Front Cell Infect Microbiol. 2025 Jul 2;15:1506687. doi: 10.3389/fcimb.2025.1506687. eCollection 2025.

ABSTRACT

BACKGROUND: Monkeypox (MPOX) is a zoonotic disease caused by the MPOX virus (MPXV). MPOX resurfaced globally in May 2022, spreading throughout six WHO regions, resulting in nearly 87,000 cases and 112 deaths. Clinical symptoms include swollen lymph nodes, fever, joint pain and several neurological complications such as headache, encephalitis, myalgia, fatigue, photophobia and seizures. Despite these manifestations, the precise mechanisms of MPXV's neurotropism remain elusive. This study aimed to explore the genetic underpinnings of MPOX-related neurological manifestations, including headache, myalgia, fatigue, and photophobia, using advanced bioinformatics tools.

METHODS: Data were sourced from the GeneCards database, which is an integrated database of human genes. Genes linked to MPOX and its neurological manifestations were identified and cross-referenced to uncover shared genes between these conditions. Network visualization was created using STRING, followed by topological analysis in Cytoscape to identify key genes based on degree and betweenness centrality. Functional enrichment analysis through ToppGene provided insights into molecular functions, biological processes, and cellular components associated with these target genes. Pathway analysis was performed using WikiPathways, and cell-type-specific enrichment was conducted using Enrichr. Additionally, we predicted functional microRNAs using mirTarbase and identified potential drug candidates via the Stitch database.

RESULTS: We identified 32 MPOX-associated genes and a large set of neurological manifestation-related genes. Ten hub genes, including CD55, CXCL1, NFKB1, CXCL8, CD4, IL6, MX1, CFH, KLRK1, and CD46 were shared between MPOX and its neurological manifestations. Five novel genes, including CFHR3, C5AR1, C3AR1, IFNA2, and CXCL3 were predicted to be associated with MPOX and its neurological complications. Gene ontology analysis highlighted biological processes such as immune regulation, viral life cycle, and lymphocyte activation, while pathway enrichment identified critical signaling mechanisms like prostaglandin signaling, toll-like receptor 4 (TLR4) signaling, complement activation, and neuroinflammation. Moreover, cell types such as T-helper cells, natural killer cells, and microglia were found to be significantly impacted by MPOX and its frequent neurological complications. We identified 11 key microRNAs associated with MPOX-neurological manifestations and repurposed eight potential drugs, offering promising therapeutic strategies.

CONCLUSION: This study emphasizes the central role of the complement system, immunological responses, and inflammatory pathways in the neurological manifestations of MPOX. The identification of novel genes and predicted therapeutic targets paves the way for future research and therapeutic interventions. Experimental validation is required to confirm these findings and determine the effectiveness of the proposed treatments.

PMID:40673003 | PMC:PMC12263605 | DOI:10.3389/fcimb.2025.1506687

J Inflamm (Lond). 2025 Jul 16;22(1):28. doi: 10.1186/s12950-025-00455-9.

ABSTRACT

BACKGROUND: Gout arthritis (GA) is an inflammatory disorder characterized by the deposition of monosodium urate (MSU) crystals within synovial joints due to increased urate concentrations in the body. The NLRP3 inflammasome drives a majority of the inflammatory response to MSU crystals; therefore, we hypothesize pharmaceutical agents that attenuate NLRP3 inflammasome activation could be used to treat GA flares.

RESULTS: We screened a drug library containing 875 FDA-approved drugs and identified five drugs that reduced NLRP3 inflammasome activation without causing cytotoxic effects in bone marrow-derived macrophages (BMDM). The best performing and therefore leading candidate, verteporfin, used to treat macular degeneration and other eye disorders, reduced Nlrp3- and Caspase-1-dependent IL-1β and IL-18 secretion by BMDM. Additionally, verteporfin-treated mice showed a marked reduction in paw swelling and pro-inflammatory cytokine/chemokine induction, including inflammasome markers (IL-1β and IL-18), in a MSU-induced mouse model of GA flares.

CONCLUSION: Collectively, these data suggest verteporfin is a NLRP3 inflammasome inhibitor that could be repurposed as a treatment for GA.

PMID:40671118 | DOI:10.1186/s12950-025-00455-9

J Antibiot (Tokyo). 2025 Jul 16. doi: 10.1038/s41429-025-00848-1. Online ahead of print.

ABSTRACT

The assignment of new therapeutic purposes to drugs, known as drug repositioning, has been an important ally in the search for new antifungal drugs. Statin compounds, which are used systemically as cholesterol-lowering, may also exert direct antifungal effects, since the statins are drugs that act to prevent sterol synthesis in both humans and fungi and for this reason they are drug promising to combat mycoses. We evaluate the in vivo efficacy of an atorvastatin-loaded topic emulgel (0.75%, 1.5%, or 3.0% m/m) in an in vivo experimental model Tinea pedis. The results showed that the cutaneous delivery-atorvastatin showed total score reduction after seven days of treatment. We concluded that atorvastatin may be a promising drug for the treatment of superficial and cutaneous mycosis.

PMID:40670739 | DOI:10.1038/s41429-025-00848-1

Am J Cancer Res. 2025 Jun 15;15(6):2701-2718. doi: 10.62347/MXZH5646. eCollection 2025.

ABSTRACT

H3K27-altered diffuse midline glioma (DMG) is a universally fatal disease with no available therapeutic strategies apart from palliative radiotherapy. Repurposing marketed non-cancer drugs in oncology is emerging as a fast-tracking approach to speed up the development of new treatment options, urgently needed for DMG. Repurposed anthelmintic mebendazole (MBZ) is in the spotlight against brain tumors, because it joins promising anticancer properties with high neuropenetrance, favorable pharmacokinetic and safety profile. Although MBZ is undergoing Phase I/II trials against brain tumors, including DMG, MBZ anticancer properties and the underlying mechanisms of actions have poorly been characterized in DMG preclinical models. We found that MBZ robustly reduced cell viability in six out of seven DMG cell lines with either K27M-mutated or wild-type H3. All IC50 values (range 102 to 958 nM) fell in a clinically attainable range. The antiproliferative MBZ properties were mediated by an arrest of DMG cells in the G2/M phase with a concomitant upregulation of the key cell cycle regulators p21 and p27, whereas p53 upregulation and activation were cell context-dependent. At the same growth-inhibitory concentrations, MBZ triggered apoptotic cell death, as evidenced by higher levels of the apoptotic markers caspase-3 and PARP cleavage. Consistently, Annexin V-Propidium iodide (PI) double staining showed MBZ dose-dependent increase in both stages of apoptosis. Of interest, the combination of MBZ with the first-in-class imipridone ONC201 sinergistically increased the antiproliferative effects in two DMG cell lines as assessed by combination scores with different algorithms, showing additive effects in two others cell lines. Mechanistically, the combination potentiated the proapoptotic activity of either MBZ or ONC201, while not changing the cytokinetic perturbations induced by the single drugs. Finally, one pair of ONC201-sensitive and ONC201-resistant DMG cell lines with acquired resistance showed same responsiveness to MBZ with similar values of IC50 and Emax. In conclusion, MBZ demonstrates high growth-inhibitory/proapoptotic activity, chemosensitization property to ONC201 and the ability to overcome ONC201 resistance in DMG cell cultures, proposing as a new low-toxicity therapeutic for DMG, with a potential to be used in second-line treatment and/or in combination protocols.

PMID:40667566 | PMC:PMC12256405 | DOI:10.62347/MXZH5646

bioRxiv [Preprint]. 2025 Jun 28:2025.06.25.661596. doi: 10.1101/2025.06.25.661596.

ABSTRACT

OBJECTIVE: Alzheimer disease (AD) is a neurodegenerative disorder leading to cognitive decline. Despite growing recognition of sex differences in epidemiology, symptomatology, and clinical outcomes of AD, the molecular mechanisms underlying these variations remain poorly defined. We performed transcriptome association studies of AD aiming to identify sex-specific and sex-dependent transcriptomic profiles that could provide insights into the molecular mechanisms underlying sex differences in AD pathogenesis.

METHODS: We conducted a meta-analysis of bulk-RNAseq data derived from human postmortem brain studies. Specifically, we analyzed gene expression differences between individuals diagnosed with AD and non-cognitively impaired (NCI) individuals across two key brain regions: the prefrontal cortex and the temporal lobe. We performed stratified differential expression analyses separately in males and females, alongside combined analyses across sexes. Additionally, we assessed the data in relation to known AD genes, proteomic studies, and drug repurposing opportunities.

RESULTS: Beyond the genes commonly dysregulated across both sexes, our meta-analyses identified multiple differentially expressed genes (DEGs) between AD and NCI that are either altered in only one sex or show different effects between sexes. Some genes are known AD genes from genetic studies, but others are novel. Correlation with proteomic data suggests that these transcriptional differences have functional significance, potentially contributing to the biological mechanisms underlying sex differences observed in AD. Finally, we identify drug compounds that are potential candidates for treatment.

INTERPRETATION: Our findings enhance our understanding of sex-related differences in disease etiology and progression, and underscore the importance of incorporating sex as a critical variable in transcriptomic studies of AD. These insights help pave the way for more precise, personalized medicine approaches that account for sex-specific molecular mechanisms.

PMID:40667349 | PMC:PMC12262414 | DOI:10.1101/2025.06.25.661596

bioRxiv [Preprint]. 2025 Jun 26:2025.06.23.660251. doi: 10.1101/2025.06.23.660251.

ABSTRACT

Pathogenic variants in the mitochondrial outer membrane GTPase MFN2 cause the peripheral neuropathy Charcot-Marie-Tooth Type 2A (CMT2A). These mutations disrupt MFN2-dependent regulation of diverse aspects of mitochondrial biology including organelle morphology, motility, mitochondrial-endoplasmic reticulum (ER) contacts (MERCs), and respiratory chain activity. However, no therapies currently exist to mitigate the mitochondrial dysfunction linked to genetic deficiencies in MFN2. Herein, we performed a drug repurposing screen to identify compounds that selectively activate the integrated stress response (ISR) - the predominant stress-responsive signaling pathway responsible for regulating mitochondrial morphology and function. This screen identified the compounds parogrelil and MBX-2982 as potent and selective activators of the ISR through the OMA1-DELE1-HRI signaling axis. We show that treatment with these compounds promotes adaptive, ISR-dependent remodeling of mitochondrial morphology and protects mitochondria against genetic and chemical insults. Moreover, we show that pharmacologic ISR activation afforded by parogrelil restores mitochondrial tubular morphology, promotes mitochondrial motility, rescues MERCs, and enhances mitochondrial respiration in MFN2 -deficient cells. These results demonstrate the potential for pharmacologic HRI activation as a viable strategy to mitigate mitochondrial dysfunction in CMT2A and other pathologies associated with MFN2 deficiency.

PMID:40666974 | PMC:PMC12262622 | DOI:10.1101/2025.06.23.660251

Front Cell Dev Biol. 2025 Jul 1;13:1551010. doi: 10.3389/fcell.2025.1551010. eCollection 2025.

ABSTRACT

INTRODUCTION: Drug repurposing is gaining consideration in cancer due to the challenges of poor outcomes and resistance associated with the current conventional modalities. Non-steroidal anti-inflammatory drugs (NSAIDs), widely used for treating inflammation, are being explored for their potential efficacy in cancer treatment, including prostate cancer (PCa). This study aims to evaluate the efficacy of Piroxicam (PXM), an NSAID, in enhancing the sensitivity of PCa cells to chemotherapy and hormonal drugs.

METHODS: Computational analysis was conducted to identify differentially expressed genes between our established murine PCa cell models, PLum-AD (androgen-dependent) and PLum-AI (androgen-independent), to uncover potential therapeutic targets. In two-dimensional (2D) cell culture, cell proliferation, viability, and migration assays were performed on PLum-AD and PLum-AI cells treated with PXM alone or in combination with docetaxel (Doc) or enzalutamide (Enz). Additionally, the impact of these treatments on stem-like progenitor cells was assessed using three-dimensional (3D)-Matrigel™-based sphere-forming and organoid formation assays.

RESULTS: Transcriptomic analysis revealed that inflammatory pathways are enriched during PCa progression, making them viable targets for NSAID-based interventions. Single treatment of PXM demonstrated significant anti-cancer effects on PLum-AD and PLum-AI cells, evidenced by reduced cell proliferation, viability, migration, sphere growth, and organoid growth.

DISCUSSION: Importantly, PXM treatment in combination with Doc or Enz resulted in more pronounced antineoplastic effects compared to single-drug exposure. Our work suggests PXM as a potential adjunctive therapy to enhance the efficacy of conventional treatments in PCa patients.

PMID:40666288 | PMC:PMC12259556 | DOI:10.3389/fcell.2025.1551010

Iran J Basic Med Sci. 2025;28(5):647-654. doi: 10.22038/ijbms.2025.83856.18143.

ABSTRACT

OBJECTIVES: Glycation is one of the primary underlying processes attributed to senescence and related diseases. No medicine currently targets this harmful manifestation. Drug repurposing is an efficient and cost-effective way of developing drugs. The present study evaluated meloxicam, a clinically used NSAID, for its ability to offer protection against glycative stress.

MATERIALS AND METHODS: Methylglyoxal (MGO; 17.25 mg/kg) was administered for two weeks to create a rat model of glycative stress. Aminoguanidine (AG; 50 mg/kg) and Meloxicam (MEL; 0.15, 0.3, and 0.6 mg/kg) were used as standard and test agents, respectively. Afterward, the cognitive (Morris Water Maze), liver (LFT), and kidney (Creatinine) functioning were evaluated. The expression of genes of interest (TNF-α, RAGE, BACE, Glyoxalase, and VEGF) were estimated (qPCR) in the liver, brain, and kidney along with histopathology (H&E staining). Carboxymethyllysine (CML) levels in rat plasma were evaluated via ELISA.

RESULTS: MEL treatment has significantly (P<0.05) protected the MGO-induced cognitive (duration in target quadrant, time taken to get to target quadrant, and the frequency of crossings via platform location), hepatic, and renal impairment. The qPCR data revealed that MEL prevented MGO-induced enhancement in the expression of genes of interest. Additionally, the CML levels were significantly (P<0.005) normalized by concomitant administration of MEL. Histopathological examination did not reveal any remarkable outcomes.

CONCLUSION: MEL has significantly mitigated the rats' MGO-induced cognitive, liver, and kidney impairments. Hence, it appears to be a potential molecule for repurposing as an antiglycation agent.

PMID:40666177 | PMC:PMC12258788 | DOI:10.22038/ijbms.2025.83856.18143

Respir Res. 2025 Jul 15;26(1):247. doi: 10.1186/s12931-025-03320-8.

ABSTRACT

RATIONALE: Individuals homozygous for the Alpha-1 Antitrypsin (AAT) Z allele (Pi*ZZ) exhibit heterogeneity in COPD risk. COPD occurrence in non-smokers with AAT deficiency (AATD) suggests that inflammatory processes may contribute to COPD risk independently of smoking. We hypothesized that inflammatory protein biomarkers in non-AATD COPD are associated with moderate-to-severe COPD in AATD individuals, after accounting for clinical factors.

METHODS: Participants from the COPDGene (Pi*MM) and AAT Genetic Modifiers Study (Pi*ZZ) were included. Proteins associated with FEV1/FVC were identified, adjusting for confounders and familial relatedness. Lung-specific protein-protein interaction (PPI) networks were constructed. Proteins associated with AAT augmentation therapy were identified, and drug repurposing analyses performed. A protein risk score (protRS) was developed in COPDGene and validated in AAT GMS using AUROC analysis. Machine learning ranked proteomic predictors, adjusting for age, sex, and smoking history.

RESULTS: Among 4,446 Pi*MM and 352 Pi*ZZ individuals, sixteen blood proteins were associated with airflow obstruction, fourteen of which were highly expressed in lung. PPI networks implicated regulation of immune system function, cytokine and interleukin signaling, and matrix metalloproteinases. Eleven proteins, including IL4R, were linked to augmentation therapy. Drug repurposing identified antibiotics, thyroid medications, hormone therapies, and antihistamines as potential adjunctive AATD treatments. Adding protRS improved COPD prediction in AAT GMS (AUROC 0.86 vs. 0.80, p = 0.0001). AGER was the top-ranked protein predictor of COPD.

CONCLUSIONS: Sixteen proteins are associated with COPD and inflammatory processes that predict airflow obstruction in AATD after accounting for age and smoking. Immune activation and inflammation are modulators of COPD risk in AATD.

PMID:40665347 | DOI:10.1186/s12931-025-03320-8

IEEE J Biomed Health Inform. 2025 Jul 15;PP. doi: 10.1109/JBHI.2025.3589290. Online ahead of print.

ABSTRACT

Accurate prediction of drug-disease associations (DDAs) is essential for drug repositioning and the development of novel therapeutic strategies. However, existing methods often suffer from limited prior knowledge and the use of oversimplified negative sampling techniques, which hinder their ability to capture the complex relationships between drugs and diseases. To break through these limitations, we propose a new model, Hierarchical Attention Mechanism-Based Negative Sampling (HA-NegS), which aims to enhance the prediction of potential DDAs. In this study, HA-NegS further computes the similarity information between drugs and diseases and constructs heterogeneous and homogeneous networks based on it. For the similarity network, HA-NegS fuses Graph Convolutional Network (GCN) and Graph Attention Network (GAT) to effectively capture the neighborhood features of the target nodes. Subsequently, the model incorporates a hierarchical sampling strategy using the PageRank algorithm to rank nodes in descending order of global importance. The attention mechanism is then used to calculate the attention score and re-rank the nodes accordingly. This approach ensures the reliability of the negative sample selection. In order to obtain optimized representations, we use graph contrastive learning methods to refine drug and disease features with homogeneous and heterogeneous neighborhood information. Experimental results on a benchmark dataset show that HA-NegS outperforms existing baseline methods in predicting DDA. In addition, case studies for Alzheimer's disease and Parkinson's disease highlight the effectiveness of HA-NegS in discovering new therapeutic applications for existing drugs.

PMID:40663664 | DOI:10.1109/JBHI.2025.3589290

OMICS. 2025 Jul 15. doi: 10.1177/15578100251359275. Online ahead of print.

ABSTRACT

Head and neck squamous cell carcinoma (HNSCC) displays significant molecular heterogeneity, which hinders effective and safe treatments and clinical outcomes. This predicament also points to the need for an individually tailored personalized/precision medicine approach in HNSCC that includes the oral, hypopharyngeal, nasopharyngeal, and laryngeal subtypes. This study, with the overarching aim of personalized/precision medicine, attempted to identify (1) a molecular target shared by the HNSCC subtypes and (2) screen for potential anticancer drugs for repurposing that may work across the HNSCC subtypes. The National Center for Biotechnology Information-Gene Expression Omnibus database was used to select the datasets (GSE127165, GSE2379, GSE37991, and GSE12452) for the analyses of differentially expressed genes in HNSCC subtypes. Our transcriptome analyses of the HNSCC subtypes revealed 305 upregulated genes. Subsequently, protein network construction with 305 genes showed three closely interconnected high-risk HNSCC prognostic clusters. Importantly, COL1A1 was identified as the pivotal target regulating the pathogenic cluster protein implicated in cancer pathways. Molecular docking with 1040 anticancer drugs identified bleomycin as a potential candidate, exhibiting a binding affinity of -12.425 kcal/mol and a favorable binding free energy of -92.05 kcal/mol. The dynamic simulations confirmed the stability of the system, with stable interactions over 200 ns. Quantum calculations provided insights into bleomycin's chemical and electronic properties, revealing crucial interactions with COL1A1. In conclusion, our study proposes COL1A1 as a promising potential therapeutic target among HNSCC subtypes, with bleomycin demonstrating notable repurposing potential for HNSCC.

PMID:40662975 | DOI:10.1177/15578100251359275

Bioinformatics. 2025 Jul 1;41(Supplement_1):i77-i85. doi: 10.1093/bioinformatics/btaf184.

ABSTRACT

MOTIVATION: Drug repositioning, identifying new therapeutic applications for existing drugs, can significantly reduce the time and cost involved in drug development. Recent studies have explored the use of Anatomical Therapeutic Chemical (ATC) codes in drug repositioning, offering a systematic framework to predict ATC codes for a drug. The ATC classification system organizes drugs according to their chemical properties, pharmacological actions, and therapeutic effects. However, its complex hierarchical structure and the limited scalability at higher levels present significant challenges for achieving accurate ATC code prediction.

RESULTS: We propose a novel approach to predict ATC codes of drugs, named Feature Aggregation and Convolution with Transformer models (FACT). This method computes three types of drug similarities, incorporating ATC code similarity with hierarchical weights and masked drug-ATC code associations. These features are then aggregated for each target drug-ATC code pair and processed through a convolution-transformer encoder to generate three embeddings. The embeddings are finally used to estimate the probability of an association between the target pair. The experimental results demonstrate that the proposed method achieves an area under the receiver operating characteristic curve (AUROC) of 0.9805 and an area under the precision-recall curve of 0.9770 at level 4 of the ATC codes, outperforming the previous methods by 15.05% and 18.42%, respectively. This study highlights the effectiveness of integrating diverse drug features and the potential of transformer-based models in ATC code prediction.

AVAILABILITY AND IMPLEMENTATION: Source code of FACT is freely available at https://github.com/knhc1234/FACT.

PMID:40662799 | DOI:10.1093/bioinformatics/btaf184

Curr Alzheimer Res. 2025 Jul 14. doi: 10.2174/0115672050393875250626065205. Online ahead of print.

ABSTRACT

INTRODUCTION: Type 2 diabetes mellitus (T2D) is a known risk factor for developing Alzheimer's disease (AD). Recent research shows that both diseases share complex and related pathophysiological processes. Network medicine approaches can help to elucidate common dysregulated processes among different diseases, such as AD and T2D. Thus, the aim of this work was to determine differentially expressed genes (DEGs) in AD and T2D and to apply a network medicine approach to identify the microRNAs (miRNAs) involved in the AD-T2D association.

METHODS: Gene expression microarray data sets consisting of 384 control samples and 399 samples belonging to AD and T2D disease were analyzed to obtain DEGs shared by both diseases; the miRNAs associated with these DEGs were predicted using a network medicine approach. Finally, potential small molecules targeting these potentially deregulated miRNAs were identified.

RESULTS: AD and T2D shared a small subset of 82 downregulated DEGs. These genes were significantly associated (p < 0.01) with the ontology terms of chemical synaptic deregulation. DEGs were associated with 12 miRNAs expressed in specific tissues for AD and T2D. Such miRNAs were also primarily associated with the ontology terms related to synaptic deregulation and cancer, and AKT signaling pathways. Steroid anti-inflammatory drugs, antineoplastics, and glucose metabolites were predicted to be potential regulators of the 12 shared miRNAs.

DISCUSSION: The network medicine approach integrating DEGs and miRNAs enabled the identification of shared, potentially deregulated biological processes and pathways underlying the pathophysiology of AD and T2D. These common molecular mechanisms were also linked to drugs currently used in clinical practice, suggesting that this strategy may inform future drug repurposing efforts. Nonetheless, further in-depth biological validation is required to confirm these findings.

CONCLUSION: Network medicine allowed identifying 12 miRNAs involved in the AD-T2D association, and these could be drug targets for the design of new treatments; however, the identified miRNAs need further experimental confirmation.

PMID:40662549 | DOI:10.2174/0115672050393875250626065205

Medicine (Baltimore). 2025 Jul 11;104(28):e42975. doi: 10.1097/MD.0000000000042975.

ABSTRACT

With the aging population, the prevalence of sarcopenia and primary osteoporosis is increasing. However, the underlying mechanisms linking these 2 diseases remain unclear. The study aims to identify potential genes and pharmacological targets associated with both diseases through bioinformatics analysis. Datasets GSE35958 (primary osteoporosis) and GSE1428 (sarcopenia) were sourced from the gene expression omnibus database. Differentially expressed genes common to both conditions were identified, and functional roles were elucidated using gene ontology and Reactome pathway analyses. Protein-protein interaction networks and hub genes were analyzed using Cytoscape with the molecular complex detection plugin. Key gene expression profiles were validated by quantitative real-time PCR, and potential gene-drug interactions were explored using the drug-gene interaction database. The identified pharmacological agents contributed to the formulation of therapeutic strategies for both diseases. Our study identified 80 commonly expressed genes through the gene expression omnibus database. Gene ontology enrichment analysis was employed to categorize these genes into biological processes, cellular components, and molecular functions. Additionally, 6 Reactome pathways were identified (P < .05), leading to the characterization of 65 genes. The study conducted a protein-protein interaction analysis and employed molecular complex detection to evaluate a set of 65 genes, ultimately identifying 5 central genes. In this investigation, cancellous bone and muscle tissues from elderly individuals with osteoporosis and sarcopenia were subjected to quantitative real-time PCR validation. The analysis revealed that 5 genes were down-regulated in muscle tissue, whereas AXL, ERBB2, and GAB1 were up-regulated in bone tissue. Subsequently, these 5 genes were analyzed for drug-gene interactions, resulting in the identification of 13 Food and Drug Administration-approved drugs with potential therapeutic applications for osteoporosis and sarcopenia. The aforementioned 5 genes (AXL, GAB1, ERBB2, NRP2, and ESR1) along with 13 pharmacological agents represent promising candidates for enhancing the treatment of osteoporosis and sarcopenia. Investigations into gene-drug correlations and analyses of potential pharmacotherapies offer novel insights for drug repurposing and the exploration of therapeutic pathways.

PMID:40660573 | DOI:10.1097/MD.0000000000042975

Ageing Res Rev. 2025 Jul 12:102835. doi: 10.1016/j.arr.2025.102835. Online ahead of print.

ABSTRACT

Aging is a complex biological process driven by genetic and immune-mediated mechanisms, yet the causal roles of immune-cell-specific gene regulation remain unclear. In this study, we integrate single-cell expression quantitative trait loci (sc-eQTL) data with Mendelian randomization (MR) and colocalization analyses to identify immune-mediated regulatory mechanisms and therapeutic targets for aging. Using data from 14 immune cell types, we systematically evaluated 8,733 eGenes for causal effects on telomere length (TL), facial aging (FA), and frailty index (FI). We identified 27 immune-cell-specific eGenes with significant causal associations and strong colocalization evidence (posterior probability for a shared causal variant, PP.H4 > 50%). Key regulators include FUBP1, TUFM, ATIC, and SLC22A5, with distinct effects across cell types and aging traits. Phenome-wide association studies (PheWAS) demonstrated minimal off-target associations for most genes, supporting their safety as therapeutic targets. Drug repurposing analysis revealed several approved or investigational compounds, such as Irofulven, zinc-based agents, and acetylcarnitine, with potential for aging-related interventions. Our findings provide new insights into the immune-genetic architecture of aging and establish a scalable framework for identifying cell-type-specific causal genes and repurposable drug targets. This approach enhances precision medicine strategies aimed at promoting healthy aging and delaying age-related decline.

PMID:40659289 | DOI:10.1016/j.arr.2025.102835

Nano Lett. 2025 Jul 14. doi: 10.1021/acs.nanolett.5c02826. Online ahead of print.

ABSTRACT

Prostate cancer is the second leading cause of cancer-related deaths in men worldwide, with bone metastasis being the predominant driver of morbidity and mortality in advanced stages. This underscores the urgent need for innovative therapeutic strategies to address skeletal metastasis and improve patient outcomes. Niclosamide, a long-standing anthelminthic drug, has emerged as a promising multipathway modulator against cancer hallmarks. Here, we propose a nanotechnology-enabled repurposing of niclosamide, hypothesizing that its formulation into smart, targeted nanohybrids could not only suppress prostate tumor growth but also inhibit its dissemination to the skeletal system. We discuss the molecular rationale, design considerations, and translational outlook for nanoengineered niclosamide in the context of metastatic prostate cancer.

PMID:40657731 | DOI:10.1021/acs.nanolett.5c02826

Int J Med Sci. 2025 Jun 12;22(12):2896-2905. doi: 10.7150/ijms.111050. eCollection 2025.

ABSTRACT

Objective: Gangrene has been a problem for many people with diabetes. Besides, the relationship and pathomechanism of diabetes-induced gangrene (DG) are still unclear. The aim of this study was to investigate the causal relationship between diabetes and gangrene through Mendelian randomization (MR) and to identify potential therapeutic agents using bioinformatics analysis. Method: Summary data from genome-wide association studies (GWAS) were utilized to evaluate the connection between two types of diabetes and gangrene risk using a two-sample MR design. Single nucleotide polymorphisms (SNPs) that were significantly associated with diabetes were selected as instrumental variables, and their validity was verified by F-statistics and other methods. Next, we used text mining and protein-protein interaction (PPI) networks to filtrate significant genes for drug-gene interaction (DGI) to identify prospective medications for the therapy of DG. Results: Through multiple methods analysis (IVW, MR-Egger and MR-PRESSO etc.), MR analysis showed that genetic susceptibility to type 1 diabetes was related to a higher risk of gangrene risk (OR: 1.19, 95% CI: 1.04-1.36, P-value: 0.0134), while type 2 diabetes mellitus (T2DM) could also increase the gangrene risk (OR: 1.57, 95% CI: 1.05-2.33, P-value: 0.0269). The outcomes of text mining disclosed 50 genes enriched in NOD-like receptor and RAGE signaling pathways commonly associated with both diabetes and gangrene for PPI analysis. Subsequent DGI analysis revealed six genes targeted by 12 drugs (DGI score > 5), presenting them as candidates for treating DG. Conclusion: In conclusion, this study not only validates the causal effect of diabetes on gangrene risk but also identifies several potential therapeutic agents (CILAZAPRIL, RESATORVID, SILTUXIMAB, and OLOKIZUMAB) by integrating bioinformatics analysis, providing new directions for future clinical interventions.

PMID:40657379 | PMC:PMC12243868 | DOI:10.7150/ijms.111050

ACS Omega. 2025 Jun 26;10(26):28446-28465. doi: 10.1021/acsomega.5c04340. eCollection 2025 Jul 8.

ABSTRACT

ROCK kinases are key players in neurodegenerative diseases such as Alzheimer's disease (AD), making them attractive therapeutic targets. In this study, we developed a pharmacophoric map of ROCK inhibitors and highlighted the key affinity sites in ROCK1/2 through molecular modeling. Virtual screening led to the identification of six approved drugs as ROCK inhibitors: ruxolitinib (36), baricitinib (37), ponatinib (38), tivozanib (39), nialamide (40), and tucatinib (41). Ruxolitinib (36) (hROCK1 IC50 = 0.025 μM; hROCK2 IC50 = 0.007 μM) and baricitinib (37) (hROCK1 IC50 = 0.019 μM; hROCK2 IC50 = 0.011 μM) showed the highest potency, while tivozanib (39) displayed 15-fold selectivity for ROCK2 over ROCK1. Molecular dynamics revealed that ruxolitinib (36) forms stable bidentate hydrogen bonds with the ROCK hinge region and has selectivity across the AGC kinase family. Biological assays confirmed ruxolitinib's (36) safety in neuronal and glial cells and its ability to reduce C3 immunolabeling, a glial inflammation marker, in LPS-treated astrocytes. These findings not only highlight ruxolitinib (36) as a promising candidate for AD but also provide a structural basis for designing novel dual JAK-ROCK inhibitors and pave the way for further in vitro and in vivo studies. Moreover, the validated pharmacophoric map for ROCK inhibition highlights the identification of an affinity pocket that can be useful for the design of new ROCK inhibitors.

PMID:40657111 | PMC:PMC12242638 | DOI:10.1021/acsomega.5c04340

Indian J Microbiol. 2025 Jun;65(2):798-819. doi: 10.1007/s12088-024-01439-8. Epub 2025 Jan 7.

ABSTRACT

Pseudomonas aeruginosa is a major cause of nosocomial infection which at times causes a grim situation in clinical setups. There is a dire need to develop strategies to overcome the increasing incidence of drug resistance in this organism. The protective outer membrane and over expressed efflux pumps serve as a major survival weapon for this pathogen, making it to resist present day antibiotics. The present review dwells on some of these strategies, with emphasis on tagging to existing antibiotics with siderophore as a carrier employing a Trojan horse strategy so that an antibiotic can creep into bacterial cell through the iron acquisition pathway. These hybrid drugs, defined as synthetic structures are likely to overcome drug resistance due to their ability to evade resistance mediated by multiple antibiotic resistance operon. The advances and challenges of siderophore-antibiotic conjugates are elaborated in this article. Moreover, several xeno-siderophore-antibiotic conjugates, currently in clinical trials, have been discussed. The present review provides insights into repurposing of fluroquinolones through siderophore targeted delivery to increase the biological activity of antibiotics. In the near future, siderophore-based Trojan horse antibiotics indeed will not only help in altering the potency of antibiotics, hence decreasing antimicrobial resistance, but also will lead to disarming the pathogen of its virulence.

PMID:40655352 | PMC:PMC12246312 | DOI:10.1007/s12088-024-01439-8

J Transl Med. 2025 Jul 13;23(1):788. doi: 10.1186/s12967-025-06739-1.

ABSTRACT

BACKGROUND: Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) helps identify potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) results with tissue-level eQTLs. Recent studies have explored brain cell type-specific eQTLs, but a systematic analysis across multiple Alzheimer's disease (AD) genome-wide association study (GWAS) datasets or comparisons between tissue-level and cell type-specific effects remain limited. Here, we integrated brain cell type-level and bulk-level eQTL datasets with AD GWAS datasets to identify potential causal genes.

METHODS: We used Summary Data-Based Mendelian Randomization (SMR) and Bayesian Colocalization (COLOC) to integrate AD GWAS summary statistics with eQTLs datasets. Combining data from five AD GWAS, two single-cell eQTL datasets, and one bulk eQTL dataset, we identified novel candidate causal genes and further confirmed known ones. We investigated gene regulation through enhancer activity using H3K27ac and ATAC-seq data, performed protein-protein interaction (PPI) and pathway enrichment, and conducted a drug/compound enrichment analysis with Drug Signatures Database (DSigDB) to support drug repurposing for AD.

RESULTS: We identified 28 candidate causal genes for AD, of which 12 were uniquely detected at the cell-type level, 9 were exclusive to the bulk level and 7 detected in both. Among the 19 cell-type level candidate causal genes, microglia contributed the highest number of candidate genes, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). PABPC1 emerged as a novel candidate causal gene in astrocytes. We generated PPI networks for the candidate causal genes and found that pathways such as membrane organization, cell migration, and ERK1/2 and PI3K/AKT signaling were enriched. The AD-risk variant associated with candidate causal gene PABPC1 is located near or within enhancers only active in astrocytes. We classified the 28 genes into three drug tiers and identified druggable interactions, with imatinib mesylate emerging as a key candidate. A drug-target gene network was created to explore potential drug targets for AD.

CONCLUSIONS: We systematically prioritized AD candidate causal genes based on cell type-level and bulk level molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates interpretation of AD GWAS results.

PMID:40653482 | DOI:10.1186/s12967-025-06739-1