ABSTRACT
A large gap remains between sequencing a microbial community and characterizing all of the organisms inside of it. Here we develop a novel method to taxonomically bin metagenomic assemblies through alignment of contigs against a reference database. We show that this workflow, BugSplit, bins metagenome-assembled contigs to species with a 33% absolute improvement in F1-score when compared to alternative tools. We perform nanopore mNGS on patients with COVID-19, and using a reference database predating COVID-19, demonstrate that BugSplit's taxonomic binning enables sensitive and specific detection of a novel coronavirus not possible with other approaches. When applied to nanopore mNGS data from cases of Klebsiella pneumoniae bacteremia and Neisseria gonorrhoeae infection, BugSplit's taxonomic binning accurately separates pathogen sequences from those of the host and microbiota, and unlocks the possibility of sequence typing, in silico serotyping, and antimicrobial resistance prediction of each organism within a sample. BugSplit is available at https://bugseq.com/academic.
Subject(s)
COVID-19 , Klebsiella InfectionsABSTRACT
The COVID-19 pandemic has underscored the need for rapid novel diagnostic strategies to detect and characterize pathogens from clinical specimens. The MinION sequencing device allows for rapid, cost-effective, high-throughput sequencing; useful features for translation to clinical laboratory settings. Metagenomic Next-Generation Sequencing (mNGS) approaches provide the opportunity to examine the entire genomic material of a sample; allowing for detection of emerging and clinically relevant pathogens that may be missed in targeted assays. Here we present a pilot study on the performance of Sequence-Independent Single Primer Amplification (SISPA) to amplify RNA randomly for the detection and characterization of SARS-CoV-2. We designed a classifier that corrects for barcode crosstalk between specimens. Our assay yielded 100% specificity overall and 95.2% sensitivity for specimens with a RT-qPCR cycle threshold value less than 30. We assembled 10 complete (>95% coverage at 20x depth), and one near-complete (>80% coverage at 20x depth) genomes from 20 specimens that were classified as positive by mNGS. We characterized these genomes through phylogenetic analysis and found that 10/11 specimens from British Columbia had a closest relative to another British Columbian specimen. Of five samples that we had both assembled genomes, as well as Variant of Concern (VOC) PCR results, we found 100% concordance between these results. Additionally, our assay was able to distinguish between the Alpha and Gamma variants, which was not possible with our VOC PCR technique. This study supports future work examining the broader feasibility of SISPA as a diagnostic strategy for the detection and characterization of viral pathogens.
Subject(s)
COVID-19ABSTRACT
Countries around the world are in a state of lockdown to help limit the spread of SARS-CoV-2. However, as the number of new daily confirmed cases begins to decrease, governments must decide how to release their populations from quarantine as efficiently as possible without overwhelming their health services. We applied an optimal control framework to an adapted Susceptible-Exposure-Infection-Recovery (SEIR) model framework to investigate the efficacy of two potential lockdown release strategies, focusing on the UK population as a test case. To limit recurrent spread, we find that ending quarantine for the entire population simultaneously is a high-risk strategy, and that a gradual re-integration approach would be more reliable. Furthermore, to increase the number of people that can be first released, lockdown should not be ended until the number of new daily confirmed cases reaches a sufficiently low threshold. We model a gradual release strategy by allowing different fractions of those in lockdown to re-enter the working non-quarantined population. Mathematical optimisation methods, combined with our adapted SEIR model, determine how to maximise those working while preventing the health service from being overwhelmed. The optimal strategy is broadly found to be to release approximately half the population two-to-four weeks from the end of an initial infection peak, then wait another three-to-four months to allow for a second peak before releasing everyone else. We also modelled an ''on-off'' strategy, of releasing everyone, but re-establishing lockdown if infections become too high. We conclude that the worst-case scenario of a gradual release is more manageable than the worst-case scenario of an on-off strategy, and caution against lockdown-release strategies based on a threshold-dependent on-off mechanism. The two quantities most critical in determining the optimal solution are transmission rate and the recovery rate, where the latter is defined as the fraction of infected people in any given day that then become classed as recovered. We suggest that the accurate identification of these values is of particular importance to the ongoing monitoring of the pandemic.