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PubMed2026

Nonviral transposon‑engineered stem cells characterization: dose‑dependency between vector copy number and transgene expression.

Genetically engineered stem cells hold substantial promises for advancing regenerative medicine, yet ensuring their genomic safety remains a critical challenge. A key safety concern is vector copy number (VCN), which defines the number of integrated transgene copies per genome. Although ddPCR is used to assess VCN in virally transduced cells, its application in transposon‑engineered systems is limited. In this study, we extended VCN determination to non‑viral, transposon‑engineered stem cells. In alignment with FDA recommendations, the primary objective was to establish a robust and quantitative framework for interim VCN determination at the time of lot release. Specifically, we demonstrate that reliable interim VCN estimates increase in a dose‑dependent manner with increasing plasmid input. In addition, strong linear correlations between VCN and both EGFP median fluorescence intensity (MFI) and gene‑of‑interest (GOI) protein expression validate the accuracy of this framework. Furthermore, comparison of two distinct GOIs revealed gene‑specific differences in expression efficiency. Together, these findings validate a standardized VCN determination workflow that quantitatively links plasmid dose, genomic integration, and functional transgene expression. This workflow provides a systematic characterization of engineered cells, offering comprehensive information to support downstream risk‑based analyses to ensure the genomic safety and stability of the final cell product.

Transgenes↗

PubMed2026

Genome-Wide Characterization of β-Glucosidase (TaBGLU) Genes in Bread Wheat and Their Expression Under Drought, Cold, and Combined Stress.

Glycoside hydrolase 1 (GH1) β-glucosidases were known to activate hormone conjugates and defense metabolites, yet their genomic organization and stress-response dynamics in wheat remained incompletely defined. We therefore performed an integrated characterization of TaBGLUs spanning phylogeny, gene structure and conserved motifs, subcellular localization, promoter cis-elements, Gene Ontology enrichment, protein-protein interaction networks, and targeted expression profiling. Wheat TaBGLUs partitioned into well-supported clades that shared canonical GH1 catalytic residues and a largely conserved motif scaffold. Subcellular localization predictions indicated predominant nuclear and chloroplast targeting, with a smaller cohort directed to secretory or endomembrane compartments. Promoters were enriched for light-responsive, hormone-related (ABA, JA/SA, auxin, GA) and stress-associated (MYB/WRKY, heat, low temperature) cis-elements, and functional annotations were consistent with roles in carbohydrate and cell-wall metabolism, hormone homeostasis, and defense. Network analysis revealed a densely connected TaBGLU submodule embedded within broader carbohydrate and defense interaction networks, suggesting coordinated or cooperative functions. Expression profiling under cold, drought, and combined drought and cold demonstrated broad stress inducibility, with early activation detected by 6 h, cold-responsive maxima typically at 12 h, drought-responsive peaks predominating at 24 h, and combined stress eliciting both earlier and more sustained expression maxima between 12-24 h. Representative strongly responsive genes included TaBGLU20, TaBGLU44, TaBGLU6, and TaBGLU23, which showed pronounced late induction under combined stress, TaBGLU30, which exhibited an earlier combined-stress peak, and TaBGLU12, which displayed a marked late drought-specific response. Taken together, this integrated genomic, regulatory, and expression atlas refined the wheat BGLU repertoire relative to previous gene model inventories, highlighted candidate TaBGLUs with central network positions and strong stress inducibility, and provided concrete entry points for functional validation and breeding for improved stress resilience.

Triticum↗

PubMed2026

Leveraging traveller genomics for LMIC diarrhoeal disease management.

Diarrhoeal pathogens impose a substantial global health burden, disproportionately affecting low- and middle-income countries (LMICs). However, in these settings, health-seeking behaviours, suboptimal microbiological capacity, and challenges in establishing genomics capacity constrain effective surveillance, including surveillance of antimicrobial resistance (AMR). In contrast, high-income countries routinely generate and share large volumes of diarrhoeal pathogen genomes through established systems, with a significant proportion originating from travellers returning from LMICs. These data reveal strong geographical structuring of lineages and clinically relevant AMR patterns, demonstrating untapped potential to support improvements in geographically granulated surveillance to support antimicrobial treatment recommendations. In this opinion article, we outline the potential to integrate traveller-derived microbial genomic data into LMIC public health decision-making and highlight the scientific, ethical, practical, and governance considerations for implementation.

antimicrobial resistance↗

PubMed2026

Endocrine-disrupting chemical-induced gene networks confer coronary heart disease risk revealed by causal inference and single-cell analyses.

BACKGROUND: Endocrine-disrupting chemicals (EDCs) are linked to coronary heart disease (CHD), but underlying mechanisms remain unclear. We aimed to identify EDC-related genes and evaluate their causal roles in CHD. METHODS: We curated EDC-related genes from a compound-gene interaction database and integrated them with CHD genome-wide association study (GWAS) summary statistics and tissue-specific expression quantitative trait loci (eQTL) data. Two-sample Mendelian randomization (MR) and Bayesian colocalization were applied to infer causality. Functional enrichment, single-cell RNA sequencing of human coronary arteries, and EDC-gene networks were further analyzed. RESULTS: After FDR correction, 39 genes were significantly associated with CHD risk via MR. Four genes-ZNF827, FCHO1, IPO9 (protective), and RPL13 (risk-increasing)-showed strong colocalization (PPH4 > 0.9). Pathway and single-cell analyses of coronary artery tissue indicated that vascular and immune pathways mediate these effects. An interaction network highlighted associations between specific EDCs and candidate genes implicated in CHD susceptibility. CONCLUSION: This integrative genomic study provides evidence that EDCs influence CHD susceptibility through distinct gene networks, revealing potential mechanisms and molecular targets for prevention and therapy.

Humans↗

PubMed2026

Norgestrel drives mitochondrial collapse and plasma membrane impairment in Pacific oyster (Crassostrea gigas) sperm by triggering premature acrosome reaction.

The toxic mechanisms of norgestrel (NGT), an emerging marine pollutant, on the sperm from externally fertilized invertebrates remain elusive. This study employed an integrated physiological and multi-omics framework to elucidate how NGT (10 and 1000 ng/L) disrupts acrosome reaction (AR) signaling machinery, thereby impairing the functional integrity of Pacific oyster (Crassostrea gigas, also known as Magallana gigas) sperm. Exposure to NGT triggered a significant, dose-dependent premature AR, characterized by elevated acrosin activity and a loss of acrosomal integrity. Multi-omics integration supports a model in which this premature exocytosis is linked to signaling disturbances, including disruption of calcium signaling and reduced transcript abundance of calmodulin (CaM) and the primary recognition protein zonadhesin (Zan). This signaling interference induced an premature AR, subsequently driving a cascade of bioenergetic and structural failures. At the mitochondrial level, NGT induced abnormal mitochondrial permeability transition pore (mPTP) opening and elevated the transcript levels of antioxidant defense genes (e.g., peroxiredoxin-5, PRDX5). These alterations indicate the occurrence of mitochondrial collapse. Concurrently, scanning electron microscopy verified localized plasma membrane wrinkling and pore formation in sperm. In addition, NGT exposure decreased the transcript abundance of cytoskeleton-related genes, including solute carrier family 26 member 6 (SLC26A6), actin (ACT), and tubulin polymerization promoting protein family member 3 (TPPP3). These molecular changes further disrupted membrane phospholipid homeostasis, as represented by altered glycerophospholipid metabolism. At the same time, cumulative cellular stress was associated with decreased transcript abundance of cytoprotective factors (e.g., baculoviral IAP repeat-containing proteins, birc2) and changes in apoptosis-related genes consistent with activation of a caspase-8-mediated apoptotic programme. In conclusion, NGT, as a representative synthetic progestin, exerts reproductive toxicity by interfering with signaling mediators to induce premature AR, which subsequently exhausts metabolic energy and triggers plasma membrane impairment. These findings provide a critical mechanistic basis for the aquatic ecological risk assessment of synthetic progestins.

Animals↗

PubMed2026

Genome-resolved analysis reveals disruption of gut microbial vitamin B and K2 biosynthesis during Toxoplasma gondii infection in mice.

UNLABELLED: Toxoplasma gondii infection remodels the gut microbiome, yet its impact on microbial vitamin biosynthetic potential and host redox metabolism remains unclear. Here, we integrated mouse gut metagenomes with publicly available metagenome-assembled genomes (MAGs) to construct a genome-resolved atlas of B-vitamin and vitamin K2 biosynthesis. From 45,697 MAGs, we curated 4,771 representative genomes, of which 2,682 met high-quality criteria (completeness ≥90%, contamination <5%). Functional annotation identified 229,717 vitamin-related genes corresponding to 177 Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs across de novo pathways for eight B vitamins, thiamine (B1), riboflavin (B2), niacin (B3), pantothenate (B5), pyridoxine (B6), biotin (B7), folate (B9), cobalamin (B12), and vitamin K2. Among the high-quality genomes, 1,665 encoded complete de novo pathways for at least one vitamin, highlighting functional specialization and community-level complementarity. Transcripts per million-normalized metagenomic read counts revealed significant differences in KEGG ortholog abundances across six of the nine vitamin pathways. Reanalysis of metagenomic data from infected mice (acute, chronic, and control; n = 10 per group) revealed a stage-dependent reduction in α-diversity of vitamin biosynthesis pathways during acute infection, and a clear β-diversity separation from chronic and control groups. Core niacin biosynthesis genes (nadB, nadA, nadC) displayed phylum-specific redistribution, indicating selective remodeling of microbial NAD+ precursor production under infection-induced metabolic stress. These results suggest that T. gondii infection disrupts cooperative vitamin biosynthetic networks while specifically modulating niacin pathways linked to host NAD+ metabolism. IMPORTANCE: Gut microbes can synthesize essential vitamins, but how infection alters this function is poorly understood. By integrating mouse gut metagenomes with genome-resolved microbial data, we show that Toxoplasma gondii infection reshapes the vitamin biosynthetic potential of the gut microbiome in a stage-dependent manner. Acute infection reduces the diversity of vitamin biosynthesis pathways and shifts the taxonomic distribution of key niacin biosynthesis genes involved in microbial NAD+ precursor production. These findings identify vitamin metabolism, especially niacin-related pathways, as a sensitive functional axis of microbiome remodeling during infection. Our work links microbial taxonomic changes to functional metabolic consequences and suggests that microbiome-mediated regulation of NAD+-related metabolism may contribute to host redox adaptation during T. gondii infection.

B vitamins↗

PubMed2026

Molecular Diagnostics for WHO Priority Bacterial Pathogens: A Bibliometric Mapping of Diagnostic Platforms, Resistance Markers, and Antimicrobial Resistance Research Trends.

Antimicrobial resistance (AMR) constrains effective treatment and carries implications for infection control, surveillance, and public health. The World Health Organization (WHO) priority bacterial pathogen framework has intensified the need for diagnostic innovation by redefining research priorities around organisms combining high disease burden with complex resistance profiles. Molecular diagnostics have accordingly moved beyond culture-based workflows, integrating rapid pathogen identification, resistance-marker detection, genomic surveillance, and clinical decision support. The present study conducted a bibliometric mapping of the literature on WHO priority pathogens. Rather than addressing resistance at a general level or a single pathogen or technology, it integrates priority pathogens, molecular platforms, and resistance markers within a single framework, tracing their joint thematic and temporal evolution along an explicit pathogen-platform-marker axis. Scopus-indexed articles and reviews (2000-2025) were retrieved, yielding 1746 publications after screening adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Analyses used Bibliometrix/Biblioshiny, R, and VOSviewer. The literature expanded markedly after 2018, led by China and the United States. Methicillin-resistant Staphylococcus aureus (MRSA), Mycobacterium tuberculosis, Enterococcus faecium, and the Enterobacterales-carbapenemase axis constituted the principal thematic cores, whereas conventional polymerase chain reaction (PCR)/nucleic acid amplification testing (NAAT) and whole-genome sequencing were the dominant platforms. Overall, the field has evolved from pathogen detection into an AMR-centered translational domain encompassing resistance prediction, genomic epidemiology, surveillance, and clinical decision support. Diagnostic development, stewardship, and surveillance depend on hybrid workflows coupling rapid marker-targeted assays with genome-based characterization, delivering actionable resistance within clinically meaningful timeframes, and extending coverage to underrepresented pathogens and platforms.

Humans↗

PubMed2026

Emerging Principles in Spatial Functional Genomics.

Spatial transcriptomic and proteomic atlases have enabled mapping of gene programs within intact tissues, but these measurements remain largely descriptive and do not define the mechanisms controlling tissue biology. Pooled CRISPR screening provides scalable causal interrogation of gene function but remains largely confined to dissociated systems that lack spatial context. In vivo spatial functional genomics (SFG) bridges these approaches by integrating genetic perturbations with in situ transcriptomic and proteomic readouts to measure gene function within intact tissue ecosystems. By preserving spatial organization, SFG enables interpretation of perturbations through effects on cell-cell interactions, diffusible signals, multicellular niches, and tissue architecture. Here, we outline key design axes of SFG: perturbation strategy, barcoding strategy, and phenotypic readout. We discuss computational challenges, including spatial autocorrelation, neighborhood dependence, and context-aware null modeling, and highlight how SFG reveals non-cell-autonomous, architecture-dependent mechanisms of gene function, advancing toward predictive models of tissue organization and gene function.

Genomics↗

PubMed2026

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89-0.94) in cross-validation and 0.88 (95% CI: 0.85-0.91) in independent validation, outperforming clinical models (ΔAUC = +0.18, p < 0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression-free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.

Humans↗

PubMed2026

Pharmacogenomic and drug interactions risk in cardio-oncology: A precision medicine perspective for India.

Cardio-oncology patients may face complex treatment regimens due to the concurrent existence of cancer and cardiovascular disease, leading to a considerable polypharmacy burden. This significantly increases the prospect of drug-drug interactions (DDIs) and gene-drug interactions. The majority of these interactions arise from comparable pharmacokinetic and pharmacological pathways associated with drug transporters and cytochrome P450 enzymes. The significance of pharmacogenomics in tailored treatment strategies are emphasised by the fact that genetic variability enhances individual differences in drug response, safety, and efficacy. This narrative review focus on the effects of key genetic polymorphisms (e.g., DPYD, CYP2C19, and CYP2C9) on the metabolism and efficacy of commonly prescribed anticancer and cardiovascular medications such as fluoropyrimidines, clopidogrel, and warfarin. In addition it explore the role of pharmacogenomic variants on drug-drug interactions within the field of cardio-oncology. The study ultimately emphasizes the necessity of precision medicine in India to address the genetic diversity and underrepresentation in global genomic databases. The absence of pharmacogenomic testing, infrastructural deficiencies, financial constraints, and insufficient clinical integration hinder the widespread use of this technology in India. The Genome India Project and other national initiatives establish the foundation for pharmacogenomic-guided therapy. Utilizing genetic data, together with artificial intelligence-based predictive tools, for clinical decision-making may enhance medication safety and yield optimal outcomes in Indian cardio-oncology patients.

Humans↗

PubMed2026

Structural and tissue-specific organisation of endocrine Fgf19 and Fgf21 signalling in rainbow trout.

Endocrine fibroblast growth factors (FGF19 subfamily) play a key role in regulating metabolic homeostasis in vertebrates. However, their functional diversification in salmonids remains poorly understood. In this study, we conducted an integrative characterisation of Fgf19 and Fgf21 signalling in rainbow trout (Oncorhynchus mykiss) by combining phylogenetic, structural and expression analyses. Phylogenetic analyses revealed the conservation of single fgf19 and fgf21 genes, despite the extensive expansion of receptors post-Ss4R (salmonid-specific fourth-round whole genome duplication). Structural modelling and molecular dynamics simulations demonstrated the stable interactions of both ligands to multiple Fgfr isoforms, with receptor-specific energetic profiles and conserved core interaction residues. Tissue expression profiling revealed clear differences from mammalian models, such as predominant hepatic fgf19 expression and the absence of hepatic fgf21 under basal conditions. In addition, there were complex and tissue-dependent distributions of fgfr and klotho transcripts. These findings support a receptor-driven diversification model of endocrine Fgf signalling in salmonids, suggesting enhanced endocrine plasticity associated with the retention of receptors following post-genomic duplication. Taken together, our findings provide new insights into the structural and regulatory organisation of endocrine Fgf signalling, as well as its potential role in metabolic regulation in rainbow trout.

Animals↗

PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

PubMed2026

Assembly and Characterization of the First Complete Mitochondrial Genome of Tussilago farfara L.: Insights into Biological Functions and Phylogenetic Relationships within the Asteraceae Family.

Tussilago farfara L., a member of the Asteraceae family, is an economically valuable species due to its edible and medicinal properties. To elucidate the structural characteristics, genetic mechanisms, and evolutionary pathways of the organelle genomes of T. farfara, we sequenced, assembled, and annotated its mitochondrial genome for the first time. The complete mitochondrial genome of T. farfara spans 306,024 bp and contains 33 mitochondrial protein-coding genes (PCGs), 3 rRNAs, and 22 tRNAs. Analysis of the nucleotide substitution rate and genetic diversity revealed that most mitochondrial genome genes may have undergone purifying selection, indicating a slow evolutionary rate and a relatively conserved genomic structure. We further identified 13 fragments of chloroplast-derived DNA integrated into the mitochondrial genome, evidencing intracellular gene transfer. Collinearity analysis showed that Arctium lappa shares the most extensive mitochondrial homologous sequences and the highest sequence similarity with T. farfara. Phylogenetic analysis based on the mitochondrial genome helped to clarify the evolutionary and taxonomic position of T. farfara within the Asteraceae family. The mitochondrial genome sequence of T. farfara provides a valuable genomic resource for species identification and for evolutionary studies within the Asteraceae family.

Genome, Mitochondrial↗

PubMed2026

Hierarchical modeling of tumor subtypes in cell lines using large-scale genomic datasets.

Cancer cell lines (CLs) are widely used to study tumor biology and drug response, yet their translational relevance is often limited by inaccurate subtype annotations. Existing CL-tumor matching approaches are frequently constrained by flat classification schemes, weak subtype definitions, and the exclusion of normal tissue references, leading to potential confounding of tumor-specific and tissue-of-origin signals. To address these limitations, a hierarchical classification (HC) framework is presented in which CLs are aligned with patient tumors across biological resolutions, from organ to molecular subtype. Gene expression profiles from 802 CLs, 5,612 tumors from The Cancer Genome Atlas (TCGA) , and 8,939 non-cancerous tissues were integrated to separate oncogenic signals from tissue-specific signals. Node-specific features were selected using maximum relevance minimum redundancy, and balanced accuracies of 89% in cross-validation and 75%, and 80% on external datasets were achieved. Through the framework, 43 CLs were reassigned, and clinically relevant underrepresented subtypes were identified.

cancer cell lines↗

PubMed2026

Genome-wide scans reveal candidate genes associated with wing morph differentiation in Tetrix japonica.

Wing dimorphism is an important dispersal-related trait in insects, but its genomic basis remains poorly understood in pygmy grasshoppers. Here, we integrated genome-wide single-nucleotide polymorphism (SNP) analyses, population structure inference, selection scans, and functional annotation to investigate genomic differentiation between long- and short-winged Tetrix japonica. Principal component analysis (PCA), ADMIXTURE, and phylogenetic analyses revealed weak genome-wide separation between morphs, indicating differentiation on a largely shared genetic background. Genome-wide scans based on the fixation index (FST), nucleotide diversity ratios, and Tajima's D, using 50-kb non-overlapping windows and empirical top-5% outlier thresholds, identified multiple candidate regions across seven chromosomes. The broader long- and short-winged candidate sets spanned 9.35 Mb and 9.37 Mb and directly overlapped 82 and 77 genes, respectively. Candidate genes were associated with signaling/hormone regulation, membrane transport, metabolism, cytoskeletal organization, extracellular matrix structure, and development. Short-winged candidate genes were significantly enriched for ABC-type transporter activity and ATP hydrolysis activity. Because all individuals originated from a single laboratory-maintained population with weak genome-wide structure, these regions should be regarded as candidate loci from a screening-stage analysis that require validation in independent populations and by functional assays, rather than as confirmed targets of selection.

Animals↗

PubMed2026

A chromosome-level, haplotype-resolved genome assembly for the barn owl, Tyto alba.

Recent advances in long-read sequencing have enabled near telomere-to-telomere (T2T) assemblies across diverse taxa. However, avian genomes remain challenging due to numerous microchromosomes, small, typically < 20Mb, DNA molecules that are gene-, GC-, and repeat-rich. As a consequence, microchromosomes are often missing from genome assemblies. Here, we present a chromosome-level, haplotype-resolved genome assembly for the Western barn owl (Tyto alba). Using a trio-binning strategy with Illumina parental reads combined with PacBio HiFi and Oxford Nanopore Technologies data, we generated two phased contig sets. These were scaffolded into 40 linkage groups using a linkage map. Comparative analyses identified unplaced HiFi scaffolds corresponding to microchromosomes, which we integrated into six additional microchromosomes using long reads information. The two assemblies present 46 chromosomes, matching the karyotype of the species. They exhibit strong synteny between parental haplotypes, except for a ∼38 Mb complex region on chromosome 7 containing nested inversions. This high-quality reference provides a haplotype-resolved and chromosome-level genome for Strigiformes, enabling fine-scale studies of structural variation and avian genome evolution.

Tyto alba↗

PubMed2026

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype↗

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↗