Dataset Information

Real-Time Nanopore-Based Recognition of Protein Translocation Success.

ABSTRACT: A growing number of new technologies are supported by a single- or multi-nanopore architecture for capture, sensing, and delivery of polymeric biomolecules. Nanopore-based single-molecule DNA sequencing is the premier example. This method relies on the uniform linear charge density of DNA, so that each DNA strand is overwhelmingly likely to pass through the nanopore and across the separating membrane. For disordered peptides, folded proteins, or block copolymers with heterogeneous charge densities, by contrast, translocation is not assured, and additional strategies to monitor the progress of the polymer molecule through a nanopore are required. Here, we demonstrate a single-molecule method for direct, model-free, real-time monitoring of the translocation of a disordered, heterogeneously charged polypeptide through a nanopore. The crucial elements are two "selectivity tags"-regions of different but uniform charge density-at the ends of the polypeptide. These affect the selectivity of the nanopore differently and enable discrimination between polypeptide translocation and retraction. Our results demonstrate exquisite sensitivity of polypeptide translocation to applied transmembrane potential and prove the principle that nanopore selectivity reports on biopolymer substructure. We anticipate that the selectivity tag technique will be broadly applicable to nanopore-based protein detection, analysis, and separation technologies, and to the elucidation of protein translocation processes in normal cellular function and in disease.

SUBMITTER: Hoogerheide DP

PROVIDER: S-EPMC5984951 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Prosthetic 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 report a real-time analytical pathway for Nanopore sequence data, designed for detecting bacterial composition of prosthetic joint infections but potentially useful for any microbial sequencing, and compare detection by direct-from-clinical-sample metagenomic nanopore sequencing with Illumina sequencing and standard microbiological diagnostic techniques. RESULTS: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. CONCLUSIONS:We demonstrate a novel, scalable pipeline for real-time analysis of MinION sequence data and use of this pipeline to show initial proof of concept that metagenomic MinION sequencing can provide rapid, accurate diagnosis for prosthetic joint infections. The high proportion of human DNA in prosthetic joint infection extracts prevents full genome analysis from complete coverage, and methods to reduce this could increase genome depth and allow antimicrobial resistance profiling. The nine samples sequenced in this pilot study have shown a proof of concept for sequencing and analysis that will enable us to investigate further sequencing to improve specificity and sensitivity.

Dataset Information

Real-Time Nanopore-Based Recognition of Protein Translocation Success.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets