BMC Cardiovasc Disord. 2025 May 7;25(1):353. doi: 10.1186/s12872-025-04753-1.

ABSTRACT

BACKGROUND: Human genetics is an important tool for identifying genes as potential drug targets, and the extensive genetic study of cardiovascular disease provides an opportunity to leverage genetics to match specific patient populations to specific drug targets to improve prioritization of patient selection for clinical studies.

METHODS: We selected well described genetic variants in the region of PCSK9 (rs11591147 and rs562556), ADRB1 (rs7076938), ACE (rs4968782 and rs4363), GLP1R (rs10305492) and ABCC8 (rs757110) for use as proxies for the effects of drugs. Time-to-event analyses were utilized to evaluate their effects on atrial fibrillation (AF) and heart failure (HF) death and/or re-hospitalization using real-world longitudinal dataset. To mitigate the effect of confounding factors for cardiovascular (CV) outcomes, we employed propensity score matching.

RESULTS: After matching, a genetic proxy for PCSK9 inhibition (rs11591147) improved survival from CV death/heart transplant in individuals following a diagnosis of ischemic heart disease (Hazard Ratio (HR) 0.78, P = 0.03). A genetic proxy for beta-blockade (rs7076938) improved freedom from rehospitalization or death in individuals with AF (HR 0.92, P = 0.001), and a genetic proxy of ACE inhibition (rs7076938) improved freedom from rehospitalization for HF or death (HR 0.8, P = 0.017) and AF (HR 0.85, P = 0.0014). A protective variant in GLP1R (rs10305492) showed decreased risk of developing HF or CV death after diagnosis of ischemic heart disease (HR = 0.82, P = 0.031) and a protective variant in ABCC8 (rs757110) showed decreased risk of CV mortality since ischemic disease diagnosis (HR = 0.88, P = 0.04) and decreased risk of AF in diabetic patients with ischemic heart disease (HR = 0.68, P = 0.001). Notably, despite smaller cohort sizes after matching, we often observed numerically smaller HRs and reduced P, indicating more pronounced effects and increased statistical association. However, not all genetic proxies replicated known treatment effects.

CONCLUSIONS: Genetic proxies for well-known drugs corroborate findings from clinical trials in cardiovascular disease. Our results demonstrate a useful analytical approach that leverages genetic evidence from a large cohort with longitudinal outcomes data to effectively select patient populations where specific drug targets may be most effective.

PMID:40335923 | DOI:10.1186/s12872-025-04753-1

Int J Biol Macromol. 2025 May 5:143959. doi: 10.1016/j.ijbiomac.2025.143959. Online ahead of print.

ABSTRACT

Drug repositioning refers to new medical application exploration for existing drugs. Heparins, beyond their well-known anticoagulant properties widely used in clinics, present the capacity to carry biosignal molecules that is responsible for other properties such as anti-inflammatory, angiogenesis. Thus, heparins interaction with different biosignal molecules such as cytokines and growth factors have recently drawn attention and have promoted heparin repositioning as an active excipient with useful applications as drug-delivery systems and biomaterial-based tissue engineering scaffolds. Indeed, biomaterial heparinization can further help in their formulation such as in self-assembled heparin-based hydrogels or nanoparticles, and improve their biocompatibility. Moreover, the capacity of heparin to carry biosignal molecules enables the direct functionalization of heparinized biomaterial for tissue engineering. Both heparin characteristics namely the biosignal molecule carrying and biomaterial heparinization are reviewed here along their combination for biomaterial functionalization in tissue engineering applications.

PMID:40334894 | DOI:10.1016/j.ijbiomac.2025.143959

Pharmacogenomics J. 2025 May 7;25(3):13. doi: 10.1038/s41397-025-00370-5.

ABSTRACT

The serotonin transporter (SLC6A4) and the serotonin autoreceptor (HTR1A) are two of the most extensively studied genes in the field of psychiatry, and their variants have been implicated in antidepressant response, specifically with selective serotonin reuptake inhibitors (SSRIs) which are widely regarded as the first-line medications for depression and anxiety. Variants of SLC6A4 and HTR1A have also been studied as risk factors for depression. In this retrospective study, we aim to investigate the relationship between all possible serotonin transporter (SLC6A4) and autoreceptor (HTR1A) variant expression combinations that may have contributed to the therapeutic failure of an SSRI and subsequent disability. In this study, we utilize data from a cohort of 302 European patients diagnosed with depression and/or anxiety who were referred to Personalized Prescribing Inc. (PPI) in 2022 as result of a mental health disability claim to determine whether statistical differences are present in this cohort as compared to general European population allele frequencies. Our data reveals the presence and relevance of significant differences in the presentation of SLC6A4 and HTR1A, specifically in a disability cohort, relative to the average European population. The SLC6A4 gene codes for the serotonin transporter; the SSRI drug target that aims to be blocked to prevent the recycling of serotonin, whereas the HTR1A plays an indirect role as an autoreceptor allowing serotonin levels to be maintained by the SSRI, as well as a direct role in modulating mood through post-synaptic serotonin interaction. This study has revealed statistically significant differences in the expression of these two genes together in increasing the likelihood of drug failure, specifically the presence of one or more G alleles at HTR1A rs6295 in combination with the SLC6A4 SS variant. The most significantly overrepresented combination in this cohort of patients suffering from depression and anxiety that have failed to achieve adequate symptom remission on previous SSRI trials is HTR1A rs6295 GG-SLC6A4 SS which is overrepresented in this study by over 74% at a p-value well below 0.01. Genotyping anti-depressant drug targets may play an important role in optimizing anti-depressant drug response and research developments for future therapies.

PMID:40335484 | DOI:10.1038/s41397-025-00370-5

Nat Commun. 2025 May 7;16(1):4235. doi: 10.1038/s41467-025-56910-x.

ABSTRACT

Chronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly.

PMID:40335470 | DOI:10.1038/s41467-025-56910-x

BMJ Open. 2025 May 6;15(5):e088456. doi: 10.1136/bmjopen-2024-088456.

ABSTRACT

INTRODUCTION: Patients with chronic limb-threatening ischaemia (CLTI) are often prescribed clopidogrel in order to reduce their risk of major adverse limb and cardiovascular events. Clopidogrel is metabolised by the CYP2C19 enzyme and genetic variations in CYP2C19 are common. These variants can influence an individual's ability to metabolise clopidogrel to its active metabolite. Few studies have investigated the relationship between patient genotype and outcomes in vascular surgery. This work aims to establish the relationship between patient genotype and outcomes after revascularisation in patients with CLTI who are prescribed clopidogrel. It will consider whether pharmacogenetics can be used to ensure patients are prescribed effective medications to optimise their outcomes.

METHODS AND ANALYSIS: This is an observational cohort study of patients undergoing lower limb surgical, endovascular or hybrid revascularisation for CLTI at Manchester University NHS Foundation Trust. Patients taking clopidogrel post-procedure, as well as those prescribed a non-clopidogrel based medication regimen, will be recruited prior to or shortly after revascularisation. Patients will undergo CYP2C19 genotyping and will be followed up using online records. The study has 90% power to detect 114 amputations with a target sample size of 483 participants. The primary outcomes are risk of amputation at 1 year and a composite endpoint for the risk of major adverse limb events (MALE) or death from any cause at 1 year. Secondary outcomes are risk of MALE at 1 year, risk of major adverse cardiovascular events (MACE) or death from any cause at 1 year, death within 30 days of revascularisation, minor re-interventions at 1 year, total number of re-interventions at 1 year and rate of systemic or gastrointestinal bleed at 1 year.Risk of amputation, MALE and MACE will be analysed using Cox models. All remaining outcomes will be analysed using negative binomial models. Potential competing events for the risk of amputation will be investigated as part of a sensitivity analysis. Patients given a non-clopidogrel-based medication will be compared as an additional analysis.

ETHICS AND DISSEMINATION: Manchester University Research Ethics Committee approval obtained as part of the Implementing Pharmacogenetics to Improve Prescribing (IPTIP) trial process (IRAS 305751). The results of the study will be published in a peer-reviewed journal and presented at international conferences.

REGISTRATION: This work is a sub-protocol for the IPTIP study which is registered as ISRCTN14050335.

PMID:40335138 | DOI:10.1136/bmjopen-2024-088456

Nat Commun. 2025 May 7;16(1):4235. doi: 10.1038/s41467-025-56910-x.

ABSTRACT

Chronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly.

PMID:40335470 | DOI:10.1038/s41467-025-56910-x

BMC Vet Res. 2025 May 8;21(1):326. doi: 10.1186/s12917-025-04802-z.

ABSTRACT

BACKGROUND: Myxomatous mitral valve disease (MMVD) represents the most prevalent cardiac disorder in dogs, frequently resulting in mitral regurgitation (MR) and congestive heart failure. Although echocardiography is the gold standard for diagnosis, it is an expensive tool that involves significant clinical training to ensure consistent application. Deep learning models offer an innovative approach to assessing MR using digital stethoscopic recordings, enabling early screening and precise prediction. Thus, in this study, we evaluated the effectiveness of a convolutional neural network 6 (CNN6) in providing an objective alternative to traditional methods for assessing MR. This study, conducted at the Seoul National University Veterinary Medicine Teaching Hospital, included 460 dogs with MMVD, classified according to the American College of Veterinary Internal Medicine guidelines. Phonocardiogram signals were recorded using digital stethoscopes and analyzed using the deep models CNN6, patch-mix audio spectrogram transformer (PaSST), and residual neural network (ResNET38), which were trained to categorize MR severity into mild, moderate, and severe based on MINE score. Performance metrics were calculated to evaluate model effectiveness.

RESULTS: The CNN6-Fbank model achieved an accuracy of 94.12% [95% confidence interval (CI): 94.11-93.12], specificity of 97.30% (95% CI: 97.30-97.34), sensitivity of 94.12% (95% CI: 93.74-94.50), precision of 92.63% (95% CI: 92.29-92.97), and F1 score of 93.32% (95% CI: 93.05-93.59), outperforming the PaSST and ResNet38 models overall and demonstrating robust performance across most metrics.

CONCLUSIONS: Deep learning models, particularly CNN6, can effectively assess MR severity in dogs with MMVD using digital stethoscope recordings. This approach provides a rapid, noninvasive, and reliable adjunct to echocardiography, potentially enhancing diagnosis and outcomes. Future studies should focus on broader clinical validation and real-time application of this technology.

PMID:40336065 | DOI:10.1186/s12917-025-04802-z

BMC Med Imaging. 2025 May 7;25(1):155. doi: 10.1186/s12880-025-01683-4.

ABSTRACT

BACKGROUND: Cardiovascular diseases are the number one cause of death globally, making cardiac magnetic resonance image segmentation a popular research topic. Existing schemas relying on manual user interaction or semi-automatic segmentation are infeasible when dealing thousands of cardiac MRI studies. Thus, we proposed a full automatic and robust algorithm for large-scale cardiac MRI segmentation by combining the advantages of deep learning localization and 3D-ASM restriction.

MATERIAL AND METHODS: The proposed method comprises several key techniques: 1) a hybrid network integrating CNNs and Transformer as a encoder with the EFG (Edge feature guidance) module (named as CTr-HNs) to localize the target regions of the cardiac on MRI images, 2) initial shape acquisition by alignment of coarse segmentation contours to the initial surface model of 3D-ASM, 3) refinement of the initial shape to cover all slices of MRI in the short axis by complex transformation. The datasets used are from the UK BioBank and the CAP (Cardiac Atlas Project). In cardiac coarse segmentation experiments on MR images, Dice coefficients (Dice), mean contour distances (MCD), and mean Hausdorff distances (HD95) are used to evaluate segmentation performance. In SPASM experiments, Point-to-surface (P2S) distances, Dice score are compared between automatic results and ground truth.

RESULTS: The CTr-HNs from our proposed method achieves Dice coefficients (Dice), mean contour distances (MCD), and mean Hausdorff distances (HD95) of 0.95, 0.10 and 1.54 for the LV segmentation respectively, 0.88, 0.13 and 1.94 for the LV myocardium segmentation, and 0.91, 0.24 and 3.25 for the RV segmentation. The overall P2S errors from our proposed schema is 1.45 mm. For endocardium and epicardium, the Dice scores are 0.87 and 0.91 respectively.

CONCLUSIONS: Our experimental results show that the proposed schema can automatically analyze large-scale quantification from population cardiac images with robustness and accuracy.

PMID:40335966 | DOI:10.1186/s12880-025-01683-4

BMC Med Imaging. 2025 May 7;25(1):156. doi: 10.1186/s12880-025-01701-5.

ABSTRACT

Medical images occupy the largest part of the existing medical information and dealing with them is challenging not only in terms of management but also in terms of interpretation and analysis. Hence, analyzing, understanding, and classifying them, becomes a very expensive and time-consuming task, especially if performed manually. Deep learning is considered a good solution for image classification, segmentation, and transfer learning tasks since it offers a large number of algorithms to solve such complex problems. PRISMA-ScR guidelines have been followed to conduct the scoping review with the aim of exploring how deep learning is being used to classify a broad spectrum of diseases diagnosed using an X-ray, MRI, or Ultrasound image modality.Findings contribute to the existing research by outlining the characteristics of the adopted datasets and the preprocessing or augmentation techniques applied to them. The authors summarized all relevant studies based on the deep learning models used and the accuracy achieved for classification. Whenever possible, they included details about the hardware and software configurations, as well as the architectural components of the models employed. Moreover, the models that achieved the highest accuracy in disease classification were highlighted, along with their strengths. The authors also discussed the limitations of the current approaches and proposed future directions for medical image classification.

PMID:40335965 | DOI:10.1186/s12880-025-01701-5

BMC Bioinformatics. 2025 May 7;26(1):123. doi: 10.1186/s12859-025-06072-w.

ABSTRACT

The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming the landscape of scientific research. However, the challenge of designing molecular drugs or materials that incorporate multi-modality prior knowledge remains a critical and complex undertaking. Specifically, achieving a practical molecular design necessitates not only meeting the diversity requirements but also addressing structural and textural constraints with various symmetries outlined by domain experts. In this article, we present an innovative approach to tackle this inverse design problem by formulating it as a multi-modality guidance optimization task. Our proposed solution involves a textural-structure alignment symmetric diffusion framework for the implementation of molecular optimization tasks, namely 3DToMolo. 3DToMolo aims to harmonize diverse modalities including textual description features and graph structural features, aligning them seamlessly to produce molecular structures adhere to specified symmetric structural and textural constraints by experts in the field. Experimental trials across three guidance optimization settings have shown a superior hit optimization performance compared to state-of-the-art methodologies. Moreover, 3DToMolo demonstrates the capability to discover potential novel molecules, incorporating specified target substructures, without the need for prior knowledge. This work not only holds general significance for the advancement of deep learning methodologies but also paves the way for a transformative shift in molecular design strategies. 3DToMolo creates opportunities for a more nuanced and effective exploration of the vast chemical space, opening new frontiers in the development of molecular entities with tailored properties and functionalities.

PMID:40335938 | DOI:10.1186/s12859-025-06072-w

BMC Med. 2025 May 7;23(1):267. doi: 10.1186/s12916-025-04104-z.

ABSTRACT

BACKGROUND: CT images and circulating tumor cells (CTCs) are indispensable for diagnosing the mediastinal lesions by providing radiological and intra-tumoral information. This study aimed to develop and validate a deep multimodal fusion network (DMFN) combining CTCs and CT images for the multi-classification of mediastinal lesions.

METHODS: In this retrospective diagnostic study, we enrolled 1074 patients with 1500 enhanced CT images and 1074 CTCs results between Jan 1, 2020, and Dec 31, 2023. Patients were divided into the training cohort (n = 434), validation cohort (n = 288), and test cohort (n = 352). The DMFN and monomodal convolutional neural network (CNN) models were developed and validated using the CT images and CTCs results. The diagnostic performances of DMFN and monomodal CNN models were based on the Paraffin-embedded pathologies from surgical tissues. The predictive abilities were compared with thoracic resident physicians, attending physicians, and chief physicians by the area under the receiver operating characteristic (ROC) curve, and diagnostic results were visualized in the heatmap.

RESULTS: For binary classification, the predictive performances of DMFN (AUC = 0.941, 95% CI 0.901-0.982) were better than the monomodal CNN model (AUC = 0.710, 95% CI 0.664-0.756). In addition, the DMFN model achieved better predictive performances than the thoracic chief physicians, attending physicians, and resident physicians (P = 0.054, 0.020, 0.016) respectively. For the multiclassification, the DMFN achieved encouraging predictive abilities (AUC = 0.884, 95%CI 0.837-0.931), significantly outperforming the monomodal CNN (AUC = 0.722, 95%CI 0.705-0.739), also better than the chief physicians (AUC = 0.787, 95%CI 0.714-0.862), attending physicians (AUC = 0.632, 95%CI 0.612-0.654), and resident physicians (AUC = 0.541, 95%CI 0.508-0.574).

CONCLUSIONS: This study showed the feasibility and effectiveness of CNN model combing CT images and CTCs levels in predicting the diagnosis of mediastinal lesions. It could serve as a useful method to assist thoracic surgeons in improving diagnostic accuracy and has the potential to make management decisions.

PMID:40335930 | DOI:10.1186/s12916-025-04104-z

Med Biol Eng Comput. 2025 May 8. doi: 10.1007/s11517-025-03365-3. Online ahead of print.

ABSTRACT

Despite the extensive use of biomarkers like PSA, AMACR, and PCA3, prostate cancer (PCa) is still a major clinical challenge, demanding the development of more precise and specific methods for diagnosis. In this study, a deep learning model was applied to identify ten key genes from a pool of 68 common differentially expressed genes in the three transcriptomic datasets. The model demonstrated high performance, with the accuracy of 0.969, R2 of 0.88, and PR-AUC of 0.98. Notably, selected genes have been previously reported as functionally important in various cancers. Among them, SCUBE2 stands out as a novel potential diagnostic biomarker in prostate cancer, showing a strong diagnostic performance in the TCGA dataset with AUC = 0.84, sensitivity = 0.76, and specificity = 0.84. SCUBE2 is a secreted glycoprotein known for its ability to suppress tumor growth, cell migration, and epithelial-mesenchymal transition (EMT) in several cancer types, including gliomas, breast, and colorectal cancers, mainly through its regulation of signaling pathways such as Hedgehog (Shh). Although its role in prostate cancer (PCa) has not been previously explored, its consistent downregulation across multiple PCa datasets in this study suggests it may act as a tumor suppressor, warranting further investigation. Another candidate, SLC16A5, showed moderate performance individually (AUC = 0.62, SP = 0.81, SE = 0.42 in GSE88808), but its combination with SCUBE2 significantly enhanced diagnostic accuracy (combined AUC = 0.76, SE = 0.75, SP = 0.71). SLC16A5 is a monocarboxylate transporter involved in metabolic reprogramming, and prior studies have linked its downregulation to immune infiltration and poor prognosis in PCa. Functional enrichment analysis of the ten identified genes revealed strong involvement of these genes in cancer-related processes, including gap junction assembly, tight junction formation, efflux transporter activity, and pathways such as Hedgehog signaling, leukocyte transendothelial migration, and cell-cell adhesion. Hub gene analysis further confirmed the central roles of identified genes such as CAV1, GJA1, AMACR, and CLDN8, which are well-documented in cancer progression, metastasis, or therapeutic resistance. In summary, this study identifies SCUBE2 as a novel potential diagnostic biomarker for prostate cancer and supports the use of AI-driven gene discovery in identifying key players in tumor biology. The combination of SCUBE2 with SLC16A5 not only enhances diagnostic precision but also opens new avenues for functional and clinical validation, ultimately contributing to the development of more accurate, multi-gene diagnostic panels for PCa.

PMID:40335872 | DOI:10.1007/s11517-025-03365-3

Interdiscip Sci. 2025 May 7. doi: 10.1007/s12539-025-00703-9. Online ahead of print.

ABSTRACT

Enhancers are short DNA fragments capable of significantly increase the frequency of gene transcription. They often exert their effects on targeted genes over long distances, either in cis or in trans configurations. Identifying enhancers poses a challenge due to their variable position and sensitivities. Genetic variants within enhancer regions have been implicated in human diseases, highlighting critical importance of enhancers identification and strength prediction. Here, we develop a two-layer predictor named iEnhancer-GDM to identify enhancers and to predict enhancer strength. To address the challenges posed by the limited size of enhancer training dataset, which could cause issues such as model overfitting and low classification accuracy, we introduce a Wasserstein generative adversarial network (WGAN-GP) to augment the dataset. We employ a dna2vec embedding layer to encode raw DNA sequences into numerical feature representations, and then integrate multi-scale convolutional neural network, bidirectional long short-term memory network and multi-head attention mechanism for feature representation and classification. Our results validate the effectiveness of data augmentation in WGAN-GP. Our model iEnhancer-GDM achieves superior performance on an independent test dataset, and outperforms the existing models with improvements of 2.45% for enhancer identification and 11.5% for enhancer strength prediction by benchmarking against current methods. iEnhancer-GDM advances the precise enhancer identification and strength prediction, thereby helping to understand the functions of enhancers and their associations on genomics.

PMID:40335860 | DOI:10.1007/s12539-025-00703-9

Nat Biotechnol. 2025 May 7. doi: 10.1038/s41587-025-02655-3. Online ahead of print.

ABSTRACT

Naturally existing enzymes have been adapted for a variety of molecular technologies, with enhancements or modifications to the enzymes introduced to improve the desired function; however, it is difficult to engineer variants with enhanced activity while maintaining specificity. Here we engineer the compact Obligate Mobile Element Guided Activity (OMEGA) RNA-guided endonuclease IscB and its guiding RNA (ωRNA) by combining ortholog screening, structure-guided protein domain design and RNA engineering, and deep learning-based structure prediction to generate an improved variant, NovaIscB. We show that the compact NovaIscB achieves up to 40% indel activity (~100-fold improvement over wild-type OgeuIscB) on the human genome with improved specificity relative to existing IscBs. We further show that NovaIscB can be fused with a methyltransferase to create a programmable transcriptional repressor, OMEGAoff, that is compact enough to be packaged in a single adeno-associated virus vector for persistent in vivo gene repression. This study highlights the power of combining natural diversity with protein engineering to design enhanced enzymes for molecular biology applications.

PMID:40335752 | DOI:10.1038/s41587-025-02655-3

Plant Cell Rep. 2025 May 8;44(6):115. doi: 10.1007/s00299-025-03498-7.

ABSTRACT

Using deep learning-based image restoration, we achieved high-resolution 4D imaging with minimal photodamage, revealing distinct localization and suggesting Lifeact-RFP-labeled actin microfilaments play a role in initiating cell plate formation. Phragmoplasts are plant-specific intracellular structures composed of microtubules, actin microfilaments (AFs), membranes, and associated proteins. Importantly, they are involved in the formation and the expansion of cell plates that partition daughter cells during cell division. While previous studies have revealed the important role of cytoskeletal dynamics in the proper functioning of the phragmoplast, the localization and the role of AFs in the initial phase of cell plate formation remain controversial. Here, we used deep learning-based image restoration to achieve high-resolution 4D imaging with minimal laser-induced damage, enabling us to investigate the dynamics of AFs during the initial phase of cell plate formation in transgenic tobacco BY-2 cells labeled with Lifeact-RFP or RFP-ABD2 (actin-binding domain 2). This computational approach overcame the limitation of conventional imaging, namely laser-induced photobleaching and phototoxicity. The restored images indicated that RFP-ABD2-labeled AFs were predominantly localized near the daughter nucleus, whereas Lifeact-RFP-labeled AFs were found not only near the daughter nucleus but also around the initial cell plate. These findings, validated by imaging with a long exposure time, highlight distinct localization patterns between the two AF probes and suggest that Lifeact-RFP-labeled AFs play a role in initiating cell plate formation.

PMID:40335746 | DOI:10.1007/s00299-025-03498-7

Nature. 2025 May 7. doi: 10.1038/s41586-025-08985-1. Online ahead of print.

ABSTRACT

The information-processing capability of the brain's cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution1,2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner.

PMID:40335689 | DOI:10.1038/s41586-025-08985-1

Nature. 2025 May 7. doi: 10.1038/s41586-025-08973-5. Online ahead of print.

ABSTRACT

Despite recent advances in understanding disease biology, treatment of group 3/4 medulloblastoma remains a therapeutic challenge in paediatric neuro-oncology1. Bulk-omics approaches have identified considerable intertumoural heterogeneity in group 3/4 medulloblastoma, including the presence of clear single-gene oncogenic drivers in only a subset of cases, whereas in most cases, large-scale copy number aberrations prevail2,3. However, intratumoural heterogeneity, the role of oncogene aberrations, and broad copy number variation in tumour evolution and treatment resistance remain poorly understood. To dissect this interplay, we used single-cell technologies (single-nucleus RNA sequencing (snRNA-seq), single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing (snATAC-seq) and spatial transcriptomics) on a cohort of group 3/4 medulloblastoma with known alterations in the oncogenes MYC, MYCN and PRDM6. We show that large-scale chromosomal aberrations are early tumour-initiating events, whereas the single-gene oncogenic events arise late and are typically subclonal, but MYC can become clonal upon disease progression to drive further tumour development and therapy resistance. Spatial transcriptomics shows that the subclones are mostly interspersed across tumour tissue, but clear segregation is also present. Using a population genetics model, we estimate medulloblastoma initiation in the cerebellar unipolar brush cell lineage starting from the first gestational trimester. Our findings demonstrate how single-cell technologies can be applied for early detection and diagnosis of this fatal disease.

PMID:40335697 | DOI:10.1038/s41586-025-08973-5

Nature. 2025 May 7. doi: 10.1038/s41586-025-08971-7. Online ahead of print.

ABSTRACT

The eukaryotic genome is packed into nucleosomes of 147 base pairs around a histone core and is organized into euchromatin and heterochromatin, corresponding to the A and B compartments, respectively1,2. Here we investigated whether individual nucleosomes contain sufficient information for 3D genomic organization into compartments, for example, in their biophysical properties. We purified native mononucleosomes to high monodispersity and used physiological concentrations of polyamines to determine their condensability. The chromosomal regions known to partition into A compartments have low condensability and those for B compartments have high condensability. Chromatin polymer simulations using condensability as the only input, without any trans factors, reproduced the A/B compartments. Condensability is also strongly anticorrelated with gene expression, particularly near the promoters and in a cell type-dependent manner. Therefore, mononucleosomes have biophysical properties associated with genes being on or off. Comparisons with genetic and epigenetic features indicate that nucleosome condensability is an emergent property, providing a natural axis on which to project the high-dimensional cellular chromatin state. Analysis using various condensing agents or histone modifications and mutations indicates that the genome organization principle encoded into nucleosomes is mostly electrostatic in nature. Polyamine depletion in mouse T cells, resulting from either knocking out or inhibiting ornithine decarboxylase, results in hyperpolarized condensability, indicating that when cells cannot rely on polyamines to translate the biophysical properties of nucleosomes to 3D genome organization, they accentuate condensability contrast, which may explain the dysfunction observed with polyamine deficiency3-5.

PMID:40335690 | DOI:10.1038/s41586-025-08971-7

Sci Rep. 2025 May 7;15(1):15886. doi: 10.1038/s41598-025-00817-6.

ABSTRACT

Isoform switching in cancer is a prevalent phenomenon with significant implications for immunotherapy, as actionable neoantigens derived from these cancer-specific events would be applicable to broad categories of patients, reducing the necessity for personalized treatments. By integrating five large-scale transcriptomic datasets comprising over 19,500 samples across 29 cancer and 54 normal tissue types, we identified cancer-associated isoform switching events common to multiple cancer types, several of which involve genes with established mechanistic roles in oncogenesis. The presence of neoantigen-containing peptides derived from these transcripts was confirmed in broad cancer and normal tissue proteome datasets and the binding affinity of predicted neoantigens to the human leukocyte antigen (HLA) complex via molecular dynamics simulations. The study presents strong evidence that isoform switching in cancer is a significant source of actionable neoantigens that have the capability to trigger an immune response. These findings suggest that isoform switching events could potentially be leveraged for broad immunotherapeutic strategies across various cancer types.

PMID:40335513 | DOI:10.1038/s41598-025-00817-6