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PubMed2026

Integrative WGBS and ATAC-seq profiling reveals epigenetic and chromatin accessibility signatures associated with clutch length in goose ovaries.

Clutch length is an important reproductive trait in geese, but its epigenetic basis remains poorly characterized. Daily egg production was recorded for 280 individually housed Zi geese, and clutch-related indices were calculated as described in our previous study. Based on these records, six geese with contrasting clutch-length phenotypes were selected and assigned to the long-clutch (LC) and short-clutch (SC) groups. Ovarian tissues from three geese per group were subjected to whole-genome bisulfite sequencing (WGBS) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) to identify candidate epigenetic signatures associated with clutch length. WGBS identified 630,909 differentially methylated regions (DMRs), whereas ATAC-seq identified 902 differentially accessible regions (DARs). Integrated analysis revealed distinct patterns of ovarian DNA methylation and chromatin accessibility between the two groups, suggesting that clutch length variation may be accompanied by epigenomic differences in ovarian tissue. Genes associated with DMRs and/or DARs were enriched in biological processes related to granulosa cell differentiation and endocrine competence, follicular fate regulation, and periovulatory cytoskeletal and signaling remodeling. RERE was prioritized as a candidate locus because it was supported by changes in both DNA methylation and chromatin accessibility, whereas FOXL2, STAR, BAK1, FGF17, PRSS35, ACTR3, and AXIN1 were supported mainly by evidence from a single omics layer. RT-qPCR analysis of selected genes showed expression trends broadly consistent with the corresponding epigenomic differences, providing additional supportive evidence for these candidate associations. Collectively, this study provides an exploratory ovarian epigenomic resource and identifies candidate epigenetic signatures, genes, and biological processes associated with clutch length variation in geese.

DNA methylation↗

PubMed2026

Beyond Morphology: Reframing Lymph-Node Metastasis Prediction Through Clonal Ecology-Decades-Long Genomic Instability and Polyclonal-to-Monoclonal Transitions as the Missing Dimension in Cancer.

Recent whole-genome, lineage-tracing, single-cell, and spatial studies have reshaped our understanding of tumor evolution, revealing that cancers can arise from polyclonal populations, undergo decades-long genomic instability before clinical detection, and progress through dynamic changes in subclonal composition, cellular state, and ecological organization. These findings challenge the assumption underlying morphology-based prediction models that metastatic risk can be inferred from static histological features alone. Here, we revisit lymph-node metastasis prediction in colorectal cancer through clonal ecology, integrating computational pathology with evolutionary oncology. Drawing on the subclonal switchboard model proposed in 2012 and subsequent artificial intelligence (AI)-enabled approaches for tracking dominant and dormant subclones, we synthesize evidence that metastatic potential reflects clonal ancestry, evolutionary timing, spatial niche architecture, cellular plasticity, intercellular interactions, dormancy, and treatment-driven shifts in subclonal fitness. We define five complementary methodological pillars for operationalizing clonal ecology: single-cell transcriptomics for resolving rare subclones, evolutionary trajectories, and adaptive cell states; lineage tracing and phylogenetics for reconstructing clonal ancestry and divergence; spatial transcriptomics and genomics for mapping subclonal geography and tumor-stromal-immune interactions; longitudinal liquid biopsy surveillance for monitoring residual disease, clonal turnover, and emerging resistance; and AI-enabled multimodal integration for connecting histopathology, genomics, spatial biology, and longitudinal data into predictive ecological-state models. Multiple-instance learning and pathology foundation models provide scalable computational foundations for evolution-aware prediction. Translationally, dormant subclones represent actionable reservoirs of recurrence. A longitudinal clinical and experimental study of KMT2A-rearranged acute myeloid leukemia further supports central predictions of the subclonal switchboard framework by demonstrating treatment-associated shifts in subclonal dominance, persistence of cryptic adaptive programs, and ecological rewiring during resistance and relapse. We propose clonal ecology as a measurable dimension for extending morphology-driven prediction toward integrative models that anticipate evolutionary transitions, identify therapeutic windows, and proactively constrain adaptive tumor ecosystems before resistant or metastatic subclones achieve clinical dominance.

Humans↗

PubMed2026

Mutual Information-based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG.

Mutual information (MI)-based approaches have increasingly been applied to cancer genomics; however, their use for genome-wide prognostic biomarker discovery remains relatively underexplored. The present article summarizes the conceptual workflow of Mutual Information-based Prognostic Omics Gene (MI-POG) based on previously published applications in breast cancer, lower-grade glioma, and other cancer datasets. The framework consists of clinical endpoint discretization, genome-wide MI-based screening, candidate ranking, and downstream validation using conventional survival-analysis approaches. Previous MI-POG applications identified solute carrier family 20 member 1 (SLC20A1) as a prognostic biomarker in hormone receptor-positive breast cancer. Elevated SLC20A1 expression was associated with unfavorable survival outcomes and was independently validated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Methodological analyses demonstrated how survival endpoints can be integrated into an information-theoretic framework through fixed-time outcome discretization, enabling model-independent assessment of molecular-clinical dependencies. Applications across multiple cancer datasets suggested the potential applicability of the framework across biologically distinct tumor types, although further validation will be required to establish its robustness and generalizability. In conclusion, MI-POG can be formalized as an information-theoretic framework for genome-wide identification of prognostic biomarkers by quantifying molecular-clinical dependencies using mutual information. Representative applications from previously published studies suggest that MI-POG may complement conventional survival-analysis approaches and provide a useful strategy for biomarker discovery, although additional benchmarking and prospective validation will be required.

Humans↗

PubMed2002

"A system biology" approach to bioinformatics and functional genomics in complex human diseases: arthritis.

Human and other annotated genome sequences have facilitated generation of vast amounts of correlative data, from human/animal genetics, normal and disease-affected tissues from complex diseases such as arthritis using gene/protein chips and SNP analysis. These data sets include genes/proteins whose functions are partially known at the cellular level or may be completely unknown (e.g. ESTs). Thus, genomic research has transformed molecular biology from "data poor" to "data rich" science, allowing further division into subpopulations of subcellular fractions, which are often given an "-omic" suffix. These disciplines have to converge at a systemic level to examine the structure and dynamics of cellular and organismal function. The challenge of characterizing ESTs linked to complex diseases is like interpreting sharp images on a blurred background and therefore requires a multidimensional screen for functional genomics ("functionomics") in tissues, mice and zebra fish model, which intertwines various approaches and readouts to study development and homeostasis of a system. In summary, the post-genomic era of functionomics will facilitate to narrow the bridge between correlative data and causative data by quaint hypothesis-driven research using a system approach integrating "intercoms" of interacting and interdependent disciplines forming a unified whole as described in this review for Arthritis.

Animals↗

PubMed2005

Liver tumors in wild flatfish: a histopathological, proteomic, and metabolomic study.

Fish play host to viral, bacterial, and parasitic diseases in addition to non-infectious conditions such as cancer. The National Marine Monitoring Programme (NMMP) provides information to the U.K. Government on the health status of marine fish stocks. An aspect of this work relates to the presence of tumors and other pathologies in the liver of the offshore sentinel flatfish species, dab (Limanda limanda). Using internationally agreed quality assurance criteria, tumors and pre-tumors are diagnosed using histopathology. The current study has expanded upon this work by integrating these traditional diagnostic approaches with ones utilizing modern technologies for analysis of proteomic and metabolomic profiles of selected lesions. We have applied SELDI and FT-ICR technologies (for proteomic and metabolomic analyses, respectively) to tumor and non-tumor samples resected from the liver of dab. This combined approach has demonstrated how these technologies are able to identify protein and metabolite profiles that are specific to liver tumors. Using histopathology to classify "analysis groups" is key to the success of such an approach since it allows for elimination of spurious samples (e.g., those containing parasite infections) that may confuse interpretation of "omic" data. As such, the pathology laboratory plays a central role in collating information relating to particular specimens and in establishing sampling groups relative to specific diagnostic questions. In this study, we present pilot data, which illustrates that proteomics and metabolomics can be used to discriminate fish liver tumors and suggest future directions for work of this type.

Adenoma, Liver Cell↗

PubMed2026

Spatial proximity sequencing maps developmental dynamics in the germinal center.

Spatial profiling of proteins and protein interactions facilitates understanding of cell functions within tissues and is essential for studies in signaling, immunity, and cancer. We present spatial proximity sequencing (Sprox-seq) for simultaneous profiling of surface proteins, protein complexes, and mRNAs, recording the tissue location of each molecule. Sprox-seq profiled 32 proteins, 528 pairwise interactions, and thousands of mRNAs with spatial resolution across human tonsils and germinal centers. Mapping tissue-wide protein interactions recapitulated RNA-defined tissue architecture but also revealed higher interaction complexity in the light zone. Protein-interaction trajectories uncovered a B cell state transition distinct from that inferred by RNA. Integrated protein-complex and mRNA analysis related spatially enriched complexes with mitotic pathways. Sprox-seq captured cell-cell interactions, such as B cell-follicular dendritic cell interactions mediated by the receptor complex VLA-4-VCAM1. Sprox-seq provides a spatially resolved multi-modal view of cell states and an integrated study of protein and cellular interactions across tissues.

Humans↗

PubMed2026

Unveiling tumor heterogeneity by single cell RNA-sequencing: From basic considerations to clinical applications.

Tumor heterogeneity-encompassing diverse cellular phenotypes, genomic alterations, and microenvironmental contexts-is a principal barrier to effective cancer therapy. Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve this complexity by capturing transcriptomes at single-cell resolution. Here, we review the technical foundations required for high-quality scRNA-seq studies. We then trace the evolution of scRNA-seq platforms from manual micromanipulation to high-throughput systems, and describe the computational pipelines that enable reliable data interpretation. The application of scRNA-seq is exemplarily shown in the context of lung cancer, where single-cell profiling has revealed (i) the clonal and sub-clonal architecture of tumors, (ii) extensive remodeling of the immune microenvironment, iii) key mechanisms underlying resistance to targeted agents and immune-checkpoint blockade, and (iv) the dynamics of neo-antigen-specific T-cell responses. Integrating machine-learning techniques-such as deep-learning classifiers and graph-based models-with single-cell transcriptomic data has markedly sped up biomarker discovery, produced more accurate risk-stratification scores, and enabled the generation of patient-specific therapeutic predictions. We surveyed the major trial registry ClinicalTrials.gov and identified ∼380 ongoing or completed studies that explicitly incorporate scRNA-seq as a correlative or pharmacodynamic endpoint. Overall, the analysis shows that scRNA-seq becomes an increasingly important component of modern trials, providing high-resolution cellular and molecular readouts that complement conventional imaging and bulk-omics endpoints. While key challenges remain, ranging from costs, scalability and need for rigorous validation before routine clinical deployment, ongoing technological advances continue to expand the potential of scRNA-seq as a cornerstone of precision medicine.

Humans↗

PubMed2026

A spatially resolved genomic-molecular atlas of human white-matter microstructure.

Human white matter has been linked to inherited variation, circulating molecular state and brain disease, but these layers have rarely been mapped onto the same tract anatomy. Here we measured genetic effects along 6,090 atlas-aligned fiber pathways sampled at 609,000 locations in 72,185 UK Biobank participants, and integrated proteomic and metabolomic profiles within the same anatomical frame. Genetic effects were not whole-tract properties: each locus formed a spatial footprint along fiber trajectories, ranging from single locations to broad multi-tract patterns and reflecting regional polygenicity rather than tract heritability. This map identified 258, 186 and 298 previously unreported loci for fractional anisotropy, mean diffusivity and axial diffusivity; spatial patterns replicated in adults and 157 of 315 FA loci replicated in adolescence in ABCD. Mendelian randomization linked localized genetic effects to neurodegenerative and psychiatric traits, with Alzheimer's disease showing directional effects across 12 of 17 tracts. Multi-omic analyses identified 97 proteomic and 161 metabolomic associations, with the broadest signals from lipid metabolites including linoleic acid and phosphatidylcholines. The strongest lipid-metabolite and genetic signals converged in the corpus callosum, placing inherited variation, disease risk and systemic lipid metabolism on the same localized tract segments.

Journal Article↗

PubMed2026

Circulating Tumor DNA in Bladder Cancer: Current Clinical Evidence and Emerging Multi-Omics Perspectives-A Narrative Review.

Background: Circulating tumor DNA (ctDNA) analysis has emerged as a promising tool for real-time disease monitoring in muscle-invasive bladder cancer (MIBC). This narrative review summarizes current clinical evidence regarding ctDNA across disease stages. Methods: We examine recent translational and clinical findings, incorporating key prospective data from practice-changing trials, as well as insights into minimal residual disease (MRD) detection, treatment escalation and de-escalation strategies, and systemic barriers to adoption. Results: Postoperative ctDNA positivity consistently identifies patients with molecular residual disease (MRD) who face a substantially higher risk of recurrence and mortality, frequently preceding radiographic relapse by several months. Prospective evidence now validates ctDNA as a predictive biomarker to guide adjuvant immunotherapy escalation, while sustained ctDNA negativity correlates with high long-term disease-free survival. Beyond plasma ctDNA, emerging multi-compartment liquid biopsies-integrating urinary tumor DNA (utDNA)-demonstrate enhanced sensitivity, particularly in bladder-sparing and local surveillance settings. Furthermore, integrating genomic ctDNA profiling with novel post-transcriptional layers like epitranscriptomics offers a functional framework to capture tumor adaptation under therapeutic pressure. However, clinical translation remains constrained by a lack of assay harmonization, variable analytical sensitivity, and the need for standardized intervention thresholds. Conclusions: Longitudinal liquid biopsies are rapidly shifting MIBC management from static, stage-based paradigms toward dynamic, molecularly informed precision oncology. While ctDNA-guided strategies show robust clinical utility, prospective interventional validation and technical standardization are required before widespread routine integration.

biomarkers↗

PubMed2026

DNA Methylation and Proteomic Profiling of Postmortem Brain Tissue Reveals Epigenetic Dysregulation and Neuroinflammatory in Fragile X-associated Tremor/Ataxia Syndrome (FXTAS).

BACKGROUND: Fragile X-associated Tremor/Ataxia Syndrome (FXTAS) is a late-onset neurodegenerative disorder caused by FMR1 premutation CGG repeat expansions (55-200 repeats). The epigenetic landscape of the FXTAS brain remains uncharacterized. We performed genome-wide DNA methylation profiling of postmortem prefrontal cortex tissue to identify differentially methylated positions (DMPs) and candidate genes, and sought protein-level support for a neuroinflammatory signal. METHODS: DNA methylation was profiled in postmortem prefrontal cortex (Brodmann area 9) from 27 male FXTAS cases and 29 male controls using the Illumina MethylationEPIC array (EPICv1 and EPICv2 platforms), merging 721,802 common probes. Surrogate variable analysis (SVA) controlled for confounders. DMPs were defined by |Δβ| > 0.10 and FDR < 0.05; exploratory Reactome 2024 pathway analysis was performed on the DMP-associated gene list. Targeted proteomic profiling was performed in the same brain region using the Olink (proximity extension assay) Inflammation panel in 9 FXTAS cases and 12 controls, with SVA-adjusted differential abundance analysis, and concordance assessment against a prior mass spectrometry dataset. RESULTS: We identified 108 significant cg-type DMPs mapping to 80 genes (50 hypermethylated, 58 hypomethylated in FXTAS). The strongest signal was CYP2E1 (7 concordant hypomethylated DMPs, mean Δβ = -0.143), an oxidative stress gene also implicated in Parkinson's disease. FTCD, a one-carbon cycle enzyme, carried 5 hypermethylated DMPs (mean Δβ = +0.210). A cluster of DMP-associated genes with established roles in innate immune and NF-κB signaling, TRAF3 (the single most significant DMP among the inflammation genes, hypermethylated), BATF, RCOR1, and MSI2; they pointed toward neuroinflammatory dysregulation. Additional genes included LINGO1 (myelination inhibitor), SYT3 (synaptic vesicle), and SLC39A4 (zinc transporter). Exploratory Reactome enrichment using the DMP-associated gene set nominated themes including neuroinflammation resolution, axonal growth inhibition, zinc homeostasis, and CYP2E1 metabolism at nominal significance (p<0.05); however, the gene-to-pathway mapping rate was low and no pathway survived correction for multiple testing. Olink proteomic analysis independently identified 60 significantly altered inflammation proteins (59 downregulated), including CXCL8, CXCL10, IL6, IL15, IL18, TLR3, IRAK1/4, and complement C1QA, which were directionally concordant with prior mass spectrometry data. CONCLUSIONS: This integrated study reveals a genome-wide epigenetic signature in the FXTAS prefrontal cortex implicating oxidative stress, myelination failure, zinc dysregulation, one-carbon cycle disruption, and most notably a coordinated set of epigenetically altered genes governing innate immune and NF-κB signaling. Convergence of TRAF3 hypermethylation with independent downregulation of TLR3 and NF-κB-pathway proteins at the protein level supports a coherent, cross-platform model of dysregulated neuroinflammatory signaling in FXTAS, identified here through individual gene- and protein-level convergence rather than formal pathway enrichment. FTCD hypermethylation proposes a self-reinforcing epigenetic loop via SAM depletion. These multi-omic findings establish FXTAS as a disorder of pervasive epigenetic reprogramming and nominate candidate genes for future mechanistic and therapeutic investigation.

CYP2E1↗

PubMed2025

Nanobioreactor detection of space-associated hematopoietic stem and progenitor cell aging.

Human hematopoietic stem and progenitor cell (HSPC) fitness declines following exposure to stressors that reduce survival, dormancy, telomere maintenance, and self-renewal, thereby accelerating aging. While previous National Aeronautics and Space Administration (NASA) research revealed immune dysfunction in low-earth orbit (LEO), the impact of spaceflight on human HSPC aging had not been studied. To study HSPC aging, our NASA-supported Integrated Space Stem Cell Orbital Research (ISSCOR) team developed bone marrow niche nanobioreactors with lentiviral bicistronic fluorescent, ubiquitination-based cell-cycle indicator (FUCCI2BL) reporter for real-time HSPC tracking in artificial intelligence (AI)-driven CubeLabs. In month-long International Space Station (ISS) missions (SpX-24, SpX-25, SpX-26, and SpX-27) compared with ground controls, FUCCI2BL reporter, whole-genome and transcriptome sequencing, and cytokine arrays demonstrated cell-cycle, inflammatory cytokine, mitochondrial gene, human repetitive element, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3 (APOBEC3) deregulation together with clonal hematopoietic mutations. Furthermore, HSPC functionally organized multi-omics aging (HSPC-FOMA) analyses revealed reduced telomere maintenance, adenosine deaminase acting on RNA1 (ADAR1) p150 self-renewal gene expression, and replating capacity indicative of space-associated HSPC aging that may limit long-duration spaceflight.

Humans↗

PubMed2026

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblasts reveals shared allelic effects between Latin American and European ancestries.

BACKGROUND: Quantitative Trait Locus (QTL) analysis of molecular data has identified genetic variants associated with traits such as gene expression, and colocalization of these functional QTL with GWAS risk loci has offered insights into the genetic basis of human disease. We employed gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) obtained from human primary fibroblasts to investigate quantitative trait loci (QTLs) in cohorts ascertained for bipolar disorder of European (n = 150) and Latin American (n = 96) ancestries. RESULTS: Leveraging data from three countries of origin (The Netherlands, Colombia, Costa Rica) within our cohort, we characterized differences among individuals at the SNP, gene, and accessible-chromatin levels to compute ancestry-specific expression (e)QTLs and chromatin-accessibility (ca)QTLs. Across ancestries, we observed R2 ≥ 0.93 for eQTL effect sizes and R2 ≥ 0.95 for caQTLs, indicating a high degree of concordance. Integrating chromatin data with expression and genotype information enabled precise fine-mapping of eQTLs, yielding 203 genes with high-confidence (posterior probability > 90%) candidate regulatory pathways. In downstream analyses, transcriptome-wide (TWAS) and chromatin-wide (CWAS) association studies with brain- and skin-related GWAS identified 36 TWAS-significant genes and 77 CWAS-significant open chromatin regions. CONCLUSIONS: These findings underscore the shared genetic regulatory mechanisms across European and Latin American ancestries, while demonstrating that ancestry-specific reference panels enhance the accuracy of TWAS and CWAS in diverse populations. More broadly, this study highlights the value of paired multi-omic datasets from diverse cohorts for interpreting disease-associated genetic variation.

Humans↗

PubMed2026

Multi-omics and spatial transcriptomics reveal that S100A10 drives CD8+ T-cell exhaustion and immune evasion in hepatocellular carcinoma through cPLA2-5-LOX-mediated arachidonic acid metabolism and ferroptosis.

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8+ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8+ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A2-arachidonate 5-lipoxygenase (cPLA2-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8+ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

Arachidonic acid metabolism↗

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↗

PubMed2025

Stepping out of the dark: how metabolomics shed light on fungal biology.

Metabolomics, a critical tool for analyzing small-molecule metabolites, integrates with genomics, transcriptomics, and proteomics to provide a systems-level understanding of fungal biology. By mapping metabolic networks, it elucidates regulatory mechanisms driving physiological and ecological adaptations. In fungal pathogenesis, metabolomics reveals host-pathogen dynamics, identifying virulence factors like gliotoxin in Aspergillus fumigatus and metabolic shifts, such as glyoxylate cycle upregulation in Candida albicans. Ecologically, it highlights fungal responses to abiotic stressors, including osmolyte production like trehalose, enhancing survival in extreme environments. These insights highlight metabolomics' role in decoding fungal persistence and niche colonization. In drug discovery, it aids target identification by profiling biosynthetic pathways, supporting novel antifungal and nanostructured therapy development. Combined with multi-omics, metabolomics advances insights into fungal pathogenesis, ecological interactions, and therapeutic innovation, offering translational potential for addressing antifungal resistance and improving treatment outcomes for fungal infections. Its progress shed light on complex fungal molecular profiles, advancing discovery and innovation in fungal biology.

Metabolomics↗

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↗

PubMed2003

Metabolomics in systems biology.

The primary aim of "omic" technologies is the nontargeted identification of all gene products (transcripts, proteins, and metabolites) present in a specific biological sample. By their nature, these technologies reveal unexpected properties of biological systems. A second and more challenging aspect of omic technologies is the refined analysis of quantitative dynamics in biological systems. For metabolomics, gas and liquid chromatography coupled to mass spectrometry are well suited for coping with high sample numbers in reliable measurement times with respect to both technical accuracy and the identification and quantitation of small-molecular-weight metabolites. This potential is a prerequisite for the analysis of dynamic systems. Thus, metabolomics is a key technology for systems biology. The aim of this review is to (a) provide an in-depth overview about metabolomic technology, (b) explore how metabolomic networks can be connected to the underlying reaction pathway structure, and (c) discuss the need to investigate integrative biochemical networks.

Models, Biological↗

PubMed2026

From dysbiosis to resilience: Microbiome engineering for sustainable shrimp aquaculture.

The intensification of shrimp aquaculture has increased exposure to disease, environmental perturbations, and antimicrobial pressure, making microbial stability increasingly relevant to sustainable production. Microbiome stability-encompassing resistance to disturbance and resilience of functional recovery-provides an ecological framework for understanding how shrimp and culture-environment microbial communities respond to intensive farming. This review examines the transition from microbial homeostasis to dysbiosis and evaluates how microbiome engineering could redirect disrupted communities towards resilient states. Evidence is integrated across the intestine, hepatopancreas, rearing water, sediment and biofloc to assess how host genetics, ontogeny, diet, culture conditions, antibiotics and pollutants shape microbiome assembly and destabilization. Disease-associated changes in acute hepatopancreatic necrosis disease, white faeces syndrome, Enterocytozoon hepatopenaei infection, and white spot syndrome virus infection are critically evaluated, with explicit separation of associations, pathogen-induced dysbiosis, and community-level causality. Established and emerging interventions-including probiotics, prebiotics, synbiotics, functional diets, biofloc management, phages, postbiotics, microbiota transplantation and synthetic microbial communities-are assessed according to their capacity to modify microbial function, persistence and recovery rather than taxonomic change alone. We further examine how multi-omics, microbiome-informed breeding, and environmental monitoring could support biomarker development, predictive decision support and context-specific intervention. We argue that progress requires a shift from taxonomic description to function-guided engineering, from endpoint comparisons to direct measurement of resilience, and from laboratory efficacy to reproducible farm-scale validation. Overall, microbiome management may contribute to more disease-resilient and sustainable shrimp production, provided that its effectiveness can be validated under commercial farming conditions.

Dysbiosis↗