Clinical performance characteristics of the Swift Normalase Amplicon Panel for sensitive recovery of SARS-CoV-2 genomes

Amplicon-based sequencing methods have been central in characterizing the diversity, transmission and evolution of SARS-CoV-2, but need to be rigorously assessed for clinical utility. Here, we validated the Swift Biosciences SARS-CoV-2 Swift Normalase Amplicon Panels using remnant clinical specimens. High quality genomes meeting our established library and sequence quality criteria were recovered from positive specimens with a 95% limit of detection of 40.08 SARS-CoV-2 copies/PCR reaction. Breadth of genome recovery was evaluated across a range of Ct values (11.3 - 36.7, median 21.6). Out of 428 positive samples, 406 (94.9%) generated genomes with < 10% Ns, with a mean genome coverage of 13,545X/SD 8,382X. No genomes were recovered from PCR-negative specimens (n = 30), or from specimens positive for non-SARS-CoV-2 respiratory viruses (n = 20). Compared to whole-genome shotgun metagenomic sequencing (n = 14) or Sanger sequencing for the spike gene (n = 11), pairwise identity between consensus sequences was 100% in all cases, with highly concordant allele frequencies (R2 = 0.99) between Swift and shotgun libraries. When samples from different clades were mixed at varying ratios, expected variants were detected even in 1:99 mixtures. When deployed as a clinical test, 268 tests were performed in the first 23 weeks with a median turnaround time of 11 days, ordered primarily for outbreak investigations and infection control.

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Culture-Independent Genotyping, Virulence and Antimicrobial Resistance Gene Identification of Staphylococcus aureus from Orthopaedic Implant-Associated Infections

Microorganisms ◽

10.3390/microorganisms9040707 ◽

2021 ◽

Vol 9 (4) ◽

pp. 707

Author(s):

J. Christopher Noone ◽

Fabienne Antunes Ferreira ◽

Hege Vangstein Aamot

Keyword(s):

Staphylococcus Aureus ◽

Antimicrobial Resistance ◽

Resistance Genes ◽

Virulence Gene ◽

Orthopaedic Implant ◽

Metagenomic Sequencing ◽

Gene Detection ◽

Shotgun Metagenomic Sequencing ◽

Antimicrobial Resistance Genes ◽

Culture Independent

Our culture-independent nanopore shotgun metagenomic sequencing protocol on biopsies has the potential for same-day diagnostics of orthopaedic implant-associated infections (OIAI). As OIAI are frequently caused by Staphylococcus aureus, we included S. aureus genotyping and virulence gene detection to exploit the protocol to its fullest. The aim was to evaluate S. aureus genotyping, virulence and antimicrobial resistance genes detection using the shotgun metagenomic sequencing protocol. This proof of concept study included six patients with S. aureus-associated OIAI at Akershus University Hospital, Norway. Five tissue biopsies from each patient were divided in two: (1) conventional microbiological diagnostics and genotyping, and whole genome sequencing (WGS) of S. aureus isolates; (2) shotgun metagenomic sequencing of DNA from the biopsies. Consensus sequences were analysed using spaTyper, MLST, VirulenceFinder, and ResFinder from the Center for Genomic Epidemiology (CGE). MLST was also compared using krocus. All spa-types, one CGE and four krocus MLST results matched Sanger sequencing results. Virulence gene detection matched between WGS and shotgun metagenomic sequencing. ResFinder results corresponded to resistance phenotype. S. aureus spa-typing, and identification of virulence and antimicrobial resistance genes are possible using our shotgun metagenomics protocol. MLST requires further optimization. The protocol has potential application to other species and infection types.

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Discriminating between JCPyV and BKPyV in Urinary Virome Data Sets

Viruses ◽

10.3390/v13061041 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1041

Author(s):

Rita Mormando ◽

Alan J. Wolfe ◽

Catherine Putonti

Keyword(s):

Jc Virus ◽

Sequence Similarity ◽

Bk Virus ◽

Data Sets ◽

Metagenomic Sequencing ◽

Significant Sequence Similarity ◽

Sequence Identity ◽

Shotgun Metagenomic Sequencing ◽

Urinary Microbiome ◽

Six Genes

Polyomaviruses are abundant in the human body. The polyomaviruses JC virus (JCPyV) and BK virus (BKPyV) are common viruses in the human urinary tract. Prior studies have estimated that JCPyV infects between 20 and 80% of adults and that BKPyV infects between 65 and 90% of individuals by age 10. However, these two viruses encode for the same six genes and share 75% nucleotide sequence identity across their genomes. While prior urinary virome studies have repeatedly reported the presence of JCPyV, we were interested in seeing how JCPyV prevalence compares to BKPyV. We retrieved all publicly available shotgun metagenomic sequencing reads from urinary microbiome and virome studies (n = 165). While one third of the data sets produced hits to JCPyV, upon further investigation were we able to determine that the majority of these were in fact BKPyV. This distinction was made by specifically mining for JCPyV and BKPyV and considering uniform coverage across the genome. This approach provides confidence in taxon calls, even between closely related viruses with significant sequence similarity.

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Automation and validation of a MALDI-TOF MS (Mass-Fix) replacement of immunofixation electrophoresis in the clinical lab

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2020-0581 ◽

2021 ◽

Vol 59 (1) ◽

pp. 155-163

Author(s):

Mindy Kohlhagen ◽

Surendra Dasari ◽

Maria Willrich ◽

MeLea Hetrick ◽

Brian Netzel ◽

...

Keyword(s):

Limit Of Detection ◽

Turnaround Time ◽

Automated System ◽

Serum Samples ◽

Maldi Tof Ms ◽

Maldi Tof ◽

Automated Method ◽

Positive Specimen ◽

Specimen Identification ◽

Tof Ms

AbstractObjectivesA matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) method (Mass-Fix) as a replacement for gel-based immunofixation (IFE) has been recently described. To utilize Mass-Fix clinically, a validated automated method was required. Our aim was to automate the pre-analytical processing, improve positive specimen identification and ergonomics, reduce paper data storage and increase resource utilization without increasing turnaround time.MethodsSerum samples were batched and loaded onto a liquid handler along with reagents and a barcoded sample plate. The pre-analytical steps included: (1) Plating immunopurification beads. (2) Adding 10 μl of serum. (3) Bead washing. (4) Eluting the immunoglobulins (Igs), and reducing to separate the heavy and light Ig chains. The resulting plate was transferred to a second low-volume liquid handler for MALDI plate spotting. MALDI-TOF mass spectra were collected. Integrated in-house developed software was utilized for sample tracking, driving data acquisition, data analysis, history tracking, and result reporting. A total of 1,029 residual serum samples were run using the automated system and results were compared to prior electrophoretic results.ResultsThe automated Mass-Fix method was capable of meeting the validation requirements of concordance with IFE, limit of detection (LOD), sample stability and reproducibility with a low repeat rate. Automation and integrated software allowed a single user to process 320 samples in an 8 h shift. Software display facilitated identification of monoclonal proteins. Additionally, the process maintains positive specimen identification, reduces manual pipetting, allows for paper free tracking, and does not significantly impact turnaround time (TAT).ConclusionsMass-Fix is ready for implementation in a high-throughput clinical laboratory.

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Analytical assessment of ortho clinical diagnostics high-sensitivity cardiac troponin I assay

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2020-1115 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Peter A. Kavsak ◽

Tara Edge ◽

Chantele Roy ◽

Paul Malinowski ◽

Karen Bamford ◽

...

Keyword(s):

Cardiac Troponin ◽

Troponin I ◽

Limit Of Detection ◽

Clinical Performance ◽

Characteristic Curve ◽

Area Under The Curve ◽

High Sensitivity ◽

Cardiac Troponin I ◽

Clinical Diagnostics ◽

Ortho Clinical Diagnostics

AbstractObjectivesTo analytically evaluate Ortho Clinical Diagnostics VITROS high-sensitivity cardiac troponin I (hs-cTnI) assay in specific matrices with comparison to other hs-cTn assays.MethodsThe limit of detection (LoD), imprecision, interference and stability testing for both serum and lithium heparin (Li-Hep) plasma for the VITROS hs-cTnI assay was determined. We performed Passing-Bablok regression analyses between sample types for the VITROS hs-cTnI assay and compared them to the Abbott ARCHITECT, Beckman Access and the Siemens ADVIA Centaur hs-cTnI assays. We also performed Receiver-operating characteristic curve analyses with the area under the curve (AUC) determined in an emergency department (ED)-study population (n=131) for myocardial infarction (MI).ResultsThe VITROS hs-cTnI LoD was 0.73 ng/L (serum) and 1.4 ng/L (Li-Hep). Stability up to five freeze-thaws was observed for the Ortho hs-cTnI assay, with the analyte stability at room temperature in serum superior to Li-Hep with gross hemolysis also affecting Li-Hep plasma hs-cTnI results. Comparison of Li-Hep to serum concentrations (n=202), yielded proportionally lower concentrations in plasma with the VITROS hs-cTnI assay (slope=0.85; 95% confidence interval [CI]:0.83–0.88). In serum, the VITROS hs-cTnI concentrations were proportionally lower compared to other hs-cTnI assays, with similar slopes observed between assays in samples frozen <−70 °C for 17 years (ED-study) or in 2020. In the ED-study, the VITROS hs-cTnI assay had an AUC of 0.974 (95%CI:0.929–0.994) for MI, similar to the AUCs of other hs-cTn assays.ConclusionsLack of standardization of hs-cTnI assays across manufacturers is evident. The VITROS hs-cTnI assay yields lower concentrations compared to other hs-cTnI assays. Important differences exist between Li-Hep plasma and serum, with evidence of stability and excellent clinical performance comparable to other hs-cTn assays.

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Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis

Journal of Clinical Microbiology ◽

10.1128/jcm.01182-18 ◽

2018 ◽

Vol 57 (2) ◽

Cited By ~ 8

Author(s):

Qun Yan ◽

Yu Mi Wi ◽

Matthew J. Thoendel ◽

Yash S. Raval ◽

Kerryl E. Greenwood-Quaintance ◽

...

Keyword(s):

Antibiotic Resistance ◽

Metagenomic Data ◽

Metagenomic Sequencing ◽

Antibacterial Resistance ◽

Sequencing Data ◽

Bacterial Detection ◽

Shotgun Metagenomic Sequencing ◽

Prosthetic Joint ◽

Validation Set ◽

Fluid Culture

ABSTRACT We previously demonstrated that shotgun metagenomic sequencing can detect bacteria in sonicate fluid, providing a diagnosis of prosthetic joint infection (PJI). A limitation of the approach that we used is that data analysis was time-consuming and specialized bioinformatics expertise was required, both of which are barriers to routine clinical use. Fortunately, automated commercial analytic platforms that can interpret shotgun metagenomic data are emerging. In this study, we evaluated the CosmosID bioinformatics platform using shotgun metagenomic sequencing data derived from 408 sonicate fluid samples from our prior study with the goal of evaluating the platform vis-à-vis bacterial detection and antibiotic resistance gene detection for predicting staphylococcal antibacterial susceptibility. Samples were divided into a derivation set and a validation set, each consisting of 204 samples; results from the derivation set were used to establish cutoffs, which were then tested in the validation set for identifying pathogens and predicting staphylococcal antibacterial resistance. Metagenomic analysis detected bacteria in 94.8% (109/115) of sonicate fluid culture-positive PJIs and 37.8% (37/98) of sonicate fluid culture-negative PJIs. Metagenomic analysis showed sensitivities ranging from 65.7 to 85.0% for predicting staphylococcal antibacterial resistance. In conclusion, the CosmosID platform has the potential to provide fast, reliable bacterial detection and identification from metagenomic shotgun sequencing data derived from sonicate fluid for the diagnosis of PJI. Strategies for metagenomic detection of antibiotic resistance genes for predicting staphylococcal antibacterial resistance need further development.

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Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples

10.1101/2021.05.04.21256618 ◽

2021 ◽

Author(s):

Jutte J.C. de Vries ◽

Julianne R. Brown ◽

Nicole Fischer ◽

Igor A. Sidorov ◽

Sofia Morfopoulou ◽

...

Keyword(s):

De Novo ◽

Respiratory Infections ◽

Limit Of Detection ◽

Bioinformatic Analysis ◽

Clinical Samples ◽

Mixed Infections ◽

Metagenomic Sequencing ◽

User Friendliness ◽

Viral Pathogens ◽

Clinical Diagnostic

Metagenomic sequencing is increasingly being used in clinical settings for difficult to diagnose cases. The performance of viral metagenomic protocols relies to a large extent on the bioinformatic analysis. In this study, the European Society for Clinical Virology (ESCV) Network on NGS (ENNGS) initiated a benchmark of metagenomic pipelines currently used in clinical virological laboratories. Methods Metagenomic datasets from 13 clinical samples from patients with encephalitis or viral respiratory infections characterized by PCR were selected. The datasets were analysed with 13 different pipelines currently used in virological diagnostic laboratories of participating ENNGS members. The pipelines and classification tools were: Centrifuge, DAMIAN, DIAMOND, DNASTAR, FEVIR, Genome Detective, Jovian, MetaMIC, MetaMix, One Codex, RIEMS, VirMet, and Taxonomer. Performance, characteristics, clinical use, and user-friendliness of these pipelines were analysed. Results Overall, viral pathogens with high loads were detected by all the evaluated metagenomic pipelines. In contrast, lower abundance pathogens and mixed infections were only detected by 3/13 pipelines, namely DNASTAR, FEVIR, and MetaMix. Overall sensitivity ranged from 80% (10/13) to 100% (13/13 datasets). Overall positive predictive value ranged from 71-100%. The majority of the pipelines classified sequences based on nucleotide similarity (8/13), only a minority used amino acid similarity, and 6 of the 13 pipelines assembled sequences de novo. No clear differences in performance were detected that correlated with these classification approaches. Read counts of target viruses varied between the pipelines over a range of 2-3 log, indicating differences in limit of detection. Conclusion A wide variety of viral metagenomic pipelines is currently used in the participating clinical diagnostic laboratories. Detection of low abundant viral pathogens and mixed infections remains a challenge, implicating the need for standardization and validation of metagenomic analysis for clinical diagnostic use. Future studies should address the selective effects due to the choice of different reference viral databases.

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Human gut microbial communities dictate efficacy of anti-PD-1 therapy in a humanized microbiome mouse model of glioma

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab023 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Kory J Dees ◽

Hyunmin Koo ◽

J Fraser Humphreys ◽

Joseph A Hakim ◽

David K Crossman ◽

...

Keyword(s):

Mouse Model ◽

Microbial Communities ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Microbial Composition ◽

Healthy Human ◽

Fecal Transplantation ◽

Host Interaction ◽

Shotgun Metagenomic Sequencing ◽

Mouse Lines

Abstract Background Although immunotherapy works well in glioblastoma (GBM) preclinical mouse models, the therapy has not demonstrated efficacy in humans. To address this anomaly, we developed a novel humanized microbiome (HuM) model to study the response to immunotherapy in a preclinical mouse model of GBM. Methods We used 5 healthy human donors for fecal transplantation of gnotobiotic mice. After the transplanted microbiomes stabilized, the mice were bred to generate 5 independent humanized mouse lines (HuM1-HuM5). Results Analysis of shotgun metagenomic sequencing data from fecal samples revealed a unique microbiome with significant differences in diversity and microbial composition among HuM1-HuM5 lines. All HuM mouse lines were susceptible to GBM transplantation, and exhibited similar median survival ranging from 19 to 26 days. Interestingly, we found that HuM lines responded differently to the immune checkpoint inhibitor anti-PD-1. Specifically, we demonstrate that HuM1, HuM4, and HuM5 mice are nonresponders to anti-PD-1, while HuM2 and HuM3 mice are responsive to anti-PD-1 and displayed significantly increased survival compared to isotype controls. Bray-Curtis cluster analysis of the 5 HuM gut microbial communities revealed that responders HuM2 and HuM3 were closely related, and detailed taxonomic comparison analysis revealed that Bacteroides cellulosilyticus was commonly found in HuM2 and HuM3 with high abundances. Conclusions The results of our study establish the utility of humanized microbiome mice as avatars to delineate features of the host interaction with gut microbial communities needed for effective immunotherapy against GBM.

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Yoghurt Consumption is Associated With Transient Changes in the Composition of the Human Gut Microbiome

10.21203/rs.3.rs-99718/v1 ◽

2020 ◽

Author(s):

Caroline Ivanne Le Roy ◽

Alexander Kurilshikov ◽

Emily Leeming ◽

Alessia Visconti ◽

Ruth Bowyer ◽

...

Keyword(s):

16S Rrna ◽

Gut Microbiota ◽

Visceral Fat ◽

Gut Microbiome ◽

Body Weight Gain ◽

Healthy Eating Index ◽

Rrna Gene ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Shotgun Metagenomic Sequencing

Abstract Background: Yoghurt contains live bacteria that could contribute via modulation of the gut microbiota to its reported beneficial effects such as reduced body weight gain and lower incidence of type 2 diabetes. To date, the association between yoghurt consumption and the composition of the gut microbiota is underexplored. Here we used clinical variables, metabolomics, 16S rRNA and shotgun metagenomic sequencing data collected on over 1000 predominantly female UK twins to define the link between the gut microbiota and yoghurt-associated health benefits. Results: According to food frequency questionnaires (FFQ), 73% of subjects consumed yoghurt. Consumers presented a healthier diet pattern (healthy eating index: beta = 2.17±0.34; P = 2.72x10-10) and improved metabolic health characterised by reduced visceral fat (beta = -28.18±11.71 g; P = 0.01). According to 16S rRNA gene analyses and whole shotgun metagenomic sequencing approach consistent taxonomic variations were observed with yoghurt consumption. More specifically, we identified higher abundance of species used as yoghurt starters Streptococcus thermophilus (beta = 0.41±0.051; P = 6.14x10-12) and sometimes added Bifidobacterium animalis subsp. lactis (beta = 0.30±0.052; P = 1.49x10-8) in the gut of yoghurt consumers. Replication in 1103 volunteers from the LifeLines-DEEP cohort confirmed the increase of S. thermophilus among yoghurt consumers. Using food records collected the day prior to faecal sampling we showed that increase in these two yoghurt bacteria could be transient. Metabolomics analysis revealed that B. animalis subsp. lactis was associated with 13 faecal metabolites including a 3-hydroxyoctanoic acid, known to be involved in the regulation of gut inflammation.Conclusions: Yoghurt consumption is associated with reduced visceral fat mass and changes in gut microbiome including transient increase of yoghurt-contained species (i.e. S. thermophilus and B. lactis).

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Considerations for performance metrics of metagenomic next generation sequencing analyses

10.1101/2020.12.17.423212 ◽

2020 ◽

Author(s):

Jason G. Kralj ◽

Stephanie L. Servetas ◽

Samuel P. Forry ◽

Scott A. Jackson

Keyword(s):

Performance Metrics ◽

Limit Of Detection ◽

Clinical Performance ◽

Ground Truth ◽

Negative Control ◽

Fitness For Purpose ◽

Performance Metric ◽

The One ◽

Sensitivity Specificity ◽

Harmonic Means

AbstractEvaluating the performance of metagenomics analyses has proven a challenge, due in part to limited ground-truth standards, broad application space, and numerous evaluation methods and metrics. Application of traditional clinical performance metrics (i.e. sensitivity, specificity, etc.) using taxonomic classifiers do not fit the “one-bug-one-test” paradigm. Ultimately, users need methods that evaluate fitness-for-purpose and identify their analyses’ strengths and weaknesses. Within a defined cohort, reporting performance metrics by taxon, rather than by sample, will clarify this evaluation. An estimated limit of detection, positive and negative control samples, and true positive and negative true results are necessary criteria for all investigated taxa. Use of summary metrics should be restricted to comparing results of similar cohorts and data, and should employ harmonic means and continuous products for each performance metric rather than arithmetic mean. Such consideration will ensure meaningful comparisons and evaluation of fitness-for-purpose.

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The human microbiome in hematopoiesis and hematologic disorders

Blood ◽

10.1182/blood-2015-04-574392 ◽

2015 ◽

Vol 126 (3) ◽

pp. 311-318 ◽

Cited By ~ 40

Author(s):

Veronica E. Manzo ◽

Ami S. Bhatt

Keyword(s):

Community Structure ◽

Human Health ◽

Ribosomal Rna ◽

Human Microbiome ◽

Human Cells ◽

Metagenomic Sequencing ◽

Hematologic Disorders ◽

Hematologic Diseases ◽

Shotgun Metagenomic Sequencing

AbstractHumans are now understood to be in complex symbiosis with a diverse ecosystem of microbial organisms, including bacteria, viruses, and fungi. Efforts to characterize the role of these microorganisms, commonly referred as the microbiota, in human health have sought to answer the fundamental questions of what organisms are present, how are they functioning to interact with human cells, and by what mechanism are these interactions occurring. In this review, we describe recent efforts to describe the microbiota in healthy and diseased individuals, summarize the role of various molecular technologies (ranging from 16S ribosomal RNA to shotgun metagenomic sequencing) in enumerating the community structure of the microbiota, and explore known interactions between the microbiota and humans, with a focus on the microbiota’s role in hematopoiesis and hematologic diseases.

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