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Identification of genes and genomic islands correlated with high pathogenicity in Streptococcus suis using whole genome tiling microarrays.

ABSTRACT: Streptococcus suis is an important zoonotic pathogen that can cause meningitis and sepsis in both pigs and humans. Infections in humans have been sporadic worldwide but two severe outbreaks occurred in China in recent years, while infections in pigs are a major problem in the swine industry. Some S. suis strains are more pathogenic than others with 2 sequence types (ST), ST1 and ST7, being well recognized as highly pathogenic. We analyzed 31 isolates from 23 serotypes and 25 STs by NimbleGen tiling microarray using the genome of a high pathogenicity (HP) ST1 strain, GZ1, as reference and a new algorithm to detect gene content difference. The number of genes absent in a strain ranged from 49 to 225 with a total of 632 genes absent in at least one strain, while 1346 genes were found to be invariably present in all strains as the core genome of S. suis, accounting for 68% of the GZ1 genome. The majority of genes are located in chromosomal blocks with two or more contiguous genes. Sixty two blocks are absent in two or more strains and defined as regions of difference (RDs), among which 26 are putative genomic islands (GIs). Clustering and statistical analyses revealed that 8 RDs including 6 putative GIs and 21 genes within these RDs are significantly associated with HP. Three RDs encode known virulence related factors including the extracellular factor, the capsular polysaccharide and a SrtF pilus. The strains were divided into 5 groups based on population genetic analysis of multilocus sequence typing data and the distribution of the RDs among the groups revealed gain and loss of RDs in different groups. Our study elucidated the gene content diversity of S. suis and identified genes that potentially promote HP.

SUBMITTER: Zheng X

PROVIDER: S-EPMC3068143 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Identification of genes and genomic islands correlated with high pathogenicity in Streptococcus suis using whole genome tiling microarrays.

Zheng Xiao X Zheng Han H Lan Ruiting R Ye Changyun C Wang Yiting Y Zhang Ji J Jing Huaiqi H Chen Chen C Segura Mariela M Gottschalk Marcelo M Xu Jianguo J

PloS one 20110330 3

Streptococcus suis is an important zoonotic pathogen that can cause meningitis and sepsis in both pigs and humans. Infections in humans have been sporadic worldwide but two severe outbreaks occurred in China in recent years, while infections in pigs are a major problem in the swine industry. Some S. suis strains are more pathogenic than others with 2 sequence types (ST), ST1 and ST7, being well recognized as highly pathogenic. We analyzed 31 isolates from 23 serotypes and 25 STs by NimbleGen til ...[more]

PMID: 21479213

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Project description:With lower manufacturing cost, high spot density, and flexible probe design, genomic tiling microarrays are ideal for comprehensive transcriptome studies. Typically, transcriptome profiling using microarrays involves reverse transcription, which converts RNA to cDNA. The cDNA is then labeled and hybridized to the probes on the arrays, thus the RNA signals are detected indirectly. Reverse transcription is known to generate artifactual cDNA, in particular the synthesis of second-strand cDNA, leading to false discovery of antisense RNA. To address this issue, we have developed an effective method using RNA that is directly labeled, thus by-passing the cDNA generation. This paper describes this method and its application to the mapping of transcriptome profiles.RNA extracted from laboratory cultures of Porphyromonas gingivalis was fluorescently labeled with an alkylation reagent and hybridized directly to probes on genomic tiling microarrays specifically designed for this periodontal pathogen. The generated transcriptome profile was strand-specific and produced signals close to background level in most antisense regions of the genome. In contrast, high levels of signal were detected in the antisense regions when the hybridization was done with cDNA. Five antisense areas were tested with independent strand-specific RT-PCR and none to negligible amplification was detected, indicating that the strong antisense cDNA signals were experimental artifacts.An efficient method was developed for mapping transcriptome profiles specific to both coding strands of a bacterial genome. This method chemically labels and uses extracted RNA directly in microarray hybridization. The generated transcriptome profile was free of cDNA artifactual signals. In addition, this method requires fewer processing steps and is potentially more sensitive in detecting small amount of RNA compared to conventional end-labeling methods due to the incorporation of more fluorescent molecules per RNA fragment.

Project description:BackgroundThere is limited information on the distribution of virulence-associated genes (VAGs) in U.S. Streptococcus suis isolates, resulting in little understanding of the pathogenic potential of these isolates. This lack also reduces our understanding of the epidemiology associated with S. suis in the United States and thus affects the efficiency of control and prevention strategies. In this study we applied whole genome sequencing (WGS)-based approaches for the characterization of S. suis and identification of VAGs.ResultsOf 208 S. suis isolates classified as pathogenic, possibly opportunistic, and commensal pathotypes, the genotype based on the classical VAGs (epf, mrp, and sly encoding the extracellular protein factor, muramidase-release protein, and suilysin, respectively) was identified in 9% (epf+/mrp+/sly+) of the pathogenic pathotype. Using the chi-square test and LASSO regression model, the VAGs ofs (encoding the serum opacity factor) and srtF (encoding sortase F) were selected out of 71 published VAGs as having a significant association with pathotype, and both genes were found in 95% of the pathogenic pathotype. The ofs+/srtF+ genotype was also present in 74% of 'pathogenic' isolates from a separate validation set of isolates. Pan-genome clustering resulted in the differentiation of a group of isolates from five swine production companies into clusters corresponding to clonal complex (CC) and virulence-associated (VA) genotypes. The same CC-VA genotype patterns were identified in multiple production companies, suggesting a lack of association between production company, CC, or VA genotype.ConclusionsThe proposed ofs and srtF genes were stronger predictors for differentiating pathogenic and commensal S. suis isolates compared to the classical VAGs in two sets of U.S. isolates. Pan-genome analysis in combination with metadata (serotype, ST/CC, VA genotype) was illustrated to be a valuable subtyping tool to describe the genetic diversity of S. suis.

Project description:BackgroundMicroRNAs (miRNAs) and trans-acting small-interfering RNAs (tasi-RNAs) are small (20-22 nt long) RNAs (smRNAs) generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs) are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery.Principal findingsWe explored rice (Oryza sativa) sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans) and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis 'orphan' hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM) was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the "ancient" (deeply conserved) class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for "new" rapidly-evolving MIRNA genes.ConclusionsAntisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other kingdoms, which can provide insight into antisense transcription, miRNA evolution, and post-transcriptional gene regulation.

Dataset Information

Identification of genes and genomic islands correlated with high pathogenicity in Streptococcus suis using whole genome tiling microarrays.

Publications

Identification of genes and genomic islands correlated with high pathogenicity in Streptococcus suis using whole genome tiling microarrays.

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