J Theor Biol. 2025 Apr 28:112135. doi: 10.1016/j.jtbi.2025.112135. Online ahead of print.

ABSTRACT

During the COVID-19 pandemic, the emergence of novel variants of concern (VoCs) prompted different responses from governments across the world aimed at mitigating the impacts of more transmissible or more harmful strains. We model the invasion of a novel VoC into a population with heterogeneous vaccine- and infection-acquired immunity using a multi-type branching process framework with immigration. We define the number of cases needed to be reached to ensure stochastic extinction of this strain is unlikely and, therefore, the strain has become established in the population. To estimate the first-passage time distribution to reach this number of cases we use a mixture of stochastic simulations and analytic results. The first-passage time distribution gives a time window that is useful for policymakers planning interventions aimed at suppressing or delaying the introduction of novel VoC. We apply our method to a model of COVID-19 in the United Kingdom, though our results are applicable to other pathogens and settings.

PMID:40306569 | DOI:10.1016/j.jtbi.2025.112135

Clin Immunol. 2025 Apr 28:110507. doi: 10.1016/j.clim.2025.110507. Online ahead of print.

ABSTRACT

This study explores immune responses in mild Omicron-era COVID-19 breakthrough cases, focusing on cytokine dysregulation, antibody dynamics, and Long COVID. Samples from 114 mild COVID-19 patients across multiple waves were analyzed at three timepoints (T1: 2-4 weeks, T2: 3-4 months, T3: 6-8 months post-infection). Persistent IP-10 elevation up to 8 months suggests prolonged low-grade immune activation. Hybrid immunity from Omicron breakthrough infections provided broad cross-variant antibody recognition but showed declining neutralization over time. Among vaccination regimens, mRNA-inclusive combinations were associated with lower Long COVID scores. CoV-229E antibody levels correlated with Long COVID scores. These findings underscore the need for extended monitoring of mild COVID-19 cases and highlight the potential of mRNA vaccines in reducing post-COVID-19 complications. Insights into immune alterations and vaccine effects can inform the development of future vaccination strategies and approaches for managing post-COVID-19 conditions.

PMID:40306350 | DOI:10.1016/j.clim.2025.110507

Semin Cell Dev Biol. 2025 Apr 29;171:103612. doi: 10.1016/j.semcdb.2025.103612. Online ahead of print.

ABSTRACT

Cardiovascular diseases remain the leading cause of death worldwide-claiming one-third of all deaths every year. Current two-dimensional in vitro cell culture systems and animal models cannot completely recapitulate the clinical complexity of these diseases in humans. Therefore, there is a dire need for higher fidelity biological systems capable of replicating these phenotypes to inform clinical outcomes and therapeutic development. Cardiac tissue engineering (CTE) strategies have emerged to fulfill this need by the design of in vitro three-dimensional myocardial tissue systems from human pluripotent stem cells. In this way, CTE systems serve as highly controllable human models for a variety of applications-including for physiological and pathological modeling, drug discovery and preclinical testing platforms, and even direct therapeutic interventions in the clinic. Although significant progress has been made in the development of these CTE technologies, critical challenges remain and necessary refinements are required to derive more advanced human heart tissue technologies. In this review, we distill three focus areas for the field to address: I) Generating cardiac muscle cell types and scalable manufacturing methods, II) Engineering tissue structure, function, and analyses, and III) Curating system design for specific application. In each of our focus areas, we emphasize the importance of designing CTE systems capable of mimicking the intricate intercellular connectivity of the human heart and discuss fundamental design considerations that subsequently arise. We conclude by highlighting cutting-edge applications that use CTE technologies for clinical modeling and the direct repair of damaged and diseased hearts.

PMID:40306230 | DOI:10.1016/j.semcdb.2025.103612

Biomed Pharmacother. 2025 Apr 29;187:118088. doi: 10.1016/j.biopha.2025.118088. Online ahead of print.

ABSTRACT

Mitochondrial dysfunction and mitophagy are closely linked with human diseases such as neurodegenerative diseases, metabolic diseases, and cancer. High-mobility group box 1 (HMGB1) has been shown to mediate a wide range of pathological responses by binding with the receptor for advanced glycation end-products (RAGE) and toll-like receptors (TLRs). Extracellular HMGB1 and its ligand RAGE stimulate the growth, metastasis, invasiveness, and treatment resistance of different cancer cells. Through extracellular signal-regulated kinase 1/2 (ERK1/2) signaling, HMGB1 and RAGE lead to the phosphorylation of Drp1-S616 and Drp1-mediated mitochondrial fission, which consequently causes autophagy. Although the structure of the RAGE and HMGB1 complex is not clearly known, the complex has emerged as a potential therapeutic target. In the present study, the structure of the RAGE and HMGB1 complex was determined at a resolution of 5.19 Å using cryogenic electron microscopy. The structure revealed that the residues P66, G70, P71, S74, and R77 in RAGE and E145, K146, E153, and E156 in HMGB1 were the sites of interaction between the two proteins. Additionally, an HMGB1 peptide (151 LKEKYEK 157) was synthesized based on the RAGE-HMGB1 complex. We investigated the inhibitory function of the HMGB1 peptide and demonstrated that it inhibits tumor growth, metastasis, and invasion by binding to the RAGE protein in lung cancers. The HMGB1 peptide significantly suppressed mitochondrial dysfunction and the initiation of autophagy. Furthermore, the HMGB1 peptide dramatically reduced cell viability, migration, and mitophagy in the colorectal and pancreatic cancer cell lines HCT-116 and AsPC-1, respectively.

PMID:40306174 | DOI:10.1016/j.biopha.2025.118088

IUBMB Life. 2025 May;77(5):e70020. doi: 10.1002/iub.70020.

ABSTRACT

Chemotherapeutic treatments for breast cancer are often associated with severe toxicity due to the requirement of high concentrations of the drugs for efficacy. The combination of chemotherapy drugs along with repurposed drugs offers a promising strategy to enhance efficacy while reducing toxicity. However, the effectiveness of such combinations is likely to be hindered by improper metabolism of the drugs due to the sharing of the same metabolizing enzymes. In this study, we explored a novel approach to enhance the efficacy of Pimozide (repurposed drug) by combining it with chemotherapeutic drugs that utilize different metabolizing enzymes than Pimozide, thereby reducing metabolic load and toxicity. The Epirubicin-SAHA(Vorinostat)-Pimozide (ESP) combination emerged as highly synergistic, reducing the IC50 of Pimozide from 16.54 to 0.57 μM in MCF-7 cells and from 17.5 to 3.35 μM in MDA-MB-231 cells, representing a significant enhancement in efficacy. Mechanistic studies revealed increased intracellular reactive oxygen species (ROS) generation and activation of the intrinsic apoptosis pathway, as indicated by a 10-fold increase in the cleaved PARP levels. In MDA-MB-231 cells, there was also a 2-fold increase in p53 and a 10-fold increase in p21 expression, with a concomitant reduction in AKT signaling. Furthermore, the ESP combination reduced cancer stemness, invasiveness, fatty acid uptake, and lipid droplet accumulation, pointing to its broad impact on cancer cell survival and metabolism. These findings suggest that the ESP combination holds promise as an effective therapeutic strategy for breast cancer, with reduced toxicity and enhanced efficacy.

PMID:40305333 | DOI:10.1002/iub.70020

Biomolecules. 2025 Apr 11;15(4):565. doi: 10.3390/biom15040565.

ABSTRACT

Beta-hemoglobinopathies such as beta-thalassemia and sickle cell disease are severe genetic blood disorders affecting the beta globin chain of haemoglobin A (α2β2). Activation of delta globin, the non-alpha globin of HbA2 (α2δ2), could represent a possible approach to improve the clinical severity of these pathologies. Notably, the therapeutic potential of delta globin has been demonstrated in previous studies using a mouse model of beta-thalassemia and sickle cell disease. The present study evaluated delta globin gene activation by small molecules in erythroid cells isolated from transgenic murine foetal liver. A screening of 119 molecules, selected for their potential in drug repurposing, was performed without prior selection based on specific pathways of interest. Three candidates-Nexturastat, Stattic and Palbociclib-were found to have high efficacy on delta globin expression. Palbociclib also proved effective in increasing gamma globin expression. All of these compounds have pharmacokinetic profiles that are beneficial for clinical application, providing potential inducer agents of HbA2 that could have therapeutic effects in the treatment of beta-hemoglobinopathies.

PMID:40305292 | DOI:10.3390/biom15040565

J Genet Couns. 2025 Jun;34(3):e70015. doi: 10.1002/jgc4.70015.

ABSTRACT

In recent years, an increasing number of affected children have been diagnosed through whole-exome sequencing (WES); however, it remains unclear whether the problems faced by the patients' parents during the undiagnosed period were resolved. This exploratory qualitative study aimed to clarify the needs of the parents of children who have been diagnosed with rare genetic diseases and determine the factors that may help provide the environment necessary for the family to understand and accept the symptoms and characteristics associated with the disease and live with their affected child. Semi-structured interviews were conducted with the parents of children (less than 18 years old) who participated in a research project, namely the Initiative on Undiagnosed and Rare Diseases (IRUD), at Kyoto University Hospital between November 2016 and December 2021. A reflective thematic analysis generated three themes: the benefits of diagnosis from the perspective of parents, the challenges to be solved after diagnosis, and the significance and issues of revealing genetic information. The results showed that the diagnoses provided psychological satisfaction for the parents. However, diagnosis of a hereditary and rare disease can lead to social and medical isolation, and it was necessary to improve the environment around the affected children's families, mainly by taking advantage of the IRUD research system. The analysis indicated the need for psychological support, which can be provided by the clinical genetic department, the need for a follow-up system in collaboration with various clinical departments, and the need to improve the general public's understanding of human genetics.

PMID:40305385 | DOI:10.1002/jgc4.70015

Am J Respir Cell Mol Biol. 2025 Apr 30. doi: 10.1165/rcmb.2024-0595LE. Online ahead of print.

NO ABSTRACT

PMID:40305670 | DOI:10.1165/rcmb.2024-0595LE

Biomolecules. 2025 Apr 5;15(4):535. doi: 10.3390/biom15040535.

ABSTRACT

Pharmacogenomics is revolutionizing precision medicine by enabling tailored therapeutic strategies based on an individual genetic and molecular profile. Circular RNAs (circRNAs), a distinct subclass of endogenous non-coding RNAs, have recently emerged as key regulators of drug resistance, tumor progression, and therapeutic responses. Their covalently closed circular structure provides exceptional stability and resistance to exonuclease degradation, positioning them as reliable biomarkers and novel therapeutic targets in cancer management. This review provides a comprehensive analysis of the interplay between circRNAs and pharmacogenomics, focusing on their role in modulating drug metabolism, therapeutic efficacy, and toxicity profiles. We examine how circRNA-mediated regulatory networks influence chemotherapy resistance, alter targeted therapy responses, and impact immunotherapy outcomes. Additionally, we discuss emerging experimental tools and bioinformatics techniques for studying circRNAs, including multi-omics integration, machine learning-driven biomarker discovery, and high-throughput sequencing technologies. Beyond their diagnostic potential, circRNAs are being actively explored as therapeutic agents and drug delivery vehicles. Recent advancements in circRNA-based vaccines, engineered CAR-T cells, and synthetic circRNA therapeutics highlight their transformative potential in oncology. Furthermore, we address the challenges of standardization, reproducibility, and clinical translation, emphasizing the need for rigorous biomarker validation and regulatory frameworks to facilitate their integration into clinical practice. By incorporating circRNA profiling into pharmacogenomic strategies, this review underscores a paradigm shift toward highly personalized cancer therapies. circRNAs hold immense potential to overcome drug resistance, enhance treatment efficacy, and optimize patient outcomes, marking a significant advancement in precision oncology.

PMID:40305280 | DOI:10.3390/biom15040535

Sci Adv. 2025 May 2;11(18):eadr1576. doi: 10.1126/sciadv.adr1576. Epub 2025 Apr 30.

ABSTRACT

Accurately predicting pathological complete response (pCR) before neoadjuvant chemotherapy (NAC) is crucial for patients with breast cancer. In this study, we developed a multimodal integrated fully automated pipeline system (MIFAPS) in forecasting pCR to NAC, using a multicenter and prospective dataset of 1004 patients with locally advanced breast cancer, incorporating pretreatment magnetic resonance imaging, whole slide image, and clinical risk factors. The results demonstrated that MIFAPS offered a favorable predictive performance in both the pooled external test set [area under the curve (AUC) = 0.882] and the prospective test set (AUC = 0.909). In addition, MIFAPS significantly outperformed single-modality models (P < 0.05). Furthermore, the high deep learning scores were associated with immune-related pathways and the promotion of antitumor cells in the microenvironment during biological basis exploration. Overall, our study demonstrates a promising approach for improving the prediction of pCR to NAC in patients with breast cancer through the integration of multimodal data.

PMID:40305609 | DOI:10.1126/sciadv.adr1576

Sci Adv. 2025 May 2;11(18):eadt5111. doi: 10.1126/sciadv.adt5111. Epub 2025 Apr 30.

ABSTRACT

Protein aggregation is a pathological hallmark of more than 50 human diseases and a major problem for biotechnology. Methods have been proposed to predict aggregation from sequence, but these have been trained and evaluated on small and biased experimental datasets. Here we directly address this data shortage by experimentally quantifying the aggregation of >100,000 protein sequences. This unprecedented dataset reveals the limited performance of existing computational methods and allows us to train CANYA, a convolution-attention hybrid neural network that accurately predicts aggregation from sequence. We adapt genomic neural network interpretability analyses to reveal CANYA's decision-making process and learned grammar. Our results illustrate the power of massive experimental analysis of random sequence-spaces and provide an interpretable and robust neural network model to predict aggregation.

PMID:40305601 | DOI:10.1126/sciadv.adt5111

J Med Internet Res. 2025 Apr 30. doi: 10.2196/75340. Online ahead of print.

ABSTRACT

BACKGROUND: Implementing machine learning models to identify clinical deterioration on the wards is associated with decreased morbidity and mortality. However, these models have high false positive rates and only use structured data.

OBJECTIVE: We aim to compare models with and without information from clinical notes for predicting deterioration.

METHODS: Adults admitted to the wards at the University of Chicago (development cohort) and University of Wisconsin-Madison (external validation cohort) were included. Predictors consisted of structured and unstructured variables extracted from notes as Concept Unique Identifiers (CUIs). We parameterized CUIs in five ways: Standard Tokenization (ST), ICD Rollup using Tokenization (ICDR-T), ICD Rollup using Binary Variables (ICDR-BV), CUIs as SapBERT Embeddings (SE), and CUI Clustering using SapBERT embeddings (CC). Each parameterization method combined with structured data and structured data-only were compared for predicting intensive care unit transfer or death in the next 24 hours using deep recurrent neural networks.

RESULTS: The development (UC) cohort included 284,302 patients, while the external validation (UW) cohort included 248,055. In total, 4.9% (N=26,281) of patients experienced the outcome. The SE model achieved the highest AUPRC (0.208), followed by CC (0.199) and the structured-only model (0.199), ICDR-BV (0.194), ICDR-T (0.166), and ST (0.158). The CC and structured-only models achieved the highest AUROC (0.870), followed by ICDR-T (0.867), ICDR-BV (0.866), ST (0.860), and SE (0.859). In terms of sensitivity and positive predictive value, the CC model achieved the greatest positive predictive value (12.53%) and sensitivity (52.15%) at the cutoff that flagged 5% of the observations in the test set. At the 15% cutoff, the ICDR-T, CC, and ICDR-BV models tied for the highest positive predictive value at 5.67%, while their sensitivities were 70.95%, 70.92%, and 70.86%, respectively. All models were well calibrated, achieving Brier scores in the range of 0.011-0.012. The modified IG method revealed that CUIs corresponding to terms such as "NPO - Nothing by mouth", "Chemotherapy", "Transplanted tissue", and "Dialysis procedure" were most predictive of deterioration.

CONCLUSIONS: A multimodal model combining structured data with embeddings using SapBERT had the highest AUPRC, but performance was similar between models with and without CUIs. Although the addition of CUIs from notes to structured data did not meaningfully improve model performance for predicting clinical deterioration, models using CUIs could provide clinicians with relevant information and additional clinical context for supporting decision-making.

PMID:40305429 | DOI:10.2196/75340

Biomolecules. 2025 Apr 16;15(4):589. doi: 10.3390/biom15040589.

ABSTRACT

Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. This study reviews the potential of artificial intelligence (AI) to optimize ICI therapy in melanoma by integrating various diagnostic tools. Through a comprehensive literature review, we analyzed studies on AI applications in melanoma immunotherapy, focusing on predictive modeling, biomarker identification, and treatment response prediction. Key findings highlight the efficacy of AI in improving ICI outcomes. Machine learning models successfully identified prognostic cytokine signatures linked to nivolumab clearance. The combination of AI with RNAseq analysis had the potential for the development of personalized treatment with ICIs. A machine learning-based approach was able to assess the risk-benefit ratio for the prediction of immune-related adverse events (irAEs) using the electronic health record (EHR) data. Deep learning algorithms demonstrated high accuracy in tumor microenvironment analysis, including tumor region identification and lymphocyte detection. AI-assisted quantification of tumor-infiltrating lymphocytes (TILs) proved prognostically valuable in primary melanoma and predictive of anti-PD-1 therapy response in metastatic cases. Integrating multiple diagnostic modalities, such as CT imaging and laboratory data, modestly enhanced predictive performance for 1-year survival in advanced cancers treated with immunotherapy. These findings underscore the potential of AI-driven approaches to refine biomarker identification, treatment prediction, and patient stratification in melanoma immunotherapy. While promising, clinical validation and implementation challenges remain.

PMID:40305346 | DOI:10.3390/biom15040589

IEEE Trans Neural Netw Learn Syst. 2025 Apr 30;PP. doi: 10.1109/TNNLS.2025.3561930. Online ahead of print.

ABSTRACT

How to learn and accurately distinguish new concepts from few samples, as humans do, is a long-standing concern in artificial intelligence (AI). Studies in brain science and neuroscience have shown that human brain perception is based on nonlinear manifolds, and high-dimensional manifolds can facilitate concept learning in neural circuits. Based on this inspiration, in this paper, we propose a heterogeneous Riemannian few-shot learning network (HRFL-Net), which is the first few-shot learning method to perform end-to-end deep learning on heterogeneous Riemannian manifolds. Specifically, to enhance the geometric invariance of the image representation, the image features are projected into three heterogeneous Riemannian manifold spaces. Then, the implicit Riemannian kernel function maps the manifolds to the separable high-dimensional reproducing Hilbert space. It is assumed that the embedded kernel features of the complementary manifolds are mapped to the same common subspace. Thus, a novel neural network-based Riemannian metric learning method is designed to solve the subspace feature vectors by imposing orthogonal normalized projection, which overcomes the data extension limitation of the Riemannian metric. Finally, with the optimization objective of increasing the interclass distance and decreasing the intraclass distance in Hilbert space, the HRFL-Net is trained with end-to-end stochastic optimization, and the optimal aggregation subspace is learned during the gradient descent process. Thus, the proposed HRFL-Net can be easily generalized to challenging nonconvex data. The evaluation of four public datasets shows that the proposed HRFL-Net has significant superiority and also achieves competitive results compared with the state-of-the-art methods.

PMID:40305249 | DOI:10.1109/TNNLS.2025.3561930

IEEE Trans Med Imaging. 2025 Apr 30;PP. doi: 10.1109/TMI.2025.3565514. Online ahead of print.

ABSTRACT

Rib fractures are a common and potentially severe injury that can be challenging and labor-intensive to detect in CT scans. While there have been efforts to address this field, the lack of large-scale annotated datasets and evaluation benchmarks has hindered the development and validation of deep learning algorithms. To address this issue, the RibFrac Challenge was introduced, providing a benchmark dataset of over 5,000 rib fractures from 660 CT scans, with voxel-level instance mask annotations and diagnosis labels for four clinical categories (buckle, nondisplaced, displaced, or segmental). The challenge includes two tracks: a detection (instance segmentation) track evaluated by an FROC-style metric and a classification track evaluated by an F1-style metric. During the MICCAI 2020 challenge period, 243 results were evaluated, and seven teams were invited to participate in the challenge summary. The analysis revealed that several top rib fracture detection solutions achieved performance comparable or even better than human experts. Nevertheless, the current rib fracture classification solutions are hardly clinically applicable, which can be an interesting area in the future. As an active benchmark and research resource, the data and online evaluation of the RibFrac Challenge are available at the challenge website (https://ribfrac. grand-challenge.org/). In addition, we further analyzed the impact of two post-challenge advancements-largescale pretraining and rib segmentation-based on our internal baseline for rib fracture detection. These findings lay a foundation for future research and development in AI-assisted rib fracture diagnosis.

PMID:40305244 | DOI:10.1109/TMI.2025.3565514

Biomolecules. 2025 Apr 3;15(4):524. doi: 10.3390/biom15040524.

ABSTRACT

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

PMID:40305228 | DOI:10.3390/biom15040524

J Vet Cardiol. 2025 Apr 5;59:126-132. doi: 10.1016/j.jvc.2025.04.001. Online ahead of print.

ABSTRACT

A 10-week-old female Oriental shorthair was referred due to stunted growth, weight loss, dyspnea, and reduced activity levels compared to her littermates. Thoracic radiography revealed a markedly enlarged cardiac silhouette and a diffuse unstructured interstitial pulmonary pattern, presumably due to cardiogenic pulmonary edema. Echocardiography showed marked left- and right-sided ventricular dilation, decreased contractility, and enlargement of both atria, without any identifiable congenital defects. Pleural and peritoneal effusion were also present. Based on these findings, a presumptive diagnosis of both left- and right-sided congestive heart failure due to a dilated cardiomyopathy phenotype was made. Cardiovascular pathological examination confirmed the echocardiographic findings. Additionally, mild interstitial myocardial fibrosis was present in the left ventricle, both atria, the interventricular septum, and, to a minimal extent, in the right ventricle. Moderate endocardial fibrosis was observed in the left atrium and left atrial appendage, while mild endocardial fibrosis was present in the left ventricle. Both antemortem and postmortem evaluations did not provide clear evidence of the underlying cause. Therefore, we consider this a rare case of feline juvenile idiopathic dilated cardiomyopathy with secondary reactive endocardial and myocardial fibrosis.

PMID:40305901 | DOI:10.1016/j.jvc.2025.04.001

Redox Biol. 2025 Apr 12;83:103635. doi: 10.1016/j.redox.2025.103635. Online ahead of print.

ABSTRACT

Inhibiting ferroptosis represents a promising strategy to combat ferroptosis-related diseases. Here we show that 428, a selenide-containing noscapine derivative, effectively inhibits ferroptosis in various cell lines by enhancing the stability and activity of GPX4. TRIM41 was identified as a novel E3 ubiquitin ligase of GPX4 and 428 was demonstrated to bind to the selenocysteine residue Sec46 of GPX4 via the formation of a transient and reversible Se-Se bond, thereby blocking the interaction between GPX4 and TRIM41, stabilizing GPX4 and enhancing its activity. This unique dynamic covalent binding mode was preliminarily validated by structure-activity relationship analysis and molecular docking studies. Importantly, we demonstrated that 428 treatment alleviates bleomycin-induced pulmonary fibrosis in vivo by inhibiting ferroptosis. Overall, our studies identified a novel stabilizer and activator of GPX4, offering a potential therapeutic approach for the treatment of ferroptosis-related diseases and uncovering a new mechanism for regulating GPX4 degradation.

PMID:40305884 | DOI:10.1016/j.redox.2025.103635

Ann Am Thorac Soc. 2025 Apr 30. doi: 10.1513/AnnalsATS.202503-361ED. Online ahead of print.

NO ABSTRACT

PMID:40305677 | DOI:10.1513/AnnalsATS.202503-361ED

Biomolecules. 2025 Apr 1;15(4):509. doi: 10.3390/biom15040509.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a disease with a poor prognosis and no curative therapies. Fibroblast activation by transforming growth factor β1 (TGFβ1) and disrupted metabolic pathways, including the arginine-polyamine pathway, play crucial roles in IPF development. Polyamines are agonists of the calcium/cation-sensing receptor (CaSR), activation of which is detrimental for asthma and pulmonary hypertension, but its role in IPF is unknown. To address this question, we evaluated polyamine abundance using metabolomic analysis of IPF patient saliva. Furthermore, we examined CaSR functional expression in human lung fibroblasts (HLFs), assessed the anti-fibrotic effects of a CaSR antagonist, NPS2143, in TGFβ1-activated normal and IPF HLFs by RNA sequencing and immunofluorescence imaging, respectively; and NPS2143 effects on polyamine synthesis in HLFs by immunoassays. Our results demonstrate that polyamine metabolites are increased in IPF patient saliva. Polyamines activate fibroblast CaSR in vitro, elevating intracellular calcium concentration. CaSR inhibition reduced TGFβ1-induced polyamine and pro-fibrotic factor expression in normal and IPF HLFs. TGFβ1 directly stimulated polyamine release by HLFs, an effect that was blocked by NPS2143. This suggests that TGFβ1 promotes CaSR activation through increased polyamine expression, driving a pro-fibrotic response. By halting some polyamine-induced pro-fibrotic changes, CaSR antagonists exhibit disease-modifying potential in IPF onset and development.

PMID:40305220 | DOI:10.3390/biom15040509