Breast Cancer Res. 2025 Feb 27;27(1):30. doi: 10.1186/s13058-025-01971-5.

ABSTRACT

OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model based on longitudinal ultrasound data and clinical features to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients.

METHODS: Between January 2018 and June 2023, 312 patients with histologically confirmed breast cancer were enrolled and randomly assigned to a training cohort (n = 219) and a test cohort (n = 93) in a 7:3 ratio. Next, pre-NAC and post-treatment 2-cycle ultrasound images were collected, and radiomics and deep learning features were extracted from NAC pre-treatment (Pre), post-treatment 2 cycle (Post), and Delta (pre-NAC-NAC 2 cycle) images. In the training cohort, to filter features, the intraclass correlation coefficient test, the Boruta algorithm, and the least absolute shrinkage and selection operator (LASSO) logistic regression were used. Single-modality models (Pre, Post, and Delta) were constructed based on five machine-learning classifiers. Finally, based on the classifier with the optimal predictive performance, the DLR model was constructed by combining Pre, Post, and Delta ultrasound features and was subsequently combined with clinical features to develop a combined model (Integrated). The discriminative power, predictive performance, and clinical utility of the models were further evaluated in the test cohort. Furthermore, patients were assigned into three subgroups, including the HR+/HER2-, HER2+, and TNBC subgroups, according to molecular typing to validate the predictability of the model across the different subgroups.

RESULTS: After feature screening, 16, 13, and 10 features were selected to construct the Pre model, Post model, and Delta model based on the five machine learning classifiers, respectively. The three single-modality models based on the XGBoost classifier displayed optimal predictive performance. Meanwhile, the DLR model (AUC of 0.827) was superior to the single-modality model (Pre, Post, and Delta AUCs of 0.726, 0.776, and 0.710, respectively) in terms of prediction performance. Moreover, multivariate logistic regression analysis identified Her-2 status and histological grade as independent risk factors for NAC response in breast cancer. In both the training and test cohorts, the Integrated model, which included Pre, Post, and Delta ultrasound features and clinical features, exhibited the highest predictive ability, with AUC values of 0.924 and 0.875, respectively. Likewise, the Integrated model displayed the highest predictive performance across the different subgroups.

CONCLUSION: The Integrated model, which incorporated pre-NAC treatment and early treatment ultrasound data and clinical features, accurately predicted pCR after NAC in breast cancer patients and provided valuable insights for personalized treatment strategies, allowing for timely adjustment of chemotherapy regimens.

PMID:40016785 | DOI:10.1186/s13058-025-01971-5

BMC Med. 2025 Feb 27;23(1):127. doi: 10.1186/s12916-025-03962-x.

ABSTRACT

BACKGROUND: Early diagnosis of biliary atresia (BA) is crucial for improving patient outcomes, yet remains a significant global challenge. This challenge may be ameliorated through the application of artificial intelligence (AI). Despite the promise of AI in medical diagnostics, its application to multimodal BA data has not yet achieved substantial breakthroughs. This study aims to leverage diverse data sources and formats to develop an intelligent diagnostic system for BA.

METHODS: We constructed the largest known multimodal BA dataset, comprising ultrasound images, clinical data, and laboratory results. Using this dataset, we developed a novel deep learning model and simplified it using easily obtainable data, eliminating the need for blood samples. The models were externally validated in a prospective study. We compared the performance of our model with human experts of varying expertise levels and evaluated the AI system's potential to enhance its diagnostic accuracy.

RESULTS: The retrospective study included 1579 participants. The multimodal model achieved an AUC of 0.9870 on the internal test set, outperforming human experts. The simplified model yielded an AUC of 0.9799. In the prospective study's external test set of 171 cases, the multimodal model achieved an AUC of 0.9740, comparable to that of a radiologist with over 10 years of experience (AUC = 0.9766). For less experienced radiologists, the AI-assisted diagnostic AUC improved from 0.6667 to 0.9006.

CONCLUSIONS: This AI-based screening application effectively facilitates early diagnosis of BA and serves as a valuable reference for addressing common challenges in rare diseases. The model's high accuracy and its ability to enhance the diagnostic performance of human experts underscore its potential for significant clinical impact.

PMID:40016769 | DOI:10.1186/s12916-025-03962-x

BMC Biol. 2025 Feb 27;23(1):63. doi: 10.1186/s12915-025-02166-2.

ABSTRACT

BACKGROUND: Cyclic peptides, known for their high binding affinity and low toxicity, show potential as innovative drugs for targeting "undruggable" proteins. However, their therapeutic efficacy is often hindered by poor membrane permeability. Over the past decade, the FDA has approved an average of one macrocyclic peptide drug per year, with romidepsin being the only one targeting an intracellular site. Biological experiments to measure permeability are time-consuming and labor-intensive. Rapid assessment of cyclic peptide permeability is crucial for their development.

RESULTS: In this work, we proposed a novel deep learning model, dubbed as MultiCycPermea, for predicting cyclic peptide permeability. MultiCycPermea extracts features from both the image information (2D structural information) and sequence information (1D structural information) of cyclic peptides. Additionally, we proposed a substructure-constrained feature alignment module to align the two types of features. MultiCycPermea has made a leap in predictive accuracy. In the in-distribution setting of the CycPeptMPDB dataset, MultiCycPermea reduced the mean squared error (MSE) by approximately 44.83% compared to the latest model Multi_CycGT (0.29 vs 0.16). By leveraging visual analysis tools, MultiCycPermea can reveal the relationship between peptide modification structures and membrane permeability, providing insights to improve the membrane permeability of cyclic peptides.

CONCLUSIONS: MultiCycPermea provides an effective tool that accurately predicts the permeability of cyclic peptides, offering valuable insights for improving the membrane permeability of cyclic peptides. This work paves a new path for the application of artificial intelligence in assisting the design of membrane-permeable cyclic peptides.

PMID:40016695 | DOI:10.1186/s12915-025-02166-2

BMC Bioinformatics. 2025 Feb 27;26(1):68. doi: 10.1186/s12859-025-06081-9.

ABSTRACT

BACKGROUND: Protein large language models (LLM) have been used to extract representations of enzyme sequences to predict their function, which is encoded by enzyme commission (EC) numbers. However, a comprehensive comparison of different LLMs for this task is still lacking, leaving questions about their relative performance. Moreover, protein sequence alignments (e.g. BLASTp or DIAMOND) are often combined with machine learning models to assign EC numbers from homologous enzymes, thus compensating for the shortcomings of these models' predictions. In this context, LLMs and sequence alignment methods have not been extensively compared as individual predictors, raising unaddressed questions about LLMs' performance and limitations relative to the alignment methods. In this study, we set out to assess the performance of ESM2, ESM1b, and ProtBERT language models in their ability to predict EC numbers, comparing them with BLASTp, against each other and against models that rely on one-hot encodings of amino acid sequences.

RESULTS: Our findings reveal that combining these LLMs with fully connected neural networks surpasses the performance of deep learning models that rely on one-hot encodings. Moreover, although BLASTp provided marginally better results overall, DL models provide results that complement BLASTp's, revealing that LLMs better predict certain EC numbers while BLASTp excels in predicting others. The ESM2 stood out as the best model among the LLMs tested, providing more accurate predictions on difficult annotation tasks and for enzymes without homologs.

CONCLUSIONS: Crucially, this study demonstrates that LLMs still have to be improved to become the gold standard tool over BLASTp in mainstream enzyme annotation routines. On the other hand, LLMs can provide good predictions for more difficult-to-annotate enzymes, particularly when the identity between the query sequence and the reference database falls below 25%. Our results reinforce the claim that BLASTp and LLM models complement each other and can be more effective when used together.

PMID:40016653 | DOI:10.1186/s12859-025-06081-9

BMC Cancer. 2025 Feb 27;25(1):367. doi: 10.1186/s12885-025-13740-w.

ABSTRACT

As reported by the International Agency for Research on Cancer (IARC), the global incidence of cancer reached nearly 20 million new cases in recent years, with cancer-related fatalities amounting to around 9.7 million. This underscores the profound impact cancer has on public health worldwide. Deep learning has become a mainstream approach in cancer recognition. Despite its significant progress, deep learning is known for its requirement of large quantities of labeled data. Few-shot learning addresses this limitation by reducing the need for extensive labeled samples. In the field of cancer recognition, data collection is particularly challenging due to the scarcity of categories compared to other fields, and current few-shot learning methods have not yielded satisfactory results. To tackle this, we propose an auxiliary meta-learning strategy for cancer recognition. During the auxiliary training phase, the feature mapping model is trained in conjunction with external data. This process neutralizes the prediction probability of misclassification, allowing the model to more readily learn distinguishing features and avoid performance degradation caused by discrepancies in external data. Additionally, the redundancy of some input principal components in the feature mapping model is reduced, while the implicit information within these components is extracted. The training process is further accelerated by utilizing depthwise over-parameterized convolutional layers. Moreover, the implementation of a three-branch structure contributes to faster training and enhanced performance. In the meta-training stage, the feature mapping model is optimized within the embedding space, utilizing category prototypes and cosine distance. During the meta-testing phase, a small number of labeled samples are employed to classify unknown data. We have conducted extensive experiments on the BreakHis, Pap smear, and ISIC 2018 datasets. The results demonstrate that our method achieves superior accuracy in cancer recognition. Furthermore, experiments on few-shot benchmark datasets indicate that our approach exhibits excellent generalization capabilities.

PMID:40016648 | DOI:10.1186/s12885-025-13740-w

Sci Rep. 2025 Feb 27;15(1):7009. doi: 10.1038/s41598-025-90646-4.

ABSTRACT

Detecting cassava leaf disease is challenging because it is hard to identify diseases accurately through visual inspection. Even trained agricultural experts may struggle to diagnose the disease correctly which leads to potential misjudgements. Traditional methods to diagnose these diseases are time-consuming, prone to error, and require expert knowledge, making automated solutions highly preferred. This paper explores the application of advanced deep learning techniques to detect as well as classify cassava leaf diseases which includes EfficientNet models, DenseNet169, Xception, MobileNetV2, ResNet models, Vgg19, InceptionV3, and InceptionResNetV2. A dataset consisting of around 36,000 labelled images of cassava leaves, afflicted by diseases such as Cassava Brown Streak Disease, Cassava Mosaic Disease, Cassava Green Mottle, Cassava Bacterial Blight, and healthy leaves, was used to train these models. Further the images were pre-processed by converting them into grayscale, reducing noise using Gaussian filter, obtaining the region of interest using Otsu binarization, Distance transformation, as well as Watershed technique followed by employing contour-based feature selection to enhance model performance. Models, after fine-tuned with ADAM optimizer computed that among the tested models, the hybrid model (DenseNet169 + EfficientNetB0) had superior performance with classification accuracy of 89.94% while as EfficientNetB0 had the highest values of precision, recall, and F1score with 0.78 each. The novelty of the hybrid model lies in its ability to combine DenseNet169's feature reuse capability with EfficientNetB0's computational efficiency, resulting in improved accuracy and scalability. These results highlight the potential of deep learning for accurate and scalable cassava leaf disease diagnosis, laying the foundation for automated plant disease monitoring systems.

PMID:40016508 | DOI:10.1038/s41598-025-90646-4

NPJ Digit Med. 2025 Feb 27;8(1):126. doi: 10.1038/s41746-025-01455-y.

ABSTRACT

Recognizing the limitations of computer-assisted tools for thyroid nodule diagnosis using static ultrasound images, this study developed a diagnostic tool utilizing dynamic ultrasound video, namely Thyroid Nodules Visualization (TNVis), by leveraging a two-stage deep learning framework that involved three-dimensional (3D) visualization. In this multicenter study, 4569 cases were included for framework development, and data from seven hospitals were employed for diagnostic validation. TNVis achieved a Dice similarity coefficient of 0.90 after internal testing. For the external validation, TNVis significantly improved radiologists' performance, reaching an AUC of 0.79, compared to their diagnostic performance without the use of TNVis (AUC: 0.66; p < 0.001) and those with partial assistance (AUC: 0.72; p < 0.001). In conclusion, the TNVis-assisted diagnostic strategy not only significantly improves the diagnostic ability of radiologists but also closely imitates their clinical diagnostic procedures and provides them with an objective 3D representation of the nodules for precise and personalized diagnosis and treatment planning.

PMID:40016505 | DOI:10.1038/s41746-025-01455-y

NPJ Digit Med. 2025 Feb 27;8(1):130. doi: 10.1038/s41746-025-01473-w.

ABSTRACT

Glaucoma, a leading cause of blindness, requires accurate early detection. We present an AI-based Glaucoma Screening (AI-GS) network comprising six lightweight deep learning models (total size: 110 MB) that analyze fundus images to identify early structural signs such as optic disc cupping, hemorrhages, and nerve fiber layer defects. The segmentation of the optic cup and disc closely matches that of expert ophthalmologists. AI-GS achieved a sensitivity of 0.9352 (95% CI 0.9277-0.9435) at 95% specificity. In real-world testing, sensitivity dropped to 0.5652 (95% CI 0.5218-0.6058) at ~0.9376 specificity (95% CI 0.9174-0.9562) for the standalone binary glaucoma classification model, whereas the full AI-GS network maintained higher sensitivity (0.8053, 95% CI 0.7704-0.8382) with good specificity (0.9112, 95% CI 0.8887-0.9356). The sub-models in AI-GS, with enhanced capabilities in detecting early glaucoma-related structural changes, drive these improvements. With low computational demands and tunable detection parameters, AI-GS promises widespread glaucoma screening, portable device integration, and improved understanding of disease progression.

PMID:40016437 | DOI:10.1038/s41746-025-01473-w

Sci Rep. 2025 Feb 27;15(1):7010. doi: 10.1038/s41598-025-92020-w.

ABSTRACT

Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as a cornerstone in diagnosing brain health issues, providing essential data for healthcare decisions. These images represent vast datasets that are increasingly harnessed by deep learning for high-performance image processing and classification tasks. In our study, we focus on classifying brain tumors-such as glioma, meningioma, and pituitary tumors-using the U-Net architecture applied to MRI scans. Additionally, we explore the effectiveness of convolutional neural networks including Inception-V3, EfficientNetB4, and VGG19, augmented through transfer learning techniques. Evaluation metrics such as F-score, recall, precision, and accuracy were employed to assess model performance. The U-Net segmentation architecture, emerged as the top performer, achieving an accuracy of 98.56%, along with an F-score of 99%, an area under the curve of 99.8%, and recall and precision rates of 99%. This study demonstrates the effectiveness of U-Net, a convolutional neural network architecture, for accurate brain tumor segmentation in early detection and treatment planning. Achieving an accuracy of 96.01% in cross-dataset validation with an external cohort, U-Net exhibited robust performance across diverse clinical scenarios. Our findings highlight the potential of U-Net and transfer learning in enhancing diagnostic accuracy and informing clinical decision-making in neuroimaging, ultimately improving patient care and outcomes.

PMID:40016334 | DOI:10.1038/s41598-025-92020-w

Eur Respir J. 2025 Feb 27;65(2):2402224. doi: 10.1183/13993003.02224-2024. Print 2025 Feb.

NO ABSTRACT

PMID:40015735 | DOI:10.1183/13993003.02224-2024

Alzheimers Res Ther. 2025 Feb 27;17(1):52. doi: 10.1186/s13195-025-01697-8.

ABSTRACT

BACKGROUND: Recent studies have focused on improving our understanding of gut microbiome dysbiosis and its impact on cognitive function. However, the relationship between gut microbiome composition, accelerated brain atrophy, and cognitive function has not yet been fully explored.

METHODS: We recruited 292 participants from South Korean memory clinics to undergo brain magnetic resonance imaging, clinical assessments, and collected stool samples. We employed a pretrained brain age model- a measure associated with neurodegeneration. Using cluster analysis, we categorized individuals based on their microbiome profiles and examined the correlations with brain age, Mental State Examination (MMSE) scores, and the Clinical Dementia Rating Sum of Box (CDR-SB).

RESULTS: Two clusters were identified in the microbiota at the phylum level that showed significant differences on a few microbiotas phylum. Greater gut microbiome dysbiosis was associated with worse cognitive function including MMSE and CDR-SB; this effect was partially mediated by greater brain age even when accounting for chronological age, sex, and education.

CONCLUSIONS: Our findings indicate that brain age mediates the link between gut microbiome dysbiosis and cognitive performance. These insights suggest potential interventions targeting the gut microbiome to alleviate age-related cognitive decline.

PMID:40016766 | DOI:10.1186/s13195-025-01697-8

BMC Biol. 2025 Feb 27;23(1):62. doi: 10.1186/s12915-025-02168-0.

ABSTRACT

BACKGROUND: Melanin pigmentation in oocytes is a critical feature for both the esthetic and developmental aspects of oocytes, influencing their polarity and overall development. Despite substantial knowledge of melanogenesis in melanocytes and retinal pigment epithelium cells, the molecular mechanisms underlying oocyte melanogenesis remain largely unknown.

RESULTS: Here, we compare the oocytes of wild-type, tyr-/- and mitf-/- Xenopus tropicalis and found that mitf-/- oocytes exhibit normal melanin deposition at the animal pole, whereas tyr-/- oocytes show no melanin deposition at this site. Transmission electron microscopy confirmed that melanogenesis in mitf-/- oocytes proceeds normally, similar to wild-type oocytes. Transcriptomic analysis revealed that mitf-/- oocytes still express melanogenesis-related genes, enabling them to complete melanogenesis. Additionally, in Xenopus tropicalis oocytes, the expression of the MiT subfamily factor tfe3 is relatively high, while tfeb, mitf, and tfec levels are extremely low. The expression pattern of tfe3 is similar to that of tyr and other melanogenesis-related genes. Thus, melanogenesis in Xenopus tropicalis oocytes is independent of Mitf and may be regulated by other MiT subfamily factors such as Tfe3, which control the expression of genes like tyr, dct, and tyrp1. Furthermore, transcriptomic data revealed that changes in the expression of genes related to mitochondrial cloud formation represent the most significant molecular changes during oocyte development.

CONCLUSIONS: Overall, these findings suggest that further elucidation of Tyr-dependent and Mitf-independent mechanisms of melanin deposition at the animal pole will enhance our understanding of melanogenesis and Oogenesis.

PMID:40016733 | DOI:10.1186/s12915-025-02168-0

Cell Res. 2025 Feb 28. doi: 10.1038/s41422-025-01084-w. Online ahead of print.

ABSTRACT

The spliceosome, a highly dynamic macromolecular assembly, catalyzes the precise removal of introns from pre-mRNAs. Recent studies have provided comprehensive structural insights into the step-wise assembly, catalytic splicing and final disassembly of the spliceosome. However, the molecular details of how the spliceosome recognizes and rejects suboptimal splicing substrates remained unclear. Here, we show cryo-electron microscopy structures of spliceosomal quality control complexes from a thermophilic eukaryote, Chaetomium thermophilum. The spliceosomes, henceforth termed B*Q, are stalled at a catalytically activated state but prior to the first splicing reaction due to an aberrant 5' splice site conformation. This state is recognized by G-patch protein GPATCH1, which is docked onto PRP8-EN and -RH domains and has recruited the cognate DHX35 helicase to its U2 snRNA substrate. In B*Q, DHX35 has dissociated the U2/branch site helix, while the disassembly helicase DHX15 is docked close to its U6 RNA 3'-end substrate. Our work thus provides mechanistic insights into the concerted action of two spliceosomal helicases in maintaining splicing fidelity by priming spliceosomes that are bound to aberrant splice substrates for disassembly.

PMID:40016598 | DOI:10.1038/s41422-025-01084-w

Commun Med (Lond). 2025 Feb 27;5(1):49. doi: 10.1038/s43856-025-00764-3.

ABSTRACT

BACKGROUND: Accurately recognizing that a person may be dying is central to improving their experience of care at the end-of-life. However, predicting dying is frequently inaccurate and often occurs only hours or a few days before death.

METHODS: We performed urinary metabolomics analysis on patients with lung cancer to create a metabolite model to predict dying over the last 30 days of life.

RESULTS: Here we show a model, using only 7 metabolites, has excellent accuracy in the Training cohort n = 112 (AUC = 0·85, 0·85, 0·88 and 0·86 on days 5, 10, 20 and 30) and Validation cohort n = 49 (AUC = 0·86, 0·83, 0·90, 0·86 on days 5, 10, 20 and 30). These results are more accurate than existing validated prognostic tools, and uniquely give accurate predictions over a range of time points in the last 30 days of life. Additionally, we present changes in 125 metabolites during the final four weeks of life, with the majority exhibiting statistically significant changes within the last week before death.

CONCLUSIONS: These metabolites identified offer insights into previously undocumented pathways involved in or affected by the dying process. They not only imply cancer's influence on the body but also illustrate the dying process. Given the similar dying trajectory observed in individuals with cancer, our findings likely apply to other cancer types. Prognostic tests, based on the metabolites we identified, could aid clinicians in the early recognition of people who may be dying and thereby influence clinical practice and improve the care of dying patients.

PMID:40016594 | DOI:10.1038/s43856-025-00764-3

Lab Anim (NY). 2025 Feb 27. doi: 10.1038/s41684-025-01506-7. Online ahead of print.

ABSTRACT

HSALR mice are the most broadly used animal model for studying myotonic dystrophy type I (DM1). However, so far, HSALR preclinical studies have often excluded female mice or failed to document the biological sex of the animals. This leaves an unwanted knowledge gap concerning the differential development of DM1 in males and females, particularly considering that the disease has a different clinical presentation in men and women. Here we compared typical functional measurements, histological features, molecular phenotypes and biochemical plasma profiles in the muscles of male and female HSALR mice in search of any significant between-sex differences that could justify this exclusion of female mice in HSALR studies and, critically, in candidate therapy assays performed with this model. We found no fundamental differences between HSALR males and females during disease development. Both sexes presented comparable functional and tissue phenotypes, with similar molecular muscle profiles. The only sex differences and significant interactions observed were in plasma biochemical parameters, which are also intrinsically variable in patients with DM1. In addition, we tested the influence of age on these measurements. We therefore suggest including female HSALR mice in regular DM1 studies, and recommend documenting the sex of animals, especially in studies focusing on metabolic alterations. This will allow researchers to detect and report any potential differences between male and female HSALR mice, especially regarding the efficacy of experimental treatments that could be relevant to patients with DM1.

PMID:40016516 | DOI:10.1038/s41684-025-01506-7

Nat Med. 2025 Feb 27. doi: 10.1038/s41591-025-03530-z. Online ahead of print.

ABSTRACT

Brain metastases frequently develop in patients with non-small cell lung cancer (NSCLC) and are a common cause of cancer-related deaths, yet our understanding of the underlying human biology is limited. Here we performed multimodal single-nucleus RNA and T cell receptor, single-cell spatial and whole-genome sequencing of brain metastases and primary tumors of patients with treatment-naive NSCLC. Chromosomal instability (CIN) is a distinguishing genomic feature of brain metastases compared with primary tumors, which we validated through integrated analysis of molecular profiling and clinical data in 4,869 independent patients, and a new cohort of 12,275 patients with NSCLC. Unbiased analyses revealed transcriptional neural-like programs that strongly enriched in cancer cells from brain metastases, including a recurring, CINhigh cell subpopulation that preexists in primary tumors but strongly enriched in brain metastases, which was also recovered in matched single-cell spatial transcriptomics. Using multiplexed immunofluorescence in an independent cohort of treatment-naive pairs of primary tumors and brain metastases from the same patients with NSCLC, we validated genomic and tumor-microenvironmental findings and identified a cancer cell population characterized by neural features strongly enriched in brain metastases. This comprehensive analysis provides insights into human NSCLC brain metastasis biology and serves as an important resource for additional discovery.

PMID:40016452 | DOI:10.1038/s41591-025-03530-z

EMBO Rep. 2025 Feb 27. doi: 10.1038/s44319-025-00389-6. Online ahead of print.

NO ABSTRACT

PMID:40016427 | DOI:10.1038/s44319-025-00389-6

NPJ Syst Biol Appl. 2025 Feb 27;11(1):21. doi: 10.1038/s41540-025-00498-x.

ABSTRACT

More than 20% of the population across the world is affected by non-communicable inflammatory skin diseases including psoriasis, atopic dermatitis, hidradenitis suppurativa, rosacea, etc. Many of these chronic diseases are painful and debilitating with limited effective therapeutic interventions. This study aims to identify common regulatory pathways and master regulators that regulate the molecular pathogenesis of inflammatory skin diseases. We designed an integrative systems biology framework to identify the significant regulators across several diseases. Network analytics unraveled 55 high-value proteins as significant regulators in molecular pathogenesis which can serve as putative drug targets for more effective treatments. We identified IKZF1 as a shared master regulator in hidradenitis suppurativa, atopic dermatitis, and rosacea with known disease-derived molecules for developing efficacious combinatorial treatments for these diseases. The proposed framework is very modular and indicates a significant path of molecular mechanism-based drug development from complex transcriptomics data and other multi-omics data.

PMID:40016271 | DOI:10.1038/s41540-025-00498-x

Nat Commun. 2025 Feb 27;16(1):2020. doi: 10.1038/s41467-025-57034-y.

ABSTRACT

G protein-coupled receptors (GPCRs) constitute a functionally diverse protein family and are targets for a broad spectrum of pharmaceuticals. Technological progress in X-ray crystallography and cryogenic electron microscopy has enabled extensive, high-resolution structural characterisation of GPCRs in different conformational states. However, as highly dynamic events underlie GPCR signalling, a complete understanding of GPCR functionality requires insights into their conformational dynamics. Here, we present a large dataset of molecular dynamics simulations covering 60% of currently available GPCR structures. Our analysis reveals extensive local "breathing" motions of the receptor on a nano- to microsecond timescale and provides access to numerous previously unexplored receptor conformational states. Furthermore, we reveal that receptor flexibility impacts the shape of allosteric drug binding sites, which frequently adopt partially or completely closed states in the absence of a molecular modulator. We demonstrate that exploring membrane lipid dynamics and their interaction with GPCRs is an efficient approach to expose such hidden allosteric sites and even lateral ligand entrance gateways. The obtained insights and generated dataset on conformations, allosteric sites and lateral entrance gates in GPCRs allows us to better understand the functionality of these receptors and opens new therapeutic avenues for drug-targeting strategies.

PMID:40016203 | DOI:10.1038/s41467-025-57034-y

Gut. 2025 Feb 27:gutjnl-2024-333944. doi: 10.1136/gutjnl-2024-333944. Online ahead of print.

ABSTRACT

BACKGROUND: Despite the success of immune checkpoint blockade, a lack of understanding of the hepatocellular carcinoma (HCC) immune microenvironment impedes its development.

OBJECTIVE: We aim to elucidate the essential function of E-twenty-six-specific sequence variant 5 (ETV5) in regulating the immune microenvironment in HCC.

DESIGN: Humanised mouse models, murine orthotopic models and diethylnitrosamine/carbon tetrachloride (DEN/CCl4)-induced HCC models were used to examine the function of ETV5. The downstream targets of ETV5 were screened using chromatin immunoprecipitation sequencing, CUT&Tag and RNA sequencing. Immune cells were examined using flow cytometry and immunofluorescence. S100 calcium-binding protein A9 (S100A9) was targeted by neutralising antibodies.

RESULTS: Overexpression of ETV5 in HCC cells facilitated HCC metastasis and immune escape by recruiting and enhancing the immunosuppressive capabilities of polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs). Mechanistically, ETV5 transactivated programmed death ligand 1 (PD-L1) and S100A9 expression. Inhibition of S100A9 or myeloid-specific knockout of toll-like receptor 4 (TLR4)/receptor for advanced glycation endproducts (RAGE), the receptors of S100A9, impeded ETV5-induced PMN-MDSC recruitment. Meanwhile, S100A9 within the tumour microenvironment elevated ETV5 expression via the extracellular signal-regulated kinase (ERK)/nuclear factor-kappa B pathway. Additionally, ETV5 transcriptionally upregulated PD-L1 in MDSCs as well, thereby augmenting their immunosuppressive functions. Myeloid-specific Etv5 knockout attenuated HCC progression. We developed monoclonal neutralising-S100A9 antibodies that effectively inhibited ETV5-mediated PMN-MDSC infiltration. Synergistic application of anti-S100A9 or TLR4/RAGE inhibitors with anti-PD-L1 therapy significantly suppressed ETV5-mediated HCC progression.

CONCLUSION: ETV5 facilitates HCC progression and metastasis by promoting the recruitment, infiltration and activation of PMN-MDSCs. Synergistic application of anti-S100A9 or TLR4/RAGE inhibitors with anti-PD-L1 therapy holds great promise as an effective combinational treatment strategy for ETV5-positive HCC.

PMID:40015948 | DOI:10.1136/gutjnl-2024-333944