Adv Sci (Weinh). 2025 Aug 11:e07044. doi: 10.1002/advs.202507044. Online ahead of print.

ABSTRACT

Peptide drugs have revolutionized modern therapeutics, offering novel treatment avenues for various diseases. Nevertheless, low design efficacy, time consumption, and high cost still hinder peptide drug design and discovery. Here, an efficient approach that integrates deep learning-based protein design with functional screening is presented, enabling the rapid design of biotechnologically important peptides with improved stability and efficacy. 10,000 de novo glucagon-like peptide-1 receptor agonists (GLP-1RAs) are designed, 60 of these satisfied the stability, efficacy, and diversity criteria in the virtual functional screening. In vitro validations reveal a 52% success rate, and in vivo experiments demonstrate that two lead GLP-1RAs (D13 and D41) exhibit extended half-lives, approximately three times longer than that of Semaglutide. In diabetic mouse models, candidate D13 results in significantly lower blood glucose levels than Semaglutide. In the obesity mouse model, D13 induces weight loss efficacy comparable to that of Semaglutide. The AI-driven peptide design pipeline-which integrates protein design, functional screening, and experimental validation-reduces the number of iterations required to find novel peptide candidates. The entire process, from design to screening, can be completed in a single cycle within two weeks.

PMID:40787887 | DOI:10.1002/advs.202507044

Compr Rev Food Sci Food Saf. 2025 Sep;24(5):e70248. doi: 10.1111/1541-4337.70248.

ABSTRACT

Cereal quality significantly influences human health, requiring thorough evaluation of authenticity, nutritional composition, and food safety hazards. Conventional detection methods are often characterized by limitations, including time-consuming intricacy, complexity, and limited sensitivity. Recently, optical imaging and spectroscopy have emerged as rapid, nondestructive, and high-throughput alternatives for assessing cereal quality. The integration of chemometrics and artificial intelligence (AI), particularly deep learning algorithms, is paramount in the processing and analysis of optical data, which is indispensable for extracting key features from large datasets. In this work, the advanced spectroscopy and optical imaging techniques are comprehensively introduced, and their recent progress in applied research is outlined, emphasizing the major innovations and practical applications of these techniques. Besides, the latest developments of these techniques and AI-driven data processing methods in various aspects of cereal quality assessment have been summarized in order to highlight the potential research directions and future trends for practical application.

PMID:40787808 | DOI:10.1111/1541-4337.70248

ACS Omega. 2025 Jul 22;10(30):33662-33674. doi: 10.1021/acsomega.5c04524. eCollection 2025 Aug 5.

ABSTRACT

The interaction between circular RNAs (circRNAs) and RNA-binding proteins (RBPs) plays a crucial role in gene regulation; however, experimental identification is costly and inefficient. Current computational methods often overlook the structural features of circRNAs, thereby limiting prediction accuracy. To address these challenges, we propose GGCRB, a deep learning framework that integrates both sequence and structural features for predicting circRNA-RBP binding sites. Sequence features are captured through five encoding schemes (HFN, ND, NCP, DPCP, and Doc2Vec), followed by convolutional layers for local pattern extraction. Structural features are derived from base-pairing adjacency matrices generated by RNAstructure and modeled using graph convolutional networks and graph attention networks to learn topological dependencies. The fused representations are further processed by bidirectional LSTM and multihead attention modules to capture global interactions. Final predictions are made through pooling and softmax layers. Extensive experiments on 16 benchmark data sets demonstrate that GGCRB significantly outperforms existing models. Ablation studies and motif analyses further confirm its effectiveness, underscoring the importance of integrating structural and sequence information for accurate prediction of circRNA-RBP interactions.

PMID:40787315 | PMC:PMC12332793 | DOI:10.1021/acsomega.5c04524

ACS Omega. 2025 Jul 23;10(30):33031-33044. doi: 10.1021/acsomega.5c01924. eCollection 2025 Aug 5.

ABSTRACT

DNA methylation is an epigenetic modification that plays a crucial role in genome stability and cellular specialization, essential for maintaining normal cellular function and development, also a manifestation indicator of some diseases. Various tools have been proposed for methylation detection, typically leveraging a third-generation sequencing technology called nanopore sequencing, which provides more accurate DNA sequencing data. However, existing tools have their own limitations and advantages in terms of computational resources and information processing, without achieving a good balance. In this situation, we developed EDNTOM (Ensemble Deep Network Tool Of Methylation), a DNA methylation detection tool based on deep learning technology. We employed ensemble learning techniques, integrating predictions from multiple pretrained single models, and introduced an attention weight mechanism to provide accurate and reliable detection, reducing the consumption of computational resources. Results demonstrate that EDNTOM outperforms individual models. Additionally, in cross-species transfer experiments, EDNTOM exhibits strong transfer learning capabilities. We hope this work can provide a more powerful and reliable solution for methylation detection, contributing to the fields of biological science and medicine. The project code is available at https://github.com/ViceMusic/EDNTOM.

PMID:40787313 | PMC:PMC12332607 | DOI:10.1021/acsomega.5c01924

Front Bioeng Biotechnol. 2025 Jul 25;13:1556748. doi: 10.3389/fbioe.2025.1556748. eCollection 2025.

ABSTRACT

INTRODUCTION: Echocardiography is a first-line noninvasive test for diagnosing coronary artery disease (CAD), but it depends on time-consuming visual assessments by experts.

METHODS: This study constructed an echocardiographic video-driven multi-task learning model, denoted Intelligent echo for CAD (IE-CAD), to facilitate CAD screening and stenosis grading. A 3DdeeplabV3+ backbone and multi-task learning were simultaneously incorporated into the core frame of the IE-CAD model to capture the dynamic myocardial contours. Multifarious features reflecting local semantic structures were extracted and integrated to yield echocardiographic metrics such as ejection fraction, strain, and myocardial work. For model training and testing, we used a total of 870 echocardiographic videos from 290 patients with clinically suspected CAD at Beijing Hospital (Beijing, China), split at an 8:2 ratio. To evaluate the model's generalizability, we used an external dataset comprising 450 echocardiographic videos from 150 patients at Fuwai Hospital (Beijing, China).

RESULTS: The IE-CAD model achieved an AUC of 0.78 and a sensitivity of 0.85 for detecting significant or severe CAD, with a pearson correlation coefficient of 0.545 for predicting the Gensini score. When applied to the external dataset, the model achieved an AUC of 0.77 and a sensitivity of 0.78 for detecting significant or severe CAD.

DISCUSSION: Thus, the IE-CAD model demonstrated effective CAD diagnosis and grading in patients with clinical suspicion.

TRIAL REGISTRATION: This work was registered at ClinicalTrials.gov on 05 April 2019 (registration number: NCT03905200).

PMID:40787200 | PMC:PMC12331746 | DOI:10.3389/fbioe.2025.1556748

Int J Biomed Imaging. 2025 Aug 1;2025:2149042. doi: 10.1155/ijbi/2149042. eCollection 2025.

ABSTRACT

Brain tumors are complex clinical lesions with diverse morphological characteristics, making accurate segmentation from MRI scans a challenging task. Manual segmentation by radiologists is time-consuming and susceptible to human error. Consequently, automated approaches are anticipated to accurately delineate tumor boundaries and quantify tumor burden, addressing these challenges efficiently. The presented work integrates a convolutional block attention module (CBAM) into a deep learning architecture to enhance the accuracy of MRI-based brain tumor segmentation. The deep learning network is built upon a VGG19-based U-Net model, augmented with depthwise and pointwise convolutions to improve feature extraction and processing efficiency during brain tumor segmentation. Furthermore, the proposed framework enhances segmentation precision while simultaneously incorporating tumor area measurement, making it a comprehensive tool for early-stage tumor analysis. Several qualitative assessments are used to assess the performance of the model in terms of tumor segmentation analysis. The qualitative metrics typically analyze the overlap between predicted tumor masks and ground truth annotations, providing information on the segmentation algorithms' accuracy and dependability. Following segmentation, a new approach is used to compute the extent of segmented tumor areas in MRI scans. This involves counting the number of pixels within the segmented tumor masks and multiplying by their area or volume. The computed tumor areas offer quantifiable data for future investigation and clinical interpretation. In general, the proposed methodology is projected to improve segmentation accuracy, efficiency, and clinical relevance compared to existing methods, resulting in better diagnosis, treatment planning, and monitoring of patients with brain tumors.

PMID:40786983 | PMC:PMC12334286 | DOI:10.1155/ijbi/2149042

FASEB J. 2025 Aug 15;39(15):e70920. doi: 10.1096/fj.202501665R.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive form of interstitial lung disease and is pathologically featured by excessive deposition of extracellular matrix in response to repetitive epithelial injury. Burgeoning evidence suggests that ketone body exerts a beneficial effect on oxidative stress and on different types of fibrotic diseases, including cardiac fibrosis, hepatic fibrosis, and renal fibrosis. However, its effect on IPF is largely unknown. In vitro in alveolar epithelial cells type II (AECII) exposed to bleomycin, β-hydroxybutyrate (BHB) treatment substantially mitigated cellular ferroptosis, as evidenced by enhanced cell viability, reduced iron content, and reduced lipid peroxidation. This beneficial action of BHB coincided with a reinforced de novo glutathione synthesis and increased glutathione peroxidase 4 (GPX4) antioxidant response. Mechanistically, BHB promoted the expression of glutamate-cysteine ligase catalytic subunit (GCLC), the rate-limiting enzyme in de novo glutathione synthesis. Indeed, RSL3, a selective inhibitor of GPX4, or knockdown of GCLC abolished, whereas selective activation of GPX4 was sufficient for the antiferroptosis and AECII protective effects of BHB. In murine models of bleomycin-induced IPF, BHB therapy promoted the expression of GCLC and reinforced GPX4 activity in AECII, resulting in lessened AECII ferroptosis and improved lung injury and fibrosis. Thus, our findings may pave the way for developing a BHB-based novel approach to therapeutic ketosis for treating IPF.

PMID:40787805 | DOI:10.1096/fj.202501665R

Ann Thorac Med. 2025 Jul-Sep;20(3):145-152. doi: 10.4103/atm.atm_132_25. Epub 2025 Jul 14.

ABSTRACT

This updated systematic review and meta-analysis pooled the results of previous clinical trials assessing the effects of pirfenidone and nintedanib on patients with pulmonary fibrosis. Scopus, the Cochrane Library, PubMed, and Web of Science were searched from the inception to April 12, 2025, to identify randomized controlled trials measuring the effect of pirfenidone and nintedanib on pulmonary fibrosis. Because of high methodological heterogeneity, we utilized a random-effects model (DerSimonian-Laird) to perform this meta-analysis. Finally, 18 articles with 20 randomized controlled trials were included in this meta-analysis. We found that compared to placebo, treatment with the two antifibrotic drugs increased forced vital capacity (FVC) predicted (weighted mean difference [WMD] 3.12%, 95% confidence interval [CI] [1.41, 4.82], I 2 = 53.30%), FVC volume (WMD 87.44 ml, 95% CI [59.32, 115.57], I 2 = 99.4%), and the distance walked in the 6-minute walk test (WMD 24.63 m, 95% CI [16.05, 33.22], I 2 = 0.00%). However, compared to placebo, treatment with the two antifibrotic drugs did not significantly change the diffusing capacity of the lungs for carbon monoxide (WMD 1.38 ml/min/mmHg, 95% CI [-9.42, 12.18], I 2 = 0.00%). Therapeutic benefits were observed for both pirfenidone and nintedanib and for both idiopathic pulmonary fibrosis (IPF) and non-IPF. Pirfenidone and nintedanib can improve lung function and functional capacity in patients with different types of pulmonary fibrosis.

PMID:40786886 | PMC:PMC12333965 | DOI:10.4103/atm.atm_132_25

Quant Imaging Med Surg. 2025 Aug 1;15(8):6604-6615. doi: 10.21037/qims-2025-380. Epub 2025 Jul 23.

ABSTRACT

BACKGROUND: In patients with idiopathic pulmonary fibrosis (IPF), computed tomography (CT) quantification using artificial intelligence (AI) has been explored as a method to assess the therapeutic response to antifibrotic agents; however, studies evaluating long-term follow-up outcomes remain scarce. We investigated AI-driven quantitative analysis for long-term follow-up chest CT of IPF patients undergoing pirfenidone treatment.

METHODS: Among the 2,223 patients diagnosed with interstitial lung disease by chest CT at Jeonbuk National University Hospital, 36 patients with a multidisciplinary diagnosis of IPF were included in the study after excluding those who had not undergone surgical lung biopsy or did not have available pulmonary function tests (PFTs). These 36 patients underwent high-resolution computed tomography (HRCT) along with concurrent PFTs over a 10-year period and were categorized into two groups: those treated with pirfenidone (n=17) and those not treated with pirfenidone (n=19). Quantitative texture analysis was performed using a deep convolutional neural network to calculate fibrotic scores, defined as the combined mean percentage of two fibrotic components-reticulation and honeycombing, with or without accompanying ground-glass opacities-across the entire lung. This analysis aimed to assess treatment response in IPF patients receiving pirfenidone. Repeated measures analysis of variance (ANOVA) was used to evaluate the correlation between changes in pulmonary function and fibrotic scores over time.

RESULTS: The final study population comprised 36 patients, with a mean age of 67.1±7.7 years. Patients (DLCO: 58.0%±21.0%) who received pirfenidone (n=17) exhibited lower DLCO values at the final follow-up compared to the untreated group (n=19) (DLCO: 69.0%±21.7%), although the difference was not statistically significant (P=0.260). However, in the treated group (n=17), patients with progression despite pirfenidone treatment (n=6) (fibrotic score: 27.1%±12.1%) showed a markedly greater increase in mean AI fibrotic scores at the final follow-up compared to those with no or little change (n=11) (fibrotic score: 10.9%±8.7%), with the difference approaching statistical significance (P=0.076). There was a significant correlation between the decrease in DLCO values and the increase in AI fibrotic score in patients with pirfenidone on long-term follow-up (P<0.01).

CONCLUSIONS: AI-driven quantitative analysis of HRCT images in patients with IPF enables objective monitoring of the effects of pirfenidone on the progression of pulmonary fibrosis on long-term follow-up.

PMID:40785882 | PMC:PMC12332572 | DOI:10.21037/qims-2025-380

J Cell Biol. 2025 Nov 3;224(11):e202506102. doi: 10.1083/jcb.202506102. Epub 2025 Aug 11.

ABSTRACT

Segmenting multidimensional microscopy data requires high accuracy across many images (e.g., time points or Z slices) and is thus a labor-intensive part of biological image processing pipelines. We present ReSCU-Nets, recurrent convolutional neural networks that use the segmentation results from the previous image in a sequence as a prompt to segment the current image. We demonstrate that ReSCU-Nets outperform state-of-the-art image segmentation models, including nnU-Net and the Segment Anything Model, in different segmentation tasks on time-lapse microscopy sequences. Furthermore, ReSCU-Nets enable human-in-the loop corrections that prevent propagation of segmentation errors throughout image sequences. Using ReSCU-Nets, we investigate the role of gap junctions during Drosophila embryonic wound healing. We show that pharmacological blocking of gap junctions slows down wound closure by disrupting cytoskeletal polarity and cell shape changes necessary to repair the wound. Our results demonstrate that ReSCU-Nets enable the analysis of the molecular and cellular dynamics of tissue morphogenesis from multidimensional microscopy data.

PMID:40788207 | DOI:10.1083/jcb.202506102

Microbiol Spectr. 2025 Aug 11:e0219124. doi: 10.1128/spectrum.02191-24. Online ahead of print.

ABSTRACT

This study integrated multisource longitudinal data with trajectory modeling to delineate the heterogeneity of humoral immune dynamics induced by inactivated SARS-CoV-2 vaccinations and their clinical implications for subsequent SARS-CoV-2 infection outcomes in 205 individuals from Sichuan Province, China. We found that preexisting infection status served as the dominant stratifier of antibody trajectory divergence across the cohort. Additionally, among individuals without prior infection before the vaccine cohort, we identified five distinct immune response patterns with clinical implications. An age-associated "minimal response" subtype was associated with an increased risk of prolonged recovery time, sore throat, and limb pain in subsequent infections. In contrast, a subtype characterized by a marked increase in S-Igs titers after the booster dose exhibited fewer symptoms, with a lower likelihood of experiencing fever and fatigue. What is more, for those with prior infection, clinical data-including viral shedding duration and total antibody levels 15 days after discharge from the initial infection-could provide valuable insights into the subsequent risk of reinfection.

IMPORTANCE: To better identify vulnerable populations in epidemic surveillance and predict their clinical manifestations post-infection for accurate diagnosis and effective management, it is vital to understand the intrinsic dynamic immune patterns among individuals and how these trajectory patterns relate to future infections and associated symptoms. In the post-COVID-19 era, conducting nuanced analyses remains of great significance, especially as longer-term observational data become available. This is the first finding from China that illustrates the dynamic characteristics of the immune response following inactivated COVID-19 vaccination over an extended observation period, including information on following infection and symptoms. These data are particularly valuable as no participants experienced COVID-19 infection during the vaccine cohort follow-ups, meaning their antibody levels solely reflect the intrinsic dynamic immune patterns triggered by the inactivated COVID-19 vaccines.

PMID:40788170 | DOI:10.1128/spectrum.02191-24

Clin Pharmacol Ther. 2025 Aug 11. doi: 10.1002/cpt.70026. Online ahead of print.

ABSTRACT

Pharmacomicrobiomics explores the role of the gut microbiota in pharmacokinetics and pharmacodynamics, paving the way for new biomarkers and intervention strategies to reduce interindividual variability in drug response. Emerging mechanistic insights and tools enable microbiome-aware approaches as a promising new facet of personalized medicine.

PMID:40788067 | DOI:10.1002/cpt.70026

ACS Omega. 2025 Jul 27;10(30):32728-32743. doi: 10.1021/acsomega.4c10983. eCollection 2025 Aug 5.

ABSTRACT

Leptospirosis, a re-emerging zoonotic disease, is challenging human and animal health due to the lack of early and rapid diagnostic tools and effective vaccines. The exoproteome of the pathogen expressed under pathogenic conditions possesses a rational diagnostic significance due to its consistent presence in body fluids. were challenged to conditions simulating infection using physiological temperatures and osmolarity. Using state-of-the-art extraction techniques, efficient enrichment of nonabundant proteins, and high-resolution LC-MS/MS, we identified 1575 exoproteins from both the pathogen surface and culture supernatant. The results indicate a significant upregulation of 155 exoproteins, of which 41 were predicted to have moonlighting properties, 35 were identified as adhesins, and several proteins were components of the type 2 secretion system (T2SS). Additionally, 10 proteins showed extracellular matrix (ECM) binding properties, out of which 4 orthologs were found using the T2SS. The overall characteristics of upregulated proteins show that they can help Leptospira establish infection, invasion, and protection from the host defense, thereby providing new insights into the pathogen to confront the host via an increased energy level, secretion system, and host ECM binding molecules. Furthermore, the study suggests potential candidates for efficient antileptospiral countermeasures.

PMID:40787326 | PMC:PMC12332550 | DOI:10.1021/acsomega.4c10983

NPJ Biol Phys Mech. 2025;2(1):20. doi: 10.1038/s44341-025-00025-3. Epub 2025 Aug 6.

ABSTRACT

This review focuses on the roles of chemical species transport and biochemical and biophysical transformation within the gastrointestinal and immune systems and interactions with tissue structure and biomechanics in the mechanisms of pathophysiological conditions including gastrointestinal reflux disease and allergic responses. Combinations of biophysical and biochemical techniques are needed to unravel the complex interplay between transport and transformation to develop more effective interventions and ultimately improve patient outcomes.

PMID:40786563 | PMC:PMC12328227 | DOI:10.1038/s44341-025-00025-3

Data Brief. 2025 Jul 22;62:111904. doi: 10.1016/j.dib.2025.111904. eCollection 2025 Oct.

ABSTRACT

Ophiocordyceps sinensis is a medicinal mushroom that has been traditionally used for promoting respiratory health and addressing diverse therapeutic needs. The data in this article provides insights into the metabolic profile of the cultivated fruiting body of Ophiocordyceps sinensis (xOs™) across different molecular weight (MW) fractions. The cold-water extract obtained from xOs™ was fractionated by MW into high, medium and low MW fractions using size-exclusion chromatography. The metabolic profile of each fraction was analysed through liquid chromatography-mass spectrometry. The identification of metabolites in xOs™ was validated by analysing the mass spectra of both precursor (MS) and daughter ions (MS2) with those documented in the mass spectrometry reference libraries. Additionally, the role of each metabolite was hypothesized based on existing literature to provide scientific rationale for the traditional applications of xOs™. Further investigations into the fractions of xOs™ were conducted using the organ bath approach, in which the airway relaxant effects of each fraction on isolated airway tissues from adult male Sprague-Dawley rats were examined. This information could shed light on the bioactivity of different xOs™ fractions in relaxing airway smooth muscles. Taken together, the metabolomics and pharmacological dataset of xOs™ presented in this study may serve as a reference to facilitate comparative metabolomic analyses involving Ophiocordyceps sinensis and also to support targeted isolation of bioactive components in future works.

PMID:40785731 | PMC:PMC12332881 | DOI:10.1016/j.dib.2025.111904

Clin Exp Med. 2025 Aug 11;25(1):288. doi: 10.1007/s10238-025-01721-z.

ABSTRACT

Immune checkpoint inhibitors (ICIs) have revolutionized cancer immunotherapy, significantly improving patient survival. However, their use is frequently associated with immune-related adverse events, including immune checkpoint inhibitor-induced liver injury (ICI-DILI), which poses substantial clinical challenges. While corticosteroid therapy remains the primary treatment for ICI-DILI, many patients with steroid-refractory cases, especially those involving non-hepatocellular injury, exhibit resistance to standard therapies. Emerging evidence suggests that factors such as MDR1 expression, PD-L1 (Programmed cell death ligand (1)) and PD-L2 (Programmed cell death ligand (2)) expression levels, and liver injury patterns contribute to steroid resistance, highlighting the need for alternative treatment strategies. Recent advancements in therapeutic research have identified promising second-line treatments, including immunosuppressants (tacrolimus, cyclosporine A, mycophenolate mofetil, and azathioprine), monoclonal antibodies (infliximab and tocilizumab), anti-thymocyte globulin, intravenous immunoglobulin, ursodeoxycholic acid for cholestatic cases, and blood purification techniques. These innovations offer new possibilities for managing steroid-refractory ICI-DILI. This review also explores critical gaps in the field, including the lack of reliable diagnostic criteria and biomarkers for hormone-refractory ICI-DILI. Furthermore, we discuss the development of novel therapies informed by evolving insights into the condition's pathogenesis and the feasibility of reintroducing ICIs after ICI-DILI resolution. These advancements mark significant progress in optimizing patient outcomes and advancing the mechanistic understanding of ICI-DILI.

PMID:40788551 | DOI:10.1007/s10238-025-01721-z

Front Pharmacol. 2025 Jul 25;16:1647088. doi: 10.3389/fphar.2025.1647088. eCollection 2025.

ABSTRACT

BACKGROUND: Drug-induced migraine represents a clinically significant yet under-investigated subtype of migraine. This study aims to evaluate the risk of drug-related migraine based on real-world data from the FDA Adverse Event Reporting System (FAERS).

METHODS: A retrospective pharmacovigilance analysis was conducted using FAERS data from Q1 2004 to Q4 2024. Migraine cases were identified via standardized MedDRA (The Medical Dictionary for Regulatory Activities) terms. Only primary suspect drugs were included. Disproportionality analyses were performed using four algorithms: ROR, PRR, MGPS, and BCPNN. Drugs were classified by therapeutic indication and mechanism of action, and stratified by BCPNN values to assess risk levels.

RESULTS: A total of 20,886 migraine-related adverse events were identified, predominantly among females (77.4%) with a mean age of 45.7 years. Sixty-six drugs yielded positive signals, and after exclusion criteria, 39 remained for further analysis. The highest-risk agents included lorcaserin (BCPNN = 3.33), tasimelteon (3.20), and botulinum toxin type A (3.06). High-risk therapeutic classes included immunosuppressants, estrogens/progestogens, and sedative-hypnotics.

CONCLUSION: This large-scale analysis identifies key drug categories and compounds associated with an elevated risk of migraine, providing actionable insights for clinicians. Especially lorcaserin, tasimelteon, and botulinum toxin as potential risk factors for migraine. Given the public health burden of migraine, pharmacovigilance efforts should incorporate such findings to mitigate iatrogenic risks. Further prospective studies are warranted to establish causal mechanisms and optimize therapeutic decision-making.

PMID:40786030 | PMC:PMC12332265 | DOI:10.3389/fphar.2025.1647088

Thorac Res Pract. 2025 Aug 4. doi: 10.4274/ThoracResPract.2025.2025-1-11. Online ahead of print.

ABSTRACT

OBJECTIVE: The Cystic Fibrosis Registry of Türkiye (CFRT) was established by the Turkish Pediatric Respiratory Diseases and Cystic Fibrosis Society and has provided detailed information on demographic, clinical, genetic, and treatment-related aspects of cystic fibrosis (CF) patients since 2017. We aimed to describe the current status of CF in Türkiye using CFRT's 2023 annual data.

MATERIAL AND METHODS: Demographic, clinical, and treatment data were taken from CFRT's 2023 record.

RESULTS: In 2023, 2,258 patients from 34 centers were recorded. The median age of patients was 9.1 years, and 46.9% were female, with a median age at diagnosis of 0.3 years. Only 14.9% of the patients were older than 18 years. Genetic analyses were completed in 97.3% of patients. The most common variant, F508del, had a total variant frequency of 22.1%. The median percent predicted FEV1 and FVC were 88.0 and 94.0 in those aged 6-17 years 71.0 and 84.0 in those aged ≥18 years, respectively. The median values of body mass index z-scores were -0.5, and -0.5 for patients 2-18 and older than 18 years, respectively. Chronic colonization with Pseudomonas aeruginosa was present in 17.2% of the patients. Most patients used inhaled recombinant human DNase (87.1%) and oral pancreatic enzyme replacement treatment (83.0%). CF transmembrane conductance regulator (CFTR) modulators were used by 15.9% of patients. Over the year, 24 patients died, with a median age at death of 13.3 years.

CONCLUSION: The CFRT report provides a valuable resource showing the clinical and laboratory data of patients with CF in the country.

PMID:40785313 | DOI:10.4274/ThoracResPract.2025.2025-1-11

J Cell Mol Med. 2025 Aug;29(15):e70751. doi: 10.1111/jcmm.70751.

ABSTRACT

Epithelial cells at many sites in the body are affected by the inherited disorder cystic fibrosis (CF). The lung was the major focus of research until recently, when effective therapeutics became available for most people with CF. There is now renewed interest in CF aetiology in other locations in the body, where the regulatory mechanisms for the CF transmembrane conductance regulator (CFTR) gene are less well-characterised. The definition of the genomic elements controlling CFTR expression and their associated transcription factors is important for the design of gene-based therapies. Here we identify the cis-regulatory elements (CREs) associated with the CFTR locus by open chromatin mapping in pancreatic adenocarcinoma cell lines, primary human pancreatic and bile duct (cholangiocyte) organoids and single cells from tissues, as well as sweat gland coil and duct epithelial cells. We show that broadly these cell types use a combination of CREs that were characterised previously either in airway or intestinal epithelial cells, though not occurring together in these two cell lineages. Moreover, the chromatin structure of the CFTR locus in pancreatic cell lines is consistent with earlier models. We also use bioinformatic tools to predict the transcription factor network in these rare cell lineages from open chromatin peaks genome-wide.

PMID:40785146 | DOI:10.1111/jcmm.70751

J Physiol. 2025 Aug 10. doi: 10.1113/JP288955. Online ahead of print.

ABSTRACT

Cystic fibrosis (CF) is a life-threatening condition caused by pathogenic mutations of the cystic fibrosis transmembrane conductance regulator (CFTR) anion channel. While most patients with frequent mutations are treated with a combination of corrector and potentiator drugs (elexacaftor-tezacaftor-ivacaftor, ETI), for many rare variants no such treatment is approved. Here we characterized the molecular pathologies of five rare CF-associated mutants (G126D, I336K, T465I, T582I and D984V), and evaluated their responsiveness to ETI treatment. Channel expression was investigated by western blot from HEK-293T cells transiently expressing mutant channels, with or without corrector drugs (elexacaftor and tezacaftor). Drug efficiency was assessed by the enhancement of channel glycosylation. Maturation was mildly (T582I, G126D) to severely (T465I) defective for all five mutants, but could be restored to wild-type levels by incubation with corrector drugs. Gating properties and responses to potentiator drugs (ivacaftor and elexacaftor) were assessed in macroscopic and single-channel inside-out patch-clamp recordings from mutant channels expressed in Xenopus laevis oocytes. Mutations G126D, T582I, I336K and T465I markedly decreased channel open probability, with the greatest reduction being caused by mutation T465I. Mutations G126D, I336K and T465I also slightly impaired unitary conductance. Applying potentiator drugs boosted gating for all variants, producing multifold increases in macroscopic currents. In conclusion, all five mutations impair channel maturation and gating to various degrees, but a normal glycosylation pattern can be restored with correctors, and channel gating can be enhanced with potentiators. These in vitro observations suggest that ETI treatment would be beneficial for CF patients carrying an allele with any of the five mutations. KEY POINTS: Mutations of the cystic fibrosis transmembrane conductance regulator (CFTR) anion channel cause cystic fibrosis. Highly efficient modulator therapy has been approved for ∼90% of cystic fibrosis patients, but a large number of rare mutations are not yet eligible for treatment. Five rare CFTR mutations found in cystic fibrosis patients in Hungary and worldwide are shown here to produce complex channel pathologies that to differing degrees affect protein maturation, channel gating and anion permeation through the open pore. All five variants respond strongly to clinically employed modulator drugs that boost channel surface expression and gating. The results help us understand how these CFTR mutations lead to cystic fibrosis, and suggest that patients carrying any of the five variants would probably benefit from modulator therapy.

PMID:40785054 | DOI:10.1113/JP288955