Bacterial diversity in the intestinal mucosa of heart failure rats treated with Sini Decoction.

BACKGROUND: Sini Decoction (SND), a classic Chinese medicine prescription, has been proved to have a good effect on heart failure (HF), whereas its underlying mechanism is still unclear. In order to explore the therapeutic mechanism of SND, we combined with 16S rRNA gene sequencing to analyze the composition of gut microflora in rats with HF. MATERIAL AND METHODS: Twenty Sprague-Dawley (SD) rats were divided into four groups (n = 5): normal group, model group, SND treatment group (SNT group), and metoprolol (Met) treatment group (Meto group). All the rats except the normal group were intraperitoneally injected with doxorubicin (concentration 2 mg/mL, dose 0.15 mL/100 g) once a week to induce HF. After successfully modeling, SND and Met were gavaged to rats, respectively. After the treatment period, blood was collected for hematological analyses, myocardial tissue and colon tissues were collected for Hematoxylin-Eosin (H&E) staining, and mucosal scrapings were collected for Illumina Miseq high-throughput sequencing. RESULTS: Echocardiographic results suggested that both left ventricular ejection fraction (LVEF) and left ventricular fraction shortening (LVFS) in Model rats decreased compared with normal rats. The results of H&E staining showed that compared with the model group, the structures of myocardial tissue and colon tissue in the SNT group and Meto group showed a recovery trend. Alpha results showed that the model group had higher species diversity and richness compared with the normal group. After treatment, the richness and diversity of intestinal bacteria in the SNT group were significantly restored, and Met also showed the effect of adjusting bacterial diversity, but its effect on bacterial richness was not ideal. At the Family level, we found that the number of several bacteria associated with HF in the model group increased significantly. Excitingly, SND and Met had shown positive effects in restoring these HF-associated bacteria. Similarly, the results of Linear discriminant analysis (LDA) showed that both SND and Met could reduce the accumulation of bacteria in the model group caused by HF. CONCLUSION: Collectively, SND can improve HF by regulating the intestinal flora. This will provide new ideas for the clinical treatment of patients with HF.

A unified frame of predicting side effects of drugs by using linear neighborhood similarity.

BACKGROUND: Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. RESULTS: In this paper, we present a novel measure of drug-drug similarity named "linear neighborhood similarity", which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method "LNSM" and its extension "LNSM-SMI" to predict side effects of new drugs, and propose the method "LNSM-MSE" to predict unobserved side effect of approved drugs. CONCLUSIONS: We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.