Suchergebnisse

The activity of enzymes is traditionally characterised through bulk-phase biochemical methods that only report on population averages. Single-molecule methods are advantageous in elucidating kinetic and population heterogeneity but are often complicated, time consuming, and lacking statistical power. We present a highly generalisable and high-throughput single-molecule assay to rapidly characterise proteins involved in DNA metabolism. The assay exclusively relies on changes in total fluorescence intensity of surface-immobilised DNA templates as a result of DNA synthesis, unwinding or digestion. Combined with an automated data-analysis pipeline, our method provides enzymatic activity data of thousands of molecules in less than an hour. We demonstrate our method by characterising three fundamentally different nucleic-acid enzyme activities: digestion by the phage λ exonuclease, synthesis by the phage Phi29 polymerase, and unwinding by the E. coli UvrD helicase. We observe a previously unknown activity of the UvrD helicase to remove proteins tightly bound to the ends of DNA. ; The authors thank Dr Jacob Lewis (University of Wollongong) and Prof. Michael O'Donnell (Rockefeller University) for contributing reagents. ; This work was supported by the Australian Research Council (research grants DP150100956 and DP180100858 to A.M.v.O. and an Australian Laureate Fellowship FL140100027 to A.M.v.O.), the National Health and Medical Research Council (NHMRC Investigator grant 2007778 to L.M.S) and an Australian Government Research Training Program Scholarship (to S.H.M). Funding for open access charge: Australian Research Council.

BACKGROUND: Outbreaks of infectious disease cause serious and costly health and social problems. Two new technologies – pathogen whole genome sequencing (WGS) and Big Data analytics – promise to improve our capacity to detect and control outbreaks earlier, saving lives and resources. However, routinely using these technologies to capture more detailed and specific personal information could be perceived as intrusive and a threat to privacy. METHOD: making pathogen WGS and linked administrative data available for public health research; using this information in concert with data linkage and machine learning to enhance communicable disease surveillance systems. Fifty participants of diverse backgrounds, mixed genders and ages were recruited by random-digit-dialling and topic-blinded social-media advertising. Each jury was presented with balanced factual evidence supporting different expert perspectives on the potential benefits and costs of technologically enhanced public health research and communicable disease surveillance and given the opportunity to question experts. RESULTS: Almost all jurors supported data linkage and WGS on routinely collected patient isolates for the purposes of public health research, provided standard de-identification practices were applied. However, allowing this information to be operationalised as a syndromic surveillance system was highly contentious with three juries voting in favour, and one against by narrow margins. For those in favour, support depended on several conditions related to system oversight and security being met. Those against were concerned about loss of privacy and did not trust Australian governments to run secure and effective systems. CONCLUSIONS: Participants across all four events strongly supported the introduction of data linkage and pathogenomics to public health research under current research governance structures. Combining pathogen WGS with event-based data surveillance systems, however, is likely to be controversial because of a lack of public trust, ...

Background: Outbreaks of infectious disease cause serious and costly health and social problems. Two new technologies – pathogen whole genome sequencing (WGS) and Big Data analytics – promise to improve our capacity to detect and control outbreaks earlier, saving lives and resources. However, routinely using these technologies to capture more detailed and specific personal information could be perceived as intrusive and a threat to privacy. Method: Four community juries were convened in two demographically different Sydney municipalities and two regional cities in New South Wales, Australia (western Sydney, Wollongong, Tamworth, eastern Sydney) to elicit the views of well-informed community members on the acceptability and legitimacy of: - making pathogen WGS and linked administrative data available for public health research - using this information in concert with data linkage and machine learning to enhance communicable disease surveillance systems Fifty participants of diverse backgrounds, mixed genders and ages were recruited by random-digit-dialling and topic-blinded social-media advertising. Each jury was presented with balanced factual evidence supporting different expert perspectives on the potential benefits and costs of technologically enhanced public health research and communicable disease surveillance and given the opportunity to question experts. Results: Almost all jurors supported data linkage and WGS on routinely collected patient isolates for the purposes of public health research, provided standard de-identification practices were applied. However, allowing this information to be operationalised as a syndromic surveillance system was highly contentious with three juries voting in favour, and one against by narrow margins. For those in favour, support depended on several conditions related to system oversight and security being met. Those against were concerned about loss of privacy and did not trust Australian governments to run secure and effective systems. Conclusions: Participants across ...