BackgroundSubstantial efforts have been made to link the gut bacterial community to many complex human diseases. Nevertheless, the gut phages are often neglected.ResultsIn this study, we used multiple bioinformatic methods to catalog gut phages from whole-community metagenomic sequencing data of fecal samples collected from both type II diabetes (T2D) patients (n = 71) and normal Chinese adults (n = 74). The definition of phage operational taxonomic units (pOTUs) and identification of large phage scaffolds (n = 2567, ≥ 10 k) revealed a comprehensive human gut phageome with a substantial number of novel sequences encoding genes that were unrelated to those in known phages. Interestingly, we observed a significant increase in the number of gut phages in the T2D group and, in particular, identified 7 pOTUs specific to T2D. This finding was further validated in an independent dataset of 116 T2D and 109 control samples. Co-occurrence/exclusion analysis of the bacterial genera and pOTUs identified a complex core interaction between bacteria and phages in the human gut ecosystem, suggesting that the significant alterations of the gut phageome cannot be explained simply by co-variation with the altered bacterial hosts.ConclusionsAlterations in the gut bacterial community have been linked to the chronic disease T2D, but the role of gut phages therein is not well understood. This is the first study to identify a T2D-specific gut phageome, indicating the existence of other mechanisms that might govern the gut phageome in T2D patients. These findings suggest the importance of the phageome in T2D risk, which warrants further investigation.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0410-y) contains supplementary material, which is available to authorized users.

Psoriasis is an autoimmune disease, which symptoms can significantly impair the patient's life quality. It is mainly diagnosed through the visual inspection of the lesion skin by experienced dermatologists. Currently no cure for psoriasis is available due to limited knowledge about its pathogenesis and development mechanisms. Previous studies have profiled hundreds of differentially expressed genes related to psoriasis, however with no robust psoriasis prediction model available. This study integrated the knowledge of three feature selection algorithms that revealed 21 features belonging to 18 genes as candidate markers. The final psoriasis classification model was established using the novel Incremental Feature Selection algorithm that utilizes only 3 features from 2 unique genes, IGFL1 and C10orf99. This model has demonstrated highly stable prediction accuracy (averaged at 99.81%) over three independent validation strategies. The two marker genes, IGFL1 and C10orf99, were revealed as the upstream components of growth signal transduction pathway of psoriatic pathogenesis.

BackgroundHigh-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This “large p, small n” paradigm in the area of biomedical “big data” may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets.ResultsThis work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature.ConclusionMcTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0990-0) contains supplementary material, which is available to authorized users.

As an alternative approach against multidrug-resistant bacterial infections, phages are now being increasingly investigated as effective therapeutic agents. Here, aiming to design an efficient phage cocktail against Aeromonas salmonicida infections, we isolated and characterized five lytic A. salmonicida phages, AS-szw, AS-yj, AS-zj, AS-sw, and AS-gz. The results of morphological and genomic analysis suggested that all these phages are affiliated to the T4virus genus of the Caudovirales order. Their heterogeneous lytic capacities against A. salmonicida strains were demonstrated by experiments. A series of phage cocktails were prepared and investigated in vitro. We observed that the cocktail combining AS-gz and AS-yj showed significantly higher antimicrobial activity than other cocktails and individual phages. Given the divergent genomes between the phages AS-yj and AS-gz, our results highlight that the heterogeneous mechanisms that phages use to infect their hosts likely lead to phage synergy in killing the host. Conclusively, our study described a strategy to develop an effective and promising phage cocktail as a therapeutic agent to combat A. salmonicida infections, and thereby to control the outbreak of relevant fish diseases. Our study suggests that in vitro investigations into phages are prerequisite to obtain satisfying phage cocktails prior to application in practice.

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Increasing evidences have revealed a close interaction between the intestinal microbes and host growth performance. The shrimp (Litopenaeus vannamei) gut harbors a diverse microbial community, yet its associations with dietary, body weight and weaning age remain a matter of debate. In this study, we analyzed the effects of different dietary (fishmeal group (NC), krill meal group (KM)) and different growth stages (age from 42 day-old to 98 day-old) of the shrimp on the intestinal microbiota. High throughput sequencing of the 16S rRNA genes of shrimp intestinal microbes determined the novelty of bacteria in the shrimp gut microbiota and a core of 58 Operation Taxonomic Units (OTUs) was present among the shrimp gut samples. Analysis results indicated that the development of the shrimp gut microbiota is a dynamic process with three stages across the age according to the gut microbiota compositions. Furthermore, the dietary of KM group did not significantly change the intestinal microbiota of the shrimps compared with NC group. Intriguingly, compared to NC group, we observed in KM group that a fluctuation of the shrimp gut microbiota coincided with the shrimp body weight gain between weeks 6–7. Six OTUs associated with the microbiota change in KM group were identified. This finding strongly suggests that the shrimp gut microbiota may be correlated with the shrimp body weight likely by influencing nutrient uptake in the gut. The results obtained from this study potentially will be guidelines for manipulation to provide novel shrimp feed management approaches.

Objective To evaluate the feasibility and safety of a revised technique of botulinum toxin type A (BTA) injections for the treatment of infantile esotropia. Methods Forty-seven patients with infantile esotropia were randomly divided into two groups. In group A, 23 cases were treated with a bilateral injection of 2.5-3.75 U BTA combined with sodium hyaluronate (SH) to the medial rectus muscle. In group B, 24 cases were treated with a bilateral injection of 2.5-3.75 U BTA solution alone to the medial rectus muscle. Electromyography was not used in the study. All patients received one injection and were evaluated 2 weeks, 3 months, and 6 months following injection. Results The measured changes between groups A and B included the frequencies of good alignment 6 months after injections (30.4% vs 37.5%), complicated ptosis (2.2% vs 20.8%), and vertical deviation (2.2% vs 2.1%). Conclusion BTA injections combined with or without SH in the absence of electromyography demonstrated effectiveness and feasibility in the treatment of infantile esotropia. A relative decrease in the frequency of complicated ptosis resulted from injections of BTA þ SH.

Background: The prediction of the prokaryotic promoter strength based on its sequence is of great importance not only in the fundamental research of life sciences but also in the applied aspect of synthetic biology. Much advance has been made to build quantitative models for strength prediction, especially the introduction of machine learning methods such as artificial neural network (ANN) has significantly improve the prediction accuracy. As one of the most important machine learning methods, support vector machine (SVM) is more powerful to learn knowledge from small sample dataset and thus supposed to work in this problem. Methods: To confirm this, we constructed SVM based models to quantitatively predict the promoter strength. A library of 100 promoter sequences and strength values was randomly divided into two datasets, including a training set (≥10 sequences) for model training and a test set (≥10 sequences) for model test. Results:The results indicate that the prediction performance increases with an increase of the size of training set, and the best performance was achieved at the size of 90 sequences. After optimization of the model parameters, a highperformance model was finally trained, with a high squared correlation coefficient for fitting the training set (R 2 > 0.99) and the test set (R 2 > 0.98), both of which are better than that of ANN obtained by our previous work.Conclusions: Our results demonstrate the SVM-based models can be employed for the quantitative prediction of promoter strength.