刺参池塘底质微生物群落功能多样性的季节变化

利用BIOLOG技术和冗余分析(Redundancy analysis,RDA)方法对刺参(Apostichopus japonicus)养殖池塘底质环境(底泥、附着基)微生物群落功能多样性的季节变化及其与环境因子的关系进行了研究。结果表明:(1)刺参池塘底泥和附着基微生物对碳源总量和单类碳源的利用均具有显著的季节变化,总体表现为春、夏、秋季节高于冬季,其中,底泥微生物利用比例较高的碳源类型为聚合物、糖类、羧酸和氨基酸,附着基微生物利用比例较高的碳源类型为聚合物、糖类、氨基酸和胺。(2)主成分分析表明,刺参池塘底泥和附着基微生物碳代谢方式均具有显著的季节变化。底泥中,与主成分显著相关的碳源有18种,其中与主成分1显著相关的主要是糖类、羧酸和氨基酸,与主成分2显著相关的主要是聚合物和糖类;附着基中,与主成分显著相关的碳源有22种,其中与主成分1显著相关的主要是聚合物、糖类、羧酸和氨基酸,与主成分2显著相关的是羧酸。(3)刺参池塘底泥和附着基微生物多样性指数Shannon、McIntosh、Simpson和S-E均匀度均存在显著的季节变化,但不同指数之间的变化有较大差异。(4)RDA分析表明,TP、NO3-N和PO4-P是影响底泥微生物功能多样性季节变化的主要因素,SOM、NO3-N和TN是影响附着基微生物功能多样性季节变化的主要因素。结论认为,刺参池塘底泥和附着基微生物功能多样性具有显著的不同的季节变化,这些变化与环境因子具有很好的相关性。     

长沙某医院产质粒AmpC酶型肺炎克雷伯菌的多重PCR检测与分析

为了了解湖南长沙某医院临床分离的肺炎克雷伯菌中质粒介导AmpC β-内酰胺酶的产生情况及其基因型,收集了该医院2008年3月至2010年10月临床分离的多重耐药肺炎克雷伯菌104株,用头孢西丁纸片扩散法对这些菌株进行表型初筛,用多重PCR确定ampC耐药基因型;结果发现其中有19株对头孢西丁纸片不敏感,疑为产AmpC酶菌株;再经多重PCR扩增,有12株菌分别在约400 bp（11株）和约350 bp（1株）出现了阳性条带,特异性PCR证明此12株菌分别携带了DHA型（11株）和ACC型（1株）ampC耐药基因;产质粒介导AmpC酶肺炎克雷伯菌的分离率为11.5%（12/104）。该医院产质粒介导AmpC酶肺炎克雷伯菌的分离率较高,应对其检测与监测给予足够重视,以指导临床合理选用抗菌药物。     

艾叶挥发油β-环糊精包合物的制备

为了探索艾叶挥发油β-环糊精包合的最佳生产工艺,提高挥发油在制剂中的稳定性.本文采用正交实验法,通过测定油利用率、包合物收得率及含油率考察包合工艺.结果表明:最佳生产工艺条件:A3 B2 C3(β-环糊精和艾叶挥发油的比例为8:1,油和乙醇的比例为1:1.5,包合温度为60℃,时间为2 h).     

小花蝽对牛角花齿蓟马的捕食作用

在室内研究了小花蝽(Orius minutus)成虫对牛角花齿蓟马(Odontothrips loti)3～4龄若虫的捕食作用和种内干扰作用.结果表明,小花蝽对牛角花齿蓟马的捕食功能反应均符合HollingⅡ型方程,在玻璃试管中,捕食量(N_a)符合Na＝1.0113N／(1＋0.04149N),在培养皿中为N_a＝0.6777N／(1＋0.03395N),在笼罩花盆中为N_a＝0.6417 N／(1＋0.03934 N).小花蝽的捕食作用有较强的种内干扰反应,捕食率与个体相互干扰的关系符合Hassell模型.在相同空间条件下,小花蝽的捕食量与猎物密度呈正相关,寻找效应与猎物密度呈负相关;随着空间增大,小花蝽对牛角花齿蓟马若虫的瞬间攻击率(a′)和最大捕食量(N_a)均下降,处置时间(T_h)则延长,但其功能反应类型不变,仍为HollingⅡ型.     

New insights into the mechanism of arsenite methylation with the recombinant human arsenic (+3) methyltransferase (hAS3MT)

The catalytic mechanism of the recombinant human arsenic (+3) methyltransferase (hAS3MT) was studied using kinetics, initial velocity and spectroscopy. The production and the distribution of methylated arsenicals changed with various concentrations of arsenite/S-adenosyl-l-methionine (SAM)/thiols, enzyme contents, and incubation times. These results suggest a sequential methylation of arsenite to monomethylated arsenicals (MMA) and dimethylated arsenicals (DMA). In addition, competition exists between the two reactions. hAS3MT showed the greatest activity at pH 8.5 with glutathione (GSH) as the reductant. This might indicate that a balance between the deprotonation and protonation of sulfhydryl groups is required. Initial velocity studies illuminate an ordered sequence for the binding of SAM and arsenite to the hAS3MT; while GSH should probably be placed either as the first reactant or as a reactant combining with the enzyme only after products have been released. The interactions between substrate/cofactors and the hAS3MT were first monitored by UV-visible and circular dichroism spectroscopy. It revealed that arsenite and SAM combined with the hAS3MT before reaction started; whereas, no interactions between GSH and the hAS3MT were detected. Integrating the results from kinetics, initial velocity and spectroscopy studies, an ordered mechanism are originally attained, with the SAM as the first reactant that adds to the hAS3MT and arsenite as the second one. Arsenite is successively methylated reductively, rather than a stepwise oxidative methylation. GSH should combine with the hAS3MT after the methylation to reduce the disulfide bond formed during the catalytic cycle in the hAS3MT to resume the active form of the enzyme.     

Phosphoproteome Analysis of Invasion and Metastasis-Related Factors in Pancreatic Cancer Cells

Mechanisms of abnormal protein phosphorylation that regulate cell invasion and metastasis in pancreatic cancer remain obscure. In this study, we used high-throughput phosphorylation array to test two pancreatic cancer cell lines (PC-1 cells with a low, and PC-1.0 cells with a high potential for invasion and metastasis). We noted that a total of 57 proteins revealed a differential expression (fold change ≥ 2.0). Six candidate proteins were further validated by western blot with results found to be accordance with the array. Of 57 proteins, 32 up-regulated proteins (e.g. CaMK1-α and P90RSK) were mainly involved in ErbB and neurotrophin signaling pathways as determined using DAVID software, while 25 down-regulated proteins (e.g. BID and BRCA1) were closely involved in apoptosis and p53 signaling pathways. Moreover, four proteins (AKT1, Chk2, p53 and P70S6K) with different phosphorylation sites were found, not only among up-regulated, but also among down-regulated proteins. Importantly, specific phosphorylation sites can affect cell biological functions. CentiScaPe software calculated topological characteristics of each node in the protein-protein interaction (PPI) network: we found that AKT1 owns the maximum node degrees and betweenness in the up-regulation protein PPI network (26 nodes, average path length: 1.89, node degrees: 6.62±4.18, betweenness: 22.23±35.72), and p53 in the down-regulation protein PPI network (17 nodes, average path length: 2.04, node degrees: 3.65±2.47, betweenness: 16.59±29.58). In conclusion, the identification of abnormal protein phosphorylation related to invasion and metastasis may allow us to identify new biomarkers in an effort to develop novel therapeutic drug targets for pancreatic cancer treatment.     

Residues in Human Arsenic (+3 Oxidation State) Methyltransferase Forming Potential Hydrogen Bond Network around S-adenosylmethionine

Residues Tyr59, Gly78, Ser79, Met103, Gln107, Ile136 and Glu137 in human arsenic (+3 oxidation state) methyltransferase (hAS3MT) were deduced to form a potential hydrogen bond network around S-adenosylmethionine (SAM) from the sequence alignment between Cyanidioschyzon merolae arsenite S-adenosylmethyltransferase (CmArsM) and hAS3MT. Herein, seven mutants Y59A, G78A, S79A, M103A, Q107A, I136A and E137A were obtained. Their catalytic activities and conformations were characterized and models were built. Y59A and G78A were completely inactive. Only 7.0%, 10.6% and 13.8% inorganic arsenic (iAs) was transformed to monomethylated arsenicals (MMA) when M103A, Q107A and I136A were used as the enzyme. The V_max (the maximal velocity of the reaction) values of M103A, Q107A, I136A and E137A were decreased to 8%, 22%, 15% and 50% of that of WT-hAS3MT, respectively. The K_M(SAM) (the Michaelis constant for SAM) values of mutants M103A, I136A and E137A were 15.7, 8.9 and 5.1 fold higher than that of WT-hAS3MT, respectively, indicating that their affinities for SAM were weakened. The altered microenvironment of SAM and the reduced capacity of binding arsenic deduced from K_M(As) (the Michaelis constant for iAs) value probably synergetically reduced the catalytic activity of Q107A. The catalytic activity of S79A was higher than that of WT despite of the higher K_M(SAM), suggesting that Ser79 did not impact the catalytic activity of hAS3MT. In short, residues Tyr59 and Gly78 significantly influenced the catalytic activity of hAS3MT as well as Met103, Ile136 and Glu137 because they were closely associated with SAM-binding, while residue Gln107 did not affect SAM-binding regardless of affecting the catalytic activity of hAS3MT. Modeling and our experimental results suggest that the adenine ring of SAM is sandwiched between Ile136 and Met103, the amide group of SAM is hydrogen bonded to Gly78 in hAS3MT and SAM is bonded to Tyr59 with van der Waals, cation-π and hydrogen bonding contacts.