J Am Soc Nephrol. 2025 May 26. doi: 10.1681/ASN.0000000706. Online ahead of print.

ABSTRACT

BACKGROUND: Alterations in pre-mRNA splicing are crucial to the pathophysiology of various diseases. However, the effects of alternative splicing of mRNA on podocytes in hypertensive nephropathy are still unknown. The Sys_CARE project aimed to identify alternative splicing events involved in the development and progression of glomerular hypertension.

METHODS: Murine podocytes were exposed to mechanical stretch, after which proteins and mRNA were analyzed by proteomics, RNA sequencing and several bioinformatic alternative splicing tools.

RESULTS: Using transcriptomic and proteomic analysis, we identified significant changes in gene expression and protein abundance due to mechanical stretch. RNA-Seq identified over 3,000 alternative spliced genes after mechanical stretch, including all types of alternative splicing events. Among these, 17 genes exhibited an alternative splicing event across four different splicing analysis tools. From this group, we focused on Myl6, a component of the myosin protein complex, and Shroom3, an actin-binding protein essential for podocyte function. We identified two Shroom3 isoforms with significant expression changes under mechanical stretch, which was validated by qRT-PCR and in situ hybridization. Additionally, we observed an expression switch of two Myl6 isoforms after mechanical stretch, accompanied by an alteration in the C-terminal amino acid sequence.

CONCLUSIONS: A comprehensive RNA-Seq analysis of mechanically stretched podocytes identified novel potential podocyte-specific biomarkers and highlighted significant alternative splicing events, notably in the mRNA of Shroom3 and Myl6.

PMID:40418580 | DOI:10.1681/ASN.0000000706

Mol Divers. 2025 May 26. doi: 10.1007/s11030-025-11214-6. Online ahead of print.

ABSTRACT

Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage. Along with tumor growth in the vicinity of blood vessels, malignant cells require more nutrients; hence, capillary sprouting occurs from parental vessels, a process known as angiogenesis. Although multiple cellular pathways have been identified, controlling them with one single biomolecule as a multi-target inhibitor could be an attractive strategy for reducing medication side effects. Three critical pathways in angiogenesis have been identified, which are activated by the vascular endothelial growth factor receptor (VEGFR), fibroblast growth factor receptor (FGFR), and epidermal growth factor receptor (EGFR). This study aimed to develop a methodology to discover multi-target inhibitors among over 2000 FDA-approved drugs. Hence, a novel ensemble approach was employed, comprising classification and regression models. First, three different deep autoencoder classifications were generated for each target individually. The top 100 trained models were selected for the high-throughput virtual screening step. After that, all identified molecules with a probability of more than 0.9 in more than 70% of the models were removed to ensure accurate consideration in the regression step. Since the ultimate aim of virtual screening is to discover molecules with the highest success rate in the pharmaceutical industry, various aspects of the molecules in different assays were considered by integrating ten different regression models. In conclusion, this paper contributes to pharmaceutical sciences by introducing eleven diverse scaffolds and eight approved drugs that can potentially be used as inhibitors of angiogenesis receptors, including VEGFR, FGFR, and EGFR. Considering three target receptors simultaneously is another central concept and contribution used. This concept could increase the chance of success, while reducing the possibility of resistance to these agents.

PMID:40418485 | DOI:10.1007/s11030-025-11214-6

AMIA Annu Symp Proc. 2025 May 22;2024:1275-1283. eCollection 2024.

ABSTRACT

Large language models (LLMs) have shown great promise in clinical medicine, but their adoption in real-world settings has been limited by their tendency to generate incorrect and sometimes even toxic statements. This study presents a reliable rare disease intelligent agent called RDguru, which incorporates authoritative and reliable knowledge sources and tools into the reasoning and response of LLMs. In addition to answering questions about rare diseases more accurately, RDguru can conduct medical consultations to provide differential diagnosis decision support for clinical users. The DQN-based multi-source fusion diagnostic model integrates three diagnostic recommendation strategies, GPT-4, PheLR, and phenotype matching. Testing on 238 real rare disease cases showed that RDguru's top 10 list of recommended diagnoses was able to recall 69.1% of real diagnoses, the top 5 recommended diagnoses were able to recall 63.6% of real diagnoses, and the top ranked diagnosis was able to achieve an accuracy rate of 41.9%.

PMID:40417483 | PMC:PMC12099370

Drug Des Devel Ther. 2025 May 20;19:4201-4220. doi: 10.2147/DDDT.S506112. eCollection 2025.

ABSTRACT

OBJECTIVE: This study analyzes the current research landscape and trends in orphan drug development, providing insights for future advancements in the field.

METHODS: Gathering pertinent material from the China National Knowledge Infrastructure (CNKI) and Web of Science databases. Microsoft Office Excel 2017, VOSview 1.6.20, and CiteSpace 6.3R2 were utilised to summarise the present research state and offer insights into research hotspots in the domain of orphan drugs.

RESULTS: A total of 3598 research papers were included, with Chinese research showing a continuous upward trend, while international research has entered a slow development stage. The field of orphan drug research has formed a sizable research team, and cooperation between international institutions is relatively mature. Meanwhile, the United States and the United Kingdom have strong influence in this research field, while China lacks international cooperation. The research focus in this field mainly involves the development and clinical application of orphan drugs, and domestic and foreign research also has its own emphasis. Maintain consistency in clinical trials and medication support for orphan drugs both domestically and internationally; And foreign research has obvious advantages in the development of orphan drugs. Keyword emergence research indicates that the clinical accessibility and regulatory approval of orphan drugs have become a prominent issue of interest both nationally and globally.

CONCLUSION: This study systematically summarises and examines the current research status and emerging trends in the global orphan drug sector by analysing relevant literature from 2000 to 2024 through bibliometric methods. It further delineates the similarities and differences in orphan drug research domestically and internationally, offering valuable references for future investigations in this domain.

PMID:40416799 | PMC:PMC12103199 | DOI:10.2147/DDDT.S506112

Front Mol Biosci. 2025 May 9;12:1583992. doi: 10.3389/fmolb.2025.1583992. eCollection 2025.

ABSTRACT

Exosomes, small membranous vesicles naturally secreted by living cells, have garnered attention for their role in intercellular communication and therapeutic potential. Their low immunogenicity, high biocompatibility, and efficient biological barrier penetration make them promising drug delivery vehicles. This review spans research developments from 2010 to 2025, covering the engineering of exosomes to optimize cargo loading and targeting specificity. We discuss their applications in treating cardiovascular diseases, liver fibrosis, immune diseases, and neurological diseases, alongside ongoing clinical trials and industry progress. Future challenges include scalability, standardization, and minimizing off-target effects. We propose strategies to address these hurdles, such as bioengineering techniques and improved isolation methods. By synthesizing current knowledge and outlining future directions, this review aims to guide researchers toward harnessing exosomes for disease treatment.

PMID:40417062 | PMC:PMC12098103 | DOI:10.3389/fmolb.2025.1583992

Int J Comput Assist Radiol Surg. 2025 May 26. doi: 10.1007/s11548-025-03422-0. Online ahead of print.

ABSTRACT

PURPOSE: Accurate patient dosimetry estimates from fluoroscopically-guided interventions (FGIs) are hindered by limited knowledge of the specific anatomy that was irradiated. Current methods use data reported by the equipment to estimate the patient anatomy exposed during each irradiation event. We propose a deep learning algorithm to automatically match 2D fluoroscopic images with corresponding anatomical regions in computational phantoms, enabling more precise patient dose estimates.

METHODS: Our method involves two main steps: (1) simulating 2D fluoroscopic images, and (2) developing a deep learning algorithm to predict anatomical coordinates from these images. For part (1), we utilized DeepDRR for fast and realistic simulation of 2D x-ray images from 3D computed tomography datasets. We generated a diverse set of simulated fluoroscopic images from various regions with different field sizes. In part (2), we employed a Residual Neural Network (ResNet) architecture combined with metadata processing to effectively integrate patient-specific information (age and gender) to learn the transformation between 2D images and specific anatomical coordinates in each representative phantom. For the Modified ResNet model, we defined an allowable error range of ± 10 mm.

RESULTS: The proposed method achieved over 90% of predictions within ± 10 mm, with strong alignment between predicted and true coordinates as confirmed by Bland-Altman analysis. Most errors were within ± 2%, with outliers beyond ± 5% primarily in Z-coordinates for infant phantoms due to their limited representation in the training data. These findings highlight the model's accuracy and its potential for precise spatial localization, while emphasizing the need for improved performance in specific anatomical regions.

CONCLUSION: In this work, a comprehensive simulated 2D fluoroscopy image dataset was developed, addressing the scarcity of real clinical datasets and enabling effective training of deep-learning models. The modified ResNet successfully achieved precise prediction of anatomical coordinates from the simulated fluoroscopic images, enabling the goal of more accurate patient-specific dosimetry.

PMID:40418509 | DOI:10.1007/s11548-025-03422-0

Mol Divers. 2025 May 26. doi: 10.1007/s11030-025-11214-6. Online ahead of print.

ABSTRACT

Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage. Along with tumor growth in the vicinity of blood vessels, malignant cells require more nutrients; hence, capillary sprouting occurs from parental vessels, a process known as angiogenesis. Although multiple cellular pathways have been identified, controlling them with one single biomolecule as a multi-target inhibitor could be an attractive strategy for reducing medication side effects. Three critical pathways in angiogenesis have been identified, which are activated by the vascular endothelial growth factor receptor (VEGFR), fibroblast growth factor receptor (FGFR), and epidermal growth factor receptor (EGFR). This study aimed to develop a methodology to discover multi-target inhibitors among over 2000 FDA-approved drugs. Hence, a novel ensemble approach was employed, comprising classification and regression models. First, three different deep autoencoder classifications were generated for each target individually. The top 100 trained models were selected for the high-throughput virtual screening step. After that, all identified molecules with a probability of more than 0.9 in more than 70% of the models were removed to ensure accurate consideration in the regression step. Since the ultimate aim of virtual screening is to discover molecules with the highest success rate in the pharmaceutical industry, various aspects of the molecules in different assays were considered by integrating ten different regression models. In conclusion, this paper contributes to pharmaceutical sciences by introducing eleven diverse scaffolds and eight approved drugs that can potentially be used as inhibitors of angiogenesis receptors, including VEGFR, FGFR, and EGFR. Considering three target receptors simultaneously is another central concept and contribution used. This concept could increase the chance of success, while reducing the possibility of resistance to these agents.

PMID:40418485 | DOI:10.1007/s11030-025-11214-6

Abdom Radiol (NY). 2025 May 26. doi: 10.1007/s00261-025-04990-0. Online ahead of print.

ABSTRACT

The rapid advancements in artificial intelligence (AI) carry the promise to reshape abdominal imaging by offering transformative solutions to challenges in disease detection, classification, and personalized care. AI applications, particularly those leveraging deep learning and radiomics, have demonstrated remarkable accuracy in detecting a wide range of abdominal conditions, including but not limited to diffuse liver parenchymal disease, focal liver lesions, pancreatic ductal adenocarcinoma (PDAC), renal tumors, and bowel pathologies. These models excel in the automation of tasks such as segmentation, classification, and prognostication across modalities like ultrasound, CT, and MRI, often surpassing traditional diagnostic methods. Despite these advancements, widespread adoption remains limited by challenges such as data heterogeneity, lack of multicenter validation, reliance on retrospective single-center studies, and the "black box" nature of many AI models, which hinder interpretability and clinician trust. The absence of standardized imaging protocols and reference gold standards further complicates integration into clinical workflows. To address these barriers, future directions emphasize collaborative multi-center efforts to generate diverse, standardized datasets, integration of explainable AI frameworks to existing picture archiving and communication systems, and the development of automated, end-to-end pipelines capable of processing multi-source data. Targeted clinical applications, such as early detection of PDAC, improved segmentation of renal tumors, and improved risk stratification in liver diseases, show potential to refine diagnostic accuracy and therapeutic planning. Ethical considerations, such as data privacy, regulatory compliance, and interdisciplinary collaboration, are essential for successful translation into clinical practice. AI's transformative potential in abdominal imaging lies not only in complementing radiologists but also in fostering precision medicine by enabling faster, more accurate, and patient-centered care. Overcoming current limitations through innovation and collaboration will be pivotal in realizing AI's full potential to improve patient outcomes and redefine the landscape of abdominal radiology.

PMID:40418375 | DOI:10.1007/s00261-025-04990-0

Abdom Radiol (NY). 2025 May 26. doi: 10.1007/s00261-025-05014-7. Online ahead of print.

ABSTRACT

PURPOSE: A critical limitation to deployment and utilization of Artificial Intelligence (AI) algorithms in radiology practice is the actual integration of algorithms directly into the clinical Picture Archiving and Communications Systems (PACS). Here, we sought to integrate an AI-based pipeline for prostate organ and intraprostatic lesion segmentation within a clinical PACS environment to enable point-of-care utilization under a prospective clinical trial scenario.

METHODS: A previously trained, publicly available AI model for segmentation of intra-prostatic findings on multiparametric Magnetic Resonance Imaging (mpMRI) was converted into a containerized environment compatible with MONAI Deploy Express. An inference server and dedicated clinical PACS workflow were established within our institution for evaluation of real-time use of the AI algorithm. PACS-based deployment was prospectively evaluated in two phases: first, a consecutive cohort of patients undergoing diagnostic imaging at our institution and second, a consecutive cohort of patients undergoing biopsy based on mpMRI findings. The AI pipeline was executed from within the PACS environment by the radiologist. AI findings were imported into clinical biopsy planning software for target definition. Metrics analyzing deployment success, timing, and detection performance were recorded and summarized.

RESULTS: In phase one, clinical PACS deployment was successfully executed in 57/58 cases and were obtained within one minute of activation (median 33 s [range 21-50 s]). Comparison with expert radiologist annotation demonstrated stable model performance compared to independent validation studies. In phase 2, 40/40 cases were successfully executed via PACS deployment and results were imported for biopsy targeting. Cancer detection rates for prostate cancer were 82.1% for ROI targets detected by both AI and radiologist, 47.8% in targets proposed by AI and accepted by radiologist, and 33.3% in targets identified by the radiologist alone.

CONCLUSIONS: Integration of novel AI algorithms requiring multi-parametric input into clinical PACS environment is feasible and model outputs can be used for downstream clinical tasks.

PMID:40418374 | DOI:10.1007/s00261-025-05014-7

Eur Radiol. 2025 May 26. doi: 10.1007/s00330-025-11671-5. Online ahead of print.

ABSTRACT

BACKGROUND: MRI sequence classification becomes challenging in multicenter studies due to variability in imaging protocols, leading to unreliable metadata and requiring labor-intensive manual annotation. While numerous automated MRI sequence identification models are available, they frequently encounter the issue of domain shift, which detrimentally impacts their accuracy. This study addresses domain shift, particularly from adult to pediatric MRI data, by evaluating the effectiveness of pre-trained models under these conditions.

METHODS: This retrospective and multicentric study explored the efficiency of a pre-trained convolutional (ResNet) and CNN-Transformer hybrid model (MedViT) to handle domain shift. The study involved training ResNet-18 and MedVit models on an adult MRI dataset and testing them on a pediatric dataset, with expert domain knowledge adjustments applied to account for differences in sequence types.

RESULTS: The MedViT model demonstrated superior performance compared to ResNet-18 and benchmark models, achieving an accuracy of 0.893 (95% CI 0.880-0.904). Expert domain knowledge adjustments further improved the MedViT model's accuracy to 0.905 (95% CI 0.893-0.916), showcasing its robustness in handling domain shift.

CONCLUSION: Advanced neural network architectures like MedViT and expert domain knowledge on the target dataset significantly enhance the performance of MRI sequence classification models under domain shift conditions. By combining the strengths of CNNs and transformers, hybrid architectures offer enhanced robustness for reliable automated MRI sequence classification in diverse research and clinical settings.

KEY POINTS: Question Domain shift between adult and pediatric MRI data limits deep learning model accuracy, requiring solutions for reliable sequence classification across diverse patient populations. Findings The MedViT model outperformed ResNet-18 in pediatric imaging; expert domain knowledge adjustment further improved accuracy, demonstrating robustness across diverse datasets. Clinical relevance This study enhances MRI sequence classification by leveraging advanced neural networks and expert domain knowledge to mitigate domain shift, boosting diagnostic precision and efficiency across diverse patient populations in multicenter environments.

PMID:40418319 | DOI:10.1007/s00330-025-11671-5

J Am Med Inform Assoc. 2025 May 26:ocaf074. doi: 10.1093/jamia/ocaf074. Online ahead of print.

ABSTRACT

OBJECTIVES: Immunotherapies have revolutionized the landscape of cancer treatments. However, our understanding of response patterns in advanced cancers treated with immunotherapy remains limited. By leveraging routinely collected noninvasive longitudinal and multimodal data with artificial intelligence, we could unlock the potential to transform immunotherapy for cancer patients, paving the way for personalized treatment approaches.

MATERIALS AND METHODS: In this study, we developed a novel artificial neural network architecture, multimodal transformer-based simple temporal attention (MMTSimTA) network, building upon a combination of recent successful developments. We integrated pre- and on-treatment blood measurements, prescribed medications, and CT-based volumes of organs from a large pan-cancer cohort of 694 patients treated with immunotherapy to predict mortality at 3, 6, 9, and 12 months. Different variants of our extended MMTSimTA network were implemented and compared to baseline methods, incorporating intermediate and late fusion-based integration methods.

RESULTS: The strongest prognostic performance was demonstrated using a variant of the MMTSimTA model with area under the curves of 0.84 ± 0.04, 0.83 ± 0.02, 0.82 ± 0.02, 0.81 ± 0.03 for 3-, 6-, 9-, and 12-month survival prediction, respectively.

DISCUSSION: Our findings show that integrating noninvasive longitudinal data using our novel architecture yields an improved multimodal prognostic performance, especially in short-term survival prediction.

CONCLUSION: Our study demonstrates that multimodal longitudinal integration of noninvasive data using deep learning may offer a promising approach for personalized prognostication in immunotherapy-treated cancer patients.

PMID:40418276 | DOI:10.1093/jamia/ocaf074

J Chem Inf Model. 2025 May 26. doi: 10.1021/acs.jcim.5c00378. Online ahead of print.

ABSTRACT

Computational protein design (CPD) refers to the use of computational methods to design proteins. Traditional methods relying on energy functions and heuristic algorithms for sequence design are inefficient and do not meet the demands of the big data era in biomolecules, with their accuracy limited by the energy functions and search algorithms. Existing deep learning methods are constrained by the learning capabilities of the networks, failing to extract effective information from sparse protein structures, which limits the accuracy of protein design. To address these shortcomings, we developed an Efficient attention-based models for computational protein design using amino acid microenvironment (EMOCPD). It aims to predict the category of each amino acid in a protein by analyzing the three-dimensional atomic environment surrounding the amino acids, and optimize the protein based on the predicted high-probability potential amino acid categories. EMOCPD employs a multihead attention mechanism to focus on important features in the sparse protein microenvironment and utilizes an inverse residual structure to optimize the network architecture. In protein design, the thermal stability and protein expression of the predicted mutants from EMOCPD show significant improvements compared to the wild type, effectively validating EMOCPD's potential in designing superior proteins. Furthermore, the predictions of EMOCPD are influenced positively, negatively, or have minimal impact based on the content of the 20 amino acids, categorizing amino acids as positive, negative, or neutral. Research findings indicate that EMOCPD is more suitable for designing proteins with lower contents of negative amino acids.

PMID:40418077 | DOI:10.1021/acs.jcim.5c00378

ACS Appl Mater Interfaces. 2025 May 26. doi: 10.1021/acsami.5c06453. Online ahead of print.

ABSTRACT

Flexible strain sensors have garnered significant attention due to their high sensitivity, rapid response, and flexibility. Recent innovations, particularly those incorporating machine learning, have significantly enhanced their stability, sensitivity, and adaptability, positioning these sensors as promising solutions in health monitoring, human-computer interaction, and smart home applications. However, challenges remain in optimizing sensor materials for enhanced responsiveness, durability, and stability. Moreover, the development of machine learning-based strain sensors faces obstacles, including algorithmic limitations, low noise tolerance in complex environments, and limited model interpretability. This review systematically evaluates the latest advancements in flexible strain sensors, emphasizing the critical role of machine learning in performance enhancement. It further explores the shift from traditional machine learning methods to deep learning approaches, elucidating the potential applications that these algorithms facilitate. Finally, we discuss future research trajectories, highlighting both opportunities and challenges that may guide the next wave of innovations in this dynamic field.

PMID:40418062 | DOI:10.1021/acsami.5c06453

Curr Med Chem. 2025 May 23. doi: 10.2174/0109298673388739250516071228. Online ahead of print.

NO ABSTRACT

PMID:40417758 | DOI:10.2174/0109298673388739250516071228

3 Biotech. 2025 Jun;15(6):180. doi: 10.1007/s13205-025-04329-y. Epub 2025 May 21.

ABSTRACT

Nannochloropsis is a microalgae with more than substantially 60-70% cellulose in its cell wall, making it a potential candidate for nanocellulose sustainable production. This study examined the effects of salts in seawater and their role on Nannochloropsis gaditana and Nannochloropsis oculata cellulose synthase activity using In-silico and In-vitro approaches for the first time. Deep-learning-based AlphaFold2 predicted model was selected as the most reliable 3D structure. Molecular docking results revealed that none of the selected ligands occupied the binding site predicted for the native substrate of the enzyme, uridine-diphosphate. To validate the In-silico results, experiments were conducted to investigate the impact of salinity stress (NaCl, NaNO3 and NaHCO3) on the cell growth and cellulose production. The assessment tools included a UV-visible spectrophotometer and a hemocytometer, with a modified Jayme-Wise method used for cellulose extraction. The results indicated that the following concentrations of 0.443 mol/L, 0.457 mol/L, and 0.469 mol/L of NaCl, 0.072 mol/L, 0.077 mol/L, and 0.082 mol/L of NaNO3, 0.0021 mol/L, 0.0022 mol/L, and 0.0023 mol/L of NaHCO3 did not lower the growth rate nor the cellulose yield of N. oculata and notable enhancement in growth was observed in cultures supplemented with 0.0023 mol/L NaHCO3. Furthermore, when NaCl (0.457 mol/L and 0.469 mol/L), NaNO3 (0.082 mol/L) and NaHCO3 (0.0022 mol/L and 0.0023 mol/L) were individually introduced to the culture, cellulose yield increased up to five times compared to the control group.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-025-04329-y.

PMID:40417659 | PMC:PMC12095769 | DOI:10.1007/s13205-025-04329-y

Nat Sci Sleep. 2025 May 19;17:975-985. doi: 10.2147/NSS.S506975. eCollection 2025.

ABSTRACT

BACKGROUND: Obstructive sleep apnea (OSA) is prevalent in patients with idiopathic pulmonary fibrosis (IPF). This study evaluated the prognostic significance of Krebs von den Lungen-6 (KL-6) levels in patients with comorbid OSA and IPF.

METHODS: This retrospective research included 115 individuals diagnosed with IPF between January 2015 and December 2020, all of whom completed sleep tests and underwent measurement of serum KL-6 levels during hospitalization. To ascertain the risk factors associated with all-cause death, a multivariate Cox regression model was employed, adjusted for confounding variables of age, sex, and pulmonary function.

RESULTS: During the 40-month follow-up, 24 (20.9%) deaths occurred, with 17 (28.8%) in the OSA group and 7 (12.5%) in the non-OSA group. Patients with OSA had higher baseline KL-6 levels than did those without OSA. Both apnea-hypopnea index (hazard ratio [HR] = 1.023, 95% confidence interval [CI] 1.000-1.047, p = 0.049) and serum KL-6 levels (HR = 1.001, 95% CI 0.999-1.002, p = 0.032) were identified as independent risk factors for death in multivariable Cox analysis. For the overall cohort of patients with IPF, those with a KL-6 levels ≥1200 U/mL had a higher risk of death in both univariate analysis (HR = 5.694, 95% CI 1.945-16.669, p = 0.002) and adjusted models (HR = 5.245, 95% CI 1.775-15.494, p = 0.003). In the subgroup analysis, the independent prognostic significance of KL-6 levels ≥1200 U/mL for death was evident only in IPF patients with concurrent OSA (HR = 4.887, 95% CI 1.082-22.067, p = 0.039), whereas it was not observed yet in IPF patients without OSA (HR = 4.652, 95% CI 0.616-35.131, p = 0.136).

CONCLUSION: KL-6 level is of prognostic value in patients with comorbid IPF and OSA. These findings underscore the need for sleep tests and KL-6 measurement for IPF patients at high risk.

PMID:40417308 | PMC:PMC12101450 | DOI:10.2147/NSS.S506975

Am J Physiol Renal Physiol. 2025 May 26. doi: 10.1152/ajprenal.00362.2024. Online ahead of print.

ABSTRACT

Diabetic kidney disease is a complication in one out of three patients with diabetes. Aberrant glucose metabolism in diabetes leads to structural and functional damage in glomerular tissue and a systemic inflammatory immune response. Complex cellular signaling is at the core of metabolic and functional derangement. Unfortunately, the mechanism underlying the role of inflammation in glomerular endothelial cell dysfunction during diabetic kidney disease is not fully understood. Mathematical models in systems biology allow the integration of experimental evidence and cellular signaling networks to understand mechanisms involved in disease progression. This study developed a logic-based ordinary differential equations model to study inflammatory crosstalk between macrophages and glomerular endothelial cells during diabetic kidney disease progression using a protein signaling network stimulated with glucose and lipopolysaccharide. This modeling approach reduced the biological parameters needed to study signaling networks. The model was fitted to and validated against available biochemical data from in vitro experiments. The model identified mechanisms for dysregulated signaling in macrophages and glomerular endothelial cells during diabetic kidney disease. In addition, the influence of signaling interactions on glomerular endothelial cell morphology through selective knockdown and downregulation was investigated. Simulation results showed that partial knockdown of VEGF receptor 1, PLC-γ, adherens junction proteins, and calcium partially improved intercellular junction integrity between glomerular endothelial cells. These findings contribute to understanding signaling and molecular perturbations that affect the glomerular endothelial cells in the early stage of diabetic kidney disease.

PMID:40418541 | DOI:10.1152/ajprenal.00362.2024

Methods Mol Biol. 2025;2923:163-180. doi: 10.1007/978-1-0716-4522-2_10.

ABSTRACT

High-throughput RNA sequencing (RNA-seq) is commonly used to quantify gene expression transcriptome-wide. While usually paired with polyadenylate (poly(A)) selection to enrich for messenger RNA (mRNA) to the exclusion of highly abundant ribosomal RNA (rRNA) in the cell, this strategy will under-quantify mRNA with short or absent poly(A) tails and can conflate changes in poly(A) tail length with changes in RNA level. This is notably an issue during early development, when cytoplasmic polyadenylation of maternal mRNA over time can be mistaken for genome activation in poly(A) + RNA-seq time courses. Here, we present a method to perform total RNA-seq using a streamlined rRNA depletion strategy customizable to any taxon. Antisense DNA oligos are designed with the aid of our Oligo-ASST web tool to sparsely tile the length of the rRNA, which are used with thermostable RNaseH to digest rRNA from a total RNA sample. After column cleanup, the mRNA-enriched sample is ready for sequencing library construction.

PMID:40418449 | DOI:10.1007/978-1-0716-4522-2_10

Methods Mol Biol. 2025;2923:89-117. doi: 10.1007/978-1-0716-4522-2_7.

ABSTRACT

The transcriptional machinery of a cell is often not homogenously distributed but rather forms clusters in the nucleus. These clusters are important for gene expression, but how they form and function is often not clear. The zebrafish embryo provides an excellent system to study these clusters of transcriptional machinery, because embryos are transparent and develop rapidly, making it easy to track proteins as they cluster and perform their function. Here, we provide a protocol for how to image nuclear clusters in living zebrafish embryos. The protocol includes information on the selection and encoding of proteins and fluorophores, embryo-embedding for live-cell microscopy, the use of a spinning disk microscope, staging of embryos post image acquisition, and image analysis. While the protocol is written in the context of our work with early zebrafish embryos, many of the tools will be useful in other contexts.

PMID:40418446 | DOI:10.1007/978-1-0716-4522-2_7

Adv Clin Exp Med. 2025 May 26. doi: 10.17219/acem/205024. Online ahead of print.

ABSTRACT

Cancer progression and therapeutic resistance are propelled by the remarkable plasticity of signaling networks, which dynamically rewire under selective pressures to maintain proliferation, enable immune evasion and promote metastasis. Despite advances in precision oncology, the dynamic crosstalk between tumor cells, non-coding genomes and the microenvironment continues to undermine treatment efficacy. This call for submissions, Revolutionizing Cancer Treatment: Navigating the Intricate Landscape of Cellular Signaling Networks, seeks cutting-edge research that dissects these adaptive mechanisms through innovative technologies - from single-cell multi-omics and spatial transcriptomics to AI-powered network modeling. We welcome studies leveraging physiomimetic models (e.g., organoids, 3D-bioprinted ecosystems) to decode tumor heterogeneity, as well as translational work targeting emergent vulnerabilities at the intersection of epigenetics, metabolic reprogramming and stromal interactions. By integrating systems biology with computational and experimental approaches, this collection aims to catalyze the design of adaptive therapies that outmaneuver cancer's evolutionary resilience.

PMID:40418208 | DOI:10.17219/acem/205024