Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “omics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 757 records · Page 42Linked to original sources
PubMed2026

Multi-dimensional profiling of primary metabolites in Heuchera micrantha varieties reveals potential for functional food development.

Heuchera micrantha is a horticultural plant with emerging pharmacological value, yet its primary metabolites remain underexplored. This study comprehensively profiled nutrient metabolites in four H. micrantha varieties using LC-MS/MS. We identified 285 metabolites, with amino acid derivatives being predominant. Multivariate analysis revealed distinct varietal accumulation patterns and 204 differential accumulated metabolites (DAMs). Integrative network pharmacology and molecular docking suggested γ-glutamyltyrosine and L-prolyl-L-phenylalanine as potential bioactive dipeptides that may interact with core hubs (MAPK1, EGFR, SRC) involved in cancer and inflammation pathways, though these predictions require experimental validation. Transcriptomics identified 39 differentially expressed genes regulating the biosynthesis of their precursor amino acids. Antioxidant assays showed varietal differences: some excelled in free radical scavenging (DPPH/ABTS) while others demonstrated superior reducing power (FRAP). This multi-omics study suggests that H. micrantha may be a rich source of therapeutically relevant primary metabolites, providing a preliminary scientific basis for its development as a functional food or nutraceutical pending further validation.

Functional Food↗
PubMed2026

Transcriptomic and metabolomic analyses revealed the action mechanism of nesfatin-1 gene on glucolipid metabolism during early development stage of largemouth bass.

Nesfatin-1 has biological roles including the suppression of food intake and the regulation of glucose and lipid metabolism. However, the information available regarding nesfatin-1 in the glycolipid metabolism in the early development stage of fish is still limited. In order to investigate the role of the nesfatin-1 gene in the early development stage of the largemouth bass (Micropterus salmoides), the nesfatin-1 gene was knocked down using siRNA interference technology. Then, we evaluated its mRNA expression levels, transcriptomes and metabolomes. The mRNA expression levels of nesfatin-1 gene were appreciably decreased at 48 h, 72 h and 96 h after injection of nesfatin-1 siRNA in the early development stage. The omics results revealed that knockdown of the nesfatin-1 gene induced 1833 differentially expressed genes (DEGs) and 2370 differentially expressed metabolites (DEMs). Bioinformatic analysis enriched the most affected molecular pathways (sphingolipid metabolism, fatty acid elongation, amino sugar and nucleotide sugar metabolism and biosynthesis of unsaturated fatty acids) and metabolic pathways (biosynthesis of unsaturated fatty acids, sphingolipid metabolism and amino sugar and nucleotide sugar metabolism) in early development stage of largemouth bass. In amino sugar and nucleotide sugar metabolism, increased expression levels of genes such as chic, chs1, and gck genes, alongside decreased expression levels of the chia.1 gene, resulted in significantly elevated concentrations of N-Acetyl-D-glucosamine, β-d-fructose 6-phosphate, β-d-Fructose, D-mannose 6-phosphate, d-glucose, d-glucose 1-phosphate, UDP-glucose, and UDP-glucuronate, whilst the concentration of UDP-N-acetyl-α-D-glucosamine was markedly reduced. Therefore, the nesfatin-1 gene may influence the early development stage of largemouth bass by affecting signaling pathways associated with glycolipid metabolism. Our findings further expand the understanding of molecular mechanisms of the nesfatin-1 gene, and provide further theoretical support for the initial breeding and feed adaptation of largemouth bass.

Animals↗
PubMed2026

Dynamic transcriptomic landscape from bulk RNA-seq reveals critical mmu-miR-181a-5p/hif1a and mmu-miR-101a-3p/col1a1 modules for deep second-degree burn wound healing.

Burn injuries constitute a significant global health challenge, with deep partial-thickness burns (deep second-degree) posing particular clinical concerns due to prolonged healing and high scarring risks stemming from reticular dermis damage. Current therapeutic strategies remain largely empirical, reflecting limited understanding of stage-specific regulatory mechanisms. This study systematically investigated the molecular basis of deep partial-thickness burn repair by establishing murine models and performing RNA-seq analysis across healing phases (0, 3, 7, 14 days post-burn, dpb). Integrated bioinformatics revealed pivotal ceRNA and PPI networks, identifying hif1a (hypoxia-responsive immunomodulator) and col1a1 (ECM remodeling hub) as nodal regulators. Mechanistically, mmu-miR-101a-3p and mmu-miR-181a-5p were validated as post-transcriptional repressors of col1a1 and hif1a, respectively. Our work pioneers the discovery of the mmu-miR-181a-5p/hif1a and mmu-miR-101a-3p/col1a1 axes as master regulators of burn repair, offering novel therapeutic targets. The multi-omics dataset and molecular networks established herein provide a foundational resource for wound healing research.

MicroRNAs↗
PubMed2026

Charting host structural variations in cervical cancer by long-read sequencing pinpoints a functional deletion in PIAS1.

Host structural variations (SVs) are critical in cancer development but their landscape and interaction with HPV integration in cervical carcinogenesis remain unclear. In this study, we performed Nanopore long-read sequencing on five HPV-positive cervical cancer tissues and two cell lines to profile host SVs. We identified thousands of SVs and statistically demonstrated their significant enrichment in genomic windows ±25 to ±50 kb from HPV integration sites. Cross-sample analysis revealed 60 shared SVs, including a recurrent deletion within the PIAS1 gene. Multi-omics integration (Hi-C, H3K27ac ChIP-seq, and TCGA data) showed that this deletion is associated with reduced PIAS1 expression, disruption of local topologically associating domains, advanced pathological tumor stage, and poorer overall survival. Functional assays confirmed that PIAS1 deficiency inhibits cervical cancer cell proliferation and migration. Our findings identify a PIAS1 deletion as a candidate driver event, and underscore the pivotal role of host genomic instability in HPV-associated oncogenesis.

Cervical cancer↗
PubMed2026

Comprehensive analysis of metabolomics and transcriptomics of radiation-induced rectal injury.

Radiation-induced rectal injury (RRI) significantly affects the quality of life in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (NCRT). Non-targeted liquid chromatography-mass spectrometry metabolomics analysis and transcriptomic analysis were conducted to explore RRI characteristics. Hematoxylin-eosin and Masson staining confirmed radiation-induced injury in rectal tissue within the radiotherapy target region. Orthogonal partial least squares discriminant analysis identified 823 differentially expressed metabolites (DEMs). Transcriptomic analysis revealed 400 differentially expressed genes (DEGs). Enrichment analysis revealed that DEMs and DEGs were primarily involved in metabolic, immune, and signal transduction pathways. Integrated analysis demonstrated significant enrichment of DEMs and DEGs in the arachidonic acid metabolism pathway. Pearson's correlation and canonical correlation analyses were used to assess the association between DEMs and DEGs within this pathway. In conclusion, this study identified key biological regulatory pathways involved in RRI through a multi-omics approach, offering potential targets for its diagnosis and treatment.

Humans↗
PubMed2026

GENKI: A generative framework for scalable and robust metabolic kinetic modeling.

GENKI (Generative ENsemble KPI-Informed) is a variational autoencoder-based framework for large-scale kinetic modeling of metabolism. Developed for metabolic engineering applications, GENKI is designed to improve the recovery of kinetically feasible models that reproduce experimentally observed phenotypes under genetic and environmental perturbations. The framework is trained on feasible kinetic model ensembles and uses phenotype-based key performance indicators (KPIs), derived from multi-omics and bioprocess data, to label and enrich models according to their agreement with mutant and condition-specific observations. This enables targeted generation of biologically relevant parameter sets with improved predictive performance. Crucially, GENKI recovers kinetic parameter sets that jointly reproduce wild-type and multiple perturbed physiologies within a single model. We apply GENKI to large-scale kinetic models of Escherichia coli and Saccharomyces cerevisiae under enzyme perturbations and oxygen shifts. In both systems, GENKI enriches kinetic ensembles with models that more accurately reproduce experimentally observed physiologies across multiple perturbations and conditions. GENKI therefore provides a practical framework for perturbation-aware kinetic model refinement within iterative Design-Build-Test-Learn workflows.

DBTL↗
PubMed2006

Solid-phase and bead-based cytokine immunoassay: a comparison.

Cytokines and chemoattractive cytokines (chemokines) are present in a wide variety of body fluids such as plasma, cerebrospinal fluid, bronchoaveolar fluid, amniotic fluid, synovial fluid, middle ear effusion fluid, and urine. Cytokines can be detected using classical solid-phase sandwich immunoassays such as enzyme-linked immunosorbent assay (ELISA) or with a bead based multiplex immunoassay (MIA). The physical chemical properties of the different body fluids (such as pH and total protein content) differ, which may have an impact on the outcome of the cytokine assay. Both ELISA as well as MIA cytokine detection systems are constructed by sandwiching the protein of interest between a capture and reporter antibody. When the biological sample contains heterophilic antibodies (such as in patients with auto-immune diseases), these non-specific antibodies can cause false positive results. During pathological conditions, cytokines may be found over a wide concentration range; likewise have to cover this dynamic range in a similar fashion. The correct (statistical) analysis of standard curves and (multiplexed) data are critical for proper interpretation. Classical ELISA based cytokine assays are robust, easy to use and very well suited for measurement of single cytokines. Due to an increased interest in the integral approach to understand biological processes (the omics era), multiplex immunoassays for detection of cytokines and the interpretation of these assays are gaining popularity.

Animals↗
PubMed2002

Nutrigenomics: exploiting systems biology in the nutrition and health arena.

Nutritional sciences are discovering the application of the so-called "omics" sciences. Propelled by the recent unravelling of the human genome and the coinciding technological developments, genotyping, transcriptomics, proteomics and metabolomics are now available to nutritional research. In the future we are likely to see new screening tools for the selection of bioactive nutrients, new biomarkers for the in vivo efficacy of nutrients, and better insight into the influence of genetic polymorphisms on nutrient metabolism. However, are these promises just based on biotechnological hype or is a real fundamental change in human nutritional sciences at hand?

Biomarkers↗
PubMed2006

The promise of a virtual lab in drug discovery.

To date, the life sciences 'omics' revolution has not lived up to the expectation of boosting the drug discovery process. The major obstacle is dealing with the volume and diversity of data generated. An enhanced-science (e-science) approach based on remote collaboration, reuse of data and methods, and supported by a virtual laboratory (VL) environment promises to get the drug discovery process afloat. The creation, use and preservation of information in formalized knowledge spaces is essential to the e-science approach. VLs include Grid computation and data communication as well as generic and domain-specific tools and methods for information management, knowledge extraction and data analysis. Problem-solving environments (PSEs) are the domain-specific experimental environments of VLs. Thus, VL-PSEs can support virtual organizations, based on the changing partnerships characteristic of successful drug discovery enterprises.

Computer Simulation↗
PubMed2005

Towards quantitative biology: integration of biological information to elucidate disease pathways and to guide drug discovery.

Developing a new drug is a tedious and expensive undertaking. The recently developed high-throughput experimental technologies, summarised by the terms genomics, transcriptomics, proteomics and metabolomics provide for the first time ever the means to comprehensively monitor the molecular level of disease processes. The "-omics" technologies facilitate the systematic characterisation of a drug target's physiology, thereby helping to reduce the typically high attrition rates in discovery projects, and improving the overall efficiency of pharmaceutical research processes. Currently, the bottleneck for taking full advantage of the new experimental technologies are the rapidly growing volumes of automatically produced biological data. A lack of scalable database systems and computational tools for target discovery has been recognised as a major hurdle. In this review, an overview will be given on recent progress in computational biology that has an impact on drug discovery applications. The focus will be on novel in silico methods to reconstruct regulatory networks, signalling cascades, and metabolic pathways, with an emphasis on comparative genomics and microarray-based approaches. Promising methods, such as the mathematical simulation of pathway dynamics are discussed in the context of applications in discovery projects. The review concludes by exemplifying concrete data-driven studies in pharmaceutical research that demonstrate the value of integrated computational systems for drug target identification and validation, screening assay development, as well as drug candidate efficacy and toxicity evaluations.

Animals↗
PubMed2005

Fusion of mass spectrometry-based metabolomics data.

A general method is presented for combining mass spectrometry-based metabolomics data. Such data are becoming more and more abundant, and proper tools for fusing these types of data sets are needed. Fusion of metabolomics data leads to a comprehensive view on the metabolome of an organism or biological system. The ideas presented draw upon established techniques in data analysis. Hence, they are also widely applicable to other types of X-omics data provided there is a proper pretreatment of the data. These issues are discussed using a real-life metabolomics data set from a microbial fermentation process.

Databases as Topic↗
PubMed2006

Scaling and normalization effects in NMR spectroscopic metabonomic data sets.

Considerable confusion appears to exist in the metabonomics literature as to the real need for, and the role of, preprocessing the acquired spectroscopic data. A number of studies have presented various data manipulation approaches, some suggesting an optimum method. In metabonomics, data are usually presented as a table where each row relates to a given sample or analytical experiment and each column corresponds to a single measurement in that experiment, typically individual spectral peak intensities or metabolite concentrations. Here we suggest definitions for and discuss the operations usually termed normalization (a table row operation) and scaling (a table column operation) and demonstrate their need in 1H NMR spectroscopic data sets derived from urine. The problems associated with "binned" data (i.e., values integrated over discrete spectral regions) are also discussed, and the particular biological context problems of analytical data on urine are highlighted. It is shown that care must be exercised in calculation of correlation coefficients for data sets where normalization to a constant sum is used. Analogous considerations will be needed for other biofluids, other analytical approaches (e.g., HPLC-MS), and indeed for other "omics" techniques (i.e., transcriptomics or proteomics) and for integrated studies with "fused" data sets. It is concluded that data preprocessing is context dependent and there can be no single method for general use.

Algorithms↗
PubMed2026

Targeting DNA Methylation: New Paradigms and the Advent of Gene-Selective Tools.

DNA methylation can function as a toxic alkylation reaction exploited by chemotherapeutic agents to induce cancer cell death. However, finely tuned DNA methylation plays a fundamental role in cellular physiology, particularly in the epigenetic regulation of gene expression. Once thought to act solely as a repressor of gene transcription, its functional role has since been elucidated as genomic locus-specific and deeply connected with other epigenetic factors. Following the clinical approval of DNA methyltransferase inhibitors, such as Azacitidine and Decitabine, for the treatment of hematological malignancies, considerable efforts have been devoted to developing pharmacological tools that modulate epigenetic DNA methylation. However, the lack of gene selectivity in these agents limits their therapeutic efficacy and increases off-target toxicity. Moreover, the non-gene-selective nature of current DNA methylation-targeting molecules fails to meet the standards required to discern the nuanced roles of DNA methylation across diverse pathophysiological contexts and genomic loci, particularly in an era where next-generation sequencing and omics technologies enable high-resolution epigenetic analyses. In this review, we examine the mechanisms and roles of DNA methylation in epigenetic regulation, evaluate the current landscape of DNA methylation modulators, from traditional DNMT inhibitors to cutting-edge CRISPR-dCas9 fusion systems and protein-protein interaction disruptors, and discuss their clinical relevance. Finally, we emphasize the need for precise, locus-specific tools to advance both cancer research and therapeutic strategies.

Humans↗
PubMed2026

Urine and Serum Proteome and Lipidome Analysis of Naturally Aging Feline Species.

Aging in companion animals such as cats closely relates to human aging in environmental exposures and disease manifestation, providing a valuable model for identifying biomarkers of age-associated decline. This study provides a combined proteomic and lipidomic analysis of serum and urine from naturally aging domestic cats aged 3.8-16 years, grouped as adult, old, and senior, to identify age-related molecular changes across biofluids. Label-free quantitative proteomics identified 901 urinary and 238 serum proteins, with 75 urinary proteins significantly altered with age that are linked to kidney disease, hypertension, neurodegeneration, and metabolic disorders. In contrast, only six serum proteins differed significantly between adult and old/senior cats, including decreased Apolipoprotein A-I (APOA1) in seniors, a protein linked with cognitive function in aging. Untargeted lipidomics revealed increases in specific serum triacylglycerols, phosphatidylcholines (PCs), and sphingomyelins, while urinary lipid profiles showed limited age-related changes, with some PCs decreasing, and diacylglycerols increasing with age. These results demonstrate distinct systemic and renal molecular remodeling during feline aging and highlight the utility of integrated omics analyses of biological fluids for identifying molecular alterations relevant to both feline and human aging.

Animals↗
PubMed2026

Unlocking the Circulating Proteome: Toward Clinical Translation.

Blood-based proteomics is approaching a translational inflection point. Driven by advances in measurement technologies, rapid expansion of analytical capabilities, and growing adoption across research and medical communities, there is increasing demand for clinically actionable biomarkers. As the field transitions away from purely large-scale discovery-oriented studies toward more informed, targeted, application-driven analyses, the generation of proteomic data is no longer the bottleneck. Instead, the central challenge is to translate these measurements into robust, reproducible, and clinically meaningful insights. In this Review, we assess recent technological and methodological developments, evaluate persistent preanalytical and interpretative limitations, and outline the key steps required for clinical translation. We focus on three deeply interconnected dimensions: the capabilities and constraints of current measurement platforms, the role of computational and machine learning approaches in extracting biological and clinical signals, and the emergence of large-scale population studies that create new opportunities for validation and generalization. Finally, we discuss a forward-looking vision in which proteomics plays a central role in dynamic, multilayered omics frameworks, where integration with genomics, temporal profiling, and imaging can deepen our understanding of health, disease, and therapeutic response.

Humans↗
PubMed2004

Using networks to identify fine structural differences between functionally distinct protein states.

The vast increase in available data from the "-omics" revolution has enabled the fields of structural proteomics and structure prediction to make great progress in assigning realistic three-dimensional structures to each protein molecule. The challenge now lies in determining the fine structural details that endow unique functions to sequences that assume a common fold. Similar problems are encountered in understanding how distinct conformations contribute to different phases of a single protein's dynamic function. However, efforts are hampered by the complexity of these large, three-dimensional molecules. To overcome this limitation, structural data have been recast as two-dimensional networks. This analysis greatly reduces visual complexity but retains information about individual residues. Such diagrams are very useful for comparing multiple structures, including (1) homologous proteins, (2) time points throughout a dynamics simulation, and (3) functionally different conformations of a given protein. Enhanced structural examination results in new functional hypotheses to test experimentally. Here, network representations were key to discerning a difference between unliganded and inducer-bound lactose repressor protein (LacI), which were previously presumed to be identical structures. Further, the interface of unliganded LacI was surprisingly similar to that of the K84L variant and various structures generated by molecular dynamics simulations. Apo-LacI appears to be poised to adopt the conformation of either the DNA- or inducer-bound structures, and the K84L mutation appears to freeze the structure partway through the conformational transition. Additional examination of the effector binding pocket results in specific hypotheses about how inducer, anti-inducer, and neutral sugars exert their effects on repressor function.

Bacterial Proteins↗
PubMedDate not supplied

Selection of biomarkers by a multivariate statistical processing of composite metabonomic data sets using multiple factor analysis.

We introduce a statistical approach for integrating data from several analytical platforms. We illustrate this approach using (1)H-(13)C Heteronuclear Multiple Bond Connectivity nuclear magnetic resonance spectroscopy ((1)H-(13)C HMBC NMR) and Pyrolysis Metastable Atom Bombardment Time-of-Flight mass spectrometry (Py-MAB-TOF-MS) to perform metabolic fingerprinting on cattle treated with anabolic steroids. Multiple factor analysis (MFA) integrates complementary aspects from NMR and MS data into a unique metabolic signature describing the biomarkers related to the dose-response. This work also indicates that, from a practical point of view, metabonomics and other "-omics" biotechnologies can benefit significantly from a generalized multi-platform integrative approach using multiple factor analysis.

Animals↗
PubMed2004

Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles.

Metabonomics can be viewed as the process of defining multivariate metabolic trajectories that describe the systemic response of organisms to physiological perturbations through time. We have explored the hypothesis that the homothetic geometry of a metabolic trajectory, i.e., the metabolic response irrespective of baseline values and overall magnitude, defines the mode of response of the organism to treatment and is hence the key property when considering the similarity between two sets of measurements. A modeling strategy to test for homothetic geometry, called scaled-to-maximum, aligned, and reduced trajectories (SMART) analysis, is presented that together with principal components analysis (PCA) facilitates the visualization of multivariate response similarity and hence the interpretation of metabonomic data. Several examples of the utility of this approach from toxicological studies are presented as follows: interlaboratory variation in hydrazine response, CCl(4) dose-response relationships, and interspecies comparison of bromobenzene toxicity. In each case, the homothetic trajectories hypothesis is shown to be an important concept for the successful multivariate modeling and interpretation of systemic metabolic change. Overall, geometric trajectory analysis based on a homothetic modeling strategy like SMART facilitates the amalgamation and comparison of metabonomic data sets and can improve the accuracy and precision of classification models based on metabolic profile data. Because interlaboratory variation, normal physiological variation, dose-response relationships, and interspecies differences are also key areas of concern in genomic and proteomic as well as metabonomic studies, the methods presented here may also have an impact on many other multilaboratory efforts to produce screenable "-omics" databases useful for gauging toxicity in safety assessment and drug discovery.

Animals↗