Cell Rep Med. 2025 Aug 7:102283. doi: 10.1016/j.xcrm.2025.102283. Online ahead of print.

ABSTRACT

Melanoma is a deadly cancer with increasing incidence and mortality rates, and biomarkers for diagnosis are urgently needed. The impact of the microbiome, genetic factors, and immunologic markers on disease outcomes is described, but a comprehensive serum metabolome profiling is missing. The serum metabolome of patients with melanoma might be valuable to identify potential biomarkers. We present an untargeted metabolomics analysis in an exploratory cohort (87 patients with melanoma), an independent validation cohort (37 additional patients with melanoma featuring late-stage tumors), and 18 healthy control individuals, revealing striking differences. We identify and validate six serum metabolites that can predict the diagnosis of melanoma with an area under the curve (AUC) >0.9544 in advanced-stage melanoma. The AUC of our lead biomarker, muramic acid, is 0.964, 0.908, and 0.9936 in patients with stage I (n = 22), stage II (n = 67), and advanced melanoma (n = 86), respectively. In summary, we identify potentially very powerful diagnostic biomarkers for clinical practice.

PMID:40795845 | DOI:10.1016/j.xcrm.2025.102283

Semin Oncol. 2025 Aug 11;52(5):152396. doi: 10.1016/j.seminoncol.2025.152396. Online ahead of print.

ABSTRACT

Stomach adenocarcinoma (STAD) is one of the deadliest malignant tumors worldwide. Carbamoyl-phosphate synthetase 2 (CAD) expression is essential for categorizing and detecting STAD initiation and development. We explored the differential expression of genes (DEGs) affected by CAD overexpression and subsequently revealed the classification module of CAD-based scoring sets using weighted gene co-expression network analysis (WGCNA). Subsequently, enrichment analysis of biological functions and signaling pathways in clinically significant modules was conducted. We constructed a CAD-based clinical scoring model using univariate and multivariate Cox regression analyses. In addition, by using immune cell infiltration analysis, we investigated the interaction between CAD-based score and the immune microenvironment, identified upstream regulatory factors, including RNA binding proteins (RBPs), that affect the transcription of the STAD-related CAD-based score, and explored potential drug targets. We identified 4,977 abnormal regulatory genes related to CAD in STAD, among which the module genes most related to CAD were significantly enriched in cancer-related signaling pathways, such as VEGF, MAPK and TGF-beta signaling pathway. The CAD-based scores, T and N were identified as independent prognostic factors for STAD patients. We also found that under the influence of high expression of CAD, the infiltration level of most immune cells is lower, such as CD4 T cells and Tfh, and CAD has an inhibitory effect on the infiltration of certain immune cells. Notably, the potential drug targets PDHB and NDUFB6 are upstream regulatory factors in STAD. This study explored the role of highly expressed CAD-related genes in STAD and explored the tumorigenesis and progression of this disease. This research identified potential diagnostic and prognostic drug targets and provided new insights into the molecular mechanisms of STAD.

PMID:40795601 | DOI:10.1016/j.seminoncol.2025.152396

Microbiol Res. 2025 Aug 8;301:128308. doi: 10.1016/j.micres.2025.128308. Online ahead of print.

ABSTRACT

Mycovirus infections significantly impact fungal virulence and physiology, inducing either hypovirulence or hypervirulence. This study investigated the hypovirulent effects of Colletotrichum gloeosporioides RNA virus 1 (CgRV1-Ssa-44.1) infection on Colletotrichum gloeosporioides using multi-omics approaches. Transcriptomic analysis identified 261 differentially expressed genes (141 up-regulated, 120 down-regulated), while LC-MS/MS-based proteomic analyses revealed 2222 proteins, including 19 unique to virus-infected samples and 649 unique to virus-free samples. These results highlighted extensive gene and protein expression alterations, emphasizing profound impacts on the host cellular process. Changes in membrane-associated terms and cell wall-related processes suggested that the virus may exploit host structures to facilitate horizontal transfer. The disruption of carbohydrate metabolism and pathways, such as the non-sense mediated mRNA decay (NMD) system, reflected sophisticated viral strategies for suppressing host defenses and redirecting resources for its benefit. Notably, Upregulated genes, such as sorbose reductase and COMPASS complex component SWD2, pointed to adaptive response to stress and survival mechanisms during viral infection. Conversely, downregulated genes like elongation factor 3, survival factor 1, and zuotin, indicated viral manipulation of host cellular machinery to subvert normal processes. Real-time PCR validated these transcriptional changes, confirming the robustness of the findings. The study demonstrates a complex host-virus interplay, where fungal metabolic and adaptive pathways are intricately targeted and exploited. These findings underscore the dual nature of viral subversion strategies, balancing host suppression with survival adaptation. Future functional analyses of key pathways will provide insights into the molecular mechanisms underlying fungal-virus interactions and coevolution. This knowledge could guide the development of novel antifungal strategies applicable to similar host-pathogen systems.

PMID:40795492 | DOI:10.1016/j.micres.2025.128308

Sci Signal. 2025 Aug 12;18(899):eadr9131. doi: 10.1126/scisignal.adr9131. Epub 2025 Aug 12.

ABSTRACT

Increases in retroelement-derived double-stranded RNAs (dsRNAs) in various types of cancer cells facilitate the activation of antitumor immune responses. The long noncoding RNA EPIC1 interacts with the histone methyltransferase EZH2 and contributes to tumor immune evasion. Here, we found that EPIC1 in tumor cells suppressed cytoplasmic dsRNA accumulation, type I interferon (IFN) responses, and antitumor immunity. In various cancer cell lines, knockdown of EPIC1 stimulated the production of dsRNA from retroelements and an antiviral-like type I IFN response that activated immune cells. EPIC1 inhibited the expression of LINE, SINE, and LTR retroelements that were also repressed by EZH2, suggesting a potential role for the EPIC1-EZH2 interaction in regulating dsRNA production. In a humanized mouse model, in vivo delivery of EPIC1-targeting oligonucleotides enhanced dsRNA accumulation in breast cancer xenografts, reduced tumor growth, and increased the infiltration of T cells and inflammatory macrophages into tumors. Furthermore, EPIC1 knockdown improved the therapeutic efficacy of the immunotherapy drug pembrolizumab, a PD-1 inhibitor, in the humanized mouse model. Together, our findings establish EPIC1 as a key regulator of dsRNA-mediated type I IFN responses and highlight its potential as a therapeutic target to improve the efficacy of immunotherapy.

PMID:40794843 | DOI:10.1126/scisignal.adr9131

Ann Am Thorac Soc. 2025 Aug 12. doi: 10.1513/AnnalsATS.202501-028OC. Online ahead of print.

ABSTRACT

RATIONALE: Elexacaftor/tezacaftor/ivacaftor (ETI) has significantly improved lung function in people with cystic fibrosis (CF), prompting the need for outcome measures that can detect mild disease. In this new era of CFTR modulator therapy, more sensitive endpoints are required to evaluate the progression of early lung disease and to determine the efficacy of new CF therapies. Prior to the availability of highly effective therapies 129Xenon magnetic resonance imaging (Xe MRI) was shown to be more sensitive to regional ventilation changes compared to spirometry.

OBJECTIVES: To evaluate the longitudinal changes in pulmonary function and Xe-MRI outcomes after treatment with ETI in children and young people with CF.

METHODS: Lung function was assessed longitudinally at baseline 1, 6, and 12 months following ETI treatment initiation in children and young people with CF between the ages of 6 and 18 years at four study sites. Ventilation defect percentage (VDP), reader-defect percentage (RDP), Lung Clearance Index (LCI) and Forced Expiratory Volume in 1 second (FEV1) were reported.

MEASUREMENTS AND MAIN RESULTS: A total of 28 participants were enrolled; 25 completed at least baseline and one-month measurements. All four measures (RDP, VDP, LCI and FEV1) improved at one month after ETI initiation with a mean (standard deviation) absolute change of -1.2 (1.7) in LCI, 6.9 (12.3) in FEV1, -4.3 (4.8) in VDP and --7.8 (9.6) in RDP, respectively. Xe MRI outcomes (RDP and VDP) showed the largest relative treatment effects with mean relative improvements of 43% and 72%, respectively. One third of participants (8/25) had improvements in VDP and RDP but did not show improvements in FEV1.

CONCLUSIONS: Xe MRI captures sustained ventilation improvements following ETI initiation. Xe MRI metrics may provide a suitable endpoint for future interventional trials-particularly for people with CF with mild lung disease.

PMID:40795189 | DOI:10.1513/AnnalsATS.202501-028OC

Proc Natl Acad Sci U S A. 2025 Aug 19;122(33):e2506137122. doi: 10.1073/pnas.2506137122. Epub 2025 Aug 12.

ABSTRACT

Sweat parameters such as volume and chloride concentration may offer invaluable clinical insights for people with CF (PwCF). Pilocarpine-induced sweat collection for chloridometry measurement is the gold standard for a CF diagnosis, but this technique is cumbersome and not suitable for remote settings or repeat measurements. We have previously reported the utility of a skin-interfaced microfluidic device (CF Patch) in conjunction with a smartphone image processing platform that enables real-time measurement of sweating rates and sodium chloride loss in laboratory and remote settings. Here, we conducted clinical studies assessing the accuracy of the CF Patch compared to chloridometry when using pilocarpine to induce sweat. We also tested the feasibility and accuracy of exercise-induced sweat chloride measurements in PwCF and healthy volunteers (HV). In the laboratory, using either pilocarpine or exercise to induce sweat, the CF Patch demonstrated strong correlations with sweat chloride measured by pilocarpine-induced chloridometry. In remote settings, exercise-induced sweat chlorides measured using the CF patch were strongly correlated with in-laboratory exercise-induced CF patch sweat chlorides in HV but had a weaker correlation in PwCF. For PwCF on CFTR modulators, there was greater day-to-day variability in sweat chloride compared to HV, which highlights the limitations of assessing CFTR modulator efficacy and pharmacodynamics based on a single in-laboratory chloridometry measurement. Moreover, these findings demonstrate that the CF Patch is suitable as a remote management device capable of measuring serial sweat chloride concentrations and offers the potential of monitoring the efficacy of CF medication regimens but should not replace pilocarpine-based chloridometry for making a CF diagnosis.

PMID:40794828 | DOI:10.1073/pnas.2506137122

Sleep. 2025 Aug 8:zsaf226. doi: 10.1093/sleep/zsaf226. Online ahead of print.

ABSTRACT

STUDY OBJECTIVES: Atrial fibrillation (AF) and obstructive sleep apnea (OSA) are interrelated conditions that substantially increase the risk of cardiovascular complications. However, concurrent detection of these conditions remains a critical unmet need in clinical practice. Current home sleep apnea test (HSAT) devices often fail to detect arrhythmias essential for diagnosing OSA-associated AF due to limited ECG monitoring capabilities, and their integration with continuous positive airway pressure (CPAP) data for treatment optimization remains underutilized.

METHODS: This study introduces SHHDeepNet, an advanced deep learning-based framework designed for the detection of OSA in patients with AF, leveraging enhanced features extracted from single-lead electrocardiogram (ECG) signals. The ECG signals were preprocessed and refined using reconstruction independent component analysis (RICA), which isolates statistically independent features for improved data representation. These features were subsequently classified using the customized SHHDeepNet architecture. SHHDeepNet utilizes advanced signal processing and deep learning techniques to enhance ECG-based detection of AF-associated OSA.

RESULTS: The framework was validated using overnight ECG recordings from 101 subjects derived from the Sleep Heart Health Study Visit 1 (SHHS1) database, encompassing 36 prevalent AF (PAF) cases, 25 incident AF (IAF) cases, and 40 OSA cases. Detection performance was evaluated through binary classification (AF AH vs. AF non-AH) and multi-class classification (AF AH, AF non-AH, non-AF AH, and non-AF non-AH). During 5-fold cross-validation (5fold-CV), the framework achieved a binary classification accuracy of 98.22%, sensitivity of 96.8%, specificity of 99%, and an area under the curve (AUC) of 0.9981. For multi-class classification, 5fold-CV yielded 98.36% accuracy, 97.14% sensitivity, 98.77% specificity, and an AUC of 0.9975. Validation using leave-one-subject-out cross-validation (LOSO-CV) achieved a binary classification accuracy of 86.42%, sensitivity of 79.4%, specificity of 90.2%, and an AUC of 0.9372. For multi-class classification under LOSO-CV, the average accuracy, sensitivity, and F1-score were 86.7%, 72.6%, and 0.7224, respectively. External validation was performed on a cohort of 123 subjects from the Osteoporotic Fractures in Men (MrOS) database, comprising 68 cases of PAF and 55 cases of OSA. The proposed method achieved a multi-class classification accuracy of 88.51%, sensitivity of 73.50%, specificity of 91.34%, and an AUC of 0.9363.

CONCLUSIONS: These findings underscore the significance of simultaneous detection of AF and OSA, providing a more comprehensive evaluation of cardiovascular health. The proposed SHHDeepNet framework offers a promising tool to support clinical decision-making, enhance management strategies, and improve patient outcomes by mitigating the risks associated with these conditions.

PMID:40795334 | DOI:10.1093/sleep/zsaf226

J Gerontol A Biol Sci Med Sci. 2025 Aug 8:glaf173. doi: 10.1093/gerona/glaf173. Online ahead of print.

ABSTRACT

BACKGROUND: Chronological age is an important component of medical risk scores and decision-making. However, there is considerable variability in how individuals age. We recently published an open-source deep learning model to assess biological age from chest radiographs (CXR-Age), which predicts all-cause and cardiovascular mortality better than chronological age. Here, we compare CXR-Age to two established epigenetic aging clocks (First generation-Horvath Age; Second generation-DNAm PhenoAge) to test which is more strongly associated with cardiopulmonary disease and frailty.

METHODS: Our cohort consisted of 2,097 participants from the Project Baseline Health Study, a prospective cohort study of individuals from four US sites. We compared the association between the different aging clocks and measures of cardiopulmonary disease, frailty, and protein abundance collected at the participant's first annual visit using linear regression models adjusted for common confounders.

RESULTS: We found that CXR-Age was associated with coronary calcium, cardiovascular risk factors, worsening pulmonary function, increased frailty, and abundance in plasma of two proteins implicated in neuroinflammation and aging. Associations with DNAm PhenoAge were weaker for pulmonary function and all metrics in middle-age adults. We identified thirteen proteins that were associated with DNAm PhenoAge, one (CDH13) of which was also associated with CXR-Age. No associations were found with Horvath Age.

CONCLUSION: These results suggest that CXR-Age may serve as a better metric of cardiopulmonary aging than epigenetic aging clocks, especially in midlife adults.

PMID:40795299 | DOI:10.1093/gerona/glaf173

Sleep. 2025 Aug 7:zsaf227. doi: 10.1093/sleep/zsaf227. Online ahead of print.

ABSTRACT

STUDY OBJECTIVES: The intricate interplay between sleep and emotion has garnered increasing attention due to their profound impact on human health and well-being, including the development of interventions using emotion-regulating medications. While qualitative studies have illuminated their association, quantitative evidence remains limited.

METHODS: To address this gap, we leverage deep learning and emotion priors to explore the quantitative relationship between sleep and emotion using EEG signals. Our approach introduces novel emotion-based features into sleep stage classification, providing additional abstract information and corroborating the sleep-emotion link.

RESULTS: This method enables targeted interventions with emotion-regulating medications tailored to specific sleep stages. Furthermore, we investigate the quantitative influence of emotional combinations (emotional codings) on sleep stages, revealing distinct "emotional fingerprints" during sleep.

CONCLUSION: These findings support the development of corresponding drug combinations for sleep interventions. These findings lay the foundation for developing scientifically grounded and quantifiable approaches to sleep and emotion regulation, paving the way for advancements in understanding and addressing sleep and emotional disorders.

PMID:40795269 | DOI:10.1093/sleep/zsaf227

J Am Med Inform Assoc. 2025 Aug 6:ocaf118. doi: 10.1093/jamia/ocaf118. Online ahead of print.

ABSTRACT

OBJECTIVES: To improve prediction of chronic kidney disease (CKD) progression to end-stage renal disease (ESRD) using machine learning (ML) and deep learning (DL) models applied to integrated clinical and claims data with varying observation windows, supported by explainable artificial intelligence (AI) to enhance interpretability and reduce bias.

MATERIALS AND METHODS: We utilized data from 10 326 CKD patients, combining clinical and claims information from 2009 to 2018. After preprocessing, cohort identification, and feature engineering, we evaluated multiple statistical, ML and DL models using 5 distinct observation windows. Feature importance and SHapley Additive exPlanations (SHAP) analysis were employed to understand key predictors. Models were tested for robustness, clinical relevance, misclassification patterns, and bias.

RESULTS: Integrated data models outperformed single data source models, with long short-term memory achieving the highest area under the receiver operating characteristic curve (AUROC) (0.93) and F1 score (0.65). A 24-month observation window optimally balanced early detection and prediction accuracy. The 2021 estimated glomerular filtration rate (eGFR) equation improved prediction accuracy and reduced racial bias, particularly for African American patients.

DISCUSSION: Improved prediction accuracy, interpretability, and bias mitigation strategies have the potential to enhance CKD management, support targeted interventions, and reduce health-care disparities.

CONCLUSION: This study presents a robust framework for predicting ESRD outcomes, improving clinical decision-making through integrated multisourced data and advanced analytics. Future research will expand data integration and extend this framework to other chronic diseases.

PMID:40795063 | DOI:10.1093/jamia/ocaf118

Brief Bioinform. 2025 Jul 2;26(4):bbaf406. doi: 10.1093/bib/bbaf406.

ABSTRACT

This paper develops a deep neural network that accepts cell descriptors and molecules of multiple administered drugs and predicts the joint dose-response hypersurface of the combinatorial treatment. Since the dose-response hypersurface over several concentration dimensions fully characterizes the interaction dynamics of the administered drugs, the model is a computational tool that guides the discovery of synergistic treatments. The neural network is a biochemistry-informed universal approximator; it can estimate any shape of a dose-response hypersurface and has desirable invariances built into its architecture. The model excels at interpolating and extrapolating dose-response surfaces; its predictions align well with known mechanisms of action (MOA). It is the first model that can estimate joint dose-response hypersurfaces of arbitrarily many drugs, including untried combinations, in the presence of arbitrary, potentially nonlinear interactions between drugs. We release the model itself as well as a database of likely synergistic drug triplets. Our code is available at https://github.com/alonsocampana/PanThera/; the database of likely synergistic drug triplets at https://zenodo.org/records/14001717.

PMID:40794956 | DOI:10.1093/bib/bbaf406

Brief Bioinform. 2025 Jul 2;26(4):bbaf401. doi: 10.1093/bib/bbaf401.

ABSTRACT

Database searching remains the main approach for mass spectrometry-based proteomics, where protein identification fundamentally requires prior inclusion in the reference database. For aquatic algal species lacking annotated genomes, six-frame translation of species-specific transcriptomes has emerged as a prevalent method. However, this approach results in databases that encompass all potential translation products, substantially increasing the database size and search space. Here, we introduce BERT-based Protein Annotation (BPA), a deep learning strategy that combines a pretrained BERT model for contextual patterns, Pseudo Amino Acid Composition for physicochemical properties, and InterProScan for functional domain prediction, to optimize reference proteome construction. These features are integrated by using a Random Forest classifier to generate dynamic Sequence Reliability Scores, enabling adaptive filtering thresholds tailored to diverse experimental designs. Based on the validation across three distinct test species, this study demonstrates a robust performance of BPA with sustained high classification accuracy (AUC > 0.95). In the application to Karenia mikimotoi, BPA achieved 90% proteome compression while maintaining 40% identification coverage, effectively resolving the peptide ambiguity from redundant translations. This framework provides a scalable and efficient solution for constructing and optimizing reference libraries, facilitating proteomic research in aquatic algae and other genomically understudied species. Source code and executables are available at (https://github.com/huangruihua/BPA.git).

PMID:40794952 | DOI:10.1093/bib/bbaf401

Brief Bioinform. 2025 Jul 2;26(4):bbaf403. doi: 10.1093/bib/bbaf403.

ABSTRACT

The advancement of traditional Chinese medicine (TCM) faces challenges, due to the absence of a deep understanding of TCM mechanism at the perspective of modern biomedical practices. This results in how TCM selects herbs to treat diseases or symptoms prevailingly rely on clinicals' experience or TCM ancient books, at least in part lacking scientific basis. Herein, we present a novel deep learning-based approach, named Negative-Correlation-based TCM Architecture for Reversal (NeCTAR), to optimize the generation and combination of TCM formulas for guiding empiric therapy, by which we could, to some degree, narrow the gap between TCM and modern biomedical science. Our approach builds on a hypothesis that pathway alterations may serve as a proxy for the corresponding physiological changes induced by a certain disease, and 'inverse-fit' those alterations would provide a feasible therapeutic strategy to treat the disease. We leveraged ribonucleic acid sequencing (RNA-seq) data with Gene Set Enrichment Analysis to establish herb-pathway associations, integrating these insights into a multilayer perceptron model that incorporates top-k sparse projection and pathway reconstruction loss to predict the most therapeutically promising herbal components. NeCTAR demonstrated high concordance with experimental data across various disease models, including fatty liver disease, type 2 diabetes mellitus, and premature ovarian failure. Notably, NeCTAR could equally apply to single cell RNA-seq data. Overall, our study put forwards a novel interpretive framework underlying TCM mechanisms using modern biomedical foundation, by which we could prioritize herbal components based on existing TCM formulas treating diseases.

PMID:40794951 | DOI:10.1093/bib/bbaf403

Brief Bioinform. 2025 Jul 2;26(4):bbaf402. doi: 10.1093/bib/bbaf402.

ABSTRACT

Antigen presentation by major histocompatibility complex (MHC) molecules is a complex pathway essential for T cell-mediated immunity. The pathway involves unfolding and processing of the antigen protein structure, yet limited work has been made evaluating the potential influence of local protein structure on the prediction of antigen processing and presentation. Here, we investigated this by integrating local structural features-disorder score, relative surface accessibility, and the probabilities of α-helix, β-sheet, and coil-into an NNAlign-based framework for predicting MHC class I and HLA-DR antigen presentation. Large-scale eluted ligand datasets were used to train and validate our models, demonstrating that for MHC class I, the addition of structural features yielded marginal, nonsignificant improvements in performance. In contrast, for HLA-DR ligands, models incorporating positional structural information showed a significant yet limited performance boost. Post-hoc analysis revealed no clear amino acid enrichment patterns associated with structural propensities. Rather the HLA-specific gain in performance was found to be linked to the number of positive instances seen in training. Stratification by cellular localisation indicated that peptides from endolysosomal proteins benefited more from structural integration than those from cytosolic sources. Our comprehensive benchmark shows that incorporating local protein structural features improves epitope prediction for MHC class II ligands.

PMID:40794948 | DOI:10.1093/bib/bbaf402

Brief Bioinform. 2025 Jul 2;26(4):bbaf393. doi: 10.1093/bib/bbaf393.

ABSTRACT

Liquid-liquid phase separation (LLPS) is a common and important phenomenon where biomolecules form dynamic, membrane-less condensates through multivalent interactions, spontaneously separating into distinct concentration-dense and dilute phases. Research has shown that LLPS is associated with a wide range of cellular functional regulation. In this work, we establish a feature fusion framework based on a Siamese network for the prediction of LLPS proteins, which can integrate automatically extracted features from the protein itself and the protein-protein interaction (PPI) networks, and achieve good accuracy even in small sample sets. We used two representative graph embedding methods, Node2vec and DeepNF, to extract the embedding features of PPI networks and compared the impact of the two methods on model performance at different feature lengths. Our work provides a way for integrating multivalent interactions between proteins that drive LLPS, as well as a flexible framework for the fusion of different types of protein features, not only for LLPS prediction but also for other downstream prediction tasks. All relevant materials can be found at https://github.com/ispotato/SiameseNetwork_LLPS.

PMID:40794947 | DOI:10.1093/bib/bbaf393

Brief Bioinform. 2025 Jul 2;26(4):bbaf400. doi: 10.1093/bib/bbaf400.

ABSTRACT

Single-cell multi-omics technologies enable comprehensive molecular profiling, offering insights into cellular heterogeneity and biological mechanisms. However, current cross-modality translation methods struggle with high-dimensional, noisy, and sparse single-cell data. We propose single-cell Diffusion models for Cross-modality Translation (scDCT), a probabilistic framework for bidirectional cross-modality translation in single-cell data, including single-cell RNA sequencing, single-cell assay for transposase-accessible chromatin sequencing, and protein expression. scDCT integrates modality-specific autoencoders with conditional denoising diffusion probabilistic models to map inputs to latent spaces and perform probabilistic translation across modalities. This design captures cell-type heterogeneity, accounts for data sparsity, and models uncertainty during translation. Extensive experiments on eight benchmark datasets demonstrate that scDCT outperforms state-of-the-art methods across paired, unpaired, cross-type, and cross-tissue settings, offering a robust and interpretable solution for single-cell multi-omics integration.

PMID:40794946 | DOI:10.1093/bib/bbaf400

Plant Physiol. 2025 Jul 24:kiaf319. doi: 10.1093/plphys/kiaf319. Online ahead of print.

ABSTRACT

Cell expansion relies on turgor pressure and acidification-dependent loosening of the rigid cell wall. Distinct cell surface-based and intracellular auxin signaling pathways synergistically activate plasma membrane H+-ATPases, acidifying the apoplast, a prerequisite for cell elongation. Unlike in shoots, auxin inhibits cell elongation in roots. This auxin paradox highlights a largely unknown antagonistic pathway, driving root apoplast alkalinization. Auxin fluxes, regulated by the TINY ROOT HAIR 1 (TRH1)/POTASSIUM (K+) UPTAKE 4 (KUP4) transporter, modulate root gravitropism and root hair morphogenesis through the acropetal and basipetal auxin transport pathways, respectively. Here, we show that under acidic conditions, wild-type Arabidopsis (Arabidopsis thaliana) seedlings develop shorter root hairs and exhibit an agravitropic response, a defect that is even more pronounced in trh1/kup4 roots. Acidic conditions also distort auxin responses in wild-type roots, with these effects further exacerbated in trh1/kup4 roots. Remarkably, exogenous auxin application restores the trh1-like developmental defects in wild-type roots, suggesting that acidity chemiosmotically impairs active auxin transport. Advanced compartmental pH imaging combined with pharmacological applications revealed cytoplasmic and vacuolar acidification in trh1/kup4 root cells, which activates AHA2, the predominant plasma membrane H+-ATPase in roots. Proton efflux leads to apoplast acidification and rhizotoxicity, thereby inhibiting primary root elongation of trh1/kup4 seedlings. Our results demonstrate that as a proton-coupled potassium transporter, TRH1/KUP4 maintains a balance between cytosolic and apoplastic proton gradients, facilitating cytoplasm neutralization and apoplast alkalization in roots. Through this regulatory mechanism, we postulate that TRH1/KUP4 enables pH-driven intracellular auxin transport and modulates cell surface pH, driving root cell elongation and shaping root system architecture.

PMID:40795096 | DOI:10.1093/plphys/kiaf319

FEMS Microbiol Lett. 2025 Aug 6:fnaf078. doi: 10.1093/femsle/fnaf078. Online ahead of print.

ABSTRACT

Drain flies (Clogmia albipunctata) are insects that thrive in humid urban environments such as bathrooms drains and sewage systems. While their role in pathogen transmission has been suggested, little is known about their microbiome or ecology in non-clinical contexts. Using 16S rRNA gene metabarcoding, we characterized the bacterial communities of drain flies from three locations in South Korea, public bathrooms from a college in Seoul, a rural port in Ulleungdo island, and a highly frequented public park in Yeouido. In total, we obtained 221 families and 1 474 features. We found significant differences in microbiome composition and diversity as well as a small core microbiome shared among locations, with environmental bacteria such as Pseudomonas and Ralstonia being the dominant taxa across samples. The majority of the detected amplicon sequence variants (ASV) were not shared among locations. These findings suggest drain fly transport a location-specific environmental bacteria. Notably, we also identified ASVs of potential clinical relevance, including Mycobacterium, Acinetobacter baumanii, Providencia, and Nocardia. This is the first metagenomic insight into the microbiome of this species and adds to a renewed interest in the role that non-hematophagous insects play in urban microbial ecology and the spread of microbes.

PMID:40795028 | DOI:10.1093/femsle/fnaf078

Brief Bioinform. 2025 Jul 2;26(4):bbaf397. doi: 10.1093/bib/bbaf397.

ABSTRACT

Differential expression analysis provides insights into fundamental biological processes and with the advent of single-cell transcriptomics, gene expression can now be studied at the level of individual cells. Many analyses treat cells as samples and assume statistical independence. As cells are pseudoreplicates, this assumption does not hold, leading to reduced robustness, reproducibility, and an inflated type 1 error rate. In this study, we investigate various methods for differential expression analysis on single-cell data, conduct extensive benchmarking, and give recommendations for method choice. The tested methods include DESeq2, MAST, DREAM, scVI, the permutation test, distinct, and the t-test. We additionally adapt hierarchical bootstrapping to differential expression analysis on single-cell data and include it in our benchmark. We found that differential expression analysis methods designed specifically for single-cell data do not offer performance advantages over conventional pseudobulk methods such as DESeq2 when applied to individual datasets. In addition, they mostly require significantly longer run times. For atlas-level analysis, permutation-based methods excel in performance but show poor runtime, suggesting to use DREAM as a compromise between quality and runtime. Overall, our study offers the community a valuable benchmark of methods across diverse scenarios and offers guidelines on method selection.

PMID:40794957 | DOI:10.1093/bib/bbaf397

Sci Signal. 2025 Aug 12;18(899):eadr9131. doi: 10.1126/scisignal.adr9131. Epub 2025 Aug 12.

ABSTRACT

Increases in retroelement-derived double-stranded RNAs (dsRNAs) in various types of cancer cells facilitate the activation of antitumor immune responses. The long noncoding RNA EPIC1 interacts with the histone methyltransferase EZH2 and contributes to tumor immune evasion. Here, we found that EPIC1 in tumor cells suppressed cytoplasmic dsRNA accumulation, type I interferon (IFN) responses, and antitumor immunity. In various cancer cell lines, knockdown of EPIC1 stimulated the production of dsRNA from retroelements and an antiviral-like type I IFN response that activated immune cells. EPIC1 inhibited the expression of LINE, SINE, and LTR retroelements that were also repressed by EZH2, suggesting a potential role for the EPIC1-EZH2 interaction in regulating dsRNA production. In a humanized mouse model, in vivo delivery of EPIC1-targeting oligonucleotides enhanced dsRNA accumulation in breast cancer xenografts, reduced tumor growth, and increased the infiltration of T cells and inflammatory macrophages into tumors. Furthermore, EPIC1 knockdown improved the therapeutic efficacy of the immunotherapy drug pembrolizumab, a PD-1 inhibitor, in the humanized mouse model. Together, our findings establish EPIC1 as a key regulator of dsRNA-mediated type I IFN responses and highlight its potential as a therapeutic target to improve the efficacy of immunotherapy.

PMID:40794843 | DOI:10.1126/scisignal.adr9131