Dataset Information

PAIPline: pathogen identification in metagenomic and clinical next generation sequencing samples.

ABSTRACT: Motivation:Next generation sequencing (NGS) has provided researchers with a powerful tool to characterize metagenomic and clinical samples in research and diagnostic settings. NGS allows an open view into samples useful for pathogen detection in an unbiased fashion and without prior hypothesis about possible causative agents. However, NGS datasets for pathogen detection come with different obstacles, such as a very unfavorable ratio of pathogen to host reads. Alongside often appearing false positives and irrelevant organisms, such as contaminants, tools are often challenged by samples with low pathogen loads and might not report organisms present below a certain threshold. Furthermore, some metagenomic profiling tools are only focused on one particular set of pathogens, for example bacteria. Results:We present PAIPline, a bioinformatics pipeline specifically designed to address problems associated with detecting pathogens in diagnostic samples. PAIPline particularly focuses on userfriendliness and encapsulates all necessary steps from preprocessing to resolution of ambiguous reads and filtering up to visualization in a single tool. In contrast to existing tools, PAIPline is more specific while maintaining sensitivity. This is shown in a comparative evaluation where PAIPline was benchmarked along other well-known metagenomic profiling tools on previously published well-characterized datasets. Additionally, as part of an international cooperation project, PAIPline was applied to an outbreak sample of hemorrhagic fevers of then unknown etiology. The presented results show that PAIPline can serve as a robust, reliable, user-friendly, adaptable and generalizable stand-alone software for diagnostics from NGS samples and as a stepping stone for further downstream analyses. Availability and implementation:PAIPline is freely available under https://gitlab.com/rki_bioinformatics/paipline.

SUBMITTER: Andrusch A

PROVIDER: S-EPMC6129269 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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PAIPline: pathogen identification in metagenomic and clinical next generation sequencing samples.

Andrusch Andreas A Dabrowski Piotr W PW Klenner Jeanette J Tausch Simon H SH Kohl Claudia C Osman Abdalla A AA Renard Bernhard Y BY Nitsche Andreas A

Bioinformatics (Oxford, England) 20180901 17

<h4>Motivation</h4>Next generation sequencing (NGS) has provided researchers with a powerful tool to characterize metagenomic and clinical samples in research and diagnostic settings. NGS allows an open view into samples useful for pathogen detection in an unbiased fashion and without prior hypothesis about possible causative agents. However, NGS datasets for pathogen detection come with different obstacles, such as a very unfavorable ratio of pathogen to host reads. Alongside often appearing fa ...[more]

PMID: 30423069

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Project description:Metagenomic sequencing is a promising new method for pathogen detection. We aimed to detect pathogens from archived plasma using metagenomic sequencing in a previously well-characterized cohort of 254 predominantly HIV-infected patients with sepsis in Uganda. We used Illumina sequencing and the Chan Zuckerberg ID metagenomics platform to sequence and identify pathogens. On average, each plasma sample yielded 3,404,737 ± 2,201,997 reads (mean ± standard deviation), of which 220,032 ± 416,691 (6.3% ± 8.6%) were identified as nonhuman reads. Using a background model filter, 414 genus-specific pathogen identifications were found in the 254 samples. Nineteen pathogens were previously detected positive by quantitative PCR (qPCR), compared to sequencing, which demonstrated 30.2% sensitivity and 99.5% specificity. Sensitivity was higher for viral pathogens than nonviral pathogens (37% versus 5%). For example, HIV viremia was detected in 69% of samples using qPCR, and sequencing revealed 70% sensitivity and 92% specificity. There were 75 genus-specific potential pathogens identified by sequencing in this cohort, including hepatitis B and Epstein-Barr virus (EBV), among several others. qPCR showed a prevalence of hepatitis B and EBV viremia of 17% and 45%, respectively. In-hospital mortality was associated with a lower qPCR threshold cycle value for EBV (adjusted odds ratio, 0.85; P < .001) but not for hepatitis B or HIV. In conclusion, a broad range of potential pathogens were identified by metagenomic sequencing in patients with sepsis in Uganda. Unexpectedly high rates of hepatitis B and EBV viremia were found. Whether these viral infections in HIV patients with sepsis are clinically important requires further study. IMPORTANCE The use of next-generation sequencing (NGS) in blood samples is an emerging technology for clinical microbiology labs. In this work, we performed NGS on plasma samples from a well-characterized cohort, where all samples had been previously tested by PCR for 43 pathogens. Therefore, we could compare sequencing performance against that of PCR and identify clinical correlates. A broad range of potential pathogens were identified by metagenomic sequencing in patients with sepsis in Uganda, particularly viruses, which we confirmed by PCR. In addition to HIV viremia, unexpectedly high rates of hepatitis B and EBV viremia were found, which may have important clinical implications.

Project description:BackgroundEarly availability of pathogen identification in urinary tract infections (UTIs) has critical importance in disease management. Metagenomic next-generation sequencing (mNGS) has the potential to transform how acute and serious infections are diagnosed by offering unbiased and culture-free pathogen detection. However, clinical experience with application of the mNGS test is relatively limited.MethodsWe therefore established a MinION-based mNGS pathogens diagnostic platform and evaluated its potential for clinical implementation in UTIs with clinical samples. 213 urine samples from patients with suspected UTIs were included and subjected to mNGS testing using the MinION platform. mNGS results were compared to the gold standard of clinical culture and composite standard of combining clinical testing, confirmatory qPCR testing, and clinical adjudication by doctors.ResultsThe mNGS exhibited a sensitivity of 81.4% and a specificity of 92.3%, along with a positive predictive value of 96.6%, a negative predictive value of 64.9%, and an overall accuracy of 84.4%, all of which were determined based on the gold standard of routine culture results. When assessed against the composite standard, the sensitivity and specificity both increased to 89.9% and 100%, respectively, while the accuracy rose to 92.4%. Notably, the positive predictive value and negative predictive value also saw improvements, reaching 100% and 76.8%, respectively. Moreover, this diagnostic platform successfully identified dsDNA viruses. Among the 65 culture-negative samples, the viral detection rate reached 33.8% (22/65) and was subsequently validated through qPCR. Furthermore, the automatic bioinformatics pipeline we developed enabled one-click analysis from data to results, leading to a significant reduction in diagnosis time.ConclusionThese results demonstrate that the pathogen detection performance of mNGS is sufficient for diagnostic testing in clinical settings. As the method is generally unbiased, it can improve diagnostic testing of UTIs and other microbial infections.

Project description:Background: Tuberculosis (TB) is now the leading cause of death from infectious disease. Rapid screening and diagnostic methods for TB are urgently required. Rapid development of metagenomics next-generation sequencing (mNGS) in recent years showed promising and satisfying application of mNGS in several kinds of infectious diseases. However, research directly evaluating the ability of mNGS in TB infection is still scarce. Methods: We conducted an adult prospective study in mainland China to evaluate the diagnostic performance of mNGS for detection of Mycobacterium tuberculosis complex (MTB) in multiple forms of direct clinical samples compared with GeneXpert MTB/RIF assay (Xpert), traditional diagnostic methods, and the clinical final diagnosis. Results: Of 123 patients presenting with suspected active TB infection between June 1, 2017, and May 21, 2018, 105 patients underwent synchronous tuberculous testing with culture, Xpert, and mNGS on direct clinical samples including sputum, cerebrospinal fluids, pus, etc. During follow-up, 45 of 105 participants had clinical final diagnosis of active TB infection, including 13 pulmonary TB cases and 32 extrapulmonary TB cases. Compared to clinical final diagnosis, mNGS produced a sensitivity of 44% for all active TB cases, which was similar to Xpert (42%) but much higher than conventional methods (29%). With only one false-positive result, mNGS had a specificity of 98% in our study. mNGS yielded significantly much higher sensitivity in pre-treatment samples (76%) than post-treatment ones (31%) (P = 0.005), which was also true for Xpert and conventional methods. Combining Xpert and mNGS together, the study identified 27 of 45 active TB cases (60%), including all 13 conventional method-identified cases, and the result reached statistical significance compared to conventional methods (McNemar-test P < 0.001). Conclusions: mNGS had a similar diagnostic ability of MTB compared with Xpert and showed potential for a variety of clinical samples. Combined mNGS and Xpert showed an overall superior advantage over conventional methods and significantly improved the etiology diagnosis of both MTB and other pathogens. The result that anti-TB treatment significantly reduced diagnostic efficacy of culture, Xpert, and mNGS highlighted the importance of collecting samples before empirical treatment.

Project description:BackgroundAccurate identification of primary pathogens in foot infections remains challenging due to the diverse microbiome. Conventional culture may show false-positive or false-negative growth, leading to ineffective postoperative antibiotic treatment. Next-generation sequencing (NGS) has been explored as an alternative to standard culture in orthopedic infections. NGS is highly sensitive and can detect an entire bacterial genome along with genes conferring antibiotic resistance in a given sample. We investigated the potential use of NGS for accurate identification and quantification of microbes in infected diabetic foot ulcer (DFU). We hypothesize that NGS will aid identification of dominant pathogen and provide a more complete profile of microorganisms in infected DFUs compared to the standard culture method.MethodsData were prospectively collected from 30 infected DFU patients who underwent operative treatment by a fellowship-trained orthopedic foot and ankle surgeon from October 2018 to September 2019. The average age of the patient was 60.4 years. Operative procedures performed were irrigation and debridement (12), toe or ray amputation (13), calcanectomies (4), and below-the-knee amputation (1). Infected bone specimens were obtained intraoperatively and processed for standard culture and NGS. Concordance between the standard culture and NGS was assessed.ResultsIn 29 of 30 patients, pathogens were identified by both NGS and culture, with a concordance rate of 70%. In standard culture, Staphylococcus aureus (58.6%) was the most common pathogen, followed by coagulase-negative Staphylococcus (24.1%), Corynebacterium striatum (17.2%), and Enterococcus faecalis (17.2%). In NGS, Finegoldia magna (44.8%) was the most common microorganism followed by S. aureus (41.4%), and Anaerococcus vaginalis (24.1%). On average, NGS revealed 5.1 (range, 1-11) pathogens in a given sample, whereas culture revealed 2.6 (range, 1-6) pathogens.ConclusionNGS is an emerging molecular diagnostic method of microbial identification in orthopedic infection. It frequently provides different profiles of microorganisms along with antibiotic-resistant gene information compared to conventional culture in polymicrobial foot infection. Clinical use of NGS for management of foot and ankle infections warrants further investigation.Level of evidenceLevel II, diagnostic study.

Dataset Information

PAIPline: pathogen identification in metagenomic and clinical next generation sequencing samples.

Publications

PAIPline: pathogen identification in metagenomic and clinical next generation sequencing samples.

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