Genome-wide mapping of methylated adenine residues in pathogenic Escherichia coli using single-molecule real-time sequencing

1] Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA. [2].
Nature Biotechnology (Impact Factor: 41.51). 11/2012; 31(6). DOI: 10.1038/nbt.2432
Source: PubMed


Single-molecule real-time (SMRT) DNA sequencing allows the systematic detection of chemical modifications such as methylation but has not previously been applied on a genome-wide scale. We used this approach to detect 49,311 putative 6-methyladenine (m6A) residues and 1,407 putative 5-methylcytosine (m5C) residues in the genome of a pathogenic Escherichia coli strain. We obtained strand-specific information for methylation sites and a quantitative assessment of the frequency of methylation at each modified position. We deduced the sequence motifs recognized by the methyltransferase enzymes present in this strain without prior knowledge of their specificity. Furthermore, we found that deletion of a phage-encoded methyltransferase-endonuclease (restriction-modification; RM) system induced global transcriptional changes and led to gene amplification, suggesting that the role of RM systems extends beyond protecting host genomes from foreign DNA.

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Available from: Diana Munera, Nov 12, 2014
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    • "Several methods are available to study DNA methylation such as bisulphite treatment, HPLC, and microarrays, although it is challenging to detect 6 mA methylation patterns using any of these methods [43]. A certain minimum sequencing coverage is necessary for methylome analysis and several recent studies have demonstrated the advantages of the use of SMRT sequencing technology [44–46]. The methylated motifs detected were all shown to represent adenine-specific methylation. "
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    ABSTRACT: Background So-called 936-type phages are among the most frequently isolated phages in dairy facilities utilising Lactococcus lactis starter cultures. Despite extensive efforts to control phage proliferation and decades of research, these phages continue to negatively impact cheese production in terms of the final product quality and consequently, monetary return. Results Whole genome sequencing and in silico analysis of three 936-type phage genomes identified several putative (orphan) methyltransferase (MTase)-encoding genes located within the packaging and replication regions of the genome. Utilising SMRT sequencing, methylome analysis was performed on all three phages, allowing the identification of adenine modifications consistent with N-6 methyladenine sequence methylation, which in some cases could be attributed to these phage-encoded MTases. Heterologous gene expression revealed that M.Phi145I/M.Phi93I and M.Phi93DAM, encoded by genes located within the packaging module, provide protection against the restriction enzymes HphI and DpnII, respectively, representing the first functional MTases identified in members of 936-type phages. Conclusions SMRT sequencing technology enabled the identification of the target motifs of MTases encoded by the genomes of three lytic 936-type phages and these MTases represent the first functional MTases identified in this species of phage. The presence of these MTase-encoding genes on 936-type phage genomes is assumed to represent an adaptive response to circumvent host encoded restriction-modification systems thereby increasing the fitness of the phages in a dynamic dairy environment.
    BMC Genomics 10/2014; 15(1). DOI:10.1186/1471-2164-15-831 · 3.99 Impact Factor
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    • " acquisitions of for - eign genetic elements . Functional studies of SPI - 5 with and without these GIs might be important to examine the possible role of both SPI5 - GIs . Both SPI5 - GIs contained genes en - coding a methylase , which could potentially regulate chro - mosome replication , cell cycle events , pathogenicity , and gene expression ( Fang et al . , 2012 ; Davis et al . , 2013 ) . The SPI - 5 genes could be considered targets for re - sequencing and biomarkers to rapidly differentiate lineages II and III . We performed positive selection tests for pipA and pipB using codon - based Z tests in MEGA6 , indicating that these two genes were under positive selection . Positive se - lection pl"
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    ABSTRACT: Abstract Salmonella enterica subspecies enterica serotype Newport is one of the common serotypes causing foodborne salmonellosis outbreaks in the United States. Salmonella Newport consists of three lineages exhibiting extensive genetic diversity. Due to the importance of Salmonella pathogenicity islands 5 and 6 (SPI-5 and SPI-6) in virulence of pathogenic Salmonella, the genetic diversity of these two SPIs may relate to different potentials of Salmonella Newport pathogenicity. Most Salmonella Newport strains from North America belong to Salmonella Newport lineages II and III. A total 28 Salmonella Newport strains of lineages II and III from diverse sources and geographic locations were analyzed, and 11 additional Salmonella genomes were used as outgroup in phylogenetic analyses. SPI-5 was identified in all Salmonella Newport strains and 146 single nucleotide polymorphisms (SNPs) were detected. Thirty-nine lineage-defining SNPs were identified, including 18 nonsynonymous SNPs. Two 40-kb genomic islands (SPI5-GI1 and SPI5-GI2) encoding bacteriophage genes were found between tRNA-ser and pipA. SPI5-GI1 was only present in Salmonella Newport multidrug-resistant strains of lineage II. SPI-6 was found in all strains but three Asian strains in Salmonella Newport lineage II, whereas the three Asian strains carried genomic island SPI6-GI1 at the same locus as SPI-6 in other Salmonella. SPI-6 exhibited 937 SNPs, and phylogenetic analysis demonstrated that clustering of Salmonella Newport isolates was a reflection of their geographic origins. The sequence diversity within SPI-5 and SPI-6 suggests possible recombination events and different virulence potentials of Salmonella Newport. The SNPs could be used as biomarkers during epidemiological investigations.
    Foodborne Pathogens and Disease 09/2014; 11(10). DOI:10.1089/fpd.2014.1784 · 1.91 Impact Factor
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    • "Despite the enormous interest in recent years to characterize the bacterial diversity of the human microbiome, particularly as it relates to gut diseases, the role that DNA methylation plays in function of the microbiome remains unknown. The PacBio RS II system is capable of detecting DNA methylation through analysis of polymerase kinetics (Flusberg et al., 2010; Clark et al., 2012; Fang et al., 2012; Murray et al., 2012). This technology was used here to discover the profound differences in the extent of Dam methylation in one dominant bacterial species in the gut between two children at high genetic risk for type 1 diabetes and 1 year prior to the development of type 1 diabetes autoimmunity in one of these children. "
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    ABSTRACT: Despite the large interest in the human microbiome in recent years, there are no reports of bacterial DNA methylation in the microbiome. Here metagenomic sequencing using the Pacific Biosciences platform allowed for rapid identification of bacterial GATC methylation status of a bacterial species in human stool samples. For this work, two stool samples were chosen that were dominated by a single species, Bacteroides dorei. Based on 16S rRNA analysis, this species represented over 45% of the bacteria present in these two samples. The B. dorei genome sequence from these samples was determined and the GATC methylation sites mapped. The Bacteroides dorei genome from one subject lacked any GATC methylation and lacked the DNA adenine methyltransferase genes. In contrast, B. dorei from another subject contained 20,551 methylated GATC sites. Of the 4970 open reading frames identified in the GATC methylated B. dorei genome, 3184 genes were methylated as well as 1735 GATC methylations in intergenic regions. These results suggest that DNA methylation patterns are important to consider in multi-omic analyses of microbiome samples seeking to discover the diversity of bacterial functions and may differ between disease states.
    Frontiers in Microbiology 07/2014; 5:361. DOI:10.3389/fmicb.2014.00361 · 3.99 Impact Factor
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