Pseudomonas aeruginosa transcriptome during human infection

Daniel M. Cornforth; Justine L. Dees; Carolyn B. Ibberson; Holly K. Huse; Inger H. Mathiesen; Klaus Kirketerp-Møller; Randy D. Wolcott; Kendra P. Rumbaugh; Thomas Bjarnsholt; Marvin Whiteley

doi:10.1073/pnas.1717525115

Pseudomonas aeruginosa transcriptome during human infection

Daniel M. Cornforth, Justine L. Dees, Carolyn B. Ibberson, Holly K. Huse, Inger H. Mathiesen, Klaus Kirketerp-Møller, Randy D. Wolcott, Kendra P. Rumbaugh, Thomas Bjarnsholt, Marvin Whiteley^*

^*Corresponding author af dette arbejde

85 Citationer (Scopus)

Abstract

Laboratory experiments have uncovered many basic aspects of bacterial physiology and behavior. After the past century of mostly in vitro experiments, we now have detailed knowledge of bacterial behavior in standard laboratory conditions, but only a superficial understanding of bacterial functions and behaviors during human infection. It is well-known that the growth and behavior of bacteria are largely dictated by their environment, but how bacterial physiology differs in laboratory models compared with human infections is not known. To address this question, we compared the transcriptome of Pseudomonas aeruginosa during human infection to that of P. aeruginosa in a variety of laboratory conditions. Several pathways, including the bacterium’s primary quorum sensing system, had significantly lower expression in human infections than in many laboratory conditions. On the other hand, multiple genes known to confer antibiotic resistance had substantially higher expression in human infection than in laboratory conditions, potentially explaining why antibiotic resistance assays in the clinical laboratory frequently underestimate resistance in patients. Using a standard machine learning technique known as support vector machines, we identified a set of genes whose expression reliably distinguished in vitro conditions from human infections. Finally, we used these support vector machines with binary classification to force P. aeruginosa mouse infection transcriptomes to be classified as human or in vitro. Determining what differentiates our current models from clinical infections is important to better understand bacterial infections and will be necessary to create model systems that more accurately capture the biology of infection.

Originalsprog	Engelsk
Tidsskrift	Proceedings of the National Academy of Sciences of the United States of America
Vol/bind	115
Udgave nummer	22
Sider (fra-til)	E5125-E5134
ISSN	0027-8424
DOI	https://doi.org/10.1073/pnas.1717525115
Status	Udgivet - 2018

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10.1073/pnas.1717525115

E5125.full.pdfForlagets udgivne version, 842 KBLicens: CC BY-NC-ND

Citationsformater

Cornforth, D. M., Dees, J. L., Ibberson, C. B., Huse, H. K., Mathiesen, I. H., Kirketerp-Møller, K., Wolcott, R. D., Rumbaugh, K. P., Bjarnsholt, T., & Whiteley, M. (2018). Pseudomonas aeruginosa transcriptome during human infection. Proceedings of the National Academy of Sciences of the United States of America, 115(22), E5125-E5134. https://doi.org/10.1073/pnas.1717525115

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title = "Pseudomonas aeruginosa transcriptome during human infection",

abstract = "Laboratory experiments have uncovered many basic aspects of bacterial physiology and behavior. After the past century of mostly in vitro experiments, we now have detailed knowledge of bacterial behavior in standard laboratory conditions, but only a superficial understanding of bacterial functions and behaviors during human infection. It is well-known that the growth and behavior of bacteria are largely dictated by their environment, but how bacterial physiology differs in laboratory models compared with human infections is not known. To address this question, we compared the transcriptome of Pseudomonas aeruginosa during human infection to that of P. aeruginosa in a variety of laboratory conditions. Several pathways, including the bacterium{\textquoteright}s primary quorum sensing system, had significantly lower expression in human infections than in many laboratory conditions. On the other hand, multiple genes known to confer antibiotic resistance had substantially higher expression in human infection than in laboratory conditions, potentially explaining why antibiotic resistance assays in the clinical laboratory frequently underestimate resistance in patients. Using a standard machine learning technique known as support vector machines, we identified a set of genes whose expression reliably distinguished in vitro conditions from human infections. Finally, we used these support vector machines with binary classification to force P. aeruginosa mouse infection transcriptomes to be classified as human or in vitro. Determining what differentiates our current models from clinical infections is important to better understand bacterial infections and will be necessary to create model systems that more accurately capture the biology of infection.",

keywords = "Chronic wounds, Cystic fibrosis, Human transcriptome, Machine learning, Pseudomonas aeruginosa",

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doi = "10.1073/pnas.1717525115",

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AU - Cornforth, Daniel M.

AU - Dees, Justine L.

AU - Ibberson, Carolyn B.

AU - Huse, Holly K.

AU - Mathiesen, Inger H.

AU - Kirketerp-Møller, Klaus

AU - Wolcott, Randy D.

AU - Rumbaugh, Kendra P.

AU - Bjarnsholt, Thomas

AU - Whiteley, Marvin

PY - 2018

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N2 - Laboratory experiments have uncovered many basic aspects of bacterial physiology and behavior. After the past century of mostly in vitro experiments, we now have detailed knowledge of bacterial behavior in standard laboratory conditions, but only a superficial understanding of bacterial functions and behaviors during human infection. It is well-known that the growth and behavior of bacteria are largely dictated by their environment, but how bacterial physiology differs in laboratory models compared with human infections is not known. To address this question, we compared the transcriptome of Pseudomonas aeruginosa during human infection to that of P. aeruginosa in a variety of laboratory conditions. Several pathways, including the bacterium’s primary quorum sensing system, had significantly lower expression in human infections than in many laboratory conditions. On the other hand, multiple genes known to confer antibiotic resistance had substantially higher expression in human infection than in laboratory conditions, potentially explaining why antibiotic resistance assays in the clinical laboratory frequently underestimate resistance in patients. Using a standard machine learning technique known as support vector machines, we identified a set of genes whose expression reliably distinguished in vitro conditions from human infections. Finally, we used these support vector machines with binary classification to force P. aeruginosa mouse infection transcriptomes to be classified as human or in vitro. Determining what differentiates our current models from clinical infections is important to better understand bacterial infections and will be necessary to create model systems that more accurately capture the biology of infection.

AB - Laboratory experiments have uncovered many basic aspects of bacterial physiology and behavior. After the past century of mostly in vitro experiments, we now have detailed knowledge of bacterial behavior in standard laboratory conditions, but only a superficial understanding of bacterial functions and behaviors during human infection. It is well-known that the growth and behavior of bacteria are largely dictated by their environment, but how bacterial physiology differs in laboratory models compared with human infections is not known. To address this question, we compared the transcriptome of Pseudomonas aeruginosa during human infection to that of P. aeruginosa in a variety of laboratory conditions. Several pathways, including the bacterium’s primary quorum sensing system, had significantly lower expression in human infections than in many laboratory conditions. On the other hand, multiple genes known to confer antibiotic resistance had substantially higher expression in human infection than in laboratory conditions, potentially explaining why antibiotic resistance assays in the clinical laboratory frequently underestimate resistance in patients. Using a standard machine learning technique known as support vector machines, we identified a set of genes whose expression reliably distinguished in vitro conditions from human infections. Finally, we used these support vector machines with binary classification to force P. aeruginosa mouse infection transcriptomes to be classified as human or in vitro. Determining what differentiates our current models from clinical infections is important to better understand bacterial infections and will be necessary to create model systems that more accurately capture the biology of infection.

KW - Chronic wounds

KW - Cystic fibrosis

KW - Human transcriptome

KW - Machine learning

KW - Pseudomonas aeruginosa

U2 - 10.1073/pnas.1717525115

DO - 10.1073/pnas.1717525115

M3 - Journal article

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AN - SCOPUS:85047919931

SN - 0027-8424

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JO - Proceedings of the National Academy of Sciences of the United States of America

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ER -

Pseudomonas aeruginosa transcriptome during human infection

Abstract

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