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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

Integrated transcriptomic and metabolomic analysis of fluoride tolerance-related pathways and differentially expressed genes in silkworm strain XSKD.

XueSong KD (XSKD) silkworm strain exhibits prominent fluoride tolerance, yet the underlying molecular mechanisms of fluoride tolerance remains unclear. In the present study, fourth-instar pre-molting XSKD silkworms were used as experimental materials for integrated transcriptomic and untargeted metabolomic analyses. In total, 572 differentially expressed genes and 90 differential metabolites were screened. GO enrichment and KEGG enrichment based on the hypergeometric distribution model revealed that 13-Hydroxy-9Z,11E-octadecadienoic acid (13-(S)-HODE) acts as the core differential metabolite, which is significantly enriched in the linoleic acid metabolism pathway. Within this pathway, LOC101737302 and CYP338A1 display opposite expression trends and show correlations with pathway metabolites. Based on multi-omics data, this study preliminarily characterizes the lipid metabolic response under fluoride stress, providing omics dataset support for further in-depth exploration of the molecular mechanism of fluoride tolerance in silkworms.

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

Free polyphenols and multi-omics traits underlying antioxidant variation across Paeonia lactiflora leaf cultivars.

Leaves of Paeonia lactiflora are underutilized by-products with potential as natural antioxidant sources. In this study, 18 cultivars were evaluated for phytochemical composition and in vitro antioxidant capacity. Total phenolic content correlated strongly with DPPH and ABTS activities, and the comprehensive antioxidant index identified 'Coral Charm' and 'Hangshao' as representative high- and low-antioxidant cultivars, respectively. Untargeted metabolomics detected 2677 metabolites and identified 908 differential metabolites between the two cultivars. Targeted phenolic profiling quantified 27 compounds, among which 11 differed significantly between the two cultivars. Catechin and epicatechin were enriched in 'Coral Charm', with contents of 6.62 and 0.397 ng/mg, respectively, compared with 0.012 and 0.002 ng/mg in 'Hangshao'. (+)-Dihydroquercetin was also more abundant in 'Coral Charm', while caffeic acid showed an upward trend. Proteomic analysis identified 423 differentially expressed proteins, mainly associated with secondary metabolite biosynthesis, redox homeostasis, and central carbon metabolism. Integrated analysis identified pyruvate metabolism as the only pathway significantly enriched in both metabolomic and proteomic datasets. Molecular docking predicted favorable binding between representative phenolics and selected proteins. These findings link cultivar-dependent antioxidant variation in peony leaves with free-phenolic accumulation and pathway-level metabolic differences, supporting the selection and utilization of antioxidant-rich peony leaf resources.

Antioxidants↗

PubMed2025

Multi-Omics Analyses Reveal the Red and Far-Red Light Combination Enhancing Heterologous Protein and Metabolite Production in Nicotiana benthamiana.

Transient expression of exogenous protein in Nicotiana benthamiana leaves via agroinfiltration offers a rapid and efficient platform for functional gene discovery and heterologous production of valuable eukaryotic proteins and metabolites. Though light quality is an important factor for plant photomorphogenesis, its impact on the efficiency of transient expression remains unexplored. In this study, we examined the influence of five representative light qualities with varying wavelength mix on the N. benthamiana growth and recombinant green fluorescent protein (GFP) production. Plants with red and far-red light treatment (LED-red) showed the highest GFP expression, 57.4% higher than white light. Further study showed that a higher dosage of post-infiltration Agrobacterium and the resulting increase in the number of transcripts contribute to the expression rate enhancement. Moreover, as for exogenous metabolites, a 76.5% increase of accumulated taxadiene was also observed in LED-red group. Integrated transcriptomic, proteomic and metabolomic revealed that LED-red plants reduced the resistance pathways before infiltration, inducing a higher dosage of post-agroinfiltration Agrobacterium. Our results suggest that N. benthamiana grown under LED-red creates a more favorable environment for Agrobacterium growth, enhancing heterologous protein and metabolite production. This study highlights the potential utilization of light quality as an implementable tool in plant synthetic biology.

Nicotiana↗

PubMed2026

Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.

Cancer remains a major global health burden, with approximately 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022. While advances in radiological imaging, molecular profiling, and clinical data have enhanced the interpretation of disease progression, the availability of multiple such modalities still does not meet the needs of a large patient population. This narrative review focuses on the role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology. Multimodal frameworks such as Pathomic Fusion illustrate how complementary histopathological and genomic information can be integrated for cancer diagnosis and prognostic modeling. Multimodal models have demonstrated potential in virtual biopsy, cancer screening, prognostic prediction, radiotherapy planning, intraoperative guidance, and clinical-trial design using digital twins and synthetic control arms. The major limitations of incorporating multimodal artificial intelligence and machine learning in oncology include data heterogeneity, demographic or institutional biases, and reproducibility challenges that hinder translation. Accordingly, appropriate data-governance strategies, fairness audits, and privacy-preserving approaches such as federated learning should be considered where appropriate. Future progress will depend on the development of standardized benchmarking datasets, robust external validation, seamless integration with electronic health records and picture archiving and communication systems, and the implementation of explainable, secure, and clinically validated multimodal artificial intelligence frameworks that support precision oncology in routine clinical practice.

deep learning↗

PubMed2026

Exposome influences: a multi-omics perspective on the combined toxic effects of pharmaceuticals and personal care products in Alzheimer's disease.

According to WHO data, approximately 57 million people worldwide were affected by dementia in 2021, with prevalence projected to rise. Alzheimer's disease (AD), responsible for 60%-80% of dementia cases, continues to be a leading cause of mortality, with current treatments offering limited efficacy and disease-modifying therapies lacking widespread adoption or conclusive safety evidence, shifting the focus toward prevention and risk modification. Risk factors for AD include both non-modifiable elements, such as age, genetics, and gender, and modifiable factors, like environmental pollution, health status, and diet. While age remains the primary non-modifiable risk factor, early-onset dementia represents only up to 9% of cases. Addressing modifiable factors is essential, as it could prevent or delay almost half of dementia cases, with interventions-such as increased physical activity, smoking cessation, alcohol limitation, and overall health management-being significantly associated with a reduced risk. In this context, the exposome approach offers a comprehensive, integrative framework in which both modifiable and non-modifiable risk factors interact to influence individual susceptibility. Within the neural exposome, chronic low-dose exposure to xenobiotics-such as industrial chemicals, pesticides, metals, pharmaceuticals and personal care products (PPCPs), and air pollutants-may induce neurodegeneration via mechanisms including oxidative stress, neuroinflammation, proteinopathies, and epigenetic modifications, although establishing causality remains challenging. Integration of genomics, transcriptomics, proteomics, metabolomics, and lipidomics, combined with artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL), provides promising avenues for biomarker discovery, enhanced preventive strategies, early non-invasive diagnosis, and therapeutic target identification by integrating multi-layered biological data with exposure profiles. This review highlights emerging AD risk factors-including PPCPs-underscoring complex, multifactorial nature of AD and exposome, and the requirement for an interdisciplinary research approach, while also addressing several critical research gaps and methodological limitations.

Alzheimer’s disease↗

PubMed2026

Heterozygous germline deletion in Hif3a exacerbates esophageal squamous cell carcinoma development.

Germline variations contribute to esophageal squamous cell carcinoma (ESCC) susceptibility. We identified a germline deletion (exons 7-8) in HIF3A in an ESCC family and investigated its functional impact using CRISPR/Cas9-engineered cells and Hif3a-eKO1 mice (heterozygous for exons 7-8 deletion). Multi-omics analysis of Hif3a-eKO1 and WT mice revealed dysregulated pathways in normal esophagus and during 4NQO-induced carcinogenesis, with key biomarkers validated by immunohistochemistry. Hif3a deficiency enhanced ESCC cell proliferation and invasion in vitro and accelerated 4NQO-induced tumorigenesis in vivo, with Hif3a-eKO1 mice developing more and larger neoplastic lesions. Multi-omics analysis revealed downregulation of cytokeratin-related genes (notably Krt17) and γδ T cells in normal esophagus of Hif3a-eKO1 compared with WT. Consistently reduced Krt17 expression in Hif3a-eKO1 was confirmed by both esophageal immunohistochemistry and cellular Western blot analyses. During 4NQO-induced carcinogenesis, Hif3a deficiency upregulated DNA damage response markers, including Krüppel-like factor 4 (Klf4) and ATR serine/threonine kinase (Atr). Notably, epithelial cells with abundant γH2AX foci lacked Krt17 expression, while Krt17-positive cells showed minimal γH2AX foci. Heterozygous germline Hif3a deletion (exons 7-8) may promote ESCC by disrupting esophageal barrier function-impairing Krt17-mediated epithelial integrity and reducing γδ T cells-while exacerbating genomic instability. These findings reveal ESCC predisposition mechanisms and therapeutic targets. © 2026 The Pathological Society of Great Britain and Ireland.

HIF3A↗

PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗

PubMed2026

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer↗

PubMed2026

Recent advancements in exosomal content analysis: the future of liquid biopsy.

Exosomes are widely acknowledged as an essential agent that carries biomarkers for specific diseases, representing the molecular status of their parent cells and providing extremely useful diagnostic insights. They can be isolated from different body fluids and contain a range of cargo molecules, including proteins, lipids, metabolites, and nucleic acids. Recent advancements in technology have greatly accelerated exosome research. Proteomics provides protein signatures linked to many pathological conditions, enabling quick and clinically scalable diagnostic tools, whereas high-throughput RNA-sequencing can be used to perform detailed transcriptome profiling. Exosomal biomarkers are showing promising clinical results in early detection of neurological diseases, infectious and cardiovascular disorders, oncology, and other medical conditions, hence accelerating therapeutic monitoring. Despite these advances, several challenges continue to hinder clinical translation including the lack of standardized isolation protocol, variability in exosome yield and purity, biological heterogeneity, and limited large-scale clinical validation. Addressing these limitations will be critical for the successful integration of exosome-based liquid biopsy into routine clinical practice. Overall, exosomes having significant potential as diagnostic tool, represent a transformative horizon in biomedical liquid biopsy research to redefine the landscape of less-invasive diagnostics and tailored clinical applications.

Humans↗

PubMed2026

Integrated transcriptomic and metabolomic analyses reveal key regulators associated with lipid metabolic differences between subcutaneous and visceral adipose tissues in sheep.

The location of fat deposition has a significant impact on meat quality and body health, and different adipose tissues exhibit significant differences in lipid metabolism and immune regulation. This study aimed to systematically compare the phenotypic characteristics, transcriptome, and metabolome of subcutaneous adipose tissue (SAT) and two types of visceral adipose tissue (VAT) in sheep, in order to reveal the metabolic differences between SAT and VAT and their potential regulatory mechanisms. The results showed that compared with VAT, SAT had stronger triglyceride deposition ability and obvious cellular hypertrophy. Through integrative analysis, 15 key lipid metabolism genes and 12 differential metabolites were identified. Among them, ACACA, FASN, ELOVL6, SCD, as well as metabolites palmitic acid and glycerol-3-phosphate, may play a central role in SAT lipid synthesis and storage; whereas IGFBP2, ADRB3, LTA4H, and metabolites arachidonic acid and leukotriene B4 may be involved in the lipolysis regulation and inflammatory response of VAT. These findings may provide deeper insights into the regulatory mechanisms of fat deposition in sheep.

Animals↗

PubMed2026

Integrating transcriptomics and metabolomics reveals the molecular landscape of sperm maturation driven by regional differentiation in the epididymis of Guizhou-Guiqian semi-fine wool sheep.

Epididymal regionalized differentiation is crucial for sperm maturation. However, little is known about the synergistic remodeling mechanisms of different epididymal segments at the transcriptional and metabolic levels during sexual maturation in ruminants (especially sheep). We investigated the caput, corpus, and cauda epididymidis of pre-pubertal (2-month-old) and post-pubertal (7-month-old) Guizhou-Guiqian semi-fine wool sheep using histology, RNA sequencing, and metabolomics. Post-pubertal tissues exhibited increased luminal diameters, cilia lengths, and abundant cauda spermatozoa. Transcriptomic analysis revealed increasing differentially expressed genes (DEGs) along the caput-corpus-cauda axis (4642, 6103, and 7698 DEGs, respectively). Metabolomics detected 786 unique differentially accumulated metabolites (DAMs). Region-specific analysis showed that in the caput, up-regulated pathways (fructose/mannose metabolism; HK2, ALDOA, HKDC1) provide energy and substrates for initial sperm motility. In the corpus, down-regulated genes associated with extracellular matrix and tight junctions suggested epithelial barrier remodeling to establish an immune-tolerant microenvironment. The cauda specifically up-regulated the pentose phosphate pathway (FBP1, GPI) and glutathione metabolism, maintaining redox homeostasis for long-term sperm storage. Additionally, glycerophospholipid metabolism was enriched across all segments, where PEMT, AGPAT5, and LCAT likely regulate sperm plasma membrane fluidity. In conclusion, during sexual maturation, the caput drives energy metabolism and glycosylation, the corpus establishes immune tolerance, and the cauda maintains antioxidant homeostasis. The glycerophospholipid network throughout the across all epididymal segments synergistically remodels sperm membrane. This study reveals the underlying multi-omics regulatory mechanisms of epididymal functional differentiation, providing a theoretical basis for elucidating the molecular mechanisms of sperm maturation in this breed and for the molecular breeding of early reproductive performance in rams.

Animals↗

PubMed2026

Molecular biology and integrated strategies for activating cryptic biosynthetic gene clusters toward next-generation antibiotic discovery.

Antimicrobial resistance (AMR) has been identified as one of the 21st century's severest global public health crises. AMR led to an estimated 4.95 million deaths in 2019 and will claim 10 million lives a year by 2050 in the absence of targeted interventions. During the same period, the number of novel antibiotics discovered has decreased drastically as many researchers are rediscovering known antibiotics, non-model microorganisms are poorly understood or difficult to culture and antibiotic research and development investment has declined drastically. However, high-throughput whole genome sequencing and the subsequent application of bioinformatics in bacterial and fungal genomes have shown that a numerous of cryptic or silent biosynthetic gene clusters (BGCs) remain latent at ambient laboratory conditions since their genes are transcriptionally inactive. Cryptic BGCs represent a vast source of unique secondary metabolites, many of which may yield novel antibacterial, antifungal, anti-cancer and other potentially valuable natural products. This review discusses the biological relevance of cryptic BGCs, the major limiting factors that restricts their activation and novel strategies that have been employed to activate them and exploit their potential to produce novel natural products. The review focuses on biological approaches including CRISPR-Cas mediation for the activation of cryptic BGCs, promoter engineering, pathway refactoring, and heterologous expression; biochemical strategies such as Osman, OsMAC, Precursor Feeding, Chemical Elicitation, Epigenetic Regulation and Co-cultivation and technology-based strategies such as Genome mining, Microfluidic Cultivation systems, High-Throughput Screening, Metabolomics, Molecular Networking and Artificial Intelligence and Machine Learning based prediction of BGCs and their metabolites. The use of multi-omics technologies combined with synthetic biology to achieve better discovery, characterization and large-scale production of novel natural products is also discussed herein. Finally, we will talk about the ecological significance and evolutionary advantage of cryptic BGCs' role in interactions between microorganisms, such as competition, communication, symbiosis and environmental adaptability, so as to provide a useful background for accelerating next-generation antibiotics.

CRISPR-Cas activation↗

PubMed2026

Mechanistic analysis of rice caryopsis morphogenesis regulated by exogenous hormones and related precursor substances under blue light conditions.

Rice caryopsis morphogenesis is regulated by light signals and hormonal networks. However, the mechanism by which exogenous hormones and related precursor substances modulate rice caryopsis morphogenesis under blue light remains elusive. In the present study, we aimed to elucidate the molecular mechanisms underlying the regulatory effects of exogenous phytohormones and related precursor substances on caryopsis development at 10 days after pollination (10 DAP) in the japonica rice cultivar 'Chujing 27' under blue light conditions. Results showed that tryptamine treatment increased caryopsis cell volume, thereby significantly driving caryopsis expansion; meanwhile, it markedly enhanced the activities of TDC and TAA, the key rate-limiting enzymes mediating the conversion of tryptophan to auxin, leading to a significant elevation in endogenous auxin content (P < 0.05). In comparison, exogenous auxin treatment significantly boosted carbohydrate accumulation and the activities of associated metabolic enzymes (P < 0.05). Integrated transcriptomic and metabolomic analyses revealed that tryptamine treatment led to significant enrichment of the starch and sucrose metabolic pathway, and drove the coordinated enhancement of carbon metabolic flux and auxin biosynthesis by upregulating key auxin biosynthetic genes (e.g., TAA1) and repressing auxin oxidative degradation. Genes Os04g0531100, Os03g0266100 and Os11g0221200 identified by weighted gene co-expression network analysis (WGCNA) may serve as important candidate targets regulating rice caryopsis morphology and physiological traits under blue light conditions. This study first uncovers the critical function of the "tryptamine-auxin axis" in regulating rice caryopsis development under blue light, laying a theoretical foundation for regulating caryopsis morphogenesis via exogenous hormones and their precursors.

Oryza↗

PubMed2026

The domestication-associated WHP10 tandem cluster of amino acid transporter genes enhances whole-plant protein accumulation in maize.

Improving protein accumulation in maize is essential for sustainable agriculture, yet the regulatory mechanisms governing the intermediate "flow" of organic nitrogen remain elusive. Here, we show that the maize stem acts as a regulatory node for nitrogen allocation. By integrating spatial transcriptomics and metabolomics with quantitative genetics, we demonstrate that a transport-oriented stem program orchestrates the high-protein phenotype of the wild maize accession Ames21814. We identified a major locus, Whole-plant High Protein 10 (WHP10), that encodes a tandemly duplicated cluster of amino acid transporter genes. WHP10 exhibits strong vascular-biased expression, driven by promoter divergence that enhances the wild allele's activity. Functional assays and genetic validation support a model in which the WHP10 cluster facilitates the transport of multiple nitrogen-rich amino acids, thereby contributing to vascular-associated amino acid transport and post-uptake organic-nitrogen partitioning. Our findings establish stem flow as a regulatory layer for protein accumulation and identify WHP10 as a high-value target for precision breeding to enhance whole-plant protein accumulation without compromising grain yield.

Zea mays↗

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↗

PubMed2026

Decoding the molecular basis of blue grain color codominance in Qingke: Integrative analysis of RNA-seq, DNA methylation, and miRNA-seq.

The grains on single spike of the F1 generation from the cross between blue- and white-grained Qingke (Hordeum vulgare L. var. nudum Hook. f.) are randomly distributed in blue and white colors. This study integrated data from RNA-seq, DNA methylation, and miRNA-seq to analyze this trait. The results showed that the HvF3'5'H gene is likely central to the development of this codominant phenotype. Through cross-validation of three omics approaches, it was found that the HvMYB gene targeted by miR858-z, as well as the WRKY24 and At3g44326 genes targeted by novel-m0152-5p, novel-m0153-5p, and novel-m0154-5p, are correlated with DNA methylation. qRT-PCR analysis confirmed that the four aforementioned genes exhibited variety-specific and developmental stage-specific expression patterns. This study dissects the regulatory network underlying the codominant blue and white grain color divergence on a single Qingke spike from a multi-omics perspective.

DNA Methylation↗