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PubMed2026

Dose-dependent IFN programs in myeloid cells after mRNA and adenovirus COVID-19 vaccination.

BACKGROUNDThe SARS-CoV-2 pandemic provided a rare opportunity to study how human immune responses develop to a novel viral antigen delivered through different vaccine platforms. However, to date, no study has directly compared immune responses to all 3 FDA-approved COVID-19 vaccines at single-cell multiomic resolution.METHODSWe longitudinally profiled SARS-CoV-2-naive adults (n = 31) vaccinated with BNT162b2, mRNA-1273, or Ad26.COV2.S, integrating plasma cytokines, antibody titers, and single-cell multiomic data (DOGMA-Seq).RESULTSWe discovered a distinct, transient IFN program termed ISG-dim, which emerged specifically 1-2 days after the first mRNA dose in approximately 10% of myeloid cells. This state was characterized by ISGF3 complex activation and its target genes (e.g., MX1, MX2, DDX58), with transcriptional and epigenetic profiles distinct from the robust IFN program observed after mRNA boosting or a single Ad26.COV2.S dose (ISG-high). In vitro stimulation of human monocytes showed that IFN-α alone recapitulates ISG-dim, whereas both IFN-α and IFN-γ are required for ISG-high.CONCLUSIONThese findings define dose-dependent IFN programming in human myeloid cells and highlight mechanistic differences between priming and boosting, with implications for optimizing vaccine platform choice, dose scheduling, and formulation.FUNDINGNIH grants AI142086, U19 AI135972, U01 AI165452, U01 AI165452, R01 AI160706, and P30 AG067988.

Humans↗
PubMed2026

AI-Based 3D Heterogeneous Network Model for Functional Prediction of Epigenetics.

Human biology and diseases are the result of constantly evolving processes within an intricately complex molecular network of interactions, such as epigenetic regulation. Epigenetics refers to heritable changes in gene expression that occur without alterations to the underlying DNA sequence. These changes, driven by mechanisms such as DNA methylation, histone modifications, and noncoding RNAs, play critical roles in regulating chromatin structure and gene activity. Epigenetic regulation offers valuable insights into biological systems, and when integrated with sophisticated analyses, it enables us to gain insights into gene regulation and cellular behavior. Here, we describe an artificial intelligence (AI)-based model that is capable of generating 3-dimensional (3D) heterogeneous network by integrating multimodal data for the functional prediction of epigenetic mechanisms, emphasizing its applications in medicine, developmental biology, and personalized therapeutics. Heterogeneous networks in biology are powerful tools for understanding the complex interactions and interdependencies within biological systems. Key advancements in AI and multiomics data integration have propelled this field, offering new insights into disease mechanisms, biomarker discovery, and therapeutic interventions.

Epigenesis, Genetic↗
PubMed2027

Teratoma Formation and Genomic Profiling Using Multi-Omics Approaches.

Teratoma formation is the gold standard assay for evaluating the developmental pluripotency of human and mouse embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). Following subcutaneous injection into immunodeficient mice, pluripotent stem cells spontaneously differentiate into derivatives representing all three embryonic germ layers-ectoderm, mesoderm, and endoderm. Beyond serving as a functional assay for pluripotency, teratomas provide a unique three-dimensional model system for studying early human development and lineage specification in vivo. This chapter describes comprehensive protocols for teratoma formation in immunodeficient mice, tissue processing for multiple downstream genomic applications, and multi-omics profiling approaches. We detail methods for embryonic stem cell culture, teratoma generation via subcutaneous injection, tissue dissection and processing for chromatin immunoprecipitation followed by sequencing (ChIP-Seq), RNA sequencing (RNA-Seq), single-cell multiome profiling combining chromatin accessibility (ATAC-Seq) and gene expression (scRNA-Seq), and histological analysis using hematoxylin and eosin (H&E) staining. Additionally, we provide bioinformatics workflows for analyzing the resulting genomic datasets to characterize the epigenetic and transcriptional landscapes of teratoma-derived tissues. These methods enable comprehensive molecular characterization of developmental processes and provide valuable resources for stem cell biologists studying pluripotency, differentiation, and early embryonic development.

Teratoma↗
PubMed2026

Post-genome-wide association study variant-to-function challenges in asthma research.

Genome-wide association studies of asthma have identified nearly 200 independent loci, yet the mechanisms through which individual loci influence asthma risk remain largely unknown. A growing array of computational and experimental tools has begun to fill these gaps by identifying causal variants and effector genes and characterizing their functions. In parallel, emerging studies are exploring the translational applications of genetic and multiomics data in asthma, including defining molecular endotypes and predicting disease risk. Here we review the strengths and limitations of current approaches for addressing the post-genome-wide association study challenges and discuss the next tier of questions and directions for the field.

Humans↗
PubMed2026

Exploring the Translation of Organ-on-a-Chip Technology for Human-Relevant Diagnostic Biomarkers.

Microphysiological systems (MPSs) are gaining traction as a viable alternative model for toxicity studies. Further characterization is necessary to explore the full translational potential of MPSs to human physiology, along with the utility of these platforms to serve as a diagnostic tool. Multiomics analyses have emerged as a key means for identifying host biomarkers associated with chemical and drug exposure. Correlations between published human omics and MPS technology omics data will inform the potential of organ chips to accurately represent human responses and provide an alternative approach for improved biomarker discovery for toxicity assessment and exposure identification. To interrogate these potential overlaps, TissUse Chip3 multiorgan chips (MOCs) seeded with kidney organoids, liver organoids, and respiratory tract tissue were exposed to low, therapeutic, and toxic doses of acetaminophen (n = 4 for each condition) for 24 h and subjected to proteomic and metabolomic analysis. The data from our organ chips are largely consistent with biomarkers and dysregulations identified in published human omics data, in vitro and in vivo data, to include the identification of several known acetaminophen metabolites and biotransformation products. These data suggest that organ chips may be a suitable surrogate for human biomarker identification and drug or hazardous chemical exposure diagnosis.

Humans↗
PubMed2026

Multimodal Integration of Protein Interactomes With Genomic and Molecular Data Discovers Distinct Rheumatoid Arthritis Endotypes.

OBJECTIVE: Rheumatoid arthritis (RA) is a heterogeneous autoimmune disease characterized by clinical and molecular heterogeneity, notably in the presence of anti-cyclic citrullinated peptide (CCP) antibodies. Patients with CCP+ RA exhibit more severe disease progression and distinct treatment responses compared to patients with CCP- RA. Although previous studies have investigated cellular and molecular differences between these subtypes, their genetic differences are understudied. METHODS: We leveraged the Rheumatoid Arthritis Comparative Effectiveness Research cohort, comprising 555 patients with CCP+/rheumatoid factor (RF)+ RA and 384 patients with CCP-/RF+ RA. Using a novel framework, we integrated a network-based genome-wide association study (GWAS) with multiomic data to uncover corresponding genetic and molecular differences. RESULTS: We uncovered a significant heritability difference between these disease groups. Network-based GWAS uncovered 14 putative gene modules, including many genes outside the HLA loci, that explained genetic differences between CCP+/RF+ and CCP-/RF+ RA. Heritability partitioning and multivariate expression analyses validated four modules, highlighting novel genetic loci underlying phenotypic differences. Module functional significance was established using multiple orthogonal cohorts, underscoring their biologic relevance. CONCLUSION: Our findings demonstrate the use of network-based approaches in revealing differential genetic risk factors underlying CCP+/RF+ and CCP-/RF+ RA. Disease-associated gene modules detected in synovial tissue were also observed in peripheral blood, indicating joint-specific molecular programs are reflected systemically. This cross-tissue concordance highlights the potential for blood-based assays to capture pathogenic mechanisms active in the joints, enabling practical patient stratification. Our findings highlight why patients with CCP+/RF+ and CCP-/RF+ RA exhibit distinct clinical courses and therapeutic responses, supporting precision-guided treatment strategy development in RA.

Humans↗
PubMed2026

The molecular landscape of chordoma: Current frontiers from multi-omics to artificial intelligence.

Chordoma is a rare and aggressive malignant bone tumor of the axial skeleton that has historically challenged clinicians due to its complex anatomical locations and a high recurrence rate of up to 85%. This review synthesizes the most recent advances in chordoma research and offers an overview of how multi-omics, advanced immunology, and artificial intelligence are reshaping the treatment paradigm. Central to its pathogenesis is the T-box transcription factor Brachyury, which this review highlights as both the pathognomonic diagnostic marker and the primary therapeutic vulnerability. Cutting-edge innovations targeting this driver include covalent small-molecule binders, targeted protein degradation, and peptide-centric CAR-T cells designed to attack the intracellular oncoprotein. The tumor immune microenvironment is functionally dynamic, and new dimensions in cellular therapy, such as dual-specific CAR constructs and NK-cell platforms, are being engineered to neutralize immunosuppressive factors. Beyond biological insights, the review emphasizes the role of computational biology, specifically how deep-learning and machine-learning models achieve expert-level precision in tumor segmentation and personalized survival forecasting. By integrating genomic, transcriptomic, epigenomic, and proteomic data, multiomics approaches can fully elucidate chordoma subtypes and underlying resistance mechanisms, ultimately paving the way for more precise and personalized therapeutic strategies.

Humans↗
PubMed2026

Low-Grade Myofibroblastic Sarcoma Represents an Epigenetically Distinct Myofibroblastic Tumor With USP6 Upregulation and Stable Genome.

Low-grade myofibroblastic sarcoma (LGMS) is a rare, indolent mesenchymal neoplasm exhibiting myofibroblastic differentiation, with a propensity for local recurrence. The molecular basis of LGMS and its precise relationship with other histological mimics have remained largely undefined. To address this gap, we conducted the first comprehensive multiomics analysis of 6 LGMS cases, integrating whole-exome sequencing, RNA sequencing, and Illumina Methylation EPICv2 array profiling with comparative analysis against public sarcoma methylation cohorts and related fibroblastic tumors. Clinically, patients (median age 35.5 years) presented with small tumors (median size 1.45 cm), predominantly located in the head and neck, displaying classic histological features of diffusely infiltrative spindle cell fascicles with patchy mononuclear inflammation. Two of the 5 patients with follow-up developed local recurrence, and none metastasized (median follow-up duration 92.5 months). Genomically, all LGMS exhibited a low tumor mutational burden (median 2.31 mut/Mb) and a minimal fraction of genome altered, with TP53 and TSC2 deletions and NTRK1 and ERBB3 amplifications found in a subset of cases. No pathogenic fusions were detected. Transcriptomic profiling revealed a distinct signature featuring prominent USP6 overexpression and upregulation of inflammatory and immune-related genes, including CD274 (PD-L1), and enrichment of inflammatory and interferon-gamma response signatures. Epigenetically, LGMS formed a unique methylation cluster closest to inflammatory myofibroblastic tumor, with numerous differentially methylated regions and higher immune infiltration, particularly monocytes, compared with other fibroblastic tumors. These findings establish LGMS as a genomically stable, epigenetically distinct myofibroblastic sarcoma driven by USP6 overexpression and an inflammation-enriched transcriptome. They support its recognition as a standalone entity, facilitate integration into methylation-based sarcoma classifiers for improved diagnostic precision, and nominate USP6-associated pathways and immune checkpoint blockade as promising therapeutic strategies for recurrent or unresectable disease.

Humans↗
PubMed2026

Proteins as Regulators of Metabolic Changes in Sepsis: Alterations in Body Fluids, Immune Cells, and Organs through the Eyes of Proteomics.

Sepsis is a life-threatening syndrome characterized by a dysregulated host response to infection and profound metabolic alterations that contribute to immune dysfunction and organ failure. This Review synthesizes proteomic evidence on sepsis-associated alterations in proteins involved in metabolic pathways across circulating biofluids, immune cells, and organs. Across plasma and urine, proteomic studies identify disturbances in lipoprotein-associated pathways, redox homeostasis, mitochondrial function, and substrate metabolism, indicating that protein signatures of metabolic dysregulation are systemic and detectable across biofluids. In immune cells, monocytes and neutrophils, proteomic analyses reveal a shift toward glycolysis with concurrent impairment of mitochondrial pathways alongside phenotype-dependent differences in lipid and redox-related programs. Organ-level studies further show that metabolic responses are heterogeneous, with distinct trajectories in the kidney, heart, liver, lung, skeletal muscle, and brain. These observations support the concept that sepsis involves compartment-specific remodeling of metabolism-associated protein networks rather than a single convergent metabolic state. Proteomics also highlights potential translational opportunities by identifying metabolism-associated proteins linked to disease severity, clinical phenotypes, and biologically distinct patient subgroups, although the current evidence remains largely exploratory and context-dependent. Overall, proteomics provides a complementary framework for understanding the molecular regulation of sepsis-associated metabolic dysfunction and may refine biological stratification and therapeutic targeting, particularly when integrated with longitudinal sampling and multiomic data.

Humans↗
PubMed2026

Imaging-Guided Omics Technologies for Resolving Rare Cancer States and Advancing Nanomedicine.

The ability to resolve rare and transient cellular states is critical for understanding metastasis, immune evasion, and therapy resistance in cancer, yet these dynamic processes often escape detection by conventional sequencing and imaging approaches. Recent advances at the interface of nanotechnology, high-resolution live-cell imaging, and single-cell/spatial multiomics methods have enabled functional profiling of cells with unprecedented precision within their native microenvironment. In this Mini-Review, we highlight emerging nanoscale platforms that couple real-time phenotypic imaging with molecular readouts, such as FUNseq and CIN-seq, to directly link functional heterogeneity to transcriptomic, proteomic, and epigenomic information. By integrating nanoscale optical imaging, microengineered perturbation tools, and AI-driven computational analysis, these technologies open up new avenues for dissecting rare metastatic, therapy-resistant, or immune-evasive subpopulations. We further discuss how these next-generation imaging-guided single-cell and spatial omics platforms not only advance fundamental cancer biology but also create opportunities to accelerate the development of nanomedicine applications.

Humans↗
PubMed2026

BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.

MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Humans↗
PubMed2026

Spatially resolved single-cell atlas reveals the macroevolutionary trajectory of animal hearts.

Animal hearts display diverse anatomical structures during adaptive evolution. Here, we present a multiomics atlas of adult hearts from 27 species across chordates, arthropods, and mollusks. Joint analysis indicates that Bilateria hearts share a core gene repertoire, taking a stepwise "add-on" approach as a universal evolutionary strategy. The "proto-heart" is populated by key cell types, including cardiomyocytes, fibroblasts, endothelial cells, and neural cells, which maintained core signatures while evolving with shifts in living environments and corresponding adaptations in the cardiovascular system. Additionally, we reveal an evolutionarily conserved cardiomyocyte state dynamic potentially linked to cardiac development and stress responses. Finally, we identify a common molecular program underpinning chamber evolution from a ventricular foundation. This work establishes a resource for understanding the intrinsic mechanisms of heart evolution.

Animals↗
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

The effect of sex and endurance exercise training on the incretin signaling pathway in 17 rat tissues.

Incretin-based pharmacotherapies, particularly glucagon-like peptide-1 (GLP-1) receptor agonists, have transformed the treatment of type 2 diabetes, with demonstrated benefits across multiple organ systems. Their success has driven the investigation of related gut-derived hormones, most prominently dual GLP-1/glucose-dependent insulinotropic polypeptide (GIP) receptor agonists, but extend to other targets with similar metabolic functions. For this class of drugs, the extent to which organ health improvements are secondary to improved systemic glycemic control versus direct tissue signaling remains unclear, partly because receptor availability across tissues is poorly annotated. We leveraged data from the Molecular Transducers of Physical Activity Consortium to annotate incretin receptor expression across 17 tissues in Fischer 344 rats and the Genotype-Tissue Expression Portal for human-level receptor expression. Furthermore, given the role of exercise in the preservation of muscle mass during weight loss, we analyzed the effects of 1, 2, 4, or 8 wk of treadmill exercise training on incretin-related signaling at the epigenetic, transcript, and protein levels. Endurance training elicited sex- and tissue-specific changes in incretin receptor expression, including downregulation of Gcgr across brown adipose, adrenal glands, and white adipose tissue (WAT). Training-induced Gipr regulation occurred in the adrenal glands, brain cortex, and hippocampus. Collectively, these findings contribute to the map of incretin receptor biology and identify exercise-responsive regulatory axes that may underlie synergistic effects of exercise and incretin-based therapies on weight management and metabolic health.NEW & NOTEWORTHY This study provides the first multiomic, multitissue description of incretin signaling receptor expression and regulation in response to endurance exercise training. We identify time point and sex-specific changes in incretin signaling across tissues, highlighting training effects on Gcgr, Gipr, and Sctr regulation in the adrenals, WAT, and brain. These findings help establish an exercise-responsive incretin signaling axis that may identify interactions from incretin-based therapies and exercise-based lifestyle interventions.

Animals↗
PubMed2026

Mechanistic Perspectives From Genomics and Pangenomics of Medicinal and Aromatic Plants: Linking Genome Architecture to Phytochemical Diversity.

Medicinal and aromatic plants (MAPs) produce a remarkable diversity of specialized metabolites with significant pharmaceutical, nutraceutical, and industrial value. Although advances in long-read sequencing, chromosome-scale genome assembly, and pangenomics have greatly expanded genomic resources, the mechanistic links between genome architecture and phytochemical diversity remain incompletely understood. The present review synthesizes current evidence describing how structural genomic variation may contribute to phytochemical diversity, while acknowledging that many proposed genome-to-metabolite relationships require further experimental validation. Examples illustrate how genome architecture is associated with specialized-metabolite biosynthesis through multiple regulatory processes. However, the strength of supporting evidence varies considerably among MAP species. Moreover, relatively few genome-to-metabolite relationships have been confirmed through direct functional validation. We further discuss how pangenomics, multiomics integration, genome editing, synthetic biology, and artificial intelligence support the discovery, validation, and engineering of specialized metabolic pathways. Casual conclusions are evaluated according to the strength of available evidence, highlighting where causal relationships have been experimentally established and where conclusions remain primarily association-based. Overall, this review provides an integrated conceptual and evidence-based perspective summarizing proposed relationships between genome architecture and phytochemical diversity and outlines future priorities for functional genomics, precision breeding, metabolic engineering, and sustainable utilization of MAPs.

artificial intelligence↗
PubMed2026

Proteogenomic features define subtypes of mantle cell lymphoma.

Mantle cell lymphoma (MCL) is a biologically heterogeneous B-cell malignancy. Although genomics and transcriptomics have delineated parts of the MCL disease spectrum, proteomics remains largely unexplored. Here, we conducted a comprehensive proteogenomic analysis integrating genomics, transcriptomics, and proteomics on peripheral blood samples from 27 patients with MCL and 4 healthy donors to investigate the translational and posttranslational dimensions of MCL. Our study identified 1296 downregulated and 468 upregulated proteins in MCL cells. The splicing pathways were significantly upregulated at both the mRNA and protein levels, suggesting a critical role for aberrant RNA splicing in MCL pathogenesis. Integration of proteomic data with genetic aberrations revealed immunoglobulin heavy chain variable mutational status and CCND1 mutation are associated with distinctive transcriptomic and proteomic profiles, which correspond to significant differences in clinical outcomes. A multiomics molecular stratification model incorporating proteomic data showed superior predictive power for patient survival compared with single-omics models (concordance index, 0.83 vs 0.74). This study provides, to our knowledge, the first comprehensive proteogenomic profile of MCL, offering novel insights into its molecular mechanisms and clinical behavior. The identification of molecular subtypes and prognostic protein signatures underscores the potential of proteomics to guide precision medicine strategies for MCL.

Humans↗
PubMed2024

Integrative omics analysis identifies biomarkers of septic cardiomyopathy.

Septic Cardiomyopathy (SCM) is a syndrome of acute cardiac dysfunction in septic patients, unrelated to cardiac ischemia. Multiomics studies including transcriptomics and proteomics have provided new insights into the mechanisms of SCM. In here, a rat model of SCM was established by intraperitoneal injection of lipopolysaccharide (LPS). Biomarkers of SCM were characterized via a multi-omics analysis. The differentially expressed (DE) mRNAs predominantly appeared in pathways linked to the immune response, inflammatory response, and the complement and coagulation cascades, while DE proteins were mainly enriched in pathways associated with the complement and coagulation cascades. On this basis, the integrated analysis was performed between transcriptome and proteome. The potential biomarkers were further verified by RT-qPCR and WB. The current proteotranscriptomic research has furnished a valuable dataset and fresh perspectives that will enhance our comprehension of the development of SCM. This, in turn, is expected to expedite the formulation of novel approaches for the prevention and management of SCM in patients.

Cardiomyopathies↗
PubMed2026

The Health Benefits of Exercise: Molecular and Cellular Mechanisms.

Exercise is a low-cost lifestyle intervention that can prevent and alleviate various diseases. It is a potent physiological stimulus that activates conserved molecular signaling pathways. Through the coordinated integration of multiple molecules, pathways, and systems, it leads to systemic health benefits. However, most studies focus on individual systems or molecular mechanisms, lacking systematic integration of the cross-system regulation induced by exercise. We summarize the molecular mechanisms of exercise in the musculoskeletal, cardiovascular, nervous systems, among others. Exercise induces the release of exerkines (e.g., irisin, interleukin-6, and brain-derived neurotrophic factor) and extracellular vesicles, which activate key signaling pathways to enhance mitochondrial function, metabolism and physiological adaptation, while suppressing inflammation and oxidative stress, thereby alleviating diseases and delaying aging through cross-system coordination. We further explore exercise-induced adaptive regulation in extreme environments, including microgravity, hyperbaria, and hypoxia, offering a multifaceted perspective on organismal health regulation. Finally, we outline the prospects and challenges of multiomics, artificial intelligence-driven precision medicine, personalized exercise prescriptions, and exercise mimetics. Overall, this review provides a more integrated perspective on the molecular basis of exercise and offers directions for future mechanistic and translational studies.

exercise↗