Quantifying the contribution of four resistance mechanisms to ciprofloxacin MIC in Escherichia coli: a systematic review.

Background: Reviews assessing the genetic basis of ciprofloxacin resistance in Escherichia coli have mostly been qualitative. However, to predict resistance phenotypes based on genotypic characteristics, it is essential to quantify the contribution of genotypic determinants to resistance. Objectives: We performed a systematic review to assess the relative contribution of known genomic resistance determinants to the MIC of ciprofloxacin in E. coli. Methods: PubMed and Web of Science were searched for English language studies that assessed ciprofloxacin MIC and presence or introduction of genetic determinants of ciprofloxacin resistance in E. coli. We included experimental and observational studies without time restrictions. Medians and ranges of MIC fold changes were calculated for individual resistance determinants and combinations thereof. Results: We included 66 studies, describing 604 E. coli isolates that carried at least one genetic ciprofloxacin resistance determinant. Mutations in gyrA and parC, genes encoding targets of ciprofloxacin, contribute to the largest fold changes in ciprofloxacin resistance in E. coli compared with the WT. Efflux and physical blocking or enzymatic modifications confer smaller increases in ciprofloxacin MIC than mutations in gyrA and parC. However, the presence of these other resistance mechanisms in addition to target alteration mutations further increases ciprofloxacin MIC, thus resulting in ciprofloxacin MIC increases ranging from 250- to 4000-fold. Conclusions: This quantitative review of genomic determinants of ciprofloxacin resistance in E. coli demonstrates the complexity of resistance phenotype prediction from genomic data and serves as a reference point for studies aiming to predict ciprofloxacin MIC from E. coli genomes.

Diagnostic Test Accuracy of a 2-Transcript Host RNA Signature for Discriminating Bacterial vs Viral Infection in Febrile Children.

IMPORTANCE: Because clinical features do not reliably distinguish bacterial from viral infection, many children worldwide receive unnecessary antibiotic treatment, while bacterial infection is missed in others. OBJECTIVE: To identify a blood RNA expression signature that distinguishes bacterial from viral infection in febrile children. DESIGN, SETTING, AND PARTICIPANTS: Febrile children presenting to participating hospitals in the United Kingdom, Spain, the Netherlands, and the United States between 2009-2013 were prospectively recruited, comprising a discovery group and validation group. Each group was classified after microbiological investigation as having definite bacterial infection, definite viral infection, or indeterminate infection. RNA expression signatures distinguishing definite bacterial from viral infection were identified in the discovery group and diagnostic performance assessed in the validation group. Additional validation was undertaken in separate studies of children with meningococcal disease (n = 24) and inflammatory diseases (n = 48) and on published gene expression datasets. EXPOSURES: A 2-transcript RNA expression signature distinguishing bacterial infection from viral infection was evaluated against clinical and microbiological diagnosis. MAIN OUTCOMES AND MEASURES: Definite bacterial and viral infection was confirmed by culture or molecular detection of the pathogens. Performance of the RNA signature was evaluated in the definite bacterial and viral group and in the indeterminate infection group. RESULTS: The discovery group of 240 children (median age, 19 months; 62% male) included 52 with definite bacterial infection, of whom 36 (69%) required intensive care, and 92 with definite viral infection, of whom 32 (35%) required intensive care. Ninety-six children had indeterminate infection. Analysis of RNA expression data identified a 38-transcript signature distinguishing bacterial from viral infection. A smaller (2-transcript) signature (FAM89A and IFI44L) was identified by removing highly correlated transcripts. When this 2-transcript signature was implemented as a disease risk score in the validation group (130 children, with 23 definite bacterial, 28 definite viral, and 79 indeterminate infections; median age, 17 months; 57% male), all 23 patients with microbiologically confirmed definite bacterial infection were classified as bacterial (sensitivity, 100% [95% CI, 100%-100%]) and 27 of 28 patients with definite viral infection were classified as viral (specificity, 96.4% [95% CI, 89.3%-100%]). When applied to additional validation datasets from patients with meningococcal and inflammatory diseases, bacterial infection was identified with a sensitivity of 91.7% (95% CI, 79.2%-100%) and 90.0% (95% CI, 70.0%-100%), respectively, and with specificity of 96.0% (95% CI, 88.0%-100%) and 95.8% (95% CI, 89.6%-100%). Of the children in the indeterminate groups, 46.3% (63/136) were classified as having bacterial infection, although 94.9% (129/136) received antibiotic treatment. CONCLUSIONS AND RELEVANCE: This study provides preliminary data regarding test accuracy of a 2-transcript host RNA signature discriminating bacterial from viral infection in febrile children. Further studies are needed in diverse groups of patients to assess accuracy and clinical utility of this test in different clinical settings.

An infected urachal cyst--a rare diagnosis in a child with acute abdominal pain.

UNLABELLED: A 2-year-old girl who presented with acute abdominal pain and spiking fever was diagnosed with an infected urachal cyst. Ultrasonography aided the diagnosis and the urachal remnant was removed successfully through a single laparoscopic procedure. Treatment is through removal of the complete structure, to prevent malignant degeneration in adulthood. CONCLUSION: Urachal cysts may cause abdominal complaints when infected. Although rare, they should be added to the differential diagnosis of acute abdominal pain in the paediatric patient, as this case illustrates.

Metagenomic DNA sequencing for semi-quantitative pathogen detection from urine: a prospective, laboratory-based, proof-of-concept study.

BACKGROUND: Semi-quantitative bacterial culture is the reference standard to diagnose urinary tract infection, but culture is time-consuming and can be unreliable if patients are receiving antibiotics. Metagenomics could increase diagnostic accuracy and speed by sequencing the microbiota and resistome directly from urine. We aimed to compare metagenomics to culture for semi-quantitative pathogen and resistome detection from urine. METHODS: In this proof-of-concept study, we prospectively included consecutive urine samples from a clinical diagnostic laboratory in Amsterdam. Urine samples were screened by DNA concentration, followed by PCR-free metagenomic sequencing of randomly selected samples with a high concentration of DNA (culture positive and negative). A diagnostic index was calculated as the product of DNA concentration and fraction of pathogen reads. We compared results with semi-quantitative culture using area under the receiver operating characteristic curve (AUROC) analyses. We used ResFinder and PointFinder for resistance gene detection and compared results to phenotypic antimicrobial susceptibility testing for six antibiotics commonly used for urinary tract infection treatment: nitrofurantoin, ciprofloxacin, fosfomycin, cotrimoxazole, ceftazidime, and ceftriaxone. FINDINGS: We screened 529 urine samples of which 86 were sequenced (43 culture positive and 43 culture negative). The AUROC of the DNA concentration-based screening was 0·85 (95% CI 0·81-0·89). At a cutoff value of 6·0 ng/mL, culture positivity was ruled out with a negative predictive value of 91% (95% CI 87-93; 26 of 297 samples), reducing the number of samples requiring sequencing by 56% (297 of 529 samples). The AUROC of the diagnostic index was 0·87 (95% CI 0·79-0·95). A diagnostic index cutoff value of 17·2 yielded a positive predictive value of 93% (95% CI 85-97) and a negative predictive value of 69% (55-80), correcting for a culture-positive prevalence of 66%. Gram-positive pathogens explained eight (89%) of the nine false-negative metagenomic test results. Agreement of phenotypic and genotypic antimicrobial susceptibility testing varied between 71% (22 of 31 samples) and 100% (six of six samples), depending on the antibiotic tested. INTERPRETATION: This study provides proof-of-concept of metagenomic semi-quantitative pathogen and resistome detection for the diagnosis of urinary tract infection. The findings warrant prospective clinical validation of the value of this approach in informing patient management and care. FUNDING: EU Horizon 2020 Research and Innovation Programme.

Thermus thermophilus DNA can be used as internal control for process monitoring of clinical metagenomic next-generation sequencing of urine samples.

INTRODUCTION: Metagenomics is increasingly considered for clinical diagnostics. In order for this technology to become integrated in the clinical microbiology laboratory, process controls are required. Molecular diagnostic tests typically integrate an internal control (IC) to detect potential sources of variation and technical failure. However, few studies report on the integration of an IC in metagenomics. AIM: We aimed to develop an easy-to-use IC method for the process control of library preparation and sequencing applied to metagenomics in clinical microbiology diagnostics using Thermus thermophilus DNA. METHODOLOGY: DNA was extracted from urine samples and sequenced on the Ion Torrent Proton in the absence and presence of incremental concentrations (0.5-2-5%) of IC. Between aliquots of each sample, we compared the IC relative abundance (RA), and after in silico subtraction of IC reads, analysed microbial composition and the RA of pathogens. The optimal IC concentration was defined as the lowest concentration still detectable in all samples with the smallest impact on the microbial composition. RESULTS: The RA of IC correlated linearly with the spiked IC concentration (r2 = 0.99). IC added in a concentration of 0.5% of the total DNA concentration was detectable in all sample aliquots, regardless of human-bacterial DNA proportion, and after in silico removal gave the smallest difference in RA of pathogens compared to the sample aliquot sequenced in the absence of IC. The microbial composition in the presence and absence of IC was highly similar after in silico removal of IC reads (median BC-dissimilarity per sample: 0.059), provided samples had a mean of >10,000 bacterial reads. CONCLUSION: T. thermophilus DNA at a percentage of 0.5% of the total DNA concentration was successfully applied for the process control of metagenomics of urine samples. We demonstrated negligible alterations in sample microbial composition after in silico subtraction of IC reads. This approach contributes toward implementation of metagenomics in the clinical microbiology laboratory.

Amplified fragment length polymorphism and whole genome sequencing: a comparison of methods in the investigation of a nosocomial outbreak with vancomycin resistant enterococci.

Background: Recognition of nosocomial outbreaks with antimicrobial resistant (AMR) pathogens and appropriate infection prevention measures are essential to limit the consequences of AMR pathogens to patients in hospitals. Because unrelated, but genetically similar AMR pathogens may circulate simultaneously, rapid high-resolution molecular typing methods are needed for outbreak management. We compared amplified fragment length polymorphism (AFLP) and whole genome sequencing (WGS) during a nosocomial outbreak of vancomycin-resistant Enterococcus faecium (VRE) that spanned 5 months. Methods: Hierarchical clustering of AFLP profiles was performed using unweighted pair-grouping and similarity coefficients were calculated with Pearson correlation. For WGS-analysis, core single nucleotide polymorphisms (SNPs) were used to calculate the pairwise distance between isolates, construct a maximum likelihood phylogeny and establish a cut-off for relatedness of epidemiologically linked VRE isolates. SNP-variations in the vanB gene cluster were compared to increase the comparative resolution. Technical replicates of 2 isolates were sequenced to determine the number of core-SNPs derived from random sequencing errors. Results: Of the 721 patients screened for VRE carriage, AFLP assigned isolates of 22 patients to the outbreak cluster. According to WGS, all 22 isolates belonged to ST117 but only 21 grouped in a tight phylogenetic cluster and carried vanB resistance gene clusters. Sequencing of technical replicates showed that 4-5 core-SNPs were derived by random sequencing errors. The cut-off for relatedness of epidemiologically linked VRE isolates was established at ≤7 core-SNPs. The discrepant isolate was separated from the index isolate by 61 core-SNPs and the vanB gene cluster was absent. In AFLP analysis this discrepant isolate was indistinguishable from the other outbreak isolates, forming a cluster with 92% similarity (cut-off for identical isolates ≥90%). The inclusion of the discrepant isolate in the outbreak resulted in the screening of 250 patients and quarantining of an entire ward. Conclusion: AFLP was a rapid and affordable screening tool for characterising hospital VRE outbreaks. For in-depth understanding of the outbreak WGS was needed. Compared to AFLP, WGS provided higher resolution typing of VRE isolates with implications for outbreak management.