Pseudouridylation is the most prevalent type of posttranscriptional modification in various stable RNAs of all organisms, which significantly affects many cellular processes that are regulated by RNA. Thus, accurate identification of pseudouridine (Ψ) sites in RNA will be of great benefit for understanding these cellular processes. Due to the low efficiency and high cost of current available experimental methods, it is highly desirable to develop computational methods for accurately and efficiently detecting Ψ sites in RNA sequences. However, the predictive accuracy of existing computational methods is not satisfactory and still needs improvement. In this study, we developed a new model, PseUI, for Ψ sites identification in three species, which are H. sapiens, S. cerevisiae, and M. musculus. Firstly, five different kinds of features including nucleotide composition (NC), dinucleotide composition (DC), pseudo dinucleotide composition (pseDNC), position-specific nucleotide propensity (PSNP), and position-specific dinucleotide propensity (PSDP) were generated based on RNA segments. Then, a sequential forward feature selection strategy was used to gain an effective feature subset with a compact representation but discriminative prediction power. Based on the selected feature subsets, we built our model by using a support vector machine (SVM). Finally, the generalization of our model was validated by both the jackknife test and independent validation tests on the benchmark datasets. The experimental results showed that our model is more accurate and stable than the previously published models. We have also provided a user-friendly web server for our model at http://zhulab.ahu.edu.cn/PseUI , and a brief instruction for the web server is provided in this paper. By using this instruction, the academic users can conveniently get their desired results without complicated calculations. In this study, we proposed a new predictor, PseUI, to detect Ψ sites in RNA sequences. It is shown that our model outperformed the existing state-of-art models. It is expected that our model, PseUI, will become a useful tool for accurate identification of RNA Ψ sites.

5-methylcytosine (m5C) is one of the most common and abundant post-transcriptional modifications (PTCMs) in RNA. Recent studies showed that m5C plays important roles in many biological functions such as RNA metabolism and cell fate decision. Because most experimental methods that determine m5C sites across the transcriptome are time-consuming and expensive, it is urgent to develop accurate computational methods to identify m5C sites effectively. A benchmark dataset is important for developing and evaluating computational methods. In this work, we constructed four different datasets according to the data redundancy and imbalance. Based on these datasets, we generated three different kinds of features, i.e., KNFs (K-nucleotide frequencies), KSNPFs (K-spaced nucleotide pair frequencies), and pseDNC (pseudo-dinucleotide composition), and then used a support vector machine (SVM) to build our models. Based on the imbalanced and nonredundant dataset, Met935, we extensively studied the three kinds of features and determined an optimal combination of the features. Based on the feature combination, we built models on the three different datasets and compared them with state-of-the-art models. According to the predictive results of the stringent jackknife test, the models based on the three features, 4NF, 1SNPF, and pseDNC, are superior or comparable to other methods. To determine the best model between the models based on the imbalanced dataset Met935 and the balanced dataset Met240, we further evaluated the two models on an independent test set Test1157. Our results demonstrate that the model based on the balanced dataset Met240 achieved the highest recall (68.79%) and the highest Matthews correlation coefficient (MCC) (0.154). In addition, the model is also superior to other state-of-the-art methods according to the integrated parameter MCC on the independent test set. Thus, we selected the model based on Met240 as our final model, which was named RNAm5CPred. In addition, a web server for RNAm5CPred (http://zhulab.ahu.edu.cn/RNAm5CPred/) has been provided to facilitate experimental research.

Curcumin, a spice component as well as a traditional Asian medicine, has been reported to inhibit proliferation of a variety of cancer cells but is limited in application due to its low potency and bioavailability. Here, we have assessed the therapeutic effects of a novel and water soluble curcumin analog, 3,5-bis(2-hydroxybenzylidene)tetrahydro-4H-pyran-4-one glutathione conjugate [EF25-(GSH)₂], on hepatoma cells. Using the MTT and colony formation assays, we determined that EF25-(GSH)₂ drastically inhibits the proliferation of hepatoma cell line HepG2 with minimal cytotoxicity for the immortalized human hepatic cell line HL-7702. Significantly, EF25-(GSH)₂ suppressed growth of HepG2 xenografts in mice with no observed toxicity to the animals. Mechanistic investigation revealed that EF25-(GSH)₂ induces autophagy by means of a biphasic mechanism. Low concentrations (10 µmol/L) triggered an arrested autophagy process with irreversible and extensive cytoplasmic vacuolization. Prolonged treatment with EF25-(GSH)₂ induced cell death through both an apoptosis-dependent and a non-apoptotic mechanism. Chloroquine, a late stage inhibitor of autophagy which promoted cytoplasmic vacuolization, led to significantly enhanced apoptosis and cytotoxicity when combined with EF25-(GSH)₂. Taken together, these data imply a fail-safe mechanism regulated by autophagy in the action of EF25-(GSH)₂, suggesting the therapeutic potential of the novel curcumin analog against hepatocellular carcinoma (HCC), while offering a novel and effective combination strategy with chloroquine for the treatment of patients with HCC.

The aim of this study was to investigate the protective effect and mechanism of selenium-enriched Spirulina platensis (S. platensis) on chronic alcohol-induced liver injury. Selenium incubation raises the nutrition quality of S. platensis by absorption enhancement of functional elements. Our results demonstrated that the effective dose of selenium-enriched S. platensis on HL7702 cells treated with alcohol was 200 μg ml⁻¹, containing 20% selenium. Selenium-enriched S. platensis could raise the cell survival rate by decreasing the expression of p53, Caspase3, LC3, and Caspase1 and by increasing the expression of p70s6k. In vivo experiments, where mice were pretreated with selenium-enriched S. platensis, exhibited obvious inhibition of the liver function index and this pretreatment enhanced the activity of GSH-Px and SOD in alcohol induced mice. In summary, our results indicate that the protective mechanism of selenium-enriched S. platensis on chronic alcoholic liver injury is associated with the activity enhancement of antioxidant enzymes and immunity, the inhibition of DNA damage and apoptosis, accompanied with autophagy and pyroptosis.