Identification of essential yeast genes involved in polyamine resistance

Metabolomic Insights into the Mechanisms of Ganoderic Acid: Protection against α-Amanitin-Induced Liver Injury

α-Amanitin is a representative toxin found in the Amanita genus of mushrooms, and the consumption of mushrooms containing α-Amanitin can lead to severe liver damage. In this study, we conduct toxicological experiments to validate the protective effects of Ganoderic acid A against α-amanitin-induced liver damage. By establishing animal models with different durations of Ganoderic acid A treatment and conducting a metabolomic analysis of the serum samples, we further confirmed the differences in serum metabolites between the AMA+GA and AMA groups. The analysis of differential serum metabolites after the Ganoderic acid A intervention suggests that Ganoderic acid A may intervene in α-amanitin-induced liver damage by participating in the regulation of retinol metabolism, tyrosine and tryptophan biosynthesis, fatty acid biosynthesis, sphingosine biosynthesis, spermidine and spermine biosynthesis, and branched-chain amino acid metabolism. This provides initial insights into the protective intervention mechanisms of GA against α-amanitin-induced liver damage and offers new avenues for the development of therapeutic drugs for α-Amanitin poisoning.

Epigenetic drugs as new emerging therapeutics: What is the scale's orientation of application and challenges?

Epigenetics is the study of heritable changes in gene expression or function without altering the DNA sequence. Important factors are part of epigenetic events, such as methylation, DNA histone rearrangements, nucleosome transposition, and non-coding RNAs. Dysregulated epigenetic mechanics are associated with various cancers' initiation, development, and metastasis. It is known that the occurrence and development of cancer can be controlled by regulating unexpected epigenetic events. Epi-drugs are used singly or in combination with chemotherapy and enhance antitumor activity, reduce drug resistance, and stimulate the host immune response. Despite these benefits, epigenetic therapy as a single therapy or in combination with other drugs leads to adverse effects. This review article introduces and compares the advantages, disadvantages, and side effects of using these drugs for the first time since their introduction. Also, this article describes the mechanism of action of various epigenetic drugs. Recommendations for future use of epigenetic drugs as cancer therapeutics are suggested as an overall conclusion.

A Screening Method to Identify Essential Yeast Genes for Responses Towards Spermine

We exploited the yeast DAmP mutant collection to identify essential genes that play a role in polyamine resistance. Herein, we described in details the methodology to obtain these genes. This approach is applicable for screening many nontoxic and toxic drugs.

Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features

The prediction of protein subcellular localization is critical for inferring protein functions, gene regulations and protein-protein interactions. With the advances of high-throughput sequencing technologies and proteomic methods, the protein sequences of numerous yeasts have become publicly available, which enables us to computationally predict yeast protein subcellular localization. However, widely-used protein sequence representation techniques, such as amino acid composition and the Chou's pseudo amino acid composition (PseAAC), are difficult in extracting adequate information about the interactions between residues and position distribution of each residue. Therefore, it is still urgent to develop novel sequence representations. In this study, we have presented two novel protein sequence representation techniques including Generalized Chaos Game Representation (GCGR) based on the frequency and distributions of the residues in the protein primary sequence, and novel statistics and information theory (NSI) reflecting local position information of the sequence. In the GCGR + NSI representation, a protein primary sequence is simply represented by a 5-dimensional feature vector, while other popular methods like PseAAC and dipeptide adopt features of more than hundreds of dimensions. In practice, the feature representation is highly efficient in predicting protein subcellular localization. Even without using machine learning-based classifiers, a simple model based on the feature vector can achieve prediction accuracies of 0.8825 and 0.7736 respectively for the CL317 and ZW225 datasets. To further evaluate the effectiveness of the proposed encoding schemes, we introduce a multi-view features-based method to combine the two above-mentioned features with other well-known features including PseAAC and dipeptide composition, and use support vector machine as the classifier to predict protein subcellular localization. This novel model achieves prediction accuracies of 0.927 and 0.871 respectively for the CL317 and ZW225 datasets, better than other existing methods in the jackknife tests. The results suggest that the GCGR and NSI features are useful complements to popular protein sequence representations in predicting yeast protein subcellular localization. Finally, we validate a few newly predicted protein subcellular localizations by evidences from some published articles in authority journals and books.