Dataset Information

Real-Time Observation of Target Search by the CRISPR Surveillance Complex Cascade.

ABSTRACT: CRISPR-Cas systems defend bacteria and archaea against infection by bacteriophage and other threats. The central component of these systems are surveillance complexes that use guide RNAs to bind specific regions of foreign nucleic acids, marking them for destruction. Surveillance complexes must locate targets rapidly to ensure timely immune response, but the mechanism of this search process remains unclear. Here, we used single-molecule FRET to visualize how the type I-E surveillance complex Cascade searches DNA in real time. Cascade rapidly and randomly samples DNA through nonspecific electrostatic contacts, pausing at short PAM recognition sites that may be adjacent to the target. We identify Cascade motifs that are essential for either nonspecific sampling or positioning and readout of the PAM. Our findings provide a comprehensive structural and kinetic model for the Cascade target-search mechanism, revealing how CRISPR surveillance complexes can rapidly search large amounts of genetic material en route to target recognition.

SUBMITTER: Xue C

PROVIDER: S-EPMC5753800 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Publications

Real-Time Observation of Target Search by the CRISPR Surveillance Complex Cascade.

Xue Chaoyou C Zhu Yicheng Y Zhang Xiangmei X Shin Yeon-Kyun YK Sashital Dipali G DG

Cell reports 20171201 13

CRISPR-Cas systems defend bacteria and archaea against infection by bacteriophage and other threats. The central component of these systems are surveillance complexes that use guide RNAs to bind specific regions of foreign nucleic acids, marking them for destruction. Surveillance complexes must locate targets rapidly to ensure timely immune response, but the mechanism of this search process remains unclear. Here, we used single-molecule FRET to visualize how the type I-E surveillance complex Cas ...[more]

PMID: 29281822

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Project description:BackgroundNovel influenza surveillance systems that leverage Internet-based real-time data sources including Internet search frequencies, social-network information, and crowd-sourced flu surveillance tools have shown improved accuracy over the past few years in data-rich countries like the United States. These systems not only track flu activity accurately, but they also report flu estimates a week or more ahead of the publication of reports produced by healthcare-based systems, such as those implemented and managed by the Centers for Disease Control and Prevention. Previous work has shown that the predictive capabilities of novel flu surveillance systems, like Google Flu Trends (GFT), in developing countries in Latin America have not yet delivered acceptable flu estimates.ObjectiveThe aim of this study was to show that recent methodological improvements on the use of Internet search engine information to track diseases can lead to improved retrospective flu estimates in multiple countries in Latin America.MethodsA machine learning-based methodology that uses flu-related Internet search activity and historical information to monitor flu activity, named ARGO (AutoRegression with Google search), was extended to generate flu predictions for 8 Latin American countries (Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay) for the time period: January 2012 to December of 2016. These retrospective (out-of-sample) Influenza activity predictions were compared with historically observed flu suspected cases in each country, as reported by Flunet, an influenza surveillance database maintained by the World Health Organization. For a baseline comparison, retrospective (out-of-sample) flu estimates were produced for the same time period using autoregressive models that only leverage historical flu activity information.ResultsOur results show that ARGO-like models' predictive power outperform autoregressive models in 6 out of 8 countries in the 2012-2016 time period. Moreover, ARGO significantly improves on historical flu estimates produced by the now discontinued GFT for the time period of 2012-2015, where GFT information is publicly available.ConclusionsWe demonstrate here that a self-correcting machine learning method, leveraging Internet-based disease-related search activity and historical flu trends, has the potential to produce reliable and timely flu estimates in multiple Latin American countries. This methodology may prove helpful to local public health officials who design and implement interventions aimed at mitigating the effects of influenza outbreaks. Our methodology generally outperforms both the now-discontinued tool GFT, and autoregressive methodologies that exploit only historical flu activity to produce future disease estimates.

Dataset Information

Real-Time Observation of Target Search by the CRISPR Surveillance Complex Cascade.

Publications

Real-Time Observation of Target Search by the CRISPR Surveillance Complex Cascade.

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