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PubMed2026

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses.

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Spatial Transcriptomics↗

PubMed2025

A complete and near-perfect rhesus macaque reference genome: lessons from subtelomeric repeats and sequencing bias.

A truly complete, telomere-to-telomere (T2T), and error-free reference genome remains a foundational resource-and long-standing goal-for unbiased comparative and functional genomics. While recent T2T assemblies of humans and other primates have made substantial progress, most still contain thousands of base-level errors, particularly within highly repetitive regions. Here, we present T2T-MMU8v2.0, a near-perfect T2T assembly of the rhesus macaque (Macaca mulatta), representing the highest base-level accuracy reported in a primate genome to date. By employing an optimized ONT-only assembly strategy, we identify subtelomeric satellite-rich regions as the principal bottleneck to improving assembly quality, owing to technological biases in long-read platforms and limitations in current hybrid assembly frameworks. We discover 268 previously unannotated repeat families and resolve ~8 Mbp of SATR satellite arrays, with over 99-fold enrichment in historically misassembled subtelomeric regions. These satellites form four distinct genomic architectures, each with unique SATR satellite composition, segmental duplication organization, and epigenetic signatures, distinct from the subtelomeric architectures observed in hominid genomes. Notably, in contrast to the largely gene-poor subtelomeric regions in African hominids, the SATR architectures in macaques harbor 58 actively transcribed genes, supported by open chromatin and expression data, suggesting gene innovation within these repetitive regions. Functionally, T2T-MMU8v2.0 improves read mappability and accuracy across sequencing platforms, and results in a 19% improvement of transcription start site enrichment scores and 5,821 additional chromatin accessibility peaks on average, thereby enhancing variant detection, regulatory annotation, and transcriptomic resolution in population genetics or single-nucleus studies. Together, this work establishes a new benchmark for genomics, offers a roadmap for resolving complex repetitive regions, and reveals previously unrecognized features of subtelomeric genome structure and evolution.

Journal Article↗

PubMed2026

Bridging Organ-on-a-Chip and Omics: A Multi-Dimensional Frontier in Biomedical Research.

Organ-on-a-Chip (OOC) technology offers a powerful platform for replicating human tissue-specific microenvironments, thereby narrowing the translational gap between conventional biomedical models and actual human physiology. Concurrently, omics technologies deliver comprehensive molecular-level insights into biological systems. This review highlights the transformative potential of integrating OOC platforms with high-throughput omics methodologies. We systematically examine the classification, structural configurations, and engineering principles underlying OOC systems, alongside the defining attributes of key omics domains-genomics, transcriptomics, proteomics, and metabolomics. The convergence of dynamic OOC models with advanced omics technologies enables high-resolution, multi-dimensional analyses across numerous biomedical applications, including drug metabolism, disease mechanisms, environmental toxicity assessments, and host-microbiome interactions. This interdisciplinary integration is driving a paradigm shift in precision and translational medicine. However, several challenges remain to be addressed, such as the development of whole-organ mimetics, adaptation of sample collection techniques, and real-time artificial intelligence-based integration of biosensor data with multi-omics datasets. Addressing these hurdles will be vital for unlocking the full potential of this technological synergy in biomedical science.

Multiomics↗

PubMed2026

Establishment and Characterization of Patient-Derived Xenograft Organoids for Personalized Treatment of Castration-Resistant Prostate Cancer.

BACKGROUND: Basic research on castration-resistant prostate cancer (CRPC) is limited by the lack of clinically relevant models. This study aimed to establish patient-derived xenografts (PDX) and PDX-derived organoids from clinical CRPC specimens to develop a bidirectional experimental platform for in vivo xenografts and ex vivo organoids. METHODS: We established a new PDX library (KUCaP PDX series) using CRPC clinical specimens and derived prostate cancer organoids. Comprehensive biological characterization of clinical specimens, PDXs, PDX-derived organoids, and organoid-derived xenografts (ODXs) was performed to confirm the preservation of the original tumor features. Using our PDX library, we conducted genetic engineering and drug testing to explore novel therapeutic approaches. RESULTS: PDX-derived organoids were successfully established from all eight KUCaP PDX lines (100%). Four of the eight lines (50%) were maintained during the long-term culture experiments for over ten passages. Key features observed in the original clinical specimens, including genetic alterations and castration responsiveness, were maintained across the PDX, PDX-derived organoid, and ODX models. RNA sequencing revealed that transcriptomic profiles were consistently maintained across clinical specimens, PDXs, PDX-derived organoids, and ODXs. One PDX and organoid (KUCaP19) which exhibited a high homologous recombination deficiency (HRD) score, without any pathogenic homologous recombination repair (HRR) gene alterations, showed sensitivity to a poly ADP-ribose polymerase (PARP) inhibitor. In contrast, KUCaP12, which had no HRR alterations and a low HRD score, did not respond to PARP inhibition. CONCLUSIONS: We developed the KUCaP library as a novel experimental platform for CRPC research by integrating clinical specimens with PDX, organoid, and ODX models along with their genomic and transcriptomic data. These models largely retained the genetic profiles and responses to castration observed in the original tumors. The bidirectional use of personalized PDX and organoids will facilitate the elucidation of the molecular mechanisms of CRPC.

Male↗

PubMed2026

CoxFormer enables spatial omics inference with multimodal generative modeling.

Gene co-expression maps transcriptome-wide gene-gene relationships, yet high-quality estimates cover less than half the genome. Meanwhile, spatial omics either profiles restricted in situ panels or lacks cellular resolution. Extending co-expression transcriptome-wide could overcome these limitations by inferring unassayed gene expression at subcellular resolution. Here we show that CoxFormer integrates literature-derived gene knowledge with co-expression networks from bulk tissues and large-scale single-cell atlases to learn 512-dimensional representations for 32,016 human genes. These embeddings capture functional gene relationships and serve as a generative prior for spatial inference across platforms and modalities. Without requiring a matched single-cell RNA-sequencing reference, CoxFormer supports four applications beyond measured genes: histology-based expression imputation, gene activity prediction from chromatin accessibility, subcellular super-resolution inference, and pathological region detection. Together, CoxFormer extends gene embedding from gene- and cell-level tasks to whole-transcriptome spatial inference, providing a unified framework for biological analysis beyond the limited gene coverage of current spatial omics technologies.

Humans↗

PubMed2007

SYSTOMONAS--an integrated database for systems biology analysis of Pseudomonas.

To provide an integrated bioinformatics platform for a systems biology approach to the biology of pseudomonads in infection and biotechnology the database SYSTOMONAS (SYSTems biology of pseudOMONAS) was established. Besides our own experimental metabolome, proteome and transcriptome data, various additional predictions of cellular processes, such as gene-regulatory networks were stored. Reconstruction of metabolic networks in SYSTOMONAS was achieved via comparative genomics. Broad data integration is realized using SOAP interfaces for the well established databases BRENDA, KEGG and PRODORIC. Several tools for the analysis of stored data and for the visualization of the corresponding results are provided, enabling a quick understanding of metabolic pathways, genomic arrangements or promoter structures of interest. The focus of SYSTOMONAS is on pseudomonads and in particular Pseudomonas aeruginosa, an opportunistic human pathogen. With this database we would like to encourage the Pseudomonas community to elucidate cellular processes of interest using an integrated systems biology strategy. The database is accessible at http://www.systomonas.de.

Bacterial Proteins↗

PubMed2026

Paired analysis of primary adenoid cystic carcinoma and derived cell lines reveals a mesenchymal and stem-like shift associated with therapy resistance.

Adenoid cystic carcinoma (ACC) is a salivary gland malignancy characterized by slow but persistent growth, frequent local recurrence, and late metastatic progression. Patients with unresectable, recurrent, or metastatic disease have limited therapeutic options. Efforts to identify effective therapeutic targets have been hindered by the limited availability of well-characterized ACC models. In this study, we established 11 ACC cell lines and performed RNA sequencing of nine cell lines and their matched primary tumors to evaluate the preservation and evolution of molecular and lineage-associated characteristics during cell line establishment. Comparative transcriptomic analysis revealed reduced epithelial and luminal differentiation programs in the cell lines, accompanied by enrichment of myoepithelial, EMT-, and cancer stem cell-associated transcriptional programs. Digital deconvolution and single-sample gene set enrichment analysis supported enrichment of hybrid EMT/stem-like states during in vitro propagation, while comparison with publicly available primary-recurrent ACC data demonstrated partial preservation of recurrence-associated plasticity and invasion programs. Protein-level validation of representative epithelial, myoepithelial, EMT, and stemness markers supported the major transcriptomic changes. In addition, a cell line with a higher stemness signature showed reduced sensitivity to cisplatin. Together, these findings indicate that ACC cell line establishment is associated with transcriptional reprogramming and enrichment of plastic, EMT/stem-like states while retaining selected ACC lineage characteristics. These models provide experimentally tractable platforms for investigating ACC progression, therapeutic response, and mechanisms of treatment resistance.

Adenoid cystic carcinoma↗

PubMed2006

The use of microarrays to define functionally-related genes that are differentially expressed in the cycling pig uterus.

In swine and other livestock, the uterine endometrium exhibits dramatic morphological and secretory changes throughout the oestrous cycle and during pregnancy. Such physiological changes are a reflection of extremely complex interactions between gene products (RNA and protein). The recent development of genomics and proteomics methods, as well as associated bioinformatics tools, has provided the means to begin characterising such interactions. Indeed, the analysis of the transcriptome and proteome of cells and tissues now comprises a new field of study known as 'systems biology'. Currently, the most powerful technique available to the systems biologist is the microarray. These platforms represent oligonucleotide or cDNA fragments spotted in a specified high-density pattern on a solid support. Hybridisation of fluorescently-tagged cDNAs from different tissue sources permits the measurement of thousands of RNAs in parallel. The method permits the identification of genes that are present at different amounts between the two tissues and, more importantly, it permits the identification of groups of genes (clusters) that are expressed in comparable patterns. Results from a recent expression profiling experiment are described. The goal of the profiling experiment was to define genes that are differentially expressed in endometrium during the oestrous cycle. The experiment used an in-house cDNA microarray with > 14,000 distinct cDNAs cloned from reproductive tissues. Total RNAs from cyclic endometrium (Days 0, 3, 6, 10, 12, 14 and 18 post-oestrus) were reverse transcribed into cDNAs, labelled with fluorescent dye and hybridised to the arrays along with cDNAs derived from a reference RNA pool. A total of 4,827 genes were found to differ significantly at some time during the oestrous cycle. Clustering methods were able to define numerous groups of similarly expressed genes. These data will help to define the complex patterns of endometrial genes acting in concert to create the environments required for fertilisation, embryo growth and conceptus development in swine.

Animals↗

PubMed2026

Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.

Agentic artificial intelligence is extending bioinformatics beyond conversational assistance by enabling systems to select tools, execute code, revise analytical plans, and interpret biological data. These capabilities may accelerate research, but they also redistribute decisions that determine whether biological conclusions are valid. We conducted a targeted, structured PubMed search in July 2026 and identified 11 peer-reviewed agentic bioinformatics systems for descriptive review based on predefined eligibility criteria for analytical decision-making, tool or code execution, iterative evaluation, or coordinated agent activity. The evidence base covered single-cell transcriptomics, microbial genomics, cancer genomics, and omics applications, together with methodological literature on reproducibility and biological validation. We examined how current systems report delegated authority, provenance, validation, evidence, abstention, and human oversight. Existing platforms implement safeguards such as sandboxed execution, restricted commands, interaction logs, evidence identifiers, automated checks, critic agents, quality scores, and expert assessment. However, published reports rarely provide a connected account linking the original biological question to samples, reference resources, analytical decisions, computational actions, statistical results, supporting evidence, validation outcomes, and final claims. We distinguish inherited bioinformatics errors, errors amplified through autonomous action, and emergent failures arising from memory, retrieval, tool interaction, or agent coordination. We further propose a multidimensional decision-rights profile, consequence-sensitive validation gates, and a claim-to-evidence provenance architecture organized through the Traceable History of Research Evidence, Agent Actions, and Decisions in Bioinformatics (THREAD-Bio) framework. Illustrative cases show that technically successful execution may still support misleading inference. Trustworthy agentic bioinformatics therefore requires claims to remain reconstructible, challengeable, validated, and proportionate to the evidence.

accountable autonomy↗

PubMed2026

Meta-Merging the Transcriptomes of Gastric Tumors Redefines the Connections among Molecular and Clinical Subtypes.

INTRODUCTION: The availability of a large number of cancer expression profiles presents an excellent opportunity to re-investigate various biological and clinical questions. While several expression profiles have been established for different cancers, merging them may provide a more powerful platform for extensively extrapolating molecular and clinical features across multiple cohorts. MATERIALS AND METHODS: In this study, five gastric tumor expression profiles from the Gene Expression Omnibus [GEO] and one in-house cohort comprising a total of 1,060 samples were merged. The batch effect was removed using non-parametric ComBat analysis, and the seamless merging of datasets was confirmed through various parameters. RESULTS: Extrapolation of ACRG [Asian Cancer Research Group] and TCGA [The Cancer Genome Atlas] molecular subtypes in the merged cohort of 1,060 gastric tumors revealed nine distinct clusters. Notably, the following patterns were observed: [i] mutual exclusivity between Epithelial to Mesenchymal Transition [EMT] and Microsatellite Instability [MSI] subtypes in 90% of tumors; [ii] overlapping occurrence of EMT and MSI subtypes in the remaining tumors; [iii] overlap between MSI and Epstein-Barr Virus [EBV] subtype tumors; [iv] both commonalities and differences between EMT and Genomically Stable [GS] subtypes; and [v] an association between EBV positivity and PI3K mutation. CONCLUSION: The current study demonstrates that compiling a larger expression profile is valuable for revisiting the molecular features and epidemiology associated with molecular subtypes, thereby aiding in the development of novel diagnostics and targeted therapeutics.

Humans↗

PubMed2006

Transcriptome amplification methods in gene expression profiling.

The increasing use of microarray expression profiling to study the molecular biology of cancer and the cellular physiology of difficult-to-isolate cell types has led to a need for methods that accurately and precisely amplify small quantities of RNA. The purpose of this review is to provide an overview of the existing methods for transcriptome amplification and to define the parameters for comparing different amplification methods. The authors propose a standardized protocol for the assessment and evaluation of amplification methods, focusing on a new whole-transcriptome amplification kit, which amplifies total RNA into cDNA fragments. Reproducibility and reliability of the method were analyzed and discussed using both quantitative real-time PCR and a high-density oligonucleotide microarray platform.

DNA, Complementary↗

PubMed2026

Transcriptome-based epigenetic screening identifies DNA hypermethylation signatures as prognostic biomarkers in oral squamous cell carcinoma.

Promoter DNA hypermethylation is a key epigenetic mechanism of gene silencing in cancer, yet the DNA hypermethylome of oral squamous cell carcinoma (OSCC) and its prognostic relevance remain poorly characterized. Here, we systematically identified and validated novel hypermethylated genes with prognostic significance in OSCC using a genome-wide discovery and multi-platform validation strategy. Candidate genes were first identified by pharmacologic demethylation combined with RNA sequencing across OSCC cell lines, then validated by quantitative RT-PCR, methylation-specific PCR, and bisulfite sequencing in OSCC cell lines, normal oral mucosa, and primary OSCC tumors, with independent confirmation in the TCGA-HNSC dataset. Immunohistochemistry confirmed protein-level silencing, and Kaplan-Meier survival analysis assessed prognostic significance across both cohorts. This pipeline identified five candidate genes, GPX3, ANG, CTGF, GPRC5B, and BAMBI, exhibiting cancer-specific promoter hypermethylation associated with transcriptional and protein silencing in OSCC. Validation in oral cavity tumor samples extracted from the TCGA-HNSC dataset confirmed tumor-specific hypermethylation and revealed significant inverse correlations between methylation and expression for GPX3, GPRC5B, and CTGF. Notably, CTGF hypermethylation was independently associated with poor overall survival in both cohorts (institutional cohort, p=0.03; oral tumor subset from TCGA-HNSC, p=0.01), and a combined ANG+CTGF methylation signature showed superior and reproducible prognostic performance across both platforms. Pathway analysis linked these genes to epithelial-mesenchymal transition and interferon response signaling. This study establishes the first validated DNA methylation biomarker panel for OSCC prognosis, identifying CTGF hypermethylation as a robust prognostic driver with translational potential for clinical risk stratification.

Humans↗

PubMed2025

Molecular differences between young and mature stria vascularis from organotypic explants and transcriptomics.

The stria vascularis (SV) is an essential component of the inner ear that regulates the ionic environment required for hearing. SV degeneration disrupts cochlear homeostasis, leading to irreversible hearing loss, yet a comprehensive understanding of the SV, and consequently therapeutic availability for SV degeneration, is lacking. We developed a whole-tissue explant model from neonatal and mature mice to create a platform for advancing SV research. We validated our model by demonstrating that the proliferative behavior of the SV in vitro mimics SV in vivo. We also provided evidence for pharmacological experimentation by investigating the role of Wnt/β-catenin signaling in SV proliferation. Finally, we performed single-cell RNA sequencing from in vivo neonatal and mature mouse SV and surrounding tissue and revealed key genes and pathways that may play a role in SV proliferation and maintenance. Together, our results contribute new insights into investigating biological solutions for SV-associated hearing loss.

Biochemistry↗

PubMed2006

The identification of human tumour antigens: Current status and future developments.

The biggest challenge facing us today in cancer control and prevention is the identification of novel biomarkers for detection and improved therapeutic interventions to reduce mortality and morbidity rates. Biomarkers are important indicators to inform us of the physiological state of the cell at a specific time. It is now clear that malignant transformation occurs by changes in cellular DNA and protein expression with subsequent clonal proliferation of the altered cells. The affected genes and their expressed protein products or biomarkers are those involved in the normal growth and maintenance of the cancerous cells. These biomarkers could prove pivotal for the identification of early cancer and people at risk of developing cancer. Altered proteins or changes in gene expression in malignant cells may lead to the expression of tumour antigens recognised by host immune system. In this review we discuss current research into the molecular technologies making possible the global genomic-wide analysis of changes in DNA (genotyping), RNA expression (transcriptomics) and protein expression (proteomics) that have accelerated the rate of new biomarker/tumour antigen discovery. To gain a comprehensive understanding of the physiology and pathophysiology of cancer an approach that harmoniously integrates the various 'omic' platforms are key to unraveling the complexity 'needle-in-a-haystack' quality of biomarker/tumour antigen discovery.

Antigens, Neoplasm↗

PubMed2006

An open-access long oligonucleotide microarray resource for analysis of the human and mouse transcriptomes.

Two collections of oligonucleotides have been designed for preparing pangenomic human and mouse microarrays. A total of 148,993 and 121,703 oligonucleotides were designed against human and mouse transcripts. Quality scores were created in order to select 25,342 human and 24,109 mouse oligonucleotides. They correspond to: (i) a BLAST-specificity score; (ii) the number of expressed sequence tags matching each probe; (iii) the distance to the 3' end of the target mRNA. Scores were also used to compare in silico the two microarrays with commercial microarrays. The sets described here, called RNG/MRC collections, appear at least as specific and sensitive as those from the commercial platforms. The RNG/MRC collections have now been used by an Anglo-French consortium to distribute more than 3500 microarrays to the academic community. Ad hoc identification of tissue-specific transcripts and a approximately 80% correlation with hybridizations performed on Affymetrix GeneChiptrade mark suggest that the RNG/MRC microarrays perform well. This work provides a comprehensive open resource for investigators working on human and mouse transcriptomes, as well as a generic method to generate new microarray collections in other organisms. All information related to these probes, as well as additional information about commercial microarrays have been stored in a freely-accessible database called MEDIANTE.

Animals↗

PubMed2026

Spatially guided in vivo single-cell functional genomics of postnatal heart.

Understanding how spatial organization and cell-cell interactions shape gene regulatory programs is central to decoding tissue development and function. The transition at birth, marked by increased circulatory demands and rapid tissue growth, requires precise spatiotemporal coordination of cardiac maturation. In this study, we generated a high-resolution spatial and temporal atlas of the postnatal mouse heart by integrating single-nucleus RNA sequencing with image-based spatial transcriptomics. This framework revealed dynamic cellular interactions, niche-specific signaling and transcriptional programs guiding cardiomyocyte maturation. To functionally test prioritized regulators in vivo and at scale, we developed PIP-seq (probe-based indel-detectable Perturb-seq), a high-throughput platform that detects single guide RNA identity, infers gene editing and profiles transcription from fixed nuclei. Applying PIP-seq to the developing postnatal heart, we identified 21 previously uncharacterized regulators of cardiomyocyte maturation, including genes essential for sarcomere assembly, metabolic reprogramming and electrophysiological transitions. Together, our findings define how microenvironmental signals and intrinsic gene programs cooperate to guide heart maturation and establish a broadly applicable framework for functional genomics in complex tissues.

Animals↗

PubMed2026

LymphGen-Sig: Integrating Genetic and Transcriptional States to Predict Therapeutic Response in Diffuse Large B-Cell Lymphoma.

PURPOSE: Genetic classification may advance precision medicine in diffuse large B-cell lymphoma (DLBCL), but existing tools like LymphGen (LG) are limited by complexity and incomplete classification and do not incorporate nongenetic features that affect disease biology and therapeutic outcomes. To address these limitations, we developed LG-sig (LGsig), a gene expression-based platform that classifies all DLBCLs and harmonizes both genetic and nongenetic dimensions of the disease. METHODS: LGsig was built on the distinct subtype-specific gene expression signature of each LG class using paired genomic and transcriptomic data (National Cancer Institute/British Columbia Cancer Agency; N = 764). Model development was restricted to DLBCLs classified into MYD88L265P and CD79B mutations (MCD), BCL6 translocation and NOTCH2 mutations (BN2), EZH2 mutations and BCL2 translocation (EZB), or SGK1 and TET2 mutations (ST2). Gene features were selected by differential gene expression, with 294 genes being optimal for classification using a nearest shrunken centroid classifier. LGsig classifications were designated as MCDsig, BN2sig, ST2sig, and EZBsig. The final model was applied to RNAseq from archival samples from the POLARIX trial (N = 678) to assess outcomes after polatuzumab vedotin-R-CHP (pola-R-CHP) or rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) for each LGsig subtype. RESULTS: LGsig accurately identified LG subtypes using transcriptional data alone and extended assignments to all previously LG-unclassified cases. Importantly, LG-unclassified DLBCLs reassigned by LGsig mirrored the transcriptional and clinical features of their corresponding LG counterparts, supporting their reclassification. In addition, LGsig reassigned LG A53 DLBCLs, characterized by aneuploidy and TP53 alterations, into more biologically and therapeutically relevant LGsig clusters. Finally, LGsig improved the performance of LG as a biomarker in the POLARIX study, by identifying distinct DLBCL subtypes exhibiting a survival benefit with pola-R-CHP over R-CHOP in both LG-classified and LG-unclassified cases. CONCLUSION: LGsig expands molecular classification beyond current genetic classifiers in DLBCL by integrating both genetic and transcriptional dimensions of the disease to better inform subtype-specific therapeutic strategies.

Journal Article↗

PubMed2026

Advancing precision tacrolimus therapy: a systems genetics dissection in BXD platform.

BACKGROUND: Tacrolimus is a core immunosuppressant in organ transplantation, but its narrow therapeutic window and significant pharmacokinetic variability hinder precision dosing. Although CYP3A5-guided strategies have established clinical relevance for tacrolimus initial dose adjustment, they do not fully account for the marked interindividual variability in tacrolimus exposure, highlighting the need for complementary models to decode more complex genetic regulation. This study aimed to identify candidate genetic modulators of tacrolimus metabolism and develop an integrated predictive framework for individualized therapy. METHODS: Using 46 BXD recombinant inbred mouse strains, we characterized transcriptomics and machine learning, and validated key genes. We then constructed a clinical model using data from 168 renal transplant recipients. RESULTS: We identified 19 genomic loci associated with tacrolimus pharmacokinetic traits and supported DBP/CYP2A6 as candidate modulators associated with tacrolimus disposition. The clinical prediction model, incorporating these genes and clinical variables, achieved robust AUROC. CONCLUSIONS: These findings support a polygenic contribution to tacrolimus metabolism and provide an experimental and computational framework for identifying candidate modulators relevant to individualized dosing. The BXD mouse platform offers a systems-genetics approach for mechanistic discovery that may inform future translational studies on tacrolimus precision dosing.

Animals↗