Cystic Fibrosis Rapid Response: Translating Multi-omics Data into Clinically Relevant Information
Abstract
Pulmonary exacerbations are the leading cause of death in cystic fibrosis (CF) patients. To track microbial dynamics during acute exacerbations, a CF rapid response (CFRR) strategy was developed. The CFRR relies on viromics, metagenomics, metatranscriptomics, and metabolomics data to rapidly monitor active members of the viral and microbial community during acute CF exacerbations. To highlight CFRR, a case study of a CF patient is presented, in which an abrupt decline in lung function characterized a fatal exacerbation. The microbial community in the patient’s lungs was closely monitored through the multi-omics strategy, which led to the identification of pathogenic shigatoxigenic Escherichia coli (STEC) expressing Shiga toxin. This case study illustrates the potential for the CFRR to deconstruct complicated disease dynamics and provide clinicians with alternative treatments to improve the outcomes of pulmonary exacerbations and expand the life spans of individuals with CF. Proper management of polymicrobial infections in patients with cystic fibrosis (CF) has extended their life span. Information about the composition and dynamics of each patient’s microbial community aids in the selection of appropriate treatment of pulmonary exacerbations. We propose the cystic fibrosis rapid response (CFRR) as a fast approach to determine viral and microbial community composition and activity duringmore »
- Authors:
-
- San Diego State Univ., CA (United States)
- Univ. of California San Diego, La Jolla, CA (United States)
- Ronin Inst., Montclair, NJ (United States); Wholon, San Diego, CA (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Univ. of Texas Southwestern Medical Center, Dallas, TX (United States)
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- Spruance Foundation; Consejo Nacional de Ciencia y Tecnologia (CONACYT); USDOE
- OSTI Identifier:
- 1593813
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- mBio (Online)
- Additional Journal Information:
- Journal Name: mBio (Online); Journal Volume: 10; Journal Issue: 2; Journal ID: ISSN 2150-7511
- Publisher:
- American Society for Microbiology
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Shiga toxins; clinical metagenomics; cystic fibrosis; metabolomics; metatranscriptome
Citation Formats
Cobián Güemes, Ana Georgina, Lim, Yan Wei, Quinn, Robert A., Conrad, Douglas J., Benler, Sean, Maughan, Heather, Edwards, Rob, Brettin, Thomas, Cantú, Vito Adrian, Cuevas, Daniel, Hamidi, Rohaum, Dorrestein, Pieter, Rohwer, Forest, and Sperandio, Vanessa. Cystic Fibrosis Rapid Response: Translating Multi-omics Data into Clinically Relevant Information. United States: N. p., 2019.
Web. doi:10.1128/mBio.00431-19.
Cobián Güemes, Ana Georgina, Lim, Yan Wei, Quinn, Robert A., Conrad, Douglas J., Benler, Sean, Maughan, Heather, Edwards, Rob, Brettin, Thomas, Cantú, Vito Adrian, Cuevas, Daniel, Hamidi, Rohaum, Dorrestein, Pieter, Rohwer, Forest, & Sperandio, Vanessa. Cystic Fibrosis Rapid Response: Translating Multi-omics Data into Clinically Relevant Information. United States. https://doi.org/10.1128/mBio.00431-19
Cobián Güemes, Ana Georgina, Lim, Yan Wei, Quinn, Robert A., Conrad, Douglas J., Benler, Sean, Maughan, Heather, Edwards, Rob, Brettin, Thomas, Cantú, Vito Adrian, Cuevas, Daniel, Hamidi, Rohaum, Dorrestein, Pieter, Rohwer, Forest, and Sperandio, Vanessa. Tue .
"Cystic Fibrosis Rapid Response: Translating Multi-omics Data into Clinically Relevant Information". United States. https://doi.org/10.1128/mBio.00431-19. https://www.osti.gov/servlets/purl/1593813.
@article{osti_1593813,
title = {Cystic Fibrosis Rapid Response: Translating Multi-omics Data into Clinically Relevant Information},
author = {Cobián Güemes, Ana Georgina and Lim, Yan Wei and Quinn, Robert A. and Conrad, Douglas J. and Benler, Sean and Maughan, Heather and Edwards, Rob and Brettin, Thomas and Cantú, Vito Adrian and Cuevas, Daniel and Hamidi, Rohaum and Dorrestein, Pieter and Rohwer, Forest and Sperandio, Vanessa},
abstractNote = {Pulmonary exacerbations are the leading cause of death in cystic fibrosis (CF) patients. To track microbial dynamics during acute exacerbations, a CF rapid response (CFRR) strategy was developed. The CFRR relies on viromics, metagenomics, metatranscriptomics, and metabolomics data to rapidly monitor active members of the viral and microbial community during acute CF exacerbations. To highlight CFRR, a case study of a CF patient is presented, in which an abrupt decline in lung function characterized a fatal exacerbation. The microbial community in the patient’s lungs was closely monitored through the multi-omics strategy, which led to the identification of pathogenic shigatoxigenic Escherichia coli (STEC) expressing Shiga toxin. This case study illustrates the potential for the CFRR to deconstruct complicated disease dynamics and provide clinicians with alternative treatments to improve the outcomes of pulmonary exacerbations and expand the life spans of individuals with CF. Proper management of polymicrobial infections in patients with cystic fibrosis (CF) has extended their life span. Information about the composition and dynamics of each patient’s microbial community aids in the selection of appropriate treatment of pulmonary exacerbations. We propose the cystic fibrosis rapid response (CFRR) as a fast approach to determine viral and microbial community composition and activity during CF pulmonary exacerbations. The CFRR potential is illustrated with a case study in which a cystic fibrosis fatal exacerbation was characterized by the presence of shigatoxigenic Escherichia coli. The incorporation of the CFRR within the CF clinic could increase the life span and quality of life of CF patients.},
doi = {10.1128/mBio.00431-19},
journal = {mBio (Online)},
number = 2,
volume = 10,
place = {United States},
year = {Tue Apr 16 00:00:00 EDT 2019},
month = {Tue Apr 16 00:00:00 EDT 2019}
}
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