Effect of Dietary Bacillus licheniformis Supplementation on Growth Performance and Microbiota Diversity of Pekin Ducks.

This experiment was conducted to investigate the effects of different concentrations of Bacillus licheniformis (B. licheniformis) on growth performance and microbiota diversity of Pekin ducks. Three hundred 1-day-old healthy Pekin ducks were randomly divided into 5 groups with 6 replicates per group and 10 ducks per replicate. The five treatments supplemented with basal diets containing: either 0 (group CON), 200 (group LLB), 400 (group MLB), and 800 (group HLB) mg/kg B. licheniformis or 150 mg/kg aureomycin (group ANT) for 42 days, respectively, and were sacrificed and sampled in the morning of the 42nd day for detection of relevant indexes. The results showed as follows: The feed conversion ratio of the LLB group and MLB groups were lower than the CON group (P < 0.05). The body weight and average daily feed intake of the MLB group were significantly higher than that of the CON group and ANT group (P < 0.05). Compared with the CON group, the MLB group significantly increased the content of IgA (P < 0.05) and proinflammatory IL-6 were significantly decreased (P < 0.05), besides, the activity of SOD and T-AOC were also significantly increased in the MLB group (P < 0.05). The 16S rRNA analysis showed that B. licheniformis treatments had no effect (P > 0.05) on the alpha diversities of the intestine. The addition of B. licheniformis had a dynamic effect on the abundance of cecal microflora of Pekin ducks, and 1-21 d increased the diversity of microflora, while 21d-42 d decreased it. Compared with the CON group, the relative abundance of Epsilonbacteraeota in the MLB group was significantly increased on Day 21 (P < 0.05), and that of Tenericutes in the LLB group was significantly increased as well (P < 0.05). At 42 d, the relative abundance of Bacteroidetes in LLB, MBL, HBL, and ANT groups was significantly increased (P < 0.05). In addition, the addition of B. licheniformis increased the amount of SCAF-producing bacteria in the intestinal microbiota, such as Lachnospiraceae, Collinsella, Christensenellaceae, and Bilophila. The PICRUSt method was used to predict the intestinal microbiota function, and it was found that lipid transport and metabolism of intestinal microbiota in the MLB group were significantly affected. Overall, these results suggest diet supplemented with B. licheniformis improved growth performance, immune status, antioxidant capacity, and modulated intestinal microbiota in Pekin ducks. The optimal dietary supplement dose is 400 mg/kg.

Prediction of S-glutathionylation sites based on protein sequences.

S-glutathionylation, the reversible formation of mixed disulfides between glutathione(GSH) and cysteine residues in proteins, is a specific form of post-translational modification that plays important roles in various biological processes, including signal transduction, redox homeostasis, and metabolism inside cells. Experimentally identifying S-glutathionylation sites is labor-intensive and time consuming, whereas bioinformatics methods provide an alternative way to this problem by predicting S-glutathionylation sites in silico. The bioinformatics approaches give not only candidate sites for further experimental verification but also bio-chemical insights into the mechanism of S-glutathionylation. In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data. Promising results are obtained by our method with an AUC (area under ROC curve) score of 0.879 in 5-fold cross-validation, which demonstrates the predictive power of our proposed method. The datasets used in this work are available at http://csb.shu.edu.cn/SGDB.

FGsub: Fusarium graminearum protein subcellular localizations predicted from primary structures.

BACKGROUND: The fungal pathogen Fusarium graminearum (telomorph Gibberella zeae) is the causal agent of several destructive crop diseases, where a set of genes usually work in concert to cause diseases to crops. To function appropriately, the F. graminearum proteins inside one cell should be assigned to different compartments, i.e. subcellular localizations. Therefore, the subcellular localizations of F. graminearum proteins can provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus. Unfortunately, there are no subcellular localization information for F. graminearum proteins available now. Computational approaches provide an alternative way to predicting F. graminearum protein subcellular localizations due to the expensive and time-consuming biological experiments in lab. RESULTS: In this paper, we developed a novel predictor, namely FGsub, to predict F. graminearum protein subcellular localizations from the primary structures. First, a non-redundant fungi data set with subcellular localization annotation is collected from UniProtKB database and used as training set, where the subcellular locations are classified into 10 groups. Subsequently, Support Vector Machine (SVM) is trained on the training set and used to predict F. graminearum protein subcellular localizations for those proteins that do not have significant sequence similarity to those in training set. The performance of SVMs on training set with 10-fold cross-validation demonstrates the efficiency and effectiveness of the proposed method. In addition, for F. graminearum proteins that have significant sequence similarity to those in training set, BLAST is utilized to transfer annotations of homologous proteins to uncharacterized F. graminearum proteins so that the F. graminearum proteins are annotated more comprehensively. CONCLUSIONS: In this work, we present FGsub to predict F. graminearum protein subcellular localizations in a comprehensive manner. We make four fold contributions to this filed. First, we present a new algorithm to cope with imbalance problem that arises in protein subcellular localization prediction, which can solve imbalance problem and avoid false positive results. Second, we design an ensemble classifier which employs feature selection to further improve prediction accuracy. Third, we use BLAST to complement machine learning based methods, which enlarges our prediction coverage. Last and most important, we predict the subcellular localizations of 12786 F. graminearum proteins, which provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus.