后河国家级自然保护区蛾类昆虫的季节多样性

后河国家级自然保护区位于湖北省与湖南省交界处,属于武陵山脉,与湖南壶瓶山国家级自然保护区相邻。该保护区自然环境优越,为昆虫繁衍提供了良好条件。为了研究后河国家级自然保护区蛾类昆虫的季节动态变化,在春、夏、秋三季,选取茅坪、湾潭和独岭为样地,采用灯光诱集方法,对后河国家自然保护区的蛾类昆虫群落多样性及季节变化进行了调查。采用α-多样性测度方法,分析了物种丰富度(S)、多样性指数(科、属及种级)(H)、均匀度指数(J)和优势度指数(D)。结果表明:后河国家自然保护区的蛾类昆虫分属21科173属227种,其中夜蛾科的丰富度最高达到62。随后是,尺蛾科和螟蛾科昆虫,它们的物种丰富度指数分别为为58和25。从各科个体数来看,尺蛾科昆虫最多有423头,灯蛾科排第二,有351头,第三的是夜蛾科昆虫有336头。蛾类昆虫的物种丰富度指数、多样性指数、均匀度指数及优势度指数随季节变化而变化。蛾类昆虫的物种丰富度指数以夏季最高,达到117;然后依次是秋季和春季。科级、属级和种级多样性指数也以夏季最高,分别为2.22、4.05和4.29。均匀度指数和优势度指数以春季最高,分为2.40和0.12。研究得出后河国家级自然保护区的生态环境好。     

Alterations and Mechanism of Gut Microbiota in Graves’ Disease and Hashimoto’s Thyroiditis

To explore the role of gut microbiota in Graves’ disease (GD) and Hashimoto’s thyroiditis (HT). Seventy fecal samples were collected, including 27 patients with GD, 27 with HT, and 16 samples from healthy volunteers. Chemiluminescence was used to detect thyroid function and autoantibodies (FT3, FT4, TSH, TRAb, TGAb, and TPOAb); thyroid ultrasound and 16S sequencing were used to analyze the bacteria in fecal samples; KEGG (Kyoto Encyclopedia of Genes and Genomes) and COG (Clusters of Orthologous Groups) were used to analyze the functional prediction and pathogenesis. The overall structure of gut microbiota in the GD and HT groups was significantly different from the healthy control group. Proteobacteria and Actinobacteria contents were the highest in the HT group. Compared to the control group, the GD and HT groups had a higher abundance of Erysipelotrichia, Cyanobacteria, and Ruminococcus_2 and lower levels of Bacillaceae and Megamonas. Further analysis of KEGG found that the “ABC transporter” metabolic pathway was highly correlated with the occurrence of GD and HT. COG analysis showed that the GD and HT groups were enriched in carbohydrate transport and metabolism compared to the healthy control group but not in amino acid transport and metabolism. Our data suggested that Bacillus, Blautia, and Ornithinimicrobium could be used as potential markers to distinguish GD and HT from the healthy population and that “ABC transporter” metabolic pathway may be involved in the pathogenesis of GD and HT.     

丙型肝炎病毒RNA打点杂交检测方法同RT－PCR方法的比较

采用ＨＣＶ基因组结构区Ｃ区ｃＤＮＡ探针和非结构区ＮＳ３－４区ｃＤＮＡ探针,建立了用打点杂交（ｄｏｔｂｌｏｔｈｙｂｒｉｄｉｚａｔｉｏｎ）检测血清中ＨＣＶＲＮＡ的方法,同采用ＨＣＶ基因组５’端非编码区的一对寡核苷酸引物通过逆转录－聚合酶链式反应（ＲＴ－ＰＣＲ）检测血清中ＨＣＶＲＮＡ的方法相比较,发现两种方法都能快速早期和特异地检出血清中ＨＣＶＲＮＡ,但ＲＴ－ＰＣＲ法敏感性优于ＲＮＡ打点杂交法。对于无血清学指标的慢性ＮＡＮＢ肝炎病人的诊断,可采用这两种方法。这两种方法的敏感性在很大程度上依赖于引物和探针的敏感性,以及ＲＮＡ提取方法。ＲＴ－ＰＣＲ法适用于诊断病毒血症和复制,打点杂交法适用于研究ＨＣＶＲＮＡ量的变化,对治疗的评价,以及为实验筛选较高滴度的ＨＣＶＲＮＡ阳性样本。     

n-Dodecyl β-D-maltoside specifically competes with general anesthetics for anesthetic binding sites

We recently demonstrated that the anionic detergent sodium dodecyl sulfate (SDS) specifically interacts with the anesthetic binding site in horse spleen apoferritin, a soluble protein which models anesthetic binding sites in receptors. This raises the possibility of other detergents similarly interacting with and occluding such sites from anesthetics, thereby preventing the proper identification of novel anesthetic binding sites. n-Dodecyl β-D-maltoside (DDM) is a non-ionic detergent commonly used during protein-anesthetic studies because of its mild and non-denaturing properties. In this study, we demonstrate that SDS and DDM occupy anesthetic binding sites in the model proteins human serum albumin (HSA) and horse spleen apoferritin and thereby inhibit the binding of the general anesthetics propofol and isoflurane. DDM specifically interacts with HSA (K_d?=?40?μM) with a lower affinity than SDS (K_d?=?2?μM). DDM exerts all these effects while not perturbing the native structures of either model protein. Computational calculations corroborated the experimental results by demonstrating that the binding sites for DDM and both anesthetics on the model proteins overlapped. Collectively, our results indicate that DDM and SDS specifically interact with anesthetic binding sites and may thus prevent the identification of novel anesthetic sites. Special precaution should be taken when undertaking and interpreting results from protein-anesthetic investigations utilizing detergents like SDS and DDM.     

A combined steepest descent and genetic algorithm (SD/GA) approach for the optimization of solvation parameters

An accurate solvation model is essential for computer modeling of protein folding and other biomolecular self-assembly processes. Compared to explicit solvent models, implicit solvent models, such as the Poisson-Boltzmann (PB) with solvent accessible surface area model (PB/SA), offer a much faster speed—the most compelling reason for the popularity of these implicit solvent models. Since these implicit solvent models typically use empirical parameters, such as atomic radii and the surface tensions, an optimal fit of these parameters is crucial for the final accuracy of properties such as solvation free energy and folding free energy. In this paper, we proposed a combined approach, namely SD/GA, which takes the advantage of both local optimization with the steepest descent (SD), and global optimization with the genetic algorithm (GA), for parameters optimization in multi-dimensional space. The SD/GA method is then applied to the optimization of solvation parameters in the non-polar cavity term of the PB/SA model. The results show that the newly optimized parameters from SD/GA not only increase the accuracy in the solvation free energies for ～200 organic molecules, but also significantly improve the free energy landscape of a β-hairpin folding. The current SD/GA method can be readily applied to other multi-dimensional parameter space optimization as well.     

Mechanism of flexibility control for ATP access of hepatitis C virus NS3 helicase

Hepatitis C virus (HCV) NS3 helicase couples adenosine triphosphate (ATP) binding and hydrolysis to polynucleotide unwinding. Understanding the regulation mechanism of ATP binding will facilitate targeting of the ATP-binding site for potential therapeutic development for hepatitis C. T324, an amino acid residue connecting domains 1 and 2 of NS3 helicase, has been suggested as part of a flexible hinge for opening of the ATP-binding cleft, although the detailed mechanism remains largely unclear. We used computational simulation to examine the mutational effect of T324 on the dynamics of the ATP-binding site. A mutant model, T324A, of the NS3 helicase apo structure was created and energy was minimized. Molecular dynamics simulation was conducted for both wild type and the T324A mutant apo structures to compare their differences. For the mutant structure, histogram analysis of pairwise distances between residues in domains 1 and 2 (E291-Q460, K210-R464 and R467-T212) showed that separation between the two domains was reduced by ～10% and the standard deviation by ～33%. Root mean square fluctuation (RMSF) analysis demonstrated that residues in close proximity to residue 324 have at least 30% RMSF value reductions in the mutant structure. Solvent RMSF analysis showed that more water molecules were trapped near D290 and H293 in domain 1 to form an extensive interaction network constraining cleft opening. We also demonstrated that the T324A mutation established a new atomic interaction with V331, revealing that an atomic interaction cascade from T324 to residues in domains 1 and 2 controls the flexibility of the ATP-binding cleft.     

A Statistical Framework for Improving Genomic Annotations of Prokaryotic Essential Genes

Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the essential gene annotations are derived from whole-genome transposon mutagenesis (TM), the most frequently used experimental approach for determining essential genes in microorganisms under defined conditions. However, there are substantial systematic biases associated with TM experiments. In this study, we developed a novel Poisson model–based statistical framework to simulate the TM insertion process and subsequently correct the experimental biases. We first quantitatively assessed the effects of major factors that potentially influence the accuracy of TM and subsequently incorporated relevant factors into the framework. Through iteratively optimizing parameters, we inferred the actual insertion events occurred and described each gene’s essentiality on probability measure. Evaluated by the definite mapping of essential gene profile in Escherichia coli, our model significantly improved the accuracy of original TM datasets, resulting in more accurate annotations of essential genes. Our method also showed encouraging results in improving subsaturation level TM datasets. To test our model’s broad applicability to other bacteria, we applied it to Pseudomonas aeruginosa PAO1 and Francisella tularensis novicida TM datasets. We validated our predictions by literature as well as allelic exchange experiments in PAO1. Our model was correct on six of the seven tested genes. Remarkably, among all three cases that our predictions contradicted the TM assignments, experimental validations supported our predictions. In summary, our method will be a promising tool in improving genomic annotations of essential genes and enabling large-scale explorations of gene essentiality. Our contribution is timely considering the rapidly increasing essential gene sets. A Webserver has been set up to provide convenient access to this tool. All results and source codes are available for download upon publication at http://research.cchmc.org/essentialgene/.