Nat Commun. 2025 Aug 12;16(1):7458. doi: 10.1038/s41467-025-62598-w.

ABSTRACT

DNA transactions introduce torsional constraints that pose an inherent risk to genome integrity. While topoisomerase 1 (TOP1) activity is essential for DNA supercoil removal, the aberrant stabilization of TOP1:DNA cleavage complexes (TOP1ccs) can result in cytotoxic DNA lesions. What protects genomic hot spots of topological stress from excessive TOP1cc accumulation remains unknown. Here, we identify chromatin context as an essential means to coordinate TOP1cc resolution. Through its ability to bind poly(ADP-ribose) (PAR), the histone variant macroH2A1.1 facilitates TOP1cc repair factor recruitment and lesion turnover, thereby preventing DNA damage in response to transcription-associated topological stress. The alternatively spliced macroH2A1.2 isoform is unable to bind PAR or protect from TOP1ccs. Impaired macroH2A1.1 splicing, a frequent cancer feature, was predictive of increased sensitivity to TOP1 poisons in a pharmaco-genomic screen in breast cancer cells, and macroH2A1.1 inactivation mirrored this effect. We propose macroH2A1 alternative splicing as an epigenetic modulator of TOP1-associated genome maintenance and a potential cancer vulnerability.

PMID:40796804 | DOI:10.1038/s41467-025-62598-w

Clin Drug Investig. 2025 Aug 12. doi: 10.1007/s40261-025-01466-3. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Reports of suspected adverse drug reactions are of a great importance for the safety monitoring of new vaccines to identify potential safety risks promptly and to ensure necessary measures for risk mitigation. We reviewed the reports of fatal adverse drug reactions after coronavirus disease 2019 (COVID-19) vaccination with Comirnaty®, Spikevax®, and Vaxzevria® during the national vaccination campaign in Sweden.

METHODS: Swedish reports of suspected adverse drug reactions with fatal outcomes after COVID-19 vaccines were retrieved from the EudraVigilance database. Vaccination data were obtained from the National vaccination register. Reporting rates were calculated by dividing the number of adverse drug reaction reports with fatal outcomes by the number of people exposed to at least one dose of the COVID-19 vaccines or by the number of vaccine doses given. A causality assessment of adverse drug reaction reports was performed by clinically qualified reviewers.

RESULTS: More than 26 million doses of COVID-19 vaccines were administered and 456 reports of suspected adverse drug reactions with fatal outcomes were reported during 27 December, 2020-31 May, 2023. The reporting rate was 5.7 fatal outcomes per 100,000 persons vaccinated with at least one dose of any COVID-19 vaccine or 1.7 per 100,000 vaccine doses given. Most of the fatalities were related to patients' pre-existing conditions, predominantly among people aged 70 years or older. Only ten of the reported fatalities (0.1 per 100,000 persons vaccinated) were assessed as consistent with a causal association to COVID-19 vaccination.

CONCLUSIONS: Adverse drug reactions with fatal outcomes after COVID-19 vaccines in Sweden were very rare. No new safety concerns were observed in this study.

PMID:40796716 | DOI:10.1007/s40261-025-01466-3

J Microbiol Immunol Infect. 2025 Aug 8:S1684-1182(25)00151-3. doi: 10.1016/j.jmii.2025.08.003. Online ahead of print.

ABSTRACT

BACKGROUND: Mycobacterium abscessus (MABS) is a clinically significant nontuberculous mycobacterium, and its drug resistance poses substantial therapeutic challenges. Comprehensive genomic and phenotypic analyses are essential for elucidating the mechanisms underlying this resistance and enhancing understanding of its epidemiology.

METHODS: Whole-genome sequencing (WGS) using the Illumina platform was conducted on 61 clinical MABS isolates obtained from patients in Thailand. MABS subspecies classification was performed using FastANI, TYGS, and NTM-Profiler. Phenotypic drug susceptibility testing (pDST) was determined using a broth microdilution method. Resistance mutations were identified through NTM-Profiler and Snippy pipelines.

RESULTS: The analysis classified MABS isolates into three subspecies: subsp. abscessus (40/61, 65.57 %), subsp. massiliense (15/61, 24.59 %), and subsp. bolletii (6/61, 9.83 %). Phylogenetic analysis revealed genetic diversity among the majority of the MABS clinical isolates. These isolates clustered into distinct clades, separate from globally recognized clinical strains and dominant circulating clones. Inducible clarithromycin resistance was detected in 60.66 % of MABS isolates, associated with the T28 variant in erm(41). The Ile80Val mutation in erm(41) was significantly associated with inducible clarithromycin resistance (χ2 = 12.61, p < 0.001). Acquired clarithromycin resistance associated with rrl mutations (A2270C, A2270G, A2271C) and amikacin resistance linked to the rrs mutation A1375G were detected in 11.48 % and 4.92 % of isolates, respectively. The categorical agreement between WGS-based DST and pDST was 95.08 %, 88.33 %, and 96.43 % for inducible clarithromycin, clarithromycin, and amikacin, respectively.

CONCLUSION: This study provides valuable insights into the genomic diversity and antimicrobial resistance of MABS isolates in Thailand, emphasizing regional variations in dominant clones and resistance mechanisms.

PMID:40796429 | DOI:10.1016/j.jmii.2025.08.003

Nat Biotechnol. 2025 Aug 12. doi: 10.1038/s41587-025-02771-0. Online ahead of print.

ABSTRACT

Precise CRISPR-based DNA integration and editing remain challenging, largely because of insufficient control of the repair process. We find that repair at the genome-cargo interface is predictable by deep learning models and adheres to sequence-context-specific rules. On the basis of in silico predictions, we devised a strategy of base-pair tandem repeat repair arms matching microhomologies at double-strand breaks. These repeat homology arms promote frame-retentive cassette integration and reduce deletions both at the target site and within the transgene. We demonstrate precise integrations at 32 loci in HEK293T cells. Germline-transmissible transgene integration and endogenous protein tagging in Xenopus and adult mouse brains demonstrated precise integration during early embryonic cleavage and in nondividing, differentiated cells. Optimized repair arms also facilitated small edits for scarless single-nucleotide or double-nucleotide changes using oligonucleotide templates in vitro and in vivo. We provide the design tool Pythia to facilitate precise genomic integration and editing for experimental and therapeutic purposes for a wide range of target cell types and applications.

PMID:40796977 | DOI:10.1038/s41587-025-02771-0

Commun Eng. 2025 Aug 12;4(1):151. doi: 10.1038/s44172-025-00488-1.

ABSTRACT

Growing energy storage demand has solidified the dominance of lithium-ion batteries (LIBs) in modern societies but intensifies recycling pressures. Precise state-of-health (SOH) assessment is crucial to grouping retired batteries from an unknown state for secondary utilization. However, batteries in the pack exhibit distinct capacity fading behaviors due to their service scenarios and working conditions. We develop a deep-learning framework for rapid, transferable SOH estimation and battery classification. This framework integrates deep neural networks with interconnected electrochemical, mechanical, and thermal features. Our model delivers optimal accuracy with a mean absolute error (MAE) of 0.822% and a root mean square error (RMSE) of 1.048% using combined features. It demonstrates robust performance across various conditions and enables SOH prediction with data from merely one previous cycle. Moreover, the well-trained model could adapt to other electrode systems with a minimal number of additional samples. This work highlights critical features for SOH estimation and enables efficient battery classification toward sustainable recycling.

PMID:40796946 | DOI:10.1038/s44172-025-00488-1

Sci Rep. 2025 Aug 12;15(1):29497. doi: 10.1038/s41598-025-12531-4.

ABSTRACT

Fistfight detection in video data is a critical task in video surveillance systems, where identifying physical altercations in real-time can enhance safety and security in public spaces. Earlier techniques primarily emphasized capturing inter-person interactions and combining individual characteristics into group-based representations, often overlooking the critical intra-person dynamics within the human bodypose point framework. However, essential individual features can be extracted by examining human skeletal movements' progression and temporal patterns. This paper presents a novel multimodal spatio-temporal fistfight detection model (MSTFDet) that integrates RGB images and human skeletal data to identify violent behaviors accurately. The proposed framework leverages both Context-Aware Encoded Transformer (CAET) for modeling interactions between individuals and their environment and Spatial-Temporal Graph Convolutional Networks (ST-GCN) for capturing intra-person and inter-person dynamics from skeletal data. The RGB module uses a combination of spatial and temporal transformers to model contextual relationships and individual actions, while the bodypose-point module processes skeletal data to capture the fine-grained motion of individuals. We conduct evaluations on two public datasets: the Surveillance Camera Fight Dataset (SCFD) and the RWF-2000 dataset, which feature complex real-world scenarios. On the SCFD and RWF-2000 datasets, MSTFDet achieved a Multi-class classification accuracy (MCA) of 92.3% and 95.2% MCA, respectively. These results highlight the effectiveness of the proposed approach in capturing both spatial and temporal features, providing a robust solution for real-time fistfight detection in diverse and challenging environments.

PMID:40796923 | DOI:10.1038/s41598-025-12531-4

Nat Commun. 2025 Aug 12;16(1):7490. doi: 10.1038/s41467-025-62826-3.

ABSTRACT

Bone marrow adipose tissue, as a distinct adipose subtype, has been implicated in the pathophysiology of skeletal, metabolic, and hematopoietic disorders. To identify its underlying genetic factors, we utilized a deep learning algorithm capable of quantifying bone marrow fat fraction (BMFF) in the vertebrae and proximal femur using magnetic resonance imaging data of over 38,000 UK Biobank participants. Genome-wide association analyses uncovered 373 significant BMFF-associated variants (P-value < 5 × 10-9), with enrichment in bone remodeling, metabolism, and hematopoiesis pathway. Furthermore, genetic correlation highlighted a significant association between BMFF and skeletal disease. In about 300,000 individuals, polygenic risk scores derived from three proximal femur BMFF were significantly associated with increased osteoporosis risk. Notably, Mendelian randomization analyses revealed a causal link between proximal femur BMFF and osteoporosis. Here, we show critical insights into the genetic determinants of BMFF and offer perspectives on the biological mechanisms driving osteoporosis development.

PMID:40796918 | DOI:10.1038/s41467-025-62826-3

Epigenetics Chromatin. 2025 Aug 12;18(1):55. doi: 10.1186/s13072-025-00618-1.

ABSTRACT

BACKGROUND: Topologically associating domains (TADs) are believed to play a role in the regulation of gene expression by constraining or guiding interactions between the regulatory elements. While the impact of TAD perturbations is typically studied in developmental genes with highly cell-type-specific expression patterns, this study examines genes with broad expression profiles separated by a strong insulator boundary. We focused on the mouse Slc29a3/Unc5b locus, which encompasses two distinct TADs containing ubiquitously expressed and essential for viability genes. We disrupted the CTCF-boundary between these TADs and analyzed the resulting changes in gene expression.

RESULTS: Deletion of four CTCF binding sites at the TAD boundary altered local chromatin architecture, abolishing pre‑existing loops and creating novel long‑range interactions that spanned the original TAD boundary. Using UMI-assisted targeted RNA-seq we evaluated transcriptional changes of Unc5b, Slc29a3, Psap, Vsir, Cdh23, and Sgpl1 across various organs. We found that TAD boundary disruption led to variable transcriptional responses, where not only the magnitude but also the direction of gene expression changes were tissue-specific. Current hypotheses on genome architecture function, such as enhancer competition and hijacking, as well as genomic deep learning models, only partially explain these transcriptional changes, highlighting the need for further investigation into the mechanisms underlying TAD function and gene regulation.

CONCLUSIONS: Disrupting the insulator element between broadly expressed genes resulted in moderate, tissue-dependent transcriptional alterations, rather than uniformly activating or silencing the target genes. These findings show that TAD boundaries contribute to context‑specific regulation even at housekeeping loci and underscore the need for refined models to predict the effects of non‑coding structural variants.

PMID:40796890 | DOI:10.1186/s13072-025-00618-1

Sci Rep. 2025 Aug 12;15(1):29519. doi: 10.1038/s41598-025-06951-5.

ABSTRACT

In the medical field, Artificial Intelligence (AI) for diagnostic processes, particularly through deep learning techniques, has become increasingly advanced. Minor trauma, such as accidental cheek biting, sharp dental edges, or poorly fitting dentures, typically causes painful mouth ulcers and bump-like sores inside the mouth. Traditionally, diagnosing these ulcers involves a dentist or physician performing a physical examination, visually assessing the sores, and asking detailed questions about their size, location, duration, and related symptoms. Our research focuses on the advanced classification of oral ulcer stages using a convolutional neural network (CNN). To evaluate performance comprehensively, we developed and tested three custom models, comparing their effectiveness in distinguishing between different stages of oral ulcers. We also explored various optimizers and activation functions to determine the best configuration for improving model performance. Although our models show promising potential as diagnostic tools for oral ulcers, they occasionally make errors. Among the models tested, UlcerNet-2 stood out for its performance. Using the RMSprop optimizer along with Softmax and SELU activation functions, UlcerNet-2 achieved a validation accuracy of 96%. These results highlight UlcerNet-2's exceptional effectiveness in classifying oral ulcer stages, achieving a commendable balance of high accuracy, precision, and recall. This suggests UlcerNet-2 has significant potential as an advanced diagnostic tool, possibly enhancing clinical practices in detecting and staging oral ulcers. The proposed model (UlcerNet) was implemented on FogBus, the cloud framework to empirically evaluate the model performance in cloud-fog interoperable scenarios.

PMID:40796791 | DOI:10.1038/s41598-025-06951-5

Sci Rep. 2025 Aug 12;15(1):29472. doi: 10.1038/s41598-025-13862-y.

ABSTRACT

Multi-Modal Medical Image Fusion (MMMIF) has become increasingly important in clinical applications, as it enables the integration of complementary information from different imaging modalities to support more accurate diagnosis and treatment planning. The primary objective of Medical Image Fusion (MIF) is to generate a fused image that retains the most informative features from the Source Images (SI), thereby enhancing the reliability of clinical decision-making systems. However, due to inherent limitations in individual imaging modalities-such as poor spatial resolution in functional images or low contrast in anatomical scans-fused images can suffer from information degradation or distortion. To address these limitations, this study proposes a novel fusion framework that integrates the Non-Subsampled Shearlet Transform (NSST) with a Convolutional Neural Network (CNN) for effective sub-band enhancement and image reconstruction. Initially, each source image is decomposed into Low-Frequency Coefficients (LFC) and multiple High-Frequency Coefficients (HFC) using NSST. The proposed Concurrent Denoising and Enhancement Network (CDEN) is then applied to these sub-bands to suppress noise and enhance critical structural details. The enhanced LFCs are fused using an AlexNet-based activity-level fusion model, while the enhanced HFCs are combined using a Pulse Coupled Neural Network (PCNN) guided by a Novel Sum-Modified Laplacian (NSML) metric. Finally, the fused image is reconstructed via Inverse-NSST (I-NSST). Experimental results prove that the proposed method outperforms existing fusion algorithms, achieving approximately 16.5% higher performance in terms of the QAB/F (edge preservation) metric, along with strong results across both subjective visual assessments and objective quality indices.

PMID:40796770 | DOI:10.1038/s41598-025-13862-y

Sci Rep. 2025 Aug 12;15(1):29467. doi: 10.1038/s41598-025-14238-y.

ABSTRACT

Pain is a multifaceted phenomenon that significantly affects a large portion of the global population. Objective pain assessment is essential for developing effective management strategies, which in turn contribute to more efficient and responsive healthcare systems. However, accurately evaluating pain remains a complex challenge due to subtle physiological and behavioural indicators, individual-specific pain responses, and the need for continuous patient monitoring. Automatic pain assessment systems offer promising, technology-driven solutions to support and enhance various aspects of the pain evaluation process. Physiological indicators offer valuable insights into pain-related states and are generally less influenced by individual variability compared to behavioural modalities, such as facial expressions. Skin conductance, regulated by sweat gland activity, and the heart's electrical signals are both influenced by changes in the sympathetic nervous system. Biosignals, such as electrodermal activity (EDA) and electrocardiogram (ECG), can, therefore, objectively capture the body's physiological responses to painful stimuli. This paper proposes a novel multi-modal ensemble deep learning framework that combines electrodermal activity and electrocardiogram signals for automatic pain recognition. The proposed framework includes a uni-modal approach (FCN-ALSTM-Transformer) comprising a Fully Convolutional Network, Attention-based LSTM, and a Transformer block to integrate features extracted by these models. Additionally, a multi-modal approach (CrossMod-Transformer) is introduced, featuring a dedicated Transformer architecture that fuses electrodermal activity and electrocardiogram signals. Experimental evaluations were primarily conducted on the BioVid dataset, with further cross-dataset validation using the AI4PAIN 2025 dataset to assess the generalisability of the proposed method. Notably, the CrossMod-Transformer achieved an accuracy of 87.52% on Biovid and 75.83% on AI4PAIN, demonstrating strong performance across independent datasets and outperforming several state-of-the-art uni-modal and multi-modal methods. These results highlight the potential of the proposed framework to improve the reliability of automatic multi-modal pain recognition and support the development of more objective and inclusive clinical assessment tools.

PMID:40796769 | DOI:10.1038/s41598-025-14238-y

Sci Rep. 2025 Aug 12;15(1):29591. doi: 10.1038/s41598-025-12370-3.

ABSTRACT

Rolling bearings are a crucial component in rotating machinery, essential for ensuring the smooth functioning of the entire system. However, their vulnerability to damage necessitates the implementation of effective fault diagnosis. Traditional deep learning methods often struggle due to the scarcity of fault samples, leading to issues like overfitting and inadequate generalization. To address this problem, a novel Siamese Neural Network (SNN) model, integrating Deep Convolutional Neural Networks with Wide First-layer Kernel (WDCNN) and Bidirectional Long Short-Term Memory (BiLSTM) network is proposed. This model constructs a feature extraction system that combines WDCNN and BiLSTM to extract local spatial features and global temporal dependencies from vibration signals. Additionally, the SNN framework is introduced to build a feature space under small sample conditions through metric learning, enhancing the ability of model to discern sample similarities. Experiments on the CWRU and HUST datasets indicate that with only 90 training samples, the model achieves diagnostic accuracy of 83.47% and 61.48%, respectively, significantly surpassing CNN, BiLSTM, and their combined models. Furthermore, the model also shows robustness against severe noise interference, making it a viable tool for efficient fault diagnosis in rolling bearings with limited data.

PMID:40796758 | DOI:10.1038/s41598-025-12370-3

Nat Microbiol. 2025 Aug 12. doi: 10.1038/s41564-025-02061-0. Online ahead of print.

ABSTRACT

Antimicrobial resistance is one of the greatest threats facing humanity, making the need for new antibiotics more critical than ever. While most antibiotics originate from bacteria and fungi, archaea offer a largely untapped reservoir for antibiotic discovery. In this study, we leveraged deep learning to systematically explore the archaeome, uncovering promising candidates for combating antimicrobial resistance. By mining 233 archaeal proteomes, we identified 12,623 molecules with potential antimicrobial activity. These peptide compounds, termed archaeasins, have unique compositional features that differentiate them from traditional antimicrobial peptides, including a distinct amino acid profile. We synthesized 80 archaeasins, 93% of which showed antimicrobial activity in vitro against Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus and Enterococcus spp. Notably, in vivo validation identified archaeasin-73 as a lead candidate, significantly reducing A. baumannii loads in mouse infection models, with effectiveness comparable to that of established antibiotics such as polymyxin B. Our findings highlight the potential of archaea as a resource for developing next-generation antibiotics.

PMID:40796684 | DOI:10.1038/s41564-025-02061-0

Nat Struct Mol Biol. 2025 Aug 12. doi: 10.1038/s41594-025-01614-5. Online ahead of print.

ABSTRACT

Pregnane X receptor (PXR) is a nuclear receptor considered to be a master transcription factor of xenobiotic metabolism. Here, using enhanced ultraviolet crosslinking and immunoprecipitation, we show that PXR can bind mRNAs in different cancer cell lines and normal liver tissues. PXR-bound mRNAs include genes related to metabolic reprogramming and lipid metabolism. Separately from its known nuclear transcriptional function, cytoplasmic PXR binds and stabilizes mature mRNA containing C+G-enriched sequences through its zinc-finger domain. Mechanistically, cytoplasmic PXR interacts with RNH1, an RNase inhibitor, to regulate RNA stability. In colorectal cancer cells, cytoplasmic PXR facilitates glucose uptake by stabilizing SLC2A1 mRNA. This process further promotes cell proliferation and cancer development. Our study unveils a previously unknown dimension of PXR-mediated gene regulation by characterizing PXR as an RNA-binding protein important for mRNA stability in the cytoplasm.

PMID:40797049 | DOI:10.1038/s41594-025-01614-5

Nat Commun. 2025 Aug 12;16(1):7436. doi: 10.1038/s41467-025-62579-z.

ABSTRACT

False positive claims of differentially expressed genes (DEGs) in scRNA-seq studies are of substantial concern. We found that DEGs from individual Parkinson's (PD), Huntington's (HD), and COVID-19 datasets had moderate predictive power for case-control status of other datasets, but DEGs from Alzheimer's (AD) and Schizophrenia (SCZ) datasets had poor predictive power. We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets, and found DEGs with improved predictive power. Specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. Up-regulated DEGs implicated chaperone-mediated protein processing in PD glia and lipid transport in AD and PD microglia, while down-regulated DEGs were in glutamatergic processes in AD astrocytes and excitatory neurons and synaptic functioning in HD FOXP2 neurons. Lastly, we evaluate factors influencing reproducibility of individual studies as a prospective guide for experimental design.

PMID:40796563 | DOI:10.1038/s41467-025-62579-z

J Proteome Res. 2025 Aug 12. doi: 10.1021/acs.jproteome.4c01060. Online ahead of print.

ABSTRACT

Bone tissue poses significant challenges for proteomic analysis due to its dense, mineral-rich matrix and predominance of collagen, overshadowing low-abundance proteins critical for understanding bone physiology during LC-MS/MS-based proteomic analysis. In this study, we present a rapid sequential two-step extraction protocol designed to enhance proteome coverage, reduce collagen interference without using collagenase, and ensure robust quantification while enabling simultaneous metabolome analysis. We systematically compared it with two previously reported methods, which attempt to reduce collagen content through enzymatic collagen digestion or by employing four sequential extractions. Performance was evaluated based on reproducible protein quantification, variance, collagen content, processing, and instrument time. Our protocol reproducibly quantified 4,518 proteins across a dynamic range of 4 orders of magnitude. It demonstrated only marginally inferior quantification performance compared to the four-step protocol while reducing extraction and measurement time by half. Further, it significantly outperformed the collagenase-based method, which quantified only 2,689 proteins. Incorporating a chloroform-methanol metabolite extraction only led to a minimal reduction in quantifiable proteins, making the protocol suitable for multiomics applications. In conclusion, this protocol facilitates comprehensive coverage of proteins after metabolite extraction, enabling comprehensive multiomics analyses and aiding in the assessment of bone diseases and therapeutic developments.

PMID:40796122 | DOI:10.1021/acs.jproteome.4c01060

Pharmacol Res. 2025 Aug 10:107905. doi: 10.1016/j.phrs.2025.107905. Online ahead of print.

ABSTRACT

Huntington's disease (HD) is a fatal neurodegenerative disorder caused by a CAG repeat expansion in the HTT gene. This leads to progressive loss of striatal neurons and motor-cognitive decline. While current gene-targeting approaches aiming at reducing somatic instability show promise - especially in case of early treatment - they cannot restore the already compromised neuronal circuitry at advanced disease stages. Thus, cell replacement therapy offers a regenerative strategy to rebuild damaged striatal circuits. Here, we report that human striatal progenitors (hSPs) derived from embryonic stem cells via a morphogen-guided protocol survive long-term when transplanted into a rodent model of HD and recapitulate key aspects of ventral telencephalic development. By employing single-nucleus RNAseq of the grafted cells, we resolved their transcriptional profile with unprecedented resolution. This has identified transcriptional signals of D1- and D2-type medium spiny neurons (MSN), Medial Ganglionic Eminence (MGE) and Caudal Ganglionic Eminence (CGE) -derived interneurons, and regionally specified astrocytes. Moreover, we demonstrate that grafted cells undergo further maturation 6 months post-transplantation, acquiring the expected regionally defined transcriptional identity. Immunohistochemistry confirmed stable graft composition over time and supported a neurogenic-to-gliogenic switch post-transplantation. Multiple complementary techniques including virus-based tracing and electrophysiology assays demonstrated anatomical and functional integration of the grafts. Notably, chemogenetic modulation of graft activity regulated striatal-dependent behaviors, further supporting effective graft integration into host basal ganglia circuits. Altogether, these results provide preclinical evidence that hSP-grafts can reconstruct striatal circuits and modulate functionally relevant behaviors. The ability to generate a scalable, molecularly defined progenitor population capable of in vivo functional integration supports the potential of hSPs for clinical application in HD and related basal ganglia disorders.

PMID:40796049 | DOI:10.1016/j.phrs.2025.107905

J R Soc Interface. 2025 Aug;22(229):20240877. doi: 10.1098/rsif.2024.0877. Epub 2025 Aug 13.

ABSTRACT

Physiological adaptation is a fundamental property of biological systems across all levels of organization, ensuring survival and proper function. Adaptation is typically formulated as an asymptotic property of the dose response (DR), defined as the level of a response variable with respect to an input parameter. In pharmacology, the input could be a drug concentration; in immunology, it might correspond to an antigen level. In contrast to the DR, this paper develops the concept of a transient, finite-time, cumulative dose response (cDR), which is obtained by integrating the response variable over a fixed time interval and viewing that integral-area under the curve-as a function of the input parameter. This study is motivated by experimental observations of cytokine accumulation under T-cell stimulation, which exhibit a non-monotonic cDR. It is known from the systems biology literature that only two types of network motifs, incoherent feedforward loops and negative integral feedback (IFB) mechanisms, can generate adaptation. Three paradigmatic such motifs-two types of incoherent loops and one integral feedback-have been the focus of much study. Surprisingly, it is shown here that these two incoherent feedforward loop motifs-despite their capacity for non-monotonic DR-always yield a monotonic cDR, and are therefore inconsistent with these experimental data. On the other hand, this work reveals that the IFB motif is indeed capable of producing a non-monotonic cDR, and is thus consistent with these data.

PMID:40795984 | DOI:10.1098/rsif.2024.0877

Cell Rep Methods. 2025 Aug 8:101140. doi: 10.1016/j.crmeth.2025.101140. Online ahead of print.

ABSTRACT

We identified a gene panel comprising 71 glycosyltransferases (GTs) that alter glycan patterns on cancer cells as they become more virulent. When these cancer-pattern GTs (CPGTs) were run through an algorithm trained on The Cancer Genome Atlas, they differentiated tumors from healthy tissue with 97% accuracy and clustered 27 cancers with 94% accuracy in external validation, revealing each variety's "biometric glycan ID." Using machine learning, we built four models for cancer classification, including two for detecting the molecular subtypes of breast cancer and glioma using even smaller CPGT sets. Our results reveal the power of using glyco-genes for diagnostics: Our breast cancer classifier was almost twice as effective in independent testing as the widely used prediction analysis of microarray 50 (PAM50) subtyping kit at differentiating between luminal A, luminal B, HER2-enriched, and basal-like breast cancers based on a comparable number of genes. Only four GT genes were needed to build a prognostic model for glioma survival.

PMID:40795869 | DOI:10.1016/j.crmeth.2025.101140

Curr Biol. 2025 Aug 6:S0960-9822(25)00952-2. doi: 10.1016/j.cub.2025.07.043. Online ahead of print.

ABSTRACT

The efficient recognition and removal of apoptotic cells and extracellular vesicles (EVs) by phagocytes is critical to prevent secondary necrosis and maintain tissue homeostasis. Such detection involves receptors and bridging molecules that recognize aminophospholipids-normally restricted to the inner leaflet of healthy cells-which become exposed on the surface of dead cells and the vesicles they produce.1,2,3,4,5 A majority of studies focus on phosphatidylserine (PS), for which there are well-established receptors that either bind to the lipid directly or indirectly via intermediary proteins.6,7,8 Phosphatidylethanolamine (PE) is even more prevalent than PS in the inner leaflet of mammalian cells9 and also becomes exposed by the action of scramblases during cell death,10,11 though little is known about the effects of PE once scrambled. Here, we report that PE can itself serve as a phagocytic ligand for macrophages by engaging CD300 family receptors. CD300a and CD300b specifically modulated the binding and uptake of PE particles, and this process involved immunoreceptor tyrosine-based activation motif (ITAM)-containing adaptors and spleen tyrosine kinase (Syk). For bacteria, which contain PE but largely lack PS in their membranes, we report that PE engagement enabled the binding and uptake of spheroplasts and bacterial extracellular vesicles (BEVs) that were unsheathed by the cell wall. The inflammatory responses of macrophages to PE particles containing lipopolysaccharide (LPS) were also curtailed by CD300a expression. Based on these observations, we posit that the direct recognition of PE facilitates mechanisms of clearance that stand to have a broad impact on the immune response.

PMID:40795848 | DOI:10.1016/j.cub.2025.07.043