Deciphering potential pharmacological mechanisms of Danhong injection to treat chronic stable angina based on drug response-related modules and genes.

ETHNOPHARMACOLOGICAL RELEVANCE: Danhong injection (DHI), a traditional Chinese medicine (TCM) injection that has been widely used to treat coronary heart disease and angina pectoris. However, its underlying pharmacological mechanisms have not been fully elucidated. Not all patients benefit from DHI to the same extent. We attempted to explore the characteristics of potential therapeutic targets in different responsive populations. AIM OF THE STUDY: This study aimed to reveal the potential molecular mechanisms of DHI in treating chronic stable angina and identify potential therapeutic targets for DHI. MATERIALS AND METHODS: Based on a previous phase IV clinical trial of DHI in treating chronic stable angina, drug response modules were identified through structural entropy and similarity. Drug response-related genes were screened out based on the correlations between drug response module/module-related genes and clinical features and were assessed using a random forest model. Further validation was conducted using a hypoxia/reoxygenation (H/R) model. RESULTS: Seven DHI-related response modules were identified. Eight drug response-related genes were screened out, and principal component analysis showed that DHI responders were distinguished from responders in the control group based on their expression values. The combination of the two most important genes, SHC4 and PIP5K1P1, discriminated between responders and nonresponders with an area under the receiver operating characteristic curve (AUC) of 0.714; however, no significant difference was found in the AUC between the combination and a single gene. Reverse transcription-polymerase chain reaction showed that middle-dose DHI treatment significantly decreased SHC4 mRNA expression compared with that in the H/R group (P = 0.026), a finding consistent with our previous analysis of differentially expressed genes. CONCLUSIONS: DHI comprehensively exerted a therapeutic effect by acting on multiple response modules related to angina pectoris and drug response-related genes. Our findings indicate that the dimensionality reduction strategy based on the target network-drug response module-therapeutic targets can contribute to revealing the mechanism of action of TCM compounds and guiding precise clinical medication.

Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks.

BACKGROUND: Discovering potential predictive risks in the super precarcinomatous phase of hepatocellular carcinoma (HCC) without any clinical manifestations is impossible under normal paradigm but critical to control this complex disease. METHODS: In this study, we utilized a proposed sequential allosteric modules (AMs)-based approach and quantitatively calculated the topological structural variations of these AMs. RESULTS: We found the total of 13 oncogenic allosteric modules (OAMs) among chronic hepatitis B (CHB), cirrhosis and HCC network used SimiNEF. We obtained the 11 highly correlated gene pairs involving 15 genes (r > 0.8, P < 0.001) from the 12 OAMs (the out-of-bag (OOB) classification error rate < 0.5) partial consistent with those in independent clinical microarray data, then a three-gene set (cyp1a2-cyp2c19-il6) was optimized to distinguish HCC from non-tumor liver tissues using random forests with an average area under the curve (AUC) of 0.973. Furthermore, we found significant inhibitory effect on the tumor growth of Bel-7402, Hep 3B and Huh7 cell lines in zebrafish treated with the compounds affected those three genes. CONCLUSIONS: These findings indicated that the sequential AMs-based approach could detect HCC risk in the patients with chronic liver disease and might be applied to any time-dependent risk of cancer.

System-wide assembly of pathways and modules hierarchically reveal metabolic mechanism of cerebral ischemia.

The relationship between cerebral ischemia and metabolic disorders is poorly understood, which is partly due to the lack of comparative fusing data for larger complete systems and to the complexity of metabolic cascade reactions. Based on the fusing maps of comprehensive serum metabolome, fatty acid and amino acid profiling, we identified 35 potential metabolic biomarkers for ischemic stroke. Our analyses revealed 8 significantly altered pathways by MetPA (Metabolomics Pathway Analysis, impact score >0.10) and 15 significantly rewired modules in a complex ischemic network using the Markov clustering (MCL) method; all of these pathways became more homologous as the number of overlapping nodes was increased. We then detected 24 extensive pathways based on the total modular nodes from the network analysis, 12 of which were new discovery pathways. We provided a new perspective from the viewpoint of abnormal metabolites for the overall study of ischemic stroke as well as a new method to simplify the network analysis by selecting the more closely connected edges and nodes to build a module map of stroke.

DanHong injection dose-dependently varies amino acid metabolites and metabolic pathways in the treatment of rats with cerebral ischemia.

AIM: To determine how the relative amino acid contents and metabolic pathways regulate the pharmacological phenotypes in rats with cerebral ischemia after treatment with varying doses of DanHong injection (DHI). METHODS: Adult male rats underwent middle cerebral artery occlusion (MCAO), and were injected with DHI (DH-1: 1 mL/kg; DH-2: 2.5 mL/kg; DH-3: 5 mL/kg, and DH-4: 10 mL/kg, iv) daily for 3 d. The neurological deficit score, body weights and infarct volume were assessed. Serum levels of 20 free amino acids were determined using HPLC, and the values were transformed through the quantitative analysis of the amino acids in the serum metabolic spectrum. Multivariate statistical analysis methods (PCA and PLS-DA) and web-based metabolomics tools (MetPa and MetaboAnalyst) were used to analyze the biological data sets for the amino acids. RESULTS: Administration of DHI dose-dependently decreased cerebral infarct volume, and ameliorated neurological deficits. A total of 5, 6, 7 and 7 non-overlapping metabolites were identified in the DH-1, DH-2, DH-3, and DH-4 groups, respectively. Eight metabolites were shared between the DHI groups and the vehicle group. In addition, the serum levels of glutamic acid, aspartic acid and serine increased with increasing DHI dose. A total of 3, 2, 2 and 5 non-overlapping metabolic pathways were identified in the DH-1, DH-2, DH-3 and DH-4 groups, respectively, and glycine, serine, threonine and histidine metabolism were identified as overlapping pathways among the 4 dose groups. CONCLUSION: Overlapping and non-overlapping amino acid metabolites and metabolic pathways are associated with the dose-dependent neuroprotective effect of DHI.

Significant overlapping modules and biological processes between stroke and coronary heart disease.

The systematical associations between stroke and coronary heart disease (CHD) remain controversial and uncertain. Network construction and modularized analysis have become powerful tools in the field of systems biology research, which can help us to mine the multidimensional characters of correlation between the two diseases in depth. A total of 218 stroke-related and 204 CHD-related genes were identified via the Online Mendelian Inheritance in Man database; text searching engine (Agilent Literature Search 2.71) and MCODE software were employed for network construction and module division, respectively. Finally, 67, 21, 7, and 196 overlapping genes, hubs, modules and modular functions were identified between stroke and CHD, respectively. The overlapping genes, which should be responsible for the similar phenotypes, highlighted the molecular signatures of the two linked diseases. Additionally, the analysis of modules and their functional annotations showed potential dependent and independent risk factors, such as atherosclerosis, cholesterol homeostasis, plasma lipoprotein particle remodeling and response to oxidative stress, etc. Moreover, the potential mechanisms by which the same biological process activating pathological cascade or risk component-based shared pathway between stroke and CHD were uncovered, which may provide useful insights for further drug development and cost saving.