Clinical Metagenomic Sequencing for Species Identification and Antimicrobial Resistance Prediction in Orthopaedic Device Infection

Background Diagnosis of orthopaedic device-related infection is challenging, and causative pathogens may be difficult to culture. Metagenomic sequencing can diagnose infections without culture, but attempts to detect antimicrobial resistance (AMR) determinants using metagenomic data have been less successful. Human DNA depletion may maximise the amount of microbial DNA sequence data available for analysis. Methods Human DNA depletion by saponin was tested in 115 sonication fluid samples generated following revision arthroplasty surgery, comprising 67 where pathogens were detected by culture and 48 culture-negative samples. Metagenomic sequencing was performed on the Oxford Nanopore Technologies GridION platform. Filtering thresholds for detection of true species versus contamination or taxonomic misclassification were determined. Mobile and chromosomal genetic AMR determinants were identified in Staphylococcus aureus-positive samples. Results Of 114 samples generating sequence data, species-level sensitivity of metagenomic sequencing was 49/65 (75%; 95%CI 63-85%) and specificity 103/114 (90%; 95%CI 83-95%) compared with culture. Saponin treatment reduced the proportion of human bases sequenced in comparison to 5um filtration from a median (IQR) 98.1% (87.0%-99.9%) to 11.9% (0.4%-67.0%), improving reference genome coverage at 10-fold depth from 18.7% (0.30%-85.7%) to 84.3% (12.9%-93.8%). Metagenomic sequencing predicted 13/15 (87%) resistant and 74/74 (100%) susceptible phenotypes where sufficient data were available for analysis. Conclusions Metagenomic nanopore sequencing coupled with human DNA depletion has the potential to detect AMR in addition to species detection in orthopaedic device-related infection. Further work is required to develop pathogen-agnostic human DNA depletion methods, improving AMR determinant detection and allowing its application to other infection types.

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MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data

F1000Research ◽

10.12688/f1000research.18866.2 ◽

2019 ◽

Vol 8 ◽

pp. 726

Author(s):

Mike W.C. Thang ◽

Xin-Yi Chua ◽

Gareth Price ◽

Dominique Gorse ◽

Matt A. Field

Keyword(s):

Microbial Communities ◽

Sequence Data ◽

Metagenomic Data ◽

Marker Genes ◽

Metagenomic Sequencing ◽

Differential Analysis ◽

Biomedical Sciences ◽

Metagenomic Sequence ◽

Differential Abundance ◽

Differential Abundance Analysis

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences. While software for detailing the composition of microbial communities using 16S rRNA marker genes is relatively mature, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results. Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs. Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files. Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics. MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.

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Molecular diagnosis of orthopaedic device infection direct from sonication fluid by metagenomic sequencing

10.1101/118026 ◽

2017 ◽

Author(s):

Teresa L. Street ◽

Nicholas D. Sanderson ◽

Bridget L. Atkins ◽

Andrew J. Brent ◽

Kevin Cole ◽

...

Keyword(s):

Diagnostic Information ◽

Species Level ◽

Metagenomic Sequencing ◽

Tissue Samples ◽

Bacterial Dna ◽

Dna Contamination ◽

Prosthetic Joint ◽

Device Infection ◽

Fluid Culture ◽

Orthopaedic Device

AbstractCulture of multiple periprosthetic tissue samples is the current gold-standard for microbiological diagnosis of prosthetic joint infections (PJI). Additional diagnostic information may be obtained through sonication fluid culture of explants. However, current techniques can have relatively low sensitivity, with prior antimicrobial therapy and infection by fastidious organisms influencing results. We assessed if metagenomic sequencing of complete bacterial DNA extracts obtained direct from sonication fluid can provide an alternative rapid and sensitive tool for diagnosis of PJI.We compared metagenomic sequencing with standard aerobic and anaerobic culture in 97 sonication fluid samples from prosthetic joint and other orthopaedic device infections. Reads from Illumina MiSeq sequencing were taxonomically classified using Kraken. Using 50 samples (derivation set), we determined optimal thresholds for the number and proportion of bacterial reads required to identify an infection and validated our findings in 47 independent samples.Compared to sonication fluid culture, the species-level sensitivity of metagenomic sequencing was 61/69(88%,95%CI 77-94%) (derivation samples 35/38[92%,79-98%]; validation 26/31[84%,66-95%]), and genus-level sensitivity was 64/69(93%,84-98%). Species-level specificity, adjusting for plausible fastidious causes of infection, species found in concurrently obtained tissue samples, and prior antibiotics, was 85/97(88%,79-93%) (derivation 43/50[86%,73-94%], validation 42/47[89%,77-96%]). High levels of human DNA contamination were seen despite use of laboratory methods to remove it. Rigorous laboratory good practice was required to prevent bacterial DNA contamination.We demonstrate metagenomic sequencing can provide accurate diagnostic information in PJI. Our findings combined with increasing availability of portable, random-access sequencing technology offers the potential to translate metagenomic sequencing into a rapid diagnostic tool in PJI.

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Technical note: overcoming host contamination in bovine vaginal metagenomic samples with Nanopore adaptive sequencing

Journal of Animal Science ◽

10.1093/jas/skab344 ◽

2021 ◽

Author(s):

Chian Teng Ong ◽

Elizabeth M Ross ◽

Gry B Boe-Hansen ◽

Conny Turni ◽

Ben J Hayes ◽

...

Keyword(s):

16S Rdna ◽

Sequence Data ◽

Amplicon Sequencing ◽

Technical Note ◽

Metagenomic Data ◽

High Coverage ◽

Long Reads ◽

Oxford Nanopore ◽

16S Rdna Amplicon Sequencing ◽

Functional Profiles

Abstract Animal metagenomic studies, in which host-associated microbiomes are profiled, are an increasingly important contribution to our understanding of the physiological functions, health and susceptibility to diseases of livestock. One of the major challenges in these studies is host DNA contamination, which limits the sequencing capacity for metagenomic content and reduces the accuracy of metagenomic profiling. This is the first study comparing the effectiveness of different sequencing methods for profiling bovine vaginal metagenomic samples. We compared the new method of Oxford Nanopore Technologies (ONT) adaptive sequencing, which can be used to target or eliminate defined genetic sequences, to standard ONT sequencing, Illumina 16S rDNA amplicon sequencing, and Illumina shotgun sequencing. The efficiency of each method in recovering the metagenomic data and recalling the metagenomic profiles was assessed. ONT adaptive sequencing yielded a higher amount of metagenomic data than the other methods per 1 Gb of sequence data. The increased sequencing efficiency of ONT adaptive sequencing consequently reduced the amount of raw data needed to provide sufficient coverage for the metagenomic samples with high host-to-microbe DNA ratio. Additionally, the long reads generated by ONT adaptive sequencing retained the continuity of read information, which benefited the in-depth annotations for both taxonomical and functional profiles of the metagenome. The different methods resulted in the identification of different taxa. Genera Clostridium, which was identified at low abundances and categorised under Order “Unclassified Clostridiales” when using the 16S rDNA amplicon sequencing method, was identified to be the dominant genera in the sample when sequenced with the three other methods. Additionally, higher numbers of annotated genes were identified with ONT adaptive sequencing, which also produced high coverage on most of the commonly annotated genes. This study illustrates the advantages of ONT adaptive sequencing in improving the amount of metagenomic data derived from microbiome samples with high host-to-microbe DNA ratio and the advantage of long reads in preserving intact information for accurate annotations.

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Fast functional annotation of metagenomic shotgun data by DNA alignment to a microbial gene catalog

10.1101/120402 ◽

2017 ◽

Author(s):

Stuart M. Brown ◽

Yuhan Hao ◽

Hao Chen ◽

Bobby P. Laungani ◽

Thahmina A. Ali ◽

...

Keyword(s):

Functional Annotation ◽

Sequence Data ◽

Human Microbiome ◽

Metagenomic Data ◽

Metagenomic Sequencing ◽

Alternative Analysis ◽

Metagenomic Sequence ◽

Shotgun Metagenomics ◽

Gene Functions ◽

Dna Alignment

AbstractBackgroundMetagenomic shotgun sequencing is becoming increasingly popular to study microbes associated with the human body and in environmental samples. A key goal of shotgun metagenomic sequencing is to identify gene functions and metabolic pathways that differ between samples or conditions. However, current methods to identify function in the large number of reads in a high-throughput sequence data file rely on the computationally intensive and low stringency approach of mapping each read to a generic database of proteins or reference microbial genomes.ResultsWe have developed an alternative analysis approach for shotgun metagenomic sequence data utilizing Bowtie2 DNA-DNA alignment of the reads to a database of well annotated genes compiled from human microbiome data. This method is rapid, and provides high stringency matches (>90% DNA sequence identity) of shotgun metagenomics reads to genes with annotated functions. We demonstrate the use of this method with synthetic data, Human Microbiome Project shotgun metagenomic data sets, and data from a study of liver disease. Differentially abundant KEGG gene functions can be detected in these experiments.ConclusionsFunctional annotation of metagenomic shotgun sequence reads can be accomplished by rapid DNA-DNA matching to a custom database of microbial sequences using the Bowtie2 sequence alignment tool. This method can be used for a variety of microbiome studies and allows functional analysis which is otherwise computationally demanding. This rapid annotation method is freely available as a Galaxy workflow within a Docker image.

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MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data

F1000Research ◽

10.12688/f1000research.18866.1 ◽

2019 ◽

Vol 8 ◽

pp. 726 ◽

Cited By ~ 1

Author(s):

Mike W.C. Thang ◽

Xin-Yi Chua ◽

Gareth Price ◽

Dominique Gorse ◽

Matt A. Field

Keyword(s):

Microbial Communities ◽

Sequence Data ◽

Metagenomic Data ◽

Marker Genes ◽

Metagenomic Sequencing ◽

Differential Analysis ◽

Biomedical Sciences ◽

Metagenomic Sequence ◽

Differential Abundance ◽

Differential Abundance Analysis

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences yet analysis workflows remain immature relative to other field such as DNASeq and RNASeq analysis pipelines. While software for detailing the composition of microbial communities using 16S rRNA marker genes is constantly improving, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results. Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs. Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files. Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics. MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.

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MetaLAFFA: a flexible, end-to-end, distributed computing-compatible metagenomic functional annotation pipeline

BMC Bioinformatics ◽

10.1186/s12859-020-03815-9 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Alexander Eng ◽

Adrian J. Verster ◽

Elhanan Borenstein

Keyword(s):

Distributed Computing ◽

Functional Annotation ◽

Sequence Data ◽

Metagenomic Data ◽

Metagenomic Sequencing ◽

Distributed Computing Systems ◽

Annotation Pipeline ◽

Shotgun Metagenomic Sequencing ◽

End To End ◽

Functional Profiles

Abstract Background Microbial communities have become an important subject of research across multiple disciplines in recent years. These communities are often examined via shotgun metagenomic sequencing, a technology which can offer unique insights into the genomic content of a microbial community. Functional annotation of shotgun metagenomic data has become an increasingly popular method for identifying the aggregate functional capacities encoded by the community’s constituent microbes. Currently available metagenomic functional annotation pipelines, however, suffer from several shortcomings, including limited pipeline customization options, lack of standard raw sequence data pre-processing, and insufficient capabilities for integration with distributed computing systems. Results Here we introduce MetaLAFFA, a functional annotation pipeline designed to take unfiltered shotgun metagenomic data as input and generate functional profiles. MetaLAFFA is implemented as a Snakemake pipeline, which enables convenient integration with distributed computing clusters, allowing users to take full advantage of available computing resources. Default pipeline settings allow new users to run MetaLAFFA according to common practices while a Python module-based configuration system provides advanced users with a flexible interface for pipeline customization. MetaLAFFA also generates summary statistics for each step in the pipeline so that users can better understand pre-processing and annotation quality. Conclusions MetaLAFFA is a new end-to-end metagenomic functional annotation pipeline with distributed computing compatibility and flexible customization options. MetaLAFFA source code is available at https://github.com/borenstein-lab/MetaLAFFA and can be installed via Conda as described in the accompanying documentation.

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Metagenomic sequencing as a clinical diagnostic tool for infectious diseases: a systematic review and meta-analysis

10.1101/2020.03.30.20043901 ◽

2020 ◽

Author(s):

Kumeren N. Govender ◽

Teresa L. Street ◽

Nicholas D. Sanderson ◽

David W. Eyre

Keyword(s):

Systematic Review ◽

Antimicrobial Resistance ◽

Infectious Diseases ◽

Meta Analysis ◽

Metagenomic Sequencing ◽

Human Dna ◽

Culture Independent ◽

Resistance Prediction ◽

Negative Controls ◽

Near Future

SummaryBackgroundMetagenomics has the potential to revolutionise infectious diseases diagnostics, from rapid species and antimicrobial resistance prediction, to finding unrecognised and sometimes untreated infections. Our aim was to summarise all literature on culture-independent metagenomic sequencing to describe the accuracy of species and antimicrobial resistance prediction and, describe the challenges and progress in the field.MethodsWe conducted a systematic review with meta-analysis from eligible studies retrieved from PubMed, Google Scholar and bioRxiv and, assessed risk of bias and quality using the QUADAS-2 tool. This study is registered with PROSPERO, number CRD42020163777.FindingsWe identified 36 studies, 22 of which used a species-agnostic approach to identify all possible pathogens. In these studies, the overall sensitivity and specificity of pathogen species detection were 88% (95%CI 81-92%) and 86% (95%CI 70-94%) respectively. Antimicrobial resistance prediction and comparison to phenotypic results was undertaken in six studies. Categorical agreement was 83% (95%CI 68-92%), very major (prediction sensitive, phenotype resistant) and major error (prediction resistant, phenotype sensitive) rates were 9% (95%CI 2-27%) and 1% (95%CI 0-20%) respectively. We report limited use of negative controls in studies 61% (22/36) which contribute to a major challenge of discriminating true pathogens from contamination, where there is no convergence on methodology. More efficient human DNA depletion methods are required as a median of 79% (IQR 62-96) [Range 7-98] of sequences were classified as human despite laboratory depletion techniques. The median time from sample to result was 23·5 hours (7-31) [4-144], with sequencing time accounting 10 hours (4·8-16) [1-16]. The average reported consumables cost per sample ranged from $128 to $685.InterpretationThe science and regulatory environment are rapidly developing, and its role as a routine test or test of last resort still needs to be determined, however it is likely that clinical metagenomics will be an increasing part of the clinician’s armamentarium to diagnose infectious diseases in the near future.FundingNone.

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Real-time analysis of nanopore-based metagenomic sequencing from orthopaedic device infection

10.1101/220616 ◽

2017 ◽

Cited By ~ 2

Author(s):

Nicholas D Sanderson ◽

Teresa L Street ◽

Dona Foster ◽

Jeremy Swann ◽

Bridget L. Atkins ◽

...

Keyword(s):

Real Time ◽

Sequence Data ◽

Metagenomic Sequencing ◽

Nanopore Sequencing ◽

Time Analysis ◽

Real Time Analysis ◽

Joint Infections ◽

Prosthetic Joint ◽

Device Infection ◽

Prosthetic Joint Infections

AbstractProsthetic joint infections are clinically difficult to diagnose and treat. Previously, we demonstrated metagenomic sequencing on an Illumina MiSeq replicates the findings of current gold standard microbiological diagnostic techniques. Nanopore sequencing offers advantages in speed of detection over MiSeq. Here, we compare direct-from-clinical-sample metagenomic Illumina sequencing with Nanopore sequencing, and report a real-time analytical pathway for Nanopore sequence data, designed for detecting bacterial composition of prosthetic joint infections.DNA was extracted from the sonication fluids of seven explanted orthopaedic devices, and additionally from two culture negative controls, and was sequenced on the Oxford Nanopore Technologies MinION platform. A specific analysis pipeline was assembled to overcome the challenges of identifying the true infecting pathogen, given high levels of host contamination and unavoidable background lab and kit contamination.The majority of DNA classified (>90%) was host contamination and discarded. Using negative control filtering thresholds, the species identified corresponded with both routine microbiological diagnosis and MiSeq results. By analysing sequences in real time, causes of infection were robustly detected within minutes from initiation of sequencing.We demonstrate initial proof of concept that metagenomic MinION sequencing can provide rapid, accurate diagnosis for prosthetic joint infections. We demonstrate a novel, scalable pipeline for real-time analysis of MinION sequence data. The high proportion of human DNA in extracts prevents full genome analysis from complete coverage, and methods to reduce this could increase genome depth and allow antimicrobial resistance profiling.

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Microbe Finder (MiFi®): Implementation of an Interactive Pathogen Detection Tool in Metagenomic Sequence Data

Plants ◽

10.3390/plants10020250 ◽

2021 ◽

Vol 10 (2) ◽

pp. 250

Author(s):

Andres S. Espindola ◽

Kitty F. Cardwell

Keyword(s):

High Throughput ◽

Web Application ◽

Pathogen Detection ◽

High Throughput Sequencing ◽

Sequence Data ◽

Metagenomic Data ◽

Diagnostic Tools ◽

Metagenomic Sequencing ◽

Metagenomic Sequence ◽

Metagenomic Sequence Data

Agricultural high throughput diagnostics need to be fast, accurate and have multiplexing capacity. Metagenomic sequencing is being widely evaluated for plant and animal diagnostics. Bioinformatic analysis of metagenomic sequence data has been a bottleneck for diagnostic analysis due to the size of the data files. Most available tools for analyzing high-throughput sequencing (HTS) data require that the user have computer coding skills and access to high-performance computing. To overcome constraints to most sequencing-based diagnostic pipelines today, we have developed Microbe Finder (MiFi®). MiFi® is a web application for quick detection and identification of known pathogen species/strains in raw, unassembled HTS metagenomic data. HTS-based diagnostic tools developed through MiFi® must pass rigorous validation, which is outlined in this manuscript. MiFi® allows researchers to collaborate in the development and validation of HTS-based diagnostic assays using MiProbe™, a platform used for developing pathogen-specific e-probes. Validated e-probes are made available to diagnosticians through MiDetect™. Here we describe the e-probe development, curation and validation process of MiFi® using grapevine pathogens as a model system. MiFi® can be used with any pathosystem and HTS platform after e-probes have been validated.

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Clin-mNGS: Automated Pipeline for Pathogen Detection from Clinical Metagenomic Data

Current Bioinformatics ◽

10.2174/1574893615999200608130029 ◽

2020 ◽

Vol 15 ◽

Author(s):

Akshatha Prasanna ◽

Vidya Niranjan

Keyword(s):

Antimicrobial Resistance ◽

High Performance ◽

Pathogen Detection ◽

Bacterial Species ◽

Workflow Management ◽

Metagenomic Data ◽

Antimicrobial Resistance Genes ◽

Culture Independent ◽

Automated Pipeline ◽

User Friendly

Background: Since bacteria are the earliest known organisms, there has been significant interest in their variety and biology, most certainly concerning human health. Recent advances in Metagenomics sequencing (mNGS), a culture-independent sequencing technology have facilitated an accelerated development in clinical microbiology and our understanding of pathogens. Objective: For the implementation of mNGS in routine clinical practice to become feasible, a practical and scalable strategy for the study of mNGS data is essential. This study presents a robust automated pipeline to analyze clinical metagenomic data for pathogen identification and classification. Method: The proposed Clin-mNGS pipeline is an integrated, open-source, scalable, reproducible, and user-friendly framework scripted using the Snakemake workflow management software. The implementation avoids the hassle of manual installation and configuration of the multiple command-line tools and dependencies. The approach directly screens pathogens from clinical raw reads and generates consolidated reports for each sample. Results: The pipeline is demonstrated using publicly available data and is tested on a desktop Linux system and a High-performance cluster. The study compares variability in results from different tools and versions. The versions of the tools are made user modifiable. The pipeline results in quality check, filtered reads, host subtraction, assembled contigs, assembly metrics, relative abundances of bacterial species, antimicrobial resistance genes, plasmid finding, and virulence factors identification. The results obtained from the pipeline are evaluated based on sensitivity and positive predictive value. Conclusion: Clin-mNGS is an automated Snakemake pipeline validated for the analysis of microbial clinical metagenomics reads to perform taxonomic classification and antimicrobial resistance prediction.

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