Whole Methylome Analysis by Ultra-Deep Sequencing Using Two-Base Encoding

Methylation, the addition of methyl groups to cytosine (C), plays an important role in the regulation of gene expression in both normal and dysfunctional cells. During bisulfite conversion and subsequent PCR amplification, unmethylated Cs are converted into thymine (T), while methylated Cs will not be converted. Sequencing of this bisulfite-treated DNA permits the detection of methylation at specific sites. Through the introduction of next-generation sequencing technologies (NGS) simultaneous analysis of methylation motifs in multiple regions provides the opportunity for hypothesis-free study of the entire methylome. Here we present a whole methylome sequencing study that compares two different bisulfite conversion methods (in solution versus in gel), utilizing the high throughput of the SOLiD™ System. Advantages and disadvantages of the two different bisulfite conversion methods for constructing sequencing libraries are discussed. Furthermore, the application of the SOLiD™ bisulfite sequencing to larger and more complex genomes is shown with preliminary in silico created bisulfite converted reads

Accurate, rapid and high-throughput detection of strain-specific polymorphisms in Bacillus anthracis and Yersinia pestis by next-generation sequencing

Background: In the event of biocrimes or infectious disease outbreaks, high-resolution genetic characterization for identifying the agent and attributing it to a specific source can be crucial for an effective response. Until recently, in-depth genetic characterization required expensive and time-consuming Sanger sequencing of a few strains, followed by genotyping of a small number of marker loci in a panel of isolates at or by gel-based approaches such as pulsed field gel electrophoresis, which by necessity ignores most of the genome. Next-generation, massively parallel sequencing (MPS) technology (specifically the Applied Biosystems sequencing by oligonucleotide ligation and detection (SOLiD™) system) is a powerful investigative tool for rapid, cost-effective and parallel microbial whole-genome characterization. Results: To demonstrate the utility of MPS for whole-genome typing of monomorphic pathogens, four Bacillus anthracis and four Yersinia pestis strains were sequenced in parallel. Reads were aligned to complete reference genomes, and genomic variations were identified. Resequencing of the B. anthracis Ames ancestor strain detected no false-positive single-nucleotide polymorphisms (SNPs), and mapping of reads to the Sterne strain correctly identified 98% of the 133 SNPs that are not clustered or associated with repeats. Three geographically distinct B. anthracis strains from the A branch lineage were found to have between 352 and 471 SNPs each, relative to the Ames genome, and one strain harbored a genomic amplification. Sequencing of four Y. pestis strains from the Orientalis lineage identified between 20 and 54 SNPs per strain relative to the CO92 genome, with the single Bolivian isolate having approximately twice as many SNPs as the three more closely related North American strains. Coverage plotting also revealed a common deletion in two strains and an amplification in the Bolivian strain that appear to be due to insertion element-mediated recombination events. Most private SNPs (that is, a, variant found in only one strain in this set) selected for validation by Sanger sequencing were confirmed, although rare falsepositive SNPs were associated with variable nucleotide tandem repeats. Conclusions: The high-throughput, multiplexing capability, and accuracy of this system make it suitable for rapid whole-genome typing of microbial pathogens during a forensic or epidemiological investigation. By interrogating nearly every base of the genome, rare polymorphisms can be reliably discovered, thus facilitating high-resolution strain tracking and strengthening forensic attribution

Data from: Novel conserved genotypes correspond to antibiotic resistance phenotypes of E. coli clinical isolates

Current efforts to understand antibiotic resistance on the whole genome scale tend to focus on known genes even as high throughput sequencing strategies uncover novel mechanisms. To identify genomic variations associated with antibiotic resistance, we employed a modified genome-wide association study; we sequenced genomic DNA from pools of E. coli clinical isolates with similar antibiotic resistance phenotypes using SOLiD technology to uncover SNPs unanimously conserved in each pool. The multidrug-resistant pools were genotypically similar to SMS-3-5, a previously sequenced multidrug-resistant isolate from a polluted environment. The similarity was evenly spread across the entire genome and not limited to plasmid or pathogenicity island loci. Among the pools of clinical isolates, genomic variation was concentrated adjacent to previously reported inversion and duplication differences between the SMS-3-5 isolate and the drug-susceptible laboratory strain, DH10B. Single nucleotide polymorphisms (SNPs) that result in non-synonymous changes in gyrA (encoding the well-known S83L allele associated with fluoroquinolone resistance), mutM, ligB, and recG were unanimously conserved in every fluoroquinolone-resistant pool. Alleles of the latter three genes are tightly linked among most sequenced E. coli genomes, and had not been implicated in antibiotic resistance previously. The changes in these genes map to amino acid positions in alpha helices that are involved in DNA binding. Plasmid- encoded complementation of null strains with either allelic variant of mutM or ligB resulted in variable responses to ultraviolet light or hydrogen peroxide treatment as markers of induced DNA damage, indicating their importance in DNA metabolism and revealing a potential mechanism for fluoroquinolone resistance. Our approach uncovered evidence that additional DNA binding enzymes may contribute to fluoroquinolone resistance and further implicate environmental bacteria as a reservoir for antibiotic resistance

Raw diBayes SNP calls for clinical isolate pools against three different reference E. coli genomes

The attached zip file contains the raw DiBayes SNP calls for the paper Swick et al, PONE-D-12-36402R1. Novel Conserved Genotypes Correspond to Antibiotic Resistance Phenotypes of E. coli Clinical Isolates. PLOS ONE. It contains 16 folders, each of which follows this structure: $PoolID >$ReferenceGenome > DB > $PoolID >$PoolID_SNP.gff3. The $ReferenceGenome codes correspond to the following NCBI accession numbers: NC_010473 - DH10B, NC_010498 - SMS-3-5, NC_012967 - REL60

Effect of variants on ciprofloxacin, hydrogen peroxide, or ultraviolet light susceptibility.

<p><b>A)</b> Ciprofloxacin (CIP) susceptibility. MICs were measured using E-test. Experiments were repeated twice with identical results. The CIP MIC for the parent strain was 0.016 µg/ml. <b>B)</b> H₂O₂ susceptibility. Cells were grown and exposed to 5 mM H₂O₂ for 20 minutes, then spread onto LB agar. Colonies were counted after overnight incubation at 37°C. Shown are the averages for three independent experiments. *<i>p</i>-value of <0.05. <b>C)</b> UV susceptibility. Cells were spread onto LB agar and exposed to various doses of UV light. Shown is a representative result of three separate experiments.</p

SNPs associated with fluoroquinolone resistance and susceptibility.

<p>Unanimous SNPs that result in non-synonymous changes in genes were computed relative to each of the three reference genomes, SMS-3-5 (multidrug-resistant with high fluoroquinolone MICs), DH10B, and REL606 (both antibiotic susceptible). Genes containing these variants for each reference genome are shown in Venn diagrams. <b>A)</b> Genes with allelic variants that were enriched in the fluoroquinolone-resistant phenotype. Variants that were called in any fluoroquinolone-susceptible pool were subtracted from those found in common among all fluoroquinolone-resistant pools. <b>B)</b> Genes with allelic variants that were enriched in the fluoroquinolone-susceptible phenotype. Underlined genes are encoded on an SMS-3-5-specific plasmid (pSMS35_130).</p