Molecular omics最新文献

AS1411 is a G-rich DNA aptamer that targets the multifunctional RNA-binding protein nucleolin. AS1411 has both antiproliferative and cell size-regulating activities and has been evaluated for clinical utility, reaching phase II trials as an anticancer agent. The mechanisms underlying cell size effects of AS1411 are not well understood and broad characterization of its molecular effects is lacking. Here, we used a multi-omics approach to profile transcriptome, proteome and lipidome changes in AS1411-treated NIH-3T3 cells, which increase in size in response to the aptamer. We found that AS1411 caused downregulation of cholesterol biosynthesis pathway enzymes at both mRNA and protein levels, without an accompanying reduction in cellular cholesterol levels or cholesterol uptake. In addition, AS1411 induced changes in several lipid classes, including increases in phosphatidylethanolamine levels. Ratiometric imaging of Di-4-ANEPPS-labeled cells showed that AS1411 decreases the fluidity of intracellular membranes. Thus, aptamer engagement of nucleolin affects lipid biosynthesis and homeostasis, likely contributing to its roles in cell size control.

Phosphoinositides (PIPs), the phosphorylated derivatives of phosphatidylinositol (PI), are low-abundance yet critical components of eukaryotic membranes. They play pivotal roles in a wide array of cellular processes, including signal transduction, membrane trafficking, and cell motility. The seven PIP subclasses, generated by phosphorylation at the 3-, 4-, and 5-positions of the inositol ring, are tightly regulated in both spatial and temporal contexts. Dysregulation of PIP metabolism is associated with a range of diseases, including cancer, myopathy, and neurodegenerative and developmental disorders. While the importance of phosphorylation of the inositol ring is well established, recent studies have clarified the role of the fatty acyl chain composition of PIPs. This has resulted in a growing interest in analytical techniques that can determine fatty acyl chain profiles of PIPs. Over the past three decades, substantial advances have been made in mass spectrometry-based techniques, enabling detailed characterization of PIP molecular species, including their phosphate regioisomers. This review provides an overview of the development of mass spectrometric methods for analyzing PIPs, with a particular focus on those enabling the separation of PIP regioisomers and the profiling of their acyl chain composition.

Obesity is a multifactorial condition projected to affect over half of the global population by 2035, posing significant clinical and socioeconomic challenges. Traditional metrics such as body mass index lack precision in predicting individual risk, disease progression, and therapeutic response due to the heterogeneous nature of obesity. Advances in omics technologies such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics have enabled the identification of molecular subtypes and candidate biomarkers that offer deeper insights into obesity pathophysiology. Genomic studies have revealed hundreds of loci associated with obesity related traits, while polygenic risk scores offer modest improvements in early risk prediction. Epigenomic profiling, particularly deoxy ribose nucleic acid (DNA) methylation signatures such as those at carnitine palmitoyl transferase 1A (CPT1A) and hypoxia inducible factor 3 subunit alpha (HIF3A), has uncovered modifiable pathways linked to adiposity and metabolic dysfunction. These findings are increasingly being integrated with other omics layers to improve stratification and therapeutic targeting. Metabolomic subtypes, including ceramide driven insulin resistance and branched chain amino acid (BCAA) dominant dysregulation, have shown potential in guiding treatment selection, such as sodium glucose cotransporter 2 (SGLT2) inhibitors or glucagon like peptide-1 (GLP-1) agonists. Proteomic markers like proprotein convertase subtilisin/kexin type 9 (PCSK9) and retinol binding protein 4 (RBP4) are being evaluated for cardiovascular risk stratification independent of body mass index (BMI). Integrative multiomics frameworks and AI driven models are beginning to bridge molecular data with clinical phenotypes, enabling patient stratification and risk modeling. However, most findings remain in research grade environments, and clinical translation is limited by cohort diversity, data harmonization challenges, and the lack of standardized validation protocols. This review synthesizes evidence from single and multiomics studies, highlights emerging biomarkers and molecular subtypes, and discusses the potential of omics guided frameworks to inform precision obesity care.

Chronic inflammation plays a central role in the progression of both infectious and vascular diseases, yet its impact on endothelial cells (ECs), which form the interface between blood and tissue, remains poorly understood. Given their constant exposure to inflammatory cytokines such as TNF-α and IFN-γ, we set out to investigate how cytokine induced inflammation shapes EC function at the molecular level. Using primary human umbilical vein endothelial cells (HUVECs), we modeled repeated cytokine exposure to simulate a chronically inflamed microenvironment. Transcriptomic and epigenetic profiling revealed that ECs respond to this chronic stimulation with durable transcriptional and chromatin changes. These responses included phenotypes resembling immune cell priming, training, and tolerance, which are commonly associated with innate immune memory, a phenomenon whereby innate immune cells mount altered responses following previous stimulation. Although we did not observe classical trained immunity pathways, several genes known to mediate immune training, including TLR2, IL1B, and HDAC9, exhibited enhanced activation following TNF-α re-exposure. IFN-γ stimulation uniquely induced sustained expression and chromatin accessibility at MHC class II loci, suggesting cytokine-specific modes of reprogramming. Functionally, re-stimulated ECs exhibited enhanced monocyte adhesion in a 3D vessel-on-chip model, highlighting the relevance of these molecular changes to vascular inflammation. Moreover, the regulatory regions altered by cytokine exposure were enriched for disease-associated SNPs, particularly those linked to COVID-19, sepsis, and cardiovascular disorders. In summary, these findings reveal that repeated exposure to cytokines as seen in chronic inflammation can induce memory-like responses in ECs and suggest that endothelial reprogramming may contribute to vascular dysfunction.

A three-dimensional (3D) spheroid culture mimics in vivo conditions and reproduces the tumor microenvironment, thus providing more physiological relevance to disease conditions. Mapping the proteome profile in 3D-cultured ovarian cancer (OC) spheroids helps identify novel and potential therapeutic targets in ovarian cancer stem cells. We used mass-spectrometry-based comparative proteome profiling for two-dimensional (2D)-cultured adherent and 3D-cultured OC spheroids and identified 94 upregulated and 54 downregulated proteins in 3D-cultured A2780 spheroids compared to 2D-cultured adherent A2780 cells. In SKOV-3 cells, we identified 127 upregulated proteins and 192 downregulated proteins in 3D-cultured spheroids compared to 2D-cultured adherent cells. The differentially expressed proteins were enriched in proteins regulating oxidative phosphorylation, the acetyl-CoA metabolic process, RNA polymerase core enzyme binding, and growth factor binding. In addition, we also mapped the proteome profile after the treatment with a mitochondrial fission inhibitor, mDivi-1, of 3D-cultured cells and defined the correlation between significantly upregulated and downregulated genes and their association with the progression-free survival of OC patients.

Although multi-omics analysis is popular for revealing diverse physiological effects and biomarkers in many branches of state-of-the-art molecular and cell biology and bioinformatics, there is still no consensus on a gold standard protocol for the integration of various multi-omics profiles into a uniformly shaped system bioinformatics platform. In the current study, we performed the integration of data on DNA methylation, and the expression of coding RNA (mRNA), micro-RNA (miRNA), and long non-coding RNA into a joint platform for calculation of signaling pathway impact analysis (SPIA) and drug efficiency index (DEI). We found that the mirrored and balanced DEI values fitted the DNA methylome data better than the original DEI. Additionally, the protein-coding mRNA-based values correlated more strongly with antisense lncRNA-based values than with miRNA-based values. The whole correlation between the mRNA-based and antisense lncRNA-based values was generally positive. This platform allowed integrative analysis of several levels of gene expression regulation of protein-coding genes and their regulators, including methylation and noncoding RNAs.

Background: cancer exhibits high molecular and clinical heterogeneity, making accurate subtyping essential for personalized treatment. Traditional single-omics approaches often fail to capture this complexity. Multi-omics integration offers a more holistic understanding, but many existing methods either lack interpretability or fail to model cross-omics correlations effectively. Methods: we developed MOFNet, a novel supervised deep learning framework for multi-omics integration, incorporating a similarity graph pooling (SGO) module and a view correlation discovery network (VCDN). MOFNet processes omics data—including mRNA expression, DNA methylation, and miRNA expression—via omics-specific graph learning and cross-omics label space fusion. Three cancer types—breast cancer (BRCA), low-grade glioma (LGG), and stomach adenocarcinoma (STAD)—were analyzed using datasets from the cancer genome atlas (TCGA). Statistical evaluation was performed using accuracy, weighted F1 score, and macro F1 score across stratified training/testing splits. Results: MOFNet achieved superior performance across all datasets. For BRCA, it obtained an accuracy of 85.17%, F1_weighted of 85.36%, and macro F1 of 80.93%, outperforming all baseline models by up to 18.25%. In LGG and STAD, MOFNet also showed robust gains, with maximum improvements of 23.72% and 21.56%, respectively. Omics ablation studies demonstrated enhanced performance with multi-omics integration. Functional enrichment analysis revealed that MOFNet-identified key features were involved in biologically relevant pathways such as cell cycle regulation, synaptic signaling, and ion transport. Conclusions: MOFNet enables scalable and interpretable multi-omics data fusion for cancer subtype classification, significantly improving predictive accuracy while retaining only 25% of input features. The integration of SGO and VCDN modules offers both biological interpretability and computational efficiency. These results suggest MOFNet's promising application in precision oncology and biomarker discovery.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has established itself as a powerful analytical technique for spatially resolved lipidomics, offering unique insights into lipid distribution and metabolism directly within plant and food matrices. Recent methodological and technological advances have markedly improved the spatial resolution, sensitivity, and selectivity of MALDI-MSI, enabling high-definition mapping of complex lipidomes down to the cellular level. This review presents the current state of MALDI-MSI applications in plant and food lipidomics, with a focus on studies that have advanced our understanding of lipid heterogeneity, metabolic pathways, and spatial lipid organization. Special attention is given to the analytical challenges associated with lipid structural diversity, particularly isomerism and isobarism, and to the strategies developed to address these limitations. Emerging applications involving stable isotope labelling, advanced ion mobility spectrometry, and chemical derivatization are also discussed, highlighting their potential to enhance lipid identification and spatial localization. Finally, the review outlines future perspectives, emphasizing the integration of MALDI-MSI with complementary omics approaches and advanced computational tools to accelerate discoveries in plant biology, food quality assessment, and nutritional science.

Indisulam, a DCAF15-based molecular glue degrader, induces widespread proteome changes with implications for cell division and chromosome segregation. While RBM39 and RBM23 are two well-characterized indisulam neo-substrates, additional targets likely exist. To identify those degradation targets, we applied a network-based approach to prioritize novel neo-substrates from large-scale omics data. Our approach integrates proteome-wide expression measurements with information from publicly accessible databases into a multilayer heterogeneous network. Utilizing a Random Walk with Restart algorithm, we identified a preliminary list of 30 neo-substrates. These proteins are likely interactors with DCAF15 in the presence of indisulam and are subject to subsequent degradation. Experimental validation of hits from the shortlisted candidates confirmed their degradation in a proteasome-dependent manner, supporting their identification as potential novel indisulam neo-substrates. Our work employs established network resources and analytical methods to effectively identify direct targets of the indisulam molecular glue degrader. This approach is readily adaptable for exploring novel targets across other molecular glue systems, enhancing its applicability and value to the drug discovery community.

Hepatic fibrosis (HF), a reversible yet critical pathological stage in chronic liver disease progression, represents a major global public health challenge. This study systematically investigated the antifibrotic mechanism of Prunus mongolica oil (OIL), an active component derived from traditional medicinal plants, through an integrated approach combining pharmacodynamics, transcriptomics, and molecular biology in carbon tetrachloride (CCl₄)-induced Sprague–Dawley rat models. Dose–response evaluation revealed optimal antifibrotic efficacy at the medium dosage (5 g kg⁻¹) compared with other concentrations (2.5 and 7.5 g kg⁻¹). Transcriptomic profiling identified 1734 differentially expressed mRNAs, 121 lncRNAs, and 82 miRNAs among model (MOD), control (CON), and OIL-treated groups. Construction of competing endogenous RNA (ceRNA) networks and functional enrichment analysis highlighted the potential association of the PPAR signaling pathway (P = 0.012, FDR = 0.27). Topological assessment using Cytoscape (v3.9.1) and the STRING database identified the Gck/rno-miR-667-5p/Cyp8b1 axis as the central regulatory node. Mechanistically, OIL exerted dual therapeutic effects: (1) upregulating PGC-1α/PPARγ expression to enhance metabolic reprogramming, and (2) suppressing TGF-β/Smad3 phosphorylation activation, thereby inhibiting hepatic stellate cell (HSC) activation and extracellular matrix (ECM) deposition. Immunohistochemical and western blot analyses validated these protein-level modulations. Our findings revealed a novel ceRNA-network-mediated mechanism wherein OIL attenuates hepatic fibrosis through coordinated regulation of PPAR and TGF-β/Smad3 pathways via the Gck/rno-miR-667-5p/Cyp8b1 axis, providing a theoretical foundation for developing multitarget phytopharmaceuticals against liver fibrosis.