Methods最新文献

Nucleosomes, composed of DNA and histone octamers, regulate gene expression through histone modifications such as lysine acetylation and methylation. These modifications are added by writers, removed by erasers, and interpreted by readers to control gene expression, chromatin structure, and essential cellular processes such as differentiation and development. Accurate PTM analysis requires high-purity histones due to the sensitivity of epigenetic assays. Recombinant histones are expressed in E. coli as inclusion bodies, requiring denaturation, refolding, and purification. These traditional purification methods involve complicated and lengthy protocols taking days and potentially exposing the histones to oxidation and proteolytic degradation. We developed a rapid method for refolding histones from inclusion bodies in a one-step purification using a desalting column achieving > 90 % purity. This method is compared to our previous HPLC-based protocol. Our single-step desalting purification reduces purification time from multiple days to one day, lowers operational cost, and eliminates the need for reverse-phase HPLC, making high-purity histone production accessible to laboratories without specialized chromatography infrastructure.

Molecular dynamics (MD) simulations and experimental analysis were performed on MgO-SrO containing bioactive glasses (MSBGs) with the composition of 60SiO₂-(31-x)CaO-4P₂O₅-5MgO-xSrO (mol%) (x = 0, 1, 3, 5, 8, 10, 15, 20; M5S0-M5S20) to evaluate the structural properties, ion clustering, and dissolution behavior as a function of SrO content. Simulation employed the Buckingham potential for short-range interactions and Coulombic potentials for long-range forces. MSBGs had Si-O and P-O bond lengths of 1.609 Å and 1.491 Å, with O-Si-O and O-P-O bond angles centered at ∼109.3° and 109.4°, respectively, confirming tetrahedral SiO₄/PO₄ coordination. Across all compositions, Si-O-Si bonds dominated the majority of the distribution (88-89 %), with Si-O-P at 11-12 % and P-O-P negligible (∼0.3 %). Densities decreased from 2.913 g·cm^-3 (M5S20) to 2.631 g·cm^-3 (M5S0), reflecting network loosening with SrO substitution. Qⁿ distribution remained stable, with Q³/Q⁴ fractions of 38-43 % and 26-30 % for Si-based tetrahedra. R-factor analysis revealed optimal homogeneity for M5S5 (R_Si/P^Sr = 0.838252), balancing reduced cation clustering and moderate network stability. ICP-AES showed M5S5 with a sustained release of Si⁴⁺, Mg²⁺, and Sr²⁺ over 24 h. Meanwhile, antibacterial study resulted in statistically significant increase in efficiency for M5S5 compared to M5S0 (***p < 0.001). The combined computational and experimental findings identify M5S5 as the most promising candidate for biomedical applications requiring structural benefits, controlled ion release, and antibacterial efficiency.

Targeting the interaction between P53 and MDM2 to re-activate P53 to induce apoptosis is an important strategy for cancer treatment. In this study, based on the unique advantages of in situ visualization, dynamic imaging, and quantitative analysis of living cell FRET imaging, a method for screening apoptotic drugs targeting p53-MDM2 interaction was developed. A stable model of Nutlin-3-induced apoptosis was established in MCF-7 cells, which was verified by reducing mitochondrial membrane potential and increasing the proportion of nuclear chromatin condensation (from 9.16 % to 50.55 %). Biochemical methods such as WB analysis found that after activating P53, BAX expression was up-regulated through a Puma-independent pathway, which promoted BAX oligomerization. Live-cell quantitative FRET imaging found that the maximum donor center FRET efficiency (E_Dmax) of CFP-p53 and YFP-MDM2 decreased from 0.50 to 0.22 after Nutlin-3 treatment, and the co-localization coefficient decreased significantly from 83 % to 22 %, confirmed that Nutlin-3 directly disrupted the interaction between P53/MDM2, promoting P53 nuclear translocation and apoptosis. This indicated that Nutlin-3 was a direct inhibitor of the P53/MDM2 interaction. Apoptosis drug screening was performed in MCF-7 cells, and we found that the E_Dmax was 0.29 and 0.31 for the cells treated with DOX and RSV, respectively, and 0.48 for the IKE-treated cells and 0.43 for the SOR-treated cells, indicating that DOX and RSV, but not IKE and SOR, were potential P53/MDM2-dependent apoptotic drugs. In addition, Nutlin-3 treatment decreased the E_Dmax value in p53 wild-type U2OS cells from 0.43 to 0.20. In summary, our method can identify p53-MDM2 interaction inhibitors in living cells, providing a quantitative in vivo supplement for traditional target-based drug discovery.

The Golgi complex is central to cellular homeostasis and serves as a key processing and sorting hub for protein trafficking. In many cell types, the Golgi complex is organized as interconnected stacks of cisternae, forming a structure known as the Golgi ribbon. This ribbon undergoes dynamic remodelling during physiological processes, such as cell division, and under pathological conditions, including cancer and neurodegeneration. A critical step in the unlinking of the Golgi ribbon involves the phosphorylation of the stacking protein GRASP65, which leads to the separation of the ribbon into individual stacks, a process necessary for the G2/M transition of the cell cycle. However, existing tools for selectively manipulating the GRASP65 role in ribbon organization are limited by non-specific effects or technical challenges. Here, we present the development and characterization of a membrane-permeable peptide, R₈-GRASP65-S277, derived from GRASP65 and containing the phosphorylation site Ser277, which is essential for Golgi unlinking. This peptide effectively inhibited Golgi unlinking and mitotic entry in several cell lines, including cancer models. In contrast, a control peptide with a non-phosphorylatable alanine substitution (R₈-GRASP65-S277A) showed no such effect, confirming the specificity of the tool. Furthermore, the R₈-GRASP65-S277 peptide reversed Golgi unlinking induced by the chemotherapeutic agent doxorubicin, demonstrating its utility in studying stress-induced Golgi disassembly. These findings establish the R8-GRASP65-S277 peptide as a specific, potent, and scalable tool for probing the molecular mechanisms of Golgi unlinking, its regulation of cell cycle progression, and its potential contributions to pathological states.

Psoriatic arthritis (PsA) is a chronic inflammatory disease characterised by unpredictable flare-ups that are difficult to forecast, particularly in patients without an acute phase response. In this paper, we propose and apply an explainable, multimodal machine learning framework that jointly leverages structured temporal electronic patient records (EPRs) - sequential blood tests, disease activity scores, comorbidity burden, medications, and demographics - and unstructured clinical referral letters pre-processed with large language models ((LLMs, (Qwen-2.5 family)) to predict PsA flares. Gradient boosting models, Light Gradient Boosting Machine (LGBM) and eXtreme Gradient Boosting (XGBoost) were used to predict PsA flares, achieving the highest predictive performance 3 months before a clinic visit (accuracy = 92.8 %, AUROC = 0.94). Model performance gradually declined for longer timeframes (6 months: 78.2 %, AUROC = 0.80; 9 months: 76.6 %, AUROC = 0.78; 12 months: 72.2 %, AUROC = 0.75). LLMs applied to unstructured GP referral letters had limited standalone predictive value, but enhanced sensitivity and specificity when combined with the structured models in an ensemble approach. SHapley Additive exPlanations (SHAP) helped explain the prediction and demonstrated comorbidity count, disease scores, and immunosuppressive medications as the top predictors. Our results show that integrating both structured longitudinal data with unstructured clinical narratives using interpretable multimodal artificial intelligence can enable time-sensitive, personalised management of PsA flares and early clinical intervention.