GacA对假单胞菌株M18两个phz基因簇和菌群传感的调控

摘要:【目的】假单胞菌株M18（Pseudomonas sp. M18）是从甜瓜根际土壤中分离获得的一株对多种植物病原菌具有显著拮抗作用的菌株,在菌群传感（quorum sensing）系统的调控下,能分泌吩嗪-1-羧酸（PCA）以及多种吩嗪（phz）类衍生物的抗真菌物质。全局性因子GacA是M18菌株吩嗪类物质的合成与菌群传感系统的重要调控因子,本文将就GacA对上述两者的调控做进一步研究。【方法】PCR基因扩增和测序研究M18菌株中PCA合成基因簇,运用RT-PCR及构建phzA-lacZ转录融合手段     

The computational analysis of scientific literature to define and recognize gene expression clusters

A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present a computational method that leverages the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in the analysis of gene expression data offers an opportunity to incorporate functional information about the genes when defining expression clusters. We have created a method that associates gene expression profiles with known biological functions. Our method has two steps. First, we apply hierarchical clustering to the given gene expression data set. Secondly, we use text from abstracts about genes to (i) resolve hierarchical cluster boundaries to optimize the functional coherence of the clusters and (ii) recognize those clusters that are most functionally coherent. In the case where a gene has not been investigated and therefore lacks primary literature, articles about well-studied homologous genes are added as references. We apply our method to two large gene expression data sets with different properties. The first contains measurements for a subset of well-studied Saccharomyces cerevisiae genes with multiple literature references, and the second contains newly discovered genes in Drosophila melanogaster; many have no literature references at all. In both cases, we are able to rapidly define and identify the biologically relevant gene expression profiles without manual intervention. In both cases, we identified novel clusters that were not noted by the original investigators.     

Evolution of Conserved Non-Coding Sequences Within the Vertebrate Hox Clusters Through the Two-Round Whole Genome Duplications Revealed by Phylogenetic Footprinting Analysis

As a result of two-round whole genome duplications, four or more paralogous Hox clusters exist in vertebrate genomes. The paralogous genes in the Hox clusters show similar expression patterns, implying shared regulatory mechanisms for expression of these genes. Previous studies partly revealed the expression mechanisms of Hox genes. However, cis-regulatory elements that control these paralogous gene expression are still poorly understood. Toward solving this problem, the authors searched conserved non-coding sequences (CNSs), which are candidates of cis-regulatory elements. When comparing orthologous Hox clusters of 19 vertebrate species, 208 intergenic conserved regions were found. The authors then searched for CNSs that were conserved not only between orthologous clusters but also among the four paralogous Hox clusters. The authors found three regions that are conserved among all the four clusters and eight regions that are conserved between intergenic regions of two paralogous Hox clusters. In total, 28 CNSs were identified in the paralogous Hox clusters, and nine of them were newly found in this study. One of these novel regions bears a RARE motif. These CNSs are candidates for gene expression regulatory regions among paralogous Hox clusters. The authors also compared vertebrate CNSs with amphioxus CNSs within the Hox cluster, and found that two CNSs in the HoxA and HoxB clusters retain homology with amphioxus CNSs through the two-round whole genome duplications.     

AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides

Background

A tremendous amount of efforts have been devoted to identifying genes for diagnosis and prognosis of diseases using microarray gene expression data. It has been demonstrated that gene expression data have cluster structure, where the clusters consist of co-regulated genes which tend to have coordinated functions. However, most available statistical methods for gene selection do not take into consideration the cluster structure.

Results

We propose a supervised group Lasso approach that takes into account the cluster structure in gene expression data for gene selection and predictive model building. For gene expression data without biological cluster information, we first divide genes into clusters using the K-means approach and determine the optimal number of clusters using the Gap method. The supervised group Lasso consists of two steps. In the first step, we identify important genes within each cluster using the Lasso method. In the second step, we select important clusters using the group Lasso. Tuning parameters are determined using V-fold cross validation at both steps to allow for further flexibility. Prediction performance is evaluated using leave-one-out cross validation. We apply the proposed method to disease classification and survival analysis with microarray data.

Conclusion

We analyze four microarray data sets using the proposed approach: two cancer data sets with binary cancer occurrence as outcomes and two lymphoma data sets with survival outcomes. The results show that the proposed approach is capable of identifying a small number of influential gene clusters and important genes within those clusters, and has better prediction performance than existing methods.     

Nucleotide sequence of the regulatory region of malB operons in E. coli

The nucleotide sequence of a cloned section of the Escherichia coli chromosome containing the promoter regions of the malB divergent operons was determined. The region of the proximal gene, malE of the malEFG operon, was identified on the basis of the known amino acid sequence of the precursor molecule of maltose-binding protein. The region of malK, the proximal gene of the malKlamB operon, was deduced from the observation that a cloned segment contains an amino-terminal portion of the malK gene. The non-coding region between malE and malK is 299 base pairs long and contains two long GC clusters. Another feature of this region that may be related to the regulation of gene expression is the presence of two palindromic structures between the GC clusters. The DNA regions binding to cyclic AMP binding protein were determined by a method using polyacrylamide gel electrophoresis. The sites are thought to be located close to GC clusters.     

Genetic and biochemical aspects of keratin synthesis by hair follicles

In this review article the data about synthesis and gene regulation of keratin by hair follicles have been summarized. It has been shown that both differentiation of hair follicle matrix cells and normal growth of hair require the coordinated activities of the genes encoding structural proteins. The keratin genes are clustered in families and are usually 5-10 kb in the genome. The separate clusters of two keratin IF gene families and five KAP gene families have been discovered and some of them have been mapped. The close relation between these clusters suggests that the "global" regulatory domains might govern their expression.     

Identification of nocobactin NA biosynthetic gene clusters in Nocardia farcinica

We identified the biosynthetic gene clusters of the siderophore nocobactin NA. The nbt clusters, which were discovered as genes highly homologous to the mycobactin biosynthesis genes by the genomic sequencing of Nocardia farcinica IFM 10152, consist of 10 genes separately located at two genomic regions. The gene organization of the nbt clusters and the predicted functions of the nbt genes, particularly the cyclization and epimerization domains, were in good agreement with the chemical structure of nocobactin NA. Disruptions of the nbtA and nbtE genes, respectively, reduced and abolished the productivity of nocobactin NA. The heterologous expression of the nbtS gene revealed that this gene encoded a salicylate synthase. These results indicate that the nbt clusters are responsible for the biosynthesis of nocobactin NA. We also found putative IdeR-binding sequences upstream of the nbtA, -G, -H, -S, and -T genes, whose expression was more than 10-fold higher in the low-iron condition than in the high-iron condition. These results suggest that nbt genes are regulated coordinately by IdeR protein in an iron-dependent manner. The ΔnbtE mutant was found to be impaired in cytotoxicity against J774A.1 cells, suggesting that nocobactin NA production is required for virulence of N. farcinica.     

The SphS-SphR two component system is the exclusive sensor for the induction of gene expression in response to phosphate limitation in synechocystis

Living organisms respond to phosphate limitation by expressing various genes whose products maintain an appropriate range of phosphate concentrations within each cell. We identified previously a two component system, which consists of histidine kinase SphS and its cognate response regulator SphR, which regulates the expression of the phoA gene for alkaline phosphatase under phosphate-limiting conditions in the cyanobacterium Synechocystis sp. PCC 6803. In the present study, we used DNA microarrays to investigate the role of SphS and SphR in the regulation of the genome-wide expression of genes in response to phosphate limitation. In wild-type cells, phosphate limitation strongly induced the expression of 12 genes with induction factors greater than 7. These genes were included in three clusters of genes, namely, the pst1 and pst2 clusters that encode phosphate transporters; the phoA gene and the nucH gene for the extracellular nuclease. Phosphate limitation strongly repressed the expression of only the urtA gene with induction factors below 0.2. Inactivation of either of SphS or SphR completely eliminated the phosphate limitation-inducible expression of the 12 genes and the phosphate limitation-repressible expression of the urtA gene. These results suggest that the SphS-SphR two component system in Synechocystis sp. PCC 6803 is the dominant sensory system that controls gene expression in response to phosphate limitation.     

Evaluation and optimization of clustering in gene expression data analysis

MOTIVATION: A measurement of cluster quality is needed to choose potential clusters of genes that contain biologically relevant patterns of gene expression. This is strongly desirable when a large number of gene expression profiles have to be analyzed and proper clusters of genes need to be identified for further analysis, such as the search for meaningful patterns, identification of gene functions or gene response analysis. RESULTS: We propose a new cluster quality method, called stability, by which unsupervised learning of gene expression data can be performed efficiently. The method takes into account a cluster's stability on partition. We evaluate this method and demonstrate its performance using four independent, real gene expression and three simulated datasets. We demonstrate that our method outperforms other techniques listed in the literature. The method has applications in evaluating clustering validity as well as identifying stable clusters. AVAILABILITY: Please contact the first author.     

Genetic organization of the Escherichia coli K10 capsule gene cluster: identification and characterization of two conserved regions in group III capsule gene clusters encoding polysaccharide transport functions

Analysis of the Escherichia coli K10 capsule gene cluster identified two regions, regions 1 and 3, conserved between different group III capsule gene clusters. Region 1 encodes homologues of KpsD, KpsM, KpsT, and KpsE proteins, and region 3 encodes homologues of the KpsC and KpsS proteins. An rfaH mutation abolished K10 capsule production, suggesting that expression of the K10 capsule was regulated by RfaH in a manner analogous to group II capsule gene clusters. An IS3 element and a phiR73-like prophage, both of which may have played a role in the acquisition of group III capsule gene clusters, were detected flanking the K10 capsule genes.     

Comparisons of graph-structure clustering methods for gene expression data

Although many numerical clustering algorithms have been applied to gene expression dataanalysis,the essential step is still biological interpretation by manual inspection.The correlation betweengenetic co-regulation and affiliation to a common biological process is what biologists expect.Here,weintroduce some clustering algorithms that are based on graph structure constituted by biological knowledge.After applying a widely used dataset,we compared the result clusters of two of these algorithms in terms ofthe homogeneity of clusters and coherence of annotation and matching ratio.The results show that theclusters of knowledge-guided analysis are the kernel parts of the clusters of Gene Ontology (GO)-Clustersoftware,which contains the genes that are most expression correlative and most consistent with biologicalfunctions.Moreover,knowledge-guided analysis seems much more applicable than GO-Cluster in a largerdataset.     

Globoside-specific adhesins of uropathogenic Escherichia coli are encoded by similar trans-complementable gene clusters.

Uropathogenic Escherichia coli frequently express globoside-specific adhesins, shown to mediate binding to uroepithelial cells. For one gene cluster pap, it recently has been demonstrated that globoside binding is not dependent on expression of the pilus subunit gene papA. Instead, two other pap genes papF and papG are specifically required for globoside binding (F. P. Lindberg et al., EMBO J. 3:1167-1173, 1984). By restriction enzyme mapping, DNA hybridization, DNA sequencing, and protein expression in minicells, we show that three gene clusters encoding globoside binding have a very similar structure and gene organization, although they were cloned from different E. coli isolates. Major differences between the adhesin clones were restricted to the central part of the pilin gene (papA) and to one of the two adhesin gene (papG). The three functional units required for biogenesis of globoside-binding pili, i.e., pilin synthesis, pilin export, and pilin assembly, as well as expression of adhesion function, were all trans complementable among the gene clusters.