Small interfering RNA targeting mcl-1 enhances proteasome inhibitor-induced apoptosis in various solid malignant tumors

<p>Abstract</p> <p>Background</p> <p>Targeting the ubiquitin-proteasome pathway is a promising approach for anticancer strategies. Recently, we found Bik accumulation in cancer cell lines after they were treated with bortezomib. However, recent evidence indicates that proteasome inhibitors may also induce the accumulation of anti-apoptotic Bcl-2 family members. The current study was designed to analyze the levels of several anti-apoptotic members of Bcl-2 family in different human cancer cell lines after they were treated with proteasome inhibitors.</p> <p>Methods</p> <p>Different human cancer cell lines were treated with proteasome inhibitors. Western blot were used to investigate the expression of Mcl-1 and activation of mitochondrial apoptotic signaling. Cell viability was investigated using SRB assay, and induction of apoptosis was measured using flow cytometry.</p> <p>Results</p> <p>We found elevated Mcl-1 level in human colon cancer cell lines DLD1, LOVO, SW620, and HCT116; human ovarian cancer cell line SKOV3; and human lung cancer cell line H1299, but not in human breast cancer cell line MCF7 after they were treated with bortezomib. This dramatic Mcl-1 accumulation was also observed when cells were treated with other two proteasome inhibitors, MG132 and calpain inhibitor I (ALLN). Moreover, our results showed Mcl-1 accumulation was caused by stabilization of the protein against degradation. Reducing Mcl-1 accumulation by Mcl-1 siRNA reduced Mcl-1 accumulation and enhanced proteasome inhibitor-induced cell death and apoptosis, as evidenced by the increased cleavage of caspase-9, caspase-3, and poly (ADP-ribose) polymerase.</p> <p>Conclusions</p> <p>Our results showed that it was not only Bik but also Mcl-1 accumulation during the treatment of proteasome inhibitors, and combining proteasome inhibitors with Mcl-1 siRNA would enhance the ultimate anticancer effect suggesting this combination might be a more effective strategy for cancer therapy.</p

Over-expression of the ATP5J gene correlates with cell migration and 5-fluorouracil sensitivity in colorectal cancer.

Recently we found that ATP5J was over-expressed in tissue samples from patients with colorectal cancer. However, the clinical significance and function of the over-expression of ATP5J in these patients remains unclear. We investigated these issues in the current study. Our results indicated that expression of ATP5J was significantly higher in colorectal cancer tissue than in adjacent tissue, and it was also significantly higher in metastatic lymph nodes than in primary cancer tissue (P<0.05). A correlation between ATP5J expression and tumor differentiation was detected, but no correlation with gender, age, T stage, lymph node metastasis, or survival status was observed. Down-regulation of ATP5J expression attenuated the ability of cell migration and increased the sensitivity to 5-fluorouracil (5-Fu) in cells of the DLD1 cell line. Inversely, up-regulation of ATP5J expression enhanced cell migration and decreased 5-Fu sensitivity, suggesting that the function of ATP5J in colorectal cancer might involve cell migration and 5-Fu sensitivity

Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit

The figure of merit (zT) is a key parameter to measure the performance of thermoelectric materials. At present, the prediction of zT values via machine leaning has emerged as a promising method for exploring high-performance materials. However, the machine learning-based predictions still suffer from unsatisfactory accuracy, and this is related to the size of the data set, the hyperparameters of models, and the quality of the data. In this work, 5038 pieces of data of thermoelectric materials were selected, and several regression models were generated to predict zT values. This large data set-driven light gradient boosting (LGB) model with 57 features performed with an excellent accuracy, achieving a coefficient of determination (R2) value of 0.959, a root mean squared error (RMSE) of 0.094, a mean absolute error (MAE) of 0.057, and a correlation coefficient (R) of 0.979. Owing to the large size of the data set, the prediction accuracy exceeds that of most reported zT predictions via machine learning. The “ME Lattice Parameter” was verified as the most important feature in the zT prediction. Furthermore, nine potential candidates were screened out from among one million pieces of data. This study solves the problem of the data set size, adjusts the hyperparameters of the models, uses feature engineering to improve data quality, and provides an efficient strategy to perform wide-ranging screening for promising materials

High‐performance diffusion model for inverse design of high Tc superconductors with effective doping and accurate stoichiometry

Abstract The pursuit of designing superconductors with high Tc has been a long‐standing endeavor. However, the widespread incorporation of doping in high Tc superconductors significantly impacts electronic structure, intricately influencing Tc. The complex interplay between the structural composition and material performance presents a formidable challenge in superconductor design. Based on a novel generative model, diffusion model, and doping adaptive representation: three‐channel matrix, we have designed a high Tc superconductors inverse design model called Supercon‐Diffusion. It has achieved remarkable success in accurately generating chemical formulas for doped high Tc superconductors. Supercon‐Diffusion is capable of generating superconductors that exhibit high Tc and excels at identifying the optimal doping ratios that yield the peak Tc. The doping effectiveness (55%) and electrical neutrality (55%) of the generated doped superconductors exceed those of traditional GAN models by more than tenfold. Density of state calculations on the structures further confirm the validity of the generated superconductors. Additionally, we have proposed 200 potential high Tc superconductors that have not been documented yet. This groundbreaking contribution effectively reduces the search space for high Tc superconductors. Moreover, it successfully establishes a bridge between the interrelated aspects of composition, structure, and property in superconductors, providing a novel solution for designing other doped materials

Down-regulation of <i>ATP5J</i> expression attenuated cell migration and increased 5-Fu sensitivity in DLD1 cells.

<p>(A) Western blot result for ATP5J protein in different clones of DLD1 cell after stable transfection with the same ATP5J shRNA plasmid. (B) Wound-healing assay for WT, Vector, and ATP5J shRNA/4 DLD1 cells. Data represent one of three similar experiments (original magnification: x100). (C) Migration of WT, Vector, and ATP5J shRNA/4 DLD1 cells was assayed using a 24-Transwell system. The pictures of migrated cells were taken 24 hours after seeding (original magnification: x100). Data represent one of three similar experiments. (D) Quantitative analyses of three cell migration assays. Values represent means ± SD. * <i>P</i><0.05; (E) Apoptotic ratios of WT, Vector, and ATP5J shRNA/4 DLD1 cells after treatment with 50 µmol/L 5-Fu for 3 days. Data represent one of two similar experiments. Values represent means ± SD. *<i>P</i><0.05.</p