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Metagenomic analysis of viral nucleic acid extraction methods in respiratory clinical samples.

ABSTRACT: BACKGROUND:Numerous protocols for viral enrichment and genome amplification have been created. However, the direct identification of viral genomes from clinical specimens using next-generation sequencing (NGS) still has its challenges. As a selected viral nucleic acid extraction method may determine the sensitivity and reliability of NGS, it is still valuable to evaluate the extraction efficiency of different extraction kits using clinical specimens directly. RESULTS:In this study, we performed qRT-PCR and viral metagenomic analysis of the extraction efficiency of four commonly used Qiagen extraction kits: QIAamp Viral RNA Mini Kit (VRMK), QIAamp MinElute Virus Spin Kit (MVSK), RNeasy Mini Kit (RMK), and RNeasy Plus Micro Kit (RPMK), using a mixed respiratory clinical sample without any pre-treatment. This sample contained an adenovirus (ADV), influenza virus A (Flu A), human parainfluenza virus 3 (PIV3), human coronavirus OC43 (OC43), and human metapneumovirus (HMPV). The quantity and quality of the viral extracts were significantly different among these kits. The highest threshold cycle(Ct)values for ADV and OC43 were obtained by using the RPMK. The MVSK had the lowest Ct values for ADV and PIV3. The RMK revealed the lowest detectability for HMPV and PIV3. The most effective rate of NGS data at 67.47% was observed with the RPMK. The other three kits ranged between 12.1-26.79% effectiveness rates for the NGS data. Most importantly, compared to the other three kits the highest proportion of non-host reads was obtained by the RPMK. The MVSK performed best with the lowest Ct value of 20.5 in the extraction of ADV, while the RMK revealed the best extraction efficiency by NGS analysis. CONCLUSIONS:The evaluation of viral nucleic acid extraction efficiency is different between NGS and qRT-PCR analysis. The RPMK was most applicable for the metagenomic analysis of viral RNA and enabled more sensitive identification of the RNA virus genome in respiratory clinical samples. In addition, viral RNA extraction kits were also applicable for metagenomic analysis of the DNA virus. Our results highlighted the importance of nucleic acid extraction kit selection, which has a major impact on the yield and number of viral reads by NGS analysis. Therefore, the choice of extraction method for a given viral pathogen needs to be carefully considered.

SUBMITTER: Zhang D

PROVIDER: S-EPMC6202819 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Metagenomic analysis of viral nucleic acid extraction methods in respiratory clinical samples.

Zhang Dan D Lou Xiuyu X Yan Hao H Pan Junhang J Mao Haiyan H Tang Hongfeng H Shu Yan Y Zhao Yun Y Liu Lei L Li Junping J Chen Jiang J Zhang Yanjun Y Ma Xuejun X

BMC genomics 20181025 1

<h4>Background</h4>Numerous protocols for viral enrichment and genome amplification have been created. However, the direct identification of viral genomes from clinical specimens using next-generation sequencing (NGS) still has its challenges. As a selected viral nucleic acid extraction method may determine the sensitivity and reliability of NGS, it is still valuable to evaluate the extraction efficiency of different extraction kits using clinical specimens directly.<h4>Results</h4>In this study ...[more]

PMID: 30359242

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Project description:The reproductive tract metagenome plays a significant role in the various reproductive system functions, including reproductive cycles, health, and fertility. One of the major challenges in bovine vaginal metagenome studies is host DNA contamination, which limits the sequencing capacity for metagenomic content and reduces the accuracy of untargeted shotgun metagenomic profiling. This is the first study comparing the effectiveness of different host depletion and DNA extraction methods for bovine vaginal metagenomic samples. The host depletion methods evaluated were slow centrifugation (Soft-spin), NEBNext Microbiome DNA Enrichment kit (NEBNext), and propidium monoazide (PMA) treatment, while the extraction methods were DNeasy Blood and Tissue extraction (DNeasy) and QIAamp DNA Microbiome extraction (QIAamp). Soft-spin and QIAamp were the most effective host depletion method and extraction methods, respectively, in reducing the number of cattle genomic content in bovine vaginal samples. The reduced host-to-microbe ratio in the extracted DNA increased the sequencing depth for microbial reads in untargeted shotgun sequencing. Bovine vaginal samples extracted with QIAamp presented taxonomical profiles which closely resembled the mock microbial composition, especially for the recovery of Gram-positive bacteria. Additionally, samples extracted with QIAamp presented extensive functional profiles with deep coverage. Overall, a combination of Soft-spin and QIAamp provided the most robust representation of the vaginal microbial community in cattle while minimizing host DNA contamination. IMPORTANCE In addition to the host tissue collected during the sampling process, bovine vaginal samples are saturated with large amounts of extracellular DNA and secreted proteins that are essential for physiological purposes, including the reproductive cycle and immune defense. Due to the high host-to-microbe genome ratio, which hampers the sequencing efficacy for metagenome samples and the recovery of the actual metagenomic profiles, bovine vaginal samples cannot benefit from the full potential of shotgun sequencing. This is the first investigation on the most effective host depletion and extraction methods for bovine vaginal metagenomic samples. This study demonstrated an effective combination of host depletion and extraction methods, which harvested higher percentages of 16S rRNA genes and microbial reads, which subsequently led to a taxonomical profile that resembled the actual community and a functional profile with deeper coverage. A representative metagenomic profile is essential for investigating the role of the bovine vaginal metagenome for both reproductive function and susceptibility to infections.

Project description:Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them.We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied to digital gene expression studies (e.g. SAGE). A web server implementation of our methods and freely available source code can be found at http://metastats.cbcb.umd.edu/.

Dataset Information

Metagenomic analysis of viral nucleic acid extraction methods in respiratory clinical samples.

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Metagenomic analysis of viral nucleic acid extraction methods in respiratory clinical samples.

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