Search Results (2,546)

Non-small cell lung cancer (NSCLC) remains a major cause of cancer-related mortality, with poor survival outcomes despite advances in surgery, chemotherapy, targeted therapy, and immunotherapy. The tumor microenvironment (TME) plays a central role in sustaining tumor growth, immune evasion, and systemic metabolic dysfunction. In this study, we performed an integrative analysis of differentially expressed microRNAs (miRNAs) to uncover their contributions to dysregulated signaling networks in NSCLC. hsa-miR-486-5p was identified as a prominent differentially expressed candidate miRNA. Using mathematical modeling and regression-based reduction, we identified Forkhead Box O1 (FOXO1) and Unc-51 like Autophagy Activating Kinase 2 (ULK2) as critical regulatory nodes that integrate oncogenic signaling with cellular homeostasis. Aberrant expression of hsa-miR-486-5p was found to modulate pathways including PI3K/AKT/mTOR, NF-κB, and JAK-STAT3, thereby promoting tumor progression and secretion of inflammatory cytokines. These cytokines, viz., IL-6, TNF-α, and IL-1β, activate muscle-specific protein degradation pathways through E3 ubiquitin ligases TRIM63 and FBXO32, linking NSCLC progression to cancer-associated sarcopenia. Quasipotential landscape analysis further revealed dynamic phenotypic transitions between stable and unstable states, highlighting the adaptability of tumor–host interactions. Collectively, our findings demonstrate that miRNA-mediated regulatory networks not only drive NSCLC progression and inflammation but also contribute to systemic muscle wasting. These insights emphasize the need for novel therapeutic strategies, including RNA-based interventions, to overcome resistance, improve survival, and address the metabolic complications associated with NSCLC. Full article

A common hypoxic scenario in tumors involves unresolved acute hypoxia that eventually leads to sustained (chronic) hypoxia. This shift drives a characteristic “HIF switch”, where the key hypoxia-responsive factors change from HIF-1α to HIF-2α over time, and importantly, this switch is closely linked to stemness regulation. However, the mechanisms underlying this switch and its impact on stemness regulation are not yet fully understood. Here, we developed a mechanistic network model integrating the HIF-1/HIF-2 signaling axis with the stemness regulators OCT4 and SOX2. We found the duration and intensity of hypoxia jointly shape the dynamics of HIF-1α and HIF-2α, ultimately regulating OCT4-mediated stemness. Under physioxia, HIF-2α–mTORC2 positive feedback supports the gradual accumulation of HIF-2α toward a modest steady level and low OCT4 expression, corresponding to a primed state. Under prolonged mild hypoxia, the concurrent induction of HIF-1α, albeit at low levels, and accelerated accumulation of HIF-2α elevate OCT4 to intermediate levels, promoting stem-like traits. Under moderate hypoxia, PHD-2-mediated negative feedback triggers pulsatile HIF-1α dynamics, driving a shift toward HIF-2α dominance. Ultimately, cooperative HIF-1α/HIF-2α signaling induces REDD1 and suppresses mTORC1-dependent protein synthesis, pushing OCT4 into a high-expression state associated with differentiation. This work presents a unified framework for understanding how the HIF signaling hierarchy coordinates metabolic and transcriptional programs to direct cell fate across varying hypoxic landscapes. Full article

Protein molecularly imprinted polymers (MIPs), as artificial antibodies, are promising for protein separation due to their low cost, easy preparation, and high stability, but their performance is limited by poor mass transfer, imprecise imprinting, and single interaction modes. Herein, dendritic mesoporous silica nanoparticles (DMSNs) were used as the support, and a self-designed multifunctional poly(ionic liquid) macromonomer (p(VIMCD-co-VAIM-co-VSIM-co-VVIM)) served as the functional monomer to achieve directional anchoring of cytochrome C (Cyt-C). Surface-imprinted microspheres (DMSNs@MPS@PILs-MIPs) were prepared via free-radical copolymerization for Cyt-C recognition. The DMSNs possessed interconnected mesoporous channels, good dispersibility, an average particle size of ~80 nm, and a specific surface area of 267.97 m²/g. Ionic liquid monomers were synthesized via alkylation, and the macromonomer was constructed through a two-step method. Molecular dynamics simulations and spectroscopic characterization revealed the macromonomer-stabilized Cyt-C conformation, with interactions dominated by van der Waals forces. The DMSNs@MPS@PILs-MIPs featured a thin imprinted layer (~5 nm) to reduce mass-transfer resistance. Adsorption studies showed Cyt-C adsorption followed Langmuir and pseudo-second-order models, with a maximum capacity of 383.14 mg/g and an imprinting factor of 2.17. Only 12% capacity loss occurred after repeated cycles, indicating robust regeneration stability. This study provides a feasible strategy for constructing protein surface-imprinted polymers based on multifunctional synergistic interactions and conformational stabilization. Full article

Multisystemic eosinophilic epitheliotropic disease (MEED) is a rare and severe equine disorder characterized by chronic eosinophilic inflammation, epithelial disruption, and multi-organ involvement, with an undefined genetic basis. We performed the high-depth (~40×) whole-genome sequencing of an affected horse and compared it to 40 control genomes. Over 6.3 million variants were identified, with moderate- and high-impact variants enriched in low-frequency categories, including rare and private variants absent from the controls. The affected horse was dominated by missense mutations, with relatively few high-impact variants, consistent with the distributed protein-altering effects rather than a single highly penetrant mutation. Gene prioritization and pathway analyses highlighted the disruption of cytoskeletal organization, microtubule dynamics, epithelial integrity, and immune regulation. The network analysis further revealed the interconnected structural and inflammatory pathways, suggesting a link between an impaired epithelial barrier function and immune homeostasis. Together, these findings provide the first population genomic insight into MEED and support a model in which cumulative mutations contribute to the epithelial instability and persistent inflammation characteristic of the disease. Full article

Cell membranes exhibit specific structural and chirality properties influencing their biological behavior and functionality. Artemia salina endothelial-like cell membranes, structurally simpler, provide insights into fundamental cellular structures, whereas human endothelial cell membranes represent complex, specialized tissues essential for understanding advanced vascular functions. This study aims to compare the structural and chiral properties of Artemia salina endothelial-like cell membranes and human endothelial cell membranes through computational molecular-level modeling, evaluating potential histological and biological implications. Membrane models for Artemia salina and human endothelial cells were developed using Protein Data Bank (PDB) structures. Computational descriptors, including radius of gyration (Rg), solvent-accessible surface area (SASA), geometric asymmetry index (GAI), chiral moment (CM), fractal dimension (FD), and additional chirality indices (SOC, HCI, ACI, CAI, ME, RDF) were calculated to assess membrane complexity, structural asymmetry, and chirality. Significant structural divergences between Artemia salina and human endothelial membranes were identified. Artemia membranes exhibited lower values of Rg, SASA, and chirality metrics, indicating simpler, more symmetrical structures. In contrast, human endothelial membranes displayed elevated structural complexity, pronounced asymmetry, higher chirality indices, and more significant structural heterogeneity, consistent with their specialized physiological functions. Principal Component Analysis (PCA) further highlighted clear structural clustering distinctions between the two models. The comparative analysis underscores fundamental structural and functional divergences between Artemia salina and human endothelial cell membranes. Artemia membranes represent simplified, uniform cellular arrangements optimized for fundamental physiological roles, while human endothelial membranes exhibit complex architectures, structural specialization, and significant chirality essential for dynamic vascular functionalities. These computational descriptors offer potential diagnostic biomarkers for evaluating endothelial functionality and pathological states. Full article

Antifungal killer toxins are cytotoxic proteins that have the potential to combat the growing threat of fungi to human health and agriculture. A lack of empirical tertiary structures has limited understanding of their mechanisms of action and their ability to target pathogens. In this study, AlphaFold and molecular dynamics simulations were used to generate tertiary structure models of all canonical Saccharomyces killer toxins and to place them in the context of historical empirical data. These models enabled the prediction of functional domains and posttranslational modifications, including proteolytic cleavage sites and disulfide bonds. They also revealed unexpected homology between Saccharomyces killer toxins, suggesting that all but K28 are likely ionophores. Structural homology to the well-studied killer toxins K1 and K2 enabled the prediction of the antifungal and immunity mechanisms of K1L, K21, K45, K74, and KHS. The understudied killer toxins Klus, KHR, and K62 were found to have homology to bacterial and plant toxins, including members of the aerolysin family and antifungal lectins. These structural similarities provide clues for the mechanisms of killer toxin carbohydrate binding, oligomerization, and membrane attack. This modeling approach will help guide the continued use of the model yeast S. cerevisiae to study killer toxins in the context of the wealth of functional data gathered in the decades since their first discovery. Full article

Nickel (Ni) is a ubiquitous trace metal, yet its physiological dynamics and dose-dependent roles in skeletal biology remain unclear. Here we combined elemental mapping, cellular assays, multi-omics and mouse models to define how Ni availability modulates osteogenesis. Ni, together with Manganese (Mn), chromium (Cr) and copper (Cu), was readily detectable in serum from both mice and humans. In situ LA–ICP–MS further showed that Ni levels in embryonic calvaria rose significantly across stages and CaO exhibited a consistent upward trend, suggesting coordinated accumulation of Ni with cranial mineralization. In vitro, Ni exerted biphasic effects on bone marrow mesenchymal stromal cells (BMSCs): high-dose Ni (100 μM) suppressed proliferation, elevated ROS, and induced time-dependent upregulation of Hmox1 and Nos2, consistent with escalating oxidative/nitrosative stress. By contrast, low-dose Ni (0.1 μM) enhanced matrix mineralization, whereas this pro-mineralization effect was attenuated at higher concentrations. In vivo, both Ni deprivation and Ni overload impaired bone formation: a Ni-free diet caused trabecular rarefaction and reduced mineral apposition, while high Ni hindered bone development of mice, especially in the early-stage intake. Mechanistically, RNA-seq and Ni-NTA proteomics identified Ni-driven osteogenic transcriptional remodeling and increased Ni-binding proteins, prioritizing integrin-linked kinase (ILK) as a Ni-inducible binder. ILK was required for osteogenic differentiation, and low-dose Ni activated AKT–mTOR signaling in an ILK-dependent manner. Finally, low-dose Ni-pretreated collagen scaffolds enhanced calvarial defect repair. Together, these findings define a narrow physiological window in which Ni supports osteogenesis via ILK–AKT–mTOR, whereas both deficiency and excess disrupt skeletal accrual. Full article

Background/Objectives: Sepsis is clinically dynamic, and isolated admission biomarker values may insufficiently capture early biological evolution after treatment initiation. This study evaluated whether routine biomarker reassessment at approximately 48 h provides incremental prognostic information beyond admission Sequential Organ Failure Assessment (SOFA) score for 28-day mortality in sepsis. The analysis was framed as an exploratory 48 h landmark prognostic assessment among patients who were alive and had complete biomarker reassessment data at 48 ± 6 h. Methods: We conducted a prospective single-center observational cohort study including adult patients with sepsis. Clinical and laboratory data were collected at baseline (M1) and repeated 48 ± 6 h later (M2). The primary outcome was 28-day mortality. Candidate biomarkers included C-reactive protein (CRP), procalcitonin (PCT), lactate (LAC), and neutrophil-to-lymphocyte ratio (NLR). PCT clearance and NLR change were calculated as relative changes between M1 and M2, whereas 48 h CRP and 48 h lactate were evaluated as early reassessment values. Exploratory logistic regression models were constructed using admission SOFA as the clinical reference model. Model discrimination and fit were summarized using receiver operating characteristic analysis, likelihood-ratio testing, and Nagelkerke R²; the models were not intended as validated individual-level risk calculators. Results: The 48 h landmark analytical cohort included 126 patients, of whom 44 (34.9%) died within 28 days. Admission biomarker values showed limited prognostic signal. SOFA alone showed fair discrimination (AUC 0.740). Among the primary SOFA-augmented models, SOFA plus PCT clearance showed the highest discrimination and explanatory performance (AUC 0.810; Nagelkerke R² 0.332) and significantly improved model fit compared with SOFA alone. SOFA plus NLR change and SOFA plus 48 h lactate also provided incremental prognostic information, although their gains were more modest. In exploratory combined modeling, SOFA plus PCT clearance and NLR change provided the most coherent additional signal, with all predictors retaining independent associations with 28-day mortality. Conclusions: In this exploratory single-center 48 h landmark analysis, selected routine biomarker reassessment measures were associated with 28-day mortality beyond admission SOFA. PCT clearance provided the clearest incremental prognostic signal, while NLR change offered complementary information. Persistent 48 h lactate elevation was also informative, whereas lactate clearance was not. These findings should be interpreted as hypothesis-generating and require validation in larger cohorts, ideally including serial organ dysfunction measures such as 48 h SOFA or SOFA change. Full article

The persistence of an immunosuppressive microenvironment remains a formidable challenge for cancer immunotherapy, particularly in tumors with immune-excluded or immune-desert phenotypes. Increasing evidence indicates that chronic inflammation and tumor progression are intrinsically linked through shared signaling hubs, including NF-κB and PI3K/Akt. Osthole, a natural coumarin compound, has been reported to exhibit both potent anti-inflammatory and antitumor activities; however, whether these effects reflect a coordinated regulation of the inflammation–cancer axis remains unclear. In this study, we deployed an integrative framework founded on network pharmacology, molecular docking, and rigorous molecular dynamics simulations, complemented by literature-based evidence synthesis, to computationally explore the potential mechanisms underlying Osthole’s dual activities. Our analysis revealed that Osthole’s predicted targets are significantly enriched in signaling pathways bridging inflammatory and oncogenic processes, most notably the PI3K/Akt, NF-κB, and TGF-β/Smad pathways. Crucially, MD simulations provided supportive computational evidence, suggesting that Osthole forms stable, energetically favorable complexes with core protein hubs (AKT1, RELA, and TGFB1) under the simulated conditions. Evidence from representative inflammatory and tumor models supports the biological plausibility of these predictions, including suppression of pro-inflammatory signaling, mitigation of maladaptive tissue remodeling, and induction of apoptosis. Furthermore, in hepatocellular carcinoma models, Osthole-mediated apoptosis appeared linked to HMGB1-related inflammatory signaling, highlighting its potential to modulate the local immune niche. Collectively, this convergence of systems-level predictions and dynamic structural evidence identifies Osthole as a promising multi-target candidate for the coordinated regulation of inflammation-associated tumor progression, providing a robust rationale for further experimental validation. Full article

Background: Targeting Apolipoprotein E4 (ApoE4) represents a frontier in Alzheimer’s disease therapeutics. This study investigates the therapeutic potential of a nutraceutical panel (Polydatin, trans-resveratrol, luteolin, and PEA) by exploring their interaction with the ApoE4 EZ-482 cavity. Methods: Using a dual-platform docking strategy (SwissDock and Schrödinger Maestro) across three structural constructs. Results and Discussion: We identified the full-length protein (1–299) as the optimal target, showing a robust correlation between normalized docking scores (Spearman ρ = 0.79). Crucially, biophysical analysis via dynamic light scattering (DLS) revealed that the ApoE4–oxLDL complex exhibits a ζ-potential of −10.97 mV, a state prone to pathological aggregation. Luteolin and PEA effectively altered this electrostatic environment, inducing significant positive shifts to +2.15 mV and +1.05 mV, respectively. The alignment between computational rankings and experimental ζ-potential perturbations supports the predictive reliability of our model. These findings suggest that nutraceuticals can modulate the ApoE4–oxLDL biophysical profile and highlight that a full structural context is mandatory for developing effective ApoE4-targeted interventions. Full article

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem remains a challenging task, particularly in the presence of noisy density maps and in large, topologically complex α/β proteins. To address this issue, we propose a fully automated, classification-based framework that infers protein secondary-structure topology directly from medium-resolution cryo-EM density maps. Specifically, we cast topology determination as a supervised classification problem in three-dimensional space, leveraging geometric learning on model-derived Cα coordinate representations to establish SSE correspondences, and a Dynamic Time Warping (DTW)-based procedure to resolve density-stick directionality. Validation on a benchmark of 38 proteins spanning both simulated and experimental cryo-EM maps and covering diverse fold classes (α, β, and α/β) demonstrates strong and consistent performance. Among the evaluated predictors, the Voronoi (1-NN) classifier achieves the highest average correspondence quality, with a mean F1-score of 96.82% across the full benchmark. The framework also scales to large, topologically dense targets containing up to 65 secondary-structure elements while preserving very fast correspondence inference (<3 ms), offering a substantial improvement over prior baselines in both accuracy and computational cost. Overall, the classification-driven strategy provides reliable SSE-to-density matching and, when coupled with DTW-based direction selection, yields stronger topology constraints that directly support model building and refinement from medium-resolution cryo-EM reconstructions, while remaining easy to integrate into existing structural interpretation pipelines. Full article

Decidualization in the human endometrium is not merely a hormone-dependent differentiation process; rather, it represents a multilayered adaptive program characterized by the tight integration of immune regulation, metabolic reprogramming, and cellular stress responses. In this review, endoplasmic reticulum (ER) stress and the associated unfolded protein response (UPR) are proposed as central regulatory mechanisms governing this process. Triggered by increased protein synthesis and secretory demand, UPR activation under physiological conditions preserves proteostasis and supports the secretory capacity of stromal cells. In contrast, chronic or dysregulated activation leads to a maladaptive response characterized by apoptosis, inflammation, and metabolic dysfunction. UPR signaling pathways shape immune tolerance through their effects on macrophage polarization, uterine natural killer (uNK) cell function, and T cell balance. At the metabolic level, adenosine monophosphate-activated protein kinase (AMPK) regulates cellular adaptation through bidirectional interactions with mitochondrial function and redox homeostasis. Within this framework, the ER stress–immune–metabolic axis operates not as a linear pathway but as a dynamic network incorporating multiple feedback loops, thereby constituting a critical threshold mechanism that determines the success of decidualization. Disruption of this axis provides a shared mechanistic basis for pathologies such as recurrent implantation failure, pregnancy loss, and preeclampsia. From a therapeutic perspective, agents including chemical chaperones, UPR modulators, AMPK activators, and anti-inflammatory compounds hold translational potential by targeting these pathological feedback circuits. However, key knowledge gaps remain, particularly regarding the cell type-specific and temporal regulation of ER stress, the molecular boundaries defining the transition from adaptive to pathological states, and interspecies differences. Future studies employing single-cell omics approaches and functional in vivo models will be essential to elucidate the dynamic organization of this axis and to enable the development of targeted and personalized therapeutic strategies. Full article

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder, characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta and the accumulation of Lewy bodies. Over recent decades, various cellular mechanisms underlying PD have been elucidated, including autophagy, mitochondrial dysfunction, neuroinflammation, and axonal transport. Among them, axonal transport plays a critical role in maintaining the dynamic homeostasis of proteins, membrane-bound organelles, and cellular metabolism within neurons. Unfortunately, a comprehensive overview of axonal transport in PD remains absent. In this review, we synthesized the current literature on axonal transport in PD, leveraging neurotoxic and genetic models to explore the causes and consequences of axonal transport alterations in PD. Through this summary, we aim to deepen our understanding of PD pathogenesis and provide potential therapeutic targets for intervention. Full article

Background/Objectives: Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global health challenge, exacerbated by the emergence of multidrug-resistant strains and limited efficacy of existing therapies. Given the involvement of multiple essential mycobacterial proteins, multitarget drug discovery represents a rational therapeutic strategy. Methods: In this study, an integrated in silico pipeline combining machine learning–based quantitative structure–activity relationship modeling, graph neural network–driven drug–target affinity prediction, molecular docking, molecular dynamics (MD) simulations, and pharmacokinetic–toxicity profiling was employed to identify potential antitubercular leads from natural products. Results: A curated library of over 0.69 million compounds from the COCONUT database was systematically screened against seven essential M. tuberculosis protein targets. Machine learning and heterogeneous graph neural network models effectively captured complex ligand–protein interaction patterns, enabling high-confidence multitarget prioritization. Structure-based docking and MM-GBSA analyses revealed favorable binding affinities, further supported by 100 ns Molecular Dynamics simulations demonstrating stable binding and conformational integrity. In silico ADMET and toxicity predictions identified pharmacokinetically balanced candidates, while density functional theory calculations corroborated favorable electronic properties. Conclusions: Notably, a myricetin-based flavonoid glycoside exhibited consistent multitarget binding and dynamic stability across all targets. Overall, this study underscores the potential of integrated artificial intelligence and structure-based approaches in accelerating natural product-based antitubercular drug discovery and supports further experimental validation of prioritized leads. Full article

Objectives: This study aimed to elucidate the multi-target therapeutic mechanisms of Camellia sinensis phytochemicals in periodontitis using an integrative multi-scale molecular modeling strategy. Methods: An integrated in silico strategy was employed, incorporating network-based pharmacological analysis, protein interaction network evaluation, molecular docking assessment, density functional theory (DFT) computations, molecular dynamics (MD) trajectory analysis, MM/PBSA-derived binding energy estimation, and residue-level energetic contribution profiling. Overlapping targets between C. sinensis and periodontitis-associated genes were identified, followed by topological screening to determine crucial hub proteins. The most promising target was subjected to detailed structural and energetic evaluation. Results: Intersection analysis identified 23 common targets, with AKT1, myeloperoxidase (MPO), MMP2, MMP3, MMP9, STAT1, IL2, BCL2, ESR1, and SERPINE1 emerging as central hubs. Functional enrichment highlighted AGE–RAGE and JAK–STAT signaling pathways and extracellular matrix remodeling processes. Docking revealed MPO as the most favorable core target. Gallate-containing catechins, particularly (−)-gallocatechin gallate (−9.63 kcal/mol) and gallocatechin 3-O-gallate (−9.52 kcal/mol), exhibited more favorable binding affinities than the standard inhibitor 4-ABAH (−6.02 kcal/mol). DFT analysis demonstrated moderate HOMO–LUMO gaps (4.31–4.78 eV) and favorable dipole moments supporting electronic stability and reactivity. MD simulations confirmed stable complex formation over 100 ns, with persistent hydrogen bonding and consistent ligand retention. MM/PBSA calculations further validated a favorable binding of (−)-gallocatechin gallate (−27.66 ± 7.53 kcal/mol) and gallocatechin 3-O-gallate (−26.09 ± 8.96 kcal/mol), comparable to or exceeding 4-ABAH (−25.88 ± 4.44 kcal/mol). Conclusions: C. sinensis phytochemicals, particularly gallate-containing catechins, exhibit stable, energetically favorable interactions with MPO, supporting their potential as competitive inhibitors that modulate oxidative stress and inflammatory pathways in periodontitis. Full article