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Performance Characteristics of Next-Generation Sequencing for the Detection of Antimicrobial Resistance Determinants in Escherichia coli Genomes and Metagenomes.

ABSTRACT: Short-read sequencing can provide detection of multiple genomic determinants of antimicrobial resistance from single bacterial genomes and metagenomic samples. Despite its increasing application in human, animal, and environmental microbiology, including human clinical trials, the performance of short-read Illumina sequencing for antimicrobial resistance gene (ARG) detection, including resistance-conferring single nucleotide polymorphisms (SNPs), has not been systematically characterized. Using paired-end 2 × 150 bp (base pair) Illumina sequencing and an assembly-based method for ARG prediction, we determined sensitivity, positive predictive value (PPV), and sequencing depths required for ARG detection in an Escherichia coli isolate of sequence type (ST) 38 spiked into a synthetic microbial community at varying abundances. Approximately 300,000 reads or 15× genome coverage was sufficient to detect ARGs in E. coli ST38, with comparable sensitivity and PPV to ~100× genome coverage. Using metagenome assembly of mixed microbial communities, ARG detection at E. coli relative abundances of 1% would require assembly of approximately 30 million reads to achieve 15× target coverage. The minimum sequencing depths were validated using public data sets of 948 E. coli genomes and 10 metagenomic rectal swab samples. A read-based approach using k-mer alignment (KMA) for ARG prediction did not substantially improve minimum sequencing depths for ARG detection compared to assembly of the E. coli ST38 genome or the combined metagenomic samples. Analysis of sequencing depths from recent studies assessing ARG content in metagenomic samples demonstrated that sequencing depths had a median estimated detection frequency of 84% (interquartile range: 30%-92%) for a relative abundance of 1%. IMPORTANCE Systematically determining Illumina sequencing performance characteristics for detection of ARGs in metagenomic samples is essential to inform study design and appraisal of human, animal, and environmental metagenomic antimicrobial resistance studies. In this study, we quantified the performance characteristics of ARG detection in E. coli genomes and metagenomes and established a benchmark of ~15× coverage for ARG detection for E. coli in metagenomes. We demonstrate that for low relative abundances, sequencing depths of ~30 million reads or more may be required for adequate sensitivity for many applications.

SUBMITTER: Rooney AM

PROVIDER: S-EPMC9238399 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Escherichia coli is an important cause of bacterial infections worldwide, with multidrug-resistant strains incurring substantial costs on human lives. Besides therapeutic concentrations of antimicrobials in health care settings, the presence of subinhibitory antimicrobial residues in the environment and in clinics selects for antimicrobial resistance (AMR), but the underlying genetic repertoire is less well understood. Here, we used machine learning to predict the population doubling time and cell growth yield of 1,407 genetically diverse E. coli strains expanding under exposure to three subinhibitory concentrations of six classes of antimicrobials from single-nucleotide genetic variants, accessory gene variation, and the presence of known AMR genes. We predicted cell growth yields in the held-out test data with an average correlation (Spearman's ρ) of 0.63 (0.36 to 0.81 across concentrations) and cell doubling times with an average correlation of 0.59 (0.32 to 0.92 across concentrations), with moderate increases in sample size unlikely to improve predictions further. This finding points to the remaining missing heritability of growth under antimicrobial exposure being explained by effects that are too rare or weak to be captured unless sample size is dramatically increased, or by effects other than those conferred by the presence of individual single-nucleotide polymorphisms (SNPs) and genes. Predictions based on whole-genome information were generally superior to those based only on known AMR genes and were accurate for AMR resistance at therapeutic concentrations. We pinpointed genes and SNPs determining the predicted growth and thereby recapitulated many known AMR determinants. Finally, we estimated the effect sizes of resistance genes across the entire collection of strains, disclosing the growth effects for known resistance genes in each individual strain. Our results underscore the potential of predictive modeling of growth patterns from genomic data under subinhibitory concentrations of antimicrobials, although the remaining missing heritability poses a challenge for achieving the accuracy and precision required for clinical use. IMPORTANCE Predicting bacterial growth from genome sequences is important for a rapid characterization of strains in clinical diagnostics and to disclose candidate novel targets for anti-infective drugs. Previous studies have dissected the relationship between bacterial growth and genotype in mutant libraries for laboratory strains, yet no study so far has examined the predictive power of genome sequence in natural strains. In this study, we used a high-throughput phenotypic assay to measure the growth of a systematic collection of natural Escherichia coli strains and then employed machine learning models to predict bacterial growth from genomic data under nontherapeutic subinhibitory concentrations of antimicrobials that are common in nonclinical settings. We found a moderate to strong correlation between predicted and actual values for the different collected data sets. Moreover, we observed that the known resistance genes are still effective at sublethal concentrations, pointing to clinical implications of these concentrations.

Project description:Activating mutations in downstream genes of the epidermal growth factor receptor (EGFR) pathway may cause anti-EGFR resistance in patients with colorectal cancers. We present performance characteristics of a next-generation sequencing assay designed to detect such mutations. In this retrospective quality assessment study, we analyzed mutation detected in the KRAS, NRAS, BRAF, and PIK3CA genes by a clinically validated next-generation sequencing assay in 310 colorectal cancer specimens. Tumor cellularity and mutant allele frequency were analyzed to identify tumor heterogeneity and mutant allele-specific imbalance. Next-generation sequencing showed precise measurement of mutant allele frequencies and detected 23% of mutations with 2-20% mutant allele frequencies. Of the KRAS mutations detected, 17% were outside of codons 12 and 13. Among PIK3CA mutations, 48% were outside of codons 542, 545, and 1047. The percentage of tumors with predicted resistance to anti-EGFR therapy increased from 40% when testing for only mutations in KRAS exon 2 to 47% when testing for KRAS exons 2-4, 48% when testing for KRAS and NRAS exons 2-4, 58% when including BRAF codon 600 mutations, and 59% when adding PIK3CA exon 20 mutations. Right-sided colorectal cancers carried a higher risk of predicted anti-EGFR resistance. A concomitant KRAS mutation was detected in 51% of PIK3CA, 23% of NRAS, and 33% of kinase-impaired BRAF-mutated tumors. Lower than expected mutant allele frequency indicated tumor heterogeneity, while higher than expected mutant allele frequency indicated mutant allele-specific imbalance. Two paired neuroendocrine carcinomas and adjacent adenomas showed identical KRAS mutations, but only PIK3CA mutations in neuroendocrine carcinomas. Next-generation sequencing is a robust tool for mutation detection in clinical laboratories. It demonstrates high analytic sensitivity and broad reportable range, and it provides simultaneous detection of concomitant mutations and a quantitative measurement of mutant allele frequencies to predict tumor heterogeneity and mutant allele-specific imbalance.

Project description:BackgroundUropathogenic Escherichia coli (UPEC) is the main etiological agent behind community-acquired and hospital-acquired urinary tract infections (UTIs), which are among the most prevalent human infections. The management of UPEC infections is becoming increasingly difficult owing to multi-drug resistance, biofilm formation, and the possession of an extensive virulence arsenal. This study aims to characterize UPEC isolates in Tanta, Egypt, with regard to their antimicrobial resistance, phylogenetic profile, biofilm formation, and virulence, as well as the potential associations among these factors.MethodsOne hundred UPEC isolates were obtained from UTI patients in Tanta, Egypt. Antimicrobial susceptibility was assessed using the Kirby-Bauer method. Extended-spectrum β-lactamases (ESBLs) production was screened using the double disk synergy test and confirmed with PCR. Biofilm formation was evaluated using the microtiter-plate assay and microscopy-based techniques. The phylogenetic groups of the isolates were determined. The hemolytic activity, motility, siderophore production, and serum resistance of the isolates were also evaluated. The clonal relatedness of the isolates was assessed using ERIC-PCR.ResultsIsolates displayed elevated resistance to cephalosporins (90-43%), sulfamethoxazole-trimethoprim (63%), and ciprofloxacin (53%). Ninety percent of the isolates were multidrug-resistant (MDR)/ extensively drug-resistant (XDR) and 67% produced ESBLs. Notably, there was an inverse correlation between biofilm formation and antimicrobial resistance, and 31%, 29%, 32%, and 8% of the isolates were strong, moderate, weak, and non-biofilm producers, respectively. Beta-hemolysis, motility, siderophore production, and serum resistance were detected in 64%, 84%, 65%, and 11% of the isolates, respectively. Siderophore production was correlated to resistance to multiple antibiotics, while hemolysis was more prevalent in susceptible isolates and associated with stronger biofilms. Phylogroups B2 and D predominated, with lower resistance and stronger biofilms in group B2. ERIC-PCR revealed considerable diversity among the isolates.ConclusionThis research highlights the dissemination of resistance in UPEC in Tanta, Egypt. The evident correlation between biofilm and resistance suggests a resistance cost on bacterial cells; and that isolates with lower resistance may rely on biofilms to enhance their survival. This emphasizes the importance of considering biofilm formation ability during the treatment of UPEC infections to avoid therapeutic failure and/or infection recurrence.

Project description:BackgroundA cellular stress response (CSR) is triggered upon recombinant protein synthesis which acts as a global feedback regulator of protein expression. To remove this key regulatory bottleneck, we had previously proposed that genes that are up-regulated post induction could be part of the signaling pathways which activate the CSR. Knocking out some of these genes which were non-essential and belonged to the bottom of the E. coli regulatory network had provided higher expression of GFP and L-asparaginase.ResultsWe chose the best performing double knockout E. coli BW25113?elaA?cysW and demonstrated its ability to enhance the expression of the toxic Rubella E1 glycoprotein by 2.5-fold by tagging it with sfGFP at the C-terminal end to better quantify expression levels. Transcriptomic analysis of this hyper-expressing mutant showed that a significantly lower proportion of genes got down-regulated post induction, which included genes for transcription, translation, protein folding and sorting, ribosome biogenesis, carbon metabolism, amino acid and ATP synthesis. This down-regulation which is a typical feature of the CSR was clearly blocked in the double knockout strain leading to its enhanced expression capability. Finally, we supplemented the expression of substrate uptake genes glpK and glpD whose down-regulation was not prevented in the double knockout, thus ameliorating almost all the negative effects of the CSR and obtained a further doubling in recombinant protein yields.ConclusionThe study validated the hypothesis that these up-regulated genes act as signaling messengers which activate the CSR and thus, despite having no casual connection with recombinant protein synthesis, can improve cellular health and protein expression capabilities. Combining gene knockouts with supplementing the expression of key down-regulated genes can counter the harmful effects of CSR and help in the design of a truly superior host platform for recombinant protein expression.

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Performance Characteristics of Next-Generation Sequencing for the Detection of Antimicrobial Resistance Determinants in Escherichia coli Genomes and Metagenomes.

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