Comput Methods Programs Biomed. 2025 May 6;267:108830. doi: 10.1016/j.cmpb.2025.108830. Online ahead of print.

ABSTRACT

OBJECTIVES: Achieving efficient analysis of multiple pathological indicators has great significance for breast cancer prognosis and therapeutic decision-making. In this study, we aim to explore a deep multi-task learning (MTL) framework for collaborative prediction of histological grade and proliferation marker (Ki-67) status in breast cancer using multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

METHODS: In the novel design of hybrid multi-task architecture (HMT-Net), co-representative features are explicitly distilled using a feature extraction backbone. A customized prediction network is then introduced to perform soft-parameter sharing between two correlated tasks. Specifically, task-common and task-specific knowledge is transmitted into tower layers for informative interactions. Furthermore, low-level feature maps containing tumor edges and texture details are recaptured by a hard-parameter sharing branch, which are then incorporated into the tower layer for each subtask. Finally, the probabilities of two histological indicators, predicted in the multi-phase DCE-MRI, are separately fused using a decision-level fusion strategy.

RESULTS: Experimental results demonstrate that the proposed HMT-Net achieves optimal discriminative performance over other recent MTL architectures and deep models based on single image series, with the area under the receiver operating characteristic curve of 0.908 for tumor grade and 0.694 for Ki-67 status.

CONCLUSIONS: Benefiting from the innovative HMT-Net, our proposed method elucidates its strong robustness and flexibility in the collaborative prediction task of breast biomarkers. Multi-phase DCE-MRI is expected to contribute valuable dynamic information for breast cancer pathological assessment in a non-invasive manner.

PMID:40334302 | DOI:10.1016/j.cmpb.2025.108830

Comput Biol Med. 2025 May 6;192(Pt B):110242. doi: 10.1016/j.compbiomed.2025.110242. Online ahead of print.

ABSTRACT

Brain tumours continue to be a primary cause of worldwide death, highlighting the critical need for effective and accurate diagnostic tools. This article presents MK-YOLOv8, an innovative lightweight deep learning framework developed for the real-time detection and categorization of brain tumours from MRI images. Based on the YOLOv8 architecture, the proposed model incorporates Ghost Convolution, the C3Ghost module, and the SPPELAN module to improve feature extraction and substantially decrease computational complexity. An x-small object detection layer has been added, supporting precise detection of small and x-small tumours, which is crucial for early diagnosis. Trained on the Figshare Brain Tumour (FBT) dataset comprising (3,064) MRI images, MK-YOLOv8 achieved a mean Average Precision (mAP) of 99.1% at IoU (0.50) and 88.4% at IoU (0.50-0.95), outperforming YOLOv8 (98% and 78.8%, respectively). Glioma recall improved by 26%, underscoring the enhanced sensitivity to challenging tumour types. With a computational footprint of only 96.9 GFLOPs (representing 37.5% of YOYOLOv8x'sFLOPs) and utilizing 12.6 million parameters, a mere 18.5% of YOYOLOv8's parameters, MK-YOLOv8 delivers high efficiency with reduced resource demands. Also, it trained on the Br35H dataset (801 images) to guarantee the model's robustness and generalization; it achieved a mAP of 98.6% at IoU (0.50). The suggested model operates at 62 frames per second (FPS) and is suited for real-time clinical processes. These developments establish MK-YOLOv8 as an innovative framework, overcoming challenges in tiny tumour identification and providing a generalizable, adaptable, and precise detection approach for brain tumour diagnostics in clinical settings.

PMID:40334297 | DOI:10.1016/j.compbiomed.2025.110242

PLoS One. 2025 May 7;20(5):e0322540. doi: 10.1371/journal.pone.0322540. eCollection 2025.

ABSTRACT

Knee osteoarthritis (KOA) is a leading cause of disability globally. Early and accurate diagnosis is paramount in preventing its progression and improving patients' quality of life. However, the inconsistency in radiologists' expertise and the onset of visual fatigue during prolonged image analysis often compromise diagnostic accuracy, highlighting the need for automated diagnostic solutions. In this study, we present an advanced deep learning model, OA-HybridCNN (OHC), which integrates ResNet and DenseNet architectures. This integration effectively addresses the gradient vanishing issue in DenseNet and augments prediction accuracy. To evaluate its performance, we conducted a thorough comparison with other deep learning models using five-fold cross-validation and external tests. The OHC model outperformed its counterparts across all performance metrics. In external testing, OHC exhibited an accuracy of 91.77%, precision of 92.34%, and recall of 91.36%. During the five-fold cross-validation, its average AUC and ACC were 86.34% and 87.42%, respectively. Deep learning, particularly exemplified by the OHC model, has greatly improved the efficiency and accuracy of KOA imaging diagnosis. The adoption of such technologies not only alleviates the burden on radiologists but also significantly enhances diagnostic precision.

PMID:40334259 | DOI:10.1371/journal.pone.0322540

PLoS One. 2025 May 7;20(5):e0322978. doi: 10.1371/journal.pone.0322978. eCollection 2025.

ABSTRACT

BACKGROUND: Predicting protein-DNA binding sites in vivo is a challenging but urgent task in many fields such as drug design and development. Most promoters contain many transcription factor (TF) binding sites, yet only a few have been identified through time-consuming biochemical experiments. To address this challenge, numerous computational approaches have been proposed to predict TF binding sites from DNA sequences. However, current deep learning methods often face issues such as gradient vanishing as the model depth increases, leading to suboptimal feature extraction.

RESULTS: We propose a model called CBR-KAN (where C represents Convolutional Neural Network (CNN), B represents Bidirectional Long Short Term Memory (BiLSTM), and R represents Residual Mechanism) to predict transcription factor binding sites. Specifically, we designed a multi-scale convolution module (ConvBlock1, 2, 3) combined with BiLSTM network, introduced KAN network to replace traditional multilayer perceptron, and promoted model optimization through residual connections. Testing on 50 common ChIP seq benchmark datasets shows that CBR-KAN outperforms other state-of-the-art methods such as DeepBind, DanQ, DeepD2V, and DeepSEA in predicting TF binding sites.

CONCLUSIONS: The CBR-KAN model significantly improves prediction accuracy for transcription factor binding sites by effectively integrating multiple neural network architectures and mechanisms. This approach not only enhances feature extraction but also stabilizes training and boosts generalization capabilities. The promising results on multiple key performance indicators demonstrate the potential of CBR-KAN in bioinformatics applications.

PMID:40334196 | DOI:10.1371/journal.pone.0322978

PLoS One. 2025 May 7;20(5):e0319476. doi: 10.1371/journal.pone.0319476. eCollection 2025.

ABSTRACT

In the context of consumer economics, it is imperative to consider the functionality of sports venues based on customer demand. However, traditional survey methods are time-consuming, resource-intensive, and coverage-limited. This paper conducted sentiment mining based on Internet big data, deep learning, topic analysis, and social network analysis to capture the satisfaction of consumers and its influencing factors. Findings indicate that activity, courses, and facilities are core factors driving positive comments. Coaches, environment, and activities are key determinants influencing neutral evaluations. Attitude, integrity, and qualifications can trigger negative reviews. The findings offer insights into developing consumer-friendly service for sports venues.

PMID:40333946 | DOI:10.1371/journal.pone.0319476

PLoS One. 2025 May 7;20(5):e0322936. doi: 10.1371/journal.pone.0322936. eCollection 2025.

ABSTRACT

Identifying relevant plant parts is one of the most significant tasks in the pharmaceutical industry. Correct identification minimizes the risk of mis-identification, which might have unfavorable effects, and it ensures that plants are used medicinally. Traditional methods for plant part identification are often time-consuming and require specific expertise. This study proposed a Depth-wise Separable Convolutional Neural Network (DWS-CNN) to enhance the accuracy of medicinal plant part identification. Furthermore, we incorporated the tuned pre-trained models such as VGG16, Res Net-50, and Inception V3 which are designed by Standard convolutional neural network (S-CNN) for comparative purposes. We trained variants of the Standard convolutional neural network (S-CNN) model with high-resolution images of medicinal plant leaves which contains 15,100 leaf images. The study used supervised learning by which leaf images are used as an identity for the other parts of the plants. We used transfer learning to tune training and model parameters. Experimental results showed that our DWS-CNN model achieved better performance compared to S-CNN models, with an accuracy of 99.84% for training data, 99.44% for F1-score and 99.44% for testing data, which improves in both accuracy and training speed. The presence of depth-wise separable convolution and batch normalization at the fully connected layer of the model made the model achieved a good classification performance.

PMID:40333881 | DOI:10.1371/journal.pone.0322936

Molecules. 2025 Apr 9;30(8):1683. doi: 10.3390/molecules30081683.

ABSTRACT

While natural products and derivatives have been crucial in drug discovery, the current databases are limited to known compounds. There is a need for tools that can automatically generate and assess novel derivatives of natural products to enhance early-stage drug discovery. We present DerivaPredict (v1.0), a user-friendly tool that generates novel natural product derivatives through chemical and metabolic transformations. It predicts binding affinities using pretrained deep learning models and assesses drug-likeness via ADMET profiling. DerivaPredict is freely accessible with a source code on GitHub.

PMID:40333643 | DOI:10.3390/molecules30081683

Plant Cell. 2025 May 7:koaf113. doi: 10.1093/plcell/koaf113. Online ahead of print.

NO ABSTRACT

PMID:40334133 | DOI:10.1093/plcell/koaf113

PLoS One. 2025 May 7;20(5):e0307859. doi: 10.1371/journal.pone.0307859. eCollection 2025.

ABSTRACT

The tumor suppressor protein p53 (TP53) is frequently mutated in various types of human malignancies, including HNSCC, which affects tumor growth, prognosis, and treatment. Gaining insight into the impact of TP53 mutations in HNSCC is crucial for developing new diagnostic and therapeutic methods. In this study, we aimed to investigate the influence of mutations on the structure and functions of the TP53 protein and miRNA expression using computational analysis. The genomic data of patients with HNSCC were obtained from TCGA, and the impact of mutations on the TP53 gene was investigated using different bioinformatics tools. Results: The findings showed that the TP53 mutations increased TP53 expression levels in HNSCC and were associated with a poor prognosis. Furthermore, hsa-mir-133b expression was reduced in TP53-mutated samples, significantly affecting patient survival in HNSCC. Six mutations, including R273C, G105C, G266E, Q136H/P, and R280G, were identified as deleterious, carcinogenic, driver, highly conserved, and exposed. These mutations were located in the P53 domain, and PTM analysis revealed that R280G and R273C are at a methylation site, and R273C, Q136H/P, and R280G are located in the protein pocket. The docking research indicated that these mutations decreased the binding affinity for DNA, with R273C, R280G, G266E, and G105C displaying the most significant differences. The molecular dynamics analysis indicates that R280G, Q136H, and G105C mutations confer a gain of function by stabilizing the TP53-substrate complex. Conclusions: Based on the research findings, the mutations on TP53 were found to have an impact on protein and miRNA expression, development, survival, and progression of HNSCC patients, and has-mir-133b could be a promising novel biomarker for monitoring the progression of HNSCC. It was discovered that G105C and Q136H/P, as novel mutations, affect the function and structure of proteins causing HNSCC, which indicates that they could be interesting subjects for further investigation, diagnostics, and therapeutic strategies. Furthermore, the precise positioning of R280G and R273C within the methylation site and Q136H/P in the binding site has been documented for the first time. Moreover, the G105C, Q136H, and R280G mutations that stabilized TP53 structure and altered its interaction dynamics with substrates may serve as novel potential diagnostic biomarkers in cancer, guiding patient stratification and personalized treatment strategies. The molecular dynamics analysis provides insights into how specific TP53 mutations impact protein structure, stability, and function upon substrate binding, highlighting their role in cancer biology and potential implications for therapeutic interventions. This paper provides a novel understanding of the mechanisms by which these mutations contribute to the development of cancer.

PMID:40333905 | DOI:10.1371/journal.pone.0307859

Int J Mol Sci. 2025 Apr 16;26(8):3761. doi: 10.3390/ijms26083761.

ABSTRACT

Novel drug discovery and repositioning remain critical challenges in biomedical research, requiring accurate prediction of drug-target interactions (DTIs). We propose the CPDP framework, which builds upon existing biomedical representation models and integrates contrastive learning with multi-dimensional representations of proteins and drugs to predict DTIs. By aligning the representation space, CPDP enables GNN-based methods to achieve zero-shot learning capabilities, allowing for accurate predictions of unseen drug data. This approach enhances DTI prediction performance, particularly for novel drugs not included in the BioHNs dataset. Experimental results demonstrate CPDP's high accuracy and strong generalization ability in predicting novel biological entities while maintaining effectiveness for traditional drug repositioning tasks.

PMID:40332398 | DOI:10.3390/ijms26083761

Int J Mol Sci. 2025 Apr 19;26(8):3881. doi: 10.3390/ijms26083881.

ABSTRACT

Antituberculosis drug-induced hepatotoxicity (ATDH) is a common adverse drug reaction often requiring treatment interruption, complicating tuberculosis management. The slow acetylator phenotype, characterized by reduced N-acetyltransferase 2 (NAT2) enzyme activity, is associated with increased hepatotoxicity risk, while rapid acetylators are associated with a higher risk of therapeutic failure. This study investigates the association between the NAT2 acetylation phenotype and ATDH occurrence, with an emphasis on its predictive value in regard to a multiethnic population and its impact on the timing of ATDH onset. A retrospective observational study was conducted on tuberculosis patients treated at Luigi Sacco Hospital, Milan, Italy (July 2020-September 2023). The NAT2 genotyping identified slow and rapid/intermediate acetylators. Cumulative incidence analysis and Fine-Gray competing risks regression models were used to assess ATDH risk and onset timing. Among 102 patients, 21.6% developed ATDH, including 16.7% with slow and 4.9% with rapid/intermediate acetylators. ATDH onset was significantly earlier in regard to slow acetylators (median 0.5 vs. 2 months, interquartile range-IQR: 0.5-3 vs. 1.7-5.5). Slow acetylators were associated with a higher risk of developing ATDH (Sub-distribution hazard ratio, SHR = 3.05; 95% confidence interval-CI: 1.17-7.95; p = 0.02), even after adjusting for confounders. The NAT2 acetylation phenotype strongly influences ATDH risk and timing. Early acetylator status identification may enable dose adjustments, enhancing treatment safety. These findings highlight the role of pharmacogenetics in optimizing antituberculosis therapy by improving efficacy and minimizing toxicity.

PMID:40332508 | DOI:10.3390/ijms26083881

Int J Mol Sci. 2025 Apr 12;26(8):3668. doi: 10.3390/ijms26083668.

ABSTRACT

Osteoarthritis (OA) of the temporomandibular joint (TMJ) is a progressive disease characterized by the progressive destruction of the internal surfaces of the joint. Certain epigenetic biomarkers have been detected in TMJ-OA. We summarized the available evidence on the epigenetic biomarkers in TMJ-OA. There is an increase in the expression of non-coding RNAs related to the degradation of the extracellular matrix, chondrocyte apoptosis, and proinflammatory cytokines, while there is a decrease in the expression of those related to COL2A1, as well as the osteogenic and chondrogenic differentiation of mesenchymal stem cells. Certain methylated genes and histone modifications in TMJ-OA were also identified. In the early stage, DNA methylation was significantly decreased; that is, the expression of inflammation-related genes such as TNF and genes associated with extracellular matrix degradation, such as Adamts, were increased. While in the late stage, there was an increase in the expression of genes associated with the TGF-β and MAPK signaling pathway and angiogenesis-related genes. Although research on the role of epigenetic markers in TMJ-OA is still ongoing, the results here contribute to improving the basis for the identification of accurate diagnostic and prognostic markers and the development of new therapeutic molecules for the prevention and management of TMJ-OA. It also represents a significant advancement in elucidating its pathogenesis.

PMID:40332184 | DOI:10.3390/ijms26083668

Int J Mol Sci. 2025 Apr 10;26(8):3582. doi: 10.3390/ijms26083582.

ABSTRACT

The N-Myc Downstream Regulated Gene 1 (NDRG1) protein, a member of a family of four, has emerged as a key regulator of various physiological and pathological processes. Extensive knowledge has been gained on the modulation of NDRG1 expression during endoplasmic reticulum stress, autophagy, and hypoxia. Moreover, new functions have emerged in recent years. Notably, NDRG1 regulates cell differentiation, metabolism, autophagy and vesicular transport. This has raised interest in the molecular mechanisms that control the cellular levels and activity of NDRG1. A series of studies have shown that NDRG1 can be finely regulated at the transcriptional, post-transcriptional, and translational levels. In addition, processes that mediate protein degradation and clearance also play key roles. Furthermore, three different NDRG1 proteoforms with distinct functions have been identified. An important question is the extent to which these proteoforms contribute to the regulation of cellular functions. Given the growing clinical interest in NDRG1, this review provides an overview of the regulatory mechanisms that control NDRG1 abundance, helping to deepen our understanding of the complex mechanisms underlying protein regulation.

PMID:40332138 | DOI:10.3390/ijms26083582

Clin Pharmacol Ther. 2025 May 7. doi: 10.1002/cpt.3685. Online ahead of print.

ABSTRACT

In pediatric oncology, pharmacogenetic guidelines are underutilized and the potential impact of pre-emptive pharmacogenetic screening remains largely unexplored despite this field's need for individualized approaches. While comprehensive pharmacogenetic guidelines are not yet available for all anticancer drugs, evidence-based recommendations exist for a subset of supportive care drugs and anticancer drugs, including thiopurines, irinotecan, capecitabine, and 5-fluorouracil. In this study, we evaluate the potential impact of pre-emptive pharmacogenetic screening by retrospectively identifying opportunities for dose or treatment adjustments within a national pediatric oncology cohort. Our analysis focused on ten genes and 28 drugs relevant to pediatric oncology, which are included in the Clinical Pharmacogenetics Implementation Consortium and the Dutch Pharmacogenetics Working Group guidelines. In a cohort of 1,151 pediatric oncology subjects, we identified that 16% of individuals could have benefited from altered drug dosing or treatment. These include dose and treatment recommendations for allopurinol, nonsteroidal anti-inflammatory drugs, phenytoin, amitriptyline, proton pump inhibitors, voriconazole, tramadol, codeine, paroxetine, tacrolimus, rasburicase, and 6-mercaptopurine. As genetic data increasingly becomes available through molecular diagnostics in pediatric oncology, there is a unique opportunity to re-utilize this data for pre-emptive pharmacogenetic screening. Leveraging genetic profiles to guide clinicians in drug selection and dose optimization can improve patient outcomes by enhancing the safety and efficacy of treatments. We therefore recommend incorporating pharmacogenetic screening into clinical workflows to advance personalized medicine in pediatric oncology.

PMID:40331624 | DOI:10.1002/cpt.3685

Int J Mol Sci. 2025 Apr 17;26(8):3788. doi: 10.3390/ijms26083788.

ABSTRACT

Genome organization is essential for precise spatial and temporal gene expression and relies on interactions between promoters and distal cis-regulatory elements (CREs), which constitute ~8% of the human genome. For the cystic fibrosis transmembrane conductance regulator (CFTR) gene, tissue-specific expression, especially in the pancreas, remains poorly understood. Unraveling its regulation could clarify the clinical heterogeneity observed in cystic fibrosis and CFTR-related disorders. To understand the role of 3D chromatin architecture in establishing tissue-specific expression of the CFTR gene, we mapped chromatin interactions and epigenomic regulation in Capan-1 pancreatic cells. Candidate CREs are validated by luciferase reporter assay and CRISPR knock-out. We identified active CREs not only around the CFTR gene but also outside the topologically associating domain (TAD). We demonstrate the involvement of multiple CREs upstream and downstream of the CFTR gene and reveal a cooperative effect of the -44 kb, -35 kb, +15.6 kb, and +37.7 kb regions, which share common predicted transcription factor (TF) motifs. We also extend our analysis to compare 3D chromatin conformation in intestinal and pancreatic cells, providing valuable insights into the tissue specificity of CREs in regulating CFTR gene expression.

PMID:40332394 | DOI:10.3390/ijms26083788

Int J Mol Sci. 2025 Apr 16;26(8):3750. doi: 10.3390/ijms26083750.

ABSTRACT

Mammalian spermatozoa undergo a series of physiological and biochemical changes in the oviduct that lead them to acquire the ability to fertilize eggs. During their transit in the oviduct, spermatozoa face a series of environmental changes that can affect sperm viability. A series of ion channels and transporters, as well as the sperm cytoskeleton, allow spermatozoa to remain viable and functional. Cl- channels such as TMEM16A (calcium-activated chloride channel), CFTR (cystic fibrosis transmembrane conductance regulator), and ClC3 (chloride voltage-gated channel 3) are some of the ion transporters involved in maintaining cellular homeostasis. They are expressed in mammalian spermatozoa and are associated with capacitation, acrosomal reaction, and motility. However, little is known about their role in maintaining sperm volume. Therefore, this study aimed to determine the mechanism through which TMEM16A maintains sperm volume during capacitation. The effects of TMEM16A were compared to those of CFTR and ClC3. Spermatozoa were capacitated in the presence of specific TMEM16A, CFTR, and ClC3 inhibitors, and the results showed that only TMEM16A inhibition increased acrosomal volume, leading to changes within the acrosome. Similarly, only TMEM16A inhibition prevented actin polymerization during capacitation. Further analysis showed that TMEM16A inhibition also prevented ERK1/2 and RhoA activation. On the other hand, TMEM16A and CFTR inhibition affected both capacitation and spontaneous acrosomal reaction, whereas ClC3 inhibition only affected the spontaneous acrosomal reaction. In conclusion, during capacitation, TMEM16A activity regulates acrosomal structure through actin polymerization and by regulating ERK1/2 and RhoA activities.

PMID:40332387 | DOI:10.3390/ijms26083750

Arq Gastroenterol. 2025 May 2;62:e24110. doi: 10.1590/S0004-2803.24612024-110. eCollection 2025.

ABSTRACT

BACKGROUND: In patients with cystic fibrosis (pwCF) acid suppression therapy, gastrointestinal dysmotility, and post-operative bowel status, may predispose to the development of small intestinal bacterial overgrowth (SIBO). SIBO may continue to be present in the progression of the disease even on modulators. Breath testing is the most simple, non-invasive and available method for diagnosing SIBO. There are some divergencies over the operational procedures used to carry out and interpret breath tests in pwCF.

OBJECTIVE: We performed a systematic review of SIBO in pwCF to assess the methods used in breath tests and the existence of causal relationship between SIBO and following CF co-morbidities: liver disease, fat absorption, and eating disorders.

METHODS: We searched the PubMed, Cochrane Library, Embase, LILACS, MEDLINE, OpenGray, medRxiv, Google Scholar, and CAPES databases up to March 20, 2024. We selected clinical cohort and case-control studies to assess SIBO in cwCF. We selected studies that met the following criteria: (1) participants - children and adolescents diagnosed with CF; (2) intervention - assessment of SIBO using H2 and CH4 breath tests; (3) control - patients without SIBO; and (4) outcome - assessment of breath tests for SIBO diagnosis and the causal relationship between SIBO and CF co-morbidities. The PRISMA statement was used to report the search. QUADAS 2 tool was used for assessing the quality of each study methodology. The protocol for this review was registered in the Prospective Registration of Systematic Review Database (CRD42024503593).

RESULTS: The search strategy identified 279 studies. After screening titles and abstracts, 36 studies were selected for full-text review and 27 were excluded; nine studies involving 206 pwCFs were reviewed. All nine studies used H2 breath tests as a diagnostic method for SIBO, and five of them used a combined H2/CH4 test. There was no consistency in the timing of cessation of antibiotic therapy prior to testing. All patients performed the test after an overnight fast. A basal sample was collected prior to substrate (glucose or lactulose) ingestion, which ranged from 7 to 20 ppm. There was great variability between respiratory sample collection times, being times 0, 15, 30, 45, 60, 90, and 120 minutes the most used protocol. The methods for performing breath tests varied widely, making it difficult to reach conclusions on the role of SIBO as a co-morbidity in pwCF. There was no association between increased serum AST, ALT, and GGT levels and positive breath tests. There was no agreement regarding the role of SIBO and nutritional deficiency, but a reduction in fat absorption and the presence of hyporexia have been described under this condition.

CONCLUSION: Data on assessment of SIBO in pwCF is limited by the small number of studies available, the lack of appropriate controls in some studies, and the varying test methodology and diagnostic cut-offs applied. Protocols to investigate and diagnosing SIBO in pwCF need to be developed.

PMID:40332310 | DOI:10.1590/S0004-2803.24612024-110

Int J Mol Sci. 2025 Apr 11;26(8):3623. doi: 10.3390/ijms26083623.

ABSTRACT

Cystic fibrosis (CF) is a genetic disease due to mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. The most frequent mutation (p.Phe508del) results in a misfolded protein (p.Phe508del-CFTR) with an altered transport to the membrane of the cells via the conventional protein secretion (CPS) pathway. Nevertheless, it can use unconventional protein secretion (UPS). Indeed, p.Phe508del-CFTR forms a complex with GRASP55 to assist its direct trafficking from the endoplasmic reticulum to the plasma membrane. While GRASP55 is a key player of UPS, it is also a key player of stress-induced autophagy. In parallel, the unfolded protein response (UPR), which is activated in the presence of misfolded proteins, is tightly linked to UPS and autophagy through the key effectors IRE1, PERK, and ATF6. A better understanding of how UPS, UPR, and stress-induced autophagy interact to manage protein trafficking in CF and other conditions could lead to novel therapeutic strategies. By enhancing or modulating these pathways, it may be possible to increase p.Phe508del-CFTR surface expression. In summary, this review highlights the critical roles of UPS- and UPR-induced autophagy in managing protein transport, offering new perspectives for therapeutic approaches.

PMID:40332143 | DOI:10.3390/ijms26083623

Am J Physiol Lung Cell Mol Physiol. 2025 May 7. doi: 10.1152/ajplung.00287.2024. Online ahead of print.

ABSTRACT

Lumacaftor, the corrector of Orkambi, enhances the processing of F508del cystic fibrosis transmembrane conductance regulator (CFTR) but its impact on the channel activity of rescued F508del CFTR (rF508del) is unclear. Using an electrode-based, real-time iodide efflux assay performed at room temperature, acute exposure to lumacaftor was shown to increase the processing of F508del CFTR without a proportional increase in channel activity in a CFBE41o- cell line stably expressing F508del CFTR (CFBE-DF). A similar effect was not observed on wild-type CFTR in a HEK293 cell line. At 37°C, rF508del channel activity is significantly inhibited in CFBE-DF cells by acute exposure to 5mM lumacaftor, but not to 5mM tezacaftor or 1mM elexacaftor, the two correctors of Trikafta. Lumacaftor's inhibitory effect was characterized by a major left-shift of the peak channel activity relative to the peak CFTR processing in the dose response chart, which is absent for tezacaftor or elexacaftor. Ussing chamber analysis on polarized CFBE-DF cells reveals an inhibitory effect for lumacaftor on the forskolin- and ivacaftor-induced change in short circuit current. Single channel patch clamp on HEK-DF cells shows that acute application of cytosolic lumacaftor significantly decreases rF508del channel open probability. Taken together, despite its strong corrector activity, lumacaftor inhibits rF508del channel activity, compromising the degree of functional rescue. This effect may contribute to the limited clinical efficacy of Orkambi.

PMID:40331529 | DOI:10.1152/ajplung.00287.2024

Expert Opin Pharmacother. 2025 May 7. doi: 10.1080/14656566.2025.2491515. Online ahead of print.

ABSTRACT

INTRODUCTION: Ensifentrine, recently approved by the FDA for chronic obstructive pulmonary disease (COPD) maintenance treatment, is a novel inhaled therapy with a dual mechanism of action targeting phosphodiesterase (PDE)3 and PDE4. While long-acting bronchodilators and inhaled corticosteroids remain initial guideline-based COPD treatments, persistent symptoms and disease exacerbations highlight an existing unmet need. Ensifentrine offers both bronchodilator and anti-inflammatory benefits, offering the potential to address this treatment gap.

AREAS COVERED: This article reviews the mechanism of action of ensifentrine, details supporting preclinical evidence, and summarizes key clinical studies. It further explores ensifentrine's potential impact on the COPD treatment landscape and its potential applicability in other pulmonary diseases.

EXPERT OPINION: Ensifentrine's dual bronchodilator and anti-inflammatory action offer a promising adjunct to standard COPD treatments, particularly for patients with persistent symptoms despite conventional therapy. It improves lung function, meaningfully reduces exacerbation frequency, reduces symptoms, and enhances quality of life. Its inhaled delivery minimizes systemic exposure and side effects commonly observed with oral PDE inhibitors. Furthermore, its anti-inflammatory properties suggest potential applications in other chronic respiratory diseases, such as asthma and non-cystic fibrosis bronchiectasis.

PMID:40331465 | DOI:10.1080/14656566.2025.2491515