JMIR Med Inform. 2025 May 6;13:e66556. doi: 10.2196/66556.

NO ABSTRACT

PMID:40327366 | DOI:10.2196/66556

Mol Biol Cell. 2025 May 6:mbcE25010009. doi: 10.1091/mbc.E25-01-0009. Online ahead of print.

ABSTRACT

The life cycle of eukaryotic microorganisms involves complex transitions between states such as dormancy, mating, meiosis, and cell division, which are often studied independently from each other. Therefore, most microbial life cycles are theoretical reconstructions from partial observations of cellular states. Here we show that complete microbial life cycles can be directly and continuously studied by combining microfluidic culturing, life cycle stage-specific segmentation of micrographs, and a novel cell tracking algorithm, FIEST, based on deep learning video frame interpolation. As proof of principle, we quantitatively imaged and compared cell growth and the activity state of the cell division kinase, Cdk1, across the life cycle of Saccharomyces cerevisiae for up to three sexually reproducing generations. Our analysis of S. cerevisiae's life cycle provided the following new insights: (1) the accumulation of cell cycle regulators, such as Whi5, is tailored to each life cycle stage; (2) cell growth always preceded exit from non-proliferative states in our conditions; (3) the temporal coordination of meiotic events is the same across sexually reproducing populations when each generation is exposed to same conditions; (4) information such as cell size and morphology resets after each sexual reproduction cycle. Image processing and tracking algorithms are available as the Python package Yeastvision, which could be used study pathogens such as Candida glabrata, Cryptococcus neoformans, Colletotrichum acutatum, and other unicellular systems. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text].

PMID:40327364 | DOI:10.1091/mbc.E25-01-0009

JMIR Mhealth Uhealth. 2025 May 6;13:e70473. doi: 10.2196/70473.

ABSTRACT

BACKGROUND: Encouraging physical activity improves mental health and is recommended in workplace mental health guidelines. Although mobile health (mHealth) interventions are promising for physical activity promotion, their impact on mental health outcomes is inconsistent. Furthermore, poor user retention rates of mHealth apps pose a major challenge.

OBJECTIVE: This study aimed to examine the effectiveness and implementation outcomes of the smartphone app ASHARE in Japanese workplace settings, leveraging a deep learning model to monitor depression and anxiety through physical activity.

METHODS: This hybrid effectiveness-implementation trial was a 3-month nonrandomized controlled trial conducted from October 2023 to September 2024. Work units and employees were recruited and allocated to the intervention or active control group based on preference. The intervention group installed the ASHARE app, whereas the control group participated in an existing multicomponent workplace program promoting physical activity. Changes in physical activity and psychological distress levels were compared between the groups. User retention rates, participation rates, acceptability, appropriateness, feasibility, satisfaction, and potential harm were also assessed.

RESULTS: A total of 84 employees from 7 work units participated (67 from 5 units in the intervention group and 17 from 2 units in the control group). In total, 78 employees completed the 3-month follow-up survey (follow-up rate: 93%). Both groups showed increased physical activity, and the intervention group showed reduced psychological distress; however, the differences between groups were not statistically significant (P=.20; P=.36). In a sensitivity analysis of protocol-compliant employees (n=21), psychological distress levels were significantly reduced in the intervention group compared with the control group (coefficient=-3.68, SE 1.65; P=.03). The app's 3-month user retention rate was 20% (12/61), which was lower than the participation rate in each component of the control programs. Implementation outcomes evaluated by employees were less favorable in the intervention group than in the control group, whereas health promotion managers found them to be similar.

CONCLUSIONS: The ASHARE app did not show superior effectiveness compared with an existing multicomponent workplace program for promoting physical activity. An implementation gap may exist between health promotion managers and employees, possibly contributing to the app's low user retention rate. Future research should focus on examining the effectiveness of strategies to get engagement from managers and from segments of employees with favorable responses in the workplace at an early stage.

PMID:40327360 | DOI:10.2196/70473

Med Biol Eng Comput. 2025 May 6. doi: 10.1007/s11517-025-03357-3. Online ahead of print.

ABSTRACT

The corticospinal tract (CST) is the primary neural pathway responsible for voluntary motor functions, and preoperative CST reconstruction is crucial for preserving nerve functions during neurosurgery. Diffusion magnetic resonance imaging-based tractography is the only noninvasive method to preoperatively reconstruct CST in clinical practice. However, for the largesize bundle CST with complex fiber geometry (fanning fibers), reconstructing its full extent remains challenging with local-derived methods without incorporating global information. Especially in the presence of tumors, the mass effect and partial volume effect cause abnormal diffusion signals. In this work, a CST reconstruction tractography method based on a novel direction filter was proposed, designed to ensure robust CST reconstruction in the clinical dataset with tumors. A direction filter based on a fourth-order differential equation was introduced for global direction estimation. By considering the spatial consistency and leveraging anatomical prior knowledge, the direction filter was computed by minimizing the energy between the target directions and initial fiber directions. On the basis of the new directions corresponding to CST obtained by the direction filter, the fiber tracking method was implemented to reconstruct the fiber trajectory. Additionally, a deep learning-based method along with tractography template prior information was employed to generate the regions of interest (ROIs) and initial fiber directions. Experimental results showed that the proposed method yields higher valid connections and lower no connections and exhibits the fewest broken fibers and short-connected fibers. The proposed method offers an effective tool to enhance CST-related surgical outcomes by optimizing tumor resection and preserving CST.

PMID:40327206 | DOI:10.1007/s11517-025-03357-3

Med Biol Eng Comput. 2025 May 6. doi: 10.1007/s11517-025-03372-4. Online ahead of print.

ABSTRACT

Rehabilitation exercises are critical for recovering from motor dysfunction caused by neurological conditions like stroke, back pain, Parkinson's disease, and spinal cord injuries. Traditionally, these exercises require constant monitoring by therapists, which is time-consuming and costly, often leading to therapist shortages. This paper introduces a deep learning model, convolutional neural network - squeeze excitation (CNN-SE), to automate rehabilitation exercise assessment. By optimizing its parameters with the grey wolf optimization algorithm, the model was fine-tuned for optimal performance. The model's effectiveness was tested on both healthy and unhealthy participants with motor dysfunction, providing a comprehensive evaluation of its capabilities. To interpret the model's decisions and understand its inner workings, we employed Shapley additive explanations (SHAP) to analyze feature importance at each time step. Our CNN-SE model achieved a state-of-the-art mean absolute deviation of 0.127 on the KIMORE dataset and a comparable MAD of 0.014 on the UI-PRMD dataset across various exercises, demonstrating its potential to provide a cost-effective, efficient alternative to traditional therapist-led evaluations.

PMID:40327204 | DOI:10.1007/s11517-025-03372-4

EJNMMI Phys. 2025 May 6;12(1):43. doi: 10.1186/s40658-025-00756-1.

ABSTRACT

PURPOSE: Dopamine transporter (DAT) SPECT is an effective tool for early Parkinson's disease (PD) detection and heavily hampered by attenuation. Attenuation correction (AC) is the most important correction among other corrections. Transfer learning (TL) with fine-tuning (FT) a pre-trained model has shown potential in enhancing deep learning (DL)-based AC methods. In this study, we investigate leveraging realistic Monte Carlo (MC) simulation data to create a pre-trained model for TL-based AC (TLAC) to improve AC performance for DAT SPECT.

METHODS: A total number of 200 digital brain phantoms with realistic 99mTc-TRODAT-1 distribution was used to generate realistic noisy SPECT projections using MC SIMIND program and an analytical projector. One hundred real clinical 99mTc-TRODAT-1 brain SPECT data were also retrospectively analyzed. All projections were reconstructed with and without CT-based attenuation correction (CTAC/NAC). A 3D conditional generative adversarial network (cGAN) was pre-trained using 200 pairs of simulated NAC and CTAC SPECT data. Subsequently, 8, 24, and 80 pairs of clinical NAC and CTAC DAT SPECT data were employed to fine-tune the pre-trained U-Net generator of cGAN (TLAC-MC). Comparisons were made against without FT (DLAC-MC), training on purely limited clinical data (DLAC-CLI), clinical data with data augmentation (DLAC-AUG), mixed MC and clinical data (DLAC-MIX), TL using analytical simulation data (TLAC-ANA), and Chang's AC (ChangAC). All datasets used for DL-based methods were split to 7/8 for training and 1/8 for validation, and a 1-/2-/5-fold cross-validation were applied to test all 100 clinical datasets, depending on the numbers of clinical data used in the training model.

RESULTS: With 8 available clinical datasets, TLAC-MC achieved the best result in Normalized Mean Squared Error (NMSE) and Structural Similarity Index Measure (SSIM) (TLAC-MC; NMSE = 0.0143 ± 0.0082/SSIM = 0.9355 ± 0.0203), followed by DLAC-AUG, DLAC-MIX, TLAC-ANA, DLAC-CLI, DLAC-MC, ChangAC and NAC. Similar trends exist when increasing the number of clinical datasets. For TL-based AC methods, the fewer clinical datasets available for FT, the greater the improvement as compared to DLAC-CLI using the same number of clinical datasets for training. Joint histograms analysis and Bland-Altman plots of SBR results also demonstrate consistent findings.

CONCLUSION: TLAC is feasible for DAT SPECT with a pre-trained model generated purely based on simulation data. TLAC-MC demonstrates superior performance over other DL-based AC methods, particularly when limited clinical datasets are available. The closer the pre-training data is to the target domain, the better the performance of the TLAC model.

PMID:40327202 | DOI:10.1186/s40658-025-00756-1

Eur Spine J. 2025 May 6. doi: 10.1007/s00586-025-08895-w. Online ahead of print.

ABSTRACT

PURPOSE: Scoliosis is a prevalent spine deformity that impacts millions of children globally. The Cobb angle, a crucial and widely-accepted metric, serves as the "gold standard" for assessing scoliosis in patients. However, the traditional manual measurement of spine curvature is time-consuming and labor-intensive. It also comes with issues like intra - and inter-observer variations. Moreover, accurately and robustly evaluating Cobb angles is extremely challenging. This is because it necessitates the correct identification of all the required vertebrae in both the anterior-posterior (AP) and lateral (LAT) views of full-spine digital radiography (DR).

METHODS: To solve these challenges, a deep learning-based framework is developed to fully automatically measure patient Cobb angels from full-spine DR of both AP and LAT views. First, a deep learning network was used to distinguish AP and LAT views. Then the region of interest (ROI) of the whole spine was located and extracted. Subsequently, a detection network was applied to detect and identify the boundaries and locations, the types, and the four corner points of each spinal vertebra. Finally, the Cobb angles was measured automatically. When taking into account the location, recognition, and key points detection of spinal vertebrae, YOLOv8 architecture with CBAM module was adopted as the backbone.

RESULTS: A total of 1,163 AP view and 1,378 LAT view DR images were used to train and evaluate the models. Experimental results in the evaluation testing showed a mean Cobb angle error of 2.56° for AP view and 2.498° for LAT view DR images. The intra-class correlation coefficient (ICC) with 95% confidence interval (CI) was 0.956 (0.932, 0.972) for AP view and 0.925 (0.888, 0.952) for LAT view. The Pearson correlation coefficient was 0.961 for AP view and 0.930 for LAT view. In the comprehensive reader study, for the major curve, a mean Cobb angle error of 3.918°, an ICC of 0.943 (0.912, 0.965), and a high correlation coefficient of 0.960 were obtained.

CONCLUSION: The results showed that the proposed framework had a significant accuracy and consistency advantage in measuring Cobb angle, which not only validated the effectiveness of the algorithm, but also provided strong support for the diagnosis of clinicians.

PMID:40327070 | DOI:10.1007/s00586-025-08895-w

Radiology. 2025 May;315(2):e240714. doi: 10.1148/radiol.240714.

ABSTRACT

Background Four-dimensional (4D) flow MRI provides assessment of thoracic aorta hemodynamic measures that are increasingly recognized as important biomarkers for risk assessment. However, long acquisition times and cumbersome data analysis limit widespread availability. Purpose To evaluate the feasibility and accuracy of a generative artificial intelligence (AI) approach (fluid physics-informed cycle generative adversarial network [FPI-CycleGAN]) in quantifying aorta hemodynamics directly from anatomic input as an alternative to 4D flow MRI. Materials and Methods Patients were retrospectively identified from a dataset of clinical cardiothoracic MRI examinations performed between November 2011 and July 2020. All patients underwent aortic 4D flow MRI, which served as a reference standard for training and testing of FPI-CycleGANs. A three-dimensional (3D) segmentation of the aortic geometry was used as the only input to predict systolic aortic hemodynamics, with separate networks for bicuspid aortic valve (BAV) (994 in the training set and 248 in the test set) and tricuspid aortic valve (TAV) (419 in the training set and 104 in the test set). Voxel-by-voxel and regional analyses were used to quantify and compare (AI vs the reference standard, 4D flow) systolic velocity vector fields, peak velocity, wall shear stress (WSS), and classification of aortic valve stenosis. Results In total, 1765 patients (median age, 53 years [IQR, 41-63 years]; 1242 patients had BAV and 523 had TAV) were included. Mean AI computation time was 0.15 second ± 0.11 (SD), and total training was 1500 and 3600 minutes for the TAV and BAV networks, respectively. The FPI-CycleGAN predicted systolic 3D velocity vector fields accurately, with low bias (<0.01 m/sec) and excellent limits of agreements (±0.06-0.08 m/sec). For peak velocities and WSS, there was strong agreement between FPI-CycleGAN and 4D flow (r2 = 0.930-0.957 [P < .001], with relative differences of 6.2%-9.8%). AI accurately classified aortic valve stenosis severity in 85.8% of patients (302 of 352) (κ = 0.80 [95% CI: 0.71, 0.89]). The FPI-CycleGAN was robust to one- and two-voxel dilation and erosion (bias, -0.05 to 0.1 m/sec) and ±5° rotation (bias, -0.02 to 0.03 m/sec) of the input data. The application of the trained FPI-CycleGAN in an external test set with contrast-enhanced MR angiography (n = 60 patients) as AI input data demonstrated strong to excellent performance for peak velocities and WSS (r2 = 0.944-0.965 [P < .001], with relative differences of 6.2%-9.2%). Conclusion Aorta 3D hemodynamics can be derived from anatomic input in less than 1 second using an FPI-CycleGAN and demonstrate strong agreement with in vivo 4D flow MRI systolic hemodynamics. © RSNA, 2025 Supplemental material is available for this article.

PMID:40326877 | DOI:10.1148/radiol.240714

J Acoust Soc Am. 2025 May 1;157(5):3508-3523. doi: 10.1121/10.0036456.

ABSTRACT

Underwater passive acoustic recognition, which focuses on classifying targets based on ship-radiated noise, is a key challenge in underwater acoustics. Deep learning-based methods have gained popularity in recent years because of their strong performance. However, these methods often fail to generalize well in real-world scenarios. This work reveals one underlying challenge: the characteristics of ship-radiated noise are influenced by factors such as vessel structures and propulsion systems. Although vessels of the same type may exhibit different patterns in these aspects, vessels of different categories share similarities. As a result, data-driven models often tend to overemphasize individual-specific features, leading to "overfitting" and poor generalization. The momentum-based adversarial training (MBAT) framework is proposed to mitigate this challenge. MBAT leverages a momentum adversarial strategy to use category information and individual vessel relationships, helping extract class-discriminative features. A homoscedastic uncertainty algorithm is employed to balance the optimization objectives of category-related and vessel-specific features. These strategies allow the model to capture category-discriminative patterns more effectively and generalize to unseen targets. Experiments on DeepShip and ShipsEar demonstrate that MBAT significantly improves generalization capability on unseen individual vessels, outperforming existing state-of-the-art methods. Visualizations further confirm the efficacy and necessity of the proposed approach.

PMID:40326792 | DOI:10.1121/10.0036456

Bioinformatics. 2025 May 6:btaf277. doi: 10.1093/bioinformatics/btaf277. Online ahead of print.

ABSTRACT

MOTIVATION: The availability of very large numbers of protein structures from accurate computational methods poses new challenges in storing, searching and detecting relationships between these structures. In particular, the new-found abundance of multi-domain structures in the AlphaFold structure database introduces challenges for traditional structure comparison methods.

RESULTS: We address these challenges using a fast, embedding-based structure comparison method called Foldclass which detects structural similarity between protein domains. We demonstrate the accuracy of Foldclass embeddings for homology detection. In combination with a recently developed deep learning-based automatic domain segmentation tool Merizo, we develop Merizo-search, which first segments multi-domain query structures into domains, and then searches a Foldclass embedding database to determine the top matches for each constituent domain. Combining the ability of Merizo to accurately segment complete chains into domains, and Foldclass to embed and detect similar domains, the Merizo-search tool can be used to rapidly detect per-domain similarities for complete chains, taking as little as 2 minutes to search all 365 million domains from the Encyclopedia of Domains. We anticipate that these tools will enable many analyses using the wealth of predicted structural data now available.

AVAILABILITY: Foldclass and Merizo-search are available at https://github.com/psipred/merizo_search. The version used in this publication is archived at https://doi.org/10.5281/zenodo.15120830. Merizo-search is also available on the PSIPRED web server at http://bioinf.cs.ucl.ac.uk/psipred.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40326701 | DOI:10.1093/bioinformatics/btaf277

Gen Physiol Biophys. 2025 May;44(3):187-200. doi: 10.4149/gpb_2025008.

ABSTRACT

The abnormal proliferation and activation of fibroblasts have been implicated in idiopathic pulmonary fibrosis. Herein, the present research explored the impacts of the relationship between hydroxysafflor yellow A (HSYA) and a disintegrin and metalloproteinase 17 (ADAM17) on fibroblast activation, which can provide novel insight into the treatment and management of idiopathic pulmonary fibrosis. MRC-5 fibroblasts were firstly activated with TGF-β1, followed by measurement of ADAM17 expression through qRT-PCR and Western blot. Fibrosis-related gene and protein expression levels, cell viability, proliferation, migration, and fibroblast-to-myofibroblast transdifferentiation were determined by qRT-PCR and Western blot, MTS, EdU, Transwell, and immunofluorescence assays, respectively. Moreover, the regulatory relationships among HSYA, ADAM17, and the NF-κB/STAT3 pathway in MRC-5 cells were analyzed by bioinformatics analysis, qRT-PCR, and Western blot. The results show that HSYA treatment could diminish the fibrosis-related gene and protein expression patterns, proliferation, migration, and fibroblast-to-myofibroblast transdifferentiation in TGF-β1-stimulated MRC-5 cells. Moreover, HSYA could repress the TGF-β1-triggered ADAM17 up-regulation, thereby suppressing the NF-κB/STAT3 pathway. Furthermore, over-expression of ADAM17 negated the inhibitory effect of HSYA on fibroblast activation induced by TGF-β1. The findings revealed that HSYA blocked the NF-κB/STAT3 pathway activation by down-regulating ADAM17, thereby inhibiting TGF-β1-induced fibroblast activation.

PMID:40326971 | DOI:10.4149/gpb_2025008

ACS Synth Biol. 2025 May 6. doi: 10.1021/acssynbio.5c00077. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa elastase is a metalloprotease with significant industrial potential but is challenging to produce due to its pathogenic origin and folding complexities. In this study, we applied rational design to engineer nonfunctional regions of elastase within Saccharomyces cerevisiae, specifically targeting propeptide and signal peptide cleavage sites, and N-glycosylation in the propeptide. This led to the development of several improved elastase variants. Integrating the yeast protein secretory model pcSecYeast with protease production characteristics, a total of 75 targets were identified and validated, comprising both model-predicted and production-feature-based targets. Notably, overexpression of POS5 enhanced protease activity to 2.43-fold that of the control, while knockout of TES1 or VPS10 further optimized production. This work demonstrates the potential of systems biology in creating yeast cell factories for protease production and highlights S. cerevisiae as a versatile host for biotechnological applications.

PMID:40327375 | DOI:10.1021/acssynbio.5c00077

ACS Synth Biol. 2025 May 6. doi: 10.1021/acssynbio.4c00742. Online ahead of print.

ABSTRACT

Historically, studying the development of brain and central nervous system (CNS) tissues has been challenging. Human pluripotent stem cell (hPSC) technology has allowed for the in vitro reconstitution of relevant, early cell trajectories by using small molecules and recombinant proteins to guide differentiation of cells toward relevant brain and CNS phenotypes. However, many of these protocols fail to recapitulate the cell-guided differentiation programs intrinsic to embryonic development, particularly the signaling centers that emerge within the neural tube during brain formation. Located on the ventral end of the neural tube, the floor plate acts as one such signaling center to pattern the dorsal/ventral axis by secreting the morphogen Sonic Hedgehog (SHH). Here, we present a method for cell-guided differentiation using the synthetic Notch (synNotch) receptor platform to regulate SHH production and subsequent cell fate specification. We show that the widely used configuration of the orthogonal synNotch ligand green fluorescent protein (GFP) mounted on a platelet-derived growth factor receptor-β transmembrane chassis does not allow for robust artificial signaling in synNotch-hPSCs ("receivers") cocultured with ligand-presenting hPSCs ("senders"). We discovered that refined designs of membrane-bound GFP-ligand allow for efficient receptor activation in hPSC receivers. A variant of this enhanced synNotch system drives the production of SHH in hPSC sender:hPSC receiver cocultures and gives rise to floor plate-like cell types seen during neural tube development. This revised synNotch platform has the potential to pattern hPSC differentiation programs in synthetic morphogenesis studies designed to uncover key paradigms of human CNS development.

PMID:40327355 | DOI:10.1021/acssynbio.4c00742

Plant Cell Rep. 2025 May 6;44(6):113. doi: 10.1007/s00299-025-03503-z.

ABSTRACT

Cotton plants undergo a drastic transcriptional reprogramming after cotton boll weevil infestation, modulating several defense pathways to cope with the damage. The global demand for cotton fiber continues to rise, but pests and pathogens significantly hinder cotton production, causing substantial losses. Among these, the cotton boll weevil (Anthonomus grandis) is one of the most destructive pests. To investigate the molecular responses of cotton (Gossypium hirsutum) to boll weevil infestation, we evaluated the global gene expression of floral buds using mRNA-seq. Additionally, we analyzed the expression of non-coding RNAs, including microRNAs (miRNAs) and long intergenic non-coding RNAs (lincRNAs). Infestation by cotton boll weevil larvae triggered a rapid and drastic transcriptional reprogramming, with 1,656 and 1.698 genes modulated after two and twelve hours, respectively. Gene ontology enrichment analysis revealed significant regulation of defense-related and developmental processes, including photosynthesis, primary metabolism, and cell organization. Transcription factor families such as ERF, WRKY, GRAS, and NAC were strongly affected, highlighting their roles in coordinating defense responses. The jasmonate pathway showed intensive modulation, alongside secondary metabolite pathways like terpenoids and phenylpropanoids, which contribute to plant defense mechanisms. Non-coding RNAs also played a critical role in the response. We identified 921 unique known and novel miRNAs, with 36 modulated by the infestation, and predicted 98,850 putative lincRNAs, several of which were differentially expressed. Understanding the genetic and molecular mechanisms underlying cotton's defense against boll weevil, particularly during early infestation stages, is vital for developing biotechnological strategies to reduce pest damage. Our findings provide critical insights to enhance cotton resilience against herbivores.

PMID:40327114 | DOI:10.1007/s00299-025-03503-z

J Exp Med. 2025 Aug 4;222(8):e20241133. doi: 10.1084/jem.20241133. Epub 2025 May 6.

ABSTRACT

In chronic viral infections, sustained CD8+ T cell response relies on TCF1+ precursor-exhausted T cells (TPEX) exhibiting stem-like properties. TPEX self-renew and respond to PD-1 blockade, underscoring their paramount importance. However, strategies for effectively augmenting TPEX remain limited. Here, we demonstrate that ZC3H12A deficiency initiates a stemness program in TPEX but also increases cell death, whereas BCOR deficiency predominantly promotes TPEX proliferation. Consequently, co-targeting of both BCOR and ZC3H12A imparts exceptional stemness and functionality to TPEX, thereby enhancing viral control. Mechanistically, BCOR and ZC3H12A collaboratively suppress a core stemness program in TPEX characterized by heightened expression of ∼216 factors. While TCF1 plays a role, this core stemness program relies on novel factors, including PDZK1IP1, IFIT3, PIM2, LTB, and POU2F2. Crucially, overexpressing POU2F2 robustly boosts TPEX and enhances antiviral immunity. Thus, a core stemness program exists in exhausted T cells, jointly repressed by BCOR and ZC3H12A, robustly controlling TPEX differentiation and providing new targets for addressing T cell exhaustion.

PMID:40327039 | DOI:10.1084/jem.20241133

Elife. 2025 May 6;14:RP101299. doi: 10.7554/eLife.101299.

ABSTRACT

3D cellular-specific epigenetic and transcriptomic reprogramming is critical to organogenesis and tumorigenesis. Here, we dissect the distinct cell fitness in 2D (normoxia vs. chronic hypoxia) vs 3D (normoxia) culture conditions for an MYC-driven murine liver cancer model. We identify over 600 shared essential genes and additional context-specific fitness genes and pathways. Knockout of the VHL-HIF1 pathway results in incompatible fitness defects under normoxia vs. 1% oxygen or 3D culture conditions. Moreover, deletion of each of the mitochondrial respiratory electron transport chain complex has distinct fitness outcomes. Notably, multicellular organogenesis signaling pathways including TGFβ-SMAD, which is upregulated in 3D culture, specifically constrict the uncontrolled cell proliferation in 3D while inactivation of epigenetic modifiers (Bcor, Kmt2d, Mettl3, and Mettl14) has opposite outcomes in 2D vs. 3D. We further identify a 3D-dependent synthetic lethality with partial loss of Prmt5 due to a reduction of Mtap expression resulting from 3D-specific epigenetic reprogramming. Our study highlights unique epigenetic, metabolic, and organogenesis signaling dependencies under different cellular settings.

PMID:40326560 | DOI:10.7554/eLife.101299

Anal Chem. 2025 May 6. doi: 10.1021/acs.analchem.5c00390. Online ahead of print.

ABSTRACT

The interpretation of proteomics data often relies on functional enrichment analysis, such as Gene Ontology (GO) enrichment, to uncover the biological functions of proteins, as well as the examination of protein expression patterns across data sets like the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. However, conventional approaches to functional enrichment frequently produce extensive and redundant term lists, complicating interpretation and synthesis. Moreover, the absence of specialized tools tailored to proteomics researchers limits the efficient exploration of protein expression within specific biological contexts. To address these challenges, we developed Ontolomics-P, a user-friendly web-based tool designed to advance proteomics data interpretation. Ontolomics-P integrates topic modeling using latent Dirichlet allocation (LDA) with GO semantic similarity analysis, enabling the consolidation of redundant terms into coherent topics. These topics are further refined and reannotated using the GPT-4o language model, creating a novel topics database that provides precise and interpretable insights into shared biological functions. Additionally, Ontolomics-P incorporates quantitative proteomic data from 10 diverse cancer types archived in the CPTAC database, allowing for a comprehensive exploration of protein expression profiles from a data-driven perspective. Through detailed case studies, we demonstrate the tool's capacity to streamline workflows, simplify interpretation, and provide actionable biological insights. Ontolomics-P represents a significant advancement in proteomics data analysis, offering innovative solutions for functional annotation, quantitative exploration, and visualization, ultimately empowering researchers to accelerate discoveries in systems biology and beyond.

PMID:40326493 | DOI:10.1021/acs.analchem.5c00390

J Chem Inf Model. 2025 May 6. doi: 10.1021/acs.jcim.5c00136. Online ahead of print.

ABSTRACT

Computational prediction of potential drug side effects plays a crucial role in reducing health risks for clinical patients and accelerating drug development. Recent methods have constructed heterogeneous graphs that represent drugs and their side effects, utilizing graph learning strategies such as graph convolutional networks to predict associations between them. However, existing approaches fail to fully exploit the diverse topologies and semantics present in multiple knowledge graphs. We propose MVDSA, a novel multi-view drug-side effect association prediction model. Our approach integrates multiple relationship semantics, local topologies of knowledge graphs, and multi-view features of drug-side effect entity pairs. First, we constructed two knowledge graphs based on drug functional and structural similarity, side effect similarity, and drug-side effect associations. These knowledge graphs capture the topological and semantic connections between drug and side effect entities from diverse perspectives. Second, considering the diverse similarities and associations between entities, we designed a space-sensitive learning strategy where a relation-gated semantic encoder is constructed for each type of relationship. This encoder adaptively adjusts the contribution of each entity feature to the relational semantic representation, facilitating the learning of entity-specific semantic features within each relational space. Third, for the two knowledge graphs, given the multiple types of connections between head and tail entities, we propose a connection-sensitive tail entity attention mechanism to integrate these diverse semantic relationships. To capture the contribution of different knowledge graphs to entity feature learning, we designed a knowledge graph-level attention mechanism to adaptively fuse the enhanced features from multiple knowledge graphs. Finally, we propose a multi-view enhanced multi-layer perceptron (MLP) strategy to encode the features of drug-side effect pairs from three perspectives and capture the potential associations between entities. Extensive experiments demonstrate that MVDSA outperforms 10 state-of-the-art methods in predicting drug-side effect associations. Ablation studies validate the contributions of the proposed innovations to improved prediction performance. Additionally, case studies on candidate side effects for five drugs highlight MVDSA's capability to discover potential drug-side effect associations.

PMID:40326886 | DOI:10.1021/acs.jcim.5c00136

ACS Infect Dis. 2025 May 6. doi: 10.1021/acsinfecdis.4c00962. Online ahead of print.

ABSTRACT

The emergence of drug-resistant malarial parasites has been a growing challenge to medical science to safeguard public health in the malaria-endemic regions of the globe. With time, the parasite develops newer resistance mechanisms to defunct the drug's action one after another. Genetic mutation is the prime weapon parasites rely upon to initiate the resistance mechanism in a case-specific manner, following various strategies such as structural changes in the target protein, metabolic alterations, and tweaking the drug-transported channels. In order to combat these resistances, different approaches have evolved among these developing inhibitors against critical parasite enzymes and metabolic pathways, combinatorial/hybrid drug therapies, exploring new drug targets and analogues of existing drugs, use of resistance-reversal agents, drug-repurposing, gene blocking/altering using RNA interference and CRISPR/Cas systems are prominent. However, the effectiveness of these approaches needs to be earnestly monitored for better management of the disease, which demands the development of a reliable diagnosis technique. Several methodologies have been investigated in search of a suitable diagnosis technique, such as in vivo, in vitro, ex vivo drug efficacy studies, and molecular techniques. A parallel effort to transform the efficient method into an inexpensive and portable diagnosis tool for rapid screening of drug resistance malaria among masses in the societal landscape is advocated. This review gives an insight into the historical perspectives of drug-resistant malaria and the recent developments in malaria diagnosis and antimalarial drug discovery. Efforts have been made to update recent strategies formulated to combat and diagnose drug-resistant malaria. Finally, a concluding remark with a future perspective on the subject has been forwarded.

PMID:40326084 | DOI:10.1021/acsinfecdis.4c00962

Clin Pharmacol Ther. 2025 May 5. doi: 10.1002/cpt.3704. Online ahead of print.

ABSTRACT

There is increasing attention on the clinical utility and value of pharmacogenetic (PGx) testing to individualize medication management. Most clinical practice guidelines from medical professional societies do not recommend routine PGx testing, with a few key exceptions. Inconsistent recommendations across clinical practice guidelines, FDA product labeling, and payer reimbursement policies have hampered widespread adoption of testing. Multiple resources exist to aid in the adoption and use of actionable PGx test results in clinical practice; however, most of these resources do not provide guidance on who should receive PGx testing and when-a critical question the clinical community continues to struggle with. There are multiple considerations when answering this question beyond the clinical validity of the drug-gene interaction itself, such as the actionable result frequency, severity of the adverse clinical outcome, predictive power of the PGx test, suitability of alternative treatments, cost, and turnaround time of test results. This perspective discusses these considerations and models for testing including preemptive screening, pretreatment testing, and reactive testing, highlighting advantages and disadvantages of each approach. The authors provide their perspectives on identifying candidates for PGx testing in the current real-world environment and how that differs from a clinically ideal scenario.

PMID:40325943 | DOI:10.1002/cpt.3704