Afr J Thorac Crit Care Med. 2024 Dec 11;30(4):e2884. doi: 10.7196/AJTCCM.2024.v30i4.2884. eCollection 2024.

NO ABSTRACT

PMID:40041417 | PMC:PMC11874178 | DOI:10.7196/AJTCCM.2024.v30i4.2884

ERJ Open Res. 2025 Mar 3;11(2):00701-2024. doi: 10.1183/23120541.00701-2024. eCollection 2025 Mar.

ABSTRACT

CFTR modulators may be valuable therapy for patients with ABCA3 pathogenic variants https://bit.ly/3TMWKK9.

PMID:40040902 | PMC:PMC11874218 | DOI:10.1183/23120541.00701-2024

iScience. 2025 Feb 1;28(3):111942. doi: 10.1016/j.isci.2025.111942. eCollection 2025 Mar 21.

ABSTRACT

Cystic fibrosis (CF) is a life-shortening disease affecting >160,000 individuals worldwide predominantly with respiratory symptoms. About 80% of individuals with CF have the p.Phe508del variant that causes the CF transmembrane conductance regulator (CFTR) protein to misfold and be targeted for premature degradation by the endoplasmic reticulum (ER) quality control (ERQC), thus preventing its plasma membrane (PM) traffic. Despite the recent approval of a "highly effective" drug rescuing p.Phe508del-CFTR, maximal lung function improvement is ∼14%. To identify global modulators of p.Phe508del traffic, we performed a high-content small interfering RNA (siRNA) microscopy-based screen of >9,000 genes and monitored p.Phe508del-CFTR PM rescue in human airway cells. This primary screen identified 227 p.Phe508del-CFTR traffic regulators, of which 35 could be validated by additional siRNAs. Subsequent mechanistic studies established GRK5 as a robust regulator whose inhibition rescues p.Phe508del-CFTR PM traffic and function in primary and immortalized cells, thus emerging as a novel potential drug target for CF.

PMID:40040803 | PMC:PMC11876911 | DOI:10.1016/j.isci.2025.111942

Genome Med. 2025 Mar 4;17(1):19. doi: 10.1186/s13073-025-01443-7.

ABSTRACT

BACKGROUND: Non-tuberculous mycobacteria (NTM) are a diverse group of environmental bacteria that are increasingly associated with human infections and difficult to treat. Plasmids, which might carry resistance and virulence factors, remain largely unexplored in NTM.

METHODS: We used publicly available complete genome sequence data of 328 NTM isolates belonging to 125 species to study gene content, genomic diversity, and clusters of 196 annotated NTM plasmids. Furthermore, we analyzed 3755 draft genome assemblies from over 200 NTM species and 5415 short-read sequence datasets from six clinically relevant NTM species or complexes including M. abscessus, M. avium complex, M. ulcerans complex and M. kansasii complex, for the presence of these plasmids.

RESULTS: Between one and five plasmids were present in approximately one-third of the complete NTM genomes. The annotated plasmids varied widely in length (most between 10 and 400 kbp) and gene content, with many genes having an unknown function. Predicted gene functions primarily involved plasmid replication, segregation, maintenance, and mobility. Only a few plasmids contained predicted genes that are known to confer resistance to antibiotics commonly used to treat NTM infections. Out of 196 annotated plasmid sequences, 116 could be grouped into 31 clusters of closely related sequences, and about one-third were found across multiple NTM species. Among clinically relevant NTM, the presence of NTM plasmids showed significant variation between species, within (sub)species, and even among strains within (sub)lineages, such as dominant circulating clones of Mycobacterium abscessus.

CONCLUSIONS: Our analysis demonstrates that plasmids are a diverse and heterogeneously distributed feature in NTM bacteria. The frequent occurrence of closely related putative plasmid sequences across different NTM species suggests they may play a significant role in NTM evolution through horizontal gene transfer at least in some groups of NTM. However, further in vitro investigations and access to more complete genomes are necessary to validate our findings, elucidate gene functions, identify novel plasmids, and comprehensively assess the role of plasmids in NTM.

PMID:40038805 | DOI:10.1186/s13073-025-01443-7

Patterns (N Y). 2025 Feb 6;6(2):101177. doi: 10.1016/j.patter.2025.101177. eCollection 2025 Feb 14.

ABSTRACT

Balancing prediction accuracy, model interpretability, and domain generalization (also known as [a.k.a.] out-of-distribution testing/evaluation) is a central challenge in machine learning. To assess this challenge, we took 120 interpretable and 166 opaque models from 77,640 tuned configurations, complemented with ChatGPT, 3 probabilistic language models, and Vec2Read. The models first performed text classification to derive principles of textual complexity (task 1) and then generalized these to predict readers' appraisals of processing difficulty (task 2). The results confirmed the known accuracy-interpretability trade-off on task 1. However, task 2's domain generalization showed that interpretable models outperform complex, opaque models. Multiplicative interactions further improved interpretable models' domain generalization incrementally. We advocate for the value of big data for training, complemented by (1) external theories to enhance interpretability and guide machine learning and (2) small, well-crafted out-of-distribution data to validate models-together ensuring domain generalization and robustness against data shifts.

PMID:40041855 | PMC:PMC11873011 | DOI:10.1016/j.patter.2025.101177

Health Sci Rep. 2025 Mar 2;8(3):e70498. doi: 10.1002/hsr2.70498. eCollection 2025 Mar.

ABSTRACT

BACKGROUND AND AIMS: The increasing elderly population presents significant challenges for healthcare systems, necessitating innovative solutions for continuous health monitoring. This study develops and validates an IoT-based elderly monitoring system designed to enhance the quality of life for elderly people. The system features a robust Android-based user interface integrated with the Firebase cloud platform, ensuring real-time data collection and analysis. In addition, a supervised machine learning technology is implemented to conduct prediction task of the observed user whether in "stable" or "not stable" condition based on real-time parameter.

METHODS: The system architecture adopts the IoT layer including physical layer, network layer, and application layer. Device validation is conducted by involving six participants to measure the real-time data of heart-rate, oxygen saturation, and body temperature, then analysed by mean average percentage error (MAPE) to define error rate. A comparative experiment is conducted to define the optimal supervised machine learning model to be deployed into the system by analysing evaluation metrics. Meanwhile, the user satisfaction aspect evaluated by the terms of usability, comfort, security, and effectiveness.

RESULTS: IoT-based elderly health monitoring system has been constructed with a MAPE of 0.90% across the parameters: heart-rate (1.68%), oxygen saturation (0.57%), and body temperature (0.44%). In machine learning experiment indicates XGBoost model has the optimal performance based on the evaluation metrics of accuracy and F1 score which generates 0.973 and 0.970, respectively. In user satisfaction aspect based on usability, comfort, security, and effectiveness achieving a high rating of 86.55%.

CONCLUSION: This system offers practical applications for both elderly users and caregivers, enabling real-time monitoring of health conditions. Future enhancements may include integration with artificial intelligence technologies such as machine learning and deep learning to predict health conditions from data patterns, further improving the system's capabilities and effectiveness in elderly care.

PMID:40041774 | PMC:PMC11873372 | DOI:10.1002/hsr2.70498

PNAS Nexus. 2025 Feb 20;4(2):pgaf054. doi: 10.1093/pnasnexus/pgaf054. eCollection 2025 Feb.

ABSTRACT

Discovering manufacturing processes has been largely experienced-based. We propose a shift to a systematic approach driven by dependencies between energy inputs and performance outputs. Uncovering these dependencies across diverse process classes requires a universal language that characterizes process inputs and performances. Traditional manufacturing languages, with their individualized syntax and terminology, hinder the characterization across varying length scales and energy inputs. To enable the evaluation of process dependencies, we propose a broad manufacturing language that facilitates the characterization of diverse process classes, which include energy inputs, tool-material interactions, material compatibility, and performance outputs. We analyze the relationships between these characteristics by constructing a dataset of over 50 process classes, which we use to train a variational autoencoder (VAE) model. This generative model encodes our dataset into a 2D latent space, where we can explore, select, and generate processes based on desired performances and retrieve the corresponding process characteristics. After verifying the dependencies derived from the VAE model match with existing knowledge on manufacturing processes, we demonstrate the usefulness of using the model to discover new potential manufacturing processes through three illustrative cases.

PMID:40041620 | PMC:PMC11878556 | DOI:10.1093/pnasnexus/pgaf054

Hortic Res. 2024 Nov 20;12(2):uhae319. doi: 10.1093/hr/uhae319. eCollection 2025 Feb.

ABSTRACT

Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions. Methodological advances allow effective integration of phenotypic, genomic (additive, nonadditive), and large-scale environmental (enviromic) data into multi-environmental genomic prediction models. These models can also account for genotype-by-environment interaction, utilize alternative relationship matrices (kernels), or substitute statistical approaches with deep learning. However, the application of multi-environmental genomic prediction in apple remained limited, likely due to the challenge of building multi-environmental datasets and structurally complex models. Here, we applied efficient statistical and deep learning models for multi-environmental genomic prediction of eleven apple traits with contrasting genetic architectures by integrating genomic- and enviromic-based model components. Incorporating genotype-by-environment interaction effects into statistical models improved predictive ability by up to 0.08 for nine traits compared to the benchmark model. This outcome, based on Gaussian and Deep kernels, shows these alternatives can effectively substitute the standard genomic best linear unbiased predictor (G-BLUP). Including nonadditive and enviromic-based effects resulted in a predictive ability very similar to the benchmark model. The deep learning approach achieved the highest predictive ability for three traits with oligogenic genetic architectures, outperforming the benchmark by up to 0.10. Our results demonstrate that the tested statistical models capture genotype-by-environment interactions particularly well, and the deep learning models efficiently integrate data from diverse sources. This study will foster the adoption of multi-environmental genomic prediction to select apple cultivars adapted to diverse environmental conditions, providing an opportunity to address climate change impacts.

PMID:40041603 | PMC:PMC11879405 | DOI:10.1093/hr/uhae319

Comput Struct Biotechnol J. 2025 Feb 14;27:631-638. doi: 10.1016/j.csbj.2025.02.007. eCollection 2025.

ABSTRACT

DNA methylations, such as 5-methylcytosine (5mC), are crucial in biological processes, and aberrant methylations are strongly linked to various human diseases. Genomic 5mC is not randomly distributed but exhibits a strong association with genomic sequences. Thus, various computational methods were developed to predict 5mC status based on DNA sequences. These methods generated promising achievements and overcome the limitations of experimental approaches. However, few studies have comprehensively investigated the dependency of 5mC on genomic sequences, and most existing methods focus on specific genomic regions. In this work, we introduce Deep5mC, a deep learning transformer-based method designed to predict 5mC methylations. Deep5mC leverages long-range dependencies within genomic sequences to estimate the probability of cytosine methylations. Through cross-chromosome evaluation, Deep5mC achieves Matthew's correlation coefficient over 0.86 and F1-score over 0.93, substantially outperforming state-of-the-art methods. Deep5mC not only confirms the influence of long-range sequence context on 5mC prediction but also paves the way for further studying 5mC-sequence dependency across species and in human diseases.

PMID:40041569 | PMC:PMC11879672 | DOI:10.1016/j.csbj.2025.02.007

Front Med (Lausanne). 2025 Feb 18;12:1561485. doi: 10.3389/fmed.2025.1561485. eCollection 2025.

NO ABSTRACT

PMID:40041465 | PMC:PMC11876553 | DOI:10.3389/fmed.2025.1561485

Proc Mach Learn Res. 2024 Jun;248:182-197.

ABSTRACT

Electronic Health Records (EHRs) contain rich patient information and are crucial for clinical research and practice. In recent years, deep learning models have been applied to EHRs, but they often rely on massive features, which may not be readily available for all patients. We propose HTP-Star, which leverages hypergraph structures with a pretrain-then-finetune framework for modeling EHR data, enabling seamless integration of additional features. Additionally, we design two techniques, namely (1) Smoothness-inducing Regularization and (2) Group-balanced Reweighting, to enhance the model's robustness during finetuning. Through experiments conducted on two real EHR datasets, we demonstrate that HTP-Star consistently outperforms various baselines while striking a balance between patients with basic and extra features.

PMID:40041452 | PMC:PMC11876795

Proc IEEE Int Conf Big Data. 2024 Dec;2024:4941-4945. doi: 10.1109/bigdata62323.2024.10825319.

ABSTRACT

Predicting seizures ahead of time will have a significant positive clinical impact for people with epilepsy. Advances in machine learning/artificial intelligence (ML/AI) has provided us the tools needed to perform such predictive tasks. To date, advanced deep learning (DL) architectures such as the convolutional neural network (CNN) and long short-term memory (LSTM) have been used with mixed results. However, highly connected activity exhibited by epileptic seizures necessitates the design of more complex ML techniques which can better capture the complex interconnected neurological processes. Other challenges include the variability of EEG sensor data quality, different epilepsy and seizure profiles, lack of annotated datasets and absence of ML-ready benchmarks. In addition, successful models will need to perform inference in almost real-time using limited hardware compute-capacity. To address these challenges, we propose a lightweight architecture, called ESPFormer, whose novelty lies in the simple and smaller model-size and a lower computational load footprint needed to infer in real-time compared to other works in the literature. To quantify the performance of this lightweight model, we compared its performance with a custom-designed residual neural network (ResNet), a pre-trained vision transformer (ViT) and a pre-trained large-language model (LLM). We tested ESPFormer on MLSPred-Bench which is the largest patient-independent seizure prediction dataset comprising 12 benchmarks. Our results demonstrate that ESPFormer provides the best performance in terms of prediction accuracy for 4/12 benchmarks with an average improvement of 2.65% compared to the LLM, 3.35% compared to the ViT and 17.65% compared to the ResNet - and comparable results for other benchmarks. Our results indicate that lightweight transformer architecture may outperform resource-intensive LLM based models for real-time EEG-based seizure predictions.

PMID:40041397 | PMC:PMC11877310 | DOI:10.1109/bigdata62323.2024.10825319

Front Plant Sci. 2025 Feb 18;16:1527508. doi: 10.3389/fpls.2025.1527508. eCollection 2025.

ABSTRACT

Currently, potato defect sorting primarily relies on manual labor, which is not only inefficient but also prone to bias. Although automated sorting systems offer a potential solution by integrating potato detection models, real-time performance remains challenging due to the need to balance high accuracy and speed under limited resources. This study presents an enhanced version of the YOLO v5s model, named YOLO v5s-ours, specifically designed for real-time detection of potato defects. By integrating Coordinate Attention (CA), Adaptive Spatial Feature Fusion (ASFF), and Atrous Spatial Pyramid Pooling (ASPP) modules, the model significantly improves detection accuracy while maintaining computational efficiency. The model achieved 82.0% precision, 86.6% recall, 84.3% F1-Score and 85.1% mean average precision across six categories - healthy, greening, sprouting, scab, mechanical damage, and rot - marking improvements of 24.6%, 10.5%, 19.4%, and 13.7%, respectively, over the baseline model. Although memory usage increased from 13.7 MB to 23.3 MB and frame rate slightly decreased to 30.7 fps, the accuracy gains ensure the model's suitability for practical applications. The research provides significant support for the development of automated potato sorting systems, advancing agricultural efficiency, particularly in real-time applications, by overcoming the limitations of traditional methods.

PMID:40041023 | PMC:PMC11876418 | DOI:10.3389/fpls.2025.1527508

Front Plant Sci. 2025 Feb 18;16:1539068. doi: 10.3389/fpls.2025.1539068. eCollection 2025.

ABSTRACT

Intelligent and accurate evaluation of KASP primer typing effect is crucial for large-scale screening of excellent markers in molecular marker-assisted breeding. However, the efficiency of both manual discrimination methods and existing algorithms is limited and cannot match the development speed of molecular markers. To address the above problems, we proposed a typing evaluation method for KASP primers by integrating deep learning and traditional machine learning algorithms, called TAL-SRX. First, three algorithms are used to optimize the performance of each model in the Stacking framework respectively, and five-fold cross-validation is used to enhance stability. Then, a hybrid neural network is constructed by combining ANN and LSTM to capture nonlinear relationships and extract complex features, while the Transformer algorithm is introduced to capture global dependencies in high-dimensional feature space. Finally, the two machine learning algorithms are fused through a soft voting integration strategy to output the KASP marker typing effect scores. In this paper, the performance of the model was tested using the KASP test results of 3399 groups of cotton variety resource materials, with an accuracy of 92.83% and an AUC value of 0.9905, indicating that the method has high accuracy, consistency and stability, and the overall performance is better than that of a single model. The performance of the TAL-SRX method is the best when compared with the different integrated combinations of methods. In summary, the TAL-SRX model has good evaluation performance and is very suitable for providing technical support for molecular marker-assisted breeding and other work.

PMID:40041015 | PMC:PMC11876144 | DOI:10.3389/fpls.2025.1539068

ERJ Open Res. 2025 Mar 3;11(2):00823-2024. doi: 10.1183/23120541.00823-2024. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: With the introduction of the antifibrotic drugs targeting progressive pulmonary fibroses, it becomes imperative to provide reliable contemporary estimates of the most common interstitial lung diseases. We aimed to provide contemporary estimates of the incidence and survival of idiopathic pulmonary fibrosis (IPF), hypersensitivity pneumonitis (HP) and connective tissue disease-associated interstitial lung disease (CTD-ILDs), and to compare their survival to that of the general population. To do this we have used data extracted from the Optimum Patient Care Research Database (OPCRD).

METHODS: In this matched cohort study, we extracted incident cases of HP, CTD-ILD and IPF, and age and sex matched controls for each case, for the years 2010-2019. We calculated annual incidence rates and analysed incidence trends over time using segmented regression modelling. We estimated survival for cases and controls using the Kaplan-Meier model.

RESULTS: We extracted data for 18 914 incident cases of interstitial lung diseases between 2010 and 2019 from the OPRCD. Incidence rates varied across the different diseases, with rates of 18.12, 7.96 and 2.63 per 100 000 person-years for IPF, CTD-ILD and HP, respectively. 5-year survival for IPF, CTD-ILD and HP was 40%, 54% and 66%, respectively, and this was generally ∼50% lower than that of the general population.

CONCLUSION: Our population-based study emphasises the considerable burden of interstitial lung diseases, with >20 000 new cases diagnosed each year in the UK, many of whom will be eligible for antifibrotic drugs.

PMID:40040895 | PMC:PMC11874205 | DOI:10.1183/23120541.00823-2024

ERJ Open Res. 2025 Mar 3;11(2):00899-2024. doi: 10.1183/23120541.00899-2024. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: Suspected interstitial lung disease (ILD) patients may be referred to an ILD-specialist centre or a non-ILD-specialist centre for diagnosis and treatment. Early referral and management of patients at ILD-specialist centres has been shown to improve survival and reduce hospitalisations. The COVID-19 pandemic has affected the ILD patient diagnostic pathway and prompted centres to adapt. This study investigates and contrasts ILD patient pathways in ILD-specialist and non-ILD-specialist centres, focusing on referrals, caseloads, diagnostic tools, multi-disciplinary team (MDT) meeting practices and resource accessibility.

METHODS: Conducted as a cross-sectional study, a global self-selecting survey ran from September 2022 to January 2023. Participants included ILD specialists and healthcare professionals (HCPs) from ILD-specialist centres and non-ILD-specialist centres.

RESULTS: Of 363 unique respondents from 64 countries, 259 were from ILD-specialist centres and 104 from non-ILD-specialist centres. ILD centres had better resource availability, exhibiting higher utilisation of diagnostic tests (median: 12 tests) than non-ILD centres (nine tests) and better access to specialist professions attending MDT meetings (median: six professions at meeting) in specialist centres than non-ILD centres (three professions at meeting). Transitioning to virtual MDT meetings allowed HCPs from other locations to join meetings in nearly 90% of all centres, increasing regular participation in 60% of specialist centres and 72% of non-ILD centres. For treatment of patients, specialist centres had better access to antifibrotic drugs (91%) compared to non-ILD centres (60%).

CONCLUSIONS: Diagnostic pathways for ILD patients diverged between specialist centres and non-ILD centres. Disparities in resource and specialist availability existed between centres.

PMID:40040894 | PMC:PMC11874298 | DOI:10.1183/23120541.00899-2024

Acta Pharm Sin B. 2025 Jan;15(1):133-150. doi: 10.1016/j.apsb.2024.08.017. Epub 2024 Aug 24.

ABSTRACT

Pharmacotranscriptomic profiles, which capture drug-induced changes in gene expression, offer vast potential for computational drug discovery and are widely used in modern medicine. However, current computational approaches neglected the associations within gene‒gene functional networks and unrevealed the systematic relationship between drug efficacy and the reversal effect. Here, we developed a new genome-scale functional module (GSFM) transformation framework to quantitatively evaluate drug efficacy for in silico drug discovery. GSFM employs four biologically interpretable quantifiers: GSFM_Up, GSFM_Down, GSFM_ssGSEA, and GSFM_TF to comprehensively evaluate the multi-dimension activities of each functional module (FM) at gene-level, pathway-level, and transcriptional regulatory network-level. Through a data transformation strategy, GSFM effectively converts noisy and potentially unreliable gene expression data into a more dependable FM active matrix, significantly outperforming other methods in terms of both robustness and accuracy. Besides, we found a positive correlation between RSGSFM and drug efficacy, suggesting that RSGSFM could serve as representative measure of drug efficacy. Furthermore, we identified WYE-354, perhexiline, and NTNCB as candidate therapeutic agents for the treatment of breast-invasive carcinoma, lung adenocarcinoma, and castration-resistant prostate cancer, respectively. The results from in vitro and in vivo experiments have validated that all identified compounds exhibit potent anti-tumor effects, providing proof-of-concept for our computational approach.

PMID:40041913 | PMC:PMC11873659 | DOI:10.1016/j.apsb.2024.08.017

Front Endocrinol (Lausanne). 2025 Feb 18;16:1472754. doi: 10.3389/fendo.2025.1472754. eCollection 2025.

ABSTRACT

INTRODUCTION: Endocrine system disorders are a serious public health burden and can be caused by deleterious genetic variants in single genes or by the combined effects of multiple variants along with environmental and lifestyle factors.

METHODS: The EndoGene database presents the results of next-generation sequencing assays used to genetically profile 5,926 patients who were diagnosed with 450 endocrine and concomitant diseases and were examined and treated at the National Medical Research Center for Endocrinology between November 2017 and January 2024. Among them, 494, 1,785, 692, and 1,941 patients were profiled using four internally developed genetic panels including 220, 250, 376, and 382 genes, respectively, selected based on a literature analysis and clinical recommendations, and 1,245 patients were profiled by whole exome sequencing covering 31,969 genes.

RESULTS: 2,711 genetic variants were reported as clinically relevant by medical geneticists and are presented here along with genomic, technical, and clinical annotations.

DISCUSSION: This publicly accessible database will be useful to those interested in genetics, epidemiology, population statistics, and a better understanding of the molecular basis of endocrine disorders.

PMID:40041282 | PMC:PMC11876052 | DOI:10.3389/fendo.2025.1472754

Heliyon. 2025 Feb 14;11(4):e42719. doi: 10.1016/j.heliyon.2025.e42719. eCollection 2025 Feb 28.

ABSTRACT

Preventing exacerbations in Chronic Obstructive Pulmonary Disease (COPD) is crucial due to the high mortality rate and the associated costs of hospitalization for patients during exacerbations. Despite the proven influence of the lung microbiome on disease control, the dynamics of bacterial communication in different stages of COPD remain unknown. This study aimed to propose a group of candidate bacteria for the differential diagnosis of different states of COPD based on the relative abundance correlation of bacteria in lung sputum samples. We compared microbiome data collected from 101 COPD patients in stable and exacerbation states, as well as 124 healthy controls from two separate general cohorts, to determine the major microbiome and keystone genera. To validate our findings, we utilized two additional distinct public datasets, each comprising 81 healthy subjects and 87 COPD patients in stable condition, exacerbation, and post-treatment phases. During COPD exacerbation, Porphyromonas, Clostridium, Moryella, and Megasphaera were identified as phenotype-specific keystone genera, while Prevotella, Streptococcus, Haemophilus, and Veillonella were consistently present across all datasets as core microbiome members. Changes in keystone genera during different COPD stages indicate rewiring of bacterial interactions, with increased keystone bacteria and network connectivity observed during dysbiosis and more severe COPD. Bifidobacterium showed probiotic potential, positively correlating with Lactobacillus during exacerbation, while Neisseria and Haemophilus increased in abundance, and negatively correlated with key probiotic bacteria. These findings indicate promising potential for the simultaneous use of Bifidobacterium along with Lactobacillus as a therapeutic candidate to prevent COPD exacerbations in lung health, underscoring the need for further research in future clinical studies.

PMID:40040961 | PMC:PMC11876909 | DOI:10.1016/j.heliyon.2025.e42719

iScience. 2025 Feb 5;28(3):111961. doi: 10.1016/j.isci.2025.111961. eCollection 2025 Mar 21.

ABSTRACT

Existing research has proven difficult to understand the interplay between upstream signaling events during NLRP3 inflammasome activation. Additionally, events downstream of inflammasome complex formation such as cytokine release and pyroptosis can exhibit variation, further complicating matters. Cell Painting has emerged as a prominent tool for unbiased evaluation of the effect of perturbations on cell morphological phenotypes. Using this technique, phenotypic fingerprints can be generated that reveal connections between phenotypes and possible modes of action. To the best of our knowledge, this was the first study that utilized Cell Painting on human THP-1 macrophages to generate phenotypic fingerprints in response to different endogenous and exogenous NLRP3 inflammasome triggers and to identify phenotypic features specific to NLRP3 inflammasome complex formation. Our results demonstrated that not only can Cell Painting generate morphological fingerprints that are NLRP3 trigger-specific but it can also identify cellular fingerprints associated with NLRP3 inflammasome activation.

PMID:40040812 | PMC:PMC11876907 | DOI:10.1016/j.isci.2025.111961