Search Results (556)

Hypertension threatens the health of aging captive giant pandas, yet its molecular signatures remain poorly characterized. Here, multi-omics sequencing was applied to explore the molecular regulatory process of hypertension and the pharmacodynamic effect of levamlodipine on this endangered species. Six aged giant pandas were divided into hypertensive and normotensive groups (three hypertensive and three normotensive pandas) based on clinical phenotypes and blood pressure measurements. A multi-omics approach was employed, including blood RNA-seq, Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq), single-cell RNA-seq (scRNA-seq) of peripheral blood mononuclear cells, and time-series RNA-seq following levamlodipine administration. Our transcriptomic analysis revealed a statistically significant decline in ACE2 transcript abundance (Padj < 0.05), which suggests a possible shift in renin–angiotensin system signaling linked to hypertensive status in giant pandas. Single-cell analysis of 88,693 cells revealed that hypertension-associated genes were predominantly enriched in monocytes and T cells, implicating immune cell activation. Time-dependent transcriptional changes after levamlodipine administration. Temporal gene dynamics showed early activation of metabolic pathways followed by delayed inhibition of ion channels and calcium signaling. This study provides a transcriptional molecular perspective into the pathogenesis of hypertension in giant pandas, which is conducive to developing more effective antihypertensive treatment strategies, thereby protecting the health of this endangered species. Full article

Chronic heart failure [CHF] is a progressive clinical syndrome characterized by structural and functional impairment of the myocardium, in which energy metabolic remodeling plays a central role. Increasing evidence suggests that metabolic disturbances in CHF are not only a consequence of reduced cardiac output but also active regulators of epigenetic remodeling, thereby contributing to disease progression. Key metabolites, including α-ketoglutarate, acetyl-CoA, NAD⁺, S-adenosylmethionine, succinate, and 2-hydroxyglutarate, influence the activity of DNA methyltransferases, histone-modifying enzymes, and other chromatin regulators, thereby linking metabolic status to transcriptional control. Through these mechanisms, metabolic abnormalities promote persistent activation of pathological gene programs associated with cardiomyocyte hypertrophy, fibrosis, inflammation, apoptosis, and mitochondrial dysfunction, forming a self-reinforcing metabolic–epigenetic feedback loop in CHF. Although current guideline-directed medical therapies improve symptoms and clinical outcomes, they do not directly target this metabolic–epigenetic axis. Traditional Chinese medicine (TCM), including bioactive compounds, herbal formulas, patent medicines, and injections, has demonstrated potential in preclinical studies to modulate myocardial energy metabolism, improve mitochondrial function, and influence epigenetic regulators such as SIRT1, AMPK, and TET/JmjC-dependent pathways. However, most available evidence is derived from experimental models, and causal relationships between metabolite regulation, epigenetic remodeling, and cardiac functional improvement remain insufficiently validated. This review summarizes current knowledge on metabolite-driven epigenetic regulation in CHF and evaluates emerging evidence on the role of TCM in modulating this network. We also critically discuss key limitations, including reliance on non-clinical models, incomplete pharmacokinetic understanding, and insufficient causal validation. Finally, we propose future directions based on multi-omics integration, single-cell and spatial technologies, and systems biology approaches to facilitate mechanistic clarification and translational development of metabolism-targeted strategies for CHF. Full article

In this paper, we consider the extremal trace problem for the coupled Laplacian on the sets homeomorphic to the Stiefel manifold defined on the complex Euclidean space. The study is implemented via various mathematical methods, including topological and probabilistic approaches. A detailed, comprehensive classification of the stationary points is given, which itself deserves to be considered as a general method in the framework of the optimization theory. Finally, an application to biologically meaningful integration of heterogeneous datasets, in which the structure of molecular interactions serves as a significant constraint for the mathematical model, is proposed. The main advantage of the elaborated method in comparison with the previously used ones is the absence of any conditions on the structure of the initial heterogeneous datasets. This paper is a continuation of a series of papers by our research group devoted to the development of new mathematical methods for integrating multi-omics data. Full article

Stroke remains one of the leading causes of death and disability worldwide, yet its clinical management is constrained by substantial biological heterogeneity that single-biomarker and single-omics approaches fail to resolve. The integration of multiple molecular data layers, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and immunomics, offers a transformative framework for investigating the underlying neuroinflammatory mechanisms of different stroke subtypes and endotypes. In this review, we synthesize the current multi-omics evidence in stroke by examining how genetic variants propagate through regulatory and immune pathways and generate measurable molecular signatures and clinically relevant biomarkers. We investigate the roles of microglia, infiltrating monocyte-derived macrophages, astrocytes, neutrophils, T cells, and endothelial cells as interacting nodes in the neuroimmune network after stroke, and analyze how spatially resolved single-cell transcriptomics illuminate state-specific programs previously undetectable in bulk tissue analyses. We discuss how proteomics and metabolomics translate these cellular programs into actionable circulating biomarkers and examine emerging evidence on blood–brain barrier disruption and neurovascular unit remodeling as multi-omics-defined targets. We then explore AI and machine learning frameworks enabling the integration of heterogeneous, high-dimensional datasets for endotype classification, patient stratification, and therapeutic response prediction. Finally, we address translational barriers, including analytical standardization, multi-ancestry generalizability, and regulatory readiness, and propose a roadmap for precision stroke medicine based on systems immunology. The core conceptual point of this review is the shift from describing omics findings in stroke cases to redefining biologically meaningful neuroinflammatory endotypes and using multi-omics to enable precision cerebrovascular medicine. Full article

Background/Objectives: While traditional transcriptomics and single-cell RNA sequencing can reveal differences in cell type and gene expression, they cannot provide spatial information within tissues. Spatial transcriptomics (ST), as an emerging technology in recent years, has achieved significant progress in resolving gene expression along the spatial dimension. This technology quantifies gene expression at defined spatial coordinates and describes the spatial distribution of transcripts and the co-localization patterns between cells within intact tissue, allowing for an integrated analysis of molecular and spatial information. This review aims to systematically trace the development of ST and highlight its application value in breast cancer research. Methods: We systematically reviewed the recent literature on ST platforms, on combined analyses of single-cell RNA sequencing (scRNA-seq) and ST, and on integrated spatial multi-omics in breast cancer. Key topics include tumor microenvironment organization, intra-tumor heterogeneity, the spatial distribution of immune cells, cancer-associated fibroblast function, treatment-response prediction, and personalized-treatment strategy development. Results: ST can characterize the spatial organization of interactions between breast cancer cells and the tumor microenvironment, describe the spatial dimensions of tumor heterogeneity, and provide multi-dimensional information that may support refined subtype classification and prognostic assessment. Existing studies indicate that ST shows significant potential to inform personalized treatment strategies, but the technology also faces bottlenecks in data integration, spatial resolution, standardization, and the need for functional validation. Conclusions: ST provides an important tool for an in-depth description of the complex spatial organization within breast cancer tumors. When integrated with functional perturbation, longitudinal cohorts, and orthogonal omics, it has the potential to ultimately improve clinical outcomes for breast cancer patients. Full article

Alzheimer’s Disease (AD) is now understood as a biologically diverse condition, with amyloid and tau pathology evolving within dynamic neuroimmune networks. This challenges the traditional view that AD-related inflammation can be broadly suppressed therapeutically. We review evidence showing that neuroinflammation in AD is stage-dependent, cell-state-specific, spatially organized, and functionally complex. Microglia and astrocytes can aid in plaque containment, debris clearance, synaptic balance, metabolic adaptation, and tissue repair, but may also exacerbate injury through type-I interferon, inflammasome, complement, tumor necrosis factor, and lipid pathways. Many failed anti-inflammatory trials likely stem from mismatches in targets, timing, spatial considerations, pathway redundancy, and biomarker selection, rather than invalidating neuroinflammation as a therapeutic target. Recent single-cell and spatial transcriptomic, proteomic, metabolomic, and network-medicine studies offer a framework for precision intervention by identifying inflammatory endotypes, anatomical niches, and pathway modules. We propose the Stage–State–Space Neuroimmune Reprogramming Model (S3-NRM), aligning AD immunotherapy with disease stage, glial/endotype state, and spatial inflammatory niche, guided by fluid, imaging, and omics biomarkers. Future therapies should selectively suppress harmful immune responses while preserving beneficial glial functions. Full article

Tumor-associated macrophages (TAMs) are among the most important myeloid cell populations in the tumor microenvironment of digestive system tumors and are characterized by marked plasticity, heterogeneity, and context dependence. This review focuses on gastric, colorectal, liver, and pancreatic cancers as representative digestive system solid tumors in which TAM spatial organization has been increasingly characterized by single-cell and spatial omics studies. Traditional M1/M2 polarization or fixed subtype-based classification is insufficient to capture the continuous state transitions of TAMs across tumor types, disease stages, and tissue regions. Recent evidence suggests that TAM heterogeneity reflects dynamic functional states shaped within distinct spatial niches by local oxygen supply, metabolic stress, stromal architecture, vascular status, and interactions with neighboring cells. From a spatial-niche perspective, this review synthesizes current evidence on TAM distribution patterns, phenotypic changes, and functional biases across six recurrent spatial contexts: the hypoxic core, invasive front, fibrotic septa, perivascular regions, tertiary lymphoid structure (TLS)-adjacent regions, and necrotic borders. By linking these niches with cross-niche functional axes and evidence-supported molecular programs, we provide a structured niche-to-function framework for comparing TAM spatial heterogeneity and its major functional dimensions, including metabolic adaptation, tissue remodeling, and immune-inflammatory regulation. This context-sensitive framework may help guide future studies of niche-specific TAM reprogramming and rational combinations with immunotherapy and other treatment strategies. Full article

Dysregulation of ribosome biogenesis is increasingly recognized as a hallmark of tumor malignancy, yet its prognostic implications in lung adenocarcinoma (LUAD) remain incompletely characterized. This study aimed to construct a ribosome biogenesis-related prognostic model for LUAD and explore its potential relevance to the tumor immune microenvironment. Single-cell and bulk RNA sequencing data were integrated to identify ribosome biogenesis-related genes (RBRGs), from which a prognostic risk score was established via Cox regression, LASSO regression, and multivariate Cox analyses and validated in two independent GEO cohorts. Associations between the risk score and tumor mutation burden, immune infiltration, and computationally inferred immunotherapy response were systematically evaluated. In vitro experiments were performed to characterize the biological function of RPS19BP1, a key gene in the model. A total of 262 RBRGs were identified, and the derived 14-gene risk score demonstrated prognostic value across three cohorts (TCGA: 1-, 2-, 3-year AUC = 73.08, 72.44, 72.20; GSE68571: 1-, 2-, 3-year AUC = 67.93, 73.24, 77.59; GSE8894: 1-, 2-, 3-year AUC = 75.56, 72.99, 71.77). The low-risk group exhibited a more immunocompetent tumor microenvironment, whereas the high-risk group was associated with an immunosuppressive phenotype. Knockdown of RPS19BP1 significantly attenuated the proliferation, migration, and invasion of LUAD cells. This multi-omics-derived prognostic model showed prognostic potential in retrospective LUAD cohorts, is associated with distinct immune infiltration patterns, and identifies RPS19BP1 as a pro-oncogenic regulator in LUAD. Full article

Background: High body mass index (BMI) is a causal risk factor for endometrial cancer, but the tumor molecular mechanisms affected by adiposity remain poorly understood. Here, we characterize the tumor multi-omic landscape of endometrial cancers that have developed on a background of lifelong germline genetic liability to elevated BMI. Methods: We built a polygenic score (PGS) for BMI in women using data on independent, genome-wide significant variants associated with adult BMI in 434,794 women. We performed germline (blood) genotype quality control and imputation on data from 354 endometrial cancer cases from The Cancer Genome Atlas (TCGA). We assigned each case in this TCGA cohort their genetically predicted BMI based on the BMI PGS. Multivariable generalized linear models adjusted for age, stage, microsatellite status and genetic principal components were used to test for associations between the BMI germline PGS and endometrial cancer tumor genome-wide genomic, transcriptomic, proteomic, epigenomic and immune traits in TCGA. Results: High BMI germline PGS was associated with (i) upregulated tumor gene expression in IL6-JAK-STAT3 signaling (FDR = 4.2 × 10⁻⁷) and in other immune/inflammatory pathways; (ii) increased estimated intra-tumor activated mast cell infiltration (FDR = 0.008); and (iii) increased single base substitution (SBS) mutational signature 1 (FDR = 0.03), implicating age-related mutagenesis. In contrast, BMI at diagnosis associated with elevated progesterone receptor expression and alterations in estrogen and androgen signaling. Conclusions: Thus, we integrated germline, somatic and clinical data to identify associations between genetically predicted lifelong liability to higher BMI and endometrial cancer tumor molecular features. These associations inform our understanding of how high BMI may influence the development of this cancer, shaping endometrial tumor biology differentially over the long term. Full article

Yaks have long inhabited the Qinghai-Tibetan Plateau. This region features low-oxygen, frigid temperatures and pronounced seasonal variation in nutrient availability. They have evolved adaptive phenotypes centered on energy metabolism reprogramming, tissue structure remodeling, and stress homeostasis maintenance. In recent years, non-coding RNAs (ncRNAs) have been confirmed as an important component of the yak’s post-transcriptional regulatory network. They play a key bridging role between environmental stress perception and phenotypic output through mechanisms such as influencing RNA splicing, stability, translation activity, and constructing competitive endogenous RNA (ceRNA) networks. This article systematically reviews the biogenesis pathways and core regulatory patterns of circular RNAs (circRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs). It focuses on summarizing the expression profile characteristics and dynamic spatiotemporal changes of these three types of ncRNAs in physiological contexts such as muscle and fat deposition, mammary gland lactation, testicular development, and hypoxia response in the heart, lungs, and vascular system of yaks. Current research evidence indicates that the regulatory network of yaks ncRNAs shows significant convergence on multiple key signaling pathways, mainly concentrating on lipid metabolism (PPAR/AMPK), nutrition and growth signals (PI3K-Akt/MAPK/mTOR), extracellular matrix remodeling (ECM-receptor interaction, Wnt/TGF-β), and cell stress fate determination (apoptosis, oxidative stress/ferroptosis) modules. Among them, some core circRNA and lncRNA-miRNA-mRNA regulatory axes have been functionally validated in vitro. Despite the phased progress, current research on ncRNA in yaks still faces bottlenecks: the multi-omics molecular atlases (encompassing genomics, transcriptomics, proteomics, and metabolomics) of key high-altitude adaptive organs remain incomplete, analysis processes lack sufficient standardization, and most studies stay at the association network level with limited causal mechanism validation. To address these limitations, future research should focus on building a standardized evidence chain, integrating multi-omics and single-cell/spatial transcriptome technologies, and conducting mechanism verification for traits in independent populations, thereby providing a solid theoretical basis for understanding the extreme environmental adaptation mechanisms of yaks and molecular breeding improvement. Full article

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by persistent synovitis, progressive cartilage destruction and bone erosion. Recent advances in single-cell and spatial omics, together with immunometabolic studies, have revealed marked state heterogeneity among synovial macrophages in RA. Their metabolic reprogramming appears to sustain pathogenic cellular states, drive aberrant intercellular communication and impair the resolution of inflammation. Rather than acting as an independent initiating factor, it more likely operates as a downstream amplifier of disease. In this review, we outline the principal functional states and metabolic features of synovial macrophages in health and RA. We focus on how the rewiring of glucose, lipid and amino acid metabolism links inflammatory transcription, tissue remodelling and bone destruction. These connections are mediated by metabolic enzymes, metabolic intermediates, redox regulation and epigenetic modifications. We further summarise the immunometabolic effects of currently available antirheumatic drugs. We also appraise the preclinical evidence and translational limitations of metabolic pathway inhibitors, natural products and nanodelivery systems. It should be noted that most existing evidence still relies on in vitro polarisation systems and rodent models. Validation of metabolic flux, cell-state specificity and causal relationships in human synovium remains limited. As a narrative review focused on recent studies of synovial macrophage metabolism in health and inflammation, this work aims to delineate how metabolic reprogramming shapes the phenotypic heterogeneity and pathogenic functions of macrophages in RA. It also seeks to appraise the potential value and current boundaries of evidence for therapeutically targeting macrophage metabolism. Full article

Background/Objectives: Interpretation of high-dimensional molecular profiling data requires computational strategies that go beyond single-layer analyses to resolve coordinated biological programs. Although multi-omics integration has advanced latent structure discovery, translating these cross-omics signals into interpretable, functionally meaningful molecular states remains a central challenge. Methods: Here, we present an interpretable, pathway-centric multi-omics integration framework that combines expression, DNA methylation, and copy-number alteration data to capture nonlinear cross-omics interactions and enable biologically grounded representations of molecular states. We apply this framework towards oesophageal adenocarcinoma, a malignancy that remains relatively underexplored in integrative multi-omics and systems-level analyses, despite its rising incidence and poor prognosis. Results: Using samples spanning Barrett’s oesophagus and oesophageal adenocarcinoma from the Oesophageal Cancer Clinical and Molecular Stratification consortium, we demonstrate the framework’s ability to resolve coordinated oncogenic, metabolic, immune, and cell-cycle programs that evolve across disease states. Conclusions: Together, this work establishes a scalable and interpretable computational strategy for pathway-based multi-omics integration, along with providing a generalisable approach for molecular state assignment in complex biological systems. Full article

High-throughput technologies such as RNA-seq and single-cell transcriptomics generate increasingly large and high-dimensional gene expression datasets in which nonlinear dependence structures are common. Because classical methods primarily capture linear associations, they may fail to characterize many biologically relevant patterns of dependence. To address this limitation, diverse nonlinear dependence measures—including information-theoretic, rank-based, kernel-based, distance-based, copula-based, and clustering-based approaches—have been developed. However, the field remains fragmented, and comparative evaluations are often inconsistent. This review organizes nonlinear methods into major methodological families and critically compares their statistical behavior, strengths, limitations, and characteristic modes of failure. We emphasize that method selection depends on matching inferential objectives to estimator assumptions, analytical constraints, and characteristic failure modes. By identifying recurring trade-offs among flexibility, robustness, interpretability, and computational scalability, we provide scenario-based guidance for method selection in transcriptomics, network inference, and functional genomics. In doing so, we aim to align inferential objectives with analytical requirements, supporting principled and application-specific use of nonlinear dependence methods in modern omics research. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains among the most therapeutically intractable malignancies, with a 5-year survival rate of approximately 10% and near-universal resistance to immune checkpoint inhibitor (ICI) therapy. This refractoriness arises from the convergence of pronounced intratumoral heterogeneity (ITH) and a profoundly immunosuppressive tumor microenvironment (TME), which together configure PDAC as a prototypical immune-excluded tumor. Beyond low tumor mutational burden, PDAC exhibits layered genetic, epigenetic, transcriptional, and metabolic heterogeneity that enables rapid adaptation and immune evasion under selective pressure, while dense desmoplastic stroma, cancer-associated fibroblasts (CAFs), and immunosuppressive immune populations collectively impose formidable physical and immunologic barriers to antitumor immunity. In this review, we synthesize multi-omics, spatial transcriptomic, and immunologic evidence to elucidate how ITH and the TME dynamically interact to reinforce immune resistance. We examine reciprocal crosstalk mechanisms—including immune-driven clonal selection, interclonal cooperation, metabolic niche specialization, and metabolic–epigenetic coupling—and discuss emerging platforms such as single-cell spatial omics, patient-derived organoid immune co-culture systems, and longitudinal circulating tumor DNA monitoring that enable high-resolution mapping of ITH–TME dynamics. Finally, we evaluate ITH–TME-guided combination therapeutic strategies targeting oncogenic drivers, stromal architecture, myeloid suppression, and metabolic checkpoints, and propose a prioritized framework for near-term and speculative clinical translation in PDAC. Full article

Single-cell omics technologies have transformed the study of cellular heterogeneity, enabling high-resolution analysis of tissue development and complex traits. In sheep and goats, these approaches have been applied to skin, hair follicles, reproductive organs, metabolic tissues, and adipose tissue, revealing cell type-specific regulatory programs underlying traits such as wool quality, fertility, growth, and fat deposition. However, most studies rely on single-cell RNA sequencing (scRNA-seq) and are limited by incomplete genome annotation, insufficient coverage of production traits, and weak integration with population genetics, restricting their application in molecular breeding. This review summarizes advances in single-cell omics in sheep and goats, focusing on tissue development and trait formation. We further discuss emerging strategies that integrate single-cell multi-omics, spatial transcriptomics, and population genetics to resolve regulatory mechanisms in a cell type-specific and spatially informed context. Finally, we discuss CRISPR/Cas9-based validation to link genotype and phenotype, accelerating gene discovery and precision breeding in small ruminants. Full article