Pharmacogenomics J. 2025 May 17;25(3):14. doi: 10.1038/s41397-025-00374-1.

ABSTRACT

Bladder cancer (BC) is a highly prevalent form of cancer worldwide, and cisplatin (CDDP) resistance poses a major challenge to patients. Cytoplasmic linker-associated protein 2 (CLASP2) is a member of the microtubule plus-end tracking protein family and is involved in the regulation of microtubule dynamics. In this study, we evaluated the influence of CLASP2 on BC progression and cisplatin resistance. Levels of CLASP2, HNRNPA1, NONO, ZRANB2, FUS, KHSRP and QKI in BC tissues and cells were tested by RT-qPCR. Protein levels of CLASP2 and KHSRP were detected by Western blot. Cell viability and IC50 of cisplatin-treated BC cells were measured by CCK-8. Cell proliferation and apoptosis were determined using colony formation assay and flow cytometry, respectively. RNA immunoprecipitation (RIP) and Co-immunoprecipitation (Co-IP) experiments were adopted to verify target genes of CLASP2. Cellular localization of CLASP2 and MAPRE1 was detected utilizing immunofluorescence staining. The xenograft tumor model was established in BALB/c nude mice. We found that iCLASP2 levels were increased in CDDP-resistant BC tissues and cells. Suppression of CLASP2 impeded BC cell proliferation and alleviated their resistance to CDDP. KHSRP positively influenced the stability of CLASP2 mRNA. There was a protein interaction between CLASP2 and MAPRE1. Silencing KHSRP or MAPRE1 reversed the effect exerted of CLASP2 on BC cells. CLASP2 decreased the sensitivity of BC to CDDP in vivo. Our results imply that CLASP2 contributes to tumorigenesis and cisplatin resistance in BC via targeting MAPRE1, thereby promoting BC progression and providing a new therapeutic target for BC treatment.

PMID:40382315 | DOI:10.1038/s41397-025-00374-1

Gastroenterology. 2025 May 15:S0016-5085(25)00755-3. doi: 10.1053/j.gastro.2025.04.025. Online ahead of print.

ABSTRACT

There is a large heterogeneity among individuals in their therapeutic responses to the same drug and in the occurrence of adverse events. A key factor increasingly recognized to contribute to this variability is the gut microbiome. The gut microbiome can be regarded as a second genome, holding significant metabolic capacity. Consequently, the field of pharmacomicrobiomics has emerged as a natural extension of pharmacogenomics for studying variations in drug responses. Pharmacomicrobiomics explores the interaction of microbiome variation with drug response and disposition. The interaction between microbes and drugs is, however, complex and bidirectional. While drugs can directly alter microbial growth or influence gut microbiome composition and functionality, the gut microbiome also modulates drug responses directly through enzymatic activities and indirectly via host-mediated immune and metabolic mechanisms. Here we review recent studies that demonstrate the interaction between drugs and the gut microbiome, focusing on cancer immunotherapy and immunomodulation in the context of inflammatory bowel disease and solid organ transplantation. Since the gut microbiome is modifiable, pharmacomicrobiomics presents promising opportunities for optimizing therapeutic outcomes, with recent clinical trials highlighting fecal microbiota transplantation as a strategy to enhance the efficacy of immune checkpoint blockade. We also shed light on the future perspectives for patients arising from this field. While multiple lines of evidence already demonstrate that the gut microbiome interacts with drugs, and vice versa, thereby affecting treatment efficacy and safety, well-designed clinical studies and integrated in vivo and ex vivo models are necessary to obtain consistent results, improve clinical translation and further unlock the gut microbiome's potential to improve drug responses.

PMID:40381958 | DOI:10.1053/j.gastro.2025.04.025

Curr Probl Cardiol. 2025 May 15:103076. doi: 10.1016/j.cpcardiol.2025.103076. Online ahead of print.

ABSTRACT

Dilated cardiomyopathy (DCM) is a prevalent cardiac disorder affecting 1 in 250-500 individuals, characterized by ventricular dilation and impaired systolic function, leading to heart failure and increased mortality, including sudden cardiac death. DCM arises from genetic and environmental factors, such as drug-induced, inflammatory, and viral causes, resulting in diverse yet overlapping phenotypes. Advances in precision medicine are revolutionizing DCM management by leveraging genetic and molecular profiling for tailored diagnostic and therapeutic approaches. This review highlights comprehensive diagnostic evaluations, genetic discoveries, and multi-omics approaches integrating genomic, transcriptomic, proteomic, and metabolomic data to enhance understanding of DCM pathophysiology. Innovative risk stratification methods, including machine learning, are improving predictions of disease progression. Despite these advancements, the current one-size-fits-all management strategy contributes to persistently high morbidity and mortality. Emerging targeted therapies, such as CRISPR/Cas9 genome editing, aetiology-specific interventions, and pharmacogenomics, are reshaping treatment paradigms. Precision medicine holds promise for optimizing DCM diagnosis, treatment, and outcomes, aiming to reduce the burden of this debilitating condition.

PMID:40381754 | DOI:10.1016/j.cpcardiol.2025.103076

J Cyst Fibros. 2025 May 16:S1569-1993(25)00771-4. doi: 10.1016/j.jcf.2025.04.005. Online ahead of print.

ABSTRACT

BACKGROUND: Advanced cystic fibrosis (CF) liver disease can necessitate liver transplantation. This study aims to investigate characteristics, waiting list dynamics, and waiting list mortality of people with CF (pwCF) registered for liver transplantation within Eurotransplant countries, to understand and improve transplant outcomes for this group.

METHODS: We analysed Eurotransplant liver transplantation registration data (January 2007-December 2022), comparing pwCF to people with no CF (pwnoCF) and non-CF age/liver disease severity score (Lab-MELD) matched controls, with subgroup comparisons between pwCF receiving isolated liver transplantation (LiverTx) and combined liver-lung transplantation (Liver+LungTx).

RESULTS: 215 out of 38,125 liver transplantation registrations were for pwCF. 65.1 % of the pwCF were listed for LiverTx and 34.9 % for Liver+LungTx. At registration, pwCF were younger (17.9 ± 0.8 vs. 55.0 ± 0.1; P < 0.05) and had lower Lab-MELD scores (10.0 ± 0.4 vs. 15.0 ± 0.0; P < 0.05) compared to pwnoCF. PwCF listed for LiverTx were younger (15.1 ± 0.9 vs. 26.0 ± 1.1; P < 0.05) and had higher Lab-MELD scores at transplantation (12.0 ± 1.0 vs. 9.0 ± 0.5; P < 0.05) compared to Liver+LungTx. PwCF had a higher 2-year waiting list mortality than non-CF matched controls (14.9 % vs. 0.0 %; P < 0.05). Within pwCF, mortality was higher for pwCF listed for Liver+LungTx than for LiverTx (30.7 % vs. 6.4 %; P < 0.05).

CONCLUSIONS: CF represents a distinct indication for liver transplantation, with increased varying waiting list mortality risks across the different CF-groups, not adequately captured by standard liver disease severity scores. PwCF considered for liver transplantation require close monitoring as their disease severity and mortality risk are not well represented in the current allocation algorithm, necessitating adaptations to the organ allocation rules.

PMID:40382307 | DOI:10.1016/j.jcf.2025.04.005

J Cyst Fibros. 2025 May 16:S1569-1993(25)01468-7. doi: 10.1016/j.jcf.2025.05.001. Online ahead of print.

ABSTRACT

Airway clearance to clear excessive sputum has long been a key part of cystic fibrosis care, however the introduction of highly effective modulator medications where many people with CF are experiencing reduced sputum loads, has raised a question about its necessity. Specifically, questions are being asked as to if exercise, historically an adjunct to airway clearance, could become a replacement. This short communication summarizes a debate that was held at the 2024 North American Cystic Fibrosis Conference, summarizing the key arguments for and against the replacement of traditional airway clearance with exercise.

PMID:40382306 | DOI:10.1016/j.jcf.2025.05.001

Drug Discov Today. 2025 May 15:104380. doi: 10.1016/j.drudis.2025.104380. Online ahead of print.

ABSTRACT

Inhaled antibiotics significantly impact respiratory-disorder management through targeted delivery with reduced systemic side effects. Advances in pharmaceutical formulations, particularly lipid-based nanomedicine, help improve biopharmaceutical performance and therapeutic efficacy. In addition, advancements in inhaler technologies ensure effective lung deposition and minimize systemic exposure. These innovations have further benefited chronic respiratory diseases like cystic fibrosis and COPD, where infections are frequent. For instance, the encapsulation of inhaled antibiotics, particularly the tobramycin liposomal system, has improved efficacy and reduced toxicity, whereas the nebulized colistin nanoformulation effectively targets multidrug-resistant pathogens, including the clinical efficacy of amikacin liposome inhalation in refractory pulmonary infections. Overall, advancements in lipid-based nanoformulation and delivery technologies have significantly enhanced the utility of inhaled antibiotics, providing safer and more-effective options for managing chronic and resistant infections.

PMID:40381726 | DOI:10.1016/j.drudis.2025.104380

Plant Methods. 2025 May 17;21(1):63. doi: 10.1186/s13007-025-01381-w.

ABSTRACT

Pest infestations in rice crops greatly affect yield and quality, making early detection essential. As most rice pests affect leaves and rhizomes, visual inspection of rice for pests is becoming increasingly important. In precision agriculture, fast and accurate automatic pest identification is essential. To tackle this issue, multiple models utilizing computer vision and deep learning have been applied. Owing to its high efficiency, deep learning is now the favored approach for detecting plant pests. In this regard, the paper introduces an effective rice pest detection framework utilizing the Transformer architecture, designed to capture long-range features. The paper enhances the original model by adding the self-developed RepPConv-block to reduce the problem of information redundancy in feature extraction in the model backbone and to a certain extent reduce the model parameters. The original model's CCFM structure is enhanced by integrating the Gold-YOLO neck, improving its ability to fuse multi-scale features. Additionally, the MPDIoU-based loss function enhances the model's detection performance. Using the self-constructed high-quality rice pest dataset, the model achieves higher identification accuracy while reducing the number of parameters. The proposed RP18-DETR and RP34-DETR models reduce parameters by 16.5% and 25.8%, respectively, compared to the original RT18-DETR and RT34-DETR models. With a threshold of 0.5, the average accuracy calculated is 1.2% higher for RP18-DETR than for RT18-DETR.

PMID:40382633 | DOI:10.1186/s13007-025-01381-w

BMC Cardiovasc Disord. 2025 May 17;25(1):371. doi: 10.1186/s12872-025-04739-z.

ABSTRACT

Globally, the high number of deaths are happening due to Myocardial infarction (MI). MI is considered as a life-threatening disease, which leads to an increase number of deaths or damage to the heart, and hence, prompt detection of MI is critical to decrease the mortality rate. Though, numerous works have addressed MI identification, an increased number suffer from over fitting and high computational burden in real-time scenarios. The proposed system introduces a novel MI detection technique using a Deep Residual Network (DRN), where the solution is optimized by the proposed Social Ski-Spider (SSS) Optimization algorithm is the novel combination of both Social Ski-driver (SSD) Optimization and the Spider Monkey Optimization (SMO). This model highly prevents the overfitting and computational burden, which increases the MI detection accuracy. Here, the proposed SSS-DRN performs detection by filtering the electrocardiography (ECG) signals. Later, the signal feature, transform feature, medical feature and statistical feature are extracted by the feature extraction phase followed by data augmentation that consists of permutation, random generation and re-sampling processes and finally, detection is accomplished by the SSS-DRN. Moreover, the developed SSS-DRN is researched for its efficiency considering metrics like accuracy, sensitivity, and specificity and observed 0.916, 0.921, and 0.926. Here, when considering the accuracy metrics, the performance gain observed by the devised model is 13.96%, 12.61%, 10.37%, 7.95%, 5%, 2.21%, and 2% higher than the traditional schemes. This indicates the devised model has high detection accuracy, which could be embedded in real-time clinical settings like hospital ECG machines, wearable ECG monitors, and mobile health applications. This improves the clinical decision-making process with increased patient outcomes.

PMID:40382575 | DOI:10.1186/s12872-025-04739-z

NPJ Digit Med. 2025 May 17;8(1):291. doi: 10.1038/s41746-025-01641-y.

ABSTRACT

Deep learning techniques have significantly enhanced the convenience and precision of ultrasound image diagnosis, particularly in the crucial step of lesion segmentation. However, recent studies reveal that both train-from-scratch models and pre-trained models often exhibit performance disparities across sex and age attributes, leading to biased diagnoses for different subgroups. In this paper, we propose APPLE, a novel approach designed to mitigate unfairness without altering the parameters of the base model. APPLE achieves this by learning fair perturbations in the latent space through a generative adversarial network. Extensive experiments on both a publicly available dataset and an in-house ultrasound image dataset demonstrate that our method improves segmentation and diagnostic fairness across all sensitive attributes and various backbone architectures compared to the base models. Through this study, we aim to highlight the critical importance of fairness in medical segmentation and contribute to the development of a more equitable healthcare system.

PMID:40382499 | DOI:10.1038/s41746-025-01641-y

Eur Radiol. 2025 May 17. doi: 10.1007/s00330-025-11666-2. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aims to develop a deep learning algorithm for differentiating aneurysmal subarachnoid hemorrhage (aSAH) from non-aneurysmal subarachnoid hemorrhage (naSAH) using non-contrast computed tomography (NCCT) scans.

METHODS: This retrospective study included 618 patients diagnosed with SAH. The dataset was divided into a training and internal validation cohort (533 cases: aSAH = 305, naSAH = 228) and an external test cohort (85 cases: aSAH = 55, naSAH = 30). Hemorrhage regions were automatically segmented using a U-Net + + architecture. A ResNet-based deep learning model was trained to classify the etiology of SAH.

RESULTS: The model achieved robust performance in distinguishing aSAH from naSAH. In the internal validation cohort, it yielded an average sensitivity of 0.898, specificity of 0.877, accuracy of 0.889, Matthews correlation coefficient (MCC) of 0.777, and an area under the curve (AUC) of 0.948 (95% CI: 0.929-0.967). In the external test cohort, the model demonstrated an average sensitivity of 0.891, specificity of 0.880, accuracy of 0.887, MCC of 0.761, and AUC of 0.914 (95% CI: 0.889-0.940), outperforming junior radiologists (average accuracy: 0.836; MCC: 0.660).

CONCLUSION: The study presents a deep learning architecture capable of accurately identifying SAH etiology from NCCT scans. The model's high diagnostic performance highlights its potential to support rapid and precise clinical decision-making in emergency settings.

KEY POINTS: Question Differentiating aneurysmal from naSAH is crucial for timely treatment, yet existing imaging modalities are not universally accessible or convenient for rapid diagnosis. Findings A ResNet-variant-based deep learning model utilizing non-contrast CT scans demonstrated high accuracy in classifying SAH etiology and enhanced junior radiologists' diagnostic performance. Clinical relevance AI-driven analysis of non-contrast CT scans provides a fast, cost-effective, and non-invasive solution for preoperative SAH diagnosis. This approach facilitates early identification of patients needing aneurysm surgery while minimizing unnecessary angiography in non-aneurysmal cases, enhancing clinical workflow efficiency.

PMID:40382487 | DOI:10.1007/s00330-025-11666-2

Sci Rep. 2025 May 17;15(1):17142. doi: 10.1038/s41598-025-98496-w.

ABSTRACT

The emergence of Deep Learning provides an opportunity for traffic flow prediction. However, uncertainty and volatility exhibited by nonlinearity and instability of traffic flow pose challenges to Deep Learning models. Therefore, a combined prediction model, VMD-GAT-MGTCN, based on variational modal decomposition (VMD), graph attention network (GAT), and multi-gated attention time convolutional network (MGTCN) is proposed to enhance short-term traffic flow prediction accuracy. In the VMD-GAT-MGTCN, VMD decomposes traffic flow data to obtain the modal components, the GAT and MGTCN are integrated to design the spatio-temporal feature model to obtain the temporal and spatial features of traffic flow. The predicted value of traffic flow modal components by spatio-temporal feature model are stacked to obtain the ultimate traffic flow prediction results. The simulation experiments with the compared models and the baseline models show that the VMD-GAT-MGTCN have superior prediction accuracy and effect. It also verifies the enhancement effect of the VMD algorithm on the prediction performance of the VMD-GAT-MGTCN and the good prediction results obtained by the VMD-GAT-MGTCN in the traffic flow mutation region.

PMID:40382484 | DOI:10.1038/s41598-025-98496-w

Sci Rep. 2025 May 17;15(1):17150. doi: 10.1038/s41598-025-94086-y.

ABSTRACT

Differential equations-based epidemiological compartmental systems and deep neural networks-based artificial intelligence can effectively analyze and combat monkeypox (MPV) transmission with Poisson random measure noise into a stochastic SEIQR (susceptible, exposed, infected, quarantined, recovered) model human population and SEI (susceptible, exposed, infected) for rodent population. Compartmental models have estimates of parameter complications, whereas machine learning algorithms struggle to understand MPV's progression and lack elucidation. This research introduces Levenberg Marquardt backpropagation neural networks (LMBNNS) in training, a new approach that combines compartmental frameworks with artificial neural networks (ANNs) to explain the complex mechanisms of MPV. Meanwhile, a model description proves the existence and uniqueness of a global positive solution. A threshold parameter is determined and employed to identify the factors that lead to infection in the general public. Furthermore, other criteria are developed to eliminate the infection within the entire population. The MPV is eliminated if [Formula: see text], but continues if [Formula: see text]. The study depends on two functional scenarios to quantitatively clarify the theoretical results. An adapted dataset is generated employing the Adam algorithm to minimize the mean square error (MSE) by setting its data effectiveness to 81% for training, 9% for testing, and 10% for validation. The solver's accuracy is validated by minimal absolute error and complementing responses to every hypothetical situation. In order to verify the adaptation's reliability and precision, productivity is measured using the error histogram, changeover state, and prediction for addressing the MPV model. Visual representations are used to illustrate the investigation and compare results. Utilizing this hybrid approach, we want to increase our comprehension of disease propagation, strengthen forecasting competencies, and influence more efficient public health actions. The combination of stochastic processes and machine learning approaches creates a powerful tool for capturing the inherent uncertainties in infectious disease dynamics, as well as a more accurate framework for real-time epidemic prediction and prevention.

PMID:40382439 | DOI:10.1038/s41598-025-94086-y

Sci Rep. 2025 May 17;15(1):17135. doi: 10.1038/s41598-025-01830-5.

ABSTRACT

With the continuous development of intelligent technology, robots have entered various industries. Firefighting robots have become a hot topic in the field of firefighting and rescue equipment. For firefighting robots, autonomous firefighting technology is the core capability. It includes three steps: flame recognition, fire source location, autonomous firefighting. At present, flame recognition has been widely studied, but the adaptability is poor for different flames. For fire source location technology only rough positioning has been achieved. For autonomous firefighting, the time-consuming water point feedback adjustment method is often used. Those are not suitable for the actual fire rescue. So we proposed to use the visual information, thermal imaging morphological and thermal data of the flame for deep learning, which greatly improves the adaptability of flame recognition. The centimeter-level high-precision positioning of the fire source is achieved. Finally, the proposed water cannon fire source projection method is used to realize rapid water cannon movement instruction generation, and achieve rapid autonomous firefighting. The test results show that the proposed fire source identification algorithm can identify all fire sources up to 15 m at a speed about 15Fps. It can rapid autonomous firefighting within 0.5 s.

PMID:40382425 | DOI:10.1038/s41598-025-01830-5

Cell Signal. 2025 May 15:111867. doi: 10.1016/j.cellsig.2025.111867. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease characterized by irreversible scarring of the lungs, predominantly affecting older adults. The limited therapeutic options available are largely due to an insufficient understanding of IPF etiology and pathogenesis. This study investigated potential biomarkers to enhance IPF diagnosis and treatment strategies. Through single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing analyses of public datasets, four hub genes-FTH1, FABP5, DCXR, and IGFBP7-were identified as strongly associated with IPF. Subsequent validation in in vivo and in vitro models confirmed IGFBP7 as a novel biomarker. Double immunofluorescence staining and scRNA-seq analysis revealed that IGFBP7 expression is elevated in IPF epithelial cells. IGFBP7 shows potential for early diagnosis of IPF and can differentiate IPF from other diseases. Gene set enrichment analysis revealed the involvement of IGFBP7 in IPF pathogenesis, particularly through its strong connection to the TGF-β signaling pathway, which drives inflammation and fibrosis. In vitro studies with the TGF-β inhibitor SB431542 showed that inhibition of the TGF-β pathway significantly reduced IGFBP7 expression. Furthermore, IGFBP7 knockdown decreased the expression of markers associated with epithelial-mesenchymal transition and fibrosis while suppressing TGF-β1 expression. These results suggest that IGFBP7 forms a positive feedback loop with TGF-β1. In conclusion, this research identified IGFBP7 as a promising biomarker with significant diagnostic and therapeutic potential for IPF. These insights pave the way for improved diagnostics and the development of targeted antifibrosis therapies, while deepening our understanding of IPF mechanisms.

PMID:40381971 | DOI:10.1016/j.cellsig.2025.111867

Lancet Rheumatol. 2025 May 14:S2665-9913(25)00026-8. doi: 10.1016/S2665-9913(25)00026-8. Online ahead of print.

ABSTRACT

BACKGROUND: In patients with systemic sclerosis, it is common practice to treat interstitial lung disease (ILD) in patients in whom progression has already occurred. We sought to clarify whether observed progression of systemic sclerosis-associated ILD confers risk for subsequent progression.

METHODS: In this multicentre observational cohort study, based on an analysis of prospectively collected data, we included patients with systemic sclerosis-associated ILD aged 18 years or older at diagnosis, who fulfilled the 2013 American College of Rheumatology-European Association of Alliances in Rheumatology systemic sclerosis classification criteria. The main cohort (diagnosed between January 2001 and December 2019) was consecutively followed up annually over 4 years at the Department of Rheumatology at the Oslo University Hospital, Norway, and the Department of Rheumatology at the University Hospital Zurich, Switzerland. We applied four definitions of ILD progression: the primary definition was forced vital capacity (FVC) decline of 5% or more, and secondary definitions included FVC decline of 10% or more, progressive pulmonary fibrosis (PPF), and progressive fibrosing ILD (PF-ILD). We applied these definitions at each annual visit after the first (visit 1). We validated our findings in an enriched cohort that included patients from the main cohort with systemic sclerosis-associated ILD and short disease duration of less than 3 years along with patients diagnosed between January 2003 and September 2019 from the Division of Rheumatology, University of Michigan, Ann Arbor, MI, USA. Multivariable logistic regression analyses were applied to predict ILD progression and its effect on mortality. There was no involvement of people with lived experience in this study.

FINDINGS: Of 231 patients with systemic sclerosis-associated ILD from the main cohort (mean age 48·0 years [SD 14·6], 176 [76%] female and 55 [24%] male), 71 (31%) had ILD progression as defined by an FVC decline of 5% or more between visit 1 and visit 2, 38 (16%) as defined by an FVC decline of 10% or more, 39 (17%) as defined by PPF, and 89 (39%) defined by PF-ILD. In multivariable logistic regression analyses, adjusted for risk factors for progressive systemic sclerosis-associated ILD and immunosuppressive treatment, we found that ILD progression, defined by FVC decline of 5% or more, from visit 1 to visit 2 reduced the risk for further progression from visit 2 to visit 3 (odds ratio [OR] 0·28 [95% CI 0·12-0·63]; p=0·002) and that there was no risk for subsequent progression using the other definitions (FVC decline of ≥10%: 0·57 [0·16-1·99; p=0·38]; PPF: 0·93 [0·39-2·22; p=0·88]; and PF-ILD: 0·69 [0·35-1·36]; p=0·28]). Using the primary definition of progression, we found the same results in the enriched systemic sclerosis-associated ILD cohort, wherein 41 (34%) of 121 patients had progression defined by an FVC decline of 5% or more (OR 0·22 [95% CI 0·06-0·87]; p=0·031). FVC decline of 5% or more was significantly associated with mortality (hazard ratio 1·66 [95% CI 1·05-2·62]; p=0·030) adjusted for other risk factors.

INTERPRETATION: Systemic sclerosis-associated ILD progression does not predict further ILD progression at the next annual follow-up visit, even in an enriched population, but progression was associated with mortality. These results have implications for clinical practice because they support a paradigm shift in treatment strategy, advocating for initiating therapy in patients at risk of progression. Further research is needed to confirm these findings.

FUNDING: None.

TRANSLATIONS: For the German and Norwegian translations of the abstract see Supplementary Materials section.

PMID:40381640 | DOI:10.1016/S2665-9913(25)00026-8

Nat Commun. 2025 May 17;16(1):4607. doi: 10.1038/s41467-025-59780-5.

ABSTRACT

Cis-regulatory elements (CREs), including enhancers, silencers, promoters and insulators, play pivotal roles in orchestrating gene regulatory mechanisms that drive complex biological traits. However, current approaches for CRE identification are predominantly sequence-based and typically focus on individual CRE types, limiting insights into their cell-type-specific functions and regulatory dynamics. Here, we present CREATE, a multimodal deep learning framework based on Vector Quantized Variational AutoEncoder, tailored for comprehensive CRE identification and characterization. CREATE integrates genomic sequences, chromatin accessibility, and chromatin interaction data to generate discrete CRE embeddings, enabling accurate multi-class classification and robust characterization of CREs. CREATE excels in identifying cell-type-specific CREs, and provides quantitative and interpretable insights into CRE-specific features, uncovering the underlying regulatory codes. By facilitating large-scale prediction of CREs in specific cell types, CREATE enhances the recognition of disease- or phenotype-associated biological variabilities of CREs, thus advancing our understanding of gene regulatory landscapes and their roles in health and disease.

PMID:40382355 | DOI:10.1038/s41467-025-59780-5

Trends Genet. 2025 May 16:S0168-9525(25)00103-9. doi: 10.1016/j.tig.2025.04.011. Online ahead of print.

ABSTRACT

LINE-1 (long interspersed nuclear element 1, L1) retrotransposons constitute ~17% of human DNA (~0.5 million genomic L1 copies) and exhibit context-dependent expression in different cell lines. Recent studies reveal that L1 is under multilayered control by diverse factors that either collaborate or compete with each other to ensure precise L1 activity. Remarkably, L1s have been co-opted as various transcription-dependent regulatory elements, such as promoters, enhancers, and topologically associating domain (TAD) boundaries, that regulate gene expression in zygotic genome activation, aging, cancer, and other disorders. This review highlights the regulation of L1 and its regulatory functions that influence disease and development.

PMID:40382218 | DOI:10.1016/j.tig.2025.04.011

Int J Radiat Oncol Biol Phys. 2025 Jun 1;122(2):524. doi: 10.1016/j.ijrobp.2025.03.057.

NO ABSTRACT

PMID:40382175 | DOI:10.1016/j.ijrobp.2025.03.057

J Mol Biol. 2025 May 15:169222. doi: 10.1016/j.jmb.2025.169222. Online ahead of print.

NO ABSTRACT

PMID:40381984 | DOI:10.1016/j.jmb.2025.169222

Neurobiol Dis. 2025 May 15:106954. doi: 10.1016/j.nbd.2025.106954. Online ahead of print.

ABSTRACT

Pathogenic variants in the neuronal Na+/K+ ATPase transmembrane ion transporter (ATP1A3) cause a spectrum of neurological disorders including alternating hemiplegia of childhood (AHC). The most common de novo pathogenic variants in AHC are p.D801N (~40 % of patients) and p.E815K (~25 % of patients), which lead to early mortality by spontaneous death in mice. Nevertheless, knowledge of the development of clinically relevant neurological phenotypes without the obstacle of premature death, is critical for the identification of pathophysiological mechanisms and ultimately, for the testing of therapeutic strategies in disease models. Here, we used hybrid vigor attempting to mitigate the fragility of AHC mice and then performed behavioral, electrophysiological, biochemical, and molecular testing to comparatively analyze mice that carry either of the two most common AHC patient observed variants in the Atp1a3 gene. Collectively, our data reveal the presence but also the differential impact of the p.D801N and p.E815K variants on disease relevant alterations such as spontaneous and stress-induced paroxysmal episodes, motor function, behavioral and neurophysiological activity, and neuroinflammation. Our alternate AHC mouse models with their phenotypic deficits open novel avenues for the investigation of disease biology and therapeutic testing for ATP1A3 research.

PMID:40381892 | DOI:10.1016/j.nbd.2025.106954