. PheWAS identified no genome-wide significant associations for HSPE1, whereas virtual screening identified candidate compounds with favorable predicted docking scores against the HSPE1 structure. CONCLUSION: This integrative multi-omics study identified HSPE1 as a candidate BD risk gene with immune-cell-related regulatory evidence, providing insight into BD pathogenesis and supporting functional validation.

Humans↗