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PubMed2024

Drug repurposing in status epilepticus.

The treatment of status epilepticus (SE) has changed little in the last 20 years, largely because of the high risks and costs of new drug development for SE. Moreover, SE poses specific challenges to drug development, such as patient diversity, logistical hurdles, and the need for acute treatment strategies that differ from chronic seizure prevention. This has reduced the appetite of industry to develop new drugs in this area. Drug repurposing is an attractive approach to address this unmet need. It offers significant advantages, including reduced development time, lower costs, and higher success rates, compared to novel drug development. Here I demonstrate how novel methods integrating biological knowledge and computational methods can be applied to drug repurposing in status epilepticus. Biological approaches focus on addressing mechanisms underlying drug resistance in SE (using for example ketamine, tacrolimus and safinamide) and longer-term consequences (using for example omaveloxolone, celecoxib and losartan). Additionally, artificial intelligence platforms, such as ChatGPT, can rapidly generate promising drug lists, while in silico methods can analyze gene expression changes to predict molecular targets. Combining AI and in silico approaches has identified several candidate drugs, including metformin, sirolimus and riluzole, for SE treatment. Despite the promise of repurposing, challenges remain, such as intellectual property issues and regulatory barriers. Nonetheless, drug repurposing presents a viable solution to the high costs and slow progress of traditional drug development for SE. This paper is based on a presentation made at the 9th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures, in April 2024.

Animals↗

PubMed2026

Drug repurposing using transcriptomics: principles and unmet needs in cardiovascular disease.

Although cardiovascular disease is the leading cause of death globally, therapeutic development in this field is slow. Given the high cost of developing new drugs and running clinical trials for cardiovascular disease, repurposing of drugs with approved safety profiles is an attractive strategy for therapeutic development that can significantly reduce the time and cost investment before phase II clinical trials. In the era of "Omics," various new methods and several large databases have been developed to enable the use of transcriptomics data for drug repurposing. This review summarizes the principles and workflow of signature mapping, which forms the foundation of statistical models used for transcriptome-based drug repurposing. We highlight the features of different analysis pipelines and databases that have been developed for signature mapping. These analysis pipelines prioritize genes that are statistically important, an approach that fundamentally differs from the pharmacological approach of identifying disease-driving and therapeutically targetable pathways. Outcomes of signature mapping pipelines are sensitive to the quality of input data, and results are not always reproducible. Moreover, all widely used RNA-seq databases are derived from cancer research and lack high-quality molecular data for cardiovascular disease. These unmet needs call for interdisciplinary collaboration and large networks of cardiovascular research-oriented biobanks to create the databases needed for transcriptomic-based signature mapping for drug repurposing efforts.

Drug Repositioning↗

PubMed2025

Integrating explainable AI with multiomics systems biology and EHR data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health record (EHR) data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; nine tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations (SHAP) identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct "subtissues" (clusters of samples); and gene-gene co-expression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six FDA-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large U.S. de-identified insurance-claims database (n = 364733), exposure to promethazine, one of the candidate drugs, was associated with a 57-62 % lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both p < 0.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multi-omics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Computational Biology↗

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

AI-Driven Precision Medicine in Alzheimer's Disease: Drug Repurposing, Digital Therapeutics and Clinical Decision Support.

Alzheimer's Disease (AD) is a neurodegenerative disease that causes significant clinical, social, and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these constraints, AI-driven precision medicine allows tailored risk assessment, treatment selection, and disease monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital therapies and clinical decision support systems. Machine and deep learning models are used to predict medication response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable- derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and hybrid AI-human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to improve patient outcomes.

Alzheimer’s disease↗

PubMed2025

Integrating explainable artificial intelligence with multiomics systems biology and electronic health record data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health records data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; 9 tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct subtissues (defined as clusters of samples within a brain tissue that share a specific expression pattern); and gene-gene coexpression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six Food and Drug Administration (FDA)-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large US de-identified insurance-claims database (n = 364 733), exposure to promethazine, one of the candidate drugs, was associated with a 57%-62% lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both P < .001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multiomics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Alzheimer Disease↗

PubMed2026

Systematic Identification of Therapeutic Targets and Repurposed Drugs for Stroke: From Genome Causal Analysis to Multilevel Validation.

BACKGROUND: Stroke is a severe cerebrovascular disease characterized by narrow time windows and complications. This study aimed to identify novel drug targets and repurposed drugs for stroke. METHODS: This study used expression quantitative trait loci data from druggable genes in brain and blood as instrumental variables. Mendelian randomization, colocalization, and phenome-wide Mendelian randomization were applied to evaluate causal relationships and potential side effects, with stroke and ischemic stroke as primary outcomes. Preclinical validation used oxygen-glucose deprivation/reperfusion and middle cerebral artery occlusion/reperfusion models. Pharmacological and behavioral assessments evaluated the therapeutic potential of candidate targets and drugs. Additionally, proteomic sequencing was performed following GGCX (γ-glutamyl carboxylase) overexpression to explore its biological functions. RESULTS: Elevated GGCX expression in brain and blood was potentially causally associated with reduced risk of stroke and ischemic stroke, supported by colocalization evidence, although potential cardiovascular risks could not be excluded. Drug repositioning identified ifenprodil as a candidate agent that reduced infarction volume, improved motor and cognitive functions, and reversed GGCX downregulation in mice. Ifenprodil treatment and GGCX overexpression alleviated oxygen-glucose deprivation/reperfusion-induced injury and upregulated GGCX expression. Mechanistically, GGCX conferred neuroprotection by regulating protein homeostasis, suppressing inflammation, promoting metabolic recovery, and modulating nuclear transcriptional regulation. CONCLUSIONS: This study established a potential causal link between GGCX and stroke risk, particularly ischemic stroke. GGCX represents a promising therapeutic target for ischemic stroke. Targeted GGCX expression upregulation and drug repurposing, particularly ifenprodil, may offer novel therapeutic avenues. Further validation is warranted to assess clinical efficacy and safety.

Animals↗

PubMed2026

AI-genomics synergy for drug repurposing in breast cancer: an interpretability-driven framework.

Breast cancer's genomic heterogeneity complicates drug discovery, making repurposing an attractive but challenging strategy. Advances in artificial intelligence now enable integration of multi-omics data to reveal drug-gene-disease relationships and generate subtype-specific repurposing hypotheses. In this Review, we examine AI-driven computational approaches from signature-based to multi-modal frameworks and propose an integrated interpretability-driven framework linking mechanistic validation with clinical translation toward more transparent and actionable precision oncology.

Journal Article↗

PubMed2026

Proteomic insights into Helicobacter pylori infection in stomach cells, revealing host response and host-targeted therapeutics repurposing.

BACKGROUND: Helicobacter pylori (H. pylori) is a globally prevalent gastric pathogen strongly associated with chronic gastritis, peptic ulcers, and gastric cancer. While bacterial factors have been extensively studied, host proteomic responses and their therapeutic potential remain largely underexplored. RESEARCH DESIGN AND METHODS: Current analyses employed a systematic proteomics-based data integration and harmonization approach (retrospective qualitative cohort study) to identify important differentially regulated host proteins. Proteomic datasets were curated from in vitro studies and analyzed for functional enrichment, protein-protein interaction networks, and hub protein identification. To explore therapeutic repurposing, drug repositioning was performed using the DrugBank database. RESULTS: Data summation describing protein differential regulation in human gastric cells as a result of the infection revealed 1672 perturbed host proteins. Bioinformatics analysis revealed 11 proteins including CSK, MET, RELA, MARK2, GRB2, FTO, PLCG1, CRKL, RPS5, RPS9, and RPS27A to be ideal host targets for therapeutic repurposing. Clinically approved drugs such as Dasatinib (targeting CSK) and Crizotinib (targeting MET) emerged as promising candidates due to favorable pharmacokinetics and known bioactivity. CONCLUSIONS: Host-directed therapeutics could offer alternative strategies to conventional antibiotic therapy, addressing challenges such as resistance and infection recurrence, providing a foundation for future experimental validation and development of host-targeted interventions for infection control.

Humans↗

PubMed2026

Mocravimod as a repurposing drug against clinical isolates of Staphylococcus aureus by targeting cell membrane.

UNLABELLED: Staphylococcus aureus infections, particularly those caused by multidrug-resistant strains and associated with biofilm formation, pose a major therapeutic challenge in clinical practice. The objective of this study was to evaluate the antibacterial and antibiofilm activity of mocravimod (KRP-203), an FDA-approved S1P receptor modulator, against clinical S. aureus isolates and to explore its underlying mechanism of action. The antibacterial activity of KRP-203 was assessed against methicillin-susceptible S. aureus (MSSA) and methicillin-resistant S. aureus (MRSA) using MIC determination, time-kill assays, and biofilm inhibition models. KRP-203 exhibited strong bactericidal activity against planktonic MSSA and MRSA, with MIC values ranging 6.25-50μM. Time-kill assays demonstrated rapid bacterial eradication at 8× MIC within 2 h, showing superior killing kinetics compared with vancomycin. At sub-inhibitory concentrations, KRP-203 inhibited biofilm formation by up to 70% and reduced viable bacterial counts in mature biofilms by >2.5 logs. To elucidate the antibacterial mechanism, whole-genome sequencing and quantitative proteomic analyses were performed. These analyses revealed mutations in membrane-associated genes, including glnQ and BCAT, and significant alterations in proteins related to membrane integrity and redox regulation. Consistently, functional assays confirmed that KRP-203 disrupts bacterial cell membrane, as evidenced by dose-dependent membrane depolarization, increased permeability, and direct binding to cardiolipin and phosphatidylglycerol. Molecular docking further predicted a favorable interaction between KRP-203 and GlnQ. In conclusion, KRP-203 demonstrated notable antibacterial and antibiofilm activity against S. aureus, likely through membrane integrity disruption. While these findings highlight its potential as a repurposed antibacterial agent, further studies are required to fully elucidate its molecular targets, optimize antibacterial efficacy, and evaluate its in vivo safety profile. IMPORTANCE: Antibiotic resistance and the formation of biofilms, which protect bacteria from medications and immunological responses, present the significant challenges for the clinical treatment of Staphylococcus aureus infections. This study reveals mocravimod hydrochloride (KRP-203), a clinically approved drug initially intended to treat leukemia, as a viable new candidate against S. aureus infection. KRP-203 quickly kills both drug-susceptible and resistant S. aureus, including difficult-to-treat biofilm-associated cells. Its membrane-disrupting activity quickly kills drug-resistant bacteria while also destroying biofilm formations, presenting a dual action rarely accomplished by conventional antibiotics. Critically, KRP-203's established safety profile in human studies may hasten its repurposing as a new weapon against biofilm-associated infections, providing possible solutions for chronic and drug-resistant S. aureus infections where existing treatments commonly fail.

Biofilms↗

PubMed2025

Network-driven discovery of repurposable drugs targeting hallmarks of aging.

Despite the thousands of genes implicated in age-related phenotypes, effective interventions for aging remain elusive, a lack of advance rooted in the multifactorial nature of longevity and the functional interconnectedness of the molecular components implicated in aging. Here, we introduce a network medicine framework that integrates 2,358 longevity-associated genes onto the human interactome to identify existing drugs that can modulate aging processes. We find that genes associated with each hallmark of aging form a connected subgraph, or hallmark module, a discovery enabling us to measure the proximity of 6,442 clinically approved or experimental compounds to each hallmark. We then introduce a transcription-based metric, pAGE, which evaluates whether the drug-induced expression shifts reinforce or counteract known age-related expression changes. By integrating network proximity and pAGE, we identify multiple drug repurposing candidate that not only target specific hallmarks but act to reverse their aging-associated transcriptional changes. Our findings are interpretable, revealing for each drug the molecular mechanisms through which it modulates the hallmark, offering an experimentally falsifiable framework to leverage genomic discoveries to accelerate drug repurposing for longevity.

Journal Article↗

PubMed2025

Genomics-informed drug-repurposing strategy identifies two therapeutic targets for preventing liver disease associated with metabolic dysfunction.

Identification of drug-repurposing targets with genetic and biological support is an economically and temporally efficient strategy for improving the treatment of diseases. We employed a cross-disciplinary approach to identify potential therapeutics for the prevention of metabolic-dysfunction-associated steatotic liver disease (MASLD) in at-risk individuals by using humans as a model organism. We identified 212 putative candidate genes associated with MASLD by using data from a large multi-ancestry genetic association study, of which 158 (74.5%) were previously unreported. From this set, we identified 57 genes that encode for druggable protein targets and for which the effects of increasing genetically predicted gene expression on MASLD risk align with the function of that drug on the protein target. We then used We then evaluated these potential targets for evidence of efficacy by using Mendelian randomization, pathway analysis, and protein structural modeling. Through these approaches, we present compelling evidence to suggest that the activation of FADS1 by icosapent ethyl, as well as S1PR2 by fingolimod, could be a promising therapeutic strategy for MASLD prevention.

Humans↗

PubMed2025

Network-based drug repurposing for psychiatric disorders using single-cell genomics.

Neuropsychiatric disorders lack effective treatments due to a limited understanding of the underlying cellular and molecular mechanisms. To address this, we integrated population-scale single-cell genomics data and analyzed 23 cell-type-level gene regulatory networks across schizophrenia, bipolar disorder, and autism. Our analysis revealed potential druggable transcription factors co-regulating known risk genes that converge into cell-type-specific co-regulated modules. We applied graph neural networks on those modules to prioritize novel risk genes and leveraged them in a network-based drug repurposing framework to identify 220 drug molecules with the potential for targeting specific cell types. We found evidence for 37 of these drugs in reversing disorder-associated transcriptional phenotypes. Additionally, we discovered 335 drug-cell quantitative trait loci (eQTLs), revealing genetic variation's influence on drug target expression at the cell-type level. Our results provide a single-cell network medicine resource that provides potential mechanistic insights for advancing treatment options for neuropsychiatric disorders.

Drug Repositioning↗

PubMed2025

FDA-approved drug repurposing in zebrafish identifies thyroid hormone and other compounds as potential antithrombotics.

Venous thromboembolism (VTE) is a highly prevalent medical condition with limited therapeutic options and an incomplete understanding of its acquired and inherited subtypes. The zebrafish is a model with the benefits of external development, fecundity, optical transparency, and hemostasis that demonstrates conservation with mammals. We utilized zebrafish as a phenotypic screening tool to identify novel therapeutic options for preventing VTE. A library of FDA-approved compounds was screened for suppression of acquired (elevated estrogen) and spontaneous (protein C deficiency) thrombosis. We found that thyroid hormone, receptor tyrosine kinase (RTK) inhibitors, and proton-pump inhibitors (PPIs) effectively modulated levels of thrombosis, particularly in the estrogen-induced model. These also showed a more favorable hemostatic profile than standard therapies, suggesting alternative mechanisms. Genome editing of thyroid hormone receptor proved that thyroid hormone action is on target. A retrospective electronic health record (EHR) analysis found that thyroid-hormone prescriptions in hormonal contraceptive users correlated with a higher VTE risk, potentially limiting direct repurposing but highlighting thyroid signaling as a pathway involved in estrogen-induced thrombosis. Together, these data identify several drug classes that can be tailored to specific subtypes of VTE and help elucidate distinct pathways driving thrombosis.

FDA-approved compounds↗

PubMed2022

Reversal of cancer gene expression identifies repurposed drugs for diffuse intrinsic pontine glioma.

Diffuse intrinsic pontine glioma (DIPG) is an aggressive incurable brainstem tumor that targets young children. Complete resection is not possible, and chemotherapy and radiotherapy are currently only palliative. This study aimed to identify potential therapeutic agents using a computational pipeline to perform an in silico screen for novel drugs. We then tested the identified drugs against a panel of patient-derived DIPG cell lines. Using a systematic computational approach with publicly available databases of gene signature in DIPG patients and cancer cell lines treated with a library of clinically available drugs, we identified drug hits with the ability to reverse a DIPG gene signature to one that matches normal tissue background. The biological and molecular effects of drug treatment was analyzed by cell viability assay and RNA sequence. In vivo DIPG mouse model survival studies were also conducted. As a result, two of three identified drugs showed potency against the DIPG cell lines Triptolide and mycophenolate mofetil (MMF) demonstrated significant inhibition of cell viability in DIPG cell lines. Guanosine rescued reduced cell viability induced by MMF. In vivo, MMF treatment significantly inhibited tumor growth in subcutaneous xenograft mice models. In conclusion, we identified clinically available drugs with the ability to reverse DIPG gene signatures and anti-DIPG activity in vitro and in vivo. This novel approach can repurpose drugs and significantly decrease the cost and time normally required in drug discovery.

Humans↗

PubMed2024

The effect of fampridine on working memory: a randomized controlled trial based on a genome-guided repurposing approach.

Working memory (WM), a key component of cognitive functions, is often impaired in psychiatric disorders such as schizophrenia. Through a genome-guided drug repurposing approach, we identified fampridine, a potassium channel blocker used to improve walking in multiple sclerosis, as a candidate for modulating WM. In a subsequent double-blind, randomized, placebo-controlled, crossover trial in 43 healthy young adults (ClinicalTrials.gov, NCT04652557), we assessed fampridine's impact on WM (3-back d-prime, primary outcome) after 3.5 days of repeated administration (10 mg twice daily). Independently of baseline cognitive performance, no significant main effect was observed (Wilcoxon P = 0.87, r = 0.026). However, lower baseline performance was associated with higher working memory performance after repeated intake of fampridine compared to placebo (rs = -0.37, P = 0.014, n = 43). Additionally, repeated intake of fampridine lowered resting motor threshold (F(1,37) = 5.31, P = 0.027, R2β = 0.01), the non-behavioral secondary outcome, indicating increased cortical excitability linked to cognitive function. Fampridine's capacity to enhance WM in low-performing individuals and to increase brain excitability points to its potential value for treating WM deficits.

Adult↗

PubMed2025

Genetic evidence for repurposing GLP-1 receptor agonists in chronic kidney disease and IgA nephropathy: Metabolic and anti-inflammatory pathways beyond glycaemic control.

AIMS: Despite observational links between glucagon-like peptide-1 receptor agonists (GLP-1RAs) and kidney benefits, causal mechanisms remain unclear. This study aims to dissect genetic causality and mediation pathways underlying the effects of GLP-1RAs on chronic kidney disease (CKD) and related renal outcomes. MATERIALS AND METHODS: Using large-scale Genome - Wide Association Study (GWAS) data, we applied two-sample Mendelian randomisation (MR) to estimate the causal effects of GLP-1RAs on CKD, estimated glomerular filtration rate (eGFR) and subtypes (IgA nephropathy, membranous nephropathy, nephrotic syndrome and chronic glomerulonephritis), with sensitivity analyses. The glycaemic markers (glycated haemoglobin [HbA1c] and blood glucose), type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN) served as positive controls. Mediation MR assessed body mass index (BMI), lipids, glycaemic markers and inflammatory proteins. Data were sourced from MRC Integrative Epidemiology Unit Open Genome - Wide Association Studies OpenGWAS, FinnGen, GWAS Catalogue and cohort-specific studies. RESULTS: Positive control analyses revealed that genetically predicted GLP-1R activation was associated with reduced levels of HbA1c (p = 4.93E-15) and blood glucose (p = 9.73E-5), as well as a decreased risk of T2DM (p = 2.45E-4) and DN (p = 6.35E-4), fully validating the reliability of the genetic instruments. Genetic proxies for GLP-1R activation lowered risks of CKD (odds ratio [OR] = 0.83, p = 9.22E-9), immunoglobulin A nephropathy (IgAN) (OR = 0.70, p = 2.11E-3) and kidney function preservation (β = 0.01, p = 9.11E-3), but showed null effects on other CKD subtypes. Mediation analyses indicated that fibroblast growth factor 23 (FGF23) suppression mediated 26.57% of the effect on eGFR and 13.50% of CKD protection, whereas metabolic traits (BMI: 2.08% for CKD, 5.51% for eGFR; high-density lipoprotein: 0.79% for CKD, 2.34% for eGFR; HbA1c: 8.25% for eGFR) partially explained the benefits on CKD and eGFR. Only BMI exhibited a mediation effect on IgAN. Sensitivity analyses confirmed minimal pleiotropy. CONCLUSIONS: This study provides robust genetic evidence for repurposing GLP-1RAs in CKD and IgAN through anti-inflammatory (FGF23) and metabolic pathways, extending their utility beyond glucose control. While European ancestry data limit generalisability, our framework prioritises FGF23 and metabolic modulation as key targets for clinical trials in renal protection.

Humans↗

PubMed2026

Are components of the histone gene expression machinery functionally repurposed in terminally differentiated cells?

The expression of metazoan replication-dependent histone genes is controlled by the nuclear protein at the ataxia-telangiectasia locus (NPAT) and U7 small nuclear ribonucleoprotein particle (snRNP). NPAT activates transcription of histone genes during S-phase, whereas U7 snRNP is a multi-subunit endonuclease that cleaves the resultant transcripts at the 3' end, yielding mature histone mRNAs. In cycling cells, NPAT and U7 snRNP with its four unique components, U7 snRNA, Lsm10, Lsm11 and FLASH, are highly enriched in histone locus bodies (HLBs), the nuclear condensates formed near histone gene loci. Here, we show that in muscle and neural cells that have ceased to replicate their chromatin and permanently exited the cell cycle, HLBs are dismantled and NPAT, FLASH and Lsm11 are detected in the cytoplasm. This observation suggests that in postmitotic cells, NPAT and U7 snRNP become repurposed for functions unrelated to generating histone mRNAs. We identified a highly conserved region in Lsm11 that engages in various protein-protein interactions and likely acts as a universal platform that controls the assembly, localization and function of Lsm11 complexes, including U7 snRNP, during cell growth and differentiation. Since the assembly of U7 snRNP requires survival motor neuron, the protein mutated in spinal muscular atrophy, our results may provide a new perspective on the pathophysiology of this neuromuscular disorder.

Histones↗