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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↗

Multi-omics analysis identifies key genes and functional loci affecting teat number in American Large White and Landrace pigs and their application in optimizing genomic selection models.

BACKGROUND: Teat number is a crucial economic trait in pigs. It directly affects the ability of sows to lactate, which in turn influences the survival and health of piglets. The teat number of French Large White pigs is close to 16, while the teat number of American Large White and Landrace pigs is about 14. In order to improve the teat number of American Landrace and Large White pigs through molecular approaches and precise breeding techniques, we genotyped 2,131 American Landrace and 4,564 American Large White with teat number phenotype using a 50 K SNP chip. Then, the SNP-chip data was imputed to the level of whole-genome sequencing (iWGS). Based on iWGS data, we conducted GWAS to identify novel, significant SNPs associated with teat number and to incorporate them into genomic selection. RESULTS: In Landrace pigs, significant SNPs for TTN mapped to SSC2, SSC7, SSC8, and SSC14; the SSC8 and SSC14 effects are novel. LTN mapped to SSC7, RTN to SSC7 and SSC8. The lead SSC7 SNP explained 2.60% of TTN phenotypic variance. In Large White pigs, significant SNPs were detected on SSC7 and SSC10 for TTN; SSC7, SSC10, and SSC12 for LTN; and SSC7 and SSC10 for RTN. The most significant locus on SSC7 accounted for 2.99% of the phenotypic variance in TTN. Additionally, a multi-population meta-analysis detected significant novel SNPs for LTN on SSC1 and SSC8. By utilizing Bayesian fine mapping, the most precise QTL confidence interval on SSC7 for both TTN and RTN in Large White pigs was reduced to 40 kb. By integrating functional gene annotation with RNA-seq and ATAC-seq data from Erhualian and Bamaxiang pigs mammary placodes at embryonic day 26, we prioritized PTPN13, TRPV3, ZDHHC13, and BRD2 as novel candidate genes for teat number. We then incorporated the significant SNPs to GBLUP and benchmarked genomic-selection accuracy. In both breeds, fitting the top SNP as fixed maximized prediction for TTN and RTN, whereas treating all significant loci as an additional random effect optimized LTN. CONCLUSIONS: Our findings provide a theoretical basis for dissecting new key genes affecting teat number and for advancing molecular breeding of teat number in pigs.

Animals↗

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

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

Humans↗

A Multifaceted Interplay Among Hemophagocytosis, Interleukin-18, and Type I Interferon Distinguishes Still Disease From Other Autoinflammatory Diseases.

OBJECTIVE: The unknown pathophysiology and the lack of specific features for systemic juvenile idiopathic arthritis and adult-onset Still disease (collectively known as Still disease; SD) delay diagnosis and appropriate treatment. The goal of this study was to identify features and mechanisms that distinguish SD from other systemic autoinflammatory diseases (SAID). METHODS: Using the SomaScan assay and RNA sequencing (RNA-Seq), we determined the plasma proteomes and immune cell microRNA (miRNA) and RNA transcriptomes of 372 patients with SAID, respectively. Proteomic findings were validated by enzyme-linked immunosorbent assays. SD (n = 72) and non-SD SAIDs (n = 300) were compared to identify distinguishing features of SD. We performed integrated and unbiased analyses of all data sets using weighted gene correlation network analysis to identify feature modules that characterize SD and stratify patients. RESULTS: Elevated plasma heme oxygenase 1 (HO-1) and interleukin-18 (IL-18) strongly correlate and characterize SD but do not associate with general inflammation. SD was characterized by ferroptosis in plasma, type I interferon (IFN) signaling in monocyte transcriptomes, and elevated natural killer cell miRNA-146a-5p, which is an IL-18 induced miRNA. Finally, we identified feature modules that distinguish SD from other SAIDs and stratified patients with SD into two distinct subgroups not attributable to disease activity or inflammation but hemophagocytosis. CONCLUSION: This unprecedented large omics data set of SAIDs revealed that complex interactions among hemophagocytosis, IL-18, and type I IFN signaling characterize SD. Furthermore, two distinct subgroups in patients with SD were distinguished by the degree of hemophagocytic activity. Finally, the large proteomics and RNA-Seq data sets generated in this study can serve as an invaluable resource for the further investigation of SD and other SAIDs.

Humans↗

Integrative analysis of rumen microbiota activity and host metabolism following methanogenesis inhibition in dairy cattle.

Enteric methane emission from dairy cattle is an environmental challenge. The most efficient mitigation strategies nowadays include the use of methanogenesis inhibitors that specifically target the rumen methanogens. Specific inhibitors, such as 3-nitrooxypropanol (3-NOP), reduce methane emissions without negative effects on the products of fermentation that serve as energy metabolites for the host. However, the concomitant effects of methanogenesis inhibition on rumen microbiota and host metabolism are poorly characterized. Thus, the objective of this study was to explore the association between rumen microbiota and host metabolism when methanogenesis is inhibited. Thirteen dairy cows were used as controls, and 12 were supplemented with 3-NOP for 6 weeks. Rumen microbiota composition and activity were characterized using metagenomics and metatranscriptomics. The host metabolism was assessed in a previous publication by a metabolomic analysis of the plasma. Microbiota data were used as explanatory variables of the metabolome data in a multiblock sparse partial least squares analysis. Overall, the association between rumen microbiota and host metabolism was moderate. Notwithstanding this, a few downregulated transcripts related to glycolysis, hydrogen transfer, and protein synthesis, together with a decrease in the proportion of taxa of the Oscillospirales order, showed a correlation with host one-carbon metabolites (|r| > 0.6). These associations raised novel hypotheses that remain to be elucidated, especially with regard to the effects of dihydrogen on the accumulation of microbial glycolysis and methanogenesis metabolite intermediates.IMPORTANCEDairy cattle produce a substantial amount of methane, a potent greenhouse gas. Several strategies have been designed to reduce methane production by targeting the rumen microbiota. One such strategy specifically inhibits methanogens with a molecule called 3-nitrooxypropanol. This study uses an integrative data analysis approach, combining rumen microbiota and host metabolome information, to explore the consequences of inhibiting methanogenesis on the holobiont. This provides additional holistic insight into the effect of methane mitigation strategies on dairy cattle.

Animals↗

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↗

A survey of laboratory and statistical issues related to farmworker exposure studies.

Developing internally valid, and perhaps generalizable, farmworker exposure studies is a complex process that involves many statistical and laboratory considerations. Statistics are an integral component of each study beginning with the design stage and continuing to the final data analysis and interpretation. Similarly, data quality plays a significant role in the overall value of the study. Data quality can be derived from several experimental parameters including statistical design of the study and quality of environmental and biological analytical measurements. We discuss statistical and analytic issues that should be addressed in every farmworker study. These issues include study design and sample size determination, analytical methods and quality control and assurance, treatment of missing data or data below the method's limits of detection, and post-hoc analyses of data from multiple studies. Key words: analytical methodology, biomarkers, laboratory, limit of detection, omics, quality control, sample size, statistics.

Agriculture↗

How to analyze and understand the human immune system.

To enhance our understanding of the pathogenesis of diseases, including rheumatic diseases, and to improve disease control, it is essential to attain a thorough understanding of the human immune system, alongside mouse immunology. Historically, the investigation of the human immune system has posed significant challenges due to methodological limitations. Nonetheless, recent advancements in genomic studies of multifactorial diseases have elucidated that numerous risk-associated genetic variants affecting quantitative differences in cell-specific gene expression. In light of these findings, we are currently examining individual genetic variations in both healthy individuals and patients, as well as categorizing cells into distinct subsets in order to construct a comprehensive dataset concerning the human immune system. This is accomplished by combining data on gene expression, factors influencing the expression mechanisms, protein expression, metabolomics, and environmental variables pertinent to immune functionality-such as gut microbiota. These datasets will facilitate the comprehensive characterization of the human immune system. Using these datasets and through the integrative analyses of data related to risk genetic variations and gene expression profiles of each disease and individual, we anticipate uncovering novel insights into the human immune system, the heterogeneity of diseases, immune function mechanisms, and their regulatory strategies that may not be achievable through murine models.

Humans↗

ChemGenXplore: an interactive tool for exploring and analysing chemical genomic data.

MOTIVATION: Chemical genomics is a powerful high-throughput approach to systematically link phenotypes to genotypes. However, the vast datasets generated remain challenging to explore due to the lack of integrated, interactive tools for visualization and analysis. Existing workflows often require multiple independent software tools, limiting data accessibility and collaboration. Therefore, we created a user-friendly platform that enables efficient exploration and sharing of chemical genomics data. RESULTS: We developed ChemGenXplore, a web-based Shiny application designed to streamline the visualization and analysis of chemical genomic screens. It offers two primary functionalities: one for exploring pre-implemented datasets and another for analysing user-uploaded datasets. ChemGenXplore enables users to visualize phenotypic profiles, assess gene-gene and condition-condition correlations, perform GO and KEGG enrichment analysis, and generate customizable, interactive heatmaps. To further support collaborative research, ChemGenXplore also facilitates the comparative analysis of chemical genomic and other omics datasets. By consolidating these features into a single interactive and accessible tool, ChemGenXplore facilitates data sharing, enhances reproducibility, and promotes collaboration within the research community. AVAILABILITY AND IMPLEMENTATION: ChemGenXplore is freely accessible as a web application at https://chemgenxplore.kaust.edu.sa/. Source code and documentation, including instructions for local installation, are provided on GitHub (https://github.com/Hudaahmadd/ChemGenXplore). A Docker image is also available on DockerHub (https://hub.docker.com/r/hudaahmad/chemgenxplore) to ensure reproducibility and simplify installation.

Software↗

From Gene Function to Precision Intervention: CRISPR/Cas9 and Stem Cell-Based Strategies as Emerging Disease-Modifying Approaches in PMOS.

Polyendocrine metabolic ovarian syndrome (PMOS) is a complex endocrine-metabolic disorder affecting up to 18% of women worldwide and remains the leading cause of anovulatory infertility. Despite extensive research, current treatments primarily target symptoms, including menstrual irregularities, hyperandrogenism, and metabolic dysfunction, without addressing the underlying molecular and tissue-level disturbances. Advances in multi‑omic profiling have identified disruptions across neuroendocrine, metabolic, inflammatory, and extracellular matrix pathways, alongside genetic susceptibility at loci such as DENND1A, CYP17A1, LHCGR, FSHR, IRS1, and PPARG. However, the functional roles of many variants remain unresolved. CRISPR/Cas9 gene editing enables precise interrogation of these pathways, while stem cell-based platforms, including mesenchymal stem cells (MSCs), exosomes, and gene-edited induced pluripotent stem cells (iPSCs), may serve as complementary platforms for regeneration and disease modeling. Preclinical studies demonstrate that MSCs and their derivatives modulate inflammation, restore ovarian structure, and improve metabolic parameters, while iPSC-based models enable patient-specific investigation of steroidogenic and metabolic abnormalities. Translational challenges remain, including targeted delivery, off-target effects, phenotypic heterogeneity, and regulatory considerations. Integrating CRISPR‑based functional genomics with stem cell research may shift PMOS management from symptom‑focused care to targeted, mechanism‑driven interventions that could modify the course of PMOS (Graphical Abstract).

Humans↗

Biochemical insights into the biodegradation mechanism of typical sulfonylureas herbicides and association with active enzymes and physiological response of fungal microbes: A multi-omics approach.

The extensive use of sulfonylurea herbicides has raised major concerns regarding their long-term soil residues and agroecological risks despite their role in agricultural protection. Microbial degradation is an important approach to remove sulfonylureas, whereas understanding the associated biodegradation mechanisms, enzymes, and physiological responses remains incomplete. Based on the rapid biodegradation of nicosulfuron by typical fungal isolate Talaromyces flavus LZM1, the dependency on cellular accumulation and environmental conditions, e.g. pH and nutrient supplies, was shown in the study. The biodegradation of nicosulfuron occurred intracellularly and followed the cascade of reactions including hydrolysis, Smile contraction rearrangement, hydroxylation, and opening of the pyrimidine ring. Besides 2-amino-4,6-dimethoxypyrimidine (ADMP) and 2-aminosulfonyl-N,N-dimethylnicotinamide (ASDM), numerous products and intermediates were newly identified and the structural forms of methoxypyrimidine and sulfonylurea bridge contraction rearrangement are predicted to be more toxic than nicosulfuron. The biodegradation should be enzymatically regulated by glycosylphosphatidylinositol transaminase (GPI-T) and P450s, which were manifested with the significant upregulation in proteomics. It is the first time that the hydrolysis of nicosulfuron into ADMP and ASDM have been associated with GPI-T. The integrated pathways of biodegradation were further elucidated through the involvement of various active enzymes. Except for the enzymatic catalysis, the physiological responses verified by metabolo-proteomics were critical not only to regulate material synthesis, uptake, utilization, and energy transfer but also to maintain antioxidant homeostasis, biodegradability, and tolerance of nicosulfuron by the differentially expressed metabolites, such as acetolactate synthase and 3-isopropylmalate dehydratase. The obtained results would help understand the biodegradation mechanism of sulfonylurea from chemicobiology and enzymology and promote the use of fungal biodegradation in pollution rehabilitation.

Herbicides↗

Convergent methodologies in prosthetic joint infection research: integrating transdisciplinary approaches to understand and prevent biofilm-driven failure of orthopaedic prostheses.

Prosthetic joint infections (PJIs) remain among the most devastating complications of arthroplasty, imposing substantial clinical, economic and patient burdens. Although culture-based diagnostics underpin current clinical practice, PJIs are biofilm-driven infections shaped by taxonomic diversity, spatial organization, host responses and surface interactions, meaning conventional approaches provide only a partial and often decontextualized view of the infection process. We examine how convergent methodologies can transform PJI research by integrating approaches that have traditionally been studied in isolation, including sequencing, transcriptomics, metabolomics, advanced imaging and culture-based characterization. We discuss how whole-genome sequencing, shotgun metagenomics, transcriptomic and metabolomic approaches resolve pathogen identity, functional activity and adaptive persistence and how cross-scale imaging and spatial biology techniques reveal where microbes colonize, interact and survive across implant surfaces. We highlight emerging opportunities to unify these datasets into coherent frameworks that capture both the molecular and physical dimensions of PJIs. Integrating these complementary approaches will enable a multi-layered understanding of PJIs that link composition, function and spatial organization. Ultimately, this provides a foundation for predictive diagnostics, precision antimicrobial strategies and improved implant design and supports a shift towards more effective, mechanism-informed management of implant-associated infection.

Prosthesis-Related Infections↗

Harnessing Probiotic LAB and Bacteriocins for Clean-Label Food Processing and Biopreservation: Omics, Molecular Innovations and Industrial Applications.

The persistence of microbial agents in foods, especially spore forming bacteria is one of the most significant challenges to food preservation and safety, undermining product quality, shelf life, and consumer health. The use of traditional control methods, including thermal processing and chemical preservatives, are increasingly limited by consumer demands for minimally processed foods, and the emergence of resistant microbial strains. Advances have been made in the use of probiotics like lactic acid bacteria (LAB) and their biometabolites like bacteriocins in food processing and preservation, particularly to control biofilm and endospore forming pathogens including Bacillus sp., Listeria sp., Staphylococcus sp., Clostridium sp., E. coli etc. in foods and food processing plants/surfaces. Given the ability of these organisms to cause foodborne illness and form resilient biofilms in the food processing ecosystem and their resistance to the conventional method of their elimination, the antimicrobial peptides (bacteriocins) are gaining increasing prominence as useful alternatives to synthetic antimicrobials in enhancing food safety and combating the threats of these pathogens. This review addresses current information on the inhibition of persistent microbial spoilage contaminants, biofilm-forming pathogens, and spore formers of interest to the food industry using LAB and their bacteriocins. Current developments in isolation, characterization, and mode of action of bacteriocins are explored, including synergistic activity with other preservative hurdle techniques such as encapsulation, and nanobiotechnology. Importantly, there is a focus on the utilization of molecular and omics-based approaches to enable a better understanding of bacteriocin biosynthesis, gene regulation, host-microbe interactions and gut microbiome regulation potential of probiotic LABs, permitting the rational development of targeted and strain-specific interventions. Developments in the incorporation of bacteriocin-producing LAB into functional starter cultures and bio-protective products, and challenges in stability, regulatory approval, and scalability for industrial use, are also discussed in the paper. Despite their considerable potential, broader translation remains constrained by regulatory requirements, production and formulation costs, variable efficacy in complex food matrices, and the limited validation of many candidate bacteriocins beyond laboratory and model-food systems. Collectively, these advances position LAB and their bacteriocins at the leading edge of developing sustainable, clean-label, and efficacious functional foods and food preservation systems. Their functionality can be expanded by integrating genomics, synthetic biology, and predictive modeling for the maximization of their biopreservative potential in diverse food matrices and in gut microbiota modulation.

Bioactive Peptides↗

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↗

Integrative Cross-platform Analysis of Kinase Inhibitor Effects on Statin-relevant Cardioprotective Pathways in Human Cardiomyocytes.

BACKGROUND/AIM: Kinase inhibitors (KIs) can cause cardiotoxicity through mechanisms overlapping with statin cardioprotective pathways, yet their effects on these pathways in cardiomyocytes remain uncertain. We evaluated six literature-defined statin-relevant gene sets using transcriptomic and proteomic data. MATERIALS AND METHODS: Pre-ranked gene set enrichment analysis was performed for 23 KIs in primary cardiac cells (GSE146096; n=319) and iPSC-derived cardiomyocytes (GSE217421; n=541), with cross-platform analysis of 21 KIs by shotgun proteomics (PXD014791; n=300). Pathway-specific concordance was assessed by Spearman correlation with Benjamini-Hochberg correction; protein scores were estimated after adjustment for cell line. RESULTS: KI effects were heterogeneous. The anti-fibrotic pathway showed nominal concordance across the two transcriptomic datasets (ρ=0.495, p=0.016, q=0.098; 91% direction concordance) and significant cell-line-adjusted transcriptomic-proteomic concordance (ρ=0.644, p=0.0016, q=0.0081). Nilotinib reproducibly upregulated NF-κB pathway genes [normalized enrichment score (NES)=+2.29 and +2.18 in discovery and validation], with targeted inter-gene-correlation-adjusted testing supporting higher NF-κB expression than under rosuvastatin (CAMERA p=3.54×10-8). No global cross-omics summary remained significant after harmonizing pathway universes and accounting for repeated pathways. CONCLUSION: KI effects on statin-relevant pathways were pathway-specific. Anti-fibrotic concordance and nilotinib-associated NF-κB upregulation are hypothesis-generating candidates for experimental validation.

Humans↗

CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.

BACKGROUND: Prostate cancer (PCa) remains one of the most common malignancies in men, posing a persistent global burden in terms of both public health and socioeconomic costs. Although early detection is essential for improving patient outcomes, existing clinical tools, including prostate-specific antigen (PSA) screening, digital rectal examination, and transrectal ultrasound-guided biopsy, are hampered by suboptimal specificity and positive predictive value, resulting in frequent overdiagnosis and overtreatment of indolent lesions while missing a subset of aggressive tumors at an early stage. In this context, the rapid advancement of high-throughput omics technologies, coupled with sophisticated machine learning (ML) algorithms, provides a powerful computational framework to dissect high-dimensional genomic data, uncover latent gene expression signatures, and identify candidate biomarkers with superior discriminative performance over conventional clinicopathological parameters. Therefore, in this study, we sought to screen for crucial ML-based biomarkers associated with PCa, with a particular focus on systematically assessing the diagnostic and prognostic value of CCNA2. Leveraging large-scale transcriptomic cohorts from public repositories, we employed an ensemble of ML approaches to prioritize candidate genes and subsequently evaluated the diagnostic performance of CCNA2 through receiver operating characteristic curve analysis, as well as its prognostic utility via Kaplan-Meier survival estimation and multivariate Cox proportional hazards modeling. Our findings are anticipated to elucidate the molecular landscape of PCa and offer a promising biomarker candidate for early detection and risk stratification. METHODS: This study integrated single-cell RNA sequencing, bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, immunofluorescence, and multiple ML algorithms with in vitro functional assays to evaluate CCNA2 expression, clinical relevance, and biological behavior in PCa. RESULTS: CCNA2 was linked to metastasis and poor prognosis. High CCNA2 expression significantly correlated with adverse survival outcomes, and knockdown of CCNA2 suppressed proliferation, migration, and invasion in PCa cell lines. Mechanistically, CCNA2 modulated the PI3K/AKT signaling pathway. An ML-based diagnostic model incorporating CCNA2 demonstrated high predictive accuracy across multiple validation cohorts. CONCLUSIONS: CCNA2 serves as a promising prognostic biomarker and therapeutic target in prostate adenocarcinoma, driving tumor progression potentially via the PI3K/AKT axis.

CCNA2↗

Genomic context-dependent roles of 5-hydroxymethylcytosine in regulating gene expression during rice drought response.

DNA methylation (5-methylcytosine, 5mC) is a key epigenetic regulator of genome stability and stress adaptation in plants. However, the functional role of its oxidative derivative, 5-hydroxymethylcytosine (5hmC), remains poorly understood in plant systems, largely due to its low abundance and unresolved enzymatic origins. Here, we integrated ACE-seq (APOBEC-coupled epigenetic sequencing) with an optimized Tn5mC-seq (transposase-based library preparation in the context of whole-genome bisulfite sequencing, WGBS) approach to generate the first single-base resolution map of 5hmC in rice (Oryza sativa), unveiling its stress-responsive dynamics and regulatory interplay with 5mC during drought adaptation. Genome-wide profiling revealed a basal 5hmC level of ~0.03 (defined as the ratio of C/(C + T) at each site), with drought triggering a pronounced reduction in 5hmC abundance and locus number, followed by incomplete recovery post-rehydration. Unlike 5mC, which accumulates in heterochromatin, 5hmC preferentially localized to euchromatic regions, including promoters, exons, and intergenic elements, and exhibited enrichment at ABA-responsive transcription factors (e.g., OsATAF1, bZIP50). Strikingly, drought induced an antagonistic relationship between 5hmC and 5mC, with the latter increasing globally to reinforce transposon silencing. Multi-omics analyses demonstrated that 5hmC depletion in promoters correlated with transcriptional downregulation, while its accumulation in gene bodies (notably 5'-UTRs) suppressed stress-responsive genes. These findings highlight 5hmC's bifunctional regulatory capacity, contingent on genomic context, and its role in balancing transcriptional plasticity with genome stability during stress. Our work establishes 5hmC as a dynamic epigenetic mark in plant environmental adaptation and provides a foundation for leveraging DNA hydroxymethylation in crop resilience engineering.

Oryza↗

Multi-omics Approaches to CCAAT/Enhancer-Binding Protein Beta in Oral Squamous Cell Carcinoma: Crosstalk Between Tumor Cells and Tumor-Associated Macrophages Driving Disease Progression.

BACKGROUND: CCAAT/Enhancer-Binding Protein Beta (CEBPB) is an important transcription factor that regulates tumor progression. However, the mechanism by which CEBPB regulates the progression of Oral Squamous Cell Carcinoma (OSCC) remains incompletely understood. Tumor progression depends on complex intercellular interactions within the tumor microenvironment. The purpose of this study was to investigate the role and epigenetic regulatory mechanisms of CEBPB in interactions between OSCC cells and tumor-infiltrating immune cells. METHODS: Bulk RNA-seq, ChIP-seq, and scRNA-seq data were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The HOMER algorithm was employed to identify enhancers and predict the CEBPB-binding motif. Cell cluster analysis, functional enrichment, and intercellular interaction analysis were performed using the "Seurat" R package. H3K27ac enrichment at GAS6 enhancers was validated by ChIP-qPCR. Metastatic OSCC cells with CEBPB knockdown or GAS6 overexpression were established and co-cultured with THP-1 cells. IL-10 and IL-6 secretion from co-cultured THP-1 cells was detected via ELISA. Chemotaxis of OSCC cells toward THP-1 cells was assessed through a Transwell assay. RESULTS: CEBPB was upregulated in OSCC and correlated with poor prognosis. By integrating H3K27ac ChIP-seq and bulk RNA-seq data, 131 CEBPB-regulated enhancer-controlled genes were identified in lymph node metastatic OSCC cells. scRNA-seq analysis revealed eight major cell clusters in primary foci and lymph node metastases, including T/NK cells, malignant epithelial cells, B/plasma cells, macrophages, fibroblasts, dendritic cells, endothelial cells, and mast cells, with the malignant epithelial cells stratified into distinct sub-clusters. CEBPB expression was elevated in malignant epithelial cells of lymph node metastases compared to primary foci. Furthermore, 15 pairs of enhanced ligand-receptor interactions were identified in lymph node metastases relative to primary foci. GAS6 was a CEBPB-regulated enhancer-controlled gene, primarily mediating interactions between malignant cells and macrophages. CEBPB knockdown in metastatic OSCC cells significantly impaired their chemotaxis toward cocultured THP-1 cells, and downregulated IL-10/IL-6 secretion and CD206 expression in cocultured THP-1 cells. Conversely, GAS6 overexpression reversed these inhibitory effects. CONCLUSION: CEBPB activated GAS6 transcription in metastatic OSCC cells. The CEBPB/ GAS6 axis in metastatic OSCC cells enhanced their chemotaxis toward macrophages and promoted the M2 polarization of macrophages, thereby facilitating the establishment of an immunosuppressive microenvironment.

Humans↗