Dataset Information

Reconstruction of the Genomes of Drug-Resistant Pathogens for Outbreak Investigation through Metagenomic Sequencing.

ABSTRACT: Culture-independent methods that target genome fragments have shown promise in identifying certain pathogens, but the holy grail of comprehensive pathogen genome detection from microbiologically complex samples for subsequent forensic analyses remains a challenge. In the context of an investigation of a nosocomial outbreak, we used shotgun metagenomic sequencing of a human fecal sample and a neural network algorithm based on tetranucleotide frequency profiling to reconstruct microbial genomes and tested the same approach using rectal swabs from a second patient. The approach rapidly and readily detected the genome of Klebsiella pneumoniae carbapenemase (KPC)-producing K. pneumoniae in the patient fecal specimen and in the rectal swab sample, achieving a level of strain resolution that was sufficient for confident transmission inference during a highly clonal outbreak. The analysis also detected previously unrecognized colonization of the patient by vancomycin-resistant Enterococcus faecium, another multidrug-resistant bacterium.IMPORTANCE The study results reported here perfectly demonstrate the power and promise of clinical metagenomics to recover genome sequences of important drug-resistant bacteria and to rapidly provide rich data that inform outbreak investigations and treatment decisions, independently of the need to culture the organisms.

SUBMITTER: Mu A

PROVIDER: S-EPMC6336080 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Reconstruction of the Genomes of Drug-Resistant Pathogens for Outbreak Investigation through Metagenomic Sequencing.

Mu Andre A Kwong Jason C JC Isles Nicole S NS Gonçalves da Silva Anders A Schultz Mark B MB Ballard Susan A SA Lane Courtney R CR Carter Glen P GP Williamson Deborah A DA Seemann Torsten T Stinear Timothy P TP Howden Benjamin P BP

mSphere 20190116 1

Culture-independent methods that target genome fragments have shown promise in identifying certain pathogens, but the holy grail of comprehensive pathogen genome detection from microbiologically complex samples for subsequent forensic analyses remains a challenge. In the context of an investigation of a nosocomial outbreak, we used shotgun metagenomic sequencing of a human fecal sample and a neural network algorithm based on tetranucleotide frequency profiling to reconstruct microbial genomes an ...[more]

PMID: 30651402

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Project description:The burden of meningitis in low-and-middle-income countries remains significant, but the infectious causes remain largely unknown, impeding institution of evidence-based treatment and prevention decisions. We conducted a validation and application study of unbiased metagenomic next-generation sequencing (mNGS) to elucidate etiologies of meningitis in Bangladesh. This RNA mNGS study was performed on cerebrospinal fluid (CSF) specimens from patients admitted in the largest pediatric hospital, a World Health Organization sentinel site, with known neurologic infections (n = 36), with idiopathic meningitis (n = 25), and with no infection (n = 30), and six environmental samples, collected between 2012 and 2018. We used the IDseq bioinformatics pipeline and machine learning to identify potentially pathogenic microbes, which we then confirmed orthogonally and followed up through phone/home visits. In samples with known etiology and without infections, there was 83% concordance between mNGS and conventional testing. In idiopathic cases, mNGS identified a potential bacterial or viral etiology in 40%. There were three instances of neuroinvasive Chikungunya virus (CHIKV), whose genomes were >99% identical to each other and to a Bangladeshi strain only previously recognized to cause febrile illness in 2017. CHIKV-specific qPCR of all remaining stored CSF samples from children who presented with idiopathic meningitis in 2017 (n = 472) revealed 17 additional CHIKV meningitis cases, exposing an unrecognized meningitis outbreak. Orthogonal molecular confirmation, case-based clinical data, and patient follow-up substantiated the findings. Case-control CSF mNGS surveys can complement conventional diagnostic methods to identify etiologies of meningitis, conduct surveillance, and predict outbreaks. The improved patient- and population-level data can inform evidence-based policy decisions.IMPORTANCE Globally, there are an estimated 10.6 million cases of meningitis and 288,000 deaths every year, with the vast majority occurring in low- and middle-income countries. In addition, many survivors suffer from long-term neurological sequelae. Most laboratories assay only for common bacterial etiologies using culture and directed PCR, and the majority of meningitis cases lack microbiological diagnoses, impeding institution of evidence-based treatment and prevention strategies. We report here the results of a validation and application study of using unbiased metagenomic sequencing to determine etiologies of idiopathic (of unknown cause) cases. This included CSF from patients with known neurologic infections, with idiopathic meningitis, and without infection admitted in the largest children's hospital of Bangladesh and environmental samples. Using mNGS and machine learning, we identified and confirmed an etiology (viral or bacterial) in 40% of idiopathic cases. We detected three instances of Chikungunya virus (CHIKV) that were >99% identical to each other and to a strain previously recognized to cause systemic illness only in 2017. CHIKV qPCR of all remaining stored 472 CSF samples from children who presented with idiopathic meningitis in 2017 at the same hospital uncovered an unrecognized CHIKV meningitis outbreak. CSF mNGS can complement conventional diagnostic methods to identify etiologies of meningitis, and the improved patient- and population-level data can inform better policy decisions.

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.

Project description:BackgroundMycobacterium tuberculosis whole-genome sequencing (WGS) has been widely used for genotypic drug susceptibility testing (DST) and outbreak investigation. For both applications, Illumina technology is used by most public health laboratories; however, Nanopore technology developed by Oxford Nanopore Technologies has not been thoroughly evaluated. The aim of this study was to determine whether Nanopore sequencing data can provide equivalent information to Illumina for transmission clustering and genotypic DST for M tuberculosis.MethodsIn this genomic analysis, we analysed 151 M tuberculosis isolates from Madagascar, South Africa, and England, which were collected between 2011 and 2018, using phenotypic DST and matched Illumina and Nanopore data. Illumina sequencing was done with the MiSeq, HiSeq 2500, or NextSeq500 platforms and Nanopore sequencing was done on the MinION or GridION platforms. Using highly reliable PacBio sequencing assemblies and pairwise distance correlation between Nanopore and Illumina data, we optimise Nanopore variant filters for detecting single-nucleotide polymorphisms (SNPs; using BCFtools software). We then used those SNPs to compare transmission clusters identified by Nanopore with the currently used UK Health Security Agency Illumina pipeline (COMPASS). We compared Illumina and Nanopore WGS-based DST predictions using the Mykrobe software and mutation catalogue.FindingsThe Nanopore BCFtools pipeline identified SNPs with a median precision of 99·3% (IQR 99·1-99·6) and recall of 90·2% (88·1-94·2) compared with a precision of 99·6% (99·4-99·7) and recall of 91·9% (87·6-98·6) using the Illumina COMPASS pipeline. Using a threshold of 12 SNPs for putative transmission clusters, Illumina identified 98 isolates as unrelated and 53 as belonging to 19 distinct clusters (size range 2-7). Nanopore reproduced 15 out of 19 clusters perfectly; two clusters were merged into one cluster, one cluster had a single sample missing, and one cluster had an additional sample adjoined. Illumina-based clusters were also closely replicated using a five SNP threshold and clustering accuracy was maintained using mixed Illumina and Nanopore datasets. Genotyping resistance variants with Nanopore was highly concordant with Illumina, having zero discordant SNPs across more than 3000 SNPs and four insertions or deletions (indels), across 60 000 indels.InterpretationIllumina and Nanopore technologies can be used independently or together by public health laboratories performing M tuberculosis genotypic DST and outbreak investigations. As a result, clinical and public health institutions making decisions on which sequencing technology to adopt for tuberculosis can base the choice on cost (which varies by country), batching, and turnaround time.FundingAcademy for Medical Sciences, Oxford Wellcome Institutional Strategic Support Fund, and the Swiss South Africa Joint Research Award (Swiss National Science Foundation and South African National Research Foundation).

Dataset Information

Reconstruction of the Genomes of Drug-Resistant Pathogens for Outbreak Investigation through Metagenomic Sequencing.

Publications

Reconstruction of the Genomes of Drug-Resistant Pathogens for Outbreak Investigation through Metagenomic Sequencing.

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