Neuron. 2025 Feb 25:S0896-6273(25)00076-5. doi: 10.1016/j.neuron.2025.01.028. Online ahead of print.

ABSTRACT

In this NeuroView, we discuss challenges and best practices when dealing with disease-detection AI models that are trained on heterogeneous clinical data, focusing on the interrelated problems of model bias, causality, and rare diseases.

PMID:40037359 | DOI:10.1016/j.neuron.2025.01.028

Cell Host Microbe. 2025 Feb 26:S1931-3128(25)00053-8. doi: 10.1016/j.chom.2025.02.005. Online ahead of print.

ABSTRACT

Diarrhea is the predominant symptom of acute gastroenteritis resulting from enteric infections and a leading cause of death in infants and young children. However, the role of the host response in diarrhea pathogenesis is unclear. Using rotavirus and neonatal mice as a model, we found that oral inoculation of UV-inactivated replication-defective rotavirus consistently induced watery diarrhea by robust activation of cytosolic double-stranded RNA sensing pathways and type III interferon (IFN-λ) secretion. Diarrhea was significantly diminished in mice lacking the IFN-λ receptor. Mechanistically, IFN-λ signaling downregulates the expression of Dra, a chloride and bicarbonate exchanger, which contributes to reduced water absorption. We confirmed these findings in mice inoculated with reovirus, as well as in donor-derived human intestinal organoids and human biopsy samples. Our data highlight a mechanism of rapid diarrhea induction by host innate immune sensing in the gastrointestinal tract and suggest that diarrhea induction is an active host defense strategy to eliminate the pathogen.

PMID:40037352 | DOI:10.1016/j.chom.2025.02.005

Int J Psychiatry Med. 2025 Mar 4:912174251326009. doi: 10.1177/00912174251326009. Online ahead of print.

ABSTRACT

BACKGROUND: Depression is prevalent among individuals with chronic kidney disease (CKD) and those undergoing dialysis, with significant impacts on morbidity and mortality. This systematic review and meta-analysis was done to evaluate the efficacy of psychosocial interventions in managing depressive symptoms in patients with CKD.

METHODS: This systematic review and meta-analysis adhered to PRISMA guidelines. A literature search was conducted across PubMed, Embase, Google Scholar, and Cochrane Library databases from January 2007 to July 2024. Randomized controlled trials (RCTs) investigating psychosocial interventions in CKD patients (Stage 4 or 5 or on hemodialysis) were included. The primary outcome was the change in depressive symptoms, measured by standardized clinical tools. Quality of life was a secondary outcome. Data extraction and bias assessment were conducted using ROB-2 and GRADEpro GDT tools.

RESULTS: Twelve studies with a total of 792 participants (420 in the intervention group and 372 in the control group) were included. Cognitive-behavioral therapy (CBT) was the most common intervention. Psychosocial interventions significantly reduced depressive symptoms compared to routine care (mean difference [MD]: -4.22; 95% CI: -6.67, -1.76; P = 0.0008). High heterogeneity (I2 = 89%) was noted. Sensitivity analysis confirmed the robustness of the results. The impact on quality of life was not statistically significant (MD: 0.94; 95% CI: -0.61, 2.49; P = 0.24).

CONCLUSIONS: Moderate-quality evidence suggests that psychosocial interventions effectively reduce depressive symptoms in CKD patients. While no significant improvement in quality of life was observed, these interventions provide an alternative to pharmacological treatments, potentially minimizing drug-related side effects.

PMID:40037371 | DOI:10.1177/00912174251326009

IEEE J Biomed Health Inform. 2025 Feb 21;PP. doi: 10.1109/JBHI.2025.3542784. Online ahead of print.

ABSTRACT

The research on identifying drug-disease associations (DDAs) is widely used in scenarios such as drug development, clinical decision-making, and drug repurposing, holding significant biological and medical significance. Existing methods for drug-disease association prediction have achieved decent performance, they primarily rely on simplistic drug-disease association graphs or similarity graphs. These methods often struggle to capture the high-order correlations of complex multimodal data, limiting their ability to handle the complexity of data associations effectively. In addition, real drug-disease associations are highly sparse, posing a significant challenge to prediction accuracy. To tackle these issues, we propose a general hypergraph neural network framework for drug-disease association prediction based on hierarchical contrastive learning and cross-attention learning. It leverages hypergraph neural networks to learn representations of drugs and diseases carrying high-order correlations and strengthens representation quality using interactive attention learning and hierarchical contrastive learning. Meanwhile, the -weighted loss function is utilized to adapt to the high sparsity property of real drug-disease associations during model training and improve prediction performance. Extensive experiments demonstrate that DD-HGNN surpasses other state-of-the-art methods in predicting drug-disease associations and further validation through case studies on Leukemia and Colorectal Neoplasms underscores its reliability.

PMID:40036538 | DOI:10.1109/JBHI.2025.3542784

J Infect Dis. 2025 Feb 26:jiaf098. doi: 10.1093/infdis/jiaf098. Online ahead of print.

ABSTRACT

Dolutegravir treatment can lead to neuropsychiatric adverse events (NPAE). This study assessed the association between NPAE and polymorphisms in dolutegravir-related pharmacogenes, determined by next-generation sequencing panel testing. Using a case-control design, 36 patients having previously discontinued dolutegravir due to NPAE were compared to 98 patients tolerating dolutegravir. In the latter group, psychometric scores were compared according to genotype, targeting polymorphisms associated with drug intolerance. NR1I2 c.-22-7659C>T was independently associated with a reduced risk of NPAE-related dolutegravir discontinuation (odds ratio of 0.36 [95% confidence interval, .15-.88] for T-variant allele carriage) and was linked to decreased anxiety scores in control-group participants.

PMID:40036182 | DOI:10.1093/infdis/jiaf098

PLoS One. 2025 Mar 4;20(3):e0318085. doi: 10.1371/journal.pone.0318085. eCollection 2025.

ABSTRACT

BACKGROUND: Current diagnostic tools are limited in their ability to diagnose cystic fibrosis liver disease (CFLD) as disease is often focal in nature. Magnetic resonance extracellular volume quantification (MRI ECV) in the liver may have diagnostic utility in CFLD as a more selective liver volume is assessed and can be performed using equipment readily available in clinical practice on a standard MRI protocol.

METHODS: Healthy volunteers (HV), CF participants with no liver disease (CF-noLD) and CF participants with cirrhosis (CF-C) aged 18 years and above had MRI ECV measured using a 3T Siemens scanner. An additional retrospective analysis was performed to calculate MRI ECV in individuals who had available images obtained using a 1.5T Siemens scanner from a previous study.

RESULTS: 16 individuals had MRI ECV measured using a 3T Siemens scanner. Mean (SD) MRI ECV was 0.316 (0.058) for HV (n = 5), 0.297 (0.034) for CF-noLD (n = 5) and 0.388 (0.067) for CF-C (n = 6 ). Post-hoc analysis showed a significant difference between CF-noLD and CF-C (p = 0.046). Of 18 individuals with available images using a 1.5T scanner, mean (SD) MRI ECV was 0.269 (0.048) in HV (n = 8), 0.310 (0.037) in CF-noLD (n = 8) and 0.362 (0.063) in CF-C (n = 2).

CONCLUSIONS: Liver MRI ECV quantification was feasible in adults with CF with no significant difference in results between 1.5T and 3T obtained images suggesting applicability across different types of MRI scanner. A higher MRI ECV was demonstrated in CF participants with cirrhosis suggesting potential utility as a diagnostic tool for those with advanced CFLD. Further evaluation in larger cohorts is warranted.

PMID:40036270 | DOI:10.1371/journal.pone.0318085

J Mech Behav Biomed Mater. 2025 Feb 25;166:106949. doi: 10.1016/j.jmbbm.2025.106949. Online ahead of print.

ABSTRACT

The role of additive manufacturing (AM) for healthcare applications is growing, particularly in the aspiration to meet subject-specific requirements. This article reviews the application of artificial intelligence (AI) to enhance pre-, during-, and post-AM processes to meet a wider range of subject-specific requirements of healthcare interventions. This article introduces common AM processes and AI tools, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. The role of AI in pre-processing is described in the core dimensions like structural design and image reconstruction, material design and formulations, and processing parameters. The role of AI in a printing process is described based on hardware specifications, printing configurations, and core operational parameters such as temperature. Likewise, the post-processing describes the role of AI for surface finishing, dimensional accuracy, curing processes, and a relationship between AM processes and bioactivity. The later sections provide detailed scientometric studies, thematic evaluation of the subject topic, and also reflect on AI ethics in AM for biomedical applications. This review article perceives AI as a robust and powerful tool for AM of biomedical products. From tissue engineering (TE) to prosthesis, lab-on-chip to organs-on-a-chip, and additive biofabrication for range of products; AI holds a high potential to screen desired process-property-performance relationships for resource-efficient pre- to post-AM cycle to develop high-quality healthcare products with enhanced subject-specific compliance specification.

PMID:40036906 | DOI:10.1016/j.jmbbm.2025.106949

Gigascience. 2025 Jan 6;14:giaf008. doi: 10.1093/gigascience/giaf008.

ABSTRACT

BACKGROUND: Precise prediction of epitope presentation on human leukocyte antigen (HLA) molecules is crucial for advancing vaccine development and immunotherapy. Conventional HLA-peptide binding affinity prediction tools often focus on specific alleles and lack a universal approach for comprehensive HLA site analysis. This limitation hinders efficient filtering of invalid peptide segments.

RESULTS: We introduce TransHLA, a pioneering tool designed for epitope prediction across all HLA alleles, integrating Transformer and Residue CNN architectures. TransHLA utilizes the ESM2 large language model for sequence and structure embeddings, achieving high predictive accuracy. For HLA class I, it reaches an accuracy of 84.72% and an area under the curve (AUC) of 91.95% on IEDB test data. For HLA class II, it achieves 79.94% accuracy and an AUC of 88.14%. Our case studies using datasets like CEDAR and VDJdb demonstrate that TransHLA surpasses existing models in specificity and sensitivity for identifying immunogenic epitopes and neoepitopes.

CONCLUSIONS: TransHLA significantly enhances vaccine design and immunotherapy by efficiently identifying broadly reactive peptides. Our resources, including data and code, are publicly accessible at https://github.com/SkywalkerLuke/TransHLA.

PMID:40036690 | DOI:10.1093/gigascience/giaf008

J Econ Entomol. 2025 Feb 27:toaf035. doi: 10.1093/jee/toaf035. Online ahead of print.

ABSTRACT

Plant-specific insect scouting and prediction are still challenging in most crop systems. In this article, a machine-learning algorithm is proposed to predict populations during whiteflies (Bemisia tabaci, Hemiptera; Gennadius Aleyrodidae) scouting and aid in determining the population distribution of adult whiteflies in cotton plant canopies. The study investigated the main location of adult whiteflies relative to plant nodes (stem points where leaves or branches emerge), population variation within and between canopies, whitefly density variability across fields, the impact of dense nodes on overall canopy populations, and the feasibility of using machine learning for prediction. Daily scouting was conducted on 64 non-pesticide cotton plants, focusing on all leaves of a node with the highest whitefly counts. A linear mixed-effect model assessed distribution over time, and machine-learning model selection identified a suitable forecasting model for the entire canopy whitefly population. Findings showed that the top 3 to 5 nodes are key habitats, with a single node potentially accounting for 44.4% of the full canopy whitefly population. The Bagging Ensemble Artificial Neural Network Regression model accurately predicted canopy populations (R² = 85.57), with consistency between actual and predicted counts (P-value > 0.05). Strategic sampling of the top nodes could estimate overall plant populations when taking a few samples or transects across a field. The suggested machine-learning model could be integrated into computing devices and automated sensors to predict real-time whitefly population density within the entire plant canopy during scouting operations.

PMID:40036620 | DOI:10.1093/jee/toaf035

Bioinformatics. 2025 Feb 27:btaf092. doi: 10.1093/bioinformatics/btaf092. Online ahead of print.

ABSTRACT

MOTIVATION: Cryogenic Electron Microscopy (cryo-EM) is a core experimental technique used to determine the structure of macromolecules such as proteins. However, the effectiveness of cryo-EM is often hindered by the noise and missing density values in cryo-EM density maps caused by experimental conditions such as low contrast and conformational heterogeneity. Although various global and local map sharpening techniques are widely employed to improve cryo-EM density maps, it is still challenging to efficiently improve their quality for building better protein structures from them.

RESULTS: In this study, we introduce CryoTEN-a three-dimensional UNETR ++ style transformer to improve cryo-EM maps effectively. CryoTEN is trained using a diverse set of 1,295 cryo-EM maps as inputs and their corresponding simulated maps generated from known protein structures as targets. An independent test set containing 150 maps is used to evaluate CryoTEN, and the results demonstrate that it can robustly enhance the quality of cryo-EM density maps. In addition, automatic de novo protein structure modeling shows that protein structures built from the density maps processed by CryoTEN have substantially better quality than those built from the original maps. Compared to the existing state-of-the-art deep learning methods for enhancing cryo-EM density maps, CryoTEN ranks second in improving the quality of density maps, while running > 10 times faster and requiring much less GPU memory than them.

AVAILABILITY AND IMPLEMENTATION: The source code and data is freely available at https://github.com/jianlin-cheng/cryoten.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40036588 | DOI:10.1093/bioinformatics/btaf092

Genomics Proteomics Bioinformatics. 2025 Feb 27:qzaf011. doi: 10.1093/gpbjnl/qzaf011. Online ahead of print.

ABSTRACT

The rapid development of biological and medical examination methods has vastly expanded personal biomedical information, including molecular, cellular, image, and electronic health record datasets. Integrating this wealth of information enables precise disease diagnosis, biomarker identification, and treatment design in clinical settings. Artificial intelligence (AI) techniques, particularly deep learning models, have been extensively employed in biomedical applications, demonstrating increased precision, efficiency, and generalization. The success of the large language and vision models further significantly extends their biomedical applications. However, challenges remain in learning these multimodal biomedical datasets, such as data privacy, fusion, and model interpretation. In this review, we provided a comprehensive overview of various biomedical data modalities, multi-modal representation learning methods, and the applications of AI in biomedical data integrative analysis. Additionally, we discussed the challenges in applying these deep learning methods and how to better integrate them into biomedical scenarios. We then proposed future directions for adapting deep learning methods with model pre-training and knowledge integration to advance biomedical research and benefit their clinical applications.

PMID:40036568 | DOI:10.1093/gpbjnl/qzaf011

IEEE J Biomed Health Inform. 2025 Feb 20;PP. doi: 10.1109/JBHI.2025.3543907. Online ahead of print.

ABSTRACT

Prostate cancer is a critical healthcare challenge globally and is one of the most prevalent types of cancer in men. Early and accurate diagnosis is essential for effective treatment and improved patient outcomes. In the existing literature, computer-aided diagnosis (CAD) solutions have been developed to assist pathologists in various tasks, including classification, diagnosis, and prostate cancer grading. Content-based image retrieval (CBIR) techniques provide valuable approaches to enhance these computer-aided solutions. This study evaluates how generative deep learning models can improve the quality of retrievals within a CBIR system. Specifically, we propose applying a Siamese Network approach, which enables us to learn how to encode image patches into latent representations for retrieval purposes. We used the ProGleason-GAN framework trained on the SiCAPv2 dataset to create similar pairs of input patches. Our observations indicate that introducing synthetic patches leads to notable improvements in the evaluated metrics, underscoring the utility of generative models within CBIR tasks. Furthermore, this work is the first in the literature where latent representations optimized for CBIR are used to train an attention mechanism for performing Gleason Scoring of a WSI.

PMID:40036556 | DOI:10.1109/JBHI.2025.3543907

IEEE Trans Cybern. 2025 Feb 21;PP. doi: 10.1109/TCYB.2025.3537809. Online ahead of print.

ABSTRACT

In the past years, latent variable models have played an important role in various industrial AI systems, among which quality prediction is one of the most representative applications. Inspired by the idea of deep learning, those basic latent variable models have been extended to deep forms, based on which the quality prediction performance has been significantly improved. However, different latent variable models have their own strengths and weaknesses, a model works well under one scenario might not provide satisfactory performance under another. The motivation of this article is based on the viewpoint of information fusion and ensemble learning for heterogeneous latent variable models. Particularly, a collaborative deep learning and model fusion framework is formulated for the purpose of industrial quality prediction. In the first stage of the framework, collaborative layer-by-layer feature extractions are implemented among different latent variable models, through which different patterns of latent variables are identified in different layers of the deep model. Then, in the second stage, an ensemble regression modeling strategy is proposed to fuse the quality prediction results from different latent variable models, which is based on a well-designed data description method. Two real industrial examples are used for performance evaluation of the proposed method, based on which we can observe that information fusions in terms of both collaborative layer-by-layer feature extraction and heterogeneous model ensemble have positive effects in improving prediction accuracy and stability.

PMID:40036535 | DOI:10.1109/TCYB.2025.3537809

IEEE Trans Cybern. 2025 Feb 21;PP. doi: 10.1109/TCYB.2025.3531441. Online ahead of print.

ABSTRACT

Multiview semi-supervised learning is a popular research area in which people utilize cross-view knowledge to overcome the limitation of labeled data in semi-supervised learning. Existing methods mainly utilize deep neural network, which is relatively time-consuming due to the complex network structure and back propagation iterations. In this article, co-training broad Siamese-like network (Co-BSLN) is proposed for coupled-view semi-supervised classification. Co-BSLN learns knowledge from two-view data and can be used for multiview data with the help of feature concatenation. Different from existing deep learning methods, Co-BSLN utilizes a simple shallow network based on broad learning system (BLS) to simplify the network structure and reduce training time. It replaces back propagation iterations with a direct pseudo inverse calculation to further reduce time consumption. In Co-BSLN, different views of the same instance are considered as positive pairs due to cross-view consistency. Predictions of views in positive pairs are used to guide the training of each other through a direct logit vector mapping. Such a design is fast and effectively utilizes cross-view consistency to improve the accuracy of semi-supervised learning. Evaluation results demonstrate that Co-BSLN is able to improve accuracy and reduce training time on popular datasets.

PMID:40036533 | DOI:10.1109/TCYB.2025.3531441

IEEE J Biomed Health Inform. 2025 Mar 4;PP. doi: 10.1109/JBHI.2025.3547741. Online ahead of print.

ABSTRACT

Precise quantification of protein-ligand interaction is critical in early-stage drug discovery. Artificial intelligence (AI) has gained massive popularity in this area, with deep-learning models used to extract features from ligand and protein molecules. However, these models often fail to capture intermolecular non-covalent interactions, the primary factor influencing binding, leading to lower accuracy and interpretability. Moreover, such models overlook the spatial structure of protein-ligand complexes, resulting in weaker generalization. To address these issues, we propose Non-covalent Interaction-aware Graph Neural Network (NciaNet), a novel method that effectively utilizes intermolecular non-covalent interactions and 3D protein-ligand structure. Our approach achieves excellent predictive performance on multiple benchmark datasets and outperforms competitive baseline models in the binding affinity task, with the benchmark core set v.2016 achieving an RMSE of 1.208 and an R of 0.833, and the core set v.2013 achieving an RMSE of 1.409 and an R of 0.805, under the high-quality refined v.2016 training conditions. Importantly, NciaNet successfully learns vital features related to protein-ligand interactions, providing biochemical insights and demonstrating practical utility and reliability. However, despite these strengths, there may still be limitations in generalizability to unseen protein-ligand complexes, suggesting potential avenues for future work.

PMID:40036511 | DOI:10.1109/JBHI.2025.3547741

JMIR Hum Factors. 2025 Mar 4;12:e66699. doi: 10.2196/66699.

ABSTRACT

BACKGROUND: Antimicrobial resistances pose significant challenges in health care systems. Clinical decision support systems (CDSSs) represent a potential strategy for promoting a more targeted and guideline-based use of antibiotics. The integration of artificial intelligence (AI) into these systems has the potential to support physicians in selecting the most effective drug therapy for a given patient.

OBJECTIVE: This study aimed to analyze the feasibility of an AI-based CDSS pilot version for antibiotic therapy in sepsis patients and identify facilitating and inhibiting conditions for its implementation in intensive care medicine.

METHODS: The evaluation was conducted in 2 steps, using a qualitative methodology. Initially, expert interviews were conducted, in which intensive care physicians were asked to assess the AI-based recommendations for antibiotic therapy in terms of plausibility, layout, and design. Subsequently, focus group interviews were conducted to examine the technology acceptance of the AI-based CDSS. The interviews were anonymized and evaluated using content analysis.

RESULTS: In terms of the feasibility, barriers included variability in previous antibiotic administration practices, which affected the predictive ability of AI recommendations, and the increased effort required to justify deviations from these recommendations. Physicians' confidence in accepting or rejecting recommendations depended on their level of professional experience. The ability to re-evaluate CDSS recommendations and an intuitive, user-friendly system design were identified as factors that enhanced acceptance and usability. Overall, barriers included low levels of digitization in clinical practice, limited availability of cross-sectoral data, and negative previous experiences with CDSSs. Conversely, facilitators to CDSS implementation were potential time savings, physicians' openness to adopting new technologies, and positive previous experiences.

CONCLUSIONS: Early integration of users is beneficial for both the identification of relevant context factors and the further development of an effective CDSS. Overall, the potential of AI-based CDSSs is offset by inhibiting contextual conditions that impede its acceptance and implementation. The advancement of AI-based CDSSs and the mitigation of these inhibiting conditions are crucial for the realization of its full potential.

PMID:40036494 | DOI:10.2196/66699

Elife. 2025 Mar 4;13:RP102451. doi: 10.7554/eLife.102451.

ABSTRACT

BACKGROUND: In idiopathic pulmonary fibrosis (IPF) patients, alveolar architectures are lost and gas transfer function would decline, which cannot be rescued by conventional anti-fibrotic therapy. P63+ lung basal progenitor cells are reported to have potential to repair damaged lung epithelium in animal models, which need further investigation in clinical trials.

METHODS: We cloned and expanded P63+ progenitor cells from IPF patients to manufacture cell product REGEND001, which were further characterized by morphology and single-cell transcriptomic analysis. Subsequently, an open-label, dose-escalation autologous progenitor cell transplantation clinical trial was conducted. We treated 12 patients with ascending doses of cells: 0.6x, 1x, 2x and 3.3x106 cells/kg bodyweight. The primary outcome was the incidence and severity of cell therapy-related adverse events (AEs); secondary outcome included other safety and efficacy evaluations.

RESULTS: P63+ basal progenitor cell was safe and tolerated at all doses, with no dose-limiting toxicity or cell therapy-related severe adverse events observed. Patients in three higher dose groups showed significant improvement of lung gas transfer function as well as exercise ability. Resolution of honeycomb lesion was observed in patients of higher dose groups.

CONCLUSIONS: REGEND001 has high safety profile and meanwhile encourages further efficacy exploration in IPF patients.

FUNDING: National High Level Hospital Clinical Research Funding (2022-PUMCH-B-108), National Key Research and Development Plan (2024YFA1108900, 2024YFA1108500), Jiangsu Province Science and Technology Special Project Funding (BE2023727), National Biopharmaceutical Technology Research Project Funding (NCTIB2023XB01011), Non-profit Central Research Institute Fund of Chinese Academy of Medical Science (2020-PT320-005), and Regend Therapeutics.

CLINICAL TRIAL NUMBER: Chinese clinical trial registry: CTR20210349.

PMID:40036154 | DOI:10.7554/eLife.102451

G3 (Bethesda). 2025 Feb 28:jkaf045. doi: 10.1093/g3journal/jkaf045. Online ahead of print.

ABSTRACT

During mammalian reproduction, sperm are delivered to the female reproductive tract bathed in a complex medium known as seminal fluid, which plays key roles in signaling to the female reproductive tract and in nourishing sperm for their onwards journey. Along with minor contributions from the prostate and the epididymis, the majority of seminal fluid is produced by a somewhat understudied organ known as the seminal vesicle. Here, we report the first single-cell RNA-seq atlas of the mouse seminal vesicle, generated using tissues obtained from 23 mice of varying ages, exposed to a range of dietary challenges. We define the transcriptome of the secretory cells in this tissue, identifying a relatively homogeneous population of the epithelial cells which are responsible for producing the majority of seminal fluid. We also define the immune cell populations - including large populations of macrophages, dendritic cells, T cells, and NKT cells - which have the potential to play roles in producing the various immune mediators present in seminal plasma. Together, our data provide a resource for understanding the composition of an understudied reproductive tissue, with potential implications for paternal control of offspring development and metabolism.

PMID:40036847 | DOI:10.1093/g3journal/jkaf045

J Proteome Res. 2025 Mar 4. doi: 10.1021/acs.jproteome.4c00795. Online ahead of print.

ABSTRACT

A nanosheath-flow capillary electrophoresis mass spectrometry (CE-MS) system with electrospray ionization was used to profile cationic metabolite cargo in exosomes secreted by nontumorigenic MCF-10A and tumorigenic MDA-MB-231 breast epithelial cells. An in-house-produced sheath liquid interface was developed and machined from PEEK to enable nanoflow volumes. Normalization of CE-MS peak areas to the total UV signal was employed to enhance quantitative accuracy and reduce variability. CE-MS-based metabolomics revealed increased purine synthesis intermediates and increased carnitine synthesis metabolites in MDA-MB-231-derived exosomes, with pathway enrichment indicating the activation of de novo purine pathways and upregulation of carnitine metabolism. In addition, nano-LC-MS-based proteomics revealed differential expression of ecto-5'-nucleotidase (NT5E) and mitochondrial aldehyde dehydrogenase (ALDH9A1), demonstrating metabolic alterations in related enzymatic steps. This study demonstrates the application of nanosheath-flow CE-MS for comprehensive and quantitative exosome metabolomics, uncovering metabolic reprogramming in purine and carnitine pathways between normal and cancerous breast cell lines and providing insight into exosome-mediated signaling of breast cancer metabolism.

PMID:40036676 | DOI:10.1021/acs.jproteome.4c00795

J Proteome Res. 2025 Mar 4. doi: 10.1021/acs.jproteome.4c00968. Online ahead of print.

ABSTRACT

Liquid chromatography-tandem mass spectrometry employing data-dependent acquisition (DDA) is a mature, widely used proteomics technique routinely applied to proteome profiling, protein-protein interaction studies, biomarker discovery, and protein modification analysis. Numerous tools exist for searching DDA data and myriad file formats are output as results. While some search and post processing tools include data visualization features to aid biological interpretation, they are often limited or tied to specific software pipelines. This restricts the accessibility, sharing and interpretation of data, and hinders comparison of results between different software pipelines. We developed Limelight, an easy-to-use, open-source, freely available tool that provides data sharing, analysis and visualization and is not tied to any specific software pipeline. Limelight is a data visualization tool specifically designed to provide access to the whole "data stack", from raw and annotated scan data to peptide-spectrum matches, quality control, peptides, proteins, and modifications. Limelight is designed from the ground up for sharing and collaboration and to support data from any DDA workflow. We provide tools to import data from many widely used open-mass and closed-mass search software workflows. Limelight helps maximize the utility of data by providing an easy-to-use interface for finding and interpreting data, all using the native scores from respective workflows.

PMID:40036265 | DOI:10.1021/acs.jproteome.4c00968