不同光质对毛脉酸模中蒽醌类成分的影响

通过红色、黄色、绿色、蓝色滤光膜对毛脉酸模进行遮膜处理。采用高效液相色谱法对毛脉酸模根样品中的蒽醌类成分含量进行测定,研究光质对毛脉酸模根次生代谢产物蒽醌类成分的影响。进行了方差分析。二年生黄膜处理中蒽醌类成分含量显著性高于处理组及空白组。一年生空白组及对照组中蒽醌类成分含量显著性高于处理组。结果表明黄膜处理显著提高二年生毛脉酸模中中蒽醌类成分含量。     

鲟源致病性豚鼠气单胞菌的分离及其生长特性

从患细菌性败血综合征的杂交鲟(Husohuso♀×Acipense rruthenus♂)肝和达氏鳇(H.dauricus)腹水中共分离出4株优势菌,经人工回归感染实验测定了其致病性,并通过ATB细菌鉴定仪对致病菌株进行了鉴定,此外,研究了致病菌株的生长特性。实验结果表明,菌株XL2-T对杂交鲟和达氏鳇具有致病性。经鉴定,菌株XL2-T为豚鼠气单胞菌(Aeromonas punctata caviae);其在无菌营养肉汤中摇床振荡培养时的生长曲线:0～1.5h为生长延迟期,1.5～28h为对数生长期,28～32h为稳定期,32h以后为衰亡期;其最适生长温度为25～30℃,最适生长pH为7,在NaCl含量范围0～10%、沙拉沙星浓度范围0～40μg/ml下均能够生长,但NaCl、沙拉沙星对其生长具有较大的抑制作用。     

A comparative analysis of genetic diversity in medicinal Chrysanthemum morifolium based on morphology, ISSR and SRAP markers

The diversity and genetic relationship among 29 populations of Chrysanthemum morifolium, one of Chrysanthemum indicum and one of Chrysanthemum nankingense from China were analyzed using morphological traits and molecular markers. Twenty morphological traits were scored as well as 182 ISSR marker-fragments, as amplified by 22 primers [the percentage of polymorphic bands (PPB): 81.87%], and 243 SRAP marker-fragments as generated by 26 primer pairs (PPB: 75.72%). Mantel’s test indicated significant correlation (r = 0.624) of morphological trait and SRAP. By contrast, the morphological trait showed low correlation with ISSR (r = 0.246). Cluster analysis showed groupings of the accessions according to all four methods correlated well with their geographic region of origin, and most populations from the south of China were classified into one cluster and most populations from the north of China were classified into another cluster. Finally, an appropriate strategy for conserving the C. morifolium germplasm was proposed.     

Identification of genes differentially expressed in hemocytes of Scylla paramamosain in response to lipopolysaccharide

Although the crab Scylla paramamosain has been cultured in China for a long time, little knowledge is available on how crabs respond to infection by bacteria. A forward suppression subtractive hybridization (SSH) cDNA library was constructed from their hemocytes and the up-regulated genes were identified in order to isolate differentially expressed genes in S. paramamosain in response to bacterial lipopolysaccharide (LPS). A total of 721 clones on the middle scale in the SSH library were sequenced. Among these genes, 271 potentially functional genes were recognized based on the BLAST searches in NCBI and were categorized into seven groups in association with different biological processes using AmiGO against the Gene Ontology database. Of the 271 genes, 269 translatable DNA sequences were predicted to be proteins, and the putative amino acid sequences were searched for conserved domains and proteins using the CD-Search service and BLASTp. Among 271 genes, 179 (66.1%) were annotated to be involved in different biological processes, while 92 genes (33.9%) were classified as an unknown-function gene group. It was noted that only 18 of the 271 genes (6.6%) had previously been reported in other crustaceans and most of the screened genes showed less similarity to known sequences based on BLASTn results, suggesting that 253 genes were found for the first time in S. paramamosain. Furthermore, two up-regulated genes screened from the SSH library were selected for full-length cDNA sequence cloning and in vivo expression study, including Sp-superoxide dismutase (Sp-Cu-ZnSOD) gene and Sp-serpin gene. The differential expression pattern of the two genes during the time course of LPS challenge was analyzed using real-time PCR. We found that both genes were significantly expressed in LPS-challenged crabs in comparison with control. Taken together, the study primarily provides the data of the up-regulated genes associated with different biological processes in S. paramamosain in response to LPS, by which the interesting genes or proteins potentially involved in the innate immune defense of S. paramamosain will be investigated in future.     

Analysis and prediction of translation rate based on sequence and functional features of the mRNA

Protein concentrations depend not only on the mRNA level, but also on the translation rate and the degradation rate. Prediction of mRNA's translation rate would provide valuable information for in-depth understanding of the translation mechanism and dynamic proteome. In this study, we developed a new computational model to predict the translation rate, featured by (1) integrating various sequence-derived and functional features, (2) applying the maximum relevance & minimum redundancy method and incremental feature selection to select features to optimize the prediction model, and (3) being able to predict the translation rate of RNA into high or low translation rate category. The prediction accuracies under rich and starvation condition were 68.8% and 70.0%, respectively, evaluated by jackknife cross-validation. It was found that the following features were correlated with translation rate: codon usage frequency, some gene ontology enrichment scores, number of RNA binding proteins known to bind its mRNA product, coding sequence length, protein abundance and 5'UTR free energy. These findings might provide useful information for understanding the mechanisms of translation and dynamic proteome. Our translation rate prediction model might become a high throughput tool for annotating the translation rate of mRNAs in large-scale.     

Predicting protein oxidation sites with feature selection and analysis approach

Protein oxidation is a ubiquitous post-translational modification that plays important roles in various physiological and pathological processes. Owing to the fact that protein oxidation can also take place as an experimental artifact or caused by oxygen in the air during the process of sample collection and analysis, and that it is both time-consuming and expensive to determine the protein oxidation sites purely by biochemical experiments, it would be of great benefit to develop in silico methods for rapidly and effectively identifying protein oxidation sites. In this study, we developed a computational method to address this problem. Our method was based on the nearest neighbor algorithm in which, however, the maximum relevance minimum redundancy and incremental feature selection approaches were incorporated. From the initial 735 features, 16 features were selected as the optimal feature set. Of such 16 optimized features, 10 features were associated with the position-specific scoring matrix conservation scores, three with the amino acid factors, one with the propensity of conservation of residues on protein surface, one with the side chain count of carbon atom deviation from mean, and one with the solvent accessibility. It was observed that our prediction model achieved an overall success rate of 75.82%, indicating that it is quite encouraging and promising for practical applications. Also, the 16 optimal features obtained through this study may provide useful clues and insights for in-depth understanding the action mechanism of protein oxidation.     

鲟源弗氏柠檬酸杆菌分离鉴定及药敏特性研究

鲟隶属于鲟形目(Acipenseriformes), 是中国近年来养殖发展较快的淡水养殖品种。虽然养殖技术及条件要求较高, 但是由于养殖鲟鱼具有很高的经济价值, 而且生长快、肉质好、全身是宝, 因而受到广大养殖户推崇1。但是近年来随着养殖量的迅速增大, 养殖集约化程度不断提高, 造成鲟养殖环境不断恶化, 使得鲟体质普遍下降, 发病率大增。鲟鱼多发细菌性疾病, 暴发性强, 死亡率高, 主要病原菌包括链球菌等革兰氏阳性菌和气单胞菌属几种27, 因此疾病逐渐成为鲟鱼健康养殖的一大障碍。       

Prediction and analysis of protein methylarginine and methyllysine based on Multisequence features

Protein methylation, one of the most important post-translational modifications, typically takes place on arginine or lysine residue. The reversible modification involves a series of basic cellular processes. Identification of methyl proteins with their sites will facilitate the understanding of the molecular mechanism of methylation. Besides the experimental methods, computational predictions of methylated sites are much more desirable for their convenience and fast speed. Here, we propose a method dedicated to predicting methylated sites of proteins. Feature selection was made on sequence conservation, physicochemical/biochemical properties, and structural disorder by applying maximum relevance minimum redundancy and incremental feature selection methods. The prediction models were built according to nearest the neighbor algorithm and evaluated by the jackknife cross-validation. We built 11 and 9 predictors for methylarginine and methyllysine, respectively, and integrated them to predict methylated sites. As a result, the average prediction accuracies are 74.25%, 77.02% for methylarginine and methyllysine training sets, respectively. Feature analysis suggested evolutionary information, and physicochemical/biochemical properties play important roles in the recognition of methylated sites. These findings may provide valuable information for exploiting the mechanisms of methylation. Our method may serve as a useful tool for biologists to find the potential methylated sites of proteins.