Sci Rep. 2025 Feb 19;15(1):6099. doi: 10.1038/s41598-025-89738-y.

ABSTRACT

Convolutional neural networks (CNNs) and mammalian visual systems share architectural and information processing similarities. We leverage these parallels to develop an in-silico CNN model simulating diseases affecting the visual system. This model aims to replicate neural complexities in an experimentally controlled environment. Therefore, we examine object recognition and internal representations of a CNN under neurodegeneration and neuroplasticity conditions simulated through synaptic weight decay and retraining. This approach can model neurodegeneration from events like tau accumulation, reflecting cognitive decline in diseases such as posterior cortical atrophy, a condition that can accompany Alzheimer's disease and primarily affects the visual system. After each degeneration iteration, we retrain unaffected synapses to simulate ongoing neuroplasticity. Our results show that with significant synaptic decay and limited retraining, the model's representational similarity decreases compared to a healthy model. Early CNN layers retain high similarity to the healthy model, while later layers are more prone to degradation. The results of this study reveal a progressive decline in object recognition proficiency, mirroring posterior cortical atrophy progression. In-silico modeling of neurodegenerative diseases can enhance our understanding of disease progression and aid in developing targeted rehabilitation and treatments.

PMID:39972104 | DOI:10.1038/s41598-025-89738-y

Sci Rep. 2025 Feb 19;15(1):6124. doi: 10.1038/s41598-025-90572-5.

ABSTRACT

This research presents a novel ensemble fuzzy deep learning approach for brain Magnetic Resonance Imaging (MRI) analysis, aiming to improve the segmentation of brain tissues and abnormalities. The method integrates multiple components, including diverse deep learning architectures enhanced with volumetric fuzzy pooling, a model fusion strategy, and an attention mechanism to focus on the most relevant regions of the input data. The process begins by collecting medical data using sensors to acquire MRI images. These data are then used to train several deep learning models that are specifically designed to handle various aspects of brain MRI segmentation. To enhance the model's performance, an efficient ensemble learning method is employed to combine the predictions of multiple models, ensuring that the final decision accounts for different strengths of each individual model. A key feature of the approach is the construction of a knowledge base that stores data from training images and associates it with the most suitable model for each specific sample. During the inference phase, this knowledge base is consulted to quickly identify and select the best model for processing new test images, based on the similarity between the test data and previously encountered samples. The proposed method is rigorously tested on real-world brain MRI segmentation benchmarks, demonstrating superior performance in comparison to existing techniques. Our proposed method achieves an Intersection over Union (IoU) of 95% on the complete Brain MRI Segmentation dataset, demonstrating a 10% improvement over baseline solutions.

PMID:39972098 | DOI:10.1038/s41598-025-90572-5

Sci Rep. 2025 Feb 19;15(1):6029. doi: 10.1038/s41598-025-90084-2.

ABSTRACT

The limited availability of labeled ECG data restricts the application of supervised deep learning methods in ECG detection. Although existing self-supervised learning approaches have been applied to ECG analysis, they are predominantly image-based, which limits their effectiveness. To address these limitations and provide novel insights, we propose a Temporal-Spatial Self-Supervised Learning (TSSL) method specifically designed for ECG detection. TSSL leverages the intrinsic temporal and spatial characteristics of ECG signals to enhance feature representation. Temporally, ECG signals retain consistent identity information over time, enabling the model to generate stable representations for the same individual across different time points while isolating representations of different leads to preserve their unique features. Spatially, ECG signals from various leads capture the heart's activity from different perspectives, revealing both commonalities and distinct patterns. TSSL captures these correlations by maintaining consistency in the relationships between signals and their representations across different leads. Experimental results on the CPSC2018, Chapman, and PTB-XL databases demonstrate that TSSL introduces new capabilities by effectively utilizing temporal and spatial information, achieving superior performance compared to existing methods and approaching the performance of full-label training with only 10% of the labeled data. This highlights TSSL's ability to provide deeper insights and enhanced feature extraction beyond mere performance improvements. We make our code publicly available on https://github.com/cwp9731/temporal-spatial-self-supervised-learning.

PMID:39972080 | DOI:10.1038/s41598-025-90084-2

Sci Rep. 2025 Feb 19;15(1):6116. doi: 10.1038/s41598-025-90418-0.

ABSTRACT

Skin diseases, a significant category in the medical field, have always been challenging to diagnose and have a high misdiagnosis rate. Deep learning for skin disease classification has considerable value in clinical diagnosis and treatment. This study proposes a skin disease classification model based on multi-scale channel attention. The network architecture of the model consists of three main parts: an input module, four processing blocks, and an output module. Firstly, the model has improved the pyramid segmentation attention module to extract multi-scale features of the image entirely. Secondly, the reverse residual structure is used to replace the residual structure in the backbone network, and the attention module is integrated into the reverse residual structure to achieve better multi-scale feature extraction. Finally, the output module consists of an adaptive average pool and a fully connected layer, which convert the aggregated global features into several categories to generate the final output for the classification task. To verify the performance of the proposed model, this study used two commonly used skin disease datasets, ISIC2019 and HAM10000, for validation. The experimental results showed that the accuracy of this study was 77.6% on the ISIC2019 skin disease series dataset and 88.2% on the HAM10000 skin disease dataset. External validation data was added for evaluation to validate the model further, and the comprehensive evaluation results proved the effectiveness of the proposed model in this paper.

PMID:39972014 | DOI:10.1038/s41598-025-90418-0

Sci Rep. 2025 Feb 20;15(1):6132. doi: 10.1038/s41598-025-90530-1.

ABSTRACT

The heart is an important organ that plays a crucial role in maintaining life. Unfortunately, heart disease is one of the major causes of mortality globally. Early and accurate detection can significantly improve the situation by enabling preventive measures and personalized healthcare recommendations. Artificial intelligence is emerging as a powerful tool for healthcare applications, particularly in predicting heart diseases. Researchers are actively working on this, but challenges remain in achieving accurate heart disease prediction. Therefore, experimenting with various models to identify the most effective one for heart disease prediction is crucial. In this view, this paper addresses this need by conducting an extensive investigation of various models. The proposed research considered 11 feature selection techniques and 21 classifiers for the experiment. The feature selection techniques considered for the research are Information Gain, Chi-Square Test, Fisher Discriminant Analysis (FDA), Variance Threshold, Mean Absolute Difference (MAD), Dispersion Ratio, Relief, LASSO, Random Forest Importance, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA). The classifiers considered for the research are Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), XGBoost, AdaBoost, Stochastic Gradient Descent (SGD), Gradient Boosting Classifier, Extra Tree Classifier, CatBoost, LightGBM, Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Convolutional Neural Network (CNN), and Hybrid Model (CNN, RNN, LSTM, GRU, BiLSTM, BiGRU). Among all the extensive experiments, XGBoost outperformed all others, achieving an accuracy of 0.97, precision of 0.97, sensitivity of 0.98, specificity of 0.98, F1 score of 0.98, and AUC of 0.98.

PMID:39972004 | DOI:10.1038/s41598-025-90530-1

Sci Rep. 2025 Feb 19;15(1):6062. doi: 10.1038/s41598-025-90768-9.

ABSTRACT

Sand plugging during hydraulic fracturing is one of the primary causes of operational failure. Existing methods for identifying sand plugging during fracturing suffer from issues such as time-consuming, low accuracy, and inability to provide real-time warning. Addressing these challenges, this study leverages offshore hydraulic fracturing operational data and reports to propose a novel method for intelligent identification and real-time warning of sand plugging. Initially, we employ an Attention Mechanism based Long-Short Term Memory Network (Att-LSTM) to establish a real-time pressure prediction model during fracturing, capable of forecasting pressure within 40 s with an accuracy exceeding 92%. Subsequently, we enhance the structure of an Attention Mechanism based Convolutional Long-Short Term Memory Network (Att-CNN-LSTM) to develop a model for identifying sand plugging during fracturing, achieving identification with an error margin of less than 1 min. Finally, through the integration of Att-LSTM and Att-CNN-LSTM networks coupled with transfer learning techniques, we introduce a continuously learning approach for sand plugging warning during fracturing operations, significantly improving accuracy and efficiency in sand plugging identification and advancing the intelligent decision-making process for hydraulic fracturing. These methodologies not only contribute theoretical innovations but also demonstrate substantial practical effectiveness, providing critical technical support and guidance to enhance safety and efficiency in hydraulic fracturing operations.

PMID:39971998 | DOI:10.1038/s41598-025-90768-9

Mol Med. 2025 Feb 19;31(1):63. doi: 10.1186/s10020-025-01119-3.

ABSTRACT

BACKGROUND: As an endoplasmic reticulum (ER) protein, Reticulum 3 (RTN3) has been reported to play a crucial role in neurodegenerative diseases, lipid metabolism, and chronic kidney disease. The involvement of RTN3 in idiopathic pulmonary fibrosis (IPF), a progressive and fatal interstitial lung disease, remains unexplored.

METHODS: In this study, we explored the role of RTN3 in pulmonary fibrosis using public datasets, IPF patient samples, and animal models. We investigated its pathogenic mechanisms in lung fibroblasts and alveolar macrophages.

RESULTS: We found decreased levels of RTN3 in IPF patients, bleomycin-induced mice, and TGFβ-treated cell lines. RTN3-null mice exhibited more severe pulmonary fibrosis phenotypes in old age or after bleomycin treatment. Collagen synthesis was significantly increased in RTN3-null mice lung tissues and lung fibroblasts. Mechanistic studies revealed that RTN3 deficiency reduced the ER-anchored CRTH2 in lung fibroblasts, which serves as an antifibrotic molecule via antagonizing collagen biosynthesis. Simultaneously, RTN3 deficiency reduced the autophagy degradation of CRTH2 which acts as an activator of profibrotic macrophage differentiation. Both effects of RTN3 and CRTH2 in lung fibroblasts and alveolar macrophages aggravated age-or bleomycin-induced pulmonary fibrosis. Additionally, we also identified a mutation of RTN3 in patients with ILD.

CONCLUSIONS: Our research demonstrated that RTN3 plays a significant role in the lung, and reduction of RTN3 levels may be a risk factor for IPF and related diseases.

PMID:39972424 | DOI:10.1186/s10020-025-01119-3

Nat Commun. 2025 Feb 19;16(1):1761. doi: 10.1038/s41467-025-57024-0.

ABSTRACT

Lung macrophages play a pivotal role in pulmonary fibrosis, with monocyte-derived alveolar macrophages driving disease progression. However, the mechanisms regulating their pro-fibrotic behavior and survival remain unclear, and effective therapeutic strategies are lacking. Here we show that triggering receptors expressed on myeloid cells 2 are predominantly expressed on monocyte-derived alveolar macrophages in fibrotic mouse lungs and are significantly elevated in lung macrophages from patients with idiopathic pulmonary fibrosis. Deletion or knockdown of this receptor disrupts intracellular survival signaling, promotes macrophage apoptosis, and attenuates their pro-fibrotic phenotype. We further demonstrate that a lipid mediator and a high-avidity ligand of this receptor, encountered by macrophages in the alveolar milieu, enhance macrophage survival and activity. Ablation of TREM2 or blocking this receptor with soluble receptors or specific antibodies effectively alleviates lung fibrosis in male mice. These findings identify this receptor as a critical regulator of macrophage-mediated fibrosis and a promising therapeutic target for intervention.

PMID:39971937 | DOI:10.1038/s41467-025-57024-0

BMJ Open Respir Res. 2025 Feb 19;12(1):e002773. doi: 10.1136/bmjresp-2024-002773.

ABSTRACT

OBJECTIVES: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease (ILD) and the most common idiopathic interstitial pneumonia. The UK IPF Registry was established in 2013 to collect data pertaining to clinical features, therapeutic approaches and outcomes. From February 2023, the Registry expanded to include any ILD with evidence of fibrosis.

DESIGN: The UK IPF Registry is a national, multicentre observational registry, including both prospective and retrospective data of patients with IPF in secondary or tertiary care. Cases eligible for inclusion were those with a diagnosis of IPF, presenting at participating centres from January 2013.

RESULTS: Between January 2013 and February 2023, 5052 IPF cases were registered from 64 participating centres. There was a male preponderance (77.8%) with mean±SD age of 74±8.1 years, 66% were ex-smokers and 76% had at least one comorbidity. Over a third (36.7%) experienced symptoms for more than 24 months prior to their first clinic visit. The majority of cases were discussed at a multidisciplinary team (MDT) meeting and the most common radiological patterns at presentation were probable (54.6%) and definite (42.7%) usual interstitial pneumonia. There was a reduction in surgical lung biopsies from 14% in 2013 to 5.5% in 2022. Antifibrotic therapy prescription rose from 36.0% in 2013 to 55.9% in 2023. The use of nintedanib (approved by National Institute of Clinical Excellence in January 2016) rose from 6.7% in 2013 to 31.5% in 2022 and pirfenidone (approved in April 2013) was initially used in around a third of cases before dropping to between 16.8% and 24.9% after nintedanib was approved.

CONCLUSION: These data reflect clinical practice across the UK and it is intended the data will have a role in informing the future of IPF care and providing a model for benchmarking, ultimately increasing knowledge and improving clinical care for this devastating disease.

PMID:39971593 | DOI:10.1136/bmjresp-2024-002773

Cell Signal. 2025 Feb 17:111667. doi: 10.1016/j.cellsig.2025.111667. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF), a fatal pulmonary condition, is marked by fibrosis and is devoid of efficacious treatments. The aim of our research was to explore the influence of miR-410-3p on the advancement of IPF. For creating a model of lung fibrosis, tracheal injections of 5 mg/kg bleomycin (BLM) were administered to mice, and added 10 ng/mL of TGF-β1 into MRC-5 cell medium. The evaluation of gene and protein expression was conducted using RT-qPCR and western blotting techniques. The assessment of fibrosis in MRC-5 cells and mouse pulmonary tissue involved the use of CCK-8, ELISA, flow cytometry, and HE staining methods. The results of our study revealed a rise in miR-410-3p levels in both TGF-β1-stimulated MRC-5 cells and BLM-exposed mouse pulmonary tissue. Inhibiting miR-410-3p improved cellular survival, lessened oxidative stress (MDA, ROS), decreased levels of inflammatory cytokines (TNF-α, IL-1β, IL-6), curtailed fibrosis-associated proteins (α-SMA, Collagen I, Collagen III, FN1), and amplified the expression of SOD and E-cadherin. The treatment effectively reduced cell fibrosis and improved lung tissue health, thus hindering the advancement of IPF. Mechanically, knocking down miR-410-3p activates AMPK/SIRT1 molecular axis to inhibit NF-κB signaling by up-regulating METRNL expression, thereby inhibiting oxidative stress and inflammation levels, and ultimately improving IPF. In summary, our research indicates that focusing on miR-410-3p might be an effective approach in IPF treatment.

PMID:39971221 | DOI:10.1016/j.cellsig.2025.111667

Am J Physiol Lung Cell Mol Physiol. 2025 Feb 19. doi: 10.1152/ajplung.00171.2024. Online ahead of print.

ABSTRACT

In this research, we delve into the association between epigenetic aging and idiopathic pulmonary fibrosis (IPF), a debilitating lung disease that progresses over time. Utilizing the Illumina MethylationEPIC array, we assessed DNA methylation levels in donated human lung tissue from IPF patients, categorizing the disease into mild, moderate, and severe stages based on clinical assessments. We employed seven epigenetic clocks to determine age acceleration, which is the discrepancy between biological (epigenetic) and chronological age. Our findings revealed a notable acceleration of biological aging in IPF tissues compared to healthy controls, with four clocks-Horvath's, Hannum's, PhenoAge, and DunedinPACE-showing significant correlations. DunedinPACE, in particular, indicated a more rapid aging process in the more severe regions within the lungs of IPF cases. These results suggest that the biological aging process in IPF is expedited and closely tied to the severity of the disease. The study underscores the potential of DNA methylation as a biomarker for IPF, providing valuable insights into the underlying methylation patterns and the dynamics of epigenetic aging in affected lung tissue. This research supports the broader application of epigenetic clocks in clinical prognosis and highlights the critical role of biological age in the context of medical research and healthcare.

PMID:39970931 | DOI:10.1152/ajplung.00171.2024

Diabetol Metab Syndr. 2025 Feb 19;17(1):63. doi: 10.1186/s13098-025-01630-x.

ABSTRACT

Posttraumatic stress disorder (PTSD) patients have a high comorbidity with type 2 diabetes (T2D). Whether PTSD influences the risk of diabetes is still not known. We used GWAS data from European ancestry of PTSD (23,121 cases and 151,447 controls) and T2D (80,154 cases and 853,816 controls) to investigate the bidirectional associations between PTSD and T2D by the Mendelian randomization (MR) analysis. We showed that PTSD was causally associated with higher odds of T2D (OR = 1.04, 95% CI: 1.01-1.06, P = 0.0086), but not vice versa. Our study suggests that PTSD may increase the risk of T2D. PTSD sufferers should be screened for T2D and its precursor known as metabolic syndrome.

PMID:39972391 | DOI:10.1186/s13098-025-01630-x

Nature. 2025 Feb;638(8051):E23-E29. doi: 10.1038/s41586-024-08497-4.

NO ABSTRACT

PMID:39972229 | DOI:10.1038/s41586-024-08497-4

Nature. 2025 Feb 19. doi: 10.1038/s41586-025-08586-y. Online ahead of print.

ABSTRACT

Neoantigen vaccines are under investigation for various cancers, including epidermal growth factor receptor (EGFR)-driven lung cancers1,2. We tracked the phylogenetic history of an EGFR mutant lung cancer treated with erlotinib, osimertinib, radiotherapy and a personalized neopeptide vaccine (NPV) targeting ten somatic mutations, including EGFR exon 19 deletion (ex19del). The ex19del mutation was clonal, but is likely to have appeared after a whole-genome doubling (WGD) event. Following osimertinib and NPV treatment, loss of the ex19del mutation was identified in a progressing small-cell-transformed liver metastasis. Circulating tumour DNA analyses tracking 467 somatic variants revealed the presence of this EGFR wild-type clone before vaccination and its expansion during osimertinib/NPV therapy. Despite systemic T cell reactivity to the vaccine-targeted ex19del neoantigen, the NPV failed to halt disease progression. The liver metastasis lost vaccine-targeted neoantigens through chromosomal instability and exhibited a hostile microenvironment, characterized by limited immune infiltration, low CXCL9 and elevated M2 macrophage levels. Neoantigens arising post-WGD were more likely to be absent in the progressing liver metastasis than those occurring pre-WGD, suggesting that prioritizing pre-WGD neoantigens may improve vaccine design. Data from the TRACERx 421 cohort3 provide evidence that pre-WGD mutations better represent clonal variants, and owing to their presence at multiple copy numbers, are less likely to be lost in metastatic transition. These data highlight the power of phylogenetic disease tracking and functional T cell profiling to understand mechanisms of immune escape during combination therapies.

PMID:39972134 | DOI:10.1038/s41586-025-08586-y

Nature. 2025 Feb 19. doi: 10.1038/s41586-025-08588-w. Online ahead of print.

ABSTRACT

The survival of malignant cells within tumours is often seen as depending on ruthless competition for nutrients and other resources1,2. Although competition is certainly critical for tumour evolution and cancer progression, cooperative interactions within tumours are also important, albeit poorly understood3,4. Cooperative populations at all levels of biological organization risk extinction if their population size falls below a critical tipping point5,6. Here we examined whether cooperation among tumour cells may be a potential therapeutic target. We identified a cooperative mechanism that enables tumour cells to proliferate under the amino acid-deprived conditions found in the tumour microenvironment. Disruption of this mechanism drove cultured tumour populations to the critical extinction point and resulted in a marked reduction in tumour growth in vivo. Mechanistically, we show that tumour cells collectively digest extracellular oligopeptides through the secretion of aminopeptidases. The resulting free amino acids benefit both aminopeptidase-secreting cells and neighbouring cells. We identified CNDP2 as the key enzyme that hydrolyses these peptides extracellularly, and loss of this aminopeptidase prevents tumour growth in vitro and in vivo. These data show that cooperative scavenging of nutrients is key to survival in the tumour microenvironment and reveal a targetable cancer vulnerability.

PMID:39972131 | DOI:10.1038/s41586-025-08588-w

Nature. 2025 Feb 19. doi: 10.1038/s41586-024-08508-4. Online ahead of print.

ABSTRACT

A fundamental challenge for cancer vaccines is to generate long-lived functional T cells that are specific for tumour antigens. Here we find that mRNA-lipoplex vaccines against somatic mutation-derived neoantigens may solve this challenge in pancreatic ductal adenocarcinoma (PDAC), a lethal cancer with few mutations. At an extended 3.2-year median follow-up from a phase 1 trial of surgery, atezolizumab (PD-L1 inhibitory antibody), autogene cevumeran1 (individualized neoantigen vaccine with backbone-optimized uridine mRNA-lipoplex nanoparticles) and modified (m) FOLFIRINOX (chemotherapy) in patients with PDAC, we find that responders with vaccine-induced T cells (n = 8) have prolonged recurrence-free survival (RFS; median not reached) compared with non-responders without vaccine-induced T cells (n = 8; median RFS 13.4 months; P = 0.007). In responders, autogene cevumeran induces CD8+ T cell clones with an average estimated lifespan of 7.7 years (range 1.5 to roughly 100 years), with approximately 20% of clones having latent multi-decade lifespans that may outlive hosts. Eighty-six percent of clones per patient persist at substantial frequencies approximately 3 years post-vaccination, including clones with high avidity to PDAC neoepitopes. Using PhenoTrack, a novel computational strategy to trace single T cell phenotypes, we uncover that vaccine-induced clones are undetectable in pre-vaccination tissues, and assume a cytotoxic, tissue-resident memory-like T cell state up to three years post-vaccination with preserved neoantigen-specific effector function. Two responders recurred and evidenced fewer vaccine-induced T cells. Furthermore, recurrent PDACs were pruned of vaccine-targeted cancer clones. Thus, in PDAC, autogene cevumeran induces de novo CD8+ T cells with multiyear longevity, substantial magnitude and durable effector functions that may delay PDAC recurrence. Adjuvant mRNA-lipoplex neoantigen vaccines may thus solve a pivotal obstacle for cancer vaccination.

PMID:39972124 | DOI:10.1038/s41586-024-08508-4

Nat Commun. 2025 Feb 19;16(1):1763. doi: 10.1038/s41467-025-57031-1.

ABSTRACT

Regulatory sequences encode crucial gene expression signals, yet the sequence characteristics that determine their functionality across species remain obscure. Deep generative models have demonstrated considerable potential in various inverse design applications, especially in engineering genetic elements. Here, we introduce DeepCROSS, a generative artificial intelligence framework for the inverse design of cross-species and species-preferred 5' regulatory sequences in bacteria. DeepCROSS constructs a meta-representation using 1.8 million regulatory sequences from thousands of bacterial genomes to depict the general constraints of regulatory sequences, employs artificial intelligence-guided massively parallel reporter assay experiments in E. coli and P. aeruginosa to explore the potential sequence space, and performs multi-task optimization to obtain de novo regulatory sequences. The optimized regulatory sequences achieve similar or better performance to functional natural regulatory sequences, with high success rates and low sequence similarities with the natural genome. Collectively, DeepCROSS efficiently navigates the sequence-function landscape and enables the inverse design of cross-species and species-preferred 5' regulatory sequences.

PMID:39971994 | DOI:10.1038/s41467-025-57031-1

Cell Death Dis. 2025 Feb 19;16(1):111. doi: 10.1038/s41419-025-07389-3.

ABSTRACT

Cardiac hypertrophy is a cellular process characterized by the increased size of cardiomyocytes in response to a high workload or stress. 17-beta estradiol (E2) has cardioprotective and anti-hypertrophic effects by maintaining mitochondrial network and function. MUL1 is a mitochondrial ubiquitin ligase directly involved in the control of mitochondrial fission and mitophagy. Studies from our group and others have previously shown that cardiomyocyte hypertrophy is associated with mitochondrial fission and dysfunction. These findings led us to study in vitro whether E2 regulates MUL1 to prevent cardiac hypertrophy, mitochondrial fission, and dysfunction induced by the catecholamine norepinephrine (NE). Our results showed that NE induces hypertrophy in cultured rat cardiomyocytes. Pre-treatment with E2 (10-100 nM) prevented the NE-dependent increases in cell perimeter and the hypertrophic stress markers ANP and BNP at both the protein and mRNA levels. NE induced the fragmentation of the mitochondrial network and reduced ATP levels, effects that were both prevented by E2. In silico analysis suggested a putative binding site for estrogen receptors on the MUL1 gene promoter. In accordance with this finding, E2 prevented increases in MUL1 mRNA and protein levels induced by NE. Our data also showed that a siRNA MUL1 knockdown counteracted NE-induced cardiomyocyte hypertrophy and mitochondrial dysfunction, mirroring the protective effect triggered by E2. In contrast, a MUL1 adenovirus did not prevent the E2 protection from cardiomyocyte hypertrophy. Further, in vivo analysis in a transgenic mouse model overexpressing MUL1 revealed that only young male mice overexpressed the protein. Consequently, they exhibited increased levels of the hypertrophic marker ANP, an elevated heart weight, and larger cardiomyocyte size. Therefore, our data demonstrate that 17-beta estradiol prevents cardiac myocyte hypertrophy by regulating MUL1.

PMID:39971924 | DOI:10.1038/s41419-025-07389-3

Cell Death Dis. 2025 Feb 19;16(1):108. doi: 10.1038/s41419-025-07420-7.

ABSTRACT

Stargardt disease is an inherited retinopathy affecting approximately 1:8000 individuals. It is characterised by biallelic variants in ABCA4 which encodes a vital protein for the recycling of retinaldehydes in the retina. Despite its prevalence and impact, there are currently no treatments available for this condition. Furthermore, 35% of STGD1 cases remain genetically unsolved. To investigate the cellular and molecular characteristics associated with STGD1, we generated iPSCs from two monoallelic unresolved (PT1 & PT2), late-onset STGD1 cases with the heterozygous complex allele - c.[5461-10 T > C;5603 A > T]. Both patient iPSCs and those from a biallelic affected control (AC) carrying -c.4892 T > C and c.4539+2001G > A, were differentiated to retinal organoids, which developed all key retinal neurons and photoreceptors with outer segments positive for ABCA4 expression. We observed patient-specific disruption to lamination with OPN1MW/LW+ cone photoreceptor retention in the retinal organoid centre during differentiation. Photoreceptor retention was more severe in the AC case affecting both cones and rods, suggesting a genotype/phenotype correlation. scRNA-Seq suggests retention may be due to the induction of stress-related pathways in photoreceptors. Whole genome sequencing successfully identified the missing alleles in both cases; PT1 reported c.-5603A > T in homozygous state and PT2 uncovered a rare hypomorph - c.-4685T > C. Furthermore, retinal organoids were able to recapitulate the retina-specific splicing defect in PT1 as shown by long-read RNA-seq data. Collectively, these results highlight the suitability of retinal organoids in STGD1 modelling. Their ability to display genotype-phenotype correlations enhances their utility as a platform for therapeutic development.

PMID:39971915 | DOI:10.1038/s41419-025-07420-7

Annu Rev Biomed Eng. 2025 Feb 19. doi: 10.1146/annurev-bioeng-102723-065309. Online ahead of print.

ABSTRACT

Understanding interaction mechanisms within cells, tissues, and organisms is crucial for driving developments across biology and medicine. Mathematical modeling is an essential tool for simulating such biological systems. Building on experiments, mechanistic models are widely used to describe small-scale intracellular networks. The development of sequencing techniques and computational tools has recently enabled multiscale models. Combining such larger scale network modeling with mechanistic modeling provides us with an opportunity to reveal previously unknown disease mechanisms and pharmacological interventions. Here, we review systems biology models from mechanistic models to multiscale models that integrate multiple layers of cellular networks and discuss how they can be used to shed light on disease states and even wellness-related states. Additionally, we introduce several methods that increase the certainty and accuracy of model predictions. Thus, combining mechanistic models with emerging mathematical and computational techniques can provide us with increasingly powerful tools to understand disease states and inspire drug discoveries.

PMID:39971380 | DOI:10.1146/annurev-bioeng-102723-065309