Zhonghua Yi Xue Za Zhi. 2025 Mar 25;105(12):867-871. doi: 10.3760/cma.j.cn112137-20241024-02396.

ABSTRACT

Type 2 diabetes mellitus and chronic kidney disease (T2DM-CKD) is common in the Chinese population and seriously threatens human health. With the development and application of new drugs, the outcome of T2DM-CKD has been improved in recent years. However, how to combine those drugs effectively and reduce side effects deserves further attention. "Renal triple therapy " (RTT) refers to combined and sequential use of three key medications in treatment of T2DM-CKD, namely renin-angiotensin system inhibitor (RASi), sodium-glucose cotransporter 2 inhibitor (SGLT-2i) and non-steroidal mineralocorticoid receptor antagonist (finerenone), targeting various pathogenic factors in T2DM-CKD. Clinical studies have demonstrated that RTT is superior for protecting the kidney and heart compared with single or dual therapy, and thus improves outcomes of patients with T2DM-CKD. Moreover, RTT can reduce the risk of drug-induced hyperkalemia. Based on research progress at home and abroad and personal clinical experience, this article discusses the origin, theoretical basis, benefits and precautions of RTT in treating T2DM-CKD, aiming to provide a good treatment strategy for primary care physicians and improve the overall level of disease prevention and control.

PMID:40113409 | DOI:10.3760/cma.j.cn112137-20241024-02396

Brief Bioinform. 2025 Mar 4;26(2):bbaf117. doi: 10.1093/bib/bbaf117.

ABSTRACT

The prediction of virus-drug associations (VDAs) is crucial for drug repositioning, contributing to the identification of latent antiviral drugs. In this study, we developed a graph-based integrated Gaussian kernel similarity (GiGs) method for predicting potential VDAs in drug repositioning. The GiGs model comprises three components: (i) collection of experimentally validated VDA information and calculation virus sequence, drug chemical structure, and drug side effect similarity; (ii) integration of viruses and drugs similarity based on the above information and Gaussian interaction profile kernel (GIPK); and (iii) utilization of similarity-constrained weight graph normalization matrix factorization to predict antiviral drugs. The GiGs model enhances correlation matrix quality through the integration of multiple biological data, improves performance via similarity constraints, and prevents overfitting and predicts missing data more accurately through graph regularization. Extensive experimental results indicated that the GiGs model outperforms five other advanced association prediction methods. A case study identified broad-spectrum drugs for treating highly pathogenic human coronavirus infections, with molecular docking experiments confirming the model's accuracy.

PMID:40112339 | DOI:10.1093/bib/bbaf117

Obstet Gynecol. 2025 Apr 1;145(4):e141-e142. doi: 10.1097/AOG.0000000000005865.

NO ABSTRACT

PMID:40112310 | DOI:10.1097/AOG.0000000000005865

Brief Bioinform. 2025 Mar 4;26(2):bbaf107. doi: 10.1093/bib/bbaf107.

ABSTRACT

A-to-I ribonucleic acid (RNA) editing detection is still a challenging task. Current bioinformatics tools rely on empirical filters and whole genome sequencing or whole exome sequencing data to remove background noise, sequencing errors, and artifacts. Sometimes they make use of cumbersome and time-consuming computational procedures. Here, we present REDInet, a temporal convolutional network-based deep learning algorithm, to profile RNA editing in human RNA sequencing (RNAseq) data. It has been trained on REDIportal RNA editing sites, the largest collection of human A-to-I changes from >8000 RNAseq data of the genotype-tissue expression project. REDInet can classify editing events with high accuracy harnessing RNAseq nucleotide frequencies of 101-base windows without the need for coupled genomic data.

PMID:40112338 | DOI:10.1093/bib/bbaf107

Hepatology. 2025 Mar 20. doi: 10.1097/HEP.0000000000001314. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Among those with primary sclerosing cholangitis (PSC), perihilar CCA (pCCA) is often diagnosed at a late-stage and is a leading source of mortality. Detection of pCCA in PSC when curative action can be taken is challenging. Our aim was to create a deep learning model that analyzed magnetic resonance imaging (MRI) to detect early-stage pCCA and compare its diagnostic performance with expert radiologists.

APPROACH AND RESULTS: We conducted a multicenter, international, retrospective cohort study involving adults with large duct PSC who underwent contrast-enhanced MRI. Senior abdominal radiologists reviewed the images. All patients with pCCA had early-stage cancer and were registered for liver transplantation. We trained a 3D DenseNet-121 model, a form of deep learning, using MRI images and assessed its performance in a separate test cohort. The study included 398 patients (training cohort n=150; test cohort n=248). pCCA was present in 230 individuals (training cohort n=64; test cohort n=166). In the test cohort, the respective performances of the model compared to the radiologists were: sensitivity 87.9% versus 50.0%, p<0.001; specificity 84.1% versus 100.0%, p<0.001; area under receiving operating curve 86.0% versus 75.0%, p<0.001. Even when a mass was absent, the model had a higher sensitivity for pCCA than radiologists (91.6% vs. 50.6%, p<0.001) and maintained good specificity (84.1%).

CONCLUSION: The 3D DenseNet-121 MRI model effectively detects early-stage pCCA in PSC patients. Compared to expert radiologists, the model missed fewer cases of cancer.

PMID:40112296 | DOI:10.1097/HEP.0000000000001314

JMIR AI. 2025 Mar 20;4:e65729. doi: 10.2196/65729.

ABSTRACT

BACKGROUND: Recent advancements in Generative Adversarial Networks and large language models (LLMs) have significantly advanced the synthesis and augmentation of medical data. These and other deep learning-based methods offer promising potential for generating high-quality, realistic datasets crucial for improving machine learning applications in health care, particularly in contexts where data privacy and availability are limiting factors. However, challenges remain in accurately capturing the complex associations inherent in medical datasets.

OBJECTIVE: This study evaluates the effectiveness of various Synthetic Data Generation (SDG) methods in replicating the correlation structures inherent in real medical datasets. In addition, it examines their performance in downstream tasks using Random Forests (RFs) as the benchmark model. To provide a comprehensive analysis, alternative models such as eXtreme Gradient Boosting and Gated Additive Tree Ensembles are also considered. We compare the following SDG approaches: Synthetic Populations in R (synthpop), copula, copulagan, Conditional Tabular Generative Adversarial Network (ctgan), tabular variational autoencoder (tvae), and tabula for LLMs.

METHODS: We evaluated synthetic data generation methods using both real-world and simulated datasets. Simulated data consist of 10 Gaussian variables and one binary target variable with varying correlation structures, generated via Cholesky decomposition. Real-world datasets include the body performance dataset with 13,393 samples for fitness classification, the Wisconsin Breast Cancer dataset with 569 samples for tumor diagnosis, and the diabetes dataset with 768 samples for diabetes prediction. Data quality is evaluated by comparing correlation matrices, the propensity score mean-squared error (pMSE) for general utility, and F1-scores for downstream tasks as a specific utility metric, using training on synthetic data and testing on real data.

RESULTS: Our simulation study, supplemented with real-world data analyses, shows that the statistical methods copula and synthpop consistently outperform deep learning approaches across various sample sizes and correlation complexities, with synthpop being the most effective. Deep learning methods, including large LLMs, show mixed performance, particularly with smaller datasets or limited training epochs. LLMs often struggle to replicate numerical dependencies effectively. In contrast, methods like tvae with 10,000 epochs perform comparably well. On the body performance dataset, copulagan achieves the best performance in terms of pMSE. The results also highlight that model utility depends more on the relative correlations between features and the target variable than on the absolute magnitude of correlation matrix differences.

CONCLUSIONS: Statistical methods, particularly synthpop, demonstrate superior robustness and utility preservation for synthetic tabular data compared with deep learning approaches. Copula methods show potential but face limitations with integer variables. Deep Learning methods underperform in this context. Overall, these findings underscore the dominance of statistical methods for synthetic data generation for tabular data, while highlighting the niche potential of deep learning approaches for highly complex datasets, provided adequate resources and tuning.

PMID:40112290 | DOI:10.2196/65729

PLoS One. 2025 Mar 20;20(3):e0319859. doi: 10.1371/journal.pone.0319859. eCollection 2025.

ABSTRACT

Deep Neural Networks (DNN) have achieved state-of-the-art performance in medical image classification and are increasingly being used for disease diagnosis. However, these models are quite complex and that necessitates the need to reduce the model complexity for their use in low-power edge applications that are becoming common. The model complexity reduction techniques in most cases comprise of time-consuming operations and are often associated with a loss of model performance in proportion to the model size reduction. In this paper, we propose a simplified model complexity reduction technique based on reducing the number of channels for any DNN and demonstrate the complexity reduction approaches for the ResNet-50 model integration in low-power devices. The model performance of the proposed models was evaluated for multiclass classification of CXR images, as normal, pneumonia, and COVID-19 classes. We demonstrate successive size reductions down to 75%, 87%, and 93% reduction with an acceptable classification performance reduction of 0.5%, 0.5%, and 0.8% respectively. We also provide the results for the model generalization, and visualization with Grad-CAM at an acceptable performance and interpretable level. In addition, a theoretical VLSI architecture for the best performing architecture has been presented.

PMID:40112278 | DOI:10.1371/journal.pone.0319859

Adv Sci (Weinh). 2025 Mar 20:e2413786. doi: 10.1002/advs.202413786. Online ahead of print.

ABSTRACT

Mental disorders are a representative type of brain disorder, including anxiety, major depressive depression (MDD), and autism spectrum disorder (ASD), that are caused by multiple etiologies, including genetic heterogeneity, epigenetic dysregulation, and aberrant morphological and biochemical conditions. Psychedelic drugs such as psilocybin and lysergic acid diethylamide (LSD) have been renewed as fascinating treatment options and have gradually demonstrated potential therapeutic effects in mental disorders. However, the multifaceted conditions of psychiatric disorders resulting from individuality, complex genetic interplay, and intricate neural circuits impact the systemic pharmacology of psychedelics, which disturbs the integration of mechanisms that may result in dissimilar medicinal efficiency. The precise prescription of psychedelic drugs remains unclear, and advanced approaches are needed to optimize drug development. Here, recent studies demonstrating the diverse pharmacological effects of psychedelics in mental disorders are reviewed, and emerging perspectives on structural function, the microbiota-gut-brain axis, and the transcriptome are discussed. Moreover, the applicability of deep learning is highlighted for the development of drugs on the basis of big data. These approaches may provide insight into pharmacological mechanisms and interindividual factors to enhance drug discovery and development for advanced precision medicine.

PMID:40112231 | DOI:10.1002/advs.202413786

PLoS One. 2025 Mar 20;20(3):e0319540. doi: 10.1371/journal.pone.0319540. eCollection 2025.

ABSTRACT

Under the increasing pressure of global climate change, water conservation (WC) in semi-arid regions is experiencing unprecedented levels of stress. WC involves complex, nonlinear interactions among ecosystem components like vegetation, soil structure, and topography, complicating research. This study introduces a novel approach combining InVEST modeling, spatiotemporal transfer of Water Conservation Reserves (WCR), and deep learning to uncover regional WC patterns and driving mechanisms. The InVEST model evaluates Xiong'an New Area's WC characteristics from 2000 to 2020, showing a 74% average increase in WC depth with an inverted "V" spatial distribution. Spatiotemporal analysis identifies temporal changes, spatial patterns of WCR and land use, and key protection areas, revealing that the WCR in Xiong'an New Area primarily shifts from the lowest WCR areas to lower WCR areas. The potential enhancement areas of WCR are concentrated in the northern region. Deep learning quantifies data complexity, highlighting critical factors like land use, precipitation, and drought influencing WC. This detailed approach enables the development of personalized WC zones and strategies, offering new insights into managing complex spatial and temporal WC data.

PMID:40112018 | DOI:10.1371/journal.pone.0319540

PLoS One. 2025 Mar 20;20(3):e0316367. doi: 10.1371/journal.pone.0316367. eCollection 2025.

ABSTRACT

Extreme heat waves are causing widespread concern for comprehensive studies on their ecological and societal implications. With the ongoing rise in global temperatures, precise forecasting of heatwaves becomes increasingly crucial for proactive planning and ensuring safety. This study investigates the efficacy of deep learning (DL) models, including Artificial Neural Network (ANN), Conolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), using five years of meteorological data from Pakistan Meteorological Department (PMD), by integrating Explainable AI (XAI) techniques to enhance the interpretability of models. Although Weather forecasting has advanced in predicting sunshine, rain, clouds, and general weather patterns, the study of extreme heat, particularly using advanced computer models, remains largely unexplored, overlooking this gap risks significant disruptions in daily life. Our study addresses this gap by collecting five years of weather dataset and developing a comprehensive framework integrating DL and XAI models for extreme heat prediction. Key variables such as temperature, pressure, humidity, wind, and precipitation are examined. Our findings demonstrate that the LSTM model outperforms others with a lead time of 1-3 days and minimal error metrics, achieving an accuracy of 96.2%. Through the utilization of SHAP and LIME XAI methods, we elucidate the significance of humidity and maximum temperature in accurately predicting extreme heat events. Overall, this study emphasizes how important it is to investigate intricate DL models that integrate XAI for the prediction of extreme heat. Making these models understood allows us to identify important parameters, improving heatwave forecasting accuracy and guiding risk-reduction strategies.

PMID:40111979 | DOI:10.1371/journal.pone.0316367

PLoS One. 2025 Mar 20;20(3):e0313946. doi: 10.1371/journal.pone.0313946. eCollection 2025.

ABSTRACT

This study aims to explore a data-driven cultural background fusion method to improve the accuracy of environmental art image classification. A novel Dual Kernel Squeeze and Excitation Network (DKSE-Net) model is proposed for the complex cultural background and diverse visual representation in environmental art images. This model combines the advantages of adaptive adjustment of receptive fields using the Selective Kernel Network (SKNet) and the characteristics of enhancing channel features using the Squeeze and Excitation Network (SENet). Constructing a DKSE module can comprehensively extract the global and local features of the image. The DKSE module adopts various techniques such as dilated convolution, L2 regularization, Dropout, etc. in the multi-layer convolution process. Firstly, dilated convolution is introduced into the initial layer of the model to enhance the original art image's feature capture ability. Secondly, the pointwise convolution is constrained by L2 regularization, thus enhancing the accuracy and stability of the convolution. Finally, the Dropout technology randomly discards the feature maps before and after global average pooling to prevent overfitting and improve the model's generalization ability. On this basis, the Rectified Linear Unit activation function and depthwise convolution are introduced after the second layer convolution, and batch normalization is performed to improve the efficiency and robustness of feature extraction. The experimental results indicate that the proposed DKSE-Net model significantly outperforms traditional Convolutional Neural Networks (CNNs) and other existing state-of-the-art models in the task of environmental art image classification. Specifically, the DKSE-Net model achieves a classification accuracy of 92.7%, 3.5 percentage points higher than the comparative models. Moreover, when processing images with complex cultural backgrounds, DKSE-Net can effectively integrate different cultural features, achieving a higher classification accuracy and stability. This enhancement in performance provides an important reference for image classification research based on the fusion of cultural backgrounds and demonstrates the broad potential of deep learning technology in the environmental art field.

PMID:40111961 | DOI:10.1371/journal.pone.0313946

IEEE Trans Neural Netw Learn Syst. 2025 Mar 20;PP. doi: 10.1109/TNNLS.2025.3543926. Online ahead of print.

ABSTRACT

This article presents a unified framework for dynamics modeling and control design using deep learning, focusing on incorporating prior side information on stabilizability. Control theory provides systematic techniques for designing feedback systems while ensuring fundamental properties such as stabilizability, which are crucial for practical control applications. However, conventional data-driven approaches often overlook or struggle to explicitly incorporate such control properties into learned models. To address this, we introduce a novel neural network (NN)-based approach that concurrently learns the system dynamics, a stabilizing feedback controller, and a Lyapunov function for the closed-loop system, thus explicitly guaranteeing stabilizability in the learned model. Our proposed deep learning framework is versatile and applicable across a wide range of control problems, including safety control, -gain control, passivation, and solutions to Hamilton-Jacobi inequalities. By embedding stabilizability as a core property within the learning process, our method allows for the development of learned models that are not only data-driven but also grounded in control-theoretic guarantees, greatly enhancing their utility in real-world control applications. This article includes examples that demonstrate the effectiveness of this approach, showcasing the stability and control performance improvements achieved in various control scenarios. The methods proposed in this article can be easily applied to modeling without control design. The code has been open-sourced and is available at https://github.com/kashctrl/Deep_Stabilizable_Models.

PMID:40111782 | DOI:10.1109/TNNLS.2025.3543926

IEEE J Biomed Health Inform. 2025 Mar 20;PP. doi: 10.1109/JBHI.2025.3553217. Online ahead of print.

ABSTRACT

Deep learning offers efficient solutions for drug-target interaction prediction, but current methods often fail to capture the full complexity of multi-modal data (i.e. sequence, graphs, and three-dimensional structures), limiting both performance and generalization. Here, we present UnitedDTA, a novel explainable deep learning framework capable of integrating multi-modal biomolecule data to improve the binding affinity prediction, especially for novel (unseen) drugs and targets. UnitedDTA enables automatic learning unified discriminative representations from multi-modality data via contrastive learning and cross-attention mechanisms for cross-modality alignment and integration. Comparative results on multiple benchmark datasets show that UnitedDTA significantly outperforms the state-of-the-art drug-target affinity prediction methods and exhibits better generalization ability in predicting unseen drug-target pairs. More importantly, unlike most "black-box" deep learning methods, our well-established model offers better interpretability which enables us to directly infer the important substructures of the drug-target complexes that influence the binding activity, thus providing the insights in unveiling the binding preferences. Moreover, by extending UnitedDTA to other downstream tasks (e.g. molecular property prediction), we showcase the proposed multi-modal representation learning is capable of capturing the latent molecular representations that are closely associated with the molecular property, demonstrating the broad application potential for advancing the drug discovery process.

PMID:40111772 | DOI:10.1109/JBHI.2025.3553217

Adv Sci (Weinh). 2025 Mar 20:e2415007. doi: 10.1002/advs.202415007. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is characterized by excessive fibroblast recruitment and persistent extracellular matrix deposition at sites of tissue injury, leading to severe morbidity and mortality. However, the precise mechanisms by which fibroblasts contribute to IPF pathogenesis remain poorly understood. The study reveals that Sema3E and its receptor Plexin D1 are significantly overexpressed in the lungs of IPF patients and bleomycin (BLM)-induced lung fibrotic mice. Elevated plasma levels of Sema3E in IPF patients are negatively correlated with lung function. Importantly, Sema3E in IPF lungs predominantly exists as the P61-Sema3E. The knockdown of Sema3E or Plexin D1 effectively inhibits fibroblast activation, proliferation, and migration. Mechanistically, Furin-mediated cleavage of P87-Sema3E into P61-Sema3E drives these pro-fibrotic activities, with P61-Sema3E-PlexinD1 axis promoting fibroblast activation, proliferation, and migration by affecting the phosphorylation of ErbB2, which subsequently activates the ErbB2 pathways. Additionally, Furin inhibition reduces fibroblast activity by decreasing P61-Sema3E production. In vivo, both whole-lung Sema3E knockdown and fibroblast-specific Sema3E knockout confer protection against BLM-induced lung fibrosis. These findings underscore the crucial role of the P61-Sema3E-Plexin D1 axis in IPF pathogenesis and suggest that targeting this pathway may hold promise for the development of novel therapeutic strategies for IPF treatment.

PMID:40112179 | DOI:10.1002/advs.202415007

Am J Physiol Cell Physiol. 2025 Mar 20. doi: 10.1152/ajpcell.00374.2024. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive and degenerative interstitial lung disease characterized by complex etiology, unclear pathogenesis, and high mortality. Long non-coding RNAs (lncRNAs) have been identified as key regulators in modulating the initiation, maintenance, and progression of pulmonary fibrosis. However, the precise pathological mechanisms through which lncRNAs are involved in IPF remain limited and require further elucidation. A novel lncABCE1-5 was identified as significantly decreased by an ncRNA microarray analysis in our eight IPF lung samples compared with three donor tissues and validated by qRT-PCR analysis in clinical lung samples. To investigate the biological function of ABCE1-5, we performed loss-and gain-of-function experiments in vitro and in vivo. LncABCE1-5 silencing promoted A549 cell migration and A549 and BEAS-2B cell apoptosis, while enhancing the expression of proteins associated with extracellular matrix deposition, whereas overexpression of ABCE1-5 partially attenuated TGF-β-induced fibrogenesis. Forced ABCE1-5 expression by intratracheal injection of adeno-associated virus 6 (AAV6) revealing the anti-fibrotic effect of ABCE1-5 in BLM-treated mice. Mechanistically, RNA pull-down-mass spectrometry and RIP assay demonstrated that ABCE1-5 directly binds to keratin14 (krt14) sequences, potentially impeding its expression by perturbing mRNA stability. Furthermore, decreased ABCE1-5 levels can promote krt14 expression and enhance the phosphorylation of both mTOR and Akt; overexpression of ABCE1-5 in BLM mouse lung tissue significantly attenuated the elevated levels of p-mTOR and p-AKT. Knockdown of krt14 reversed the activation of mTOR signaling mediated by ABCE1-5 silencing. Collectively, the downregulation of ABCE1-5 mediated krt14 activation, thereby activating mTOR/AKT signaling, to facilitate pulmonary fibrosis progression in IPF.

PMID:40111939 | DOI:10.1152/ajpcell.00374.2024

Plant Physiol. 2025 Mar 20:kiaf109. doi: 10.1093/plphys/kiaf109. Online ahead of print.

ABSTRACT

Herbivores sharing host plants are often temporally and spatially separated, limiting direct interactions between them. Nevertheless, as observed in numerous aboveground study systems, they can reciprocally influence each other via systemically induced plant responses. In contrast, examples of such plant-mediated interactions between belowground herbivores are scarce; however, we postulated that they similarly occur, given the large diversity of root-interacting soil organisms. To test this hypothesis, we analyzed the performance of cabbage root fly (Delia radicum) larvae feeding on the main roots of field mustard (Brassica rapa) plants whose fine roots were infected by the root-knot nematode (Meloidogyne incognita). Simultaneously, we studied the effects of M. incognita on D. radicum-induced defense responses and the accumulation of primary metabolites in the main root. We observed that almost 1.5 times as many D. radicum adults emerged from nematode-infected plants, indicating a facilitation effect of M. incognita infection. Although we observed increases in the accumulation of proteins and two essential amino acids, the strongest effect of nematode infection was visible in the defense response to D. radicum. We observed a 1.5 times higher accumulation of the defense-related phytohormone JA-Ile in response to D. radicum on nematode-infected plants, coinciding with a 75% increase in indole glucosinolate concentrations. Contrastingly, concentrations of aliphatic glucosinolates, secondary metabolites negatively affecting D. radicum, were 10-25% lower in nematode-infected plants. We hypothesize that the attenuated aliphatic glucosinolate concentrations result from antagonistic interactions between biosynthetic pathways of both glucosinolate classes, which was reflected in the expression of key biosynthesis genes. Our results provide explicit evidence of plant-mediated interactions between belowground organisms, likely via systemically induced responses in roots.

PMID:40112263 | DOI:10.1093/plphys/kiaf109

Plant Physiol. 2025 Mar 20:kiaf105. doi: 10.1093/plphys/kiaf105. Online ahead of print.

ABSTRACT

Abiotic and biotic stresses pose serious threats to plant productivity. Elucidating the gene regulatory networks involved in plant stress responses is essential for developing future breeding programs and innovative agricultural products. Here, we introduce the AtSRGA (Arabidopsis thaliana Stress Responsive Gene Atlas), a user-friendly application facilitating the retrieval of stress-responsive genes in Arabidopsis (Arabidopsis thaliana). The application was developed using 1,131 microarray and 1,050 RNA sequencing datasets obtained from public databases. These datasets correspond to 11 stress-related conditions, namely abscisic acid, cold, drought, heat, high light, hypoxia, osmotic stress, oxidative stress, salt, wounding, and Pseudomonas syringae pv. tomato DC3000. Using a modified meta-analysis technique known as the vote-counting method, we computed integrated scores to evaluate stress responsiveness for each condition across multiple studies. AtSRGA visualizes gene behavior under 11 stress conditions and offers an interactive, user-friendly interface accessible to all researchers. It presents a comprehensive heatmap of stress-responsive genes, facilitating the comparative analysis of individual stress responses and groups of genes responding to multiple stresses. We validated the expression patterns of several high-scoring genes of unknown function under cold and heat stress using RT-qPCR, thus demonstrating that our application helps select targets to understand stress-responsive gene networks in Arabidopsis. AtSRGA will improve the screening of stress-responsive genes in Arabidopsis, thereby supporting the advancement of plant science toward a sustainable society.

PMID:40112239 | DOI:10.1093/plphys/kiaf105

J Proteome Res. 2025 Mar 20. doi: 10.1021/acs.jproteome.5c00092. Online ahead of print.

ABSTRACT

Proteomics data-dependent acquisition data sets collected with high-resolution mass-spectrometry (MS) can achieve very high-quality results, but nearly every analysis yields results that are thresholded at some accepted false discovery rate, meaning that a substantial number of results are incorrect. For study conclusions that rely on a small number of peptide-spectrum matches being correct, it is thus important to examine at least some crucial spectra to ensure that they are not one of the incorrect identifications. We present Quetzal, a peptide fragment ion spectrum annotation tool to assist researchers in annotating and examining such spectra to ensure that they correctly support study conclusions. We describe how Quetzal annotates spectra using the new Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) mzPAF standard for fragment ion peak annotation, including the Python-based code, a web-service end point that provides annotation services, and a web-based application for annotating spectra and producing publication-quality figures. We illustrate its functionality with several annotated spectra of varying complexity. Quetzal provides easily accessible functionality that can assist in the effort to ensure and demonstrate that crucial spectra support study conclusions. Quetzal is publicly available at https://proteomecentral.proteomexchange.org/quetzal/.

PMID:40111914 | DOI:10.1021/acs.jproteome.5c00092

J Comp Physiol B. 2025 Mar 20. doi: 10.1007/s00360-025-01608-1. Online ahead of print.

ABSTRACT

Widespread species often display traits of generalists, yet local adaptations may limit their ability to cope with diverse environmental conditions. With climate change being a pressing issue, distinguishing between the general ecological and physiological capacities of a species and those of individual populations is vital for assessing the capability to adapt rapidly to changing habitats. Despite its importance, physiological variation across broad range distributions, particularly among free-ranging bats in natural environments, has rarely been assessed. Studies focusing on physiological variation among different populations across seasons are even more limited. We investigated physiological variation in the Malagasy Trident Bat Triaenops menamena across three different roost types in Madagascar during the wet and dry season, examining aspects such as energy regimes, body temperature, and roost microclimates. We focused on patterns of torpor in relation to roosting conditions. We hypothesized that torpor occurrence would be higher during the colder, more demanding dry season. We predicted that populations roosting in more variable microclimates would expend less energy than those in mores stable ones due to more frequent use of torpor and greater metabolic rate reductions. Our findings highlight complex thermoregulatory strategies, with varying torpor expression across seasons and roosts. We observed an overall higher energy expenditure during the wet season but also greater energy savings during torpor in that season, regardless of roost type. We found that reductions in metabolic rate were positively correlated with greater fluctuations in ambient conditions, demonstrating these bats' adaptability to dynamic environments. Notably, we observed diverse torpor patterns, indicating the species' ability to use prolonged torpor under extreme conditions. This individual-level variation is crucial for adaptation to changing environmental conditions. Moreover, the flexibility in body temperature during torpor suggests caution in relying solely on it as an indicator for torpor use. Our study emphasizes the necessity to investigate thermoregulatory responses across different populations in their respective habitats to fully understand a species' adaptive potential.

PMID:40111435 | DOI:10.1007/s00360-025-01608-1

Microbiol Spectr. 2025 Mar 20:e0298724. doi: 10.1128/spectrum.02987-24. Online ahead of print.

ABSTRACT

Candida albicans filamentation and biofilm formation are key virulence factors tied to tissue invasion and antifungal tolerance. Pilocarpine hydrochloride (PHCl), a muscarinic receptor agonist, inhibits biofilm maturation, although its mechanism remains unclear. We explored PHCl effects by analyzing sphingolipid and lipid composition and proteomics in treated C. albicans SC5314 biofilms. PHCl significantly decreased polar lipid and ergosterol levels in biofilms while inducing phytoceramide and glucosylceramide accumulation. PHCl also induced reactive oxygen species and early apoptosis. Proteomic analysis revealed that PHCl treatment downregulated proteins associated with metabolism, cell wall remodeling, and DNA repair in biofilms to levels comparable to those observed in planktonic cells. Consistent with ergosterol reduction, Erg2 was found to be reduced. Overall, PHCl disrupts key pathways essential for biofilm integrity, decreasing its stability and promoting surface detachment, underscoring its potential as a versatile antifungal compound.

IMPORTANCE: Candida albicans filamentation and biofilm formation represent crucial virulence factors promoting fungus persistence and drug tolerance. The common eukaryotic nature of mammalian cells poses significant limitations to the development of new active nontoxic compounds. Understanding the mechanism underlying PHCl inhibitory activity on yeast-hypha transition, biofilm adhesion, and maturation can pave the way to efficient drug repurposing in a field where pharmaceutical investment is lacking.

PMID:40111054 | DOI:10.1128/spectrum.02987-24