Discovery of Drug Candidates for Specific Human Disease Based on Natural Products of Gut Microbes.

The beneficial metabolites of the microbiome could be used as a tool for screening drugs that have the potential for the therapy of various human diseases. Narrowing down the range of beneficial metabolite candidates in specific diseases was primarily a key step for further validation in model organisms. Herein, we proposed a reasonable hypothesis that the metabolites existing commonly in multiple beneficial (or negatively associated) bacteria might have a high probability of being effective drug candidates for specific diseases. According to this hypothesis, we screened metabolites associated with seven human diseases. For type I diabetes, 45 out of 88 screened metabolites had been reported as potential drugs in the literature. Meanwhile, 18 of these metabolites were specific to type I diabetes. Additionally, metabolite correlation could reflect disease relationships in some sense. Our results have demonstrated the potential of bioinformatics mining gut microbes' metabolites as drug candidates based on reported numerous microbe-disease associations and the Virtual Metabolic Human database. More subtle methods would be developed to ensure more accurate predictions.

Geptop 2.0: Accurately Select Essential Genes from the List of Protein-Coding Genes in Prokaryotic Genomes.

Computational tool composites alternative way to identify essential genes and it is low-cost and time-efficient. Based on experimental essentiality sets deposited in the databases DEG and OGEE as reference, we developed an automatically computational tool named Geptop to select essential genes from the set of protein-coding genes in a prokaryotic genome, which utilizes the strategy of reciprocally best hit for homology search and evolutionary distance for weight assigning. The latest version of Geptop is 2.0 ( http://guolab.whu.edu.cn/geptop ), which can predict gene essentiality with the mean AUC 0f 0.84 in prokaryotes and is more stable. The chapter is to briefly introduce the tool and tell how to use it.

Comprehensive review of the identification of essential genes using computational methods: focusing on feature implementation and assessment.

Essential genes have attracted increasing attention in recent years due to the important functions of these genes in organisms. Among the methods used to identify the essential genes, accurate and efficient computational methods can make up for the deficiencies of expensive and time-consuming experimental technologies. In this review, we have collected researches on essential gene predictions in prokaryotes and eukaryotes and summarized the five predominant types of features used in these studies. The five types of features include evolutionary conservation, domain information, network topology, sequence component and expression level. We have described how to implement the useful forms of these features and evaluated their performance based on the data of Escherichia coli MG1655, Bacillus subtilis 168 and human. The prerequisite and applicable range of these features is described. In addition, we have investigated the techniques used to weight features in various models. To facilitate researchers in the field, two available online tools, which are accessible for free and can be directly used to predict gene essentiality in prokaryotes and humans, were referred. This article provides a simple guide for the identification of essential genes in prokaryotes and eukaryotes.

Study on components efficacy of Salviae Miltiorrhizae Radix et Rhizoma based on systematic traditional Chinese medicine / 中国中药杂志

This paper aimed to establish efficacy systems of tanshinones and salvianolic acids, two representative substances in Salviae Miltiorrhizae Radix et Rhizoma by using literature mining and biological network construction, based on systematic traditional Chinese medicine theory. The systematic study on the efficacy of traditional Chinese medicine was carried out from the basic unit, the structure and relationship between the basic units, the boundary of the research object and the function of the system, so as to explain the overall efficacy of the two kinds of components at the molecular level. Firstly, we collected the elements of the efficacy systems of these two kinds of components by literature mining, and defined their boundaries based on biological processes. After that, the structure of the efficacy systems was clarified according to the relationship in the KEGG database. Finally, the function of the efficacy systems was analyzed from the level of pharmacology, pharmacodynamics, and efficacy, revealing the scientific connotation of traditional Chinese medicine efficacy system. The results showed that there were 201 targets(elements), 12 target sets(boundary), and 12 pathway networks(structure) in salvianolic acids' efficacy system. Meanwhile, there were 189 targets(elements), 11 target sets(boundary), and 11 pathway networks(structure) in tanshinones' efficacy system. The results suggested that the functions of salvia-nolic acids' and tanshinones' efficacy systems were different in pharmacology and pharmacodynamics from aspects of elements, boundary, relationship and structure, but they were same in functional level as both of them could promote blood circulation, remove blood stasis, clear away heart-fire, relieve restlessness, and soothe the nerves. Based on systematic traditional Chinese medicine, we constructed the efficacy system of two representative components in Salviae Miltiorrhizae Radix et Rhizoma in this paper, elucidated the overall efficacy and builded the bridge between reductionism and holism in traditional Chinese medicine.

CasLocusAnno: a web-based server for annotating cas loci and their corresponding (sub)types.

In prokaryotes, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated protein (Cas) systems constitute adaptive immune systems against mobile genetic elements (MGEs). Here, we introduce the Markov cluster algorithm (MCL) to Makarova et al.'s method in order to select a more reasonable profile. Additionally, our new Maximum Continuous Cas Subcluster (MCCS) method helps identification of tightly clustered loci. The comparison with two other commonly used programs shows that the method could identify Cas proteins with higher accuracy and lower Additional Prediction Rate (APR). Moreover, we developed a web-based server, CasLocusAnno (http://cefg.uestc.cn/CasLocusAnno), capable of annotating Cas proteins, cas loci and their (sub)types less than ~ 28 s following the whole proteome sequence submission. Its standalone version can be downloaded at https://github.com/RiversDong/CasLocusAnno.

Geptop 2.0: An Updated, More Precise, and Faster Geptop Server for Identification of Prokaryotic Essential Genes.

Geptop has performed effectively in the identification of prokaryotic essential genes since its first release in 2013. It estimates gene essentiality for prokaryotes based on orthology and phylogeny. Genome-scale essentiality data of more prokaryotic species are available, and the information has been collected into public essential gene repositories such as DEG and OGEE. A faster and more accurate toolkit is needed to meet the increasing prokaryotic genome data. We updated Geptop by supplementing more validated essentiality data into reference set (from 19 to 37 species), and introducing multi-process technology to accelerate the computing speed. Compared with Geptop 1.0 and other gene essentiality prediction models, Geptop 2.0 can generate more stable predictions and finish the computation in a shorter time. The software is available both as an online server and a downloadable standalone application. We hope that the improved Geptop 2.0 will facilitate researches in gene essentiality and the development of novel antibacterial drugs. The gene essentiality prediction tool is available at http://cefg.uestc.cn/geptop.

Comprehensive exploration of the enzymes catalysing oxygen-involved reactions and COGs relevant to bacterial oxygen utilization.

To better understand the mechanisms of bacterial adaptation in oxygen environments, we explored the aerobic living-associated genes in bacteria by comparing Clusters of Orthologous Groups of proteins' (COGs) frequencies and gene expression analyses and 38 COGs were detected at significantly higher frequencies (p-value less than 1e-6) in aerobes than in anaerobes. Differential expression analyses between two conditions further narrowed the prediction to 27 aerobe-specific COGs. Then, we annotated the enzymes associated with these COGs. Literature review revealed that 14 COGs contained enzymes catalysing oxygen-involved reactions or products involved in aerobic pathways, suggesting their important roles for survival in aerobic environments. Additionally, protein-protein interaction analyses and step length comparisons of metabolic networks suggested that the other 13 COGs may function relevantly with the 14 enzymes-corresponding COGs, indicating that these genes may be highly associated with oxygen utilization. Phylogenetic and evolutionary analyses showed that the 27 COGs did not have similar trees, and all suffered purifying selection pressures. The divergent times of species containing or lacking aerobic COGs validated that the appearing time of oxygen-utilizing gene was approximately 2.80 Gyr ago. In addition to help better understand oxygen adaption, our method may be extended to identify genes relevant to other living environments.