Dataset Information

Metagenomic Approach for Identification of the Pathogens Associated with Diarrhea in Stool Specimens.

ABSTRACT: The potential to rapidly capture the entire microbial community structure and/or gene content makes metagenomic sequencing an attractive tool for pathogen identification and the detection of resistance/virulence genes in clinical settings. Here, we assessed the consistency between PCR from a diagnostic laboratory, quantitative PCR (qPCR) from a research laboratory, 16S rRNA gene sequencing, and metagenomic shotgun sequencing (MSS) for Clostridium difficile identification in diarrhea stool samples. Twenty-two C. difficile-positive diarrhea samples identified by PCR and qPCR and five C. difficile-negative diarrhea controls were studied. C. difficile was detected in 90.9% of C. difficile-positive samples using 16S rRNA gene sequencing, and C. difficile was detected in 86.3% of C. difficile-positive samples using MSS. CFU inferred from qPCR analysis were positively correlated with the relative abundance of C. difficile from 16S rRNA gene sequencing (r(2) = -0.60) and MSS (r(2) = -0.55). C. difficile was codetected with Clostridium perfringens, norovirus, sapovirus, parechovirus, and anellovirus in 3.7% to 27.3% of the samples. A high load of Candida spp. was found in a symptomatic control sample in which no causative agents for diarrhea were identified in routine clinical testing. Beta-lactamase and tetracycline resistance genes were the most prevalent (25.9%) antibiotic resistance genes in these samples. In summary, the proof-of-concept study demonstrated that next-generation sequencing (NGS) in pathogen detection is moderately correlated with laboratory testing and is advantageous in detecting pathogens without a priori knowledge.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC4733167 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Metagenomic Approach for Identification of the Pathogens Associated with Diarrhea in Stool Specimens.

Zhou Yanjiao Y Wylie Kristine M KM El Feghaly Rana E RE Mihindukulasuriya Kathie A KA Elward Alexis A Haslam David B DB Storch Gregory A GA Weinstock George M GM

Journal of clinical microbiology 20151204 2

The potential to rapidly capture the entire microbial community structure and/or gene content makes metagenomic sequencing an attractive tool for pathogen identification and the detection of resistance/virulence genes in clinical settings. Here, we assessed the consistency between PCR from a diagnostic laboratory, quantitative PCR (qPCR) from a research laboratory, 16S rRNA gene sequencing, and metagenomic shotgun sequencing (MSS) for Clostridium difficile identification in diarrhea stool sample ...[more]

PMID: 26637379

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Project description:UNLABELLED:Screening for thousands of viruses and other pathogenic microorganisms, including bacteria, fungi, and parasites, in human tumor tissues will provide a better understanding of the contributory role of the microbiome in the predisposition for, causes of, and therapeutic responses to the associated cancer. Metagenomic assays designed to perform these tasks will have to include rapid and economical processing of large numbers of samples, supported by straightforward data analysis pipeline and flexible sample preparation options for multiple input tissue types from individual patients, mammals, or environmental samples. To meet these requirements, the PathoChip platform was developed by targeting viral, prokaryotic, and eukaryotic genomes with multiple DNA probes in a microarray format that can be combined with a variety of upstream sample preparation protocols and downstream data analysis. PathoChip screening of DNA plus RNA from formalin-fixed, paraffin-embedded tumor tissues demonstrated the utility of this platform, and the detection of oncogenic viruses was validated using independent PCR and deep sequencing methods. These studies demonstrate the use of the PathoChip technology combined with PCR and deep sequencing as a valuable strategy for detecting the presence of pathogens in human cancers and other diseases. IMPORTANCE:This work describes the design and testing of a PathoChip array containing probes with the ability to detect all known publicly available virus sequences as well as hundreds of pathogenic bacteria, fungi, parasites, and helminths. PathoChip provides wide coverage of microbial pathogens in an economical format. PathoChip screening of DNA plus RNA from formalin-fixed, paraffin-embedded tumor tissues demonstrated the utility of this platform, and the detection of oncogenic viruses was validated using independent PCR and sequencing methods. These studies demonstrate that the PathoChip technology is a valuable strategy for detecting the presence of pathogens in human cancers and other diseases.

Project description:BackgroundVirus screening and viral genome reconstruction are urgent and crucial for the rapid identification of viral pathogens, i.e., tracing the source and understanding the pathogenesis when a viral outbreak occurs. Next-generation sequencing (NGS) provides an efficient and unbiased way to identify viral pathogens in host-associated and environmental samples without prior knowledge. Despite the availability of software, data analysis still requires human operations. A mature pipeline is urgently needed when thousands of viral pathogen and viral genome reconstruction samples need to be rapidly identified.ResultsIn this paper, we present a rapid and accurate workflow to screen metagenomics sequencing data for viral pathogens and other compositions, as well as enable a reference-based assembler to reconstruct viral genomes. Moreover, we tested our workflow on several metagenomics datasets, including a SARS-CoV-2 patient sample with NGS data, pangolins tissues with NGS data, Middle East Respiratory Syndrome (MERS)-infected cells with NGS data, etc. Our workflow demonstrated high accuracy and efficiency when identifying target viruses from large scale NGS metagenomics data. Our workflow was flexible when working with a broad range of NGS datasets from small (kb) to large (100 Gb). This took from a few minutes to a few hours to complete each task. At the same time, our workflow automatically generates reports that incorporate visualized feedback (e.g., metagenomics data quality statistics, host and viral sequence compositions, details about each of the identified viral pathogens and their coverages, and reassembled viral pathogen sequences based on their closest references).ConclusionsOverall, our system enabled the rapid screening and identification of viral pathogens from metagenomics data, providing an important piece to support viral pathogen research during a pandemic. The visualized report contains information from raw sequence quality to a reconstructed viral sequence, which allows non-professional people to screen their samples for viruses by themselves (Additional file 1).

Project description:Short-chain fatty acids (SCFA) are principal nutrient substrates of intestinal epithelial cells that regulate the epithelial barrier in yaks. Until now, metagenomics sequencing has not been reported in diarrheal yaks. Scarce information is available regarding the levels of fecal SCFA and diarrhea in yaks. So, our study aims to identify the potential pathogens that cause the emerging diarrhea and explore the potential relationship of short-chain fatty acids in this issue. We estimated diarrhea rate in yaks after collecting an equal number of fecal samples from affected animals. Metagenomics sequencing and quantitative analysis of SCFA were performed, which revealed 15%-25% and 5%-10% prevalence of diarrhea in yak's calves and adults, respectively. Violin box plot also showed a higher degree of dispersion in gene abundance distribution of diarrheal yaks, as compared to normal yaks. We found 366,163 significant differential abundance genes in diarrheal yaks, with 141,305 upregulated and 224,858 downregulated genes compared with normal yaks via DESeq analysis. Metagenomics binning analysis indicated the higher significance of bin 33 (Bacteroidales) (p < 0.05) in diarrheal animals, while bin 10 (p < 0.0001), bin 30 (Clostridiales) (p < 0.05), bin 51 (Lactobacillales) (p < 0.05), bin 8 (Lachnospiraceae) (p < 0.05), and bin 47 (Bacteria) (p < 0.05) were significantly higher in normal yaks. At different levels, a significant difference in phylum (n = 4), class (n = 8), oder (n = 8), family (n = 16), genus (n = 17), and species (n = 30) was noticed, respectively. Compared with healthy yaks, acetic acid (p < 0.01), propionic acid (p < 0.01), butyric acid (p < 0.01), isobutyric acid (p < 0.01), isovaleric acid (p < 0.05), and caproic acid (p < 0.01) were all observed significantly at a lower rate in diarrheal yaks. In conclusion, besides the increased Staphylococcus aureus, Babesia ovata, Anaplasma phagocytophilum, Bacteroides fluxus, viruses, Klebsiella pneumonia, and inflammation-related bacteria, the decrease of SCFA caused by the imbalance of intestinal microbiota was potentially observed in diarrheal yaks.

Dataset Information

Metagenomic Approach for Identification of the Pathogens Associated with Diarrhea in Stool Specimens.

Publications

Metagenomic Approach for Identification of the Pathogens Associated with Diarrhea in Stool Specimens.

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