Chemical Biology & Drug Design最新文献

Diabetic nephropathy (DN) is a severe microvascular complication of diabetes. This study aimed to identify hub genes and explore potential therapeutic drugs for DN. Differentially expressed genes were analyzed between DN and normal samples from the GSE104948 and GSE47183 datasets. Hub genes were identified through weighted gene coexpression network analysis and protein–protein interaction network analysis and validated using two external datasets. Receiver operating characteristic curves were employed to assess the diagnostic accuracy of the hub genes. Virtual screening identified potential small-molecule compounds targeting the hub genes CD163 and transmembrane immune signaling adaptor TYROBP (TYROBP). High-glucose (HG)-induced human glomerular mesangial cells (HGMCs) were used to construct an in vitro DN model. Cell viability was assessed using a cell counting kit-8, and inflammatory factors were measured by enzyme-linked immunosorbent assay, and mesangial extracellular matrix-related protein levels were detected by Western blot. Two hub genes, CD163 and TYROBP, were identified as significantly upregulated in DN samples and HG-induced HGMCs, demonstrating high specificity and sensitivity for DN diagnosis. Demethyleneberberine (DMB) and dehydroevodiamine (DHE) exhibited strong binding affinity to CD163 and TYROBP and effectively suppressed HG-induced HGMC proliferation, inflammatory responses, and extracellular matrix deposition. In conclusion, CD163 and TYROBP serve as key biomarkers and therapeutic targets for DN, while DMB and DHE show promise as natural compounds for DN treatment.

Psoriasis is a prevalent chronic skin disease. Cycloastragenol (CAG) has been shown to activate autophagy and alleviate epidermal keratinocyte hyperproliferation in psoriasis. This study aimed to clarify the mechanism of CAG-mediated autophagy in psoriasis-like models. We treated C57BL/6 mice with imiquimod cream and stimulated HaCaT cells with a cytokine mixture (C-mix) to establish mouse and cell models. Psoriasis area and severity index scores were used to evaluate pathological changes. Autophagy flux was monitored using a monomeric red fluorescent protein–green fluorescent protein–microtubule-associated protein 1 light chain 3 assay. The interaction between sirtuin 1 (SIRT1) and zinc finger containing Krüppel-associated box and SCAN domain 3 (ZKSCAN3), as well as ZKSCAN3 acetylation, was examined using co-immunoprecipitation. Our results found that CAG alleviated autophagy inhibition in the imiquimod-induced psoriasis-like mouse model and enhanced autophagy by upregulating SIRT1 expression. ZKSCAN3 inhibited autophagy in the C-mix-stimulated psoriasis-like cellular model, while SIRT1 reduced the nuclear localization of ZKSCAN3 through deacetylation. ZKSCAN3 overexpression reversed SIRT1-mediated autophagy enhancement, whereas CAG promoted autophagy by regulating the nuclear localization of ZKSCAN3. In conclusion, our findings demonstrate that CAG ameliorates autophagy inhibition by modulating the SIRT1/ZKSCAN3 axis in psoriasis.

To develop and validate an interpretable machine-learning model for early prediction of biochemical recurrence (BCR) by fusing multiparametric MRI radiomics, clinical variables, and hematological biomarkers. A total of 172 patients (115 BCR-positive, 57 BCR-negative), who underwent RP with a median follow-up of 37 months, from Taizhou People's Hospital were retrospectively enrolled. Preoperative ADC and T2-weighted images were manually segmented to extract radiomic features; laboratory and clinical data included PSA, routine blood counts, lipid profile, and coagulation indices. After LASSO selection, seven machine-learning algorithms were trained and internally validated. The best-performing model was interpreted with SHAP analysis to quantify feature contributions. PSA emerged as the dominant predictor, exhibiting the highest mean absolute SHAP value and thereby exerting the greatest influence on model output. Among coagulation parameters, D-dimer and APTT were significantly associated with BCR (p = 0.007 and p = 0.036, respectively). Integration of ADC + T2 radiomics with clinical/hematological variables via a SVM classifier yielded the highest performance: AUC 0.916 (95% CI: 0.891–0.941) in the training set and 0.820 (95% CI: 0.762–0.878) in the validation set. SHAP revealed that ADC/T2 radiomic features constituted 60% of the top-ten predictors, while D-dimer exhibited a paradoxical negative SHAP value, suggesting a potential protective association. Multimodal fusion of mpMRI radiomics, clinical feature, and hematological biomarkers substantially improves BCR prediction. The interpretable model highlights PSA, and coagulation dysfunction, as key recurrence drivers, offering a clinically actionable tool for personalized management.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a multifaceted inflammatory condition that significantly affects patients clinically. It is vital to comprehend the basic molecular mechanisms and to discover new biomarkers. This research combines multi-omics data, molecular docking, and dynamic simulations to investigate gene functions and possible biomarkers related to CRSwNP. Initially, we discovered 555 differentially expressed genes (DEGs) induced by alpha-toxin (Hla) and 8119 DEGs associated with CRSwNP obtained from the Gene Expression Omnibus (GEO) database. Utilizing GO/KEGG enrichment analysis, we identified the NF-κB signaling pathway as significant. We identified 16 genes associated with TGF-β receptor signaling in epithelial–mesenchymal transition (EMT) through PathCards and subsequently intersected these with the differentially expressed genes from two datasets. This analysis revealed the presence of one gene, PRKCZ. Additionally, single-cell sequencing offered deeper insights into cellular diversity, elucidating the expression patterns of PRKCZ across various cell types. Molecular docking and dynamics simulations established that the proteasome inhibitor MG132 can create a stable complex with PRKCZ. In conclusion, qRT-PCR, Western blot, immunohistochemistry, and fluorescence assays validated that Hla stimulation markedly heightened the levels of inflammatory pathway markers in human nasal mucosal epithelial cells. Importantly, the inhibition of PRKCZ by MG132 resulted in a significant reduction of these marker expression levels, offering a foundation for targeted therapeutic interventions. The current research demonstrates that alpha-toxin from Staphylococcus aureus promotes EMT in CRSwNP via the NF-κB/TGF-β signaling axis. A significant gene that may serve as a potential therapeutic target is PRKCZ.

Fifteen new hydrazine derivatives were synthesized and structurally characterized using NMR (¹H NMR and ¹³C NMR) spectroscopy techniques. Furthermore, the crystal structure of compound 1647 was figured out by the single crystal X-ray analysis. The in vitro antimicrobial activity was reviewed against five species of pathogenic bacteria (Escherichia coli [ATCC 25922], Serratia marcescens, Klebsiella pneumoniae [ATCC 13883], Staphylococcus aureus [ATCC 29213], Enterococcus faecalis [ATCC 2942]) and three fungal species (Candida albicans [ATCC 24433], Candida krusei [ATCC 6258], and Candida parapsilopsis [ATCC 22019]). Compounds 1646 and 1647 with MIC of 1.95 μg/mL was found to be most active against E. coli (ATCC 25922). The presence of bromine or tert-butyl groups in the para position of the phenyl ring increased the antibacterial activity. It has been shown by SEM study that these compounds cause deterioration in the morphology of E. coli. Using DFT, the X-ray geometry and model geometry of compound 1647 were optimized and compared in terms of both energy and geometry, and it was determined that they were conformers of each other with a difference of 0.11 kcal/mol.

Cancer is among the leading causes of death in the world today. Several strategies were used to control cancer including chemotherapy, radiotherapy and targeted therapy. Among the problems related to antiproliferative chemotherapy against cancer is the medication resistance which require continuous generation of new agents. These new potential agents could be extracted from natural sources or prepared synthetically. This research represents the preparation of new heterocycles incorporating an indenopyridazine nucleus as potential antiproliferative agents. The synthetic strategy was initiated by the treatment of ninhydrin with cyanoacetohydrazide resulting in the formation of the key intermediate indeno[2,1-c]pyridazine scaffold 4, which undergoes condensation with various nitrogenous nucleophilic reagents (namely: phenyl hydrazine, substituted benzohydrazide, and thiosemicarbazide). Cyclization of thiosemicarbazone derivative 8 with ethyl bromoacetate furnished the target compound thiazolin-4-one derivative 9, which was condensed with three 5-aryl-1H-pyrazole-4-carbaldehydes to furnish the corresponding 5-arylidene-thiazolin-4-one derivatives 11a-c. In addition, thiosemicarbazone derivative 8 undergoes reactions with α-chloroesters 12 or α-chloroketones 14 to yield the indenopyridazine-thiazole hybrids 13 and 15, respectively. The cytotoxic activity of the targeted indeno[2,1-c]pyridazine compounds was explored against four cancer cell lines in vitro. Indenopyridazine-thiazolin-4-one hybrid 11c exhibited the highest cytotoxicity against HepG2 (IC₅₀ = 7.43 μM) and MCF-7 cells (IC₅₀ = 4.37 μM). Their IC₅₀ values were very close to the reference drug 5-fluorouracil. Also, compound 7b was found to show a very strong cytotoxic effect against HepG2 (IC₅₀ = 10.20 μM) and MCF-7 (IC₅₀ = 7.39 μM). Moreover, molecular docking indicated that compounds 11b, 7b, and 11c had the best docking score against vascular endothelial growth factor recetor-2 (VEGFR-2) which is the potential target predicted by target fishing.

The use of transformer models in drug design is gradually emerging. The models are applied in chemical structure prediction and target discovery. Textual sequences and graphs are commonly used to describe molecules. In this review, we discussed the application of transformers and their advantages in different prediction tasks. We further elaborated on their applications in various aspects of drug discovery, including the SMILES principle, spectrum prediction, drug response prediction, chemical structure prediction, drug–drug interaction and activity prediction, drug target interaction, and protein prediction. We compared the differences between transformer and traditional drug discovery and then presented our proposed resolutions to this challenge. In the future, advancements will lead to the development of more efficient models with superior parameters. Transformer models can incorporate any input. Combining the transformer with different models and algorithms can also enhance the operational performance for multimodal and multitask prediction.