The University of Southampton
University of Southampton Institutional Repository

An integrated model system to gain mechanistic insights into biofilm-associated antimicrobial resistance in Pseudomonas aeruginosa MPAO1

An integrated model system to gain mechanistic insights into biofilm-associated antimicrobial resistance in Pseudomonas aeruginosa MPAO1
An integrated model system to gain mechanistic insights into biofilm-associated antimicrobial resistance in Pseudomonas aeruginosa MPAO1

Pseudomonas aeruginosa MPAO1 is the parental strain of the widely utilized transposon mutant collection for this important clinical pathogen. Here, we validate a model system to identify genes involved in biofilm growth and biofilm-associated antibiotic resistance. Our model employs a genomics-driven workflow to assemble the complete MPAO1 genome, identify unique and conserved genes by comparative genomics with the PAO1 reference strain and genes missed within existing assemblies by proteogenomics. Among over 200 unique MPAO1 genes, we identified six general essential genes that were overlooked when mapping public Tn-seq data sets against PAO1, including an antitoxin. Genomic data were integrated with phenotypic data from an experimental workflow using a user-friendly, soft lithography-based microfluidic flow chamber for biofilm growth and a screen with the Tn-mutant library in microtiter plates. The screen identified hitherto unknown genes involved in biofilm growth and antibiotic resistance. Experiments conducted with the flow chamber across three laboratories delivered reproducible data on P. aeruginosa biofilms and validated the function of both known genes and genes identified in the Tn-mutant screens. Differential protein abundance data from planktonic cells versus biofilm confirmed the upregulation of candidates known to affect biofilm formation, of structural and secreted proteins of type VI secretion systems, and provided proteogenomic evidence for some missed MPAO1 genes. This integrated, broadly applicable model promises to improve the mechanistic understanding of biofilm formation, antimicrobial tolerance, and resistance evolution in biofilms.

Varadarajan, Adithi R.
ff277cc5-a4fc-46cd-9410-fda3a909b017
Allan, Raymond N.
390a7d0a-38e1-410a-8dfe-c8ef8408f5e1
Valentin, Jules D.P.
63941168-7e62-40a5-8ec8-c2053974ea12
Castañeda Ocampo, Olga E.
6fab38dc-e991-4de5-8aff-48282c4ce935
Somerville, Vincent
72437425-207b-4f59-bf63-c0dc62e9ec93
Pietsch, Franziska
0f11350d-36dd-4046-80f0-506593c97816
Buhmann, Matthias T.
461f0681-68e0-4054-937d-b280bdbb97bd
West, Jonathan
f1c2e060-16c3-44c0-af70-242a1c58b968
Skipp, Paul J.
1ba7dcf6-9fe7-4b5c-a9d0-e32ed7f42aa5
van der Mei, Henny C.
41a13fec-7b1c-41ce-b765-a53e30030662
Ren, Qun
fdc42b84-e0e0-4626-b7dd-0bd1d25619bf
Schreiber, Frank
996cc9b1-c439-4fae-8a46-43b94608d034
Webb, Jeremy S.
ec0a5c4e-86cc-4ae9-b390-7298f5d65f8d
Ahrens, Christian H.
70789dbf-86c6-4725-beca-8dfa4b4e1323
Varadarajan, Adithi R.
ff277cc5-a4fc-46cd-9410-fda3a909b017
Allan, Raymond N.
390a7d0a-38e1-410a-8dfe-c8ef8408f5e1
Valentin, Jules D.P.
63941168-7e62-40a5-8ec8-c2053974ea12
Castañeda Ocampo, Olga E.
6fab38dc-e991-4de5-8aff-48282c4ce935
Somerville, Vincent
72437425-207b-4f59-bf63-c0dc62e9ec93
Pietsch, Franziska
0f11350d-36dd-4046-80f0-506593c97816
Buhmann, Matthias T.
461f0681-68e0-4054-937d-b280bdbb97bd
West, Jonathan
f1c2e060-16c3-44c0-af70-242a1c58b968
Skipp, Paul J.
1ba7dcf6-9fe7-4b5c-a9d0-e32ed7f42aa5
van der Mei, Henny C.
41a13fec-7b1c-41ce-b765-a53e30030662
Ren, Qun
fdc42b84-e0e0-4626-b7dd-0bd1d25619bf
Schreiber, Frank
996cc9b1-c439-4fae-8a46-43b94608d034
Webb, Jeremy S.
ec0a5c4e-86cc-4ae9-b390-7298f5d65f8d
Ahrens, Christian H.
70789dbf-86c6-4725-beca-8dfa4b4e1323

Varadarajan, Adithi R., Allan, Raymond N., Valentin, Jules D.P., Castañeda Ocampo, Olga E., Somerville, Vincent, Pietsch, Franziska, Buhmann, Matthias T., West, Jonathan, Skipp, Paul J., van der Mei, Henny C., Ren, Qun, Schreiber, Frank, Webb, Jeremy S. and Ahrens, Christian H. (2020) An integrated model system to gain mechanistic insights into biofilm-associated antimicrobial resistance in Pseudomonas aeruginosa MPAO1. NPJ Biofilms and Microbiomes, 6 (1), [46]. (doi:10.1038/s41522-020-00154-8).

Record type: Article

Abstract

Pseudomonas aeruginosa MPAO1 is the parental strain of the widely utilized transposon mutant collection for this important clinical pathogen. Here, we validate a model system to identify genes involved in biofilm growth and biofilm-associated antibiotic resistance. Our model employs a genomics-driven workflow to assemble the complete MPAO1 genome, identify unique and conserved genes by comparative genomics with the PAO1 reference strain and genes missed within existing assemblies by proteogenomics. Among over 200 unique MPAO1 genes, we identified six general essential genes that were overlooked when mapping public Tn-seq data sets against PAO1, including an antitoxin. Genomic data were integrated with phenotypic data from an experimental workflow using a user-friendly, soft lithography-based microfluidic flow chamber for biofilm growth and a screen with the Tn-mutant library in microtiter plates. The screen identified hitherto unknown genes involved in biofilm growth and antibiotic resistance. Experiments conducted with the flow chamber across three laboratories delivered reproducible data on P. aeruginosa biofilms and validated the function of both known genes and genes identified in the Tn-mutant screens. Differential protein abundance data from planktonic cells versus biofilm confirmed the upregulation of candidates known to affect biofilm formation, of structural and secreted proteins of type VI secretion systems, and provided proteogenomic evidence for some missed MPAO1 genes. This integrated, broadly applicable model promises to improve the mechanistic understanding of biofilm formation, antimicrobial tolerance, and resistance evolution in biofilms.

This record has no associated files available for download.

More information

Accepted/In Press date: 7 October 2020
e-pub ahead of print date: 30 October 2020
Published date: 1 December 2020

Identifiers

Local EPrints ID: 447652
URI: http://eprints.soton.ac.uk/id/eprint/447652
PURE UUID: 54b62c84-436b-43b0-aea2-f4dec26ad93b
ORCID for Jonathan West: ORCID iD orcid.org/0000-0002-5709-6790
ORCID for Paul J. Skipp: ORCID iD orcid.org/0000-0002-2995-2959
ORCID for Jeremy S. Webb: ORCID iD orcid.org/0000-0003-2068-8589

Catalogue record

Date deposited: 17 Mar 2021 17:39
Last modified: 18 Mar 2024 03:23

Export record

Altmetrics

Contributors

Author: Adithi R. Varadarajan
Author: Raymond N. Allan
Author: Jules D.P. Valentin
Author: Olga E. Castañeda Ocampo
Author: Vincent Somerville
Author: Franziska Pietsch
Author: Matthias T. Buhmann
Author: Jonathan West ORCID iD
Author: Paul J. Skipp ORCID iD
Author: Henny C. van der Mei
Author: Qun Ren
Author: Frank Schreiber
Author: Jeremy S. Webb ORCID iD
Author: Christian H. Ahrens

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×