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A Novel Approach to Helicobacter pylori Pan-Genome Analysis for Identification of Genomic Islands.

ABSTRACT: Genomes of a given bacterial species can show great variation in gene content and thus systematic analysis of the entire gene repertoire, termed the pan-genome, is important for understanding bacterial intra-species diversity, population genetics, and evolution. Here, we analyzed the pan-genome from 30 completely sequenced strains of the human gastric pathogen Helicobacter pylori belonging to various phylogeographic groups, focusing on 991 accessory (not fully conserved) orthologous groups (OGs). We developed a method to evaluate the mobility of genes within a genome, using the gene order in the syntenically conserved regions as a reference, and classified the 991 accessory OGs into five classes: Core, Stable, Intermediate, Mobile, and Unique. Phylogenetic networks based on the gene content of Core and Stable classes are highly congruent with that created from the concatenated alignment of fully conserved core genes, in contrast to those of Intermediate and Mobile classes, which show quite different topologies. By clustering the accessory OGs on the basis of phylogenetic pattern similarity and chromosomal proximity, we identified 60 co-occurring gene clusters (CGCs). In addition to known genomic islands, including cag pathogenicity island, bacteriophages, and integrating conjugative elements, we identified some novel ones. One island encodes TerY-phosphorylation triad, which includes the eukaryote-type protein kinase/phosphatase gene pair, and components of type VII secretion system. Another one contains a reverse-transcriptase homolog, which may be involved in the defense against phage infection through altruistic suicide. Many of the CGCs contained restriction-modification (RM) genes. Different RM systems sometimes occupied the same (orthologous) locus in the strains. We anticipate that our method will facilitate pan-genome studies in general and help identify novel genomic islands in various bacterial species.

SUBMITTER: Uchiyama I

PROVIDER: S-EPMC4978471 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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A Novel Approach to Helicobacter pylori Pan-Genome Analysis for Identification of Genomic Islands.

Uchiyama Ikuo I Albritton Jacob J Fukuyo Masaki M Kojima Kenji K KK Yahara Koji K Kobayashi Ichizo I

PloS one 20160809 8

Genomes of a given bacterial species can show great variation in gene content and thus systematic analysis of the entire gene repertoire, termed the pan-genome, is important for understanding bacterial intra-species diversity, population genetics, and evolution. Here, we analyzed the pan-genome from 30 completely sequenced strains of the human gastric pathogen Helicobacter pylori belonging to various phylogeographic groups, focusing on 991 accessory (not fully conserved) orthologous groups (OGs) ...[more]

PMID: 27504980

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Project description:BACKGROUND:Helicobacter pylori is associated with inflammation of different areas, such as the duodenum and stomach, causing gastritis and gastric ulcers leading to lymphoma and cancer. Pathogenic islands are a type of clustered mobile elements ranging from 10-200 Kb contributing to the virulence of the respective pathogen coding for one or more virulence factors. Virulence factors are molecules expressed and secreted by pathogen and are responsible for causing disease in the host. Bacterial genes/virulence factors of the pathogenic islands represent a promising source for identifying novel drug targets. OBJECTIVE:The study aimed at identifying novel drug targets from pathogenic islands in H. pylori. MATERIAL & METHODS:The genome of 23 H. pylori strains were screened for pathogenic islands and bacterial genes/virulence factors to identify drug targets. Protein-protein interactions of drug targets were predicted for identifying interacting partners. Further, host-pathogen interactions of interacting partners were predicted to identify important molecules which are closely associated with gastric cancer. RESULTS:Screening the genome of 23 H. pylori strains revealed 642 bacterial genes/virulence factors in 31 pathogenic islands. Further analysis identified 101 genes which were non-homologous to human and essential for the survival of the pathogen, among them 31 are potential drug targets. Protein-protein interactions for 31 drug targets predicted 609 interacting partners. Predicted interacting partners were further subjected to host-pathogen interactions leading to identification of important molecules like TNF receptor associated factor 6, (TRAF6) and MAPKKK7 which are closely associated with gastric cancer. CONCLUSION:These provocative studies enabled us to identify important molecules in H. pylori and their counter interacting molecules in the host leading to gastric cancer and also a pool of novel drug targets for therapeutic intervention of gastric cancer.

Project description:BACKGROUND:It remains unclear whether the low incidence of gastric cancer in Indonesia is due to low infection rates only or is also related to low Helicobacter pylori pathogenicity. We collected H. pylori strains from the five largest islands in Indonesia and evaluated genetic virulence factors. METHODS:The genotypes of H. pylori virulence factors were determined by polymerase chain reaction (PCR)-based sequencing. Histological severity of the gastric mucosa was classified into 4 grades, according to the updated Sydney system. RESULTS:A total of 44 strains were analyzed. Forty-three (97.7 %) were cagA-positive: 26 (60.5 %) were East-Asian-type-cagA, 9 (20.9 %) were Western-type-cagA, and 8 (18.6 %) were novel ABB-type, most of which were obtained from Papuan. EPIYT sequences were more prevalent than EPIYA sequences (P = 0.01) in the EPIYA-B motif of all types of cagA. The majority of cagA-positive strains (48.8 %, 21/43) had a 6-bp deletion in the first pre-EPIYA region. Subjects infected with East-Asian-type-cagA strains with a 6-bp deletion had significantly lower inflammation and atrophy scores in the corpus than those infected with Western-type-cagA strains (both P = 0.02). In total, 70.4 % of strains possessed the vacA s1m1 genotype and 29.5 % were m2. All strains from peptic ulcer patients were of the iceA1 genotype, which occurred at a significantly higher proportion in peptic ulcer patients than that in gastritis patients (55.3 %, P = 0.04). The double positive genotype of jhp0562/?-(1,3)galT was predominant (28/44, 63.6 %), and subjects infected with this type had significantly higher inflammation scores in the corpus than those with the jhp0562 negative/?-(1,3)galT positive genotype (mean [median]; 1.43 [1] vs. 0.83 [1], P = 0.04). There were significant differences in cagA and pre-EPIYA cagA type, oipA status, and jhp0562/?-(1,3)galT type among different ethnic groups (P < 0.05). CONCLUSIONS:In addition to a low H. pylori infection rate, the low incidence of gastric cancer in Indonesia might be attributed to less virulent genotypes in predominant strains, which are characterized by the East-Asian-type-cagA with a 6-bp deletion and EPIYT motif, a high proportion of m2, dupA negative or short type dupA, and the jhp0562/?-(1,3)galT double positive genotype.

Project description:BackgroundAlthough previous epidemiological studies have examined the potential risk factors that increase the likelihood of acquiring Helicobacter pylori infections, most of these analyses have utilized conventional statistical models, including logistic regression, and have not benefited from advanced machine learning techniques.ObjectiveWe examined H. pylori infection risk factors among school children using machine learning algorithms to identify important risk factors as well as to determine whether machine learning can be used to predict H. pylori infection status.MethodsWe applied feature selection and classification algorithms to data from a school-based cross-sectional survey in Ethiopia. The data set included 954 school children with 27 sociodemographic and lifestyle variables. We conducted five runs of tenfold cross-validation on the data. We combined the results of these runs for each combination of feature selection (e.g., Information Gain) and classification (e.g., Support Vector Machines) algorithms.ResultsThe XGBoost classifier had the highest accuracy in predicting H. pylori infection status with an accuracy of 77%-a 13% improvement from the baseline accuracy of guessing the most frequent class (64% of the samples were H. Pylori negative.) K-Nearest Neighbors showed the worst performance across all classifiers. A similar performance was observed using the F1-score and area under the receiver operating curve (AUROC) classifier evaluation metrics. Among all features, place of residence (with urban residence increasing risk) was the most common risk factor for H. pylori infection, regardless of the feature selection method choice. Additionally, our machine learning algorithms identified other important risk factors for H. pylori infection, such as; electricity usage in the home, toilet type, and waste disposal location. Using a 75% cutoff for robustness, machine learning identified five of the eight significant features found by traditional multivariate logistic regression. However, when a lower robustness threshold is used, machine learning approaches identified more H. pylori risk factors than multivariate logistic regression and suggested risk factors not detected by logistic regression.ConclusionThis study provides evidence that machine learning approaches are positioned to uncover H. pylori infection risk factors and predict H. pylori infection status. These approaches identify similar risk factors and predict infection with comparable accuracy to logistic regression, thus they could be used as an alternative method.

Dataset Information

A Novel Approach to Helicobacter pylori Pan-Genome Analysis for Identification of Genomic Islands.

Publications

A Novel Approach to Helicobacter pylori Pan-Genome Analysis for Identification of Genomic Islands.

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