Insights Imaging. 2025 May 23;16(1):110. doi: 10.1186/s13244-025-01984-w.

ABSTRACT

OBJECTIVES: For patients with colorectal liver metastases (CRLM), total tumor volume (TTV) is prognostic. A deep-learning segmentation model for CRLM to assess TTV called COlorectal cAncer Liver metastases Assessment (COALA) has been developed. This study evaluated COALA's performance and practical utility in the radiological picture archiving and communication system (PACS). A secondary aim was to provide lessons for future researchers on the implementation of artificial intelligence (AI) models.

METHODS: Patients discussed between January and December 2023 in a multidisciplinary meeting for CRLM were included. In those patients, CRLM was automatically segmented in portal-venous phase CT scans by COALA and integrated with PACS. Eight expert abdominal radiologists completed a questionnaire addressing segmentation accuracy and PACS integration. They were also asked to write down general remarks.

RESULTS: In total, 57 patients were evaluated. Of those patients, 112 contrast-enhanced portal-venous phase CT scans were analyzed. Of eight radiologists, six (75%) evaluated the model as user-friendly in their radiological workflow. Areas of improvement of the COALA model were the segmentation of small lesions, heterogeneous lesions, and lesions at the border of the liver with involvement of the diaphragm or heart. Key lessons for implementation were a multidisciplinary approach, a robust method prior to model development and organizing evaluation sessions with end-users early in the development phase.

CONCLUSION: This study demonstrates that the deep-learning segmentation model for patients with CRLM (COALA) is user-friendly in the radiologist's PACS. Future researchers striving for implementation should have a multidisciplinary approach, propose a robust methodology and involve end-users prior to model development.

CRITICAL RELEVANCE STATEMENT: Many segmentation models are being developed, but none of those models are evaluated in the (radiological) workflow or clinically implemented. Our model is implemented in the radiological work system, providing valuable lessons for researchers to achieve clinical implementation.

KEY POINTS: Developed segmentation models should be implemented in the radiological workflow. Our implemented segmentation model provides valuable lessons for future researchers. If implemented in clinical practice, our model could allow for objective radiological evaluation.

PMID:40410643 | DOI:10.1186/s13244-025-01984-w

Sci Rep. 2025 May 23;15(1):18025. doi: 10.1038/s41598-025-99681-7.

ABSTRACT

Smartphone addiction among students has emerged as a critical issue, negatively impacting their academic performance, emotional well-being, and social behavior. This paper introduces the Theory of Mind integrated with Video Modelling (TMVM) framework, a novel deep learning-based approach aimed at recognizing and mitigating smartphone addiction. The TMVM framework leverages Theory of Mind AI to analyze students' facial emotions via smartphone cameras while watching videos. Based on detected emotions such as happiness, sadness, or anger, the system dynamically shuffles motivational videos using advanced algorithms like Fisher-Yates and Durstenfeld shuffling techniques to promote behavioral change. The framework also incorporates Behavior Parameters (BHP) evaluation, grounded in the Social Identity Model of Deindividuation Effects (SIDE) theory, to assess key behavioral metrics such as social identity, self-awareness, anonymity, responsibility, and accountability. Additionally, face emotion detection algorithms tuned with MnasNet-Teaching Learning Based Optimization (TLBO) and Convolution Neural Networks (CNN)-Cuckoo Search Optimization (CSO) are employed for accurate emotion recognition. Experimental results demonstrate significant improvements in students' behavior and reductions in smartphone usage post-intervention. The TMVM system achieves high accuracy in emotion detection and behavioral outcome prediction while fostering engagement in school and social activities. . TMVM method is tested in 750 students with low BHP and evaluated the behavioural parameters. After the intervention of TMVM the students showed more than 90% improvement in their BHP parameters. A paired sample t-test revealed notable reductions in mean scores from pre- to post-intervention across all measured dimensions. Social identity decreased from 4.07 to 2.21 (t(55) = 16.125, p < 0.001), anonymity from 4.11 to 2.01 (t(55) = 15.699, p < 0.001), self-awareness from 3.95 to 1.93 (t(55) = 15.103, p < 0.001), loss of individuality from 4.04 to 2.07 (t(55) = 13.364, p < 0.001), while sense of responsibility and accountability improved with mean differences of 1.18 and 2.0, respectively, both statistically significant at p < 0.001.The results showed 85% improvement in students' knowledge and attitudes.

PMID:40410532 | DOI:10.1038/s41598-025-99681-7

Sci Rep. 2025 May 23;15(1):17920. doi: 10.1038/s41598-025-99588-3.

ABSTRACT

The current ϕ-OTDR vibration localization and recognition methods based on predominantly relies on assumptions such as bare fiber sensing, simulated experimental environments, or single known laying scenario. Most of them either focus on the localization or recognition of events, while even some studies that consider both ignore the improvement of performance to meet real-time requirements, which limits their practical application in multiple laying scenarios. To solve the above problems, we propose a method for vibration area localization and event recognition of the underground power optical cable based on PGSD-YOLO and 1DCNN-BiGRU-AFM. First, with real multiple laying scenarios of buried underground and manholes, using an underground power optical cable as distributed optical fiber vibration sensing, a ϕ-OTDR system is built to collect signals of vibration events. And then, high-pass and low-pass filters are combined for denoising to improve the signal quality. Secondly, PGSD-YOLO is designed to localize the vibration area and obtain its laying scenario. PGSD-YOLO combines the YOLOv11 with the multi-scale attention of PMSAM to enhance the feature extraction ability. Through the dynamic sampling strategy of DySample, the information loss of signals is reduced, and GSConv and VoVGSCSP are used to optimize feature fusion. Finally, based on the obtained scenario labels and the time-domain signals, 1DCNN-BiGRU-AFM is designed to recognize vibration events. 1DCNN-BiGRU-AFM combines the feature extraction ability of 1DCNN and the timing analysis ability of BiGRU, and optimizes feature fusion through the AFM mechanism. From experimental results, both PGSD-YOLO and 1DCNN-BiGRU-AFM meet the real-time and performance requirements in multiple scenarios.

PMID:40410528 | DOI:10.1038/s41598-025-99588-3

Sci Rep. 2025 May 23;15(1):18006. doi: 10.1038/s41598-025-96203-3.

ABSTRACT

In the contemporary technological world, fortifying cybersecurity defense against dynamic threat landscapes is imperative. Malware detectors play a critical role in this endeavor, utilizing various techniques such as statistical analysis, static and dynamic analysis, and machine learning (ML) to compare signatures and identify threats. Deep learning (DL) aids in accurately classifying complex malware features. The cross-domain research in data fusion strives to integrate information from multiple sources to augment reliability and minimize errors in detecting sophisticated cyber threats. This collaborative approach is the least addressed and pivotal for protecting against the advancing environment of modern malware attacks. This study presents a state-of-the-art malware analysis framework that employs a multimodal approach by integrating malware images and numeric features for effective malware classification. The experiments are performed sequentially, encompassing data preprocessing, feature selection using Neighbourhood Component Analysis (NCA), and dataset balancing with Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, the late fusion technique is utilized for multimodal classification by employing Random Under Sampling and Boosting (RUSBoost) and the proposed ensemble deep neural network. The RUSBoost technique involves random undersampling and adaptive boosting to moderate bias toward majority classes while improving minority class (malware) detection. Multimodal Late fusion experimental results (95.36%) of RUSBoost (numeric) and the proposed model (imagery) outperform the standalone prevailing results for imagery (95.02%) and numeric (93.36%) data. The effectiveness of the proposed model is verified through the evaluation metrics such as Recall (86.5%), F1-score (85.0%), and Precision (79.9%). The multimodal late fusion of numeric and visual data makes the model more robust in detecting diverse malware variants. The experimental outcomes demonstrate that multimodal analysis may efficiently increase the identification strength and accuracy, especially when majority vote and bagging are employed for late fusion.

PMID:40410526 | DOI:10.1038/s41598-025-96203-3

Interdiscip Sci. 2025 May 23. doi: 10.1007/s12539-025-00714-6. Online ahead of print.

ABSTRACT

BACKGROUNDS: During the development of new drugs, it is essential to assess their effectiveness and examine the potential mechanisms behind side effects. This process typically involves combining the analysis of drugs under development with relevant existing drugs to more precisely evaluate the effects of drugs and targets. The use of deep learning methods to analyze this problem is currently a research hotspot, but several limitations remain: (i) how to deepen the analysis from the molecular level to the atomic level and analyze the key substructures that affect interactions on the basis of pharmaceutical mechanisms; (ii) how to integrate biomedical analysis with deep learning methods to make it medically sound and enhance interpretability.

METHODS: To address the limitations of existing research, based on Deep Graph Convolutional Network (Deep-GCN) and Bilinear Attention Network (BAN), we have constructed an interpretable deep learning framework, WDGBANDTI, to analyze and predict drug‒target interactions at the substructure level and enhance the prediction capability of the model with respect to unidentified target pairings by adding modules.

RESULTS: For different application scenarios, we validated the model via several commonly used and highly covered datasets. We also selected several state-of-the-art computer methods as comparison objects, and our model demonstrates advantages in accuracy, sensitivity, specificity, and other deep learning features. More importantly, the model can identify the substructures that play a role in drug‒target interactions through BAN, highlighting its excellent interpretability.

CONCLUSION: In conclusion, we believe that our work will contribute to advancements in drug development and side effect experiments and provide meaningful guidance for drug design.

PMID:40410523 | DOI:10.1007/s12539-025-00714-6

Nat Biotechnol. 2025 May 23. doi: 10.1038/s41587-025-02654-4. Online ahead of print.

ABSTRACT

The dominant success of deep learning techniques on protein structure prediction has challenged the necessity and usefulness of traditional force field-based folding simulations. We proposed a hybrid approach, deep-learning-based iterative threading assembly refinement (D-I-TASSER), which constructs atomic-level protein structural models by integrating multisource deep learning potentials with iterative threading fragment assembly simulations. D-I-TASSER introduces a domain splitting and assembly protocol for the automated modeling of large multidomain protein structures. Benchmark tests and the most recent critical assessment of protein structure prediction, 15 experiments demonstrate that D-I-TASSER outperforms AlphaFold2 and AlphaFold3 on both single-domain and multidomain proteins. Large-scale folding experiments further show that D-I-TASSER could fold 81% of protein domains and 73% of full-chain sequences in the human proteome with results highly complementary to recently released models by AlphaFold2. These results highlight a new avenue to integrate deep learning with classical physics-based folding simulations for high-accuracy protein structure and function predictions that are usable in genome-wide applications.

PMID:40410405 | DOI:10.1038/s41587-025-02654-4

Sci Rep. 2025 May 23;15(1):17921. doi: 10.1038/s41598-025-02491-0.

ABSTRACT

Remote sensing images (RSI), such as aerial or satellite images, produce a large-scale view of the Earth's surface, which gets them used to track and monitor vehicles from several settings, like border control, disaster response, and urban traffic surveillance. Vehicle detection and classification using RSIs is a vital application of computer vision and image processing. It contains locating and identifying vehicles from the image. It is done using many approaches that have object detection approaches, namely YOLO, Faster R-CNN, or SSD, which utilize deep learning (DL) to locate and identify the image. Additionally, the classification of vehicles from RSIs contains classification of them based on their variety, such as trucks, motorcycles, cars or buses, utilizing machine learning (ML) techniques. This article designed and developed an automated vehicle type detection and classification using a chaotic equilibrium optimization algorithm with deep learning (VDTC-CEOADL) on high-resolution RSIs. The VDTC-CEOADL technique presented examines high-quality RSIs for the accurate detection and classification of vehicles. The VDTC-CEOADL technique employs a YOLO-HR object detector with a residual network as the backbone model to accomplish this. In addition, CEOA based hyperparameter optimizer is designed for the parameter tuning of the ResNet model. For the vehicle classification process, the VDTC-CEOADL technique exploits the attention-based long-short-term memory (ALSTM) mod-el. Performance validation of the VDTC-CEOADL technique is validated on a high-resolution RSI dataset, and the results portrayed the supremacy of the VDTC-CEOADL technique in terms of different measures.

PMID:40410394 | DOI:10.1038/s41598-025-02491-0

Brain Res Bull. 2025 May 21:111402. doi: 10.1016/j.brainresbull.2025.111402. Online ahead of print.

ABSTRACT

BACKGROUND: The mechanistic relationship between respiratory disorders and brain function remains poorly understood, despite growing evidence of cognitive and neurological manifestations in respiratory diseases. We aim to identify whether specific brain network connectivity patterns causally influence respiratory disease susceptibility, while respiratory conditions might reciprocally affect brain network architecture.

METHODS: We performed bidirectional Mendelian randomization analyses using genome-wide association studies (GWAS) of brain network connectivity from UK Biobank resting-state functional MRI data (N=31,453) and GWAS data from ten major respiratory conditions: chronic obstructive pulmonary disease (COPD), asthma, idiopathic pulmonary fibrosis (IPF), sleep apnea syndrome (SAS), lung squamous carcinoma (LUSC), lung adenocarcinoma (LUAD), small cell lung carcinoma (SCLC), hospitalized COVID-19, very severe COVID-19, and bronchiectasis. Five MR methods, inverse variance weighted (IVW) with multiplicative random-effect model, weighted median, weighted mode, MR Egger, and MR-robust adjusted profile score (MR-RAPS) were employed to ensure causal inference.

RESULTS: In forward analysis, five respiratory disorders - asthma, IPF, SAS, LUSC, and very severe COVID-19 - showed significant causal associations (p<1.31×10-4) with 11 rs-fMRI phenotypes, spanning multiple brain networks including the central executive, subcortical-cerebellum, motor, limbic, attention, salience, visual, and default mode networks. In reverse analysis, twelve brain functional networks demonstrated genetic associations with eight respiratory conditions (COPD, asthma, IPF, SAS, LUSC, SCLC, hospitalized COVID-19, and very severe COVID-19), predominantly involving attention, salience, default mode, visual, and central executive networks.

CONCLUSIONS: Our study provides preliminary genetic evidence suggesting potential causal relationships between brain network connectivity and respiratory disorders, contributing to our understanding of the lung-brain axis. While the identification of disease-specific network alterations offers promising insights, further clinical validation is needed before these findings can be translated into therapeutic interventions.

PMID:40409599 | DOI:10.1016/j.brainresbull.2025.111402

Cell Rep Med. 2025 May 14:102144. doi: 10.1016/j.xcrm.2025.102144. Online ahead of print.

ABSTRACT

Efficient gene delivery vectors are crucial for respiratory and lung disease therapies. We report that AAV.CPP.16, an engineered adeno-associated virus (AAV) variant derived from AAV9, efficiently transduces airway and lung cells in mice and non-human primates via intranasal administration. AAV.CPP.16 outperforms AAV6 and AAV9, two wild-type AAVs with demonstrated tropism for respiratory tissues, and efficiently targets key respiratory cell types. It supports gene supplementation and editing therapies in two clinically relevant mouse models of respiratory and lung diseases. A single intranasal dose of AAV.CPP.16 expressing a dual-target, vascular endothelial growth factor (VEGF)/transforming growth factor (TGF)-β1-neutralizing protein protected lungs from idiopathic pulmonary fibrosis, while a similar application of AAV.CPP.16 carrying an "all-in-one" CRISPR-Cas13d system inhibited transcription of the SARS-CoV-2-derived RNA-dependent RNA polymerase (Rdrp) gene. Our findings highlight AAV.CPP.16 as a promising vector for respiratory and lung gene therapy.

PMID:40409263 | DOI:10.1016/j.xcrm.2025.102144

Eur J Med Chem. 2025 May 16;294:117768. doi: 10.1016/j.ejmech.2025.117768. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with high mortality and limited treatment options. Targeting multiple kinase-driven pathological processes offers a promising strategy. Using epithelial-mesenchymal transition (EMT) phenotypic screening, we optimized a series of o-aminopyridinyl alkynyl compounds derived from CSF-1R relatively selective inhibitor, compound 1, through a structure-activity relationship (SAR) study, integrating liver and kidney cytotoxicity evaluations. Compound 22, emerged as the potent antifibrotic candidate, exhibiting low cytotoxic effects against human kidney (HEK293) and hepatocyte (L02) cell lines, and minimal hERG inhibition. In addition, 22 showed significant inhibition against other IPF-related processes, including fibroblast-to-myofibroblast transition (FMT)-driven fibrosis in both human fetal lung fibroblasts cell line (HFL1) and primary human lung fibroblasts (HLFs), as well as pro-fibrotic M2 polarization. In vivo, compound 22 exhibited the acceptable PK properties and low toxicity profiles. In addition, oral administration of 22 demonstrated superior anti-fibrotic efficacy compared to Nintedanib, significantly attenuating bleomycin-induced lung fibrosis, reducing inflammation and pro-fibrotic M2-associated cytokine levels, and improving lung function. Preliminary kinase profiling indicates that compound 22 likely targets CSF-1R, PDGFR-α and Src family kinases to inhibit IPF progression, while sparing VEGFRs, FGFRs and Abl to minimize off-target toxicity commonly associated with multi-kinase inhibitor treatment. These findings highlight the advantages and therapeutic potential of a multi-kinase targeting strategy, enabling selective inhibition key IPF-associated kinases to develop more effective and safer anti-IPF agents.

PMID:40409055 | DOI:10.1016/j.ejmech.2025.117768

Res Involv Engagem. 2025 May 23;11(1):53. doi: 10.1186/s40900-025-00726-y.

ABSTRACT

BACKGROUND: Ethnic minority communities are often recognised as experiencing decreased accessibility to vital medical services as well as increased barriers to participation in research studies. These issues stem from a variety of social, cultural and economic factors, all of which must be taken into consideration when designing engagement initiatives for a particular community. Invisible Spectrum is an annual engagement initiative which seeks to promote effective communication and outreach to often-overlooked ethnic minority communities within Ireland, primarily those of Bangladeshi origin. The programme was developed in response to the traditionally low levels of engagement with healthcare services observed within these communities and seeks to empower communities in their own healthcare decision making.

METHODS: This study reports a programme of community participation activity with an embedded empirical research component based on participatory action research. The team included researchers and leaders within the Bangladeshi and Arabic Muslim communities in Ireland. Over the course of four years, feedback polls, pre- and post-event surveys and in-depth interviews gathered the views and recommendations of attendees.

RESULTS: We held 4 annual events as part of the Invisible Spectrum programme, from 2019 to 2023. Feedback we collected from participants consistently demonstrated high levels of satisfaction within the target communities while quantitative survey data also indicated improvements in key areas such as recognition of potential cancer symptoms and greater awareness of available screening services.

CONCLUSION: There is a significant need to continually promote patient involvement and minority inclusion in healthcare and research initiatives. In line with this goal and after 4 successful years of running the Invisible Spectrum programme, we have developed a set of recommendations and guidelines for the successful development and organisation of minority community engagement initiatives. It is our hope that the Invisible Spectrum programme could be used as a model for future endeavours of a similar nature.

PMID:40410864 | DOI:10.1186/s40900-025-00726-y

J Inflamm (Lond). 2025 May 23;22(1):20. doi: 10.1186/s12950-025-00446-w.

ABSTRACT

BACKGROUND: Levels of the human cationic antimicrobial host defence peptide LL-37 are enhanced in the lungs during neutrophilic airway inflammation. LL-37 drives Th17 differentiation, and Th17 cells produce IL-17A and IL-17F which form the biologically active heterodimer IL-17A/F. While IL-17 is a critical mediator of neutrophilic airway inflammation, LL-37 exhibits contradictory functions; LL-37 can both promote and mitigate neutrophil recruitment depending on the inflammatory milieu. The impact of LL-37 on IL-17-induced responses in the context of airway inflammation remains largely unknown. Therefore, we examined signaling intermediates and downstream responses mediated by the interplay of IL-17A/F and LL-37 in human bronchial epithelial cells (HBEC). As LL-37 can become citrullinated during airway inflammation, we also examined LL-37-mediated downstream responses compared to that with citrullinated LL-37 (citLL-37) in HBEC.

RESULTS: Using an aptamer-based proteomics approach, we identified proteins that are altered in response to IL-17A/F in HBEC. Proteins enhanced in response to IL-17A/F were primarily neutrophil chemoattractants, including chemokines and proteins associated with neutrophil migration such as lipocalin-2 (LCN-2). We showed that selective depletion of LCN-2 mitigates neutrophil migration, functionally demonstrating LCN-2 as a critical neutrophil chemoattractant. We further demonstrated that LL-37 and citLL-37 selectively suppress IL-17A/F-induced LCN-2 abundance in HBEC. Mechanistic studies revealed that LL-37 and citLL-37 suppresses IL-17 A/F-mediated enhancement of C/EBPβ, a transcription factor required for LCN-2 production. In contrast, LL-37 and citLL-37 enhance the abundance of ribonuclease Regnase-1, which is a negative regulator of IL-17 and LCN-2 in HBEC. In an animal model of allergen-challenged airway inflammation with elevated IL-17A/F and neutrophil elastase in the lungs, we demonstrated that CRAMP (mouse orthologue of LL-37) negatively correlates with LCN-2.

CONCLUSIONS: Overall, our findings showed that LL-37 and citLL-37 can selectively suppress the abundance of IL-17A/F-mediated LCN-2, a protein that is critical for neutrophil migration in HBEC. These results suggest that LL-37, and its modified citrullinated form, have the potential to negatively regulate IL-17-mediated neutrophil migration during airway inflammation. To our knowledge, this is the first study to report that the immunomodulatory function of LL-37 enhances the RNA binding protein Regnase-1, suggesting that a post-transcriptional mechanism of action is mediated by the peptide.

PMID:40410820 | DOI:10.1186/s12950-025-00446-w

Nat Biomed Eng. 2025 May 23. doi: 10.1038/s41551-025-01381-0. Online ahead of print.

ABSTRACT

Liver fibrosis is an over-reacted wound healing that becomes lethal in its late stage, when hepatic stellate cells (HSCs) trigger fibrotic response, proliferation of connective tissue and build-up of directional fibrous tissue bands (septa). Current in vitro models of liver fibrosis cannot reproduce liver lobule structure and the dynamic formation of septa at the same time, and the known biochemical cues underlying the progression of liver fibrosis cannot explain directional formation of fibrotic tissue. Here we report a microfabricated in vitro model that reproduces both the hexagonal liver lobule structure and the dynamic directionality of septa formation. By using collagen and primary mouse HSCs or human HSC lines, we found that tension was necessary to coordinate the cell migration that contributes to the band-like cell distribution and that HSCs sensed directional biophysical cues through liquid-liquid phase separation. This system allows the study of the biophysical interaction of HSCs and collagen during the formation of septa structures, and could be used to deepen our understanding of liver fibrosis progression.

PMID:40410557 | DOI:10.1038/s41551-025-01381-0

Commun Biol. 2025 May 23;8(1):791. doi: 10.1038/s42003-025-08148-y.

ABSTRACT

The SARS-CoV-2 pandemic has reemphasized the urgent need for broad-spectrum antiviral therapies. We developed a computational workflow using scRNA-Seq data to assess cellular metabolism during viral infection. With this workflow we predicted the capacity of cells to sustain SARS-CoV-2 virion production in patients and found a tissue-wide induction of metabolic pathways that support viral replication. Expanding our analysis to influenza A and dengue viruses, we identified metabolic targets and inhibitors for potential broad-spectrum antiviral treatment. These targets were highly enriched for known interaction partners of all analyzed viruses. Indeed, phenformin, an NADH:ubiquinone oxidoreductase inhibitor, suppressed SARS-CoV-2 and dengue virus replication. Atpenin A5, blocking succinate dehydrogenase, inhibited SARS-CoV-2, dengue virus, respiratory syncytial virus, and influenza A virus with high selectivity indices. In vivo, phenformin showed antiviral activity against SARS-CoV-2 in a Syrian hamster model. Our work establishes host metabolism as druggable for broad-spectrum antiviral strategies, providing invaluable tools for pandemic preparedness.

PMID:40410544 | DOI:10.1038/s42003-025-08148-y

Eur J Hum Genet. 2025 May 23. doi: 10.1038/s41431-025-01876-z. Online ahead of print.

ABSTRACT

Biallelic inactivating variants in ZNF142 underlie a clinically variable neurodevelopmental disorder. ZNF142 is a zinc-finger transcription factor with potential roles on chromatin organization, implying a possible association of ZNF142 loss of function with perturbed genome-wide DNA methylation (DNAm) pattern. We performed EPIC array-based methylation profiling of peripheral blood-derived DNA samples from 27 individuals with biallelic ZNF142 inactivating variants, together with 6 heterozygous carriers and 40 controls. A DNAm signature discovery pipeline was applied by using 440 controls for discovery and validation analyses, and a machine-learning model was trained to classify 8 individuals carrying ZNF142 variants of uncertain clinical significance. Analyses directed to explore the genome-wide DNAm landscape in affected individuals revealed 88 differentially methylated probes constituting the minimal informative set specific to ZNF142 loss of function. This reproducible pattern of DNAm changes involved regulatory regions of a small number of genes. The DNAm signature derived from peripheral blood allowed us to diagnose individuals carrying biallelic inactivating ZNF142 variants when applied to fibroblasts. Our findings provide evidence that biallelic loss-of-function ZNF142 variants result in a specific and robust DNAm signature. The identified DNAm pattern suggests occurrence of a methylation disturbance involving a small number of loci that appears to be shared by different cell lineages.

PMID:40410387 | DOI:10.1038/s41431-025-01876-z

Mol Syst Biol. 2025 May 23. doi: 10.1038/s44320-025-00120-6. Online ahead of print.

ABSTRACT

Glycosaminoglycan (GAG)-binding proteins regulating essential processes such as cell growth and migration are essential for cell homeostasis. As both GAGs and the lipid A disaccharide core of Gram-negative bacteria contain negatively charged disaccharide units, we hypothesized that GAG-binding proteins could also recognize LPS and enclose cryptic antibiotic motifs. Here, we report novel antimicrobial peptides (AMPs) derived from heparin-binding proteins (HBPs), with specific activity against Gram-negative bacteria and high LPS binding. We used computational tools to locate antimicrobial regions in 82% of HBPs, most of those colocalizing with putative heparin-binding sites. To validate these results, we synthesized five candidates [HBP-1-5] that showed remarkable activity against Gram-negative bacteria, as well as a strong correlation between heparin and LPS binding. Structural characterization of these AMPs shows that heparin or LPS recognition promotes a conformational arrangement that favors binding. Among all analogs, HBP-5 displayed the highest affinity for both heparin and LPS, with antimicrobial activities against Gram-negative bacteria at the nanomolar range. These results suggest that GAG-binding proteins are involved in LPS recognition, which allows them to act also as antimicrobial proteins. Some of the peptides reported here, particularly HBP-5, constitute a new class of AMPs with specific activity against Gram-negative bacteria.

PMID:40410382 | DOI:10.1038/s44320-025-00120-6

Sci Rep. 2025 May 23;15(1):17969. doi: 10.1038/s41598-025-01912-4.

ABSTRACT

The tumorigenesis of small intestinal neuroendocrine tumors (siNETs) is not understood and comprehensive genomic and transcriptomic data sets are limited. Therefore, we performed whole genome and transcriptome analysis of 39 well differentiated siNET samples. Our genomic data revealed a lack of recurrent driver mutations and demonstrated that multifocal siNETs from individual patients can arise genetically independently. We detected germline mutations in Fanconi anemia DNA repair pathway (FANC) genes, involved in homologous recombination (HR) DNA repair, in 9% of patients and found mutational signatures of defective HR DNA repair in late-stage tumor evolution. Furthermore, transcriptomic analysis revealed low expression of the transcriptional repressor REST. Summarizing, we identify a novel common transcriptomic signature of siNETs and demonstrate that genomic alterations alone do not explain initial tumor formation, while impaired DNA repair likely contributes to tumor evolution and represents a potential pharmaceutical target in a subset of patients.

PMID:40410286 | DOI:10.1038/s41598-025-01912-4

NPJ Aging. 2025 May 23;11(1):40. doi: 10.1038/s41514-025-00237-w.

ABSTRACT

Frailty is an age-related geriatric syndrome. We performed a longitudinal study of aging female (n = 40) and male (n = 47) C57BL/6NIA mice, measured frailty index and derived metabolomics data from plasma. We identify age-related differentially abundant metabolites, determine frailty-related metabolites, and generate frailty features, both in the whole cohort and sex-stratified subgroups. Using the features, we perform an association study and build a metabolomics-based frailty clock. We find that frailty-related metabolites are enriched for amino acid metabolism and metabolism of cofactors and vitamins, include ergothioneine, tryptophan and alpha-ketoglutarate, and present sex dimorphism. We identify B vitamin metabolism related flavin-adenine dinucleotide and pyridoxate as female-specific frailty biomarkers, and lipid metabolism related sphingomyelins, glycerophosphoethanolamine and glycerophosphocholine as male-specific frailty biomarkers. These associations are confirmed in a validation cohort, with ergothioneine and perfluorooctanesulfonate identified as robust frailty biomarkers. Our results identify sex-specific metabolite frailty biomarkers, and shed light on potential mechanisms.

PMID:40410187 | DOI:10.1038/s41514-025-00237-w

Nat Commun. 2025 May 23;16(1):4790. doi: 10.1038/s41467-025-59476-w.

ABSTRACT

Malaria parasite undergoes interesting developmental transition in human and mosquito host. While it divides asynchronously in the erythrocytes, it switches to sexual forms, which is critical for disease transmission. We report a novel signalling pathway involving Protein Phosphatase PfPPM2, which regulates asexual division of Plasmodium falciparum as well as its conversion to sexual forms. PfPPM2 may regulate the phosphorylation of key proteins involved in chromatin remodelling and protein translation. One of the key PfPPM2-targets was Heterochromatin Protein 1 (HP1), a regulator of heritable gene silencing which contributes to both mitotic proliferation as well as sexual commitment of the parasite. PfPPM2 promotes sexual conversion by regulating the interaction between HP1, H3K9me3 and chromatin and it achieves this by dephosphorylating S33 of HP1. PfPPM2 also regulates protein synthesis in the parasite by repressing the phosphorylation of initiation factor eIF2α, which is likely to contribute to parasite division and possibly sexual differentiation.

PMID:40410154 | DOI:10.1038/s41467-025-59476-w

Cell Stem Cell. 2025 May 21:S1934-5909(25)00179-1. doi: 10.1016/j.stem.2025.04.012. Online ahead of print.

ABSTRACT

Isocitrate dehydrogenase 1/2 (IDH) mutations are early initiating events in acute myeloid leukemia (AML). The complex clonal architecture and cellular heterogeneity in IDH-mutant AML underlies the heterogeneous clinical presentation and outcomes. Integrating single-cell genotyping and transcriptomics, we demonstrate a stem-like and inflammatory phenotype of IDH-mutant AML and identify clone-specific programs associated with NPM1, NRAS, and SRSF2 co-mutations. Furthermore, these clones had distinct responses to treatment with combination IDH inhibitors and chemotherapy, including elimination, reconstitution of myeloid differentiation, or retention within progenitor populations. At relapse after IDH inhibitor monotherapy, we identify upregulated stemness, inflammation, mitochondrial metabolism, and anti-apoptotic factors, as well as downregulated major histocompatibility complex (MHC) class II antigen presentation. At the pre-leukemic stage, we observe upregulation of IDH2-associated pathways, including inflammation. We deliver a detailed phenotyping of IDH-mutant AML and a framework for dissecting contributions of recurrently mutated genes in AML at diagnosis and following therapy, with implications for precision medicine.

PMID:40409258 | DOI:10.1016/j.stem.2025.04.012