Huihui Zhao

A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.

Metabolomic identification of diagnostic plasma biomarkers in humans with chronic heart failure.

Chronic heart failure (CHF), as a progressive clinical syndrome, is characterized by failure of enough blood supply from the heart to meet the bodys metabolic demands, and there is intense interest in identifying novel biomarkers that could make contributions to the diagnosis of CHF. Metabolomics, compared with current diagnostic approaches, could investigate many metabolic perturbations within biological systems. The overarching goal of the work discussed here is to apply a high-throughput approach to identify metabolic signatures and plasma diagnostic biomarkers underlying CHF by 1H-NMR spectroscopy. Plasma samples from 39 patients with CHF and 15 controls were analyzed by NMR spectroscopy. After processing the data, orthogonal partial least square discriminant analysis (OPLS-DA) was performed. The statistical model revealed good explained variance and predictability, and the diagnostic performance assessed by leave-one-out analysis exhibited 92.31% sensitivity and 86.67% specificity. The OPLS-DA score plots of spectra revealed good separation between case and control on the level of metabolites, and multiple biochemical changes indicated hyperlipidemia, alteration of energy metabolism and other potential biological mechanisms underlying CHF. It was concluded that the NMR-based metabolomics approach demonstrated good performance to identify diagnostic plasma markers and provided new insights into metabolic process related to CHF.

Wnt/β-catenin coupled with HIF-1α/VEGF signaling pathways involved in galangin neurovascular unit protection from focal cerebral ischemia.

Microenvironmental regulation has become a promising strategy for complex disease treatment. The neurovascular unit (NVU), as the key structural basis to maintain an optimal brain microenvironment, has emerged as a new paradigm to understand the pathology of stroke. In this study, we investigated the effects of galangin, a natural flavonoid isolated from the rhizome of Alpina officinarum Hance, on NVU microenvironment improvement and associated signal pathways in rats impaired by middle cerebral artery occlusion (MCAO). Galangin ameliorated neurological scores, cerebral infarct volume and cerebral edema and reduced the concentration of Evans blue (EB) in brain tissue. NVU ultrastructural changes were also improved by galangin. RT-PCR and western blot revealed that galangin protected NVUs through the Wnt/β-catenin pathway coupled with HIF-1α and vascular endothelial growth factor (VEGF). VEGF and β-catenin could be the key nodes of these two coupled pathways. In conclusion, Galangin might function as an anti-ischemic stroke drug by improving the microenvironment of NVUs.

Study on TCM Syndrome Identification Modes of Coronary Heart Disease Based on Data Mining.

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and high mortality. TCM has played an important role in the treatment of CHD. Syndrome differentiation based on information from traditional four diagnostic methods has met challenges and questions with the rapid development and wide application of system biology. In this paper, methods of complex network and CHAID decision tree were applied to identify the TCM core syndromes of patients with CHD, and to establish TCM syndrome identification modes of CHD based on biological parameters. At the same time, external validation modes were also constructed to confirm the identification modes.

BUILDING AND EVALUATING AN ANIMAL MODEL FOR SYNDROME IN TRADITIONAL CHINESE MEDICINE IN THE CONTEXT OF UNSTABLE ANGINA (MYOCARDIAL ISCHEMIA) BY SUPERVISED DATA MINING APPROACHES

Building an animal model for a disease is a better avenue to understand the inner mechanism of it. Traditional Chinese Medicine accumulated much practical experience and a large amount of literature to heal diseases during the past 3000 years. However, as there is no available animal model for TCM research because syndrome, the core of TCM theory, it is hard to be diagnosed from animals. In this paper, we present a novel strategy to build and evaluate an animal model for syndrome in TCM in the context of a disease. We first carried out a clinical epidemiology survey for a syndrome ( Blood stasis syndrome, BSS) diagnosed by TCM experts in the context of a disease ( Unstable angina, UA). Meanwhile, the blood samples of patients included in the survey were collected and measured as physical and chemical specifications by laboratory examinations. Alternatively, we used supervised data mining methods to build association between the specifications and the syndrome in the context of UA. The accuracy of classification was used to evaluate performance of the association built. Finally, we built an animal model for myocardial ischemia and validated the model by established diagnosis criterion of myocardial ischemia. Furthermore, the built association was used to evaluate whether an animal is with BSS. The results indicated that the strategy successfully evaluates and separates the animal model for syndrome in TCM from the counterpart for myocardial ischemia. The novel strategy presented in the paper provides a better insight to understand the nature of syndrome in TCM and pave a basis for personalized therapies of UA.

Clinical Data Mining of Phenotypic Network in Angina Pectoris of Coronary Heart Disease

Coronary heart disease (CHD) is the leading causes of morbidity and mortality in China. The diagnosis of CHD in Traditional Chinese Medicine (TCM) was mainly based on experience in the past. In this paper, we proposed four MI-based association algorithms to analyze phenotype networks of CHD, and established scale of syndromes to automatically generate the diagnosis of patients based on their phenotypes. We also compared the change of core syndromes that CHD were combined with other diseases, and presented the different phenotype spectra.

Discovery of Diagnosis Pattern of Coronary Heart Disease with Qi Deficiency Syndrome by the T-Test-Based Adaboost Algorithm

Coronary heart disease (CHD) is still the leading cause of death for adults worldwide. Traditional Chinese medicine (TCM) has a history of 1000 years fighting against the disease and provides a complementary and alternative treatment to it. Syndrome is the core of TCM diagnosis and it is traditionally diagnosed based on macroscopic symptoms as well as tongue and pulse recognitions of patients. Establishment of the diagnosis method in the microcosmic level is an urgent and major problem in TCM. The aim of this study was to establish characteristic diagnosis pattern for CHD with Qi deficiency syndrome (QDS). Thirty-four biological parameters were detected in 52 patients having unstable angina (UA) with or without QDS. Then, we presented a novel data mining method, t-test-based Adaboost algorithm, to establish highest prediction accuracy with the least number of biological parameters for UA with QDS. We gained a pattern composed of five biological parameters that distinguishes UA with QDS patients from non-QDS patients. The diagnosis accuracy of the patterns could reach 84.5% based on a 3-fold cross validation technique. Moreover, we included 85 UA cases collected from hospitals located in the north and south of China to further verify the association between the pattern and QDS. The classification accuracy is 83.5%, which keeps consistent with the accuracy obtained by the cross-validation technique. The association between a symptom and the five biological parameters was established by the data mining method and it reached an accuracy of ∼80%. These results showed that the t-test-based Adaboost algorithm might be a powerful technique for diagnosing syndrome in TCM in the context of CHD.

Study on Qi Deficiency Syndrome Identification Modes of Coronary Heart Disease Based on Metabolomic Biomarkers

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and mortality. With the era of systems biology bursting into reality, the analysis of the whole biological systems whether they are cells, tissues, organs, or the whole organisms has now become the norm of biological researches. Metabolomics is the branch of science concerned with the quantitative understandings of the metabolite complement of integrated living systems and their dynamic responses to the changes of both endogenous and exogenous factors. The aim of this study is to discuss the characteristics of plasma metabolites in CHD patients and CHD Qi deficiency syndrome patients and explore the composition and concentration changes of the plasma metabolomic biomarkers. The results show that 25 characteristic metabolites related to the CHD patients comparing with the healthy people, and 4 identifiable variables had significant differences between Qi deficiency and non-Qi deficiency patients. On the basis of identifying the different plasma endogenous metabolites between CHD patients and healthy people, we further prompted the metabolic rules, pathogenesis, and biological essence in Qi deficiency syndrome patients.

The Effects of Jiang-Zhi-Ning and Its Main Components on Cholesterol Metabolism

To examine how Jiang-Zhi-Ning (JZN) regulates cholesterol metabolism and compare the role of its four main components. We established a beagle model of hyperlipidemia, fed with JZN extract and collected JZN-containing serum 0, 1, 2, 4, and 6 h later. Human liver cells Bel-7402 were stimulated with 10% JZN-containing serum as well as the four main components of JZN and Atorvastatin. The mRNA expression of LDL receptor (LDL-R), 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMG-CoAR), cytochrome P450 7A1 (CYP7A1), and acetyl-Coenzyme A acetyltransferase 2 (ACAT2) was measured by real-time PCR. LDL-R surface expression and LDL-binding and internalization were examined by flow cytometry. The results showed that JZN-containing serum significantly increased the mRNA expression of LDL-R, HMG-CoAR, and CYP7A1 in Bel-7402 cells. All the four components significantly increased the mRNA and protein expression of LDL-R and HMG-CoAR and decreased the mRNA and protein expression of ACAT2 in Bel-7402 cells. Hyperinand chrysophanol also markedly increased the mRNA expression of CYP7A1. Stimulation with stilbene glycosidesignificantly increased the surface expression of LDL-R and the binding and internalization of LDL. In conclusion, JZN and its four components have close relationship with the process of cholesterol metabolism, emphasizing their promising application as new drug candidates in the treatment of hyperlipidemia.

Chinese Herbal Medicine in the Treatment of Chronic Heart Failure: Three-Stage Study Protocol for a Randomized Controlled Trial.

Background. Chinese herbal medicine (CHM) has been used in the treatment of chronic heart failure (CHF) for a long time. Treatment based on syndrome differentiation and the main characteristic of TCM is the fundamental principle of TCM practice. In this study protocol, we have designed a trial to assess the efficacy and safety of CHM on CHF based on syndrome differentiation. Methods/Design. This is a three-stage trial of CHM in the treatment of CHF. The first stage is a literature review aiming to explore the common syndromes of CHF. The second is a multicentral, randomized, placebo-controlled trial to evaluate the efficacy and safety of CHM for the treatment of CHF. The third is a multicentral, randomized controlled clinical trial aiming to make cost-effectiveness analysis and evaluate the feasibility, compliance, and universality of CHM on CHF. Discussion. This trial will evaluate the efficacy, safety, feasibility, compliance, and universality of CHM on CHF. The expected outcome is to provide evidence-based recommendations for CHM on CHF and develop a prescription of CHM in the treatment of CHF. This trial is registered with NCT01939236 (Stage Two of the whole trial).