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At least 937 records · Page 52Linked to original sources
PubMed2025

Unveiling the Genetic Landscape of Coronary Artery Disease Through Common and Rare Structural Variants.

BACKGROUND: Genome-wide association studies have identified several hundred susceptibility single nucleotide variants for coronary artery disease (CAD). Despite single nucleotide variant-based genome-wide association studies improving our understanding of the genetics of CAD, the contribution of structural variants (SVs) to the risk of CAD remains largely unclear. METHOD AND RESULTS: We leveraged SVs detected from high-coverage whole genome sequencing data in a diverse group of participants from the National Heart Lung and Blood Institute's Trans-Omics for Precision Medicine program. Single variant tests were performed on 58 706 SVs in a study sample of 11 556 CAD cases and 42 907 controls. Additionally, aggregate tests using sliding windows were performed to examine rare SVs. One genome-wide significant association was identified for a common biallelic intergenic duplication on chromosome 6q21 (P=1.54E-09, odds ratio=1.34). The sliding window-based aggregate tests found 1 region on chromosome 17q25.3, overlapping USP36, to be significantly associated with coronary artery disease (P=1.03E-10). USP36 is highly expressed in arterial and adipose tissues while broadly affecting several cardiometabolic traits. CONCLUSIONS: Our results suggest that SVs, both common and rare, may influence the risk of coronary artery disease.

Humans↗
PubMed2026

Multiomic Integration Reveals Novel miRNA-mRNA-Protein Expression Profile in the Aged Female Retina.

PURPOSE: Aging is a leading risk factor for retinal degeneration. MicroRNAs (miRNAs) regulate posttranscriptional gene suppressors and influence inflammation and oxidative stress, two processes disrupted during retinal aging. This study aimed to identify age-related miRNA-mRNA-protein associations between young and older retinas and uncover dysregulated pathways that may contribute to retinal degeneration. METHODS: Retinal function was assessed using electroretinography (ERG), and microgliosis was quantified by microglial immunohistochemistry (IHC). A multiomics approach was used to examine molecular changes in older (30-month-old) female C57BL/6J mouse retinas and compared with young female (3-month-old) controls. Illumina sequencing profiled short miRNAs (20 bp) and bulk mRNAs (150 bp), while total proteomics via mass spectrometry assessed protein expression. Bioinformatic analyses included targetome analysis (miRNet), pathway enrichment (Gene Ontology), and clustering to identify age-associated molecular targets and pathways. RESULTS: Retinas from older mice displayed neuronal dysfunction and increased microgliosis. Sequencing revealed significant dysregulation of miRNAs linked to immune and inflammatory pathways, supported by enrichment of their predicted mRNA targets. In the older mice, mRNA expression showed broad inflammatory activation, though only 14% of dysregulated mRNAs overlapped with predicted miRNA targets. Proteomic profiling revealed a disconnect between RNA and protein expression, yet all omics layers showed enrichment in inflammatory pathways. Integrated analysis identified associations involving several gene regulatory networks in the older retina. CONCLUSIONS: This study demonstrates that at an advanced age, miRNA expression and their predicted downstream regulatory networks are dysregulated, highlighting potential molecular mechanisms underlying age-related retinal degeneration.

Animals↗
PubMed2026

The extracellular matrix in cancer-associated fibrosis: molecular mechanisms and clinical relevance.

The ECM is a dynamic component of the tumor microenvironment with a critical role in cancer progression, invasion, metastasis, immune exclusion, and response to therapy. Recent advances in proteomic analyses investigating the insoluble ECM fractions (termed "matrisome analysis"), along with single-cell RNA sequencing and spatial transcriptomics, have revealed cancer-specific patterns of ECM remodeling. These studies have identified a panel of recurrently upregulated ECM proteins, including annexin A1, fibrillin-1, fibronectin, periostin, and tenascin-C, actively contributing to tumor growth, invasion, angiogenesis, and immune exclusion. The expression of the cancer-associated ECM is largely driven by cancer-associated fibroblasts (CAFs), whose molecular diversity has been dissected through single-cell profiling and consolidated in emerging CAF atlases across cancers. By investigating the matrisome composition and CAF heterogeneity, these studies have unraveled the pivotal role of the stroma in shaping tumor biology. Based on these discoveries, ECM proteins and CAFs are now being explored as biomarkers and therapeutic targets. Future integration of multi-omics datasets with clinical outcomes will help to translate these insights into novel biomarkers for patient stratification and stroma-directed therapeutic interventions.

Humans↗
PubMed2026

The molecular similarity landscape of preclinical cancer models to patient tumors.

Selecting appropriate preclinical models is fundamental for translational oncology, yet a large-scale, multi-omic quantitative comparison of their similarity to primary human tumors is lacking. To address this, we integrated transcriptomic, proteomic, and genomic profiles from over 10,000 primary tumors from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), alongside 4,000 preclinical models. Using a robust computational framework, we revealed a clear hierarchy of transcriptomic and proteomic similarity to patient tumors: with patient-dervied xenografts (PDXs) having greater transcriptomic and proteomic similarity to patient tumors (>) compared with patient-derived organoids (PDOs), which are equal in hierarchy to that of PDX-dervied organoids (PDXOs) > cell lines. We also quantified high molecular conservation (Pearson correlation coefficient = 0.96) across paired in vitro to in vivo platform (organoids to PDX) transitions. Furthermore, genomic analysis demonstrated that whole-exome sequencing (WES) outperforms RNA-seq in detecting DNA variants, and it identified a clonal complexity hierarchy (cell lines > PDXOs > PDXs > PDOs) reflecting the effect of passaging history on intratumor heterogeneity. Ultimately, this study delivers a comprehensive quantitative benchmark, establishing a population-level hierarchy of molecular similarity between preclinical models and primary tumors and providing a data-driven reference for model selection. These findings offer a data-driven framework for selecting models that balance biological representativeness with experimental practicality.

Humans↗
PubMed2004

The feasibility of replacing animal testing for assessing consumer safety: a suggested future direction.

At present, we are unable to use much of the data derived from alternative (non-animal) tests for human health risk assessment. This brief Comment outlines why it is plausible that new paradigms could be developed to enable risk assessment to support consumer safety decisions, without the need to generate data in animal tests. The availability of technologies that did not exist 10 years ago makes this new approach possible. The approach is based on the concept that data and information derived from applying existing and new technologies to non-animal models can be interpreted in terms of harm and disease in man. A prerequisite is that similar data and information generated in a clinical setting are available to permit this "translation". The incorporation of this additional translation step should make it possible to use data and information generated in non-animal models as inputs to risk assessment. The new technologies include genomics, transcriptomics, proteomics and metabonomics. Their application to in vitro and human "models" enables large amounts of data to be generated very quickly. The processing, interpretation and translation of these data need to be supported by powerful informatics capabilities and statistical tools. The use of integrated "systems biology" approaches will further support the interpretation by providing better understanding of the underlying biological complexity and mechanisms of toxicity. Clinical medicine is using the opportunities offered by the new "omics" technologies to advance the understanding of disease. The application of these technologies in clinical medicine will generate massive amounts of data that will need processing and interpretation to allow clinicians to better diagnose disease and understand the patients' responses to therapeutic interventions. Support from clinical epidemiology will be essential. If these data and information can be made generally accessible in an ethical and legal way, they should also permit the "translation" of experimental non-animal data, so that they can then be used in risk assessment.

Animal Testing Alternatives↗
PubMed2026

Imaging and genomics in stroke.

Imaging after ischemic and hemorrhagic stroke may allow measurement of key phenotypes of injury and recovery for which targeted therapies are still lacking. Such imaging endophenotypes provide quantifiable and heritable biomarkers that can represent mechanistic aspects of disease processes better than clinical measures. Artificial intelligence is allowing extraction of these imaging biomarkers in large cohorts, which can be paired with genomic and other omics data. This will allow the evaluation of what genetic and other biologic variations impact stroke injury and recovery. Integration of these analyses with bioinformatics tools (such as Mendelian randomization and multi-trait analysis) could further dissect how stroke complications overlap with other biologic processes and how they may be causally linked to risk factors. Further work is required to confirm the translational impact of these methods in elucidating mechanisms and drug targets for stroke. However, global collaborations are accelerating analyses on large multi-ethnic stroke cohorts, with availability of imaging data facilitated by federally-funded repositories such as the Imaging Repository for the Cerebrovascular Disease Knowledge Portal (iCDKP).

Humans↗
PubMed2026

Integrated transcriptomic and immunogenomic analysis unravels the immunological functions and prognostic landscape of WD repeat domain 76.

BackgroundWD Repeat Domain 76 (WDR76) plays a potential role in cellular regulation; however, its comprehensive landscape across human malignancies and its specific biological function in hepatocellular carcinoma (HCC) remain largely unexplored.MethodsWe conducted a systematic pan-cancer analysis utilizing multi-omics data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) atabases to evaluate WDR76 expression, subcellular localization, and its correlation with clinicopathologic features, genomic instability, and immune infiltration. Diagnostic and prognostic values were assessed via Receiver operating characteristic (ROC) and Kaplan-Meier analyses. Furthermore, the functional role of WDR76 in HCC was validated in vitro using Hep-3B and Huh7 cell lines through siRNA-mediated knockdown, followed by CCK-8, wound-healing, and transwell assays.ResultsWDR76 was significantly upregulated in the majority of tumor types, including LIHC, LUAD, and COAD, while exhibiting nuclear localization. Elevated WDR76 expression correlated with advanced tumor staging, metastasis, and poor clinical outcomes across multiple cohorts, particularly in ACC, KIRP, and LIHC. ROC analysis highlighted its exceptional diagnostic precision in cancers such as GBM and LIHC. Immunologically, WDR76 expression was intricately linked to immune cell infiltration, immune checkpoint markers, and genomic instability parameters, suggesting a role in shaping the tumor microenvironment. Drug sensitivity profiling revealed that high WDR76 levels correlate with resistance to specific chemotherapeutic agents. Experimentally, silencing WDR76 in HCC cells significantly suppressed cell proliferation, migration, and invasion capabilities.ConclusionOur study establishes WDR76 as a robust pan-cancer prognostic biomarker and a potential immunotherapeutic target. Specifically, we provide experimental evidence that WDR76 functions as an oncogenic driver in liver cancer, promoting malignant phenotypes and offering a novel avenue for targeted therapeutic intervention.

Humans↗
PubMedDate not supplied

Signaling protein networks as targets of new antineoplastic drugs.

In-depth analysis of molecular regulatory networks in cancer holds the promise of improved knowledge of the pathophysiology of tumor cells so that it will become possible to design a detailed molecular tumor taxonomy. This knowledge will also offer new opportunities for the identification and validation of key molecular tumor targets to be exploited for novel therapeutic approaches. Some signaling proteins have already been identified as such, e.g. c-Myc, Cyclin D1, Bcl-XL, kinases and some nuclear receptors. This has led to the successful development of a few function-modulatory drugs (Glivec, SERM, Iressa), providing proof-of-principle of the validity of this approach. Further developments are likely to derive from "-omic" approaches, aimed at the understanding of signaling networks and of the mechanism of action of newfound lead molecules. High-throughput screening of small drug-like molecules from combinatorial chemical libraries or from microbial extracts will identify novel, "intelligent" drug candidates. An additional medicinal chemistry strategy (via 40-50 unit rosary-bead chains) has the potential to be much more effective than small molecules in interfering with protein-protein interactions. This may lead to considerably higher selectivity and effectiveness compared with historical approaches in drug discovery.

Antineoplastic Agents↗
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↗
PubMed2005

Modeling Lactococcus lactis using a genome-scale flux model.

BACKGROUND: Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) were used as modeling frameworks. RESULTS: The metabolic network was reconstructed using the annotated genome sequence from L. lactis ssp. lactis IL1403 together with physiological and biochemical information. The established network comprised a total of 621 reactions and 509 metabolites, representing the overall metabolism of L. lactis. Experimental data reported in the literature was used to fit the model to phenotypic observations. Regulatory constraints had to be included to simulate certain metabolic features, such as the shift from homo to heterolactic fermentation. A minimal medium for in silico growth was identified, indicating the requirement of four amino acids in addition to a sugar. Remarkably, de novo biosynthesis of four other amino acids was observed even when all amino acids were supplied, which is in good agreement with experimental observations. Additionally, enhanced metabolic engineering strategies for improved diacetyl producing strains were designed. CONCLUSION: The L. lactis metabolic network can now be used for a better understanding of lactococcal metabolic capabilities and potential, for the design of enhanced metabolic engineering strategies and for integration with other types of 'omic' data, to assist in finding new information on cellular organization and function.

Bacteria, Anaerobic↗
PubMed2006

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is important to significantly increase the knowledge of the biology of its causative agents, and is motivated, on a longer term, by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should, therefore, be as reliable and versatile as possible. In this context, five aspects of the organization and mining of malaria genomic and post-genomic data were examined: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes, particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Recent progress towards a grid-enabled chemogenomic knowledge space is discussed.

Animals↗
PubMed2003

A need for a 'whole-istic functional genomics' approach in complex human diseases: arthritis.

'Genomic tools', such as gene/protein chips, single nucleotide polymorphism and haplotype analyses, are empowering us to generate staggering amounts of correlative data, from human/animal genetics and from normal and disease-affected tissues obtained from complex diseases such as arthritis. These tools are transforming molecular biology into a 'data rich' science, with subjects with an '-omic' suffix. These disciplines have to converge and integrate at a systemic level to examine the structure and dynamics of cellular and organismal function ('functionomics') simultaneously, using a multidimensional approach for cells, tissues, organs, rodents and Zebra fish models, which intertwines various approaches and readouts to study the development and homeostasis of a system. In summary, the postgenomic era of functionomics will facilitate narrowing the bridge between correlative data and causative data, thus integrating 'intercoms' of interacting and interdependent disciplines and forming a unified whole.

Arthritis↗
PubMed2006

International Agency for Research on Cancer workshop on 'Expression array analyses in breast cancer taxonomy'.

In May 2006, a workshop on Expression array analyses in breast cancer taxonomy was held at the International Agency for Research on Cancer (IARC). The workshop covered an array of topics from the validity of the currently defined breast tumor subtypes and other expression profile-based signatures to the technical limitations of expression analysis and the types of platforms on which these omics results will eventually reach clinical practice. Overall, the workshop participants believed firmly that tumor taxonomy is likely to yield improved prognostic and predictive markers. Even so, further standardization and validation are required before clinical trials are set in motion.

Breast Neoplasms↗
PubMed2003

MatchMiner: a tool for batch navigation among gene and gene product identifiers.

MatchMiner is a freely available program package for batch navigation among gene and gene product identifier types commonly encountered in microarray studies and other forms of 'omic' research. The user inputs a list of gene identifiers and then uses the Merge function to find the overlap with a second list of identifiers of either the same or a different type or uses the LookUp function to find corresponding identifiers.

Algorithms↗
PubMed2004

Computational prediction of human metabolic pathways from the complete human genome.

BACKGROUND: We present a computational pathway analysis of the human genome that assigns enzymes encoded therein to predicted metabolic pathways. Pathway assignments place genes in their larger biological context, and are a necessary first step toward quantitative modeling of metabolism. RESULTS: Our analysis assigns 2,709 human enzymes to 896 bioreactions; 622 of the enzymes are assigned roles in 135 predicted metabolic pathways. The predicted pathways closely match the known nutritional requirements of humans. This analysis identifies probable omissions in the human genome annotation in the form of 203 pathway holes (missing enzymes within the predicted pathways). We have identified putative genes to fill 25 of these holes. The predicted human metabolic map is described by a Pathway/Genome Database called HumanCyc, which is available at http://HumanCyc.org/. We describe the generation of HumanCyc, and present an analysis of the human metabolic map. For example, we compare the predicted human metabolic pathway complement to the pathways of Escherichia coli and Arabidopsis thaliana and identify 35 pathways that are shared among all three organisms. CONCLUSIONS: Our analysis elucidates a significant portion of the human metabolic map, and also indicates probable unidentified genes in the genome. HumanCyc provides a genome-based view of human nutrition that associates the essential dietary requirements of humans with a set of metabolic pathways whose existence is supported by the human genome. The database places many human genes in a pathway context, thereby facilitating analysis of gene expression, proteomics, and metabolomics datasets through a publicly available online tool called the Omics Viewer.

Arabidopsis↗
PubMed2005

Living on the edge.

A report on the Second EMBL/EMBO Symposium on Functional Genomics: 'Exploring the Edges of Omics', European Molecular Biology Laboratory (EMBL), Heidelberg, Germany, 16-19 October 2004.

Animals↗
PubMed2005

Biochip sensors for the rapid and sensitive detection of viral disease.

Recent advances in DNA and protein microarray methodology and the emerging technology of cell-based sensors have massively increased the speed and sensitivity with which we can detect viral infections. The advantages of the multi-parameter microarray technologies could be combined with the speed and sensitivity of cell-based systems to give 'cell-omic' sensors.

Biosensing Techniques↗
PubMed2025

Integrative analysis of gene expression and histone modifications for DES, DSP, GJA1 and SMOC2 in adipose tissue reveals potential relationship to cardiometabolic health.

BACKGROUND: Adipose tissue influences cardiometabolic health through its endocrine activity and its role in regulating inflammation, lipid metabolism, and cardiovascular function. The expression of cardiac-associated genes within adipose tissue may reflect or contribute to cardiometabolic risk, yet this relationship remains poorly understood. This study investigates the expression profiles of the cardiac function associated genes GJA1, DES, DSP and SMOC2 in human adipose tissue, and analyses their associations with cardiometabolic traits. Additionally, we explore epigenomic mechanisms that may underlie their differential gene expression. METHODS: Expression profiling and functional enrichment analyses were conducted to identify depot-specific cardiac gene expression patterns. Quantitative PCR validated gene expression in paired subcutaneous (SAT) and omental visceral adipose tissue (OVAT) samples from 78 individuals with obesity. Gene expression was further validated in three independent cohorts (N = 1,548 total). Associations with clinical traits were assessed using Spearman correlations and multivariate linear regression, adjusted for age, sex, and BMI. Integration with transcriptomic and proteomic datasets publicly available from the Adipose Tissue Knowledge Portal was performed to strengthen clinical relevance. Epigenomic profiling using genome-wide ChIP-seq for histone marks (H3K4me3, H3K4me1, H3K27ac, H3K27me3) was conducted in paired SAT and OVAT samples from five individuals. RESULTS: DES, DSP, GJA1, and SMOC2 were significantly upregulated in OVAT compared to SAT. DES, DSP, and SMOC2 showed consistent expression patterns across all cohorts, while GJA1 exhibited context-dependent regulation. Gene expression in SAT was negatively correlated with cardiometabolic traits, including blood pressure, insulin resistance, and liver function markers. These associations were confirmed by regression analysis and supported by publicly available multi-omics data. Epigenetic analyses revealed OVAT-specific enrichment of active histone marks and reduced repressive marks, supporting higher differential transcriptional activity in OVAT. CONCLUSIONS: Depot-specific gene expression of DES, DSP, and SMOC2 in adipose tissue is robustly linked to cardiometabolic traits and supported by distinct epigenetic landscapes in OVAT vs SAT, highlighting their potential as novel biomarkers for cardiometabolic health.

Humans↗