Correction: Diagnosis of Bacterial Bloodstream Infections: A 16S Metagenomics Approach.
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ABSTRACT:
SUBMITTER: Decuypere S
PROVIDER: S-EPMC5533317 | biostudies-other | 2017 Jul
REPOSITORIES: biostudies-other
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Project description:BackgroundBacterial bloodstream infection (bBSI) is one of the leading causes of death in critically ill patients and accurate diagnosis is therefore crucial. We here report a 16S metagenomics approach for diagnosing and understanding bBSI.Methodology/principal findingsThe proof-of-concept was delivered in 75 children (median age 15 months) with severe febrile illness in Burkina Faso. Standard blood culture and malaria testing were conducted at the time of hospital admission. 16S metagenomics testing was done retrospectively and in duplicate on the blood of all patients. Total DNA was extracted from the blood and the V3-V4 regions of the bacterial 16S rRNA genes were amplified by PCR and deep sequenced on an Illumina MiSeq sequencer. Paired reads were curated, taxonomically labeled, and filtered. Blood culture diagnosed bBSI in 12 patients, but this number increased to 22 patients when combining blood culture and 16S metagenomics results. In addition to superior sensitivity compared to standard blood culture, 16S metagenomics revealed important novel insights into the nature of bBSI. Patients with acute malaria or recovering from malaria had a 7-fold higher risk of presenting polymicrobial bloodstream infections compared to patients with no recent malaria diagnosis (p-value = 0.046). Malaria is known to affect epithelial gut function and may thus facilitate bacterial translocation from the intestinal lumen to the blood. Importantly, patients with such polymicrobial blood infections showed a 9-fold higher risk factor for not surviving their febrile illness (p-value = 0.030).Conclusions/significanceOur data demonstrate that 16S metagenomics is a powerful approach for the diagnosis and understanding of bBSI. This proof-of-concept study also showed that appropriate control samples are crucial to detect background signals due to environmental contamination.
| S-EPMC4771206 | biostudies-literature
Project description:While 16S rRNA PCR-Sanger sequencing has paved the way for the diagnosis of culture-negative bacterial infections, it does not provide the composition of polymicrobial infections. We aimed to evaluate the performance of the Nanopore-based 16S rRNA metagenomic approach, using both partial and full-length amplification of the gene, and to explore its feasibility and suitability as a routine diagnostic tool for bacterial infections in a clinical laboratory. Thirty-one culture-negative clinical samples from mono- and polymicrobial infections based on Sanger-sequencing results were sequenced on MinION using both the in-house partial amplification and the Nanopore dedicated kit for the full-length amplification of the 16S rRNA gene. Contamination, background noise definition, bacterial identification, and time-effectiveness issues were addressed. Cost optimization was also investigated with the miniaturized version of the flow cell (Flongle). The partial 16S approach had a greater sensitivity compared to the full-length kit that detected bacterial DNA in only 24/31 (77.4%) samples. Setting a threshold of 1% of total reads overcame the background noise issue and eased the interpretation of clinical samples. Results were obtained within 1 day, discriminated polymicrobial samples, and gave accurate bacterial identifications compared to Sanger-based results. We also found that multiplexing and using Flongle flow cells was a cost-effective option. The results confirm that Nanopore technology is user-friendly as well as cost- and time-effective. They also indicate that 16S rRNA targeted metagenomics is a suitable approach to be implemented for the routine diagnosis of culture-negative samples in clinical laboratories.
| S-EPMC9329087 | biostudies-literature
Project description:Repeated quantitative measurement of bacterial DNA on whole blood has been shown to be a promising method for monitoring bloodstream infection (BSI) with selected bacterial species. To enable broad use of this method, we developed a quantitative droplet digital PCR (ddPCR) method for 16S rDNA. It was validated with species-specific ddPCRs for Staphylococcus aureus (nuc), Streptococcus pneumoniae (lytA), and Escherichia coli (uidA) on spiked whole blood samples and on repeated whole blood samples (days 0, 1-2, 3-4, 6-8, and 13-15) from 83 patients with BSI with these pathogens. In these patients, 16S rDNA and species-specific DNA were detected in 60% and 61%, respectively, at least at one time-point. The highest positivity rates were seen in S. aureus BSI, where 92% of the patients were 16S rDNA-positive and 85% nuc-positive. Quantitative 16S rDNA and species-specific DNA showed strong correlations in spiked samples (r = 0.98; p < 0.0001) and clinical samples (r = 0.84; p < 0.0001). Positivity for 16S rDNA was rapidly cleared in patients with S. pneumoniae and E. coli BSI, but more slowly and sometimes persisted, in those with S. aureus BSI. The initial 16S rDNA load was higher in BSI patients with sepsis (Sepsis-3 definition) than without sepsis (median 2.38 vs. 0 lg10 copies/mL; p = 0.031) and in non-survivors than in survivors (median 2.83 vs. 0 lg10 copies/mL; p = 0.006). 16S rDNA ddPCR appears to be a promising method for bacterial DNA monitoring during BSI. The clinical value of such monitoring should be further studied.
| S-EPMC6853374 | biostudies-literature
Project description:ObjectiveWe aim to evaluate the accuracy of the 16S ribosomal ribonucleic acid (rRNA) gene polymerase chain reaction (PCR) test in the diagnosis of bloodstream infections through a systematic review and meta-analysis.MethodsA computerized literature search was conducted to identify studies that assessed the diagnostic value of 16S rRNA gene PCR test for bloodstream infections. Study quality was assessed using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We calculated the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and their 95% confidence intervals (95% CI) for each study. Summary receiver operating characteristic (SROC) curve was used to summarize overall test performance. Statistical analysis was performed in Meta-DiSc 1.4 and Stata/SE 12.0 software.ResultsTwenty-eight studies were included in our meta-analysis. Using random-effect model analysis, the pooled sensitivity, specificity, PLR, NLR, and DOR were 0.87 (95% CI, 0.85-0.89), 0.94 (95% CI, 0.93-0.95), 12.65 (95% CI, 8.04-19.90), 0.14 (95% CI, 0.08-0.24), and 116.76 (95% CI, 52.02-262.05), respectively. The SROC curve indicated that the area under the curve (AUC) was 0.9690 and the maximum joint sensitivity and specificity (Q*) was 0.9183. In addition, heterogeneity was statistically significant but was not caused by the threshold effect.ConclusionExisting data suggest that 16S rRNA gene PCR test is a practical tool for the rapid screening of sepsis. Further prospective studies are needed to assess the diagnostic value of PCR amplification and DNA microarray hybridization of 16S rRNA gene in the future.
| S-EPMC4440735 | biostudies-literature
Project description:Classifying individual bacterial species comprising complex, polymicrobial patient specimens remains a challenge for culture-based and molecular microbiology techniques in common clinical use. We therefore adapted practices from metagenomics research to rapidly catalog the bacterial composition of clinical specimens directly from patients, without need for prior culture. We have combined a semiconductor deep sequencing protocol that produces reads spanning 16S ribosomal RNA gene variable regions 1 and 2 (?360 bp) with a de-noising pipeline that significantly improves the fraction of error-free sequences. The resulting sequences can be used to perform accurate genus- or species-level taxonomic assignment. We explore the microbial composition of challenging, heterogeneous clinical specimens by deep sequencing, culture-based strain typing, and Sanger sequencing of bulk PCR product. We report that deep sequencing can catalog bacterial species in mixed specimens from which usable data cannot be obtained by conventional clinical methods. Deep sequencing a collection of sputum samples from cystic fibrosis (CF) patients reveals well-described CF pathogens in specimens where they were not detected by standard clinical culture methods, especially for low-prevalence or fastidious bacteria. We also found that sputa submitted for CF diagnostic workup can be divided into a limited number of groups based on the phylogenetic composition of the airway microbiota, suggesting that metagenomic profiling may prove useful as a clinical diagnostic strategy in the future. The described method is sufficiently rapid (theoretically compatible with same-day turnaround times) and inexpensive for routine clinical use.
| S-EPMC3666980 | biostudies-literature
Project description:This study aimed to develop an early prediction model for identifying patients with bloodstream infections. The data resource was taken from 2015 to 2019 at Taichung Veterans General Hospital, and a total of 1647 bloodstream infection episodes and 3552 non-bloodstream infection episodes in the intensive care unit (ICU) were included in the model development and evaluation. During the data analysis, 30 clinical variables were selected, including patients' basic characteristics, vital signs, laboratory data, and clinical information. Five machine learning algorithms were applied to examine the prediction model performance. The findings indicated that the area under the receiver operating characteristic curve (AUROC) of the prediction performance of the XGBoost model was 0.825 for the validation dataset and 0.821 for the testing dataset. The random forest model also presented higher values for the AUROC on the validation dataset and testing dataset, which were 0.855 and 0.851, respectively. The tree-based ensemble learning model enabled high detection ability for patients with bloodstream infections in the ICU. Additionally, the analysis of importance of features revealed that alkaline phosphatase (ALKP) and the period of the central venous catheter are the most important predictors for bloodstream infections. We further explored the relationship between features and the risk of bloodstream infection by using the Shapley Additive exPlanations (SHAP) visualized method. The results showed that a higher prothrombin time is more prominent in a bloodstream infection. Additionally, the impact of a lower platelet count and albumin was more prominent in a bloodstream infection. Our results provide additional clinical information for cut-off laboratory values to assist clinical decision-making in bloodstream infection diagnostics.
| S-EPMC8268222 | biostudies-literature
Project description:Achieving a rapid microbiological diagnosis is crucial for decreasing morbidity and mortality of patients with a bloodstream infection, as it leads to the administration of an appropriate empiric antimicrobial therapy. Molecular methods may offer a rapid alternative to conventional microbiological diagnosis involving blood culture. In this study, the performance of a new technology that uses broad-spectrum PCR coupled with mass spectrometry (PCR/ESI-MS) was evaluated for the detection of microorganisms directly from whole blood. A total of 247 whole blood samples and paired blood cultures were prospectively obtained from 175 patients with a suspicion of sepsis. Both sample types were analyzed using the PCR/ESI-MS technology, and the results were compared with those obtained by conventional identification methods. The overall agreement between conventional methods and PCR/ESI-MS performed in blood culture aliquots was 94.2% with 96.8% sensitivity and 98.5% specificity for the molecular method. When comparing conventional methods with PCR/ESI-MS performed in whole blood specimens, the overall agreement was 77.1% with 50% sensitivity and 93.8% specificity for the molecular method. Interestingly, the PCR/ESI-MS technology led to the additional identification of 13 pathogens that were not found by conventional methods. Using the PCR/ESI-MS technology the microbiological diagnosis of bloodstream infections could be anticipated in about half of the patients in our setting, including a small but significant proportion of patients newly diagnosed. Thus, this promising technology could be very useful for the rapid diagnosis of sepsis in combination with traditional methods.
| S-EPMC3633912 | biostudies-other
Project description:To test the ability of high-throughput DNA sequencing to detect bacterial pathogens, we used it on DNA from a patient's feces during and after diarrheal illness. Sequences showing best matches for Campylobacter jejuni were detected only in the illness sample. Various bacteria may be detectable with this metagenomic approach.
| S-EPMC2630750 | biostudies-literature
Project description:In an integrated poultry-fish (IPF) farming system, fish and bird are reared simultaneously. It is a common practice in Sub-Saharan Africa countries like Nigeria, Cameroon, Madagascar, and Benin, offering economic benefits to farmers and minimizing farm running costs. It seems like another way for farmers to manage poultry waste as it is a common practice in IPF farm settings to feed reared fishes with wastes emanating from the poultry. This work provides dataset on the bacterial taxonomic profile and abundance in IPF farm pond water samples using the 16S rRNA sequencing approach. Using ZymoBIOMICS®-96 MagBead DNA Kit, total DNA was extracted from pond water samples collected from IPF farm located at Ila-Orangun, Osun State, Southwest Nigeria (Long: 8° 1' N; Lat: 4° 54' E) during two sampling visits. The V3-V4 region of the rRNA gene was amplified and sequenced on the Miseq Illumina sequencing platform. Raw reads obtained after demultiplexing were analyzed using DADA2 pipeline to obtain distinctive or unique amplicon sequence variants which were grouped into Operational Taxonomic Units (OTUs) based on similarities. Taxonomy assignment was performed using UCLUST and Bayesian classifier from QIIME v.1.9.1 with the Zymo Research Database as reference. The phyla Proteobacteria (26.7%), Actinobacteria (26.0%), Firmicutes (13.1%), and Cyanobacteria (10.1%) dominated the 35 phyla obtained from the OTUs. Interestingly, the abundance of bacterial pathogens commonly associated with human infections was low. The sequence and sample data have been deposited in NCBI database under Sequence Read Archive (SRA) with Bioproject identification number PRJNA760919 (Accession number: SRX12020336 - SRX12020346). The dataset obtained can bridge the gap of limited information on the impact of IPF farming on pond bacterial diversity, a critical factor for considerations as regards food safety, fish, and public health.
| S-EPMC9130544 | biostudies-literature
Project description:BackgroundRodents are major reservoirs of pathogens responsible for numerous zoonotic diseases in humans and livestock. Assessing their microbial diversity at both the individual and population level is crucial for monitoring endemic infections and revealing microbial association patterns within reservoirs. Recently, NGS approaches have been employed to characterize microbial communities of different ecosystems. Yet, their relative efficacy has not been assessed. Here, we compared two NGS approaches, RNA-Sequencing (RNA-Seq) and 16S-metagenomics, assessing their ability to survey neglected zoonotic bacteria in rodent populations.Methodology/principal findingsWe first extracted nucleic acids from the spleens of 190 voles collected in France. RNA extracts were pooled, randomly retro-transcribed, then RNA-Seq was performed using HiSeq. Assembled bacterial sequences were assigned to the closest taxon registered in GenBank. DNA extracts were analyzed via a 16S-metagenomics approach using two sequencers: the 454 GS-FLX and the MiSeq. The V4 region of the gene coding for 16S rRNA was amplified for each sample using barcoded universal primers. Amplicons were multiplexed and processed on the distinct sequencers. The resulting datasets were de-multiplexed, and each read was processed through a pipeline to be taxonomically classified using the Ribosomal Database Project. Altogether, 45 pathogenic bacterial genera were detected. The bacteria identified by RNA-Seq were comparable to those detected by 16S-metagenomics approach processed with MiSeq (16S-MiSeq). In contrast, 21 of these pathogens went unnoticed when the 16S-metagenomics approach was processed via 454-pyrosequencing (16S-454). In addition, the 16S-metagenomics approaches revealed a high level of coinfection in bank voles.Conclusions/significanceWe concluded that RNA-Seq and 16S-MiSeq are equally sensitive in detecting bacteria. Although only the 16S-MiSeq method enabled identification of bacteria in each individual reservoir, with subsequent derivation of bacterial prevalence in host populations, and generation of intra-reservoir patterns of bacterial interactions. Lastly, the number of bacterial reads obtained with the 16S-MiSeq could be a good proxy for bacterial prevalence.
| S-EPMC4540314 | biostudies-literature