Enrichment and characteristics of mixed methane-oxidizing bacteria from a Chinese coal mine

In methane-rich environments, methane-oxidizing bacteria usually occur predominantly among consortia including other types of microorganisms. In this study, artificial coal bed gas and methane gas were used to enrich mixed methanotrophic cultures from the soil of a coal mine in China, respectively. The changes in microbial community structure and function during the enrichment were examined. The microbial diversity was reduced as the enrichment proceeded, while the capacity for methane oxidation was significantly enhanced by the increased abundance of methanotrophs. The proportion of type II methanotrophs increased greatly from 7.84 % in the sampled soil to about 50 % in the enrichment cultures, due to the increase of methane concentration. After the microbial community of the cultures got stable, Methylomonas and Methylocystis became the dominant type I and type II methanotrophs, while Methylophilus was the prevailing methylotroph. The sequences affiliated with pigment-producing strains, Methylomonas rubra, Hydrogenophaga sp. AH-24, and Flavobacterium cucumis, could explain the orange appearance of the cultures. Comparing the two cultures, the multi-carbon sources in the artificial coal bed gas caused more variety of non-methanotrophic bacteria, but did not help to maintain the diversity or to increase the quantity and activity of methanotrophs. The results could help to understand the succession and interaction of microbial community in a methane-driven ecosystem.     

30年泰山繁殖鸟类种类变化

泰山位于山东省中部,是华北中东部地区海拔最高的山脉,地形多样,生境复杂,是鸟类重要的繁殖地和迁徙停歇地。根据1984~1988年和2013~2015年两个时期野外调查及访谈调查结果,比较了近30年泰山繁殖鸟类种类的组成变化。相比20世纪80年代记录的繁殖鸟类13目35科72种,2013~2015年的调查记录到繁殖鸟类11目34科84种,与30年前相比净增长12种繁殖鸟类,其中新增繁殖鸟类记录32种,消失鸟类记录20种。新增种类主要是林地和灌丛鸟类,多为东洋种,消失的种类主要是湿地和农田草地鸟类,多为古北种和广布种。鸟类新增和消失的原因多样,但主要是栖息地面积和质量的改变,同时私自放生也是一个重要原因。本研究结果可丰富泰山地区随时间尺度变化的鸟类本底数据,并为泰山鸟类的保护提供科学依据。     

FLEXc: protein flexibility prediction using context-based statistics,predicted structural features,and sequence information

Background

The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.

Results

In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context.The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor.

Conclusions

Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc.

     

Inhibition of dengue virus replication by diisopropyl chrysin-7-yl phosphate

Dengue fever is a tropical disease and caused by dengue virus (DENV), which is transmitted by mosquitoes and infects about 400 million people annually. With the development of international trade and travel, China is facing a growing threat. Over 40 thousands of people were infected during the 2014 DENV outbreak in Guangdong. Neither licensed vaccine nor therapeutic drug has been available. In this report, we isolated two clinical DENV strains. The full-length genome was sequenced and characterized. We also applied a flavonoid, CPI, into an anti-DENV assay. Replication of viral RNA and expression of viral protein was all strongly inhibited. These results indicated that CPI may serve as potential protective agents in the treatment of patients with chronic DENV infection.