Identification of Proteins Implicated in the Increased Heart Rate in ShenSongYangXin-Treated Bradycardia Rabbits by iTRAQ-Based Quantitative Proteomics

Analysis of prescription medication rules of traditional Chinese medicine for bradyarrhythmia treatment based on data mining

BACKGROUND

Multiple studies have revealed that Traditional Chinese Medicine (TCM) prescriptions can provide protective effect on the cardiovascular system, increase the heart rate and relieve the symptoms of patients with bradyarrhythmia. In China, the TCM treatment of bradyarrhythmia is very common, which is also an effective complementary therapy. In order to further understand the application of Chinese medicines in bradyarrhythmia, we analyzed the medication rules of TCM prescriptions for bradyarrhythmia by data mining methods based on previous clinical studies.

METHODS

We searched studies reporting the clinical effect of TCM on bradyarrhythmia in the PubMed and Chinese databases China National Knowledge Infrastructure database, and estimated publication bias by risk of bias tools ROB 2. Descriptive analysis, hierarchical clustering analysis and association rule analysis based on Apriori algorithm were carried out by Microsoft Excel, SPSS Modeler, SPSS Statistics and Rstidio, respectively. Association rules, co-occurrence and clustering among Chinese medicines were found.

RESULTS

A total of 48 studies were included in our study. Among the total 99 kinds of Chinese medicines, 22 high-frequency herbs were included. Four new prescriptions were obtained by hierarchical cluster analysis. 81 association rules were found based on association rule analysis, and a core prescription was intuitively based on the grouping matrix of the top 15 association rules (based on confidence level), of which Guizhi, Zhigancao, Wuweizi, Chuanxiong, Danshen, Danggui, Huangqi, Maidong, Dangshen, Rougui were the most strongly correlated herbs and in the core position.

CONCLUSION

In this study, data mining strategy was applied to explore the TCM prescription for the treatment of bradyarrhythmia, and high-frequency herbs and core prescription were found. The core prescription was in line with the treatment ideas of TCM for bradyarrhythmia, which could intervene the disease from different aspects and adjust the patient's Qi, blood, Yin and Yang, so as to achieve the purpose of treatment.

Circular RNA, microRNA and Protein Profiles of the Longissimus Dorsi of Germany ZIKA and Sichuan White Rabbits

Due to the dietetic properties and remarkable nutritive value of rabbit meat, its industry is increasing rapidly. However, the association between circular RNAs, microRNAs, and proteins and muscle fiber type, and meat quality of rabbit is still unknown. Here, using deep sequencing and iTRAQ proteomics technologies we first identified 3159 circRNAs, 356 miRNAs, and 755 proteins in the longissimus dorsi tissues from Sichuan white (SCWrabs) and Germany great line ZIKA rabbits (ZIKArabs). Next, we identified 267 circRNAs, 3 miRNAs, and 29 proteins differentially expressed in the muscle tissues of SCWrabs and ZIKArabs. Interaction network analysis revealed some key regulation relationships between noncoding RNAs and proteins that might be associated with the muscle fiber type and meat quality of rabbit. Further, miRNA isoforms and gene variants identified in SCWrabs and ZIKArabs revealed some pathways and biological processes related to the muscle development. This is the first study of noncoding RNA and protein profiles for the two rabbit breeds. It provides a valuable resource for future studies in rabbits and will improve our understanding of the molecular regulation mechanisms in the muscle development of livestock. More importantly, the output of our study will benefit the researchers and producers in the rabbit breeding program.

Improving Prediction of Self-interacting Proteins Using Stacked Sparse Auto-Encoder with PSSM profiles

Self-interacting proteins (SIPs) play a significant role in the execution of most important molecular processes in cells, such as signal transduction, gene expression regulation, immune response and enzyme activation. Although the traditional experimental methods can be used to generate SIPs data, it is very expensive and time-consuming based only on biological technique. Therefore, it is important and urgent to develop an efficient computational method for SIPs detection. In this study, we present a novel SIPs identification method based on machine learning technology by combing the Zernike Moments (ZMs) descriptor on Position Specific Scoring Matrix (PSSM) with Probabilistic Classification Vector Machines (PCVM) and Stacked Sparse Auto-Encoder (SSAE). More specifically, an efficient feature extraction technique called ZMs is firstly utilized to generate feature vectors on Position Specific Scoring Matrix (PSSM); Then, Deep neural network is employed for reducing the feature dimensions and noise; Finally, the Probabilistic Classification Vector Machine is used to execute the classification. The prediction performance of the proposed method is evaluated on S.erevisiae and Human SIPs datasets via cross-validation. The experimental results indicate that the proposed method can achieve good accuracies of 92.55% and 97.47%, respectively. To further evaluate the advantage of our scheme for SIPs prediction, we also compared the PCVM classifier with the Support Vector Machine (SVM) and other existing techniques on the same data sets. Comparison results reveal that the proposed strategy is outperforms other methods and could be a used tool for identifying SIPs.