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

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

Relationship of pool consensus sequences to susceptible genome DH10B and resistant genome SMS-3-5.

<p><b>A)</b> Total numbers of unanimous SNPs in each pool relative to DH10B and SMS-3-5. <b>B)</b> The similarity of each pool to DH10B and SMS-3-5. The logarithm of (unanimous SNPs relative to SMS-3-5/unanimous SNPs relative to DH10B) was plotted for each pool. Blue bars are “S” pools; red bars are “H” pools; gray bars are “M” pools. <b>C)</b> Dot plot of genomic similarity for each pool. The genomic similarity of each pool was calculated relative to each DH10B and SMS-3-5, resulting in a hierarchical clustering of the pools. Blue squares are “S” pools; red squares are “H” pools; black squares are “M” pools.</p

Antibiotic resistance phenotypes of pools.

<p>Individual isolates were pooled by <i>k</i>-means clustering and consensus resistance phenotypes (defined as the resistance profile of a majority of the isolates in a pool) were visually confirmed. Pools labeled “S” contain strains that are susceptible to fluoroquinolones and non-multidrug-resistant (non-MDR). Pools labeled “M” contain strains that are resistant to fluoroquinolones and may be MDR_≥3 (resistant to at least 3 separate drug classes) or MDR_≥5 (resistant to at least 5 separate drug classes) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065961#pone.0065961-Swick1" target="_blank">[21]</a>. Pools labeled “H” contain strains that are MDR_≥3 or MDR_≥5 and have extremely high fluoroquinolone MICs. CIP = ciprofloxacin; GAT = gatifloxacin; LVX = levofloxacin; NOR = norfloxacin; AMP = ampicillin; SXT = trimethoprim-sulfamethoxazole; FQs = All four fluoroquinolones; FOX = cefoxitin; CFZ = cefazolin; GEN = gentamicin; TIM-ticarcillin-clavulanic acid; AMC = amoxicillin-clavulanic acid; NIT = nitrofurantoin; AMK = amikacin; IPM = imipenem.</p

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

Sequencing and curation strategies for identifying candidate glioblastoma treatments

Abstract Background Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians. Methods A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions. Results WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time. Conclusion These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments