Dataset Information

Rapid and Accurate Determination of Nanopore Ionic Current Using a Steric Exclusion Model.

ABSTRACT: Nanopore sensing has emerged as a versatile approach to detection and identification of biomolecules. Presently, researchers rely on experience and intuition for choosing or modifying the nanopores to detect a target analyte. The field would greatly benefit from a computational method that could relate the atomic-scale geometry of the nanopores and analytes to the blockade nanopore currents they produce. Existing computational methods are either computationally too expensive to be used routinely in experimental laboratories or not sensitive enough to account for the atomic structure of the pore and the analytes. Here, we demonstrate a robust and inexpensive computational approach-the steric exclusion model (SEM) of nanopore conductance-that is orders of magnitude more efficient than all-atom MD and yet is sensitive enough to account for the atomic structure of the nanopore and the analyte. The method combines the computational efficiency of a finite element solver with the atomic precision of a nanopore conductance map to yield unprecedented speed and accuracy of ionic current prediction. We validate our SEM approach through comparison with the current blockades computed using the all-atom molecular dynamics method for a range of proteins confined to a solid-state nanopore, biological channels embedded in a lipid bilayer membranes, and blockade currents produced by DNA homopolymers in MspA. We illustrate potential applications of SEM by computing blockade currents produced by nucleosome proteins in a solid-state nanopore, individual amino acids in MspA, and by testing the effect of point mutations on amino acid distinguishability. We expect our SEM approach to become an integral part of future development of the nanopore sensing field.

SUBMITTER: Wilson J

PROVIDER: S-EPMC6489136 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Rapid and Accurate Determination of Nanopore Ionic Current Using a Steric Exclusion Model.

Wilson James J Sarthak Kumar K Si Wei W Gao Luyu L Aksimentiev Aleksei A

ACS sensors 20190313 3

Nanopore sensing has emerged as a versatile approach to detection and identification of biomolecules. Presently, researchers rely on experience and intuition for choosing or modifying the nanopores to detect a target analyte. The field would greatly benefit from a computational method that could relate the atomic-scale geometry of the nanopores and analytes to the blockade nanopore currents they produce. Existing computational methods are either computationally too expensive to be used routinely ...[more]

PMID: 30821441

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Project description:Differentiation between mitis group streptococci (MGS) bacteria in routine laboratory tests has become important for obtaining accurate epidemiological information on the characteristics of MGS and understanding their clinical significance. The most reliable method of MGS species identification is multilocus sequence analysis (MLSA) with seven house-keeping genes; however, because this method is time-consuming, it is deemed unsuitable for use in most clinical laboratories. In this study, we established a scheme for identifying 12 species of MGS (S. pneumoniae, S. pseudopneumoniae, S. mitis, S. oralis, S. peroris, S. infantis, S. australis, S. parasanguinis, S. sinensis, S. sanguinis, S. gordonii, and S. cristatus) using the MinION nanopore sequencer (Oxford Nanopore Technologies, Oxford, UK) with the taxonomic aligner "What's in My Pot?" (WIMP; Oxford Nanopore's cloud-based analysis platform) and Kraken2 pipeline with the custom database adjusted for MGS species identification. The identities of the species in reference genomes (n = 514), clinical isolates (n = 31), and reference strains (n = 4) were confirmed via MLSA. The nanopore simulation reads were generated from reference genomes, and the optimal cut-off values for MGS species identification were determined. For 31 clinical isolates (S. pneumoniae = 8, S. mitis = 17 and S. oralis = 6) and 4 reference strains (S. pneumoniae = 1, S. mitis = 1, S. oralis = 1, and S. pseudopneumoniae = 1), a sequence library was constructed via a Rapid Barcoding Sequencing Kit for multiplex and real-time MinION sequencing. The optimal cut-off values for the identification of MGS species for analysis by WIMP and Kraken2 pipeline were determined. The workflow using Kraken2 pipeline with a custom database identified all 12 species of MGS, and WIMP identified 8 MGS bacteria except S. infantis, S. australis, S. peroris, and S. sinensis. The results obtained by MinION with WIMP and Kraken2 pipeline were consistent with the MGS species identified by MLSA analysis. The practical advantage of whole genome analysis using the MinION nanopore sequencer is that it can aid in MGS surveillance. We concluded that MinION sequencing with the taxonomic aligner enables accurate MGS species identification and could contribute to further epidemiological surveys.

Project description:ObjectiveWe aimed to evaluate the performance of nanopore amplicon sequencing detection for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical samples.MethodWe carried out a single-center, prospective cohort study in a Wuhan hospital and collected a total of 86 clinical samples, including 54 pharyngeal swabs, 31 sputum samples, and 1 fecal sample, from 86 patients with coronavirus disease 2019 (COVID-19) from Feb 20 to May 15, 2020. We performed parallel detection with nanopore-based genome amplification and sequencing (NAS) on the Oxford Nanopore Technologies (ONT) minION platform and routine reverse transcription quantitative polymerase chain reaction (RT-qPCR). In addition, 27 negative control samples were detected using the two methods. The sensitivity and specificity of NAS were evaluated and compared with those of RT-qPCR.ResultsThe viral read number and reference genome coverage were both significantly different between the two groups of samples, and the latter was a better indicator for SARS-CoV-2 detection. Based on the reference genome coverage, NAS revealed both high sensitivity (96.5%) and specificity (100%) compared with RT-qPCR (80.2 and 96.3%, respectively), although the samples had been stored for half a year before the detection. The total time cost was less than 15 h, which was acceptable compared with that of RT-qPCR (∼2.5 h). In addition, the reference genome coverage of the viral reads was in line with the cycle threshold value of RT-qPCR, indicating that this number could also be used as an indicator of the viral load in a sample. The viral load in sputum might be related to the severity of the infection, particularly in patients within 4 weeks after onset of clinical manifestations, which could be used to evaluate the infection.ConclusionOur results showed the high sensitivity and specificity of the NAS method for SARS-CoV-2 detection compared with RT-qPCR. The sequencing results were also used as an indicator of the viral load to display the viral dynamics during infection. This study proved the wide application prospect of nanopore sequencing detection for SARS-CoV-2 and may more knowledge about the clinical characteristics of COVID-19.

Dataset Information

Rapid and Accurate Determination of Nanopore Ionic Current Using a Steric Exclusion Model.

Publications

Rapid and Accurate Determination of Nanopore Ionic Current Using a Steric Exclusion Model.

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