Dataset Information

Computational prediction and experimental validation of novel markers for detection of STEC O157:H7.

ABSTRACT: To identify and assess the novel makers for detection of Shiga toxin producing Escherichia coli (STEC) O157:H7 with an integrated computational and experimental approach.High-throughput NCBI blast (E-value cutoff e-5) was used to search homologous genes among all sequenced prokaryotic genomes of each gene encoded in each of the three strains of STEC O157:H7 with complete genomes, aiming to find unique genes in O157:H7 as its potential markers. To ensure that the identified markers from the three strains of STEC O157:H7 can serve as general markers for all the STEC O157:H7 strains, a genomic barcode approach was used to select the markers to minimize the possibility of choosing a marker gene as part of a transposable element. Effectiveness of the markers predicted was then validated by running polymerase chain reaction (PCR) on 18 strains of O157:H7 with 5 additional genomes used as negative controls.The blast search identified 20, 16 and 20 genes, respectively, in the three sequenced strains of STEC O157:H7, which had no homologs in any of the other prokaryotic genomes. Three genes, wzy, Z0372 and Z0344, common to the three gene lists, were selected based on the genomic barcode approach. PCR showed an identification accuracy of 100% on the 18 tested strains and the 5 controls.The three identified novel markers, wzy, Z0372 and Z0344, are highly promising for the detection of STEC O157:H7, in complementary to the known markers.

SUBMITTER: Wang GQ

PROVIDER: S-EPMC3080728 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computational prediction and experimental validation of novel markers for detection of STEC O157:H7.

Wang Guo-Qing GQ Zhou Feng-Feng FF Olman Victor V Su Ying-Ying YY Xu Ying Y Li Fan F

World journal of gastroenterology 20110401 14

<h4>Aim</h4>To identify and assess the novel makers for detection of Shiga toxin producing Escherichia coli (STEC) O157:H7 with an integrated computational and experimental approach.<h4>Methods</h4>High-throughput NCBI blast (E-value cutoff e-5) was used to search homologous genes among all sequenced prokaryotic genomes of each gene encoded in each of the three strains of STEC O157:H7 with complete genomes, aiming to find unique genes in O157:H7 as its potential markers. To ensure that the ident ...[more]

PMID: 21528067

Similar Datasets

Project description:The objective of this study was to develop a canonical, parsimoniously-informative SNP panel for subtyping Shiga-toxin producing Escherichia coli (STEC) O157:H7 that would be consistent with epidemiological, PFGE, and MLVA clustering of human specimens. Our group had previously identified 906 putative discriminatory SNPs, which were pared down to 391 SNPs based on their prevalence in a test set. The 391 SNPs were screened using a high-throughput form of TaqMan PCR against a set of clinical isolates that represent the most diverse collection of O157:H7 isolates from outbreaks and sporadic cases examined to date. Another 30 SNPs identified by others were also screened using the same method. Two additional targets were tested using standard TaqMan PCR endpoint analysis. These 423 SNPs were reduced to a 32 SNP panel with the almost the same discriminatory value. While the panel partitioned our diverse set of isolates in a manner that was consistent with epidemiological data and PFGE and MLVA phylogenies, it resulted in fewer subtypes than either existing method and insufficient epidemiological resolution in 10 of 47 clusters. Therefore, another round of SNP discovery was undertaken using comparative genomic resequencing of pooled DNA from the 10 clusters with insufficient resolution. This process identified 4,040 potential SNPs and suggested one of the ten clusters was incorrectly grouped. After its removal, there were 2,878 SNPs, of which only 63 were previously identified and 438 occurred across multiple clusters. Among highly clonal bacteria like STEC O157:H7, linkage disequilibrium greatly limits the number of parsimoniously informative SNPs. Therefore, it is perhaps unsurprising that our panel accounted for the potential discriminatory value of numerous other SNPs reported in the literature. We concluded published O157:H7 SNPs are insufficient for effective epidemiological subtyping. However, the 438 multi-cluster SNPs we identified may provide the additional information required.

Project description:The Hedgehog (Hh) signaling pathway, acting through three homologous transcription factors (GLI1, GLI2, GLI3) in vertebrates, plays multiple roles in embryonic organ development and adult tissue homeostasis. At the level of the genome, GLI factors bind to specific motifs in enhancers, some of which are hundreds of kilobases removed from the gene promoter. These enhancers integrate the Hh signal in a context-specific manner to control the spatiotemporal pattern of target gene expression. Importantly, a number of genes that encode Hh pathway molecules are themselves targets of Hh signaling, allowing pathway regulation by an intricate balance of feed-back activation and inhibition. However, surprisingly few of the critical enhancer elements that control these pathway target genes have been identified despite the fact that such elements are central determinants of Hh signaling activity. Recently, ChIP studies have been carried out in multiple tissue contexts using mouse models carrying FLAG-tagged GLI proteins (GLI(FLAG)). Using these datasets, we tested whether a meta-analysis of GLI binding sites, coupled with a machine learning approach, could reveal genomic features that could be used to empirically identify Hh-regulated enhancers linked to loci of the Hh signaling pathway.A meta-analysis of four existing GLI(FLAG) datasets revealed a library of GLI binding motifs that was substantially more restricted than the potential sites predicted by previous in vitro binding studies. A machine learning method (kmer-SVM) was then applied to these datasets and enriched k-mers were identified that, when applied to the mouse genome, predicted as many as 37,000 potential Hh enhancers. For functional analysis, we selected nine regions which were annotated to putative Hh pathway molecules and found that seven exhibited GLI-dependent activity, indicating that they are directly regulated by Hh signaling (78% success rate).The results suggest that Hh enhancer regions share common sequence features. The kmer-SVM machine learning approach identifies those features and can successfully predict functional Hh regulatory regions in genomic DNA surrounding Hh pathway molecules and likely, other Hh targets. Additionally, the library of enriched GLI binding motifs that we have identified may allow improved identification of functional GLI binding sites.

Dataset Information

Computational prediction and experimental validation of novel markers for detection of STEC O157:H7.

Publications

Computational prediction and experimental validation of novel markers for detection of STEC O157:H7.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets