Targeting RNA with Next- and Third-Generation Sequencing Improves Pathogen Identification in Clinical Samples.
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ABSTRACT:
SUBMITTER: Zhao N
PROVIDER: S-EPMC8655164 | biostudies-literature | 2021 Dec
REPOSITORIES: biostudies-literature
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Targeting RNA with Next- and Third-Generation Sequencing Improves Pathogen Identification in Clinical Samples.
Zhao Na N Cao Jiabao J Xu Jiayue J Liu Beibei B Liu Bin B Chen Dingqiang D Xia Binbin B Chen Liang L Zhang Wenhui W Zhang Yuqing Y Zhang Xuan X Duan Zhimei Z Wang Kaifei K Xie Fei F Xiao Kun K Yan Wei W Xie Lixin L Zhou Hongwei H Wang Jun J
Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20211023 23
Fast and accurate identification of microbial pathogens is critical for the proper treatment of infections. Traditional culture-based diagnosis in clinics is increasingly supplemented by metagenomic next-generation-sequencing (mNGS). Here, RNA/cDNA-targeted sequencing (meta-transcriptomics using NGS (mtNGS)) is established to reduce the host nucleotide percentage in clinic samples and by combining with Oxford Nanopore Technology (ONT) platforms (meta-transcriptomics using third-generation sequen ...[more]
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Project description:MotivationNext 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.ResultsWe 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 implementationPAIPline is freely available under https://gitlab.com/rki_bioinformatics/paipline.
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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.
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