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Cited by 9

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Two-Step Source Tracing Strategy of Yersinia pestis and Its Historical Epidemiology in a Specific Region

Yan, Yanfeng, Wang, Hu, Li, Dongfang, Yang, Xianwei, Wang, Zuyun, Qi, Zhizhen, Zhang, Qingwen, Cui, Baizhong, Guo, Zhaobiao, Yu, Chang, Wang, Jun, Wang, Jian, Liu, Guangming, Song, Yajun, Li, Yingrui, Cui, Yujun, Yang, Ruifu·2014·PLoS ONE
DOI 10.1371/journal.pone.0085374PMC PMC3887043
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Figure 1 CPU running time and RAM consumption of phylogenetic reconstruction based on simulated mega dataset. The points represent the CPU running time (panel A) and RAM consumption (panel B) for reconstructing the MLTree based on different numbers of genomes and variations. The red curves represent the changes of both running time and RAM consumption, which follow the power function according to the formulas in the figure.
Figure 1 CPU running time and RAM consumption of phylogenetic reconstruction based on simulated mega dataset. The points represent the CPU running time (panel A) and RAM consumption (panel B) for reconstructing the MLTree based on different numbers of genomes and variations. The red curves represent the changes of both running time and RAM consumption, which follow the power function according to the formulas in the figure.CC BY · PubMed Central
Figure 2 First step of source tracing: defining the phylogenetic position of outbreak strains. (A) MLTree of 2009 outbreak strains and 24 representative Y. pestis strains with global diversity. The branch length reveals genetic distance between isolates, except for the branch of 0.PE3, which was truncated for visual effect. The root was defined by the ancestor Y. pseudotuberculosis, IP32953. Each strain is labeled with different symbols and colors that correspond to its geographical distribution on the map in panel B. Strains that are not mapped in panel B are labeled with an asterisk. As no variations exist across outbreak strains, the corresponding branches were collapsed in the tree. All nodes have a bootstrap support of more than 90%. (B) Geographic information of the strains in the MLTree.
Figure 2 First step of source tracing: defining the phylogenetic position of outbreak strains. (A) MLTree of 2009 outbreak strains and 24 representative Y. pestis strains with global diversity. The branch length reveals genetic distance between isolates, except for the branch of 0.PE3, which was truncated for visual effect. The root was defined by the ancestor Y. pseudotuberculosis, IP32953. Each strain is labeled with different symbols and colors that correspond to its geographical distribution on the map in panel B. Strains that are not mapped in panel B are labeled with an asterisk. As no variations exist across outbreak strains, the corresponding branches were collapsed in the tree. All nodes have a bootstrap support of more than 90%. (B) Geographic information of the strains in the MLTree.CC BY · PubMed Central
Figure 3 Second step of source tracing: Investigating the phylogenetic relationship within the particular phylogroup and historical isolates in the outbreak region. (A) MSTree of strains in the phylogroup 1.IN2 (a to q), 2009 outbreak strains (r), and historical strains in Xinghai County (s, t, u, and 0.PE7b). The circle size represents the number of strains it contains. The designation of hypothetical nodes was obtained from previous research [32], and the SNP numbers (>2) between the nodes are labeled in gray on each branch. Strains that were close to the MRCA of phylogroup 1.IN2 (node N12) are marked in blue, strains isolated from Xinghai are in green, and the other strains are in yellow. (B) Geographic distribution of strains used in MSTree.
Figure 3 Second step of source tracing: Investigating the phylogenetic relationship within the particular phylogroup and historical isolates in the outbreak region. (A) MSTree of strains in the phylogroup 1.IN2 (a to q), 2009 outbreak strains (r), and historical strains in Xinghai County (s, t, u, and 0.PE7b). The circle size represents the number of strains it contains. The designation of hypothetical nodes was obtained from previous research [32], and the SNP numbers (>2) between the nodes are labeled in gray on each branch. Strains that were close to the MRCA of phylogroup 1.IN2 (node N12) are marked in blue, strains isolated from Xinghai are in green, and the other strains are in yellow. (B) Geographic distribution of strains used in MSTree.CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 39

  1. D Zhou, 2006, Molecular and physiological insights into plague transmission, virulence and etiology, Microbes Infect, 8, 273, 10.1016/j.micinf.2005.06.006 · DOI
  2. RD Perry, 1997, Yersinia pestis – etiologic agent of plague, Clin Microbiol Rev, 10, 35, 10.1128/CMR.10.1.35 · DOI
  3. MB Prentice, 2007, Plague, Lancet, 369, 1196, 10.1016/S0140-6736(07)60566-2 · DOI
  4. 2003, Human plague in 2000 and 2001, Wkly Epidemiol Rec, 78, 130
  5. J Kaiser, 2005, Select agents. Researchers relieved by final biosecurity rules, Science, 308, 31, 10.1126/science.308.5718.31a · DOI
  6. B Budowle, 2005, Microbial forensics: the next forensic challenge, Int J Legal Med, 119, 317, 10.1007/s00414-005-0535-y · DOI
  7. RF Yang, 2012, Microbial Forensics: A Powerful Tool for Pursuing Bioterrorism Perpetrators and the Need for an International Database, J Bioterr Biodef, S3, 007
  8. M Achtman, 2008, Evolution, population structure, and phylogeography of genetically monomorphic bacterial pathogens, Annu Rev Microbiol, 62, 53, 10.1146/annurev.micro.62.081307.162832 · DOI
  9. G Morelli, 2010, Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity, Nat Genet, 42, 1140, 10.1038/ng.705 · DOI · Paper page
  10. M Achtman, 2004, Microevolution and history of the plague bacillus, Yersinia pestis, Proc Natl Acad Sci U S A, 101, 17837, 10.1073/pnas.0408026101 · DOI · Paper page
  11. Y Li, 2008, Different region analysis for genotyping Yersinia pestis isolates from China, PLoS One, 3, e2166, 10.1371/journal.pone.0002166 · DOI
  12. Y Li, 2009, Genotyping and phylogenetic analysis of Yersinia pestis by MLVA: insights into the worldwide expansion of Central Asia plague foci, PLoS One, 4, e6000, 10.1371/journal.pone.0006000 · DOI
  13. D Zhou, 2004, DNA microarray analysis of genome dynamics in Yersinia pestis: insights into bacterial genome microevolution and niche adaptation, J Bacteriol, 186, 5138, 10.1128/JB.186.15.5138-5146.2004 · DOI
  14. Y Cui, 2008, Insight into microevolution of Yersinia pestis by clustered regularly interspaced short palindromic repeats, PLoS One, 3, e2652, 10.1371/journal.pone.0002652 · DOI
  15. JM Girard, 2004, Differential plague-transmission dynamics determine Yersinia pestis population genetic structure on local, regional, and global scales, Proc Natl Acad Sci U S A, 101, 8408, 10.1073/pnas.0401561101 · DOI
  16. I Comas, 2009, Genotyping of genetically monomorphic bacteria: DNA sequencing in Mycobacterium tuberculosis highlights the limitations of current methodologies, PLoS One, 4, e7815, 10.1371/journal.pone.0007815 · DOI
  17. Y Li, 2013, Features of Variable Number of Tandem Repeats in Yersinia pestis and the Development of a Hierarchical Genotyping Scheme, PLoS One, 8, e66567, 10.1371/journal.pone.0066567 · DOI
  18. T Pearson, 2004, Phylogenetic discovery bias in Bacillus anthracis using single-nucleotide polymorphisms from whole-genome sequencing, Proc Natl Acad Sci U S A, 101, 13536, 10.1073/pnas.0403844101 · DOI
  19. TD Read, 2002, Comparative genome sequencing for discovery of novel polymorphisms in Bacillus anthracis, Science, 296, 2028, 10.1126/science.1071837 · DOI
  20. P Keim, 2001, Molecular investigation of the Aum Shinrikyo anthrax release in Kameido, Japan, J Clin Microbiol, 39, 4566, 10.1128/JCM.39.12.4566-4567.2001 · DOI
  21. H Takahashi, 2004, Bacillus anthracis incident, Kameido, Tokyo, 1993, Emerg Infect Dis, 10, 117, 10.3201/eid1001.030238 · DOI
  22. RE Colman, 2009, Fine-scale identification of the most likely source of a human plague infection, Emerg Infect Dis, 15, 1623, 10.3201/eid1510.090188 · DOI
  23. SR Harris, 2010, Evolution of MRSA during hospital transmission and intercontinental spread, Science, 327, 469, 10.1126/science.1182395 · DOI
  24. KE Holt, 2008, High-throughput sequencing provides insights into genome variation and evolution in Salmonella Typhi, Nat Genet, 40, 987, 10.1038/ng.195 · DOI
  25. M He, 2010, Evolutionary dynamics of Clostridium difficile over short and long time scales, Proc Natl Acad Sci U S A, 107, 7527, 10.1073/pnas.0914322107 · DOI
  26. T Pearson, 2009, Phylogeographic reconstruction of a bacterial species with high levels of lateral gene transfer, BMC Biol, 7, 78, 10.1186/1741-7007-7-78 · DOI
  27. CU Koser, 2012, Rapid whole-genome sequencing for investigation of a neonatal MRSA outbreak, N Engl J Med, 366, 2267, 10.1056/NEJMoa1109910 · DOI
  28. JL Gardy, 2011, Whole-genome sequencing and social-network analysis of a tuberculosis outbreak, N Engl J Med, 364, 730, 10.1056/NEJMoa1003176 · DOI
  29. H Wang, 2011, A dog-associated primary pneumonic plague in Qinghai Province, China, Clin Infect Dis, 52, 185, 10.1093/cid/ciq107 · DOI
  30. Y Cui, 2013, Historical variations in mutation rate in an epidemic pathogen, Yersinia pestis, Proc Natl Acad Sci U S A, 110, 577, 10.1073/pnas.1205750110 · DOI
  31. Ji S (1988) Plague. BeijingChina: People's medical publishing house. 10–24 p. · DOI
  32. H Rohde, 2011, Open-source genomic analysis of Shiga-toxin-producing E. coli O104:H4, N Engl J Med, 365, 718, 10.1056/NEJMoa1107643 · DOI
  33. M Li, 2005, Asymptomatic Yersinia pestis infection, China, Emerg Infect Dis, 11, 1494, 10.3201/eid1109.041147 · DOI
  34. R Li, 2010, De novo assembly of human genomes with massively parallel short read sequencing, Genome Res, 20, 265, 10.1101/gr.097261.109 · DOI · Paper page
  35. R Li, 2008, SOAP: short oligonucleotide alignment program, Bioinformatics, 24, 713, 10.1093/bioinformatics/btn025 · DOI · Paper page
  36. S Kurtz, 2004, Versatile and open software for comparing large genomes, Genome Biol, 5, R12, 10.1186/gb-2004-5-2-r12 · DOI · Paper page
  37. S Guindon, 2003, A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood, Syst Biol, 52, 696, 10.1080/10635150390235520 · DOI
  38. S Kumar, 2008, MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences, Brief Bioinform, 9, 299, 10.1093/bib/bbn017 · DOI
  39. A Stamatakis, 2005, RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees, Bioinformatics, 21, 456, 10.1093/bioinformatics/bti191 · DOI

Cited by

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Metadata

Authors
Yan, Y., Wang, H., Li, D., Yang, X., Wang, Z., Qi, Z., Zhang, Q., Cui, B., Guo, Z., Yu, C., Wang, J., Wang, J., Liu, G., Song, Y., Li, Y., Cui, Y., & Yang, R.
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2014-01-09
Volume
9
Issue
1
Pages
e85374
ISSN
1932-6203
DOI
10.1371/journal.pone.0085374
PMC
PMC3887043
Cited by
9
References
2
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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