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Dellicour S, Bastide P, Rocu P, Fargette D, Hardy OJ, Suchard MA, Guindon S, Lemey P. How fast are viruses spreading in the wild? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588821. [PMID: 38645268 PMCID: PMC11030353 DOI: 10.1101/2024.04.10.588821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used to estimate dispersal metrics allowing to unveil the dispersal dynamics and evaluate the capacity to spread among hosts. Heterogeneous sampling intensity of genomic sequences can however impact the accuracy of dispersal insights gained through phylogeographic inference. In our study, we implement a simulation framework to evaluate the robustness of three dispersal metrics - a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance signal metric - to the sampling effort. Our results reveal that both the diffusion coefficient and isolation-by-distance signal metrics appear to be robust to the number of samples considered for the phylogeographic reconstruction. We then use these two dispersal metrics to compare the dispersal pattern and capacity of various viruses spreading in animal populations. Our comparative analysis reveals a broad range of isolation-by-distance patterns and diffusion coefficients mostly reflecting the dispersal capacity of the main infected host species but also, in some cases, the likely signature of rapid and/or long-distance dispersal events driven by human-mediated movements through animal trade. Overall, our study provides key recommendations for the lineage dispersal metrics to consider in future studies and illustrates their application to compare the spread of viruses in various settings.
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Wang Y, Chen H, Lin K, Han Y, Gu Z, Wei H, Mu K, Wang D, Liu L, Jin R, Song R, Rong Z, Wang S. Ultrasensitive single-step CRISPR detection of monkeypox virus in minutes with a vest-pocket diagnostic device. Nat Commun 2024; 15:3279. [PMID: 38627378 PMCID: PMC11021474 DOI: 10.1038/s41467-024-47518-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 04/03/2024] [Indexed: 04/19/2024] Open
Abstract
The emerging monkeypox virus (MPXV) has raised global health concern, thereby highlighting the need for rapid, sensitive, and easy-to-use diagnostics. Here, we develop a single-step CRISPR-based diagnostic platform, termed SCOPE (Streamlined CRISPR On Pod Evaluation platform), for field-deployable ultrasensitive detection of MPXV in resource-limited settings. The viral nucleic acids are rapidly released from the rash fluid swab, oral swab, saliva, and urine samples in 2 min via a streamlined viral lysis protocol, followed by a 10-min single-step recombinase polymerase amplification (RPA)-CRISPR/Cas13a reaction. A pod-shaped vest-pocket analysis device achieves the whole process for reaction execution, signal acquisition, and result interpretation. SCOPE can detect as low as 0.5 copies/µL (2.5 copies/reaction) of MPXV within 15 min from the sample input to the answer. We validate the developed assay on 102 clinical samples from male patients / volunteers, and the testing results are 100% concordant with the real-time PCR. SCOPE achieves a single-molecular level sensitivity in minutes with a simplified procedure performed on a miniaturized wireless device, which is expected to spur substantial progress to enable the practice application of CRISPR-based diagnostics techniques in a point-of-care setting.
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Affiliation(s)
- Yunxiang Wang
- Bioinformatics Center of AMMS, 100850, Beijing, China
| | - Hong Chen
- Bioinformatics Center of AMMS, 100850, Beijing, China
| | - Kai Lin
- Department of Clinical Laboratory, Air Force Medical Center, Air Force Medical University, 100142, Beijing, China
| | - Yongjun Han
- Bioinformatics Center of AMMS, 100850, Beijing, China
| | - Zhixia Gu
- Beijing Ditan Hospital, Capital Medical University, 100015, Beijing, China
| | - Hongjuan Wei
- Bioinformatics Center of AMMS, 100850, Beijing, China
| | - Kai Mu
- Bioinformatics Center of AMMS, 100850, Beijing, China
| | - Dongfeng Wang
- Bioinformatics Center of AMMS, 100850, Beijing, China
| | - Liyan Liu
- Bioinformatics Center of AMMS, 100850, Beijing, China
| | - Ronghua Jin
- Beijing Ditan Hospital, Capital Medical University, 100015, Beijing, China.
| | - Rui Song
- Beijing Ditan Hospital, Capital Medical University, 100015, Beijing, China.
| | - Zhen Rong
- Bioinformatics Center of AMMS, 100850, Beijing, China.
| | - Shengqi Wang
- Bioinformatics Center of AMMS, 100850, Beijing, China.
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