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Rapid identification and strain-typing of respiratory pathogens for epidemic surveillance

Open access
Ecker, David J., Sampath, Rangarajan, Blyn, Lawrence B., Eshoo, Mark W., Ivy, Cristina, Ecker, Joseph A., Libby, Brian, Samant, Vivek, Sannes-Lowery, Kristin A., Melton, Rachael E., Russell, Kevin, Freed, Nikki, Barrozo, Chris, Wu, Jianguo, Rudnick, Karl, Desai, Anjali, Moradi, Emily, Knize, Duane J., Robbins, David W., Hannis, James C., Harrell, Patina M., Massire, Christian, Hall, Thomas A., Jiang, Yun, Ranken, Raymond, Drader, Jared J., White, Neill, McNeil, John A., Crooke, Stanley T., Hofstadler, Steven A.·2005·Proceedings of the National Academy of Sciences

Abstract

Epidemic respiratory infections are responsible for extensive morbidity and mortality within both military and civilian populations. We describe a high-throughput method to simultaneously identify and genotype species of bacteria from complex mixtures in respiratory samples. The process uses electrospray ionization mass spectrometry and base composition analysis of PCR amplification products from highly conserved genomic regions to identify and determine the relative quantity of pathogenic bacteria present in the sample. High-resolution genotyping of specific species is achieved by using additional primers targeted to highly variable regions of specific bacterial genomes. This method was used to examine samples taken from military recruits during respiratory disease outbreaks and for follow up surveillance at several military training facilities. Analysis of respiratory samples revealed high concentrations of pathogenic respiratory species, including Haemophilus influenzae , Neisseria meningitidis , and Streptococcus pyogenes . When S. pyogenes was identified in samples from the epidemic site, the identical genotype was found in almost all recruits. This analysis method will provide information fundamental to understanding the polymicrobial nature of explosive epidemics of respiratory disease.

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Metadata

Authors
Ecker, D. J., Sampath, R., Blyn, L. B., Eshoo, M. W., Ivy, C., Ecker, J. A., Libby, B., Samant, V., Sannes-Lowery, K. A., Melton, R. E., Russell, K., Freed, N., Barrozo, C., Wu, J., Rudnick, K., Desai, A., Moradi, E., Knize, D. J., Robbins, D. W., Hannis, J. C., Harrell, P. M., Massire, C., Hall, T. A., Jiang, Y., Ranken, R., Drader, J. J., White, N., McNeil, J. A., Crooke, S. T., & Hofstadler, S. A.
Venue
Proceedings of the National Academy of Sciences
Published
2005-05-23
Volume
102
Issue
22
Pages
8012-8017
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2109971578
Cited by
172
References
0
FWCI
9.35
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Rapid identification and strain-typing of respiratory pathogens for epidemic surveillance. https://app.cochat.ai/papers/10.1073/pnas.0409920102
The paper
Ecker, D. J., Sampath, R., Blyn, L. B., Eshoo, M. W., Ivy, C., Ecker, J. A., Libby, B., Samant, V., Sannes-Lowery, K. A., Melton, R. E., Russell, K., Freed, N., Barrozo, C., Wu, J., Rudnick, K., Desai, A., Moradi, E., Knize, D. J., Robbins, D. W., . . . Hofstadler, S. A. (2005). Rapid identification and strain-typing of respiratory pathogens for epidemic surveillance. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0409920102

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.