Tracking the Dynamic Relationship between Cellular Systems and

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Tracking the dynamic relationship between cellular systems and extracellular subproteomes in Pseudomonas aeruginosa biofilms Amber Jane Park, Kathleen Murphy, Matthew D Surette, Christopher Bandoro, Jonathan R Krieger, Paul Taylor, and Cezar M Khursigara J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b00262 • Publication Date (Web): 17 Sep 2015 Downloaded from http://pubs.acs.org on October 1, 2015

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Journal of Proteome Research

Tracking the dynamic relationship between cellular systems and extracellular subproteomes in Pseudomonas aeruginosa biofilms Amber J. Parka, Kathleen Murphya, Matthew D. Surettea, Christopher Bandoroa, Jonathan R. Kriegerb, Paul Taylorb, Cezar M. Khursigaraa*

a

Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada,

N1G 2W1; bSPARC BioCentre, The Hospital for Sick Children, Toronto, ON, Canada, M5G 0A4.

ACS Paragon Plus Environment

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Abstract. The transition of the opportunistic pathogen Pseudomonas aeruginosa from freeliving bacteria into surface-associated biofilm communities represents a viable target for the prevention and treatment of chronic infectious disease. We have established a proteomics platform that identified 2443 and 1142 high-confidence proteins in P. aeruginosa whole cells and outer membrane vesicles (OMVs), respectively, at three time points during biofilm development (ProteomeXchange identifier PXD002605). Analysis of cellular systems, specifically the phenazine biosynthetic pathway, demonstrates that whole cell protein abundance correlates to end product (i.e., pyocyanin) concentrations in biofilm but not planktonic cultures. Furthermore, increased cellular protein abundance in this pathway results in quantifiable pyocyanin in early biofilm OMVs, and OMVs from both growth modes isolated at later time points. Overall, our data indicate that the OMVs being released from the surface of the biofilm whole cells have unique proteomes in comparison to their planktonic counterparts. The relative abundance of OMV proteins from various subcellular sources showed considerable differences between the two growth modes over time, supporting the existence and preferential activation of multiple OMV biogenesis mechanisms under different conditions. The consistent detection of cytoplasmic proteins in all of the OMV subproteomes challenges the notion that OMVs are comprised of outer membrane and periplasmic proteins alone. Direct comparisons of outer membrane protein abundance levels between OMVs and whole cells shows ratios that vary greatly from 1:1, and supports previous studies that advocate specific inclusion, or “packaging”, of proteins into OMVs. The quantitative analysis of packaged protein groups suggests biogenesis mechanisms that involve untethered, rather than absent, peptidoglycan-binding proteins. Collectively, individual protein and biological system analyses of biofilm OMVs show that drugbinding cytoplasmic proteins and porins are potentially shuttled from the whole cell into the


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OMVs, and may contributing to the antibiotic resistance of P. aeruginosa whole cells within biofilms.

Key words: biofilms, outer membrane vesicles, quantitative proteomics, Pseudomonas aeruginosa, systems biology


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Introduction Bacterial biofilms are complex and highly dynamic communities. As a consequence, the biological assessment of these communities benefits from a whole system approach. This strategy is particularly suited to studying the opportunistic pathogen Pseudomonas aeruginosa as it transitions from free-living planktonic into surface-associated biofilms. P. aeruginosa has many adaptive mechanisms that allow it to thrive in a range of environmental conditions1-5, and is considered a model pathogen for both antibiotic-resistance and biofilm formation. Its recalcitrance and adaptive abilities contribute to the colonization of P. aeruginosa and its persistence in a variety of chronic infections6,7, including those in the lungs of patients with the fatal genetic disease cystic fibrosis (CF)8-10. Historically these bacterial communities have been characterized by examining the communal genotype11-13. While these transcriptomes reflect the core genome and environmental influences on the system, recent evidence from both eukaryotic and prokaryotic studies suggest the correlation between transcript levels and protein abundance is often low14-16. Therefore, we can gain a more accurate understanding of the biofilm phenotype by studying the abundance of proteins present at a certain time under specific conditions. While this approaches does not account for individual protein activity levels, or subpopulation characteristics within a heterogeneous biofilm, it does provide an important high-level view of these coordinated communities, and is an integral step in defining the proteotype of P. aeruginosa biofilms. Using protein-based approaches we can also examine extracellularcomponents, such as outer membrane vesicles (OMVs), which are shed from the surface of the bacteria into the environment. These ubiquitous structures have been shown to play a role in cellcell communication, adaptation and survival, as well as virulence (for review17,18). Importantly, initial studies suggest that P. aeruginosa OMVs with different protein content can produce


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varying amounts of inflammation in eukaryotic cell culture models19. Accordingly, the systematic collection of data related to environmental influences on OMV protein composition has the potential to inform both anti-virulence and vaccine-based antipseudomonal therapies. Previously, we published a semi-quantitative analysis of proteins found in both free-living (planktonic) and biofilm-associated whole cell P. aeruginosa PAO1 across three time points20, with organism-specific functional information ascribed to the significantly increased or decreased proteins. In this study, we now create a systematic network of whole cell and OMV proteomes that illustrates how the comprehensive analysis of high-resolution mass spectrometry (MS) data can be used to track an opportunistic pathogen as it transitions from a planktonic population into a biofilm. Accordingly, this study models the progression of transient acute P. aeruginosa infections into chronic infections seen in patients with CF. We examine how cellular abundance levels of pyocyanin biosynthetic proteins correlate to end-product quantities in both planktonic and biofilm cultures and OMVs. Outstanding questions in the OMV field guide our analysis of P. aeruginosa biofilm OMV protein content, with a specific focus on OMV function, biogenesis, and cargo sorting. High-level summaries of this extensive quantitative dataset showcase the utility of the experimental platform and identify key characteristics of these communities for future study. Selected detailed analysis illustrates the power of proteomic surveys for identifying robust patterns in biologically significant groups of proteins over time. This data significantly adds to our understanding of biofilm development, and the characterization of biofilm-specific OMVs.


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Material and Methods Bacterial strains and media. The parental strain for all studies was P. aeruginosa PAO1. Planktonic samples were grown in static cultures of tryptic soy broth (TSB) (BD, Franklin Lakes, NJ, USA). Unless otherwise noted, biofilm samples were grown on tryptic soy agar (TSA) (BD) solidified in a sterile glass dish (190  100 mm; Corning, Tewksbury, MA, USA). Triplicate cultures were normalized by optical density (OD600nm) before further processing. Biofilm material depicted in Fig. 2A was grown using a standard drip reactor. A detailed description of the sample preparation and processing is provided in SI Materials and Methods. OMV isolation. OMV isolation was based on previously described methods21 and are detailed in SI Materials and Methods. Sample purity was assessed at various stages throughout the isolation procedure via transmission electron microscopy (TEM, see methods below), 1-D protein electrophoresis, and quantification of flagellar proteins (Fig. S3). Imaging. Agar biofilms were imaged using scanning EM (SEM). Drip reactor whole cells and OMVs were imaged using TEM. Full details can be found in SI Materials and Methods. Digestion of proteins. In-solution digestions of whole cells and OMVs were completed using the method of Foster, Hoog, and Mann22 as previously reported20. Full details can be found in SI Materials and Methods. LC-MS/MS. Liquid chromatography (LC) tandem mass spectrometry (MS/MS) was completed on 5

l of digested protein as previously described[20]. Briefly, samples were

separated on an EASY-nLC 1000 chromatography system (Thermo Fisher Scientific, Waltham, MA, USA), injected into a Q Exactive mass spectrometer (Thermo Fisher Scientific) through an


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EASY-Spray integrated emitter (Thermo Fisher Scientific), and then ionized using electrospray ionization. Spectrum and peak list generation was performed using Q Exactive 2.2 and Xcalibur 2.2 (Thermo Fisher Scientific). Raw data files were extracted and searched against the UniProtKB – P. aeruginosa-ATCC15692 database (5564 entries) using MaxQuant quantitative proteomics software23 (version 1.4.0.5, Max Planck Institute of Biochemistry, Martinsried, Germany). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium24 via the PRIDE25 partner repository with the dataset identifier PXD002605. Full details can be found in SI Materials and Methods. Data analysis. Functional annotations were obtained from the Pseudomonas Genome Database26 and manually matched to the identified proteins. Label free-quantification (LFQ) intensities calculated in MaxQuant were loaded into Perseus (version 1.4.0.11, Max Planck Institute of Biochemistry, Martinsried, Germany), transformed [log2(x)], and asses using a variety of methods including histograms, Venn diagrams, and heat-maps. Significantly increased or decreased proteins were identified using two-sample t-tests with cut-offs determined using a permutation-based FDR of 0.05 based on 250 re-sampling iterations. Pathway analysis was completed using proteins identified in the PseudoCAP26 “phenazine biosynthesis” pathway, and diagrammed in Adobe Illustrator (Adobe Systems Incorporated, San Jose, CA, USA). All other data calculations were completed in Microsoft Excel (Microsoft, Redmond, WA, USA) or Prism (GraphPad Software, CA, USA). Full details are provided in SI Materials and Methods. Endogenous pyocyanin (PYO) measurement and cellular responses to exogenous PYO. Endogenous PYO was measured in cell-free supernatant (CFS) (normalized to whole cell density) or OMV samples (normalized to 150 g protein) using a Dionex UHPLC UltiMate 3000 liquid chromatograph interfaced to an amaZon SL ion trap mass spectrometer (Bruker Daltonics,


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MA, USA). Quantitation of pyocyanin was determined using the QuantAnalysis software (Bruker Daltonics). The Microtiter Dish Biofilm Formation Assay27 was completed with TSB supplemented with PYO in order to measure planktonic growth (i.e., non-adherent bacteria) and biofilm formation of P. aeruginosa PAO1 exposed to exogenous PYO. Commercially available enzyme-linked immunosorbant assays (ELISAs) were used to measure Interleukin-6 (IL-6) in CFS collected from 16HBE14ο- human bronchial epithelial (HBE) cell cultures grown in LHC-8 media supplemented with PYO. LC-MS calibration curves and PYO supplementation were completed with commercially available pyocyanin P0046 (Sigma-Aldrich, MO, USA). Full details are provided in SI Materials and Methods. Results and Discussion Creating a high-resolution proteomic database for the opportunistic pathogen P. aeruginosa. To identify key changes in functional pathways and subcellular proteomes we first established a database of high confidence proteins. We used MaxQuant, a quantitative proteomics platform with high mass accuracy that provides quantitative ion intensity-based peptide abundance measurements23. This analysis resulted in the identification of 2443 and 1142 high confidence protein groups in the whole cell and OMV samples, respectively (Fig. 1A-D), with a false discovery rate (FDR) of

Tracking the Dynamic Relationship between Cellular Systems and

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