Cell Rep Methods. 2025 Feb 10:100984. doi: 10.1016/j.crmeth.2025.100984. Online ahead of print.

ABSTRACT

Probabilistic graphical models are powerful tools to quantify, visualize, and interpret network dependencies in complex biological systems such as high-throughput -omics. However, many graphical models assume sample homogeneity, limiting their effectiveness. We propose a flexible Bayesian approach called graphical regression (GraphR), which (1) incorporates sample heterogeneity at different scales through a regression-based formulation, (2) enables sparse sample-specific network estimation, (3) identifies and quantifies potential effects of heterogeneity on network structures, and (4) achieves computational efficiency via variational Bayes algorithms. We illustrate the comparative efficiency of GraphR against existing state-of-the-art methods in terms of network structure recovery and computational cost across multiple settings. We use GraphR to analyze three multi-omic and spatial transcriptomic datasets to investigate inter- and intra-sample molecular networks and delineate biological discoveries that otherwise cannot be revealed by existing approaches. We have developed a GraphR R package along with an accompanying Shiny App that provides comprehensive analysis and dynamic visualization functions.

PMID:39954675 | DOI:10.1016/j.crmeth.2025.100984

Cell Rep Methods. 2025 Feb 10:100985. doi: 10.1016/j.crmeth.2025.100985. Online ahead of print.

ABSTRACT

Cell-cell interaction (CCI) networks are key to understanding disease progression and treatment response. However, existing methods for inferring these networks often aggregate data across patients or focus on cell-type level interactions, providing a generalized overview but overlooking patient heterogeneity and local network structures. To address this, we introduce "random cell-cell interaction generator" (RaCInG), a model based on random graphs to derive personalized networks leveraging prior knowledge on ligand-receptor interactions and bulk RNA sequencing data. We applied RaCInG to 8,683 cancer patients to extract 643 network features related to the tumor microenvironment and unveiled associations with immune response and subtypes, enabling prediction and explanation of immunotherapy responses. RaCInG demonstrated robustness and showed consistencies with state-of-the-art methods. Our findings highlight RaCInG's potential to elucidate patient-specific network dynamics, offering insights into cancer biology and treatment responses. RaCInG is poised to advance our understanding of complex CCI s in cancer and other biomedical domains.

PMID:39954673 | DOI:10.1016/j.crmeth.2025.100985

Cell Rep Methods. 2025 Feb 10:100990. doi: 10.1016/j.crmeth.2025.100990. Online ahead of print.

ABSTRACT

The need for a deeper understanding of adverse drug reaction (ADR) mechanisms is vital for improving drug safety and repurposing. This study introduces Drug Adverse Reaction Mechanism Explainer (DREAMER), a network-based framework that uses a comprehensive knowledge graph to uncover molecular mechanisms underlying ADRs and disease phenotypes. By examining shared phenotypes of drugs and diseases and their effects on protein-protein interaction networks, DREAMER identifies proteins linked to ADR mechanisms. Applied to 649 ADRs, DREAMER identified molecular mechanisms for 67 ADRs, including ventricular arrhythmia and metabolic acidosis, and emphasized pathways like GABAergic signaling and coagulation proteins in personality disorders and intracranial hemorrhage. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes, emphasizing the importance of network-based analyses with integrative data for enhancing drug safety and accelerating the discovery of novel therapeutic strategies.

PMID:39954672 | DOI:10.1016/j.crmeth.2025.100990

Biosens Bioelectron. 2025 Feb 6;276:117228. doi: 10.1016/j.bios.2025.117228. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) poses a significant global health burden, with escalating incidence rates and substantial mortality. The predominant etiological factors include liver cirrhosis (LC) and chronic hepatitis B infections (CHB). Surveillance primarily relies on ultrasound and Alpha-fetoprotein (AFP), yet their efficacy, particularly in early HCC detection, is limited. Hence, there is a critical need for accurate non-invasive biomarkers to enhance surveillance and early diagnosis. Extracellular vesicles (EVs) hold promises as stable carriers of signaling molecules, offering potential in tumor diagnosis. Our study developed a novel tidal microfluidic chip for label-free EV isolation, enabling rapid and efficient enrichment from small plasma volumes. Through transcriptome sequencing and single-cell analysis, we identified HMMR and B4GALT2 as promising HCC-associated biomarkers in EVs. In a comprehensive clinical evaluation, bi-mRNAs in EVs exhibited superior diagnostic performance over AFP, particularly in distinguishing early-stage HCC or AFP-negative cases from high-risk individuals (CHB/LC). Notably, our study demonstrated the potential of bi-mRNAs to complement imaging examinations, enabling early detection of HCC lesions. In conclusion, the tidal microfluidic chip offers a practical solution for EV isolation, with the integration of EV-based biomarkers presenting opportunities for improved early detection and management of HCC in clinical practice.

PMID:39954520 | DOI:10.1016/j.bios.2025.117228

Europace. 2025 Feb 15:euaf034. doi: 10.1093/europace/euaf034. Online ahead of print.

NO ABSTRACT

PMID:39953950 | DOI:10.1093/europace/euaf034

J Inherit Metab Dis. 2025 Mar;48(2):e70013. doi: 10.1002/jimd.70013.

ABSTRACT

The Leloir pathway was elucidated decades ago, unraveling how galactose is metabolized in the body. Different inborn errors of metabolism in this pathway are known, the most frequent and well-studied being Classic Galactosemia (CG) (OMIM 230400) due to pathogenic variants in the GALT gene. Substrate reduction using dietary restriction of galactose is currently the only available treatment option. Although this burdensome diet resolves the life-threatening clinical picture in neonates, patients still face long-term complications, including cognitive and neurological deficits as well as primary ovarian insufficiency. Emerging therapies aim to address these challenges on multiple fronts: (1) restoration of GALT activity with nucleic acid therapies, pharmacological chaperones, or enzyme replacement; (2) influencing the pathological cascade of events to prevent accumulation of metabolites (Galactokinase 1 (GALK1) inhibitors, aldose reductase inhibitors), address myo-inositol deficiency, or alleviate cellular stress responses; (3) substrate reduction with synthetic biotics or galactose uptake inhibitors to eliminate the need for lifelong diet; and (4) novel approaches to mitigate existing symptoms, such as non-invasive brain stimulation and reproductive innovations. Early, personalized intervention remains critical for optimizing patient outcomes. We review the advances in the development of different treatment modalities for CG and reflect on the factors that need to be considered and addressed to reshape the landscape of treatment.

PMID:39953772 | DOI:10.1002/jimd.70013

Clin Transl Sci. 2025 Feb;18(2):e70080. doi: 10.1111/cts.70080.

ABSTRACT

Selection of rational antagonists of P2Y12 receptor for CAD patients who inherit CYP2C19 LoF alleles remains still conflicting. This study compared the clinical outcomes in CAD patients inheriting CYP2C19 LoF alleles undergoing PCI and treated with clopidogrel against alternative antagonists of P2Y12 receptor. A thorough literature search was performed across multiple scientific databases following the PRISMA guidelines and PICO model. Setting the statistical significance at p < 0.05 and RevMan software was used to calculate the risk ratios (RRs). Estimation of the pooled analysis revealed a significant 62% increased risk of major adverse cardiovascular events (MACE) in CAD patients inheriting CYP2C19 LoF alleles and treated with clopidogrel against those treated with alternative P2Y12 receptor antagonists such as prasugrel or ticagrelor (RR 1.62; 95% CI 1.42-1.86; p < 0.00001). In addition, Asian CAD patients were found at a significantly higher risk of MACE (RR 1.93; 95% CI: 1.49-2.49; p < 0.00001) juxtaposed to CAD patients of other ethnicities (RR 1.51; 95% CI: 1.29-1.78; p < 0.00001). Conversely, between these two treatment groups, taking clopidogrel against prasugrel/ticagrelor, who possess CYP2C19 LoF alleles, no significant differences in bleeding events were observed (RR 0.94; 95% CI 0.79-1.11; p = 0.47). CAD patients undergoing PCI who inherited CYP2C19 LoF alleles and treated with clopidogrel were associated with significantly higher risk of MACE against those treated with alternative antagonists of P2Y12 receptor, that is, prasugrel or ticagrelor.

PMID:39953666 | DOI:10.1111/cts.70080

Cancer Med. 2025 Feb;14(4):e70688. doi: 10.1002/cam4.70688.

ABSTRACT

BACKGROUND: Individualized dose prediction is critical for optimizing radiation treatment planning. This study introduces DESIRE, an enhanced UNet-based dose prediction model with progrEssive feature fuSion and dIfficult Region lEarning, tailored for nasopharyngeal carcinoma (NPC) patients receiving volumetric modulated arc therapy. We aimed to assess the impact of integrating DESIRE into the treatment planning process to improve plan quality.

METHODS: This retrospective study included 131 NPC patients diagnosed at Jiangxi Cancer Hospital between 2017 and 2020. Twenty patients were randomly allocated to a testing cohort, while the remaining 111 comprised a training cohort. Target delineation included three planning target volumes (PTVs): PTV70, PTV60, and PTV55, along with several organs at risk (OARs). The DESIRE model predicted dose distributions, and discrepancies between DESIRE's predictions and the ground truth (GT) were quantified using dosimetric metrics and gamma pass rates. Two junior physicians used DESIRE's predictions for treatment planning, and their plans were compared to the GT.

RESULTS: Most of DESIRE's predicted dosimetric metrics closely aligned with GT (mean difference < 1 Gy), with no significant differences (p > 0.05) in Dmean and D1 values across OARs. While significant differences were observed in PTV metrics, the mean differences in D98, D95, D50, and Dmean between DESIRE and GT did not exceed 1 Gy. Assisted by DESIRE, the junior physicians' plans were comparable to the GT in nearly all OARs, with no significant differences in dosimetric metrics. The conformity index (CI) and homogeneity index (HI) for PTV70 surpassed the GT (0.847 ± 0.036 vs. 0.827 ± 0.037 for CI, and 0.057 ± 0.009 vs. 0.052 ± 0.008 for HI). The average three-dimensional gamma passing rates were 0.85 for PTV70 and 0.87 for the 35-Gy isodose line.

CONCLUSIONS: The DESIRE model shows promise for patient-specific dose prediction, enhancing junior physicians' treatment planning capabilities and improving plan quality.

PMID:39953816 | DOI:10.1002/cam4.70688

New Phytol. 2025 Feb 15. doi: 10.1111/nph.70009. Online ahead of print.

ABSTRACT

Stomata regulate plant gas exchange via repeated turgor-driven changes of guard cell shape, thereby adjusting pore apertures. Grasses, which are among the most widespread plant families on the planet, are distinguished by their unique stomatal structure, which is proposed to have significantly contributed to their evolutionary and agricultural success. One component of their structure, which has received little attention, is the presence of a discontinuous adjoining cell wall of the guard cell pair. Here, we demonstrate the presence of these symplastic connections in a range of grasses and use finite element method simulations to assess hypotheses for their functional significance. Our results show that opening of the stomatal pore is maximal when the turgor pressure in dumbbell-shaped grass guard cells is equal, especially under the low pressure conditions that occur during the early phase of stomatal opening. By contrast, we demonstrate that turgor pressure differences have less effect on the opening of kidney-shaped guard cells, characteristic of the majority of land plants, where guard cell connections are rarely or not observed. Our data describe a functional mechanism based on cellular mechanics, which plausibly facilitated a major transition in plant evolution and crop development.

PMID:39953834 | DOI:10.1111/nph.70009

Sci Rep. 2025 Feb 14;15(1):5473. doi: 10.1038/s41598-025-89202-x.

ABSTRACT

Speech emotion recognition has seen a surge in transformer models, which excel at understanding the overall message by analyzing long-term patterns in speech. However, these models come at a computational cost. In contrast, convolutional neural networks are faster but struggle with capturing these long-range relationships. Our proposed system, MemoCMT, tackles this challenge using a novel "cross-modal transformer" (CMT). This CMT can effectively analyze local and global speech features and their corresponding text. To boost efficiency, MemoCMT leverages recent advancements in pre-trained models: HuBERT extracts meaningful features from the audio, while BERT analyzes the text. The core innovation lies in how the CMT component utilizes and integrates these audio and text features. After this integration, different fusion techniques are applied before final emotion classification. Experiments show that MemoCMT achieves impressive performance, with the CMT using min aggregation achieving the highest unweighted accuracy (UW-Acc) of 81.33% and 91.93%, and weighted accuracy (W-Acc) of 81.85% and 91.84% respectively on benchmark IEMOCAP and ESD corpora. The results of our system demonstrate the generalization capacity and robustness for real-world industrial applications. Moreover, the implementation details of MemoCMT are publicly available at https://github.com/tpnam0901/MemoCMT/ for reproducibility purposes.

PMID:39953105 | DOI:10.1038/s41598-025-89202-x

Sci Rep. 2025 Feb 14;15(1):5561. doi: 10.1038/s41598-024-82027-0.

ABSTRACT

Parkinson's disease is a neurological disorder, and early diagnosis is crucial for the treatment and quality of life of patients. Gait movement disorder is a significant manifestation of PD, and automated gait assessment is key to achieving automated detection of PD patients. With the development of deep learning, in order to improve the accuracy of early Parkinson's disease detection and enhance the robustness of motion recognition models, this study introduces an innovative deep learning approach, namely Multi-area Attention Spatiotemporal Directed Graph Convolutional Network (Ma-ST-DGN). The model effectively captures temporal and spatial information from the movement data of subjects to better understand subtle movement abnormalities in patients. Simultaneously, by reconstructing human skeleton features using directed graphs and introducing a multi-area self-attention mechanism, the model can adaptively focus on key information in different areas and apply more effective fusion strategies on features from different areas, thereby increasing sensitivity to potential signs of Parkinson's disease. By more effectively integrating global and local area information, the model captures subtle manifestations of PD. We use the first Parkinson's disease gait dataset, PD-Walk, consisting of walking videos of 95 PD patients and 96 healthy individuals. Extensive experiments on this clinical video dataset demonstrate that the model achieves the best performance to date, with an accuracy of 88.7%, far superior to existing sensor and vision-based Parkinson's gait assessment methods. Therefore, the method proposed in this study may be effective for early diagnosis of PD in clinical practice.

PMID:39952991 | DOI:10.1038/s41598-024-82027-0

Nat Commun. 2025 Feb 14;16(1):1651. doi: 10.1038/s41467-025-56832-8.

ABSTRACT

For the intricate and infrequent safety issues of batteries, online safety fault diagnosis over stochastic working conditions is indispensable. In this work, we employ deep learning methods to develop an online fault diagnosis network for lithium-ion batteries operating under unpredictable conditions. The network integrates battery model constraints and employs a framework designed to manage the evolution of stochastic systems, thereby enabling fault real-time determination. We evaluate the performance using a dataset of 18.2 million valid entries from 515 vehicles. The results demonstrate our proposed algorithm outperforms other relevant approaches, enhancing the true positive rate by over 46.5% within a false positive rate range of 0 to 0.2. Meanwhile, we identify the trigger probability for four safety fault samples, namely, electrolyte leakage, thermal runaway, internal short circuit, and excessive aging. The proposed network is adaptable to packs of varying structures, thereby reducing the cost of implementation. Our work explores the application of deep learning for real-state prediction and diagnosis of batteries, demonstrating potential improvements in battery safety and economic benefits.

PMID:39952987 | DOI:10.1038/s41467-025-56832-8

Sci Rep. 2025 Feb 14;15(1):5521. doi: 10.1038/s41598-025-86110-y.

ABSTRACT

While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG PET) data have shown promise in the accurate identification of Alzheimer's disease, their clinical applicability is hindered by the assumption that both modalities are always available during model inference. In practice, clinicians adjust diagnostic tests based on available information and specific clinical contexts. We propose a novel MRI- and FDG PET-based multi-modal deep learning approach that mimics clinical decision-making by incorporating uncertainty estimates of an MRI-based model (generated using Monte Carlo dropout and evidential deep learning) to determine the necessity of an FDG PET scan, and only inputting the FDG PET to a multi-modal model when required. This approach significantly reduces the reliance on FDG PET scans, which are costly and expose patients to radiation. Our approach reduces the need for FDG PET by up to 92% without compromising model performance, thus optimizing resource use and patient safety. Furthermore, using a global model explanation technique, we provide insights into how anatomical changes in brain regions-such as the entorhinal cortex, amygdala, and ventricles-can positively or negatively influence the need for FDG PET scans in alignment with clinical understanding of Alzheimer's disease.

PMID:39952976 | DOI:10.1038/s41598-025-86110-y

Virol J. 2025 Feb 15;22(1):38. doi: 10.1186/s12985-025-02647-4.

ABSTRACT

BACKGROUND: Repurposing off-label drugs during epidemics or pandemics with unknown/known pathogens, particularly when their side effects and complications are already known, can be a strategic approach, as seen during the COVID-19 pandemic. Developing surrogate in vitro experimental models (passage-to-passage), which mimic epidemic/pandemic-like transmission (human-to-human), may enhance this repurposing process. This study evaluates montelukast sodium (MLS), a US FDA-approved leukotriene receptor antagonist for asthma, to explore its potential repurposing antiviral effects against bovine respiratory syncytial virus (BRSV), which has basic similarities to human respiratory syncytial virus (HRSV) as both belong to the Pneumoviridae family.

METHODS: An in vitro serial passage model was developed using MDBK cells infected with a local wild-type strain of BRSV (43TR2018). The cytotoxicity of MLS was assessed via the trypan blue exclusion method, identifying non-toxic concentrations. The impact of MLS on viral spread and infectivity was measured through TCID50 values over 10 passages. Viral loads were confirmed by nested RT-PCR and quantified using qPCR, while apoptosis, necrosis, and nitric oxide production were evaluated through staining and nitrite assays. Data were analyzed using ANOVA and Tukey's test (p < 0.05).

RESULTS: Control cells exhibited 97.16% viability, with 10 µM and 20 µM MLS concentrations maintaining viabilities of 89.2% and 87.3%, respectively. Viral titers significantly decreased at higher concentrations of MLS (up to 99.94% inhibition). Apoptosis rates decreased in MLS-treated cells, and live cell percentages improved, especially at 20 µM. Nitric oxide levels showed no significant differences across groups.

CONCLUSION: MLS demonstrated a dose-dependent antiviral effect against BRSV, achieving 99% viral inhibition properties in MDBK cells. These promising results warrant further investigation into the antiviral mechanisms of MLS.

PMID:39953515 | DOI:10.1186/s12985-025-02647-4

J Biomed Inform. 2025 Feb 12:104786. doi: 10.1016/j.jbi.2025.104786. Online ahead of print.

ABSTRACT

OBJECTIVE: Drug repurposing accelerates therapeutic development by finding new indications for approved drugs. However, accounting for individual patient differences is challenging. This study introduces a Precision Drug Repurposing (PDR) framework at single-patient resolution, integrating individual-level data with a foundational biomedical knowledge graph to enable personalized drug discovery.

METHODS: We developed a framework integrating patient-specific data from the UK Biobank (Polygenic Risk Scores, biomarker expressions, and medical history) with a comprehensive biomedical knowledge graph (61,146 entities, 1,246,726 relations). Using Alzheimer's Disease as a case study, we compared three diverse patient-specific models with a foundational model through standard link prediction metrics. We evaluated top predicted candidate drugs using patient medication history and literature review.

RESULTS: Our framework maintained the robust prediction capabilities of the foundational model. The integration of patient data, particularly Polygenic Risk Scores (PRS), significantly influenced drug prioritization (Cohen's d = 1.05 for scoring differences). Ablation studies demonstrated PRS's crucial role, with effect size decreasing to 0.77 upon removal. Each patient model identified novel drug candidates that were missed by the foundational model but showed therapeutic relevance when evaluated using patient's own medication history. These candidates were further supported by aligned literature evidence with the patient-level genetic risk profiles based on PRS.

CONCLUSION: This exploratory study demonstrates a promising approach to precision drug repurposing by integrating patient-specific data with a foundational knowledge graph.

PMID:39952626 | DOI:10.1016/j.jbi.2025.104786

Orphanet J Rare Dis. 2025 Feb 14;20(1):74. doi: 10.1186/s13023-025-03552-3.

ABSTRACT

In a recent publication by Klein et al., the need for real-world data on rare diseases is highlighted. We strongly support this need, and the collaboration with the patient community to collect data, as promoted in this publication. Our concern, however, is that this paper may be misunderstood as suggesting that the Sanofi-run Rare Disease Registries (RDRs) are sufficient to provide the datasets needed to evaluate current and future therapies. Industry-driven registries focus on their own product(s) and, therefore, do not provide the opportunity to compare products from different companies. Today, multiple companies produce treatments for all diseases included in the RDRs. Each company will have to run its own registry for regulatory purposes. This will lead to data fragmentation, which is prohibitive of truly understanding the effects of the various treatment options for these rare diseases. Therefore, independently funded and owned registries are essential to generate real-world evidence (RWE) unrelated to specific products. We discuss options for this for Pompe disease, including the International Pompe Survey, which has collected patient-reported outcomes independently from industry since 2002. This letter aims to raise awareness of the problem of siloed data and advocate for a new way forward where independent registries provide post-marketing surveillance data.

PMID:39953542 | DOI:10.1186/s13023-025-03552-3

J Biomed Inform. 2025 Feb 12:104788. doi: 10.1016/j.jbi.2025.104788. Online ahead of print.

ABSTRACT

BACKGROUND: If hospital Clinical Data Warehouses are to address today's focus in personalized medicine, they need to be able to track patients longitudinally and manage the large data sets generated by whole genome sequencing, RNA analyses, and complex imaging studies. Current Clinical Data Warehouses address neither issue. This paper reports on methods to enrich current systems by providing provenance data allowing patient histories to be followed longitudinally and managing the linking and versioning of large data sets from whatever source. The methods are open source and applicable to any clinical data warehouse system, whether data schema it uses.

METHOD: We introduce gitOmmix, an approach that overcomes these limitations, and illustrate its usefulness in the management of medical omics data. gitOmmix relies on (i) a file versioning system: git, (ii) an extension that handles large files: git-annex, (iii) a provenance knowledge graph: PROV-O, and (iv) an alignment between the git versioning information and the provenance knowledge graph.

RESULTS: Capabilities inherited from git and git-annex enable retracing the history of a clinical interpretation back to the patient sample, through supporting data and analyses. In addition, the provenance knowledge graph, aligned with the git versioning information, enables querying and browsing provenance relationships between these elements.

CONCLUSION: gitOmmix adds a provenance layer to CDWs, while scaling to large files and being agnostic of the CDW system. For these reasons, we think that it is a viable and generalizable solution for omics clinical studies.

PMID:39952627 | DOI:10.1016/j.jbi.2025.104788

Mol Med. 2025 Feb 14;31(1):59. doi: 10.1186/s10020-025-01123-7.

ABSTRACT

BACKGROUND: Patients with higher-risk (HR) myelodysplastic syndrome (MDS), ineligible for allogeneic hematopoietic stem cell transplantation (alloHSCT), require prompt therapeutic interventions, such as treatment with hypomethylating agents (HMAs) to restore normal DNA methylation patterns, mainly of oncosuppressor genes, and consequently to delay disease progression and increase overall survival (OS). However, response assessment to HMA treatment relies on conventional methods with limited capacity to uncover a wide spectrum of underlying molecular events.

METHODS: We implemented liquid chromatography-tandem mass spectrometry (LC-MS/MS) to assess 5' methyl-2' deoxycytidine (5mdC), 5' hydroxy-methyl-2'-deoxycytidine (5hmdC) levels and global adenosine/thymidine ([dA]/[T]) ratio in bone marrow aspirates from twenty-one HR MDS patients, pre- and post-HMA treatment. Additionally, targeted methylation analysis was performed by interpretation of NGS-methylation (MeD-seq) data obtained from the same patient cohort.

RESULTS: LC/MS-MS analysis revealed a significant hypomethylation status in responders (Rs), already established at baseline and a trend for further DNA methylation reduction post-HMA treatment. Non-responders (NRs) reached statistical significance for DNA hypomethylation only post-HMA treatment. The 5hmdC epigenetic mark was approximately detected at 37.5-40% among NRs and Rs, implying the impairment of the natural active demethylation pathway, mediated by the ten-eleven (TET) 5mdC dioxygenases. R and NR subgroups displayed a [dA]/[T] ratio < 1 (0.727 - 0.633), supporting high frequences of 5mdC transition to thymidine. Response to treatment, according to whole genome MeD-seq data analysis, was associated with specific, scattered hypomethylated DMRs, rather than presenting a global effect across genome. MeD-seq analysis identified divergent epigenetic effects along chromosomes 7, 9, 12, 16, 18, 21, 22, X and Y. Within statistically significant selected chromosomal bins, genes encoding for proteins and non-coding RNAs with reversed methylation profiles between Rs and NRs, were highlighted.

CONCLUSIONS: Implementation of powerful analytical tools to identify the dynamic DNA methylation changes in HR MDS patients undergoing HMA therapy demonstrated that LC-MS/MS exerts high efficiency as a broad-based but rapid and cost-effective methodology (compared to MeD-seq) to decode different perspectives of the epigenetic background of HR MDS patients and possess discriminative efficacy of the response phenotype to HMA treatment.

PMID:39953389 | DOI:10.1186/s10020-025-01123-7

Sci Rep. 2025 Feb 14;15(1):5458. doi: 10.1038/s41598-025-89160-4.

ABSTRACT

Glucuronidation is a crucial pathway for the metabolism and detoxification of drugs and endobiotics, and primarily occurs in the liver. UGT2B17 is one of the 22 glycosyltransferases (UGT) that catalyze this reaction. In a large proportion of the population, UGT2B17 is absent due to complete gene deletion. We hypothesized that a UGT2B17 human deficiency affects the composition and function of the liver proteome, potentially provoking compensatory responses, and altering interconnected pathways and regulatory networks. The objective was to elucidate the liver proteome of UGT2B17-deficient individuals. Liver specimens from UGT2B17-deficient and proficient individuals were compared by mass spectrometry-based proteomics using data-independent acquisition. In UGT2B17-deficient livers, 80% of altered proteins showed increased abundance with a notable enrichment in various metabolic and chemical defense pathways, cellular stress and immune-related responses. Enzymes involved in the homeostasis of steroids, nicotinamide, carbohydrate and energy metabolism, and sugar pathways were also more abundant. Some of these changes support compensatory mechanisms, but do not involve other UGTs. An increased abundance of non-metabolic proteins suggests an adaptation to endoplasmic reticulum stress, and activation of immune responses. Data implies a disrupted hepatocellular homeostasis in UGT2B17-deficient individuals and offers new perspectives on functions and phenotypes associated with a complete UGT2B17 deficiency.

PMID:39953065 | DOI:10.1038/s41598-025-89160-4

Pharmacol Rev. 2025 Jan;77(1):100014. doi: 10.1124/pharmrev.124.001049. Epub 2024 Nov 22.

ABSTRACT

Precision cancer medicine is widely established, and numerous molecularly targeted drugs for various tumor entities are approved or are in development. Personalized pharmacotherapy in oncology has so far been based primarily on tumor characteristics, for example, somatic mutations. However, the response to drug treatment also depends on pharmacological processes summarized under the term ADME (absorption, distribution, metabolism, and excretion). Variations in ADME genes have been the subject of intensive research for >5 decades, considering individual patients' genetic makeup, referred to as pharmacogenomics (PGx). The combined impact of a patient's tumor and germline genome is only partially understood and often not adequately considered in cancer therapy. This may be attributed, in part, to the lack of methods for combined analysis of both data layers. Optimized personalized cancer therapies should, therefore, aim to integrate molecular information, which derives from both the tumor and the germline genome, and taking into account existing PGx guidelines for drug therapy. Moreover, such strategies should provide the opportunity to consider genetic variants of previously unknown functional significance. Bioinformatic analysis methods and corresponding algorithms for data interpretation need to be developed to integrate PGx data in cancer therapy with a special meaning for interdisciplinary molecular tumor boards, in which cancer patients are discussed to provide evidence-based recommendations for clinical management based on individual tumor profiles. SIGNIFICANCE STATEMENT: The era of personalized oncology has seen the emergence of drugs tailored to genetic variants associated with cancer biology. However, the full potential of targeted therapy remains untapped owing to the predominant focus on acquired tumor-specific alterations. Optimized cancer care must integrate tumor and patient genomes, guided by pharmacogenomic principles. An essential prerequisite for realizing truly personalized drug treatment of cancer patients is the development of bioinformatic tools for comprehensive analysis of all data layers generated in modern precision oncology programs.

PMID:39952686 | DOI:10.1124/pharmrev.124.001049