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At least 1,621 records · Page 90Linked to original sources
PubMed2006

Combined transcript and metabolite profiling of Arabidopsis leaves reveals fundamental effects of the thiol-disulfide status on plant metabolism.

In this study, we used gas chromatography-mass spectrometry analysis in combination with flux analysis and the Affymetrix ATH1 GeneChip to survey the metabolome and transcriptome of Arabidopsis (Arabidopsis thaliana) leaves in response to manipulation of the thiol-disulfide status. Feeding low concentrations of the sulfhydryl reagent dithiothreitol for 1 h at the end of the dark period led to posttranslational redox activation of ADP-glucose pyrophosphorylase and major alterations in leaf carbon partitioning, including an increased flux into major respiratory pathways, starch, cell wall, and amino acid synthesis, and a reduced flux to sucrose. This was accompanied by a decrease in the levels of hexose phosphates, while metabolites in the second half of the tricarboxylic acid cycle and various amino acids increased, indicating a stimulation of anaplerotic fluxes reliant on alpha-ketoglutarate. There was also an increase in shikimate as a precursor of secondary plant products and marked changes in the levels of the minor sugars involved in ascorbate synthesis and cell wall metabolism. Transcript profiling revealed a relatively small number of changes in the levels of transcripts coding for components of redox regulation, transport processes, and cell wall, protein, and amino acid metabolism, while there were no major alterations in transcript levels coding for enzymes involved in central metabolic pathways. These results provide a global picture of the effect of redox and reveal the utility of transcript and metabolite profiling as systemic strategies to uncover the occurrence of redox modulation in vivo.

Arabidopsis↗
PubMed2006

UPSC-BASE--Populus transcriptomics online.

The increasing accessibility and use of microarrays in transcriptomics has accentuated the need for purpose-designed storage and analysis tools. Here we present UPSC-BASE, a database for analysis and storage of Populus DNA microarray data. A microarray analysis pipeline has also been established to allow consistent and efficient analysis (from small to large scale) of samples in various experimental designs. A range of optimized experimental protocols is provided for each step in generating the data. Within UPSC-BASE, researchers can perform standard and advanced microarray analysis procedures in a user-friendly environment. Background corrections, normalizations, quality-control tools, visualizations, hypothesis tests and export tools are provided without requirements for expert-level knowledge. Although the database has been developed primarily for handling Populus DNA microarrays, most of the tools are generic and can be used for other types of microarray. UPSC-BASE is also a repository of Populus microarray information, providing data from 21 experiments on a total of 407 microarray hybridizations in the public domain of the database. There are also an additional 10 experiments containing 347 hybridizations, where the automatically analysed data are searchable.

Databases, Genetic↗
PubMed2025

Developing a disease-specific accessible transcriptional signature as a biomarker for ataxia with oculomotor apraxia type 2.

BACKGROUND: Genetic ataxias are clinically heterogenous neurodegenerative conditions often involving rare or private mutations and it is often difficult to assign pathogenicity to rare gene variants solely based on DNA sequencing. An effective functional assay from an easy-to-obtain biospecimen would aid this assessment and be of high clinical value. SETX encodes a ubiquitous DNA/RNA helicase crucial for resolving R-loops and maintaining genome stability. Loss-of-function mutations cause a recessive disorder, Ataxia with Oculomotor Apraxia Type 2 (AOA2). METHODS: Here we utilize Weighted Gene Co-expression Network Analysis (WGCNA) from patient blood to construct an AOA2-specific transcriptomic signature as a biomarker to evaluate SETX variants in patients clinically suspected of having AOA2. RESULTS: WGCNA from peripheral blood RNA of 11 AOA2 patients from 7 families initially identified a single gene module that was modestly effective in distinguishing individuals with AOA2 from controls (sensitivity 73%, specificity 97%) and was able to robustly differentiate AOA2 patients from those with genetically distinct, yet phenotypically similar, neurological disorders (sensitivity 100%, specificity 100%). An independent derivation of the transcriptional biomarker identified a dual module model that was able to better distinguish individuals with AOA2 from controls (sensitivity 100%, specificity 97%). As validation, we examined a second cohort of 21 patients from 13 families and demonstrate that this dual module transcriptional biomarker could discriminate patients clinically suspected of AOA2 from controls (57%, 95%CI: 34%-78%). Overall, the transcriptional biomarker was able to separate AOA2 subjects (n = 32) from controls (n = 35) with 72% sensitivity and 97% specificity. Notably, this transcriptomic biomarker enabled verification of the first pathogenic SETX mutation found in a non-canonical transcript, expanding the spectrum of mutations that contribute to AOA2. CONCLUSIONS: Our study identified a transcriptional biomarker that was able to differentiate AOA2 from controls and from other related neurological disorders, consequently expanding the spectrum of known pathogenic mutations. This proof-of-concept study illustrates that transcriptional biomarkers may be used to validate variants of uncertain significance in known genetic diseases.

Humans↗
PubMed2026

Metabolic-cell-death gene trio predicts survival and cuproptosis sensitivity in colorectal cancer.

BACKGROUND: Metabolic cell death (MCD) modulates colorectal cancer (CRC) progression, yet its prognostic value remains unexplored. We aimed to build an MCD-centred gene signature for outcome prediction and precision therapy. METHODS: Transcriptomes of 1,174 CRC patients were integrated. Weighted gene co-expression network analysis, differential expressions and least absolute shrinkage and selection operator (LASSO) + random survival forest were successively applied to derive a three-gene (CDKN2A/MPC1/AHCY) risk model. Functional, immune-infiltration, drug-sensitivity and genomic analyses were performed, followed by validation in fresh clinical specimens and cell lines. RESULTS: Integrative metabolic-death transcriptomics identified CDKN2A, MPC1 and AHCY as the hub drivers of CRC. Their three-gene signature robustly stratified patients into high- and low-risk subsets [3-year area under the curve (AUC) 0.83-0.85, P<0.001]. High-risk tumors were enriched for extracellular matrix (ECM)-receptor-interaction pathways, displayed abundant myeloid-derived suppressor cell (MDSC) infiltration and were more vulnerable to AZD8186, AZ960 and JAK inhibitors. Guided by these in-silico findings, we functionally confirmed that CDKN2A silencing markedly repressed proliferation, invasion and migration of SW480/HCT116 cells and potentiated cuproptosis via up-regulation of lipoylated DLAT/DLST and CTR1. CONCLUSIONS: We report the first MCD-derived prognostic platform for CRC that simultaneously predicts survival and therapeutic response. Targeting CDKN2A-enhanced cuproptosis represents a promising metabolic-precision strategy for high-risk patients.

Colorectal cancer (CRC)↗
PubMed2000

The mouse brain transcriptome by SAGE: differences in gene expression between P30 brains of the partial trisomy 16 mouse model of Down syndrome (Ts65Dn) and normals.

Trisomy 21, or Down syndrome (DS), is the most common genetic cause of mental retardation. Changes in the neuropathology, neurochemistry, neurophysiology, and neuropharmacology of DS patients' brains indicate that there is probably abnormal development and maintenance of central nervous system structure and function. The segmental trisomy mouse (Ts65Dn) is a model of DS that shows analogous neurobehavioral defects. We have studied the global gene expression profiles of normal and Ts65Dn male and normal female mice brains (P30) using the serial analysis of gene expression (SAGE) technique. From the combined sample we collected a total of 152,791 RNA tags and observed 45,856 unique tags in the mouse brain transcriptome. There are 14 ribosomal protein genes (nine under expressed) among the 330 statistically significant differences between normal male and Ts65Dn male brains, which possibly implies abnormal ribosomal biogenesis in the development and maintenance of DS phenotypes. This study contributes to the establishment of a mouse brain transcriptome and provides the first overall analysis of the differences in gene expression in aneuploid versus normal mammalian brain cells.

Animals↗
PubMed2026

hypeR-GEM: connecting metabolite signatures to enzyme-coding genes via genome-scale metabolic models.

MOTIVATION: Enrichment analysis is a cornerstone of "omics" data interpretation, enabling researchers to connect analysis results to biological processes and generate testable hypotheses. Enrichment analysis in metabolomics poses distinct challenges for interpretation and multi-omics integration due to the lack of well-defined and consistent connections to well-curated gene-centered biological knowledge repositories. To address these challenges, we developed hypeR-GEM, a methodology and associated R package that adapts gene set enrichment analysis to metabolomics. hypeR-GEM leverages genome-scale metabolic models (GEMs) to infer reaction-based links between metabolites and enzyme-coding genes, enabling the mapping of metabolite signatures to gene signatures and their subsequent annotation via gene set enrichment analysis. RESULTS: We validated hypeR-GEM using paired metabolomics-proteomics and metabolomics-transcriptomics datasets by assessing whether genes mapped from metabolites significantly overlapped with differentially expressed proteins or transcripts. We further evaluated whether pathways enriched via hypeR-GEM-mapped genes corresponded to those derived from paired proteomic or transcriptomic data. In most datasets analyzed, both the predicted enzyme-coding genes and the associated enriched pathways showed significant concordance with independently derived omics signatures, supporting the utility and robustness of hypeR-GEM. Finally, we applied hypeR-GEM to the analysis of age-associated metabolic signatures from the New England Centenarian Study. The results revealed consistent enrichment of lipid-related pathways, aligning with the well-established role of lipid metabolism in aging, and highlighted additional pathways not captured in the metabolites' annotation, demonstrating hypeR-GEM's practical utility in a real-world use case. AVAILABILITY AND IMPLEMENTATION: The hypeR-GEM R package, documentation, and workflow examples are freely available at https://github.com/montilab/hypeR-GEM and archived at https://doi.org/10.5281/zenodo.20586748.

Metabolomics↗
PubMed2025

Root growth promotion by Penicillium melinii : mechanistic insights and agricultural applications.

This study characterizes Penicillium melinii , an endophytic fungus isolated from Arabidopsis thaliana roots, as a plant growth-promoting fungus with potential use as a model to study root development and as a biostimulant for sustainable agriculture. Although endophytes are known to promote plant growth, the underlying molecular mechanisms often remain poorly understood. Here, we aimed to elucidate how P. melinii enhances root system development and to assess its applicability across different crops. Phenotypic assays were conducted in Arabidopsis, quinoa and tomato under in vitro , greenhouse and field conditions. Root architecture and biomass were quantified using image-based phenotyping. Transcriptomic and phytohormone profiling assessed plant responses, and fungal genome sequencing coupled with secretome analysis was used to identify candidate effectors and metabolic traits. P. melinii consistently promoted root growth and increased plant biomass across species and environments, both in vitro and in the greenhouse. In tomato field trials, this translated into a significant increase in yield. The fungus colonized root surfaces without vascular penetration and triggered a mild transcriptomic response: early activation of stress-response genes followed by their attenuation and sustained upregulation of auxin-related pathways. Notably, the interaction modulates the SLR-ARF-LBD pathway and the number of pre-branch sites probably through increased auxin signalling in the oscillation zone. Additional hormonal changes were limited and mainly associated with the attenuation of the plant response to microorganisms. P. melinii enhances lateral root formation through a subtle molecular and metabolic dialogue with the host plant, underscoring its relevance as a model for studying root developmental plasticity. Its strong and reproducible growth-promoting effect, demonstrated with different fungal strains and under controlled and field conditions, supports its potential as a biostimulant for sustainable crop production.

Journal Article↗
PubMed2003

New trends in bioinformatics: from genome sequence to personalized medicine.

Molecular medicine requires the integration and analysis of genomic, molecular, cellular, as well as clinical data and it thus offers a remarkable set of challenges to bioinformatics. Bioinformatics nowadays has an essential role both, in deciphering genomic, transcriptomic, and proteomic data generated by high-throughput experimental technologies, and in organizing information gathered from traditional biology and medicine. The evolution of bioinformatics, which started with sequence analysis and has led to high-throughput whole genome or transcriptome annotation today, is now going to be directed towards recently emerging areas of integrative and translational genomics, and ultimately personalized medicine.Therefore considerable efforts are required to provide the necessary infrastructure for high-performance computing, sophisticated algorithms, advanced data management capabilities, and-most importantly-well trained and educated personnel to design, maintain and use these environments. This review outlines the most promising trends in bioinformatics, which may play a major role in the pursuit of future biological discoveries and medical applications.

Computational Biology↗
PubMed2003

DNA microarrays in vaccine research.

DNA microarrays represent the technology platform for a wide variety of analytical methods built around the detection of sequence-specific nucleic acid hybridization. They allow the analysis of complex biological systems on the basis of the genome and transcriptome with high simplicity, efficiency, sensitivity and specificity. This positions DNA microarrays as ideal tools for novel approaches in biomedical research and explains the tremendous pace at which they are being applied to an enormous variety of systems and projects. Recently, vaccine researchers began to use DNA microarrays for the identification of vaccine candidates against bacterial, parasitic and viral pathogens, as well as tumors. Here, DNA microarrays as a technology platform in these areas of vaccine research are reviewed and their limitations, as well as their potential for future applications in vaccinology, critically evaluated.

Bacterial Vaccines↗
PubMed2006

Metabolic-state-dependent remodeling of the transcriptome in response to anoxia and subsequent reoxygenation in Saccharomyces cerevisiae.

We conducted a comprehensive genomic analysis of the temporal response of yeast to anaerobiosis (six generations) and subsequent aerobic recovery ( approximately 2 generations) to reveal metabolic-state (galactose versus glucose)-dependent differences in gene network activity and function. Analysis of variance showed that far fewer genes responded (raw P value of or=2 generations) with the activation of Upc2- and Mot3-regulated networks involved in sterol and cell wall homeostasis. The response to reoxygenation was rapid (<5 min) and similar in both media, dominated by Yap1 networks involved in oxidative stress/redox regulation and the concomitant activation of heme-regulated ones. Our analyses revealed extensive networks of genes subject to combinatorial regulation by both heme-dependent (e.g., Hap1, Hap2/3/4/5, Rox1, Mot3, and Upc2) and heme-independent (e.g., Yap1, Skn7, and Puf3) factors under these conditions. We also uncover novel functions for several cis-regulatory sites and trans-acting factors and define functional regulons involved in the physiological acclimatization to changes in oxygen availability.

Anaerobiosis↗
PubMed2026

Insights into glandular trichome biology from analysis of organ-specific gene expression programmes in cannabis, hop and tomato.

Glandular trichomes (GTs) are epidermal outgrowths in which diverse specialised (secondary) metabolites are synthesised and stored. Cannabis (Cannabis sativa L.) and its close relative hop (Humulus lupulus L.) have pharmaceutical and industrial significance due to the presence of these metabolites in their GTs. We examined the conservation or divergence of the specific transcriptional programmes underlying GT biology. To achieve this, we generated transcriptome atlases of trichomes, flower, leaf, stem and root for cannabis, hop and tomato. We found that 12.9, 10.1 and 16.8% of cannabis, hop and tomato genes, respectively, were expressed organ/tissue specifically across all organs/tissues. Transcription factors (TFs) on average accounted for 7.5% of the organ-specific transcriptome and likely regulate organ-specific functions. We also conducted weighted gene co-expression network analysis and gene regulatory network (GRN) analysis to identify key regulators of GT function across the species and validated our predictions by DNA affinity purification sequencing for a subset of the cannabis and tomato GT TFs. The GRNs specific to cannabis or hop GTs were enriched for TFs and target genes associated with specialised metabolism, reflecting their species-specific nature. Conversely, the shared GRN components (identified via orthology analysis) were involved in highly conserved processes, such as flavonoid biosynthesis, solute transport and metabolite storage. Together, these GRNs and the associated transcriptome atlases are valuable resources to improve our knowledge of GT function and organ-specific genome regulation.

Solanum lycopersicum↗
PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗
PubMed2005

Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data.

Genome-wide expression profiling is a powerful tool for implicating novel gene ensembles in cellular mechanisms of health and disease. The most popular platform for genome-wide expression profiling is the Affymetrix GeneChip. However, its selection of probes relied on earlier genome and transcriptome annotation which is significantly different from current knowledge. The resultant informatics problems have a profound impact on analysis and interpretation the data. Here, we address these critical issues and offer a solution. We identified several classes of problems at the individual probe level in the existing annotation, under the assumption that current genome and transcriptome databases are more accurate than those used for GeneChip design. We then reorganized probes on more than a dozen popular GeneChips into gene-, transcript- and exon-specific probe sets in light of up-to-date genome, cDNA/EST clustering and single nucleotide polymorphism information. Comparing analysis results between the original and the redefined probe sets reveals approximately 30-50% discrepancy in the genes previously identified as differentially expressed, regardless of analysis method. Our results demonstrate that the original Affymetrix probe set definitions are inaccurate, and many conclusions derived from past GeneChip analyses may be significantly flawed. It will be beneficial to re-analyze existing GeneChip data with updated probe set definitions.

Alleles↗
PubMed2002

Association of interferon regulatory factor-1, nucleophosmin, nuclear factor-kappaB, and cyclic AMP response element binding with acquired resistance to Faslodex (ICI 182,780).

To identify genes associated with survival from antiestrogens, both serial analysis of geneexpression and gene expression microarrays were used to explore the transcriptomes of antiestrogen-responsive (MCF7/LCC1) and -resistant variants(MCF7/LCC9) of the MCF-7 human breast cancer cell line. Structure of the gene microarray expression data was visualized at the top level using a novel algorithm that derives the first three principal components,fitted to the antiestrogen-resistant and -responsive gene expression data, from Fisher's information matrix. The differential regulation of several candidate genes was confirmed. Functional studies of the basal expression and endocrine regulation of transcriptional activation of implicated transcription factors were studied using promoter-reporter assays. The putative tumor suppressor interferon regulatory factor-1 is down-regulated in resistant cells, whereas its nucleolar phosphoprotein inhibitor nucleophosmin is up-regulated. Resistant cells also up-regulate the transcriptional activation of cyclic AMP response element (CRE) binding and nuclear factor kappaB (NFkappaB) while down-regulating epidermal growth factor receptor protein expression. Inhibition of NFkappaB activity by ICI 182,780 is lost in resistant cells, but CRE activity is not regulated by ICI 182,780 in either responsive or resistant cells. Parthenolide, a potent and specific inhibitor of NFkappaB, inhibits the anchorage-dependent proliferation of antiestrogen-resistant but not antiestrogen-responsive cells. This observation implies a greater reliance on their increased NFkappaB signaling for proliferation in cells that have survived prolonged exposure to ICI 182,780. These data from serial analysis of gene expression and gene microarray studies implicate changes in a novel signaling pathway, involving interferon regulatory factor-1, nucleophosmin, NFkappaB, and CRE binding in cell survival after antiestrogen exposure. Cells can up-regulate some estrogen-responsive genes while concurrently losing the ability of antiestrogens to regulate their expression. Signaling pathways that are not regulated by estrogens also can be up-regulated. Thus, some breast cancer cells may survive antiestrogen treatment by bypassing specific growth inhibitory signals induced by antagonist-occupied estrogen receptors.

Animals↗
PubMed2026

Decoding tumor immune microenvironment heterogeneity by single-cell and spatial multi-omics: From immunotherapy resistance to translational biomarkers.

Immune checkpoint blockade has transformed cancer therapy, yet primary and acquired resistance remain major clinical challenges. Increasing evidence indicates that immunotherapy resistance cannot be fully explained by tumor-intrinsic alterations or conventional biomarkers such as PD-L1 expression, tumor mutational burden, or microsatellite instability. Instead, therapeutic response is shaped by the tumor immune microenvironment (TIME) as a heterogeneous, spatially organized, and dynamically evolving ecosystem. Single-cell omics has revealed diverse immune and stromal cell states, including progenitor and terminally exhausted T cells, suppressive myeloid programs, B-cell/TLS-associated immune-reactive states, and CAF-mediated exclusion phenotypes. Spatial transcriptomics, spatial proteomics, and imaging-based approaches further demonstrate that these cell states assemble into distinct immune niches, including immune-inflamed, T-cell-excluded, myeloid-suppressive, metabolic/hypoxic, and TLS-associated niches. These spatial ecosystems determine whether antitumor immune cells can access malignant cells, receive antigen-presenting support, or become restrained by stromal, vascular, metabolic, and myeloid barriers. In this review, we summarize how single-cell and spatial multi-omics redefine TIME heterogeneity in immunotherapy resistance, highlight ligand-receptor communication networks linking cell states to spatial immune dysfunction, and discuss emerging translational biomarkers for patient stratification. We further propose that future immunotherapy biomarkers should evolve from static single-marker assays toward longitudinal, spatially resolved, and interpretable multi-omics models that guide precision combination immunotherapy.

Humans↗
PubMed2003

Serial analysis of gene expression (SAGE): advances, analysis and applications to pigment cell research.

As cells progress from normal to diseased states, they may undergo a series of gene expression changes. Advances in molecular biology allow us to examine a host of these changes at once, in a high throughput fashion. Serial analysis of gene expression (SAGE) allows for the expression profiling of the complete transcriptome of a given cell, and has the potential for identifying novel genes as well as those in low abundance. In this review, we will outline the technique, how one analyzes the massive amounts of data generated, and describe pigment cell libraries currently in the making.

Gene Expression Profiling↗
PubMed2005

Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes.

BACKGROUND: The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods. RESULTS: In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85%) were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis. CONCLUSION: Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies. Predictive models generated by this approach are better validated than those generated on a single data set, while showing high predictive power and improved generalization performance.

Animals↗
PubMed2026

Multi-omics panorama of glaucoma: Pathogenesis, biomarkers, and novel therapeutic strategies.

Glaucoma is a group of irreversible, blinding eye diseases characterized by progressive loss of retinal ganglion cells, leading to gradual visual field defects that severely impact patients' quality of life. Its complex pathophysiological mechanisms remain incompletely understood, limiting the development of early diagnostic and effective therapeutic strategies. Advances in omics technologies have provided new insights into elucidating the pathophysiology of glaucoma. We summarize specific alterations in genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics associated with glaucoma. We emphasize the systematic analysis of disease mechanisms, identification of clinically applicable biomarkers, and discovery of novel therapeutic targets through the integration of these data. This approach paves new pathways for glaucoma subtype diagnosis and personalized treatment, while also outlining future research directions and challenges.

Humans↗