Nat Biotechnol. 2025 Jun 2. doi: 10.1038/s41587-025-02675-z. Online ahead of print.

ABSTRACT

Alpha-1 antitrypsin (A1AT) deficiency (AATD) is caused by a mutation in the SERPINA1 gene (PiZ allele), where misfolded A1AT liver accumulation leads to liver damage, and A1AT deficiency in the lungs results in emphysema due to unregulated neutrophil elastase activity. Base editing offers a potential cure for A1AT; however, effective treatment is hindered by the absence of dual-target delivery systems that can target key tissues. We developed Dual Selective ORgan-Targeting lipid nanoparticles (SORT LNPs) to deliver base editors to the liver and lungs. Dual SORT LNPs correct the PiZ mutation, achieving 40% correction editing in liver cells and 10% in lung AT2 cells. The liver maintains stable editing for 32 weeks, reducing Z-A1AT levels by over 80% and restoring a normal liver phenotype. In parallel, 89% neutrophil elastase inhibition is achieved in lung bronchoalveolar lavage fluid. Taken together, Dual SORT LNP therapy offers a promising approach for long-lasting genome correction for multi-organ diseases such as AATD.

PMID:40457105 | DOI:10.1038/s41587-025-02675-z

Reprod Biol Endocrinol. 2025 Jun 2;23(1):85. doi: 10.1186/s12958-025-01417-9.

ABSTRACT

BACKGROUND: Nearly all males with cystic fibrosis (MwCF) are infertile and, thus, require the use of assisted reproductive technology (ART) to have biologic children. This study aims to describe the fertility and family-building knowledge, experiences, and care utilization of this population and to compare these findings to the general United States (US) population.

METHODS: We conducted an anonymous cross-sectional study of self-reported survey data compared to data from the 2017-2019 US National Survey for Family Growth (NSFG). We recruited MwCF age 15 years and older at seven US cystic fibrosis (CF) centers.

RESULTS: A total of 532 MwCF (mean age 35.3 ± 11.6 years) completed the survey. 83% knew that almost all MwCF are infertile and 84% were aware that MwCF can have biological children. 71% correctly identified the most common cause of male CF infertility. One third of MwCF stated they had never been told by anyone they were infertile due to their CF (mean age of discussion 19.3 ± 8.8 years). 31% reported being a parent. Among parents, 66% were a parent to a biological child born of a partner's pregnancy, 20% via step-parenthood, 15% adoption, 4.3% surrogacy, and 0.6% foster parenthood. Compared to 44% of NSFG males, 18% of MwCF age 15-49 years reported being a parent to a biological child born of their partner's pregnancy (p < 0.001). Among all MwCF, 82% with a biological child reported that they required medical assistance. Among those age 15-49 years, 87% of MwCF with a biological child required medical assistance compared to 9.4% of NSFG males (p < 0.001). Nearly three-quarters (73%) of MwCF who were biological parents underwent sperm retrieval via a variety of extraction techniques. 91% of those utilizing ART underwent in vitro fertilization and 9% intrauterine insemination of their partner.

CONCLUSIONS: MwCF face significant disease-related fertility and family-building implications with suboptimal counseling. Most MwCF who are parents pursue biological parenthood via a variety of ART services, but one-third chose alternative paths to parenthood. Further research is needed to best understand and support the family-building of MwCF.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40457440 | DOI:10.1186/s12958-025-01417-9

Respir Med. 2025 May 31:108187. doi: 10.1016/j.rmed.2025.108187. Online ahead of print.

ABSTRACT

BACKGROUND: Haemoptysis is common in adults with cystic fibrosis (CF). Tranexamic acid (TA), an antifibrinolytic agent, blocks the binding of plasminogen and plasmin to fibrin to inhibit clot breakdown. Despite its theoretical benefits, there is limited data on the use of inhaled TA for management of haemoptysis in CF.

METHODS: A 3-year retrospective audit of inhaled TA in CF patients with haemoptysis was conducted. Baseline demographics, hospitalisation status, haemoptysis volume, adverse events, adjuvant therapies and time to resolution of haemoptysis were extracted. We report on the safety, efficacy of nebulised TA, and outline our hospital-specific protocol for its use.

RESULTS: Twenty-six adults [female, 12; age (yrs), 27.2 ± 3.1 95% CI] with haemoptysis were trialled on nebulised TA, in addition to standard therapy. Nebulised TA was generally well tolerated; six patients reported chest tightness during dosing, which resolved in all cases with bronchodilators or a reduction in TA dose. In 65.4% of cases, haemoptysis resolved within 48 hours with TA use. A home haemoptysis management plan, with guidance for outpatient TA use was provided to 19 adults.

CONCLUSIONS: Nebulised TA is a well-tolerated, non-invasive and inexpensive pharmacological option for in- and outpatient management of haemoptysis in CF. Our data support the use of TA to minimise further bleeding in CF outpatients, while waiting for further intervention from their CF service. However, first-dose trials should be supervised to assess for safety and side effects, and a personalised home haemoptysis management plan should be provided prior to use.

PMID:40456472 | DOI:10.1016/j.rmed.2025.108187

Magn Reson Med. 2025 Jun 2. doi: 10.1002/mrm.30591. Online ahead of print.

ABSTRACT

PURPOSE: To examine the effect of incorporating self-supervised denoising as a pre-processing step for training deep learning (DL) based reconstruction methods on data corrupted by Gaussian noise. K-space data employed for training are typically multi-coil and inherently noisy. Although DL-based reconstruction methods trained on fully sampled data can enable high reconstruction quality, obtaining large, noise-free datasets is impractical.

METHODS: We leverage Generalized Stein's Unbiased Risk Estimate (GSURE) for denoising. We evaluate two DL-based reconstruction methods: Diffusion Probabilistic Models (DPMs) and Model-Based Deep Learning (MoDL). We evaluate the impact of denoising on the performance of these DL-based methods in solving accelerated multi-coil magnetic resonance imaging (MRI) reconstruction. The experiments were carried out on T2-weighted brain and fat-suppressed proton-density knee scans.

RESULTS: We observed that self-supervised denoising enhances the quality and efficiency of MRI reconstructions across various scenarios. Specifically, employing denoised images rather than noisy counterparts when training DL networks results in lower normalized root mean squared error (NRMSE), higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) across different SNR levels, including 32, 22, and 12 dB for T2-weighted brain data, and 24, 14, and 4 dB for fat-suppressed knee data.

CONCLUSION: We showed that denoising is an essential pre-processing technique capable of improving the efficacy of DL-based MRI reconstruction methods under diverse conditions. By refining the quality of input data, denoising enables training more effective DL networks, potentially bypassing the need for noise-free reference MRI scans.

PMID:40457510 | DOI:10.1002/mrm.30591

Mol Cancer. 2025 Jun 2;24(1):159. doi: 10.1186/s12943-025-02369-9.

ABSTRACT

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the modern era of oncology, offering sophisticated tools across the range of cancer care. In this review, we performed a systematic survey of the current status of AI technologies used for cancer diagnoses and therapeutic approaches. We discuss AI-facilitated imaging diagnostics using a range of modalities such as computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and digital pathology, highlighting the growing role of deep learning in detecting early-stage cancers. We also explore applications of AI in genomics and biomarker discovery, liquid biopsies, and non-invasive diagnoses. In therapeutic interventions, AI-based clinical decision support systems, individualized treatment planning, and AI-facilitated drug discovery are transforming precision cancer therapies. The review also evaluates the effects of AI on radiation therapy, robotic surgery, and patient management, including survival predictions, remote monitoring, and AI-facilitated clinical trials. Finally, we discuss important challenges such as data privacy, interpretability, and regulatory issues, and recommend future directions that involve the use of federated learning, synthetic biology, and quantum-boosted AI. This review highlights the groundbreaking potential of AI to revolutionize cancer care by making diagnostics, treatments, and patient management more precise, efficient, and personalized.

PMID:40457408 | DOI:10.1186/s12943-025-02369-9

BMC Bioinformatics. 2025 Jun 2;26(1):148. doi: 10.1186/s12859-025-06173-6.

ABSTRACT

BACKGROUND: Understanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their spatial coordinates.

RESULTS: We present SC2Spa, a deep learning-based approach that learns intricate spatial relationships from spatial transcriptomics (ST) data. Benchmarking tests show that SC2Spa outperformed other predictors and accurately detected tissue architecture from transcriptome. SC2Spa successfully mapped single cell RNA sequencing (scRNA-seq) to Visium assay, providing an approach to enhance the resolution for low resolution ST data. Our test showed that SC2Spa performs well for various ST technologies and robust to spatial resolution. In addition, SC2Spa can suggest spatially variable genes that cannot be identified from previous approaches.

CONCLUSIONS: SC2Spa is a robust and accurate approach to provide single cells with their spatial location and identify spatially meaningful genes.

PMID:40457183 | DOI:10.1186/s12859-025-06173-6

NPJ Digit Med. 2025 Jun 2;8(1):293. doi: 10.1038/s41746-025-01571-9.

ABSTRACT

We employed a three-phase approach, culminating in a randomized controlled trial, to assess the efficacy of 3D-printed liver models in hepatobiliary surgical planning. Phase one involved developing and selecting 35 optimal 3DP models based on timeliness, cost, precision, and alignment with digital simulations. Phase two utilized deep learning algorithms to optimize the 3D reconstruction process, significantly enhancing efficiency and accuracy compared to manual segmentation. In phase three, a randomized controlled trial with 64 patients compared surgical outcomes between those planned with AI-enhanced physical 3DP models and those with traditional digital simulations. Results demonstrated that 3DP models were produced rapidly (3.52 h at $152 each) with high precision, AI-assisted reconstruction reduced processing time (303.5 vs. 557 min), and patients using AI-enhanced physical 3DP models experienced less intraoperative blood loss. Integrating deep learning with 3D printing offers a cost-effective, scalable method to enhance surgical planning and outcomes in hepatobiliary surgery.

PMID:40457016 | DOI:10.1038/s41746-025-01571-9

J Imaging Inform Med. 2025 Jun 2. doi: 10.1007/s10278-025-01559-7. Online ahead of print.

ABSTRACT

This study addresses the critical challenge of detecting out-of-distribution (OOD) chest X-rays, where subtle view differences between lateral and frontal radiographs can lead to diagnostic errors. We develop a GAN-based framework that learns the inherent feature distribution of frontal views from the MIMIC-CXR dataset through latent space optimization and Kolmogorov-Smirnov statistical testing. Our approach generates similarity scores to reliably identify OOD cases, achieving exceptional performance with 100% precision, and 97.5% accuracy in detecting lateral views. The method demonstrates consistent reliability across operating conditions, maintaining accuracy above 92.5% and precision exceeding 93% under varying detection thresholds. These results provide both theoretical insights and practical solutions for OOD detection in medical imaging, demonstrating how GANs can establish feature representations for identifying distributional shifts. By significantly improving model reliability when encountering view-based anomalies, our framework enhances the clinical applicability of deep learning systems, ultimately contributing to improved diagnostic safety and patient outcomes.

PMID:40457001 | DOI:10.1007/s10278-025-01559-7

J Imaging Inform Med. 2025 Jun 2. doi: 10.1007/s10278-025-01564-w. Online ahead of print.

ABSTRACT

Scoliosis is a three-dimensional spinal deformity that is the most common among spinal deformities and causes extremely serious posture disorders in advanced stages. Scoliosis can lead to various health problems, including pain, respiratory dysfunction, heart problems, mental health disorders, stress, and emotional difficulties. The current gold standard for grading scoliosis and planning treatment is based on the Cobb angle measurement on X-rays. The Cobb angle measurement is performed by physical medicine and rehabilitation specialists, orthopedists, radiologists, etc., in branches dealing with the musculoskeletal system. Manual calculation of the Cobb angle for this process is subjective and takes more time. Deep learning-based systems that can evaluate the Cobb angle objectively have been frequently used recently. In this article, we propose an enhanced ViT that allows doctors to evaluate the diagnosis of scoliosis more objectively without wasting time. The proposed model uses a custom attention mechanism instead of the standard multi-head attention mechanism for the ViT model. A dataset with 7 different classes was obtained from 1456 patients in total from Elazığ Fethi Sekin City Hospital Physical Medicine and Rehabilitation Clinic. Multiple models were used to compare the proposed architecture in the classification of scoliosis disease. The proposed improved ViT architecture exhibited the best performance with 95.21% accuracy. This result shows that a superior classification success was achieved compared to ResNet50, Swin Transformer, and standard ViT models.

PMID:40457000 | DOI:10.1007/s10278-025-01564-w

J Imaging Inform Med. 2025 Jun 2. doi: 10.1007/s10278-025-01553-z. Online ahead of print.

ABSTRACT

Distinguishing benign from malignant vertebral compression fractures is critical for clinical management but remains challenging on contrast-enhanced abdominal CT, which lacks the soft tissue contrast of MRI. This study evaluates and compares radiomic feature-based machine learning and convolutional neural network-based deep learning models for classifying VCFs using abdominal CT. A retrospective cohort of 447 vertebral compression fractures (196 benign, 251 malignant) from 286 patients was analyzed. Radiomic features were extracted using PyRadiomics, with Recursive Feature Elimination selecting six key texture-based features (e.g., Run Variance, Dependence Non-Uniformity Normalized), highlighting textural heterogeneity as a malignancy marker. Machine learning models (XGBoost, SVM, KNN, Random Forest) and a 3D CNN were trained on CT data, with performance assessed via precision, recall, F1 score, accuracy, and AUC. The deep learning model achieved marginally superior overall performance, with a statistically significant higher AUC (77.66% vs. 75.91%, p < 0.05) and better precision, F1 score, and accuracy compared to the top-performing machine learning model (XGBoost). Deep learning's attention maps localized diagnostically relevant regions, mimicking radiologists' focus, whereas radiomics lacked spatial interpretability despite offering quantifiable biomarkers. This study underscores the complementary strengths of machine learning and deep learning: radiomics provides interpretable features tied to tumor heterogeneity, while DL autonomously extracts high-dimensional patterns with spatial explainability. Integrating both approaches could enhance diagnostic accuracy and clinician trust in abdominal CT-based VCF assessment. Limitations include retrospective single-center data and potential selection bias. Future multi-center studies with diverse protocols and histopathological validation are warranted to generalize these findings.

PMID:40456998 | DOI:10.1007/s10278-025-01553-z

Sci Rep. 2025 Jun 2;15(1):19298. doi: 10.1038/s41598-025-04503-5.

ABSTRACT

Retinal disorders, posing significant risks of the loss of vision or blindness, are increasingly prevalent, due to factors such as the aging population and chronic conditions like diabetes. Traditional diagnostic methods, relying on manually analyzing images, often have problems making an early detection and with their accuracy and efficiency, largely due to the subjectivity of human judgment and the time-consuming nature of the process. This study introduces a novel AI-based framework for diagnosing retinal disease, referred to as RetinaDNet. This framework leverages dual-branch input, incorporating both retinal images and vessel segmentation images, along with transfer learning and ensemble learning algorithms. This enhances the accuracy of the diagnoses and the stability of the model, particularly in scenarios with small sample sizes. By using vascular features and mitigating the risk of overfitting, this framework demonstrates superior performance in terms of multiple metrics. In particular, a soft voting classifier combined with the ResNet50 model attains accuracy rate of 99.2% on the diabetic retinopathy diagnosis task, and 98.8% on the retina disease classification task. The source code can be accessed at https://github.com/yu0809/Dual-branch-retinal-diseases .

PMID:40456971 | DOI:10.1038/s41598-025-04503-5

Sci Rep. 2025 Jun 2;15(1):19274. doi: 10.1038/s41598-025-03766-2.

ABSTRACT

Hand recognition, the process of identifying or characterizing human hands in images or video streams, plays significant role in the biometrics, robotics, computer vision, and human-computer interaction. This technology relies on analyzing hand attributes such as shape, size, color, texture, and motion to perform tasks as gesture identification, hand tracking, and sign language interpretation. In particular, hand movement decoding from electromyography (EMG) signals has shown promise for understanding neuromuscular function and aiding in diagnosis and therapy for neuromuscular issues. Existing approaches range from deep learning techniques such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) to conventional machine learning methods like Support Vector Machines (SVM) and Random Forest. Deep learning automates the process, reducing the dependency on manual feature extraction. However, the performance of these models is heavily influenced by hyperparameters such as the number of neurons, hidden layers, and learning rates. This study proposes a novel method that uses the Grey Wolf Optimizer (GWO) to fine-tune the hyperparameters of a Bi-LSTM-based EMG classification system. Implemented in MATLAB R2021a, this approach aims to enhance the accuracy of Bi-LSTM models in categorizing EMG signals. Performance metrics such as accuracy of 95%, precision of 93%, F1-score of 94%, and recall of 91% are used for thorough evaluation. By leveraging GWO for hyperparameter optimization, the study aims to achieve more accurate diagnosis and efficient tracking of rehabilitation outcomes for patients with neuromuscular disorders. This research demonstrates the potential of integrating biomedical engineering and computational intelligence to empower individuals with neuromuscular disabilities, thereby enhancing their quality of life.

PMID:40456840 | DOI:10.1038/s41598-025-03766-2

Sci Rep. 2025 Jun 2;15(1):19309. doi: 10.1038/s41598-025-02649-w.

ABSTRACT

The rapid evolution of smart grids, driven by rising global energy demand and renewable energy integration, calls for intelligent, adaptive, and energy-efficient resource allocation strategies. Traditional energy management methods, based on static models or heuristic algorithms, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy wastage. To overcome these challenges, this paper presents ORA-DL (Optimized Resource Allocation using Deep Learning) an advanced framework that integrates deep learning, Internet of Things (IoT)-based sensing, and real-time adaptive control to optimize smart grid energy management. ORA-DL employs deep neural networks, reinforcement learning, and multi-agent decision-making to accurately predict energy demand, allocate resources efficiently, and enhance grid stability. The framework leverages both historical and real-time data for proactive power flow management, while IoT-enabled sensors ensure continuous monitoring and low-latency response through edge and cloud computing infrastructure. Experimental results validate the effectiveness of ORA-DL, achieving 93.38% energy demand prediction accuracy, improving grid stability to 96.25%, and reducing energy wastage to 12.96%. Furthermore, ORA-DL enhances resource distribution efficiency by 15.22% and reduces operational costs by 22.96%, significantly outperforming conventional techniques. These performance gains are driven by real-time analytics, predictive modelling, and adaptive resource modulation. By combining AI-driven decision-making, IoT sensing, and adaptive learning, ORA-DL establishes a scalable, resilient, and sustainable energy management solution. The framework also provides a foundation for future advancements, including integration with edge computing, cybersecurity measures, and reinforcement learning enhancements, marking a significant step forward in smart grid optimization.

PMID:40456783 | DOI:10.1038/s41598-025-02649-w

Int J Pharm. 2025 May 31:125750. doi: 10.1016/j.ijpharm.2025.125750. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is characterized by symptoms such as shortness of breath, persistent dry cough, and hypoxemia. The disease can progress rapidly, often leading to respiratory failure. Given its complex and multifactorial nature, pulmonary fibrosis involves multiple pathological progress, such as inflammation, oxidative stress, and fibroblast activation. A single drug cannot effectively address pulmonary fibrosis through multiple mechanisms, but combining drugs maybe create a synergistic effect, target different aspects of the pathological process and improve treatment efficacy. In our previous study, bergenin can improve pulmonary fibrosis. Vitexin is reported to have anti-inflammatory, antioxidant, and anticancer properties, which maybe influence pathways associated with pulmonary fibrosis. Therefore, bergenin and vitexin were chosen for the combined treatment of pulmonary fibrosis. To improve the targeting ability and the affinity of lung fibroblasts, mouse lung fibroblasts-derived exosomes are used as the carriers for drugs. In this study, exosomes loaded with bergenin and vitexin (Exo-Ber + Vit) were successfully prepared using ultracentrifugation and ultrasonication, with an average particle size of approximately 180 nm. Wound-healing assay showed that Exo-Ber + Vit significantly inhibited the excessive proliferation of TGF-β1 induced Mlg and NIH-3 T3 cells compared with bergenin and vitexin alone. Cell uptake experiments showed that exosomes enhanced the uptake of coumarin 6 in Mlg and 3 T3 cells. In vivo studies, compared to bergenin-loaded exosomes, vitexin-loaded exosomes, and the combination of bergenin and vitexin, Exo-Ber + Vit demonstrated superior effects in reducing pulmonary fibrosis area, collagen deposition, and improving lung function. In conclusion, the co-delivery strategy of bergenin and vitexin via Mlg-derived exosomes offers a promising new approach to the treatment of IPF.

PMID:40456424 | DOI:10.1016/j.ijpharm.2025.125750

Genome Med. 2025 Jun 2;17(1):65. doi: 10.1186/s13073-025-01486-w.

ABSTRACT

BACKGROUND: Human leukocyte antigen class II (HLA-II) genes are highly polymorphic affecting the specificity of human antibody responses, as presentation of processed antigen peptides by HLA-II on B cells is essential for T helper cell dependent affinity maturation and class switching. The combination of high-throughput immunoassays and genome-wide association studies has recently revealed strong associations between HLA-II variants and antibody responses against specific antigens. However, factors underlying these associations remain incompletely understood.

METHODS: Here, we have leveraged paired data sets of SNP arrays and functional antibody epitope repertoires against 344,000 peptide antigens in 1940 individuals to mine for key determinants linking genetics and antibody specificity.

RESULTS: We show that secreted proteins and antigens presented in small modules (i.e., viruses) are significantly more frequently associated with HLA-II alleles, than membrane bound or intracellular proteins. This data suggests a model in which antibody responses against separate antigen units composed of single or few proteins dominate HLA-II associations. In contrast, the presence of manifold intracellular or membrane proteins (peptides of which could be bound by different HLA-II alleles) on bacterial cells dilutes potential associations to antibody specificities.

CONCLUSIONS: Hence, genetic associations to antibody specificities are shaped by antigen intrinsic properties. Given the prominent role of HLA-II alleles in infection, autoimmune diseases, allergies, and cancer, our work provides a theoretical framework to study antigen/HLA-II risk factors in these disease settings and will fuel the design of improved immunogenetics screens.

PMID:40457459 | DOI:10.1186/s13073-025-01486-w

Nat Genet. 2025 Jun 2. doi: 10.1038/s41588-025-02198-y. Online ahead of print.

ABSTRACT

The completion of the Human Genome Project catalyzed the development of 'omics' technologies, enabling the detailed exploration of biological systems at an unprecedented molecular scale. Microfluidics has transformed the omics toolbox by facilitating large-scale, high-throughput and highly accurate measurements of DNA and RNA, driving the transition from bulk to single-cell analyses. This transition has ushered in a new era, moving beyond a gene- and protein-centric perspective toward a holistic understanding of cellular phenotypes. This emerging 'single-cell phenomics era' integrates diverse omics datasets with spatial, morphological and temporal phenotypes to provide a comprehensive perspective on cellular function. This Review highlights how microfluidics addressed key challenges in the transition to single-cell omics and explores how lessons learned from these efforts will propel the single-cell phenomics revolution. Furthermore, we discuss emerging opportunities in which integrative single-cell phenomics could serve as a foundation for transformative discoveries in biology.

PMID:40457076 | DOI:10.1038/s41588-025-02198-y

Nat Neurosci. 2025 Jun 2. doi: 10.1038/s41593-025-01952-z. Online ahead of print.

ABSTRACT

Aging is one of the most prominent risk factors for neurodegeneration, yet the molecular mechanisms underlying the deterioration of old neurons are mostly unknown. To efficiently study neurodegeneration in the context of aging, we transdifferentiated primary human fibroblasts from aged healthy donors directly into neurons, which retained their aging hallmarks, and we verified key findings in aged human and mouse brain tissue. Here we show that aged neurons are broadly depleted of RNA-binding proteins, especially spliceosome components. Intriguingly, splicing proteins-like the dementia- and ALS-associated protein TDP-43-mislocalize to the cytoplasm in aged neurons, which leads to widespread alternative splicing. Cytoplasmic spliceosome components are typically recruited to stress granules, but aged neurons suffer from chronic cellular stress that prevents this sequestration. We link chronic stress to the malfunctioning ubiquitylation machinery, poor HSP90α chaperone activity and the failure to respond to new stress events. Together, our data demonstrate that aging-linked deterioration of RNA biology is a key driver of poor resiliency in aged neurons.

PMID:40456907 | DOI:10.1038/s41593-025-01952-z

Nat Commun. 2025 Jun 2;16(1):5120. doi: 10.1038/s41467-025-60233-2.

ABSTRACT

Inflammatory bowel diseases (IBDs) are chronic disorders involving dysregulated immune responses. Despite the role of disrupted host-microbial interaction in the pathophysiology of IBD, the underlying metabolic principles are not fully understood. We densely profiled microbiome, transcriptome and metabolome signatures from longitudinal IBD cohorts before and after advanced drug therapy initiation and reconstructed metabolic models of the gut microbiome and the host intestine to study host-microbiome metabolic cross-talk in the context of inflammation. Here, we identified concomitant changes in metabolic activity across data layers involving NAD, amino acid, one-carbon and phospholipid metabolism. In particular on the host level, elevated tryptophan catabolism depleted circulating tryptophan, thereby impairing NAD biosynthesis. Reduced host transamination reactions disrupted nitrogen homeostasis and polyamine/glutathione metabolism. The suppressed one-carbon cycle in patient tissues altered phospholipid profiles due to limited choline availability. Simultaneously, microbiome metabolic shifts in NAD, amino acid and polyamine metabolism exacerbated these host metabolic imbalances. Leveraging host and microbe metabolic models, we predicted dietary interventions remodeling the microbiome to restore metabolic homeostasis, suggesting novel therapeutic strategies for IBD.

PMID:40456745 | DOI:10.1038/s41467-025-60233-2

Int J Cancer. 2025 Jun 2. doi: 10.1002/ijc.35490. Online ahead of print.

ABSTRACT

During cancer development and progression, many genetic alterations lead to the acquisition of novel features that confer selective advantage to cancer cells and that resemble developmental programs. SRY-box transcription factor 2 (SOX2) is one of the key pluripotency transcription factors, expressed during embryonic development and active in adult stem cells. In cancer, SOX2 is frequently dysregulated and associated with tumor stemness and poor patient survival. SOX2 expression is suppressed in differentiated cells by tumor suppressor proteins that form a transcriptional repressive complex. We previously identified some of these proteins and found that their absence combined with deficiency in Trp53 leads to maximal dysregulated expression of Sox2. Using cancer cell lines of different origin and with different p53 status, we show here that manipulating TP53 to restore or decrease its activity results in repression or induction of SOX2, respectively. Mechanistically, we observed that the regulation of SOX2 expression by TP53 is transcriptional and identified Trp53 bound to the promoter region and the Sox2 Regulatory Region 2 enhancer of Sox2. Forcing high levels of SOX2 in cancer cells leads to morphological changes that molecularly correspond to the acquisition of a more mesenchymal phenotype, correlating with an increased migratory capacity. Finally, the analysis of human breast cancer samples shows that this correlation between TP53 status, levels of expression of SOX2, and a more metastatic phenotype is also observed in cancer patients. Our results support the notion that lack of TP53 in tumor cells results in deregulated expression of developmental gene SOX2 with phenotypic consequences related to increased malignization.

PMID:40456627 | DOI:10.1002/ijc.35490

J Food Prot. 2025 May 31:100551. doi: 10.1016/j.jfp.2025.100551. Online ahead of print.

ABSTRACT

To enhance control of human pathogenic microorganisms in plant production systems, an EU COST Action (HUPLANTcontrol CA16110) was initiated, bringing together microbiologists in food, environmental and plant microbial ecology. This article summarizes the outcomes of multiple workshops and the four main lessons learned: (i) many terminologies need further explanation to facilitate multidisciplinary communication on the behavior of human pathogens from pre-harvest plant production to post-harvest food storage, (ii) the complexity of bacterial taxonomy pushes microbial hazard identification for greater resolution of characterisation (to subspecies, or even strain level) needing a multi-method approach, (iii) hazard characterisation should consider a range of factors to evaluate the weight of evidence for adverse health effects in humans, including strain pathogenicity, host susceptibility, and the impact of the plant, food, or human gut microbiome, (iv) a wide diversity of microorganisms in varying numbers and behaviours co-exist in the plant microbiome, including good (beneficial for plant or human health), bad (established human or plant pathogens) or ugly (causing spoilage or opportunistic disease). In conclusion, active listening in communication and a multi-perspective approach are the foundation for every successful conversation when plant science meets food safety.

PMID:40456365 | DOI:10.1016/j.jfp.2025.100551