Biology subjects

Janes, V. A.

Publications and source records attributed to Janes, V. A..

3 recordsLinked to original sources

bioRxiv2020

Internal Control for process monitoring of clinical metagenomic next-generation sequencing of urine samples

BackgroundProcess control for clinical metagenomic next-generation sequencing (mNGS) is not yet widely applied, while technical sources of bias are plentiful. We present an easy-to-use internal control (IC) method focussing on technical process control applied to metagenomics in clinical diagnostics. MethodsDNA of nine urine samples was sequenced in the absence and presence of Thermus thermophilus DNA as IC in incremental concentrations (0.5-2-5%). Between aliquots of each sample, we compared the IC relative abundance (RA), and after in silico subtraction of IC reads, the microbiota and the RA of pathogens. The optimal IC spike-in concentration was defined as the lowest concentration still detectable in all samples. ResultsThe RA of IC correlated linearly with the spiked IC concentration (r2=0.99). IC added in a concentration of 0.5% of total DNA concentration was detectable in all samples, regardless of human/bacterial composition and after in silico removal gave the smallest difference in RA of pathogens compared to the unspiked aliquot of the sample. The microbiota of sample aliquots sequenced in the presence and absence of IC was highly similar after in silico removal of IC reads (median BC-dissimilarity per sample of 0.059), provided samples had sufficient bacterial read counts. ConclusionT. thermophilus DNA at a percentage of 0.5% of the total DNA concentration can be applied for the process control of mNGS of urine samples. We demonstrated negligible alterations in sample microbial composition after in silico subtraction of IC sequence reads. This approach contributes toward implementation of mNGS in the clinical microbiology laboratory.

microbiology↗

bioRxiv2018

Quantifying the contribution of four resistance mechanisms to ciprofloxacin minimum inhibitory concentration in Escherichia coli: a systematic review

SynopsisO_ST_ABSIntroductionC_ST_ABSCiprofloxacin resistance in Escherichia coli is widespread and adds to the burden of E. coli infections. Reviews assessing the genetic basis of ciprofloxacin resistance have mostly been qualitative. However, to allow for the prediction of a resistance phenotype of clinical relevance based on genotypic characteristics, it is essential to quantify the contribution of prevalent genotypic determinants to resistance. We carried out a systematic review to assess the relative contribution of currently known genomic resistance determinants to the minimum inhibitory concentration (MIC) of ciprofloxacin in E. coli.\n\nMethodsPubMed and Web of Science were searched for English language studies that assessed both ciprofloxacin MIC and the 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 each resistance determinant and for combinations of determinants.\n\nResultsWe included 66 studies, describing 604 E. coli isolates that carried at least one genetic resistance determinant. Genes coding for targets of ciprofloxacin (gyrA and parC) are strongest contributors to ciprofloxacin resistance, with median MIC fold increases ranging from 24 (range 4-133) for single Ser83Leu (gyrA) mutants to 1533 (range 256-8533) for triple Ser83Leu, Asp87Asn/Gly (gyrA) and Ser80Ile/Arg (parC) mutants. Other resistance mechanisms, including efflux, physical blocking or enzymatic modification, conferred smaller increases in ciprofloxacin MIC (median MIC fold increases typically around 15, range 1-125). However, the (combined) presence of these other resistance mechanisms further increases resistance with median MIC fold increases of up to 4000, and even in the absence of gyrA and parC mutations up to 250.\n\nConclusionThis report provides a comprehensive and quantitative overview of the contribution of different genomic determinants to ciprofloxacin resistance in E. coli. Additionally, the data demonstrate the complexity of resistance phenotype prediction from genomic data and could serve as a reference point for studies aiming to address ciprofloxacin resistance prediction using genomics, in E. coli.

microbiology↗

bioRxiv2017

Metagenomic sequencing to replace semi-quantitative urine culture for detection of urinary tract infections: a proof of concept

ABSTRACTSemi-quantitative bacterial culture is the standard method to diagnose urinary tract infections (UTI), but bacterial growth rate limits diagnostic speed and it is unreliable when patients have been pre-treated with antibiotics. Metagenomics could increase diagnostic speed and accuracy by sequencing the microbiome and resistome directly from urine samples, bypassing culture. However, a semi-quantitative approach - as needed for diagnosing UTIs - has not been established.\n\nMetagenomics was deployed to identify and semi-quantify bacterial presence indicative of UTI, predict antimicrobial susceptibility (AMR), and results were compared to semi-quantitative culture. Whole genome sequencing of the corresponding uropathogens was done for comparison. Analysis time and cost were tracked.\n\nForty-one consecutive urine samples underwent metagenomic analysis. All culture positive samples contained >200ng of DNA, suggestive of a threshold below which UTI could be ruled out solely based on DNA quantity. A semi-quantitative Diagnostic Index (DI) was created by multiplying the total DNA quantity by the relative abundance of uropathogens per urine sample. The DI allowed discrimination of UTI from non-UTI samples in all but 1 case. Metagenomic detection of AMR determinants correctly predicted the phenotype of uropathogens in 20 of 32 cases. The metagenomic work-flow was 31h and cost {euro}116 per sample, but could be reduced to 4.5h and {euro}5 for low-DNA-yield non-UTI samples.\n\nThe genomic determinants of AMR and their distribution across uropathogens need to be better understood for prediction of AMR phenotypes by metagenomics. The introduction of the DI demonstrates the potential of semi-quantitative metagenomics to replace culture as rapid diagnostic method for UTI.

microbiology↗