Detection of pathogens and antimicrobial resistance genes directly from urine samples in patients suspected of urinary tract infection by metagenomics nanopore sequencing: A large-scale multi-centre study.
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SUBMITTER: Liu M
PROVIDER: S-EPMC10131482 | biostudies-literature | 2023 Apr
REPOSITORIES: biostudies-literature
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Detection of pathogens and antimicrobial resistance genes directly from urine samples in patients suspected of urinary tract infection by metagenomics nanopore sequencing: A large-scale multi-centre study.
Liu Manjiao M Yang Simin S Wu Susheng S Chen Li L Li Shan S Li Zhenzhong Z Zhou Mingzhe M Wang Lili L Xu Hui H Liu Ryon R Fang Yi Y Huang Weichun W Zhang Min M Guo Wenzheng W Dai Yan Y Ren Yong Y Guo Hao H Wu Wenjuan W
Clinical and translational medicine 20230401 4
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Project description:Urinary tract infections (UTIs) are among the most common acquired bacterial infections in humans. The current gold standard method for identification of uropathogens in clinical laboratories is cultivation. However, culture-based assays have substantial drawbacks, including long turnaround time and limited culturability of many potential pathogens. Nanopore sequencing technology can overcome these limitations and detect pathogens while also providing reliable predictions of drug susceptibility in clinical samples. Here, we optimized a metagenomic nanopore sequencing (mNPS) test for pathogen detection and identification in urine samples of 76 patients with acute uncomplicated UTIs. We first used twenty of these samples to show that library preparation by the PCR Barcoding Kit (PBK) led to the highest agreement of positive results with gold standard clinical culture tests, and enabled antibiotic resistance detection in downstream analyses. We then compared the detection results of mNPS with those of culture-based diagnostics and found that mNPS sensitivity and specificity of detection were 86.7% [95% confidence interval (CI), 73.5-94.1%] and 96.8% (95% CI, 82.4-99.9%), respectively, indicating that the mNPS method is a valid approach for rapid and specific detection of UTI pathogens. The mNPS results also performed well at predicting antibiotic susceptibility phenotypes. These results demonstrate that our workflow can accurately diagnose UTI-causative pathogens and enable successful prediction of drug-resistant phenotypes within 6 h of sample receipt. Rapid mNPS testing is thus a promising clinical diagnostic tool for infectious diseases, based on clinical urine samples from UTI patients, and shows considerable potential for application in other clinical infections.
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Project description:Empirical gonorrhea treatment at initial diagnosis reduces onward transmission. However, increasing resistance to multiple antibiotics may necessitate waiting for culture-based diagnostics to select an effective treatment. There is a need for same-day culture-free diagnostics that identify infection and detect antimicrobial resistance. We investigated if Nanopore sequencing can detect sufficient Neisseria gonorrhoeae DNA to reconstruct whole genomes directly from urine samples. We used N. gonorrhoeae-spiked urine samples and samples from gonorrhea infections to determine optimal DNA extraction methods that maximize the amount of N. gonorrhoeae DNA sequenced while minimizing contaminating host DNA. In simulated infections, the Qiagen UCP pathogen mini kit provided the highest ratio of N. gonorrhoeae to human DNA and the most consistent results. Depletion of human DNA with saponin increased N. gonorrhoeae yields in simulated infections but decreased yields in clinical samples. In 10 urine samples from men with symptomatic urethral gonorrhea, ≥92.8% coverage of an N. gonorrhoeae reference genome was achieved in all samples, with ≥93.8% coverage breath at ≥10-fold depth in 7 (70%) samples. In simulated infections, if ≥104 CFU/ml of N. gonorrhoeae was present, sequencing of the large majority of the genome was frequently achieved. N. gonorrhoeae could also be detected from urine in cobas PCR medium tubes and from urethral swabs and in the presence of simulated Chlamydia coinfection. Using Nanopore sequencing of urine samples from men with urethral gonorrhea, sufficient data can be obtained to reconstruct whole genomes in the majority of samples without the need for culture.
| S-EPMC7041563 | biostudies-literature
Project description:BackgroundAlthough urinary tract infections (UTIs) are extremely common, isolation of causative uropathogens is not always routinely performed, with antibiotics frequently prescribed empirically. This study determined the susceptibility of urinary isolates from two Health and Social Care Trusts (HSCTs) in Northern Ireland to a range of antibiotics commonly used in the treatment of UTIs. Furthermore, we determined if detection of trimethoprim resistance genes (dfrA) could be used as a potential biomarker for rapid detection of phenotypic trimethoprim resistance in urinary pathogens and from urine without culture.MethodsSusceptibility of E. coli and Klebsiella spp. isolates (n = 124) to trimethoprim, amoxicillin, ceftazidime, ciprofloxacin, co-amoxiclav and nitrofurantoin in addition to susceptibility of Proteus mirabilis (n = 61) and Staphylococcus saprophyticus (n = 17) to trimethoprim was determined by ETEST® and interpreted according to EUCAST breakpoints. PCR was used to detect dfrA genes in bacterial isolates (n = 202) and urine samples(n = 94).ResultsResistance to trimethoprim was observed in 37/124 (29.8%) E. coli and Klebsiella spp. isolates with an MIC90 > 32 mg/L. DfrA genes were detected in 29/37 (78.4%) trimethoprim-resistant isolates. Detection of dfrA was highly sensitive (93.6%) and specific (91.4%) in predicting phenotypic trimethoprim resistance among E. coli and Klebsiella spp. isolates. The dfrA genes analysed were detected using a culture-independent PCR method in 16/94 (17%) urine samples. Phenotypic trimethoprim resistance was apparent in isolates cultured from 15/16 (94%) dfrA-positive urine samples. There was a significant association (P < 0.0001) between the presence of dfrA and trimethoprim resistance in urine samples containing Gram-negative bacteria (Sensitivity = 75%; Specificity = 96.9%; PPV = 93.8%; NPV = 86.1%).ConclusionsThis study demonstrates that molecular detection of dfrA genes is a good indicator of trimethoprim resistance without the need for culture and susceptibility testing.
| S-EPMC9128081 | biostudies-literature
Project description:(1) Background: surface-enhanced Raman spectroscopy (SERS) is a novel method for bacteria identification. However, reported applications of SERS in clinical diagnosis are limited. In this study, we used cylindrical SERS chips to detect urine pathogens in urinary tract infection (UTI) patients. (2) Methods: Urine samples were retrieved from 108 UTI patients. A 10 mL urine sample was sent to conventional bacterial culture as a reference. Another 10 mL urine sample was loaded on a SERS chip for bacteria identification and antibiotic susceptibility. We concentrated the urine specimen if the intensity of the Raman spectrum required enhancement. The resulting Raman spectrum was analyzed by a recognition software to compare with spectrum-form reference bacteria and was further confirmed by principal component analysis (PCA). (3) Results: There were 97 samples with single bacteria species identified by conventional urine culture and, among them, 93 can be successfully identified by using SERS without sample concentration. There were four samples that needed concentration for bacteria identification. Antibiotic susceptibility can also be found by SERS. There were seven mixed flora infections found by conventional culture, which can only be identified by the PCA method. (4) Conclusions: SERS can be used in the diagnosis of urinary tract infection with the aid of the recognition software and PCA.
| S-EPMC6321215 | biostudies-literature
Project description:Urine culture is time consuming, which may take days to get the results and impede further timely treatment. Our objective is to evaluate whether the fast urinalysis and bacterial discrimination system called Sysmex UF-5000 may predict urinary tract infections (UTIs) (within minutes) compared with the clinical routine test in suspected UTI patients. In addition, we aimed to explore the accuracy of microbiologic information by UF-5000. Consecutive patients who were admitted from the emergency department at Queen Mary Hospital (a tertiary hospital in Hong Kong) from June 2019 to February 2020 were enrolled in the present study. The dipstick test, manual microscopic test with culture, and Sysmex UF-5000 test were performed in the urine samples at admission. A total of 383 patients were finally included in the present study. UF-5000 urinalysis (area under the receiver operator characteristic curve, AUC=0.821, confidence interval, 95%CI: 0.767-0.874) outperformed the dipstick test (AUC=0.602, 95%CI: 0.550-0.654, P=1.32×10-10) for predicting UTIs in patients without prior antibiotic treatment. A significant net benefit from UF-5000 was observed compared with the dipstick test (NRI=39.9%, 95%CI: 19.4-60.4, P=1.36 × 10-4). The urine leukocyte tested by UF-5000 had similar performance (AUC) for predicting UTI compared with the manual microscopic test (P=0.27). In patients without a prior use of antibiotics, the concordance rates between UF-5000 and culture for predicting Gram-positive or -negative bacteriuria and a negative culture were 44.7% and 96.2%, respectively. UF-5000 urinalysis had a significantly better predictive value than the dipstick urine test for predicting UTIs.
| S-EPMC9551190 | biostudies-literature
Project description:(1) Background: Urinary tract infection (UTI) is a leading cause of emergency department visits and hospital admissions. Despite many studies identifying UTI-related risk factors for bacteremia or sepsis, a significant gap remains in developing predictive models for in-hospital mortality or the necessity for emergent intensive care unit admission in the emergency department. This study aimed to construct interpretable machine learning models capable of identifying patients at high risk for critical outcomes. (2) Methods: This was a retrospective study of adult patients with urinary tract infection (UTI), extracted from the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database. The critical outcome is defined as either in-hospital mortality or transfer to an intensive care unit within 12 h. ED visits were randomly partitioned into a 70%/30% split for training and validation. The extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) algorithms were constructed using variables selected from the stepwise logistic regression model. The XGBoost model was then compared to the traditional model and clinical decision rules (CDRs) on the validation data using the area under the curve (AUC). (3) Results: There were 3622 visits among 3235 unique patients diagnosed with UTI. Of the 2535 patients in the training group, 836 (33%) experienced critical outcomes, and of the 1087 patients in the validation group, 358 (32.9%) did. The AUCs for different machine learning models were as follows: XGBoost, 0.833; RF, 0.814; and SVM, 0.799. The XGBoost model performed better than others. (4) Conclusions: Machine learning models outperformed existing traditional CDRs for predicting critical outcomes of ED patients with UTI. Future research should prospectively evaluate the effectiveness of this approach and integrate it into clinical practice.
| S-EPMC11394224 | biostudies-literature
Project description:Because some organisms causing urinary tract infection (UTI) may be difficult to culture, examination of bacterial gene sequences in the urine may provide a more accurate view of bacteria present during a UTI. Our objective was to estimate how often access to 16S rRNA gene amplicon sequencing alters diagnosis and/or clinical management. The study was designed as a cross-sectional study of a convenience sample of children with suspected UTI. The setting was the emergency department or outpatient clinic at six pediatric centers. Participants included children 2 months to 10 years of age suspected of UTI. We categorized the results of urine culture as follows: "likely UTI" (≥100,000 CFU/ml of a single uropathogen), "possible UTI" (10,000 to 99,000 CFU/ml of a uropathogen or ≥100,000 CFU/ml of a single uropathogen plus other growth), and "unlikely UTI" (no growth or growth of nonuropathogens). Similarly, we categorized the results of 16S rRNA gene sequencing into the same three categories using the following criteria: likely UTI (≥90% relative abundance of a uropathogen), possible UTI (50 to 89% relative abundance of a uropathogen), and unlikely UTI (remainder of samples). The main study outcome was concordance between conventional culture results and 16S rRNA gene sequencing. Concordance between the two methods was high in children with likely and unlikely UTI by conventional culture (95% and 87%, respectively). In children with possible UTI according to conventional culture, 71% had a single uropathogen at a relative abundance of ≥90% according to 16S rRNA gene sequencing data. Concordance between conventional culture and 16S rRNA gene amplicon sequencing appears to be high. In children with equivocal culture results, 16S rRNA gene results may provide information that may help clarify the diagnosis. IMPORTANCE Concordance between conventional culture and 16S rRNA gene amplicon sequencing appears to be high. In children with equivocal culture results, 16S rRNA gene results may provide information that may help clarify the diagnosis.
| S-EPMC8694219 | biostudies-literature
Project description:Drug resistance is becoming alarming with the passage of time worldwide in general and in third world countries in particular. Human urine specimens of patients of urinary tract infection at Sheikh Zayed hospital, Lahore, Pakistan were analyzed for drug resistance in Escherichia coli. A total of 69 Escherichia coli isolates from human urine specimens were obtained and screened for their antibiograms. A total of seven antibiotic resistance profiles were obtained with over 65% of the isolates showing multi-drug resistance. Very high resistance levels were detected against augmentin and gentamicin (87.5 &77.5 % respectively) while imipenem and tazocin recorded the least resistance levels (32.5% and 12.5% respectively) among the isolates.
| S-EPMC4135289 | biostudies-literature
Project description:BackgroundThe identification of uropathogens (UPBs) and urinary tract colonizing bacteria (UCB) conduces to guide the antimicrobial therapy to reduce resistant bacterial strains and study urinary microbiota. This study established a nomogram based on the nanopore-targeted sequencing (NTS) and other infectious risk factors to distinguish UPB from UCB.MethodsBasic information, medical history, and multiple urine test results were continuously collected and analyzed by least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression was used to determine the independent predictors and construct nomogram. Receiver operating characteristics, area under the curve, decision curve analysis, and calibration curves were used to evaluate the performance of the nomogram.ResultsIn this study, the UPB detected by NTS accounted for 74.1% (401/541) of all urinary tract microorganisms. The distribution of ln(reads) between UPB and UCB groups showed significant difference (OR = 1.39; 95% CI, 1.246-1.551, p < 0.001); the reads number in NTS reports could be used for the preliminary determination of UPB (AUC=0.668) with corresponding cutoff values being 7.042. Regression analysis was performed to determine independent predictors and construct a nomogram, with variables ranked by importance as ln(reads) and the number of microbial species in the urinary tract of NTS, urine culture, age, urological neoplasms, nitrite, and glycosuria. The calibration curve showed an agreement between the predicted and observed probabilities of the nomogram. The decision curve analysis represented that the nomogram would benefit clinical interventions. The performance of nomogram with ln(reads) (AUC = 0.767; 95% CI, 0.726-0.807) was significantly better (Z = 2.304, p-value = 0.021) than that without ln(reads) (AUC = 0.727; 95% CI, 0.681-0.772). The rate of UPB identification of nomogram was significantly higher than that of ln(reads) only (χ2 = 7.36, p-value = 0.009).ConclusionsNTS is conducive to distinguish uropathogens from colonizing bacteria, and the nomogram based on NTS and multiple independent predictors has better prediction performance of uropathogens.
| S-EPMC10229875 | biostudies-literature
Project description:Examination of E. coli transcripts present in bacteria in urine samples from 8 patients attending a urology clinic with symptoms of cystitis, as compared to transcripts present in the same E. coli strains during mid-exponential growth in filter-sterilized human urine in vitro. A 48 array study using total RNA recovered from eight clinical E. coli isolates immediately following collection from women and following culture in pooled human urine ex vivo. Each array measures the expression of the 5,379 ORFs in the CFT073 genome, using an average of 14 60-mer probes per ORF. Arrays were performed in triplicate: biological replicates of the in vitro-cultured samples and technical replicates of the in vivo samples.
2010-11-12 | E-GEOD-24478 | biostudies-arrayexpress