J Inflamm Res. 2025 Apr 25;18:5573-5586. doi: 10.2147/JIR.S516160. eCollection 2025.

ABSTRACT

PURPOSE: Macrophages play a pivotal role in the progression of atherosclerosis (AS), and targeting macrophage-associated pathological processes has emerged as a promising therapeutic strategy for AS. Flavonoids have demonstrated potent antioxidant properties with potential anti-atherosclerotic effects. This study aimed to investigate the therapeutic effects of the flavonoid calycosin-7-glucoside (CG) on AS and elucidate its underlying molecular mechanisms.

METHODS: Macrophages were differentiated from human monocytic THP-1 cells by treatment with phorbol-12-myristate-13-acetate (PMA). Foam cell formation was induced by exposing differentiated macrophages to oxidized low-density lipoprotein (ox-LDL). Protein and inflammatory cytokine expression levels were assessed using RT-qPCR, Western blot, and ELISA assays. Total cholesterol and free cholesterol levels were quantified using commercial kits, and lipid droplet accumulation was visualized using Nile red staining.

RESULTS: Activation of activating transcription factor 1 (ATF-1) was found to mediate CG-induced suppression of inflammatory responses and foam cell formation in ox-LDL-exposed THP-1-derived macrophages. CG treatment enhanced p38 MAPK activity, which was responsible for ATF-1 activation and subsequent inhibition of inflammation and foam cell formation. Mechanistically, ATF-1 facilitated CG-induced anti-atherosclerotic effects by upregulating liver X receptor beta (LXR-β) and cystic fibrosis transmembrane conductance regulator (CFTR), which are critical for lipid metabolism and inflammation regulation, respectively.

CONCLUSION: CG attenuates ox-LDL-induced foam cell formation and inflammatory responses in THP-1-derived macrophages by activating the p38 MAPK/ATF-1 signaling pathway, leading to the upregulation of LXR-β and CFTR. These findings highlight the potential of CG as a therapeutic agent for AS.

PMID:40303003 | PMC:PMC12039851 | DOI:10.2147/JIR.S516160

J Paediatr Child Health. 2025 Apr 29. doi: 10.1111/jpc.70072. Online ahead of print.

ABSTRACT

INTRODUCTION: Neonatal cholestasis is a group of disorders characterised by conjugated hyperbilirubinemia in the newborns and young infants. Advances in genetic testing have facilitated the identification of specific aetiology. This study examines the genetic and clinical profiles of neonates with cholestasis, focusing on genotype-phenotype correlations and diagnostic outcomes.

METHODS: A retrospective review of children with neonatal cholestasis treated between 1997 and 2024 was conducted. Extrahepatic causes were excluded, and genetic testing, including a targeted cholestasis panel and whole exome sequencing (WES), was employed. Clinical and biochemical data, including gamma-glutamyl transferase (GGT) levels, were collected.

RESULTS: Genetic disorders were identified in 28.0% of 378 cases, including mutations in ATP8B1, ABCB11, ABCB4, DCDC2, DGUOK, KIF12, USP53, and genes related to bile acid synthesis (HSD3B7, PEX1). GGT levels played a significant role in diagnosis: patients with low or normal GGT were frequently diagnosed with progressive familial intrahepatic cholestasis (PFIC)1 and 2, or bile acid synthesis defects, while high GGT levels were associated with PFIC3, alpha-1 antitrypsin deficiency, and cystic fibrosis. Consanguinity was noted in 56.0% of genetically diagnosed cases. After 2010, 35.5% of patients received a genetic diagnosis, compared to 18.2% before 2010.

CONCLUSION: Genetic diseases are a major cause of neonatal cholestasis, and GGT levels serve as a useful diagnostic tool in differentiating subtypes. The increasing availability of genetic testing has improved early diagnosis and personalised management. Expanded genetic testing in clinical practice is critical for timely and accurate diagnosis of these rare disorders.

PMID:40302296 | DOI:10.1111/jpc.70072

Proteins. 2025 Apr 30. doi: 10.1002/prot.26833. Online ahead of print.

ABSTRACT

The Critical Assessment of Structure Prediction (CASP) brings together a diverse group of scientists, from deep learning experts to NMR specialists, all aimed at developing accurate prediction algorithms that can effectively characterize the structural aspects of biomolecules relevant to their functions. Engagement within the CASP community has traditionally been limited to the prediction season and the conference, with limited discourse in the 1.5 years between CASP seasons. CASP special interest groups (SIGs) were established in 2023 to encourage continuous dialogue within the community. The online seminar series has drawn global participation from across disciplines and career stages. This has facilitated cross-disciplinary discussions fostering collaborations. The archives of these seminars have become a vital learning tool for newcomers to the field, lowering the barrier to entry.

PMID:40304050 | DOI:10.1002/prot.26833

Front Genet. 2025 Apr 15;16:1568270. doi: 10.3389/fgene.2025.1568270. eCollection 2025.

ABSTRACT

Long noncoding RNAs (lncRNAs) regulate physiological processes via interactions with macromolecules such as miRNAs, proteins, and genes, forming disease-associated regulatory networks. However, predicting lncRNA-disease associations remains challenging due to network complexity and isolated entities. Here, we propose MVIGCN, a graph convolutional network (GCN)-based method integrating multimodal data to predict these associations. Our framework constructs a heterogeneous network combining disease semantics, lncRNA similarity, and miRNA-lncRNA-disease interactions to address isolation issues. By modeling topological features and multiscale relationships through deep learning with attention mechanisms, MVIGCN prioritizes critical nodes and edges, enhancing prediction accuracy. Cross-validation demonstrated improved reliability over single-view methods, highlighting its potential to identify disease-related lncRNA biomarkers. This work advances network-based computational strategies for decoding lncRNA functions in disease biology and provides a scalable tool for prioritizing therapeutic targets.

PMID:40303981 | PMC:PMC12037633 | DOI:10.3389/fgene.2025.1568270

Front Med Technol. 2025 Apr 15;6:1434799. doi: 10.3389/fmedt.2024.1434799. eCollection 2024.

ABSTRACT

The microbiome of the gut is a complex ecosystem that contains a wide variety of microbial species and functional capabilities. The microbiome has a significant impact on health and disease by affecting endocrinology, physiology, and neurology. It can change the progression of certain diseases and enhance treatment responses and tolerance. The gut microbiota plays a pivotal role in human health, influencing a wide range of physiological processes. Recent advances in computational tools and artificial intelligence (AI) have revolutionized the study of gut microbiota, enabling the identification of biomarkers that are critical for diagnosing and treating various diseases. This review hunts through the cutting-edge computational methodologies that integrate multi-omics data-such as metagenomics, metaproteomics, and metabolomics-providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. By highlighting the synergy between traditional bioinformatics tools and advanced AI techniques, this review underscores the potential of these approaches in enhancing biomarker discovery and developing personalized therapeutic strategies. The convergence of computational advancements and microbiome research marks a significant step forward in precision medicine, paving the way for novel diagnostics and treatments tailored to individual microbiome profiles. Investigators have the ability to discover connections between the composition of microorganisms, the expression of genes, and the profiles of metabolites. Individual reactions to medicines that target gut microbes can be predicted by models driven by artificial intelligence. It is possible to obtain personalized and precision medicine by first gaining an understanding of the impact that the gut microbiota has on the development of disease. The application of machine learning allows for the customization of treatments to the specific microbial environment of an individual.

PMID:40303946 | PMC:PMC12037385 | DOI:10.3389/fmedt.2024.1434799

Front Pharmacol. 2025 Apr 15;16:1541509. doi: 10.3389/fphar.2025.1541509. eCollection 2025.

ABSTRACT

Traditional Chinese Medicine (TCM) utilizes multi-metabolite and multi-target interventions to address complex diseases, providing advantages over single-target therapies. However, the active metabolites, therapeutic targets, and especially the combination mechanisms remain unclear. The integration of advanced data analysis and nonlinear modeling capabilities of artificial intelligence (AI) is driving the transformation of TCM into precision medicine. This review concentrates on the application of AI in TCM target prediction, including multi-omics techniques, TCM-specialized databases, machine learning (ML), deep learning (DL), and cross-modal fusion strategies. It also critically analyzes persistent challenges such as data heterogeneity, limited model interpretability, causal confounding, and insufficient robustness validation in practical applications. To enhance the reliability and scalability of AI in TCM target prediction, future research should prioritize continuous optimization of the AI algorithms using zero-shot learning, end-to-end architectures, and self-supervised contrastive learning.

PMID:40303920 | PMC:PMC12037568 | DOI:10.3389/fphar.2025.1541509

Transbound Emerg Dis. 2023 Sep 14;2023:5046932. doi: 10.1155/2023/5046932. eCollection 2023.

ABSTRACT

A novel Omicron subvariant named BQ.1 emerged in Nigeria in July 2022 and has since become a dominant strain, causing a significant number of repeated infections even in countries with high-vaccination rates. Due to the high flow of people between Western Africa and other non-African countries, there is a high risk of Omicron BQ.1 being introduced to other countries from Western Africa. In this context, we developed a model based on deep neural networks to estimate the probability that the Omicron BQ.1 introduced to other countries from Western Africa based on the incomplete population mobility data from Western Africa to other non-African countries. Our study found that the highest risk was in France and Spain during the study period, while the importation risk of other 13 non-African countries including Canada and the United States is also high. Our approach sheds light on how deep learning techniques can assist in the development of public health policies, and it has the potential to be extended to other types of viruses.

PMID:40303718 | PMC:PMC12016809 | DOI:10.1155/2023/5046932

Int J Med Sci. 2025 Apr 9;22(9):2088-2102. doi: 10.7150/ijms.105422. eCollection 2025.

ABSTRACT

Artificial intelligence (AI), as a new technology that can assist or even replace some human functions, can collect and analyse large amounts of textual, visual and auditory data through techniques such as Reinforcement Learning, Machine Learning, Deep Learning and Natural Language Processing to establish complex, non-linear relationships and construct models. These can support doctors in disease prediction, diagnosis, treatment and management, and play a significant role in clinical risk prediction, improving the accuracy of disease diagnosis, assisting in the development of new drugs, and enabling precision treatment and personalised management. In recent years, AI has been used in the prediction, diagnosis, treatment and management of allergic diseases. Allergic diseases are a type of chronic non-communicable disease that have the potential to affect a number of different systems and organs, seriously impacting people's mental health and quality of life. In this paper, we focus on asthma and summarise the application and research progress of AI in asthma, atopic dermatitis, food allergies, allergic rhinitis and urticaria, from the perspectives of disease prediction, diagnosis, treatment and management. We also briefly analyse the advantages and limitations of various intelligent assistance methods, in order to provide a reference for research teams and medical staff.

PMID:40303497 | PMC:PMC12035833 | DOI:10.7150/ijms.105422

Front Med (Lausanne). 2025 Apr 15;12:1511487. doi: 10.3389/fmed.2025.1511487. eCollection 2025.

ABSTRACT

INTRODUCTION: Accurate segmentation of pelvic fractures from computed tomography (CT) is crucial for trauma diagnosis and image-guided reduction surgery. The traditional manual slice-by-slice segmentation by surgeons is time-consuming, experience-dependent, and error-prone. The complex anatomy of the pelvic bone, the diversity of fracture types, and the variability in fracture surface appearances pose significant challenges to automated solutions.

METHODS: We propose an automatic pelvic fracture segmentation method based on deep learning, which effectively isolates hipbone and sacrum fragments from fractured pelvic CT. The method employs two sequential networks: an anatomical segmentation network for extracting hipbones and sacrum from CT images, followed by a fracture segmentation network that isolates the main and minor fragments within each bone region. We propose a distance-weighted loss to guide the fracture segmentation network's attention on the fracture surface. Additionally, multi-scale deep supervision and smooth transition strategies are incorporated to enhance overall performance.

RESULTS: Tested on a curated dataset of 150 CTs, which we have made publicly available, our method achieves an average Dice coefficient of 0.986 and an average symmetric surface distance of 0.234 mm.

DISCUSSION: The method outperformed traditional max-flow and a transformer-based method, demonstrating its effectiveness in handling complex fracture.

PMID:40303367 | PMC:PMC12039937 | DOI:10.3389/fmed.2025.1511487

Transbound Emerg Dis. 2024 Jan 17;2024:6621199. doi: 10.1155/2024/6621199. eCollection 2024.

ABSTRACT

Leishmania, a single-cell parasite prevalent in tropical and subtropical regions worldwide, can cause varying degrees of leishmaniasis, ranging from self-limiting skin lesions to potentially fatal visceral complications. As such, the parasite has been the subject of much interest in the scientific community. In recent years, advances in diagnostic techniques such as flow cytometry, molecular biology, proteomics, and nanodiagnosis have contributed to progress in the diagnosis of this deadly disease. Additionally, the emergence of artificial intelligence (AI), including its subbranches such as machine learning and deep learning, has revolutionized the field of medicine. The high accuracy of AI and its potential to reduce human and laboratory errors make it an especially promising tool in diagnosis and treatment. Despite the promising potential of deep learning in the medical field, there has been no review study on the applications of this technology in the context of leishmaniasis. To address this gap, we provide a scoping review of deep learning methods in the diagnosis of the disease, drug discovery, and vaccine development. In conducting a thorough search of available literature, we analyzed articles in detail that used deep learning methods for various aspects of the disease, including diagnosis, drug discovery, vaccine development, and related proteins. Each study was individually analyzed, and the methodology and results were presented. As the first and only review study on this topic, this paper serves as a quick and comprehensive resource and guide for the future research in this field.

PMID:40303156 | PMC:PMC12019899 | DOI:10.1155/2024/6621199

J Med Imaging (Bellingham). 2025 Nov;12(Suppl 2):S22006. doi: 10.1117/1.JMI.12.S2.S22006. Epub 2025 Apr 28.

ABSTRACT

PURPOSE: Predictive models for contrast-enhanced mammography often perform better at detecting and classifying enhancing masses than (non-enhancing) microcalcification clusters. We aim to investigate whether incorporating synthetic data with simulated microcalcification clusters during training can enhance model performance.

APPROACH: Microcalcification clusters were simulated in low-energy images of lesion-free breasts from 782 patients, considering local texture features. Enhancement was simulated in the corresponding recombined images. A deep learning (DL) model for lesion detection and classification was trained with varying ratios of synthetic and real (850 patients) data. In addition, a handcrafted radiomics classifier was trained using delineations and class labels from real data, and predictions from both models were ensembled. Validation was performed on internal (212 patients) and external (279 patients) real datasets.

RESULTS: The DL model trained exclusively with synthetic data detected over 60% of malignant lesions. Adding synthetic data to smaller real training sets improved detection sensitivity for malignant lesions but decreased precision. Performance plateaued at a detection sensitivity of 0.80. The ensembled DL and radiomics models performed worse than the standalone DL model, decreasing the area under this receiver operating characteristic curve from 0.75 to 0.60 on the external validation set, likely due to falsely detected suspicious regions of interest.

CONCLUSIONS: Synthetic data can enhance DL model performance, provided model setup and data distribution are optimized. The possibility to detect malignant lesions without real data present in the training set confirms the utility of synthetic data. It can serve as a helpful tool, especially when real data are scarce, and it is most effective when complementing real data.

PMID:40302983 | PMC:PMC12036226 | DOI:10.1117/1.JMI.12.S2.S22006

J Anus Rectum Colon. 2025 Apr 25;9(2):202-212. doi: 10.23922/jarc.2024-085. eCollection 2025.

ABSTRACT

OBJECTIVES: Predicting the response to chemotherapy can lead to the optimization of neoadjuvant chemotherapy (NAC). The present study aimed to develop a non-invasive prediction model of therapeutic response to NAC for rectal cancer (RC).

METHODS: A dataset of the prechemotherapy computed tomography (CT) images of 57 patients from multiple institutions who underwent rectal surgery after three courses of S-1 and oxaliplatin (SOX) NAC for RC was collected. The therapeutic response to NAC was pathologically confirmed. It was then predicted whether they were pathologic responders or non-responders. Cases were divided into training, validation and test datasets. A CT patch-based predictive model was developed using a residual convolutional neural network and the predictive performance was evaluated. Binary logistic regression analysis of prechemotherapy clinical factors showed that none of the independent variables were significantly associated with the non-responders.

RESULTS: Among the 49 patients in the training and validation datasets, there were 21 (42.9%) and 28 (57.1%) responders and non-responders, respectively. A total of 3,857 patches were extracted from the 49 patients. In the validation dataset, the average sensitivity, specificity and accuracy was 97.3, 95.7 and 96.8%, respectively. Furthermore, the area under the receiver operating characteristic curve (AUC) was 0.994 (95% CI, 0.991-0.997; P<0.001). In the test dataset, which included 750 patches from 8 patients, the predictive model demonstrated high specificity (89.9%) and the AUC was 0.846 (95% CI, 0.817-0.875; P<0.001).

CONCLUSIONS: The non-invasive deep learning model using prechemotherapy CT images exhibited high predictive performance in predicting the pathological therapeutic response to SOX NAC.

PMID:40302856 | PMC:PMC12035344 | DOI:10.23922/jarc.2024-085

Front Plant Sci. 2025 Apr 15;16:1539641. doi: 10.3389/fpls.2025.1539641. eCollection 2025.

ABSTRACT

Peanut (Arachis hypogaea L.), a key oilseed crop in the U.S., plays a significant role in agriculture and the economy but faces challenges from biotic and abiotic stresses, including aflatoxin contamination caused by Aspergillus flavus and A. parasiticus. Despite many large-effect QTLs identified for yield and key traits, their use in breeding is limited by unfavorable genetic interactions. To overcome this, we aimed to identify consensus genomic regions and candidate genes linked to key traits by analyzing QTL data from 30 independent studies conducted over the past 12 years, focusing on biotic, abiotic, aflatoxin, morphological, nutritional, phenological, and yield-associated traits. Using genetic map information, we constructed consensus maps and performed a meta-analysis on 891 QTLs, leading to the identification of 70 Meta-QTLs (MQTLs) with confidence intervals ranging from 0.07 to 9.63 cM and an average of 2.33 cM. This reduction in confidence intervals enhances the precision of trait mapping, making the identified MQTLs more applicable for breeding purposes. Furthermore, we identified key genes associated with aflatoxin resistance in MQTL5.2 (serine/threonine-protein kinase, BOI-related E3 ubiquitin-protein ligase), MQTL5.3, MQTL7.3, and MQTL13.1. Similarly, for yield-related traits in MQTL3.1-MQTL3.4 (mitogen-activated protein kinase, auxin response factor), MQTL11.2 (MADS-box protein, squamosa promoter-binding protein), and MQTL14.1. Genes related to oil composition within MQTL5.2 (fatty-acid desaturase FAD2, linoleate 9S-lipoxygenase), MQTL9.3, MQTL19.1 (acyl-CoA-binding protein, fatty acyl-CoA reductase FAR1), MQTL19.4, and MQTL19.5. Nutritional traits like iron and zinc content are linked to MQTL1.1 (probable methyltransferase, ferredoxin C), MQTL10.1, and MQTL12.1. These regions and genes serve as precise targets for marker-assisted breeding to enhance peanut yield, resilience, and quality.

PMID:40303861 | PMC:PMC12038908 | DOI:10.3389/fpls.2025.1539641

Hortic Res. 2025 Mar 11;12(6):uhaf076. doi: 10.1093/hr/uhaf076. eCollection 2025 Jun.

NO ABSTRACT

PMID:40303429 | PMC:PMC12038890 | DOI:10.1093/hr/uhaf076

medRxiv [Preprint]. 2025 Apr 11:2025.04.11.25325572. doi: 10.1101/2025.04.11.25325572.

ABSTRACT

BACKGROUND: The catechol-O-methyltransferase (COMT) gene is involved in brain catecholamine metabolism, but its association with Parkinson's disease (PD) risk remains unclear.

OBJECTIVE: To investigate the relationship between COMT genetic variants and PD risk across diverse ancestries.

METHODS: We analyzed COMT variants in 2,251 PD patients and 2,835 controls of European descent using whole-genome sequencing from the Accelerating Medicines Partnership-Parkinson Disease (AMP-PD), along with 20,427 PD patients and 11,837 controls from 10 ancestries using genotyping data from the Global Parkinson's Genetics Program (GP2).

RESULTS: Utilizing the largest case-control datasets to date, no significant enrichment of COMT risk alleles in PD patients was observed across any ancestry group after correcting for multiple testing. Among Europeans, no correlations with cognitive decline, motor function, motor complications, or time to LID onset were observed.

CONCLUSIONS: These findings emphasize the need for larger, diverse cohorts to confirm the role of COMT in PD development and progression.

PMID:40297458 | PMC:PMC12036390 | DOI:10.1101/2025.04.11.25325572

Sci Rep. 2025 Apr 29;15(1):15093. doi: 10.1038/s41598-025-99539-y.

ABSTRACT

Computational methods for identifying gene-disease associations can use both genomic and phenotypic information to prioritize genes and variants that may be associated with genetic diseases. Phenotype-based methods commonly rely on comparing phenotypes observed in a patient with databases of genotype-to-phenotype associations using measures of semantic similarity. They are constrained by the quality and completeness of these resources as well as the quality and completeness of patient phenotype annotation. Genotype-to-phenotype associations used by these methods are largely derived from the literature and coded using phenotype ontologies. Large Language Models (LLMs) have been trained on large amounts of text and data and have shown their potential to answer complex questions across multiple domains. Here, we evaluate the effectiveness of LLMs in prioritizing disease-associated genes compared to existing bioinformatics methods. We show that LLMs can prioritize disease-associated genes as well, or better than, dedicated bioinformatics methods relying on pre-defined phenotype similarity, when gene sets range from 5 to 100 candidates. We apply our approach to a cohort of undiagnosed patients with rare diseases and show that LLMs can be used to provide diagnostic support that helps in identifying plausible candidate genes. Our results show that LLMs may offer an alternative to traditional bioinformatics methods to prioritize disease-associated genes based on disease phenotypes. They may, therefore, potentially enhance diagnostic accuracy and simplify the process for rare genetic diseases.

PMID:40301638 | DOI:10.1038/s41598-025-99539-y

Nat Commun. 2025 Apr 29;16(1):3997. doi: 10.1038/s41467-025-59431-9.

ABSTRACT

Adverse drug-drug interaction events (DDIEs) pose serious risks to patient safety, yet rare but severe interactions remain challenging to identify due to limited clinical data. Existing computational methods rely heavily on abundant samples, failing to identify rare DDIEs. Here we introduce RareDDIE, a metric-based meta-learning model that employs a dual-granular structure-driven pair variational representation to enhance rare DDIE prediction. To further address the challenge of zero-shot DDIE identification, we develop the Biological Semantic Transferring (BST) module, integrating large-scale sentence embeddings to form the ZetaDDIE variant. Our model outperforms existing methods in few-sample and zero-sample settings. Furthermore, we verify that knowledge transfer from DDIE can improve drug synergy predictions, surpassing existing models. Case studies on antiplatelet activity reduction and non-small cell lung cancer drug synergy further illustrate the practical value of RareDDIE. By analyzing the meta-knowledge construction process, we provide interpretability into the model's decision-making. This work establishes an effective computational framework for rare DDIE prediction, leveraging meta-learning and knowledge transfer to overcome key challenges in data-limited scenarios.

PMID:40301328 | DOI:10.1038/s41467-025-59431-9

J Neuroinflammation. 2025 Apr 29;22(1):125. doi: 10.1186/s12974-025-03455-x.

ABSTRACT

BACKGROUND: Epalrestat (EPS), an aldose reductase inhibitor, is used to alleviate peripheral nerve disorder of diabetic patients in clinical therapy. Even though EPS exerted effects in central nervous system diseases, the neuroprotection and underlying molecular mechanism in neurodegenerative diseases, especially Parkinson's disease (PD), remains obscure. Our study aimed to investigate the potential of EPS suppressed PD progression both in vivo and in vitro.

METHODS: We used 1-methyl-4-phenylpyridillium ion (MPP+)-treated PD cells and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated PD mice to investigate the protective function and molecular mechanism of EPS in PD. EPS was administered three times daily through oral route 3 days before model establishment for 5 consecutive days. Behavioral manifestation of mice was conducted using open field test, rotarod test and CatWalk gait analysis. Immunofluorescence was used to detect dopaminergic (DAergic) neurons survival in the substantia nigra. Subsequently, oxidative stress, mitochondrial function and KEAP1/Nrf2 signaling pathway in PD models were detected through molecular biology methods to assess the effect and downstream mechanisms of EPS on PD. Molecular docking, surface plasmon resonance and cellular thermal shift assay were used to verify the direct binding of EPS and KEAP1.

RESULTS: We found that EPS exhibited potent antiparkinsonian activity in PD models both in vivo and in vitro. PD models treated with EPS manifested alleviated oxidative stress and mitochondrial dysfunction. Furthermore, we found EPS activated the Nrf2 signaling pathway which contributed to DAergic neurons survival in PD models. Particularly, we firstly confirmed that EPS competitively binds to KEAP1 and enhanced its degradation, thereby activating the Nrf2 signaling pathway.

CONCLUSIONS: Collectively, EPS attenuates oxidative stress and mitochondrial dysfunction by directly binding KEAP1 to activate the KEAP1/Nrf2 signaling pathway, further reducing DAergic neurons damage. These findings suggest that EPS has great potential to become a therapeutic for PD as a clinically effective and safe medicine.

PMID:40301912 | DOI:10.1186/s12974-025-03455-x

Nat Commun. 2025 Apr 29;16(1):3851. doi: 10.1038/s41467-025-59132-3.

ABSTRACT

Genomic variation drives phenotypic diversity, including individual differences in drug response. While coding polymorphisms linked to drug efficacy and adverse reactions are well characterized, the contribution of noncoding regulatory elements remains underexplored. Using CAGE (Cap Analysis of Gene Expression), profiling transcription initiations of mRNAs and enhancer RNAs, we identify candidate cis-regulatory elements (CREs) and assessed their activities simultaneously in HepG2 cells expressing the drug-responsive transcription factor pregnane X receptor (PXR). Comparison with GWAS data reveals strong enrichment of the drug-induced CREs near variants associated with bilirubin and vitamin D levels. Among those bound by PXR in primary hepatocytes, we identify enhancers of UGT1A1, TSKU, and CYP24A1 and functional alleles that alter regulatory activities. We also find that TSKU influences expression of vitamin D-metabolizing enzymes. This study expands the landscape of PXR-mediated regulatory elements and uncovers noncoding variants impacting drug response, providing insights into the genomic basis of adverse drug reactions.

PMID:40301309 | DOI:10.1038/s41467-025-59132-3