Expert Opin Drug Saf. 2025 Feb 10. doi: 10.1080/14740338.2025.2465865. Online ahead of print.

ABSTRACT

BACKGROUND: During the 2019 coronavirus disease (COVID-19) pandemic, although patients were advised to continue using angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), it remains unclear whether the pandemic influenced the occurrence of adverse reactions to these drugs. This study aims to analyze and compare changes in ACEIs and ARBs adverse events before and during the COVID-19 pandemic, exploring its potential impact on the safety of these medications.

METHODS: We used real-world data to explore the impact of the COVID-19 pandemic on adverse events related to ACEIs and ARBs.

RESULTS: During the pandemic, ACEI-related adverse events (70 cases) and ARB-related adverse events (7 cases) showed increased reporting rates and RORs, with a notable rise in ACEI-related ear and labyrinth disorders. Additionally, 170 new adverse event signals were detected for ACEIs (8 with significantly increased risk) and 191 signals for ARBs (2 with significantly increased risk).

CONCLUSIONS: This study, based on real-world data, revealed significant signals indicating that ACEI use during the COVID-19 pandemic may have increased the risk of renal adverse events and ear labyrinth diseases. The study emphasized the need for increased caution when using ACEIs and ARBs during the pandemic.

PMID:39927493 | DOI:10.1080/14740338.2025.2465865

Disabil Rehabil Assist Technol. 2025 Feb 10:1-8. doi: 10.1080/17483107.2025.2463548. Online ahead of print.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) is a genetic disorder affecting multiple organs, primarily the lungs. Rehabilitation is crucial in managing respiratory symptoms. Telerehabilitation, which provides remote rehabilitation services via digital platforms, gained importance during the COVID-19 pandemic. Despite its growing use, there is little clarity on the available tools and devices for CF telerehabilitation.

OBJECTIVE: This scoping review aims to map the existing tools and devices used in telerehabilitation for pediatric and adult CF patients.

METHODS: The review was conducted following the Joanna Briggs Institute methodology, using the PRISMA-ScR checklist. Comprehensive searches were performed across seven databases, alongside grey literature. Studies involving CF patients and telerehabilitation interventions were included. Data were extracted and analyzed both numerically and thematically.

RESULTS: Eighteen studies were included, involving 622 CF patients. The review identified 10 platforms, seven telemonitoring devices, and three rehabilitation devices. Most studies focused on motor rehabilitation with individual, synchronous sessions. Commonly used platforms included Zoom, Skype, and Google Meet, while only three studies used platforms specifically designed for CF.

CONCLUSIONS: Telerehabilitation for CF is in its early stages and less developed than for other conditions. There is a need for dedicated platforms and devices that address CF patients' specific motor and respiratory needs. Future efforts should focus on developing these tools to improve patient engagement and outcomes.

PMID:39928374 | DOI:10.1080/17483107.2025.2463548

Med Biol Eng Comput. 2025 Feb 10. doi: 10.1007/s11517-025-03319-9. Online ahead of print.

ABSTRACT

Deformable registration between brain tumor images and brain atlas has been an important tool to facilitate pathological analysis. However, registration of images with tumors is challenging due to absent correspondences induced by the tumor. Furthermore, the tumor growth may displace the tissue, causing larger deformations than what is observed in healthy brains. Therefore, we propose a new reconstruction-driven cascade feature warping (RCFW) network for brain tumor images. We first introduce the symmetric-constrained feature reasoning (SFR) module which reconstructs the missed normal appearance within tumor regions, allowing a dense spatial correspondence between the reconstructed quasi-normal appearance and the atlas. The dilated multi-receptive feature fusion module is further introduced, which collects long-range features from different dimensions to facilitate tumor region reconstruction, especially for large tumor cases. Then, the reconstructed tumor images and atlas are jointly fed into the multi-stage feature warping module (MFW) to progressively predict spatial transformations. The method was performed on the Multimodal Brain Tumor Segmentation (BraTS) 2021 challenge database and compared with six existing methods. Experimental results showed that the proposed method effectively handles the problem of brain tumor image registration, which can maintain the smooth deformation of the tumor region while maximizing the image similarity of normal regions.

PMID:39928283 | DOI:10.1007/s11517-025-03319-9

Environ Monit Assess. 2025 Feb 10;197(3):258. doi: 10.1007/s10661-025-13722-2.

ABSTRACT

The threat of snakebites to public health, particularly in tropical and subtropical regions, requires effective mitigation strategies to avoid human-snake interactions. With the development of an IoT-based smart snake-trapping device, an innovative non-invasive solution for preventing snakebites is presented, autonomously capturing and identifying snakes. Using artificial intelligence (AI) and Internet of Things (IoT) technologies, the entire system is designed to improve the safety and efficiency of snake capture, both in rural and urban areas. A camera and sensors are installed in the device to detect heat and vibration signatures, mimicking the natural prey of snakes using tungsten wire and vibration motors to attract them into the trap. A real-time classification algorithm based on deep learning determines whether a snake is venomous or non-venomous as soon as the device detects it. This algorithm utilizes a transfer learning approach using a convolutional neural network (CNN) and has been trained using snake images, achieving an accuracy of 91.3%. As a result of this identification process, appropriate actions are taken, such as alerting authorities or releasing non-venomous snakes into the environment in a safe manner. Through the integration of IoT technology, users can receive real-time notifications and data regarding the trap via a smartphone application. The system's connectivity allows for timely intervention in case of venomous species, reducing snakebite risks. Additionally, the system provides information regarding snake movement patterns and species distribution, contributing to the study of broader ecological issues. An automated and efficient method of managing snakes could be implemented in snakebite-prone regions with the smart trapping device.

PMID:39928180 | DOI:10.1007/s10661-025-13722-2

Anal Chem. 2025 Feb 10. doi: 10.1021/acs.analchem.4c06604. Online ahead of print.

ABSTRACT

Electronic nose (E-nose) has been applied many times for exhale biomarker detection for lung cancer, which is a leading cause of cancer-related mortality worldwide. These noninvasive breath testing techniques can be used for the early diagnosis of lung cancer patients and help improve their five year survival. However, there are still many key challenges to be addressed, including accurately identifying the kind of volatile organic compounds (VOCs) biomarkers in human-exhaled breath and the concentrations of these VOCs, which may vary at different stages of lung cancer. Recent research has mainly focused on E-nose based on a metal oxide semiconductor sensor array with proposed single gas qualitative and quantitative algorithms, but there are few breakthroughs in the detection of multielement gaseous mixtures. This work proposes two hybrid deep-learning models that combine the Transformer and CNN algorithms for the identification of VOC types and the quantification of their concentrations. The classification accuracy of the qualitative model reached 99.35%, precision reached 99.31%, recall was 99.00%, and kappa was 98.93%, which are all higher than those of the comparison algorithms, like AlexNet, MobileNetV3, etc. The quantitative model achieved an average R2 of 0.999 and an average RMSE of only 0.109 on the mixed gases. Otherwise, the parameter count and FLOPs of only 0.7 and 50.28 M, respectively, of the model proposed in this work were much lower than those of the comparison models. The detailed experiments demonstrated the potential of our proposed models for screening patients with early stage lung cancer.

PMID:39928114 | DOI:10.1021/acs.analchem.4c06604

Ann Med. 2025 Dec;57(1):2461679. doi: 10.1080/07853890.2025.2461679. Epub 2025 Feb 10.

ABSTRACT

Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health, and at the same time, its treatment greatly consumes the health care resources of all countries in the world. The diagnosis of GC is usually based on histopathologic examination, and it is very important to be able to detect and identify cancerous lesions at an early stage, but some endoscopists' lack of diagnostic experience and fatigue at work lead to a certain rate of under diagnosis. The rapid and striking development of Artificial intelligence (AI) has helped to enhance the ability to extract abnormal information from endoscopic images to some extent, and more and more researchers are applying AI technology to the diagnosis of GC. This initiative has not only improved the detection rate of early gastric cancer (EGC), but also significantly improved the survival rate of patients after treatment. This article reviews the results of various AI-assisted diagnoses of EGC in recent years, including the identification of EGC, the determination of differentiation type and invasion depth, and the identification of borders. Although AI has a better application prospect in the early diagnosis of ECG, there are still major challenges, and the prospects and limitations of AI application need to be further discussed.

PMID:39928093 | DOI:10.1080/07853890.2025.2461679

Sleep Breath. 2025 Feb 10;29(1):95. doi: 10.1007/s11325-025-03247-w.

ABSTRACT

PURPOSE: This exploratory study introduces a portable, wearable device using a single accelerometer to monitor twelve sleep positions. Targeted for home use, the device aims to assist patients with mild conditions such as gastroesophageal reflux disease (GERD) by tracking sleep postures, promoting healthier habits, and improving both reflux symptoms and sleep quality without requiring hospital-based monitoring.

METHODS: The study developed AnpoNet, a lightweight deep learning model combining 1D-CNN and LSTM, optimized with BN and Dropout. The 1D-CNN captures short-term movement features, while the LSTM identifies long-term temporal dependencies. Experiments were conducted on data from 15 participants performing twelve sleep positions, with each position recorded for one minute at a sampling frequency of 50 Hz. The model was evaluated using 5-Fold cross-validation and unseen participant data to assess generalization.

RESULTS: AnpoNet achieved a classification accuracy of 94.67% ± 0.80% and an F1-score of 92.94% ± 1.35%, outperforming baseline models. Accuracy was computed as the mean of accuracies obtained for three participants in the test set, averaged over five independent random seeds. This evaluation approach ensures robustness by accounting for variability in both individual participant performance and model initialization, underscoring its potential for real-world, home-based applications.

CONCLUSION: This study provides a foundation for a portable system enabling continuous, non-invasive sleep posture monitoring at home. By addressing the needs of GERD patients, the device holds promise for improving sleep quality and supporting positional therapy. Future research will focus on larger cohorts, extended monitoring durations, and user-friendly interfaces for broader adoption.

PMID:39928075 | DOI:10.1007/s11325-025-03247-w

Med Phys. 2025 Feb 10. doi: 10.1002/mp.17672. Online ahead of print.

ABSTRACT

BACKGROUND: Given the recent increased emphasis on multimodal neural networks to solve complex modeling tasks, the problem of outcome prediction for a course of treatment can be framed as fundamentally multimodal in nature. A patient's response to treatment will vary based on their specific anatomy and the proposed treatment plan-these factors are spatial and closely related. However, additional factors may also have importance, such as non-spatial descriptive clinical characteristics, which can be structured as tabular data. It is critical to provide models with as comprehensive of a patient representation as possible, but inputs with differing data structures are incompatible in raw form; traditional models that consider these inputs require feature engineering prior to modeling. In neural networks, feature engineering can be organically integrated into the model itself, under one governing optimization, rather than performed prescriptively beforehand. However, the native incompatibility of different data structures must be addressed. Methods to reconcile structural incompatibilities in multimodal model inputs are called data fusion. We present a novel joint early pre-spatial (JEPS) fusion technique and demonstrate that differences in fusion approach can produce significant model performance differences even when the data is identical.

PURPOSE: To present a novel pre-spatial fusion technique for volumetric neural networks and demonstrate its impact on model performance for pretreatment prediction of overall survival (OS).

METHODS: From a retrospective cohort of 531 head and neck patients treated at our clinic, we prepared an OS dataset of 222 data-complete cases at a 2-year post-treatment time threshold. Each patient's data included CT imaging, dose array, approved structure set, and a tabular summary of the patient's demographics and survey data. To establish single-modality baselines, we fit both a Cox Proportional Hazards model (CPH) and a dense neural network on only the tabular data, then we trained a 3D convolutional neural network (CNN) on only the volume data. Then, we trained five competing architectures for fusion of both modalities: two early fusion models, a late fusion model, a traditional joint fusion model, and the novel JEPS, where clinical data is merged into training upstream of most convolution operations. We used standardized 10-fold cross validation to directly compare the performance of all models on identical train/test splits of patients, using area under the receiver-operator curve (AUC) as the primary performance metric. We used a two-tailed Student t-test to assess the statistical significance (p-value threshold 0.05) of any observed performance differences.

RESULTS: The JEPS design scored the highest, achieving a mean AUC of 0.779 ± 0.080. The late fusion model and clinical-only CPH model scored second and third highest with 0.746 ± 0.066 and 0.720 ± 0.091 mean AUC, respectively. The performance differences between these three models were not statistically significant. All other comparison models scored significantly worse than the top performing JEPS model.

CONCLUSION: For our OS evaluation, our JEPS fusion architecture achieves better integration of inputs and significantly improves predictive performance over most common multimodal approaches. The JEPS fusion technique is easily applied to any volumetric CNN.

PMID:39928034 | DOI:10.1002/mp.17672

J Chem Inf Model. 2025 Feb 10. doi: 10.1021/acs.jcim.5c00006. Online ahead of print.

ABSTRACT

Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of antimicrobial peptides is both time-consuming and laborious. In recent years, the development of computational technologies (especially deep learning) has provided new opportunities for antimicrobial peptide prediction. Various computational models have been proposed to predict antimicrobial peptide. In this review, we focus on deep learning models for antimicrobial peptide prediction. We first collected and summarized available data resources for antimicrobial peptides. Subsequently, we summarized existing deep learning models for antimicrobial peptides and discussed their limitations and challenges. This study aims to help computational biologists design better deep learning models for antimicrobial peptide prediction.

PMID:39927895 | DOI:10.1021/acs.jcim.5c00006

Echocardiography. 2025 Feb;42(2):e70098. doi: 10.1111/echo.70098.

ABSTRACT

Artificial intelligence (AI) has transformed medical imaging by detecting insights and patterns often imperceptible to the human eye, enhancing diagnostic accuracy and efficiency. In cardiovascular imaging, numerous AI models have been developed for cardiac computed tomography (CCT), a primary tool for assessing coronary artery disease (CAD). CCT provides comprehensive, non-invasive assessment, including plaque burden, stenosis severity, and functional assessments such as CT-derived fractional flow reserve (FFRct). Its prognostic value in predicting major adverse cardiovascular events (MACE) has increased the demand for CCT, consequently adding to radiologists' workloads. This review aims to examine AI's role in CCT for ischemic heart disease, highlighting its potential to streamline workflows and improve the efficiency of cardiac care through machine learning and deep learning applications.

PMID:39927866 | DOI:10.1111/echo.70098

Brief Bioinform. 2024 Nov 22;26(1):bbaf046. doi: 10.1093/bib/bbaf046.

ABSTRACT

The interactions between long noncoding RNA (lncRNA) and microRNA (miRNA) play critical roles in life processes, highlighting the necessity to enhance the performance of state-of-the-art models. Here, we introduced TEC-LncMir, a novel approach for predicting lncRNA-miRNA interaction using Transformer Encoder and convolutional neural networks (CNNs). TEC-LncMir treats lncRNA and miRNA sequences as natural languages, encodes them using the Transformer Encoder, and combines representations of a pair of microRNA and lncRNA into a contact tensor (a three-dimensional array). Afterward, TEC-LncMir treats the contact tensor as a multi-channel image, utilizes a four-layer CNN to extract the contact tensor's features, and then uses these features to predict the interaction between the pair of lncRNA and miRNA. We applied a series of comparative experiments to demonstrate that TEC-LncMir significantly improves lncRNA-miRNA interaction prediction, compared with existing state-of-the-art models. We also trained TEC-LncMir utilizing a large training dataset, and as expected, TEC-LncMir achieves unprecedented performance. Moreover, we integrated miRanda into TEC-LncMir to show the secondary structures of high-confidence interactions. Finally, we utilized TEC-LncMir to identify microRNAs interacting with lncRNA NEAT1, where NEAT1 performs as a competitive endogenous RNA of the microRNAs' targets (mRNAs) in brain cells. We also demonstrated the regulatory mechanism of NEAT1 in Alzheimer's disease via transcriptome analysis and sequence alignment analysis. Overall, our results demonstrate the effectivity of TEC-LncMir, suggest a potential regulation of miRNAs by NEAT1 in Alzheimer's disease, and take a significant step forward in lncRNA-miRNA interaction prediction.

PMID:39927859 | DOI:10.1093/bib/bbaf046

J Chem Inf Model. 2025 Feb 10. doi: 10.1021/acs.jcim.4c01877. Online ahead of print.

ABSTRACT

Nanobodies have emerged as a versatile class of biologics with promising therapeutic applications, driving the need for robust tools to predict their epitopes, a critical step for in silico affinity maturation and epitope-targeted design. While molecular docking has long been employed for epitope identification, it requires substantial expertise. With the advent of AI driven tools, epitope identification has become more accessible to a broader community increasing the risk of models' misinterpretation. In this study, we critically evaluate the nanobody epitope prediction performance of two leading models: AlphaFold3 and AlphaFold2-Multimer (v.2.3.2), highlighting their strengths and limitations. Our analysis revealed that the overall success rate remains below 50% for both tools, with AlphaFold3 achieving a modest overall improvement. Interestingly, a significant improvement in AlphaFold3's performance was observed within a specific nanobody class. To address this discrepancy, we explored factors influencing epitope identification, demonstrating that accuracy heavily depends on CDR3 characteristics, such as its 3D spatial conformation and length, which drive binding interactions with the antigen. Additionally, we assessed the robustness of AlphaFold3's confidence metrics, highlighting their potential for broader applications. Finally, we evaluated different strategies aimed at improving the prediction success rate. This study can be extended to assess the accuracy of emerging deep learning models adopting an approach similar to that of AlphaFold3.

PMID:39927847 | DOI:10.1021/acs.jcim.4c01877

Am J Respir Crit Care Med. 2025 Feb 10. doi: 10.1164/rccm.202411-2157OC. Online ahead of print.

ABSTRACT

RATIONALE: Pulmonary hypertension (PH) commonly complicates idiopathic pulmonary fibrosis (IPF). However, the rate of change in pulmonary hemodynamics in IPF remains poorly defined.

OBJECTIVES: To examine the rate of change in pulmonary hemodynamics among patients with IPF.

METHODS: The rate of change in mean pulmonary artery pressure (mPAP) and pulmonary vascular resistance (PVR) was examined in patients with IPF listed for lung transplantation. The 5th and 7th World Symposium on Pulmonary Hypertension definitions for precapillary PH were employed in this analysis.

MEASUREMENTS AND MAIN RESULTS: There were 496 patients with IPF that had at least two right heart catheterizations (RHCs) while listed for lung transplantation. The median time between repeated RHCs was 9 months (interquartile range [IQR]: 6-14). PH was present in 25.8% and 46.8% at the first RHC, while 42.9% and 64.3% had PH by the two definitions respectively, at the time of the final RHC. The median rate of change in the mPAP and PVR were 3.8 mmHg/year (IQR: -0.9-11.8) and 0.8 Wood Units/year (IQR: -0.2-2.4), respectively. The rate of PVR change was slower for those with established PH compared with those without PH. 28.6% of the patients had accelerated progression of their hemodynamics, arbitrarily defined as an increase in PVR of ≥ 2 Wood Units/year.

CONCLUSIONS: PH associated with IPF tends to progress in an unpredictable fashion, with some patients demonstrating an accelerated phenotype. Among patients with RHC hemodynamics below the threshold for therapy, close vigilance is warranted with consideration for an early repeat RHC. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

PMID:39928362 | DOI:10.1164/rccm.202411-2157OC

Brief Bioinform. 2024 Nov 22;26(1):bbaf033. doi: 10.1093/bib/bbaf033.

ABSTRACT

In single-cell studies, cells can be characterized with multiple sources of heterogeneity (SOH) such as cell type, developmental stage, cell cycle phase, activation state, and so on. In some studies, many nuisance SOH are of no interest, but may confound the identification of the SOH of interest, and thus affect the accurate annotate the corresponding cell subpopulations. In this paper, we develop B-Lightning, a novel and robust method designed to identify marker genes and cell subpopulations corresponding to an SOH (e.g. cell activation status), isolating it from other SOH (e.g. cell type, cell cycle phase). B-Lightning uses an iterative approach to enrich a small set of trustworthy marker genes to more reliable marker genes and boost the signals of the SOH of interest. Multiple numerical and experimental studies showed that B-Lightning outperforms existing methods in terms of sensitivity and robustness in identifying marker genes. Moreover, it increases the power to differentiate cell subpopulations of interest from other heterogeneous cohorts. B-Lightning successfully identified new senescence markers in ciliated cells from human idiopathic pulmonary fibrosis lung tissues, new T-cell memory and effector markers in the context of SARS-COV-2 infections, and their synchronized patterns that were previously neglected, new AD markers that can better differentiate AD severity, and new dendritic cell functioning markers with differential transcriptomics profiles across breast cancer subtypes. This paper highlights B-Lightning's potential as a powerful tool for single-cell data analysis, particularly in complex data sets where SOH of interest are entangled with numerous nuisance factors.

PMID:39927857 | DOI:10.1093/bib/bbaf033

JCI Insight. 2025 Feb 10;10(3):e165837. doi: 10.1172/jci.insight.165837.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is an age-related interstitial lung disease, characterized by inadequate alveolar regeneration and ectopic bronchiolization. While some molecular pathways regulating lung progenitor cells have been described, the role of metabolic pathways in alveolar regeneration is poorly understood. We report that expression of fatty acid oxidation (FAO) genes is significantly diminished in alveolar epithelial cells of IPF lungs by single-cell RNA sequencing and tissue staining. Genetic and pharmacological inhibition in AT2 cells of carnitine palmitoyltransferase 1a (CPT1a), the rate-limiting enzyme of FAO, promoted mitochondrial dysfunction and acquisition of aberrant intermediate states expressing basaloid, and airway secretory cell markers SCGB1A1 and SCGB3A2. Furthermore, mice with deficiency of CPT1a in AT2 cells show enhanced susceptibility to developing lung fibrosis with an accumulation of epithelial cells expressing markers of intermediate cells, airway secretory cells, and senescence. We found that deficiency of CPT1a causes a decrease in SMAD7 protein levels and TGF-β signaling pathway activation. These findings suggest that the mitochondrial FAO metabolic pathway contributes to the regulation of lung progenitor cell repair responses and deficiency of FAO contributes to aberrant lung repair and the development of lung fibrosis.

PMID:39927460 | DOI:10.1172/jci.insight.165837

Neurogenetics. 2025 Feb 10;26(1):27. doi: 10.1007/s10048-025-00806-x.

ABSTRACT

Alzheimer's disease (AD) is a slow brain degeneration disorder in which the accumulation of beta-amyloid precursor plaque and an intracellular neurofibrillary tangle of hyper-phosphorylated tau proteins in the brain have been implicated in neurodegeneration. In this study, we identified the most important genes that are unique and sensitive in the entorhinal region of the brain to target AD effectively. At first, microarrays data are selected and constructed protein-protein interaction network (PPIN) and gene regulatory network (GRN) from differentially expressed genes (DEGs) using Cytoscape software. Then, networks analysis was performed to determine hubs, bottlenecks, clusters, and signaling pathways in AD. Finally, critical genes were selected as targets for repurposing drugs. Analyzing the constructed PPIN and GRN identified CD44, ELF1, HSP90AB1, NOC4L, BYSL, RRP7A, SLC17A6, and RUVBL2 as critical genes that are dysregulated in the entorhinal region of AD suffering patients. The functional enrichment analysis revealed that DEG nodes are involved in the synaptic vesicle cycle, glutamatergic synapse, PI3K-Akt signaling pathway, retrograde endocannabinoid signaling, endocrine and other factor-regulated calcium reabsorption, ribosome biogenesis in eukaryotes, and nicotine addiction. Gentamicin, isoproterenol, and tumor necrosis factor are repurposing new drugs that target CD44, which plays an important role in the development of AD. Following our model validation using the existing experimental data, our model based on previous experimental reports suggested critical molecules and candidate drugs involved in AD for further investigations in vitro and in vivo.

PMID:39928227 | DOI:10.1007/s10048-025-00806-x

J Clin Immunol. 2025 Feb 10;45(1):73. doi: 10.1007/s10875-025-01867-1.

ABSTRACT

Signal transducer and activator of transcription 3 (STAT3) plays a key role in leukocytic and non-leukocytic cells. Germ line mutations in STAT3, which are mainly found in the SH2, DNA binding and transactivation domains, can be loss- or gain-of-function (LOF and GOF). STAT3 N-terminal domain (NTD) mutations are rare, and their biological effects remain incompletely understood. We explored the significance of STAT3 NTD p.Trp37* variant in a patient with chronic pulmonary aspergillosis and a low Hyper-IgE syndrome (HIES) score. In cell culture models, the expression of full-length p.Trp37* allele showed shorter STAT3 protein expression suggesting a re-initiation (Met99 or Met143). STAT3 activity using luciferase reporter assay showed a twofold-increased activity of the STAT3 p.Trp37* STAT3 protein compared with WT STAT3 at basal level and upon IL-6 stimulation. In contrast, the activity of the short pTrp37* peptide (amino acids 1 to 37) was amorphic but without dominant negative (DN) effect on transcriptional activity or STAT3 Tyr705 phosphorylation. The proteins initiated at Met99 and Met143 were surprisingly hypermorphic. In carriers' peripheral blood mononuclear cells (PBMCs), both WT and mutated STAT3 mRNA were equally present and the global amount of STAT3 protein was not significantly reduced. In stimulated heterozygous carriers' PBMCs, however, STAT3 Tyr705 phosphorylation and Th17 were reduced but not completely abolished. This suggests a DN effect of an unknown product of the p.Trp37* allele. Transcriptomics analysis of PBMCs from the index revealed selectively distinct gene expression. We conclude that heterozygosity for the NTD p.Trp37* STAT3 mutation defines a novel allelic form of STAT3 deficiency, associated with a chronic pulmonary aspergillosis and minor signs of HIES.

PMID:39928202 | DOI:10.1007/s10875-025-01867-1

J Cell Biol. 2025 Apr 7;224(4):e202407105. doi: 10.1083/jcb.202407105. Epub 2025 Feb 10.

ABSTRACT

During autophagy, toxic cargo is encapsulated by autophagosomes and trafficked to lysosomes for degradation. NBR1, an autophagy receptor targeting ubiquitinated aggregates, serves as a model for studying the multivalent, heterotypic interactions of cargo-bound receptors. Here, we find that three critical NBR1 partners-ATG8-family proteins, FIP200, and TAX1BP1-each bind to distinct, overlapping determinants within a short linear interaction motif (SLiM). To explore whether overlapping SLiMs extend beyond NBR1, we analyzed >100 LC3-interacting regions (LIRs), revealing that FIP200 and/or TAX1BP1 binding to LIRs is a common phenomenon and suggesting LIRs as protein interaction hotspots. Phosphomimetic peptides demonstrate that phosphorylation generally enhances FIP200 and ATG8-family binding but not TAX1BP1, indicating differential regulation. In vivo, LIR-mediated interactions with TAX1BP1 promote optimal NBR1 flux by leveraging additional functionalities from TAX1BP1. These findings reveal a one-to-many binding modality in the LIR motif of NBR1, illustrating the cooperative mechanisms of autophagy receptors and the regulatory potential of multifunctional SLiMs.

PMID:39928048 | DOI:10.1083/jcb.202407105

Glycobiology. 2025 Feb 10:cwaf005. doi: 10.1093/glycob/cwaf005. Online ahead of print.

ABSTRACT

A key nutrient sensing process in all animal tissues is the dynamic attachment of O-linked N-acetylglucosamine (O-GlcNAc). Determining the targets and roles of O-GlcNAc glycoproteins has the potential to reveal insights into healthy and diseased metabolic states. In cell studies, thousands of proteins are known to be O-GlcNAcylated, but reference datasets for most tissue types in animals are lacking. Here, we apply a chemoenzymatic labeling study to compile a high coverage dataset of quadriceps skeletal muscle O-GlcNAc glycoproteins from mice. Our dataset contains over 550 proteins, and > 80% of the dataset matched known O-GlcNAc proteins. This dataset was further annotated via bioinformatics, revealing the distribution, protein interactions, and gene ontology (GO) functions of these skeletal muscle proteins. We compared these quadriceps glycoproteins with a high-coverage O-GlcNAc enrichment profile from mouse hearts and describe the key overlap and differences between these tissue types. Quadriceps muscles can be used for biopsies, so we envision this dataset to have potential biomedical relevance in detecting aberrant glycoproteins in metabolic diseases and physiological studies. This new knowledge adds to the growing collection of tissues with high-coverage O-GlcNAc profiles, which we anticipate will further the systems biology of O-GlcNAc mechanisms, functions, and roles in disease.

PMID:39927985 | DOI:10.1093/glycob/cwaf005

Brief Bioinform. 2024 Nov 22;26(1):bbaf060. doi: 10.1093/bib/bbaf060.

NO ABSTRACT

PMID:39927860 | DOI:10.1093/bib/bbaf060