Journal of cellular biochemistry最新文献

Metastatic colorectal carcinoma (mCRC) is one of the prevalent subtypes of human cancers and is caused by the alterations of various lifestyle and diet-associated factors. β-catenin, GSK-3β, PI3K-α, AKT1, and NF-κB p50 are known to be the critical regulators of tumorigenesis and immunopathogenesis of mCRC. Unfortunately, current drugs have limited efficacy, side effects and can lead to chemoresistance. Therefore, searching for a nontoxic, efficacious anti-mCRC agent is crucial and of utmost interest. The present study demonstrates the identification of a productive and nontoxic anti-mCRC agent through a five-targets (β-catenin, GSK-3β, PI3K-α, AKT1, and p50)-based and three-tier (binding affinity, pharmacokinetics, and pharmacophore) screening strategy involving a series of 30 phytocompounds having a background of anti-inflammatory/anti-mCRC efficacy alongside 5-fluorouracil (FU), a reference drug. Luteolin (a phyto-flavonoid) was eventually rendered as the most potent and safe phytocompound. This inference was verified through three rounds of validation. Firstly, luteolin was found to be effective against the different mCRC cell lines (HCT-15, HCT-116, DLD-1, and HT-29) without hampering the viability of non-tumorigenic ones (RWPE-1). Secondly, luteolin was found to curtail the clonogenicity of CRC cells, and finally, it also disrupted the formation of colospheroids, a characteristic of metastasis. While studying the mechanistic insights, luteolin was found to inhibit β-catenin activity (a key regulator of mCRC) through direct physical interactions, promoting its degradation by activating GSK3-β and ceasing its activation by inactivating AKT1 and PI3K-α. Luteolin also inhibited p50 activity, which could be useful in mitigating mCRC-associated proinflammatory milieu. In conclusion, our study provides evidence on the efficacy of luteolin against the critical key regulators of immunopathogenesis of mCRC and recommends further studies in animal models to determine the effectiveness efficacy of this natural compound for treating mCRC in the future.

This network pharmacology study represents a significant step in understanding the potential of Resveratrol as an antidiabetic agent and its molecular targets. Targets for Type 2 diabetes were obtained from the MalaCards and DisGeNET databases, while targets for Resveratrol were sourced from the STP and CTD databases. Subsequently, we performed matching to identify common disease-compound targets. The identified genes were analyzed using the ShinGO-0.76.3 database for functional enrichment analysis and KEGG pathway mapping. A protein-protein interaction network was then constructed using Cytoscape software, and hub genes were identified. These hub genes were subjected to molecular docking and dynamic simulations using AutoDock Vina and Gromacs software. According to functional enrichment and KEGG pathway analysis, Resveratrol influences insulin receptors, endoplasmic reticulum functions, and oxidoreductase activity and is involved in the estrogen and HIF-1 pathways. Ten hub genes were identified, including ESR1, PTGS2, SRC, NOS3, MMP9, IGF1R, CYP19A1, MTOR, MMP2, and PIK3CA. The proteins associated with these genes exhibited high interaction with Resveratrol in the molecular docking analysis, and molecular dynamics showed a stable interaction of Resveratrol with ESR1, MMP9, PIK3CA, and PTGS2. In conclusion, our work enhances the understanding of the antidiabetic activity of Resveratrol, which future studies should experimentally corroborate.

This study investigates the repurposing potential of non-nucleosidic reverse transcriptase inhibitors (NNRTIs), specifically Rilpivirine and Etravirine, as L858R/T790M tyrosine kinase inhibitors for addressing acquired resistance in non-small cell lung cancer (NSCLC). Using in silico molecular docking, Rilpivirine demonstrated a docking score of -7.534 kcal/mol, comparable to established epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) like Osimertinib and WZ4002. Molecular dynamics (MD) simulations over 200 ns revealed the stability of the Rilpivirine-EGFR complex, with RMSD values ranging from 2.5 to 3.5 Å. The in vitro antiproliferative assays showed that Rilpivirine had an IC₅₀ value of 2.3 µM against H1975 cells, while WZ4002 had an IC₅₀ of 0.291 µM, indicating moderate efficacy. Enzymatic assays revealed that Rilpivirine inhibited the double mutant epidermal growth factor receptor tyrosine kinase (EGFR TK) with an IC₅₀ value of 54.22 nM and spared the wild-type EGFR TK with an IC₅₀ of 22.52 nM. These findings suggest Rilpivirine's potential as a therapeutic agent for NSCLC with EGFR L858R/T790M mutations.

Cover Caption: The cover image is based on the article SARS-CoV-2 Spike Protein Induces Time-Dependent CTSL Upregulation in HeLa Cells and Alveolarspheres by Magdalena M. Bolsinger et al., https://doi.org/10.1002/jcb.30627.

The lack of amino acids triggers the autophagic response. Some studies have shown such starvation conditions also induce mitochondrial fusion, revealing a close correlation between the two processes. Although Mitofusin-2 (MFN2) has been demonstrated to play a role in fusion regulation, its role in the autophagic response and the variables that activate MFN2 under stress remain unknown. In this investigation, we screened and confirmed that forkhead box protein O3 (FOXO3) participates in MFN2's expression during short periods of starvation. Luciferase reporter test proved that FOXO3 facilitates MFN2's transcription by binding to its promoter region, and FOXO3 downregulation directly depresses MFN2's expression. Consequently, inhibiting the FOXO3-MFN2 axis results in the loss of mitochondrial fusion, disrupting the normal morphology of mitochondria, impairing the degradation of substrates, and reducing autophagosome accumulation, ultimately leading to the blockage of the autophagy. In conclusion, our work demonstrates that the FOXO3-MFN2 pathway is essential for adaptive changes in mitochondrial morphology and cellular autophagy response under nutritional constraints.

The Type III secretion effectors (T3SEs) are bacterial proteins synthesized by Gram-negative pathogens and delivered into host cells via the Type III secretion system (T3SS). These effectors usually play a pivotal role in the interactions between bacteria and hosts. Hence, the precise identification of T3SEs aids researchers in exploring the pathogenic mechanisms of bacterial infections. Since the diversity and complexity of T3SE sequences often make traditional experimental methods time-consuming, it is imperative to explore more efficient and convenient computational approaches for T3SE prediction. Inspired by the promising potential exhibited by pre-trained language models in protein recognition tasks, we proposed a method called PLM-T3SE that utilizes protein language models (PLMs) for effective recognition of T3SEs. First, we utilized PLM embeddings and evolutionary features from the position-specific scoring matrix (PSSM) profiles to transform protein sequences into fixed-length vectors for model training. Second, we employed the extreme gradient boosting (XGBoost) algorithm to rank these features based on their importance. Finally, a MLP neural network model was used to predict T3SEs based on the selected optimal feature set. Experimental results from the cross-validation and independent test demonstrated that our model exhibited superior performance compared to the existing models. Specifically, our model achieved an accuracy of 98.1%, which is 1.8%-42.4% higher than the state-of-the-art predictors based on the same independent data set test. These findings highlight the superiority of the PLM-T3SE and the remarkable characterization ability of PLM embeddings for T3SE prediction.