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PubMed2024

Tumor specimen cold ischemia time impacts molecular cancer drug target discovery.

Tumor tissue collections are used to uncover pathways associated with disease outcomes that can also serve as targets for cancer treatment, ideally by comparing the molecular properties of cancer tissues to matching normal tissues. The quality of such collections determines the value of the data and information generated from their analyses including expression and modifications of nucleic acids and proteins. These biomolecules are dysregulated upon ischemia and decompose once the living cells start to decay into inanimate matter. Therefore, ischemia time before final tissue preservation is the most important determinant of the quality of a tissue collection. Here we show the impact of ischemia time on tumor and matching adjacent normal tissue samples for mRNAs in 1664, proteins in 1818, and phosphosites in 1800 cases (tumor and matching normal samples) of four solid tumor types (CRC, HCC, LUAD, and LUSC NSCLC subtypes). In CRC, ischemia times exceeding 15 min impacted 12.5% (mRNA), 25% (protein), and 50% (phosphosites) of differentially expressed molecules in tumor versus normal tissues. This hypoxia- and decay-induced dysregulation increased with longer ischemia times and was observed across tumor types. Interestingly, the proteomics analysis revealed that specimen ischemia time above 15 min is mostly associated with a dysregulation of proteins in the immune-response pathway and less so with metabolic processes. We conclude that ischemia time is a crucial quality parameter for tissue collections used for target discovery and validation in cancer research.

Humans↗

PubMed2026

Genetic and epigenetic underpinnings of biological aging: a multi-omics study integrating Mendelian randomization, spatial transcriptomics, and drug target discovery.

Inflammaging represents a hallmark of biological aging, yet the causal inflammatory mediators driving multi-dimensional epigenetic aging and their effector genes remain poorly characterized at the genetic level. We developed a four-tier analytical framework integrating causal screening, multi-omics effector gene mapping, spatial transcriptomics, and drug target evaluation. Two-sample Mendelian randomization (MR) of 91 circulating inflammatory proteins against six aging phenotypes identified IL-12B, IFNG, and IL-2 as the most robust pro-aging mediators with consistent effects across independent outcomes. Using multi-omics summary-based MR (SMR) as the core analytical engine, we integrated four-layer whole-blood molecular QTL resources eQTL (eQTLGen, n = 31,684), sQTL (GTEx, n = 755), pQTL (INTERVAL + SCALLOP, n = 34,232), and mQTL (McRae et al., n = 1,980) - with GWAS summary statistics for four epigenetic age acceleration measures. At a stringent threshold (P_SMR < 1×10⁻¹²), seven high-confidence effector genes were identified: NHLRC1, TPMT, SELP, and RIPPLY3 for IEAA; ZNF373A and PLDN for HannumAA; and EDARADD for PhenoAA. The chromosome 6p21 NHLRC1-TPMT locus, overwhelmingly driven by methylation QTL signals (-log₁₀P = 26.06), emerged as the dominant genetic node of epigenetic aging. Spatial projection via gsMap onto a mouse E16.5 embryo atlas (121,767 cells) revealed preferential enrichment in smooth muscle and lung, with EDARADD showing marked specificity in mucosal epithelium. Cross-database drug target mining classified TPMT and SELP as repurposable known targets and NHLRC1 as a high-priority novel druggable candidate. This study provides multi-omics convergent causal evidence for inflammation-driven epigenetic aging and delivers genetically anchored targets for precision anti-aging intervention.

Aging↗

PubMed2026

Integrating multi-ancestry common and rare variant mapping accelerates therapeutic target discovery.

Integrating human genetics into therapeutic discovery accelerates drug development. However, ancestral biases in historical cohorts have left critical functional variation largely uncharted. Here, we leverage the diverse NIH All of Us Research Program to conduct comprehensive common- and rare-variant association analyses for 624 quantitative traits across 369,655 ancestrally diverse individuals. We identified 6,181 genome-wide significant locus-trait associations (526 novel) and 416 gene-trait associations (105 novel) via rare-variant burden testing. By integrating fine-mapping with computational variant-effect predictors, we systematically prioritized rare, likely causal variants driving these signals. Jointly modeling common and rare variation with protein-class annotations significantly improved the identification of known drug targets compared to common-variant analysis alone. Notably, we identified NRG4 as a high-confidence candidate therapeutic target for preserving kidney function. Our findings demonstrate that characterization of rare and common variation across diverse populations enhances causal gene discovery and identifies novel, actionable therapeutic targets.

Journal Article↗

PubMed2026

Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization.

Alzheimer's disease (AD) is a multifactorial neurodegenerative condition in which accumulating genetic and molecular evidence implicates dysregulation of peripheral immune processes in disease pathogenesis. Nevertheless, the contribution of distinct peripheral immune cell subsets and associated gene regulatory landscapes to AD risk remains incompletely defined. To address this gap, we integrated single-cell expression quantitative trait loci (sc‑eQTL) data from the OneK1K cohort with AD GWAS summary statistics. We systematically interrogated immune cell-specific genes for their contributions to AD risk by integrating genetic causal inference with Bayesian colocalization analyses, and identified 24 eGenes that passed both the MR significance threshold (P < 0.05) and the criterion for strong shared genetic signals (PP.H4 > 0.8). Notable candidates included GATS, HLA-DOB, HLA-DQA1, PM20D1, and others, with each gene demonstrating a cell-type-specific association restricted to its corresponding immune cell type, such as monocytes, CD8 + T cells, or B cells. Independent peripheral blood single-cell transcriptomic data further supported disease-associated shifts in cell-type-specific expression patterns in AD. Phenome-wide association studies (PheWAS) indicated limited associations with off-target traits, indicating a favorable safety profile for therapeutic intervention, with the exceptions of B4GALNT3, PM20D1, and CNN2. Integration of immune gene targets with pharmacological databases yielded three candidate compound, including NSC321521 (targeting HLA-DQA1), phenoxybenzamine (targeting GSTP1), and rimexolone (targeting BIN1). Among these compounds, Predicted blood-brain barrier permeability was observed only for phenoxybenzamine and rimexolone, with docking studies indicating stable interactions, such as those between NSC321521 and HLA-DQA1, phenoxybenzamine and GSTP1, and rimexolone and BIN1. This integrative approach highlights key immune‑cell‑specific genes involved in AD and proposes repurposable drugs with central nervous system potential, paving the way for more targeted immunomodulatory strategies in AD.

Humans↗

PubMed2026

Application of emerging technologies in the antiviral field.

Viral diseases pose a serious threat to global public health, agriculture, and biosecurity. Conventional antiviral strategies are often limited by an incomplete understanding of disease mechanisms, poor targeting precision, and slow response times. Emerging technologies are now reshaping the landscape of antiviral research. This review examines the roles of four key frontiers, including organoid models, gene editing, AI-driven molecular design, and synthetic biology. Organoids provide physiologically relevant platforms that model virus-host interactions and disease progression. Viral infections remain a major challenge to human and animal health, agriculture, and biosecurity. Progress in antiviral research is constrained by the complexity of viral pathogenesis, the diversity and rapid evolution of viruses, and the limited translational relevance of some traditional model systems. Recent advances in organoid technology, gene editing, artificial intelligence, and synthetic biology are expanding the toolkit available for antiviral research and development. In this review, we discuss how these four technological frontiers contribute to disease modeling, target discovery, molecular design, and translational innovation. Organoids, in particular, provide physiologically relevant systems for investigating viral infection, tissue tropism, host responses, and pathogenesis. Gene editing tools, such as CRISPR, enable precise manipulation of host and viral genomes, facilitating the development of resistant organisms and next-generation vaccine platforms. AI technologies, including AlphaFold for structure prediction and platforms for de novo protein design, address long-standing bottlenecks in structural biology and offer powerful means to engineer antiviral proteins, antibodies, and vaccine antigens. Synthetic biology, guided by the Design-Build-Test-Learn cycle, integrates computational design, genetic assembly, and functional validation into a cohesive pipeline. Together, these technologies form a synergistic workflow that spans disease modeling, target discovery, molecular design, construction, testing, and iterative optimization. This integrated approach is shifting antiviral development from traditional empirical methods toward more precise, intelligent strategies. The review also highlights ongoing challenges in integration and scalability, stressing that high-quality biological datasets and stronger interdisciplinary collaboration are essential for realizing translational potential. By presenting a cohesive view of these converging methodologies, this review offers a framework to guide the intelligent evolution of antiviral strategies in both human and animal health.

Antiviral↗

PubMed2026

Inhibition of the atypical kinase WNK1 as a therapeutic strategy in TAL-related T-cell acute lymphoblastic leukemia.

Driver mutations in T-cell acute lymphoblastic leukemia (T-ALL) rarely affect druggable kinases. However, these kinases can be aberrantly activated or repressed as secondary oncogenic events. Thus, integrating unbiased phosphoproteomics with genomic approaches may offer novel opportunities for target discovery and therapeutic interventions. In our study, we identified WNK1 (with no lysine [K]) as a potential target in T-ALL by pairing a list of vulnerable kinases with data from a phosphoproteomic screen of T-ALL cell lines. We subsequently validated WNK1 by loss-of-function-based studies and tested WNK inhibitors in several in vitro and in vivo T-ALL models and clinical T-ALL samples. We showed that therapeutic WNK1 repression promotes polyploidy, resulting in cell proliferation arrest, and morphometric changes, such as incomplete cell division or chromosome segregation through altered mitotic spindle assembly and abscission defects. Furthermore, we found that WNK1 is overexpressed in the TAL1/2-related subgroup, but not in normal thymus or lymph nodes, suggesting a potential translational area for clinical exploitation in poor-prognosis T-ALL carrying PTEN mutations and del(6q). Our work also reports a functional contribution of WNK1 in the leukemia establishment and progression. Structurally WNK1 is an atypical serine/threonine kinase that diverges from canonical kinases by lacking the conserved lysine in subdomain II, instead featuring a cysteine in subdomain I, which is critical for adenosine triphosphate (ATP) binding. This unusual structural configuration creates a distinct ATP-binding pocket with limited sequence similarity to conventional kinases, offering a unique opportunity to develop highly selective small molecules. Targeting this atypical ATP domain could thus provide a therapeutic advantage and broaden the treatment landscape for T-ALL.

WNK Lysine-Deficient Protein Kinase 1↗

PubMed2026

A single-cell atlas of multiple myeloma defines malignant archetypes and proliferative states.

Multiple myeloma (MM) is a plasma-cell malignancy with extensive genomic and transcriptional heterogeneity, limiting disease classification and precision therapy. Here we generated a clinically annotated, population-scale, single-cell atlas of MM from 341 individuals spanning the disease and treatment continuum. We identified five recurrent malignant transcriptional archetypes and an orthogonal proliferative program associated with genomic features, therapeutic resistance and clinical outcomes. Validation in the independent CoMMpass cohort demonstrated robustness, prognostic relevance and portability across platforms. We developed a single-cell, target-discovery pipeline prioritizing malignant enrichment, cell-type specificity and tissue restriction, identifying FCRL2 as a plasma-restricted or B cell-lineage-restricted surface target expressed by malignant plasma cells. FCRL2-targeted chimeric antigen receptor T cells demonstrated antigen-specific activity in vitro and survival benefit in vivo. Together, these data provide a clinically actionable blueprint for patient stratification and precision target nomination in plasma-cell malignancies.

Multiple Myeloma↗

PubMed2025

Discovery of Isonitrile Lipopeptide Chalkophores from Pathogenic Mycobacteria.

The virulence-associated isonitrile lipopeptide (INLP) biosynthetic gene cluster is conserved across Mycobacterium tuberculosis and many nontuberculous mycobacteria (NTM) pathogens, yet the corresponding mycobacterial metabolites have not been fully characterized, and their biological functions are still debated. Here, we report a precursor neutral loss chromatography based mass spectrometry strategy that enables the targeted discovery of INLPs from Mycobacterium fortuitum, a fast-growing NTM pathogen. By monitoring a characteristic neutral loss of 27.1 Da corresponding to hydrogen cyanide, we identified a family of INLPs directly from bacterial culture extracts. Structural elucidation of a representative compound using NMR and high-resolution MS revealed a distinctive terminal methylated carboxyl group, contrasting with previously reported INLPs bearing linear alcohol, acetal, or cyclic motifs. Bioinformatic analysis and in vitro enzymatic assays identified a methyltransferase encoded within the INLP BGC responsible for methyl ester formation. Furthermore, metal-binding assays demonstrated selective chelation of Cu(I) and Cu(II) by the isolated INLP, but no detectable interaction with Zn(II), suggesting a role in copper homeostasis. These findings represent the first full structural characterization of an INLP from pathogenic mycobacteria, expand our understanding of the enzymes involved in INLP modification, and unequivocally support the copper-binding activity of INLPs from these pathogens.

Lipopeptides↗

PubMed2026

Locus-specific stratification and prioritization unveil genetic risk mechanism underlying complex diseases.

Although genome-wide association studies have identified thousands of disease-associated loci, the mechanistic understanding and drug target discovery remain challenging, particularly for complex diseases. The multi-signal architecture of complex diseases complicates the interpretation of genetic contributions. To address this challenge, we develop an approach comprising locus-specific stratification (LSS) and gene regulatory prioritization score (GRPS), which uniquely considers multi-signals during fine-mapping and target gene identification. LSS significantly enhances the interpretability of genetic risk associated with complex diseases. For loci associated with serum urate levels, the method identifies candidate causal genes in 34.43% of loci, surpassing the performance of other methods by 5.47% to 25.14%. GRPS considers the regulatory network of LSS-variants comprehensively and successfully nominates under-explored drug targets for hyperuricemia with high confidence such as SLC17A4, which is further validated using epigenetic activation and phenotypic assays. This study introduces an approach to efficiently and comprehensively address the multi-signal challenges in complex diseases.

Humans↗

PubMed2026

Comparative and Subtractive Genomics Analysis of Multidrug-Resistant Klebsiella pneumoniae Strains for Novel Target Identification and Drug Repurposing Strategies.

The rapid rise of multidrug-resistant (MDR) Klebsiella pneumoniae has created a major global health challenge due to the limited availability of conserved therapeutic targets effective across diverse resistant strains. In this study, an integrative computational target-discovery and drug-repurposing framework was applied to six clinically relevant K. pneumoniae strains. Comparative genomic analysis identified 3012 conserved genes, which were subsequently filtered to nine essential, non-host homologous proteins. Among these, three conserved cytoplasmic proteins (accD, cpxR, and mraZ) were prioritized for functional analysis, with acetyl-CoA carboxylase subunit beta (accD) emerging as the most promising therapeutic target based on sequence conservation, predicted essentiality, subcellular localization, and pathway association. Structural assessment supported the reliability of the predicted accD model, whereas consensus binding-site analysis identified key residues suitable for ligand interaction. Virtual screening of FDA-approved drugs followed by molecular docking identified several compounds with favorable binding profiles toward accD. Subsequent molecular dynamics simulations, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), hydrogen-bond occupancy, principal component analysis (PCA), and PCA-based free energy landscape (FEL) analyses, consistently identified tenapanor, micafungin, deferoxamine, and cobicistat as the most stable protein-ligand complexes, with tenapanor exhibiting the most favorable overall structural and thermodynamic stability profile. These findings identify accD as a promising therapeutic target in MDR K. pneumoniae and suggest several FDA-approved compounds as potential candidates for drug repurposing. Although experimental validation is needed to confirm their biological activity and therapeutic potential, this study demonstrates the potential of integrating comparative genomics with molecular dynamics analyses to support antimicrobial target identification and drug repurposing against MDR bacterial pathogens.

Klebsiella pneumoniae↗

PubMed2026

Proteomic and phosphoproteomic profiles of time-dependent dynamic changes in LPS-induced macrophage polarization.

The temporal proteomic and phosphoproteomic reprogramming during early M1 macrophage polarization (0-6 h) remains poorly understood. We performed time-resolved proteomic and phosphoproteomic analyses of LPS-stimulated RAW264.7 macrophages at seven time points within 6 h. Time-clustering of differentially expressed molecules revealed two patterns: initial change with partial recovery, and sustained dysregulation. Upregulated proteins and phosphorylation sites were enriched in the Rho GTPase signaling pathway, T-cell receptor signaling pathway, NF-κB cascade, osteoclast differentiation pathway, and antiviral immune pathway. Downregulated pathways were associated with cell cycle regulation, chromatin remodeling, RNA metabolism, and mRNA processing, indicating resource reallocation to prioritize acute inflammatory responses. Kinase-substrate network analysis confirmed the mitogen-activated protein kinase (MAPK), cyclin-dependent kinase (CDK), protein kinase B (AKT), and ribosomal S6 kinase (RSK) families as core upstream phosphorylation regulators. Integrated analysis revealed synergistic and antagonistic relationships between proteomic and phosphoproteomic changes. This study provides a temporal molecular atlas of M1 polarization, delineating inflammatory signaling dynamics and offering a basis for therapeutic target discovery in inflammatory diseases. SIGNIFICANCE: Macrophage M1 polarization is a central event in innate immune defense against pathogenic invasion, yet its dysregulation is a pivotal driver of the onset and progression of a broad spectrum of inflammation-associated disorders, spanning autoimmune diseases, infectious conditions and inflammatory bone diseases, making the dissection of its molecular regulatory mechanisms an urgent research priority in immunology and translational medicine. Dynamic molecular events within 0-6 h after LPS stimulation are critical for initiating and shaping M1 inflammatory activation, yet systematic time-resolved proteomic and phosphoproteomic profiling remains insufficient.In this study, we comprehensively characterized temporal proteome and phosphoproteome changes at seven consecutive time points during macrophage polarization, clarified two distinct dynamic molecular patterns, identified core signaling pathways and key kinase regulators involved in inflammatory reprogramming, and uncovered the leading role of post-translational phosphorylation modifications in initiating polarization. This work delineates the time-series molecular atlas of early macrophage activation, provides novel insights into the temporal regulatory mechanism of inflammatory signaling networks, and lays a solid experimental foundation for exploring new intervention targets and regulatory nodes in clinical translational research.

Lipopolysaccharides↗

PubMed2025

Pitavastatin, Procollagen Pathways, and Plaque Stabilization in Patients With HIV: A Secondary Analysis of the REPRIEVE Randomized Clinical Trial.

IMPORTANCE: In a mechanistic substudy of the Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) randomized clinical trial, pitavastatin reduced noncalcified plaque (NCP) volume, but specific protein and gene pathways contributing to changes in coronary plaque remain unknown. OBJECTIVE: To use targeted discovery proteomics and transcriptomics approaches to interrogate biological pathways beyond low-density lipoprotein cholesterol (LDL-C), relating statin outcomes to reduce NCP volume and promote plaque stabilization among people with HIV (PWH). DESIGN, SETTING, AND PARTICIPANTS: This was a post hoc analysis of the double-blind, placebo-controlled, REPRIEVE randomized clinical trial. Participants underwent coronary computed tomography angiography (CTA), plasma protein analysis, and transcriptomic analysis at baseline and 2-year follow-up. The trial enrolled PWH from April 2015 to February 2018 at 31 US research sites. PWH without known cardiovascular diseases taking antiretroviral therapy and with low to moderate 10-year cardiovascular risk were eligible. Data analyses were conducted from October 2023 to February 2024. INTERVENTION: Oral pitavastatin calcium, 4 mg per day. MAIN OUTCOMES AND MEASURES: Relative change in plasma proteomics, transcriptomics, and noncalcified plaque volume among those receiving treatment vs placebo. RESULTS: Among 558 individuals (mean [SD] age, 51 [6] years; 455 male [82%]) included in the proteomics assessment, 272 (48.7%) received pitavastatin and 286 (51.3%) received placebo. After adjusting for false discovery rates, pitavastatin increased abundance of procollagen C-endopeptidase enhancer 1 (PCOLCE), neuropilin 1 (NRP-1), major histocompatibility complex class I polypeptide-related sequence A (MIC-A) and B (MIC-B), and decreased abundance of tissue factor pathway inhibitor (TFPI), tumor necrosis factor ligand superfamily member 10 (TRAIL), angiopoietin-related protein 3 (ANGPTL3), and mannose-binding protein C (MBL2). Among these proteins, the association of pitavastatin with PCOLCE (a rate-limiting enzyme of collagen deposition) was greatest, with an effect size of 24.3% (95% CI, 18.0%-30.8%; P < .001). In a transcriptomic analysis, individual collagen genes and collagen gene sets showed increased expression. Among the 195 individuals with plaque at baseline (88 [45.1%] taking pitavastatin, 107 [54.9%] taking placebo), changes in NCP volume were most strongly associated with changes in PCOLCE (%change NCP volume/log2-fold change = -31.9%; 95% CI, -42.9% to -18.7%; P < .001), independent of changes in LDL-C level. Increases in PCOLCE related most strongly to change in the fibro-fatty (<130 Hounsfield units) component of NCP (%change fibro-fatty volume/log2-fold change = -38.5%; 95% CI, -58.1% to -9.7%; P = .01) with a directionally opposite, although nonsignificant, increase in calcified plaque (%change calcified volume/log2-fold change = 34.4%; 95% CI, -7.9% to 96.2%; P = .12). CONCLUSIONS AND RELEVANCE: Results of this secondary analysis of the REPRIEVE randomized clinical trial suggest that PCOLCE may be associated with the atherosclerotic plaque stabilization effects of statins by promoting collagen deposition in the extracellular matrix transforming vulnerable plaque phenotypes to more stable coronary lesions. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02344290.

Humans↗

PubMed2026

Unveiling Aziridine-Containing Natural Products by Genomic and Spectroscopic Approaches.

Aziridine-containing natural products are prized for their potent bioactivities, yet their scarcity and poorly understood biosynthesis have limited systematic exploration. Here, we address this by integrating genome mining with a 1H-13C coupled HSQC metabolomic approach that exploits the distinctive NMR signatures of aziridines, enabling their direct detection from complex extracts. This strategy unveiled the desertolides, the first macrolides incorporating a rare terminal 2-methyl-aziridine-2-carboxylate moiety. Genetic and isotopic studies identified a dedicated biosynthetic subcluster (desA-desN) that assembles and installs this unit from glutamate, and heterologous expression confirmed the self-sufficiency of this subcluster. Direct MS evidence reveals the aziridine moiety covalently bound to the active-site Cys113 of DesN, establishing this KAS III homolog as the first dedicated aziridine-transferase and a promising tool for polyketide engineering. Bioinformatic analysis uncovered over 50 biosynthetic gene clusters, suggesting that this aziridine-associated biosynthetic logic may be more widespread than currently appreciated. This work establishes a tractable platform for the targeted discovery and engineered biosynthesis of aziridine-containing natural products, opening this underexplored pharmacophore to systematic interrogation.

Aziridines↗

PubMed2026

De novo transcriptome meta-analysis reveals candidate genes involved in life-stage transitions for RNAi-mediated management of the citrus root weevil (Diaprepes abbreviatus).

BACKGROUND: The citrus root weevil, Diaprepes abbreviatus, is a destructive agricultural pest for which molecular control options remain limited due to historically sparse genomic resources. Leveraging a comprehensive de novo transcriptome, we investigated developmental gene regulation across larval, pupal, and adult stages and identified essential targets for RNA interference (RNAi)-based intervention. RESULTS: Stage-resolved transcriptomic analyses revealed extensive transcriptional reprogramming associated with metabolism, detoxification, cuticle biosynthesis, endocrine signaling, and sensory perception. Among these, chitin synthase (DaCHS) emerged as a critical developmental gene, exhibiting pronounced up-regulation during late larval and pupal stages corresponding to intensive cuticle synthesis. Phylogenetic and structural analyses demonstrated that DaCHS is highly conserved among insects and retains canonical catalytic domains and transmembrane topology. Alpha Fold-based structural modeling and molecular docking confirmed stable interaction of DaCHS with its substrate, N-acetylglucosamine, supporting functional conservation of enzymatic activity. Oral delivery of DaCHS double-stranded RNA induced robust transcript suppression, leading to significant mortality and severe developmental defects, including larval and pupal abnormalities, and adults with disrupted wing and abdominal morphogenesis. CONCLUSION: These findings establish DaCHS as an indispensable gene for D. abbreviates development and validate transcriptome-guided RNAi as a powerful framework for target discovery. This work provides a strong molecular foundation for developing RNAi-based strategies that can be integrated into sustainable management programs for citrus root weevil control. © 2026 Society of Chemical Industry.

Animals↗

PubMed2024

Decoding the genetic landscape of allergic rhinitis: a comprehensive network analysis revealing key genes and potential therapeutic targets.

BACKGROUND: Allergic Rhinitis (AR), an inflammatory affliction impacting the upper respiratory tract, has been registering a substantial surge in incidence across the globe. METHODS: We embarked on examination of differentially expressed genes (DEGs) and the Weighted Gene Co-Expression Network Analysis (WGCNA). With this armory of genes identified, we engaged the tools of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our study continued with the establishment of a protein-protein interaction (PPI) network and the application of LASSO regression. Finally, we leveraged a docking model to elucidate potential drug-gene interactions involving these key genes. RESULTS: Through WGCNA and different express genes screening, PPI network was performed, identifying top 20 key genes, including CD44, CD69, CD274. LASSO regression identified three independent factors, STARD5, CST1, and CHAC1, that were significantly associated with AR. A predictive model was developed with an AUC value over 0.75. Also, 105 potential therapeutic agents were discovered, including Fluorouracil, Cyclophosphamide, Doxorubicin, and Hydrocortisone, offering promising therapeutic strategies for AR. CONCLUSION: By fuzing DEGs with key genes derived from WGCNA, this study has illuminated a comprehensive network of gene interactions involved in the pathogenesis of AR, paving the way for future biomarker and therapeutic target discovery in AR.

Humans↗

PubMed2026

Hepatic metabolic adaptation to endurance exercise: temporal and sex differences by multiomics integration and validation.

BACKGROUND: Although endurance exercise benefits liver health, sex-specific adaptive trajectories remain unclear. This study mapped dynamic liver adaptation in males and females during prolonged training and identified underlying molecular programs. METHODS: Using publicly available time-resolved liver multi-omics data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we established a computational pipeline for differential analysis of transcriptomic, proteomic, phosphoproteomic, and metabolomic data with FDR correction, followed by FGSEA pathway enrichment. Kinase activities were inferred through ortholog mapping and PhosphoSitePlus. Cross-omics co-expression networks were constructed using WGCNA and topological overlap to link omics features with physiological phenotypes. For experimental validation, liver tissues were collected from endurance-trained Sprague-Dawley rats, and key nodes were confirmed by Western blotting, qRT-PCR, and immunofluorescence/immunohistochemical staining. Public scRNA-seq data were further integrated to map multi-omics signals to single-cell resolution and assess functional changes in specific cell types. RESULTS: The hepatic response to exercise stress was stage-specific, shifting from early transcriptional activation to later proteomic and metabolic remodeling. Multi-omics integration revealed distinct sex-associated adaptive trajectories: males were more strongly associated with energy metabolism, redox-related programs, and amino acid/organic acid catabolism, whereas females showed prominent membrane lipid remodeling, proteostasis -related programs, and mitochondrial/ribosomal translational features. Single-cell analysis showed that tissue remodeling occurred without major lineage turnover, instead involving altered communication among pre-existing cell communities. Validation of PPP1R3G identified a protein-dominant exercise-responsive marker, supporting the contribution of post-transcriptional or protein-level regulation. CONCLUSIONS: Hepatic adaptation to endurance stress follows a cross-omics evolutionary pattern with sex-specific reprogramming of energy supply and homeostatic maintenance. This time-resolved framework clarifies how exercise improves liver function and supports sex-oriented metabolic interventions and therapeutic target discovery.

Animals↗

PubMed2026

Biological Foundation Models for Complex Disease Research and Clinical Translation.

Complex diseases, including cancer, rare genetic disorders, neurodevelopmental and psychiatric conditions, and neurodegenerative diseases, arise from interactions among genetic variation, gene regulation, and cellular states that are difficult to capture using a single data type or biological scale. Biological foundation models address this challenge by treating nucleotides and genes as tokens and learning representations that can be transferred to downstream biomedical and clinical tasks. In this review, we examine two major model classes, genomic sequence foundation models and cell foundation models, and compare their tokenization strategies, model architectures, pretraining objectives, and adaptation methods. We summarize their emerging applications in regulatory variant interpretation, disease-associated cell-state analysis, drug-response prediction, and therapeutic target discovery across complex diseases. We distinguish applications supported by experimental or retrospective validation from those that remain primarily computational or conceptual. We further discuss key challenges to clinical translation, including multimodal data integration, model interpretability, benchmarking, patient-specific prediction, and privacy protection. We highlight future opportunities to integrate biological foundation models with emerging frameworks of medical digital twins, agentic AI, and federated learning. By linking model design to translational goals, this review provides a practical framework for evaluating biological foundation models and their readiness for complex disease research and clinical use.

biological foundation model↗

PubMed2026

A Mendelian Randomization Study of Immune Cell Traits and Plasma Metabolites in Hashimoto's Thyroiditis.

Hashimoto's thyroiditis (HT) is an autoimmune disorder of the thyroid. While immune cells are implicated in its pathogenesis, their specific roles have yet to be fully clarified. A two-sample Mendelian randomization (MR) analysis was conducted integrating genome-wide association study (GWAS) summary statistics from large public datasets for immune cell traits (ebi-a-GCST90001391 to ebi-a-GCST90002121), plasma metabolites (GCST90199621-9020102), and HT (ebi-a-GCST90018855). Causal effects were estimated using inverse-variance weighted (IVW) methods, with MR-Egger, weighted median, and leave-one-out analyses to assess pleiotropy and robustness. Bidirectional and mediation MR analyses were further applied to test directionality and identify potential metabolite-mediated pathways. CD3⁺CD4⁺CD25⁺CD39⁺Treg cells were quantified in peripheral blood samples using flow cytometry. Isovalerylcarnitine (C5) was measured by liquid chromatography tandem mass spectrometry. IVW analysis identified 32 immune cell phenotypes significantly associated with HT risk (P < 0.05 after FDR correction). Reverse MR analysis demonstrated that HT was positively causally linked with 2 immune characteristics, while 4 immune characteristics (all P < 0.05) were inversely associated with HT. Sensitivity analyses revealed no horizontal pleiotropy or heterogeneity. Additionally, the IVW method preliminarily identified 9 plasma metabolites as causally related to HT, including risk-enhancing C5 (OR = 1.120, 95% CI: 1.032-1.215, P = 0.006) and protective ergothioneine (OR = 0.958, 95% CI: 0.927-0.990, P = 0.010). Two-step MR mediation identified C5 as a candidate mediator connecting CD3⁺ CD39⁺ Treg to HT (mediation proportion 8.89%, 95% CI: 2.34%-15.4%, P = 0.008). Flow cytometry elevated CD39⁺Treg levels and plasma C5 in HT patients, with C5 positively correlated with CD39⁺Treg cells proportion. This study establishes novel causal links between immune cell phenotypes and HT, and highlights plasma metabolites, particularly C5, as potential mediators in HT pathogenesis. These findings deepen mechanistic understanding of autoimmune thyroid disease and may guide future biomarker and therapeutic target discovery.

Humans↗