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Comprehensive Studies On the Outer Membrane Subproteome of Caulobacter crescentus Using Mass Spectrometry Based Shotgun Proteomics A dissertation presented by Yuan Cao B.S., Peking University, Beijing, China, 2001 to The Graduate Studies Council Department of Chemistry Brown University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Chemistry Brown University Providence, Rhode Island May 2013 © Copyright 2012 by Yuan Cao All Rights Reserved This dissertation by Yuan Cao is accepted in its present form by the Department of Chemistry as satisfying the dissertation requirements for the Degree of Doctor of Philosophy Date ________________ ______________________________ Dr. Carthene R. Bazemore-Walker, Director Recommend to the Graduate Council Date ________________ ______________________________ Dr. J. William Suggs, Reader Date ________________ ______________________________ Dr. Arthur R. Salomon, Reader Approved by the Graduate Council Date ________________ ______________________________ Dr. Peter M. Weber, Dean of the Graduate School ‐ iii ‐ Curriculum Vitae Yuan Cao Tel.: 401-865-9380 E-mail: [email protected] Date of Birth: 11/11/1982 Education ·Department of Chemistry, Brown University Ph. D. in Chemistry (05/2012) Overall GPA 3.95 ·College of Chemistry and Molecular Engineering, Peking University B. S. in Chemistry (07/ 2005) Overall GPA 3.50 Major GPA 3.66 ·Dongzhimen High School, Beijing Publications y Cao, Yuan, Johnson, Helen M. and Bazemore-Walker, Carthene R. “Improved Enrichment and Proteomics Identification of Outer Membrane Proteins from a Gram-Negative Bacterium: Focus on Caulobacter crescentus”. Proteomics. 2011. doi: 10.1002/pmic.201100288. y Poston, Chloe N., Duong, Ellen, Cao, Yuan and Bazemore-Walker, Carthene R. “Proteomic Analysis of Lipid Raft-enriched Membranes Isolated from Internal Organelles”. Biochemistry and Biophysical Research Communications. 2011. doi: 10.1016/j.bbrc.2011.10.07. y Cao, Yuan and Bazemore-Walker, Carthene R. “Deletion of the HfsA Exopolysaccharide Export Protein Changes the Composition of the Outer Membrane Proteome of Caulobacter crescentus”. 2012. In preparation. y Cao, Yuan and Bazemore-Walker, Carthene R. “Global profiling of the cell surface proteome of Caulobacter crescentus via biotinylation”. 2012. In preparation. y Hou, Sicong, Cao, Yuan, Xiong, Wei, Liu, Haichao and Kou, Yuan. “Site Requirements for the Oxidative Coupling of Methane on SiO2-Supported Mn Catalysts”. Ind. Eng. Chem. Res., 2006. y Hou, Sicong, Cao, Yuan, Xiong, Wei, Liu, Haichao and Kou, Yuan. “In Situ XRD Study on Sodium Salt-Modified Mn/SiO2 Catalysts for the Oxidative Coupling of Methane”. Chinese Journal of Catalysis, 2006. ‐ iv ‐ Curriculum Vitae Teaching Experiences ·Teaching Assistantship of Chemistry 33 and Chemistry 50, Brown University (09/2006-07/2011) ·Teaching Assistantship of Chemistry 166, Brown University (02/2012-05/2012) Honors and Awards ·Bruker Daltonics Travel Grant Award, University of New Hampshire (June 2008) ·Fellowship Award, Brown University (01/2007-09/2007) ·Excellent Thesis Award, Peking University (06/2005) ‐ v ‐ Abstract of comprehensive studies on the outer membrane subproteome of Caulobacter crescentus using mass spectrometry based shotgun proteomics, by Yuan Cao, Ph. D., Brown University, May, 2013. Efforts to characterize proteins found in the outer membrane of gram-negative bacteria have been steadily increasing due to the promise of expanding our understanding of fundamental bacterial processes such as cell wall biogenesis as well as the promise of finding potential vaccine- or drug-targets for virulent bacteria. Meanwhile, rapid development in mass spectrometry based proteomics, especially quantitative analysis of targeted proteome, received increased research interest as it provides information showing temporal and spatial change of a particular protein within the proteome. We have developed a mass spectrometry-compatible experimental strategy that resulted in increased coverage of the outer membrane (OM) proteome of a model organism, Caulobacter crescentus. The specificity of the OM enrichment step was improved by using detergent solubilization, low-density cell culture, and a surface-layer deficient cell line. Additionally, efficient gel-assisted digestion, high resolution RP/RP-MS/MS led to the identification of 234 proteins using strict identification criteria. Eighty-four of the detected proteins were predicted to localize to the OM or extracellular space, which accounted for ~77% of the total relative abundance in the OM fraction. In addition, biotinylation of bacterial surface-exposed proteins has been applied as an alternative approach to characterize bacterial membrane subproteome. Labeled proteins were purified by immobilized avidin and identified using LC/MS/MS. There were 91 putative surface-exposed membrane proteins identified in this work, out of which 63 (70%) proteins were also identified in the previous OM fraction. These comprehensive analytical approaches, which considers important experimental variables not previously explored in published outer membrane protein studies, can be applied to ‐ vi ‐ other OM proteomic endeavors “as is” or with slight modification and should improve the largescale study of this especially challenging subproteome. ‐ vii ‐ Acknowledgements First and foremost, I would like to express my deepest appreciation to my supervisor, Professor Carthene R. Bazemore-Walker, for granting me the opportunity to study in her research group and for her invaluable guidance, inspiration, encouragement and advice during the course of my research. It has been a wonderful 5-year journey for me and I have learned a significant amount from her and I am sure it will continue to benefit my life and career. I would like to thank the other members of my supervisory committee, Professor J. William Suggs, Professor Arthur R. Salomon, Professor Dwight A. Sweigart and Professor Peter M. Weber, for their active participation during my oral examinations, their thorough reviews and comments on this thesis, and their valuable advice on my research. My deep gratitude goes to the people with whom I collaborated. I especially thank Professor Jay. X. Tang and Research Assistant Professor Guanglai Li, from the Department of Physics at Brown University for their guidance on C. crescentus cell cultures and fluorescent microscopy. I am very grateful to Professor Yves Brun and Dr. Gail Hardy from the Department of Microbiology at Indiana University for creating the RsaA- and RsaA- + HfsA- mutant strains of C. crescentus which were used throughout all three projects. Many thanks to Professor Arthur Salomon from Molecular Biology, Cell Biology, & Biochemistry and Chemistry Department at Brown University and Dr. James Cliffton from Rhode Island NSF/EPSCoR Center for Proteomics at Brown University for instruction on running LTQ-FT and LTQ-Orbitrap Velos mass spectrometers. I also thank ‐ viii ‐ Dr. Tun-Li Shen for his professional training on the MALDI instrument used for preliminary method development on gel-assisted digestion and solid phase extraction of glycopeptides. I am also grateful to former undergraduates Seth Levin, Helen M. Johnson at Brown University for their assistance in biological sample preparations. I would like to thank many members in Professor Bazemore-Walker's research group including Dr. Hongbo Gu and Dr. Chao Gong for their practical advice on performing instruments and protocols. The friendship with them goes along with my whole life. My appreciation also extends to other members in the group, in no particular order, Chloe N. Poston, Michael Ellisor, Yiying Zhu, Zhuo Chen and Shuomin Yao for their helpful discussions and assistance. I would also like to thank the departmental staff in the general and purchasing offices, the mailroom, and the electronics and machine shops for their kind help. I especially thank my parents, Mr. Guangcai Cao and Mrs. Peizhen Lu for their endless love, understanding, patience and encouragement over these years. I realize how big a sacrifice they have made while I grew up, studied abroad and completed my degree. I will be forever grateful for that. And of course, I wish to express my gratitude to my girlfriend Yifan Zhang for her admirable patience and support, and for tolerating my unreasoning and ignoring behavior in the last intensive month of my Ph.D. study. ‐ ix ‐ Contents Chapter 1 Introduction.............................................................................................. 1 1.1 Caulobacter crescentus....................................................................................... 2 1.2 Why Apply Proteomic Technologies for Characterizing Proteins from Complex sBiological System?..................................................................................................... 4 1.3 MS-based Shotgun Proteomics ........................................................................... 6 Chapter 2 Improved Enrichment and Proteomic Identification of Outer Membrane Proteins from a Gram-negative Bacterium: Focus on Caulobacter crescentus ............................................................................................. 19 2.1 Introduction....................................................................................................... 20 2.2 Matrails and Mehtods ....................................................................................... 22 2.2.1 Bacterial Growth and Cell Lysis....................................................... 22 2.2.2 Enrichment of C. crescentus OMPs.................................................. 22 2.2.3 SDS-PAGE and Gel Staining............................................................ 23 2.2.4 In-gel Tryptic Digestion.................................................................... 24 2.2.5 Gel-assisted (Ga) Digestion .............................................................. 24 2.2.6 Wheat Germ Agglutinin (WGA) Western Blotting .......................... 25 2.2.7 High-pH Reversed-phase (RP) HPLC .............................................. 25 2.2.8 LC-MS/MS Analysis ........................................................................ 26 2.2.9 Database Searching........................................................................... 26 2.2.10 Spectral Counting and Statistical Analysis ..................................... 27 2.2.11 Bioinformatics................................................................................. 28 2.3 Results.............................................................................................................. 28 2.3.1 SLS Successfully Isolates OMs from C. crescentus......................... 28 2.3.2 Gel-assisted (Ga) Digestion coupled to RP/RP-MS/MS (2DLC) Results in Increased C. crescentus OMP Identifications .......................... 33 ‐ x ‐ 2.3.3 Low Density Culture Improves C. crescentus OMP Extraction Specificity .................................................................................................. 37 2.3.4 Deletion of the Surface-Layer Protein Improves C. crescentus OM Proteome Coverage.................................................................................... 38 2.3.5 Nearly 70% of the Expected C. crescentus OM/extracellular Proteome is Detected Using the Optimized Procedure.............................. 41 2.4 Discussion ......................................................................................................... 47 2.5 References......................................................................................................... 53 Chapter 3 Identification of Putative Surface-Exposed Proteins of C. crescentus Using a Biotinylation Approach ...................................................... 60 3.1 Introduction....................................................................................................... 61 3.2 Matrails and Mehtods ....................................................................................... 64 3.2.1 Bacterial Growth and In vivo Cell Surface Labeling........................ 64 3.2.2 Affinity Capture of Labeled Cell Surface Proteins/Peptides ............ 65 3.2.3 Streptavidin Western Blotting........................................................... 66 3.2.4 In-solution Tryptic Digestion............................................................ 67 3.2.5 nanoLC-MS/MS Analysis................................................................. 67 3.2.6 Protein Identification by Database Search........................................ 68 3.2.7 Bioinformatics................................................................................... 69 3.3 Results.............................................................................................................. 69 3.3.1 Visualization of C. crescentus Surface Proteins ............................... 69 3.3.2 Identifications of Biotinylated Proteins by LC-MS/MS: Protein-level Analysis...................................................................................................... 71 3.3.3 Identifications of Biotinylated Proteins by LC-MS/MS: Peptide-level Analysis...................................................................................................... 77 3.3.4 Functional Characterization of Biotinylated Proteins from RsaA- ... 81 ‐ xi ‐ 3.4 Discussion ......................................................................................................... 84 3.5 References......................................................................................................... 92 ‐ xii ‐ List of Figures Figure 1.1: Schematic of C. crescentus cell cycle………..................................................3 Figure 1.2: General MS-based proteomics workflow.........................................................6 Figure 1.3: Schmetic diagram of QSTAR Elite (ESI-QTOF) mass spectrometer (ABSciex) in our lab……………………………………..................................................10 Figure 1.4: The nomenclature and structure of sequence-specific fragmentation ions in MS/MS………………………………………………………………………....................11 Figure 1.5: Identification of an outer membrane protein, RsaF using 2DLC-MS/ MS….14 Figure 2.1: Schematic diagram of OM enrichment procedure.........................................29 Figure 2.2: The OM fraction, which contains holdfast, is most successfully isolated using SLS.....................................................................................................................................31 Figure 2.3: Na2CO3 does not effectively separate the OM fraction from IM fraction….32 Figure 2.4: Overview of the GeLC and Ga2DLC methods..............................................34 Figure 2.5: Graphical representation of the predicted/annotated subcellular distribution of proteins identified in enriched OM fractions from C. crescentus using either the GeLC or Ga2DLC method............................................................................................................36 Figure 2.6: Graphical representation of the predicted/annotated subcellular distribution of proteins identified in enriched OM fractions from C. crescentus using either WT or RsaA- cells.........................................................................................................................38 Figure 2.7: Bacterial growth profiles for WT and RsaA- C. crescentus cells..................40 Figure 2.8: Venn diagram illustrating the common and uniquely identified OMPs across ‐ xiii ‐ all four datasets..................................................................................................................42 Figure 2.9: The majority of the identified OMPs are conserved hypothetical/hypothetical proteins, transport/binding proteins, or cell envelope proteins..........................................43 Figure 2.10: Distribution of proteins containing (A) α-helical or (B) β-barrel motifs along with calculated (C) GRAVY values……………………………………………….45 Figure 2.11: Venn diagram illustrating the overlapping and uniquely identified proteins using either the GeLC or gel-assisted methodologies........................................................50 Figure 2.12: Venn diagrams illustrating the common and uniquely identified proteins between our studies and Dr. Phadke’s work......................................................................52 Figure 3.1: Schematic of biotinylation strategies for identification of surface-exposed proteins using MS-based shotgun proteomics...................................................................63 Figure 3.2: Schematic diagram of biotinylation labeling and enrichment strategy……..64 Figure 3.3: Cell surface exposed proteins were successfully labeled by biotin reagents.70 Figure 3.4: Venn diagram representation of protein identifications in both protein-level and peptide-level purified fractions (negative controls)....................................................72 Figure 3.5: A MS/MS spectrum of a biotinylated peptides assigning to the TonBdependent receptor, a typical OMP in C. crescentus………………………….................79 Figure 3.6: Venn diagrams illustrating the common and uniquely identified biontinylated proteins using both Q-TOF and LTQ-Orbitrap LC-MS/MS..............................................80 Figure 3.7: Graphical representation of the predicted/annotated subcellular distribution of biotinylated proteins identified a peptide-level purified fractions from C. crescentus ‐ xiv ‐ RsaA- using Q-TOF or LTQ-Orbitrap……………………...............................................81 Figure 3.8: Functional classification of total 91 putative surface-exposed proteins identified using both Q-TOF and LTQ-Orbitrap method..................................................82 Figure 3.9: Distribution of biotinylated proteins with tranmembrane α-helices………..83 Figure 3.10: Venn diagrams illustrating the common and uniquely identified proteins between biotinylation experiments and OM isolation experiments...................................88 Figure 3.11: Venn diagram representation of protein identifications in ‘protein-level’ purified biotin labeling fractions in RsaA-…………………………………………...…..73 Figure 3.12: Graphical representation of the predicted/annotated subcellular distribution of biotinylated proteins identified in protein-level purified fractions from C. crescentus RsaA- using Q-TOF……………………………………...................................................74 Figure 3.13: FtsZ forms a ring-structure to mediate the peptidoglycan (PG) synthesis near midcell compartment during C. crescentus cell cycle…………………….………..91 ‐ xv ‐ List of Tables Table 2.1: Summary of proteins detected in C. crescentus OM fractions……………………...35 Table 3.1: Summary of detected biotinylated proteins in C. crescentus RsaA- fractions using Q-TOF and LTQ-Orbitrap........................................................................................78 Table 3.2: Summary of C. crescentus proteins identified in both biotinylation experiments and in OM isolation experiments…………………….....................................85 Table 3.3: Summary of C. crescentus proteins identified in protein-level biotinylation experiments………………………………………………………………………....…………...75 ‐ xvi ‐ Chapter1 INTRODUCTION ‐ 1 ‐ 1.1 Caulobacter crescentus Caulobacter crescentus is an Gram-negative, alpha-proteobacterium first isolated from fresh water [1]. One of the first features noticed about this organism was that after cell division, the two daughter cells appeared different: only one end contained a stalked appendage while the other end contained a single polar flagellum. This morphological difference implied that each daughter cell was differentiated to serve a specific purpose. The asymmetric morphology during cell cycle makes C. crescentus an excellent model for the study of bacterial cell morphogenesis [2]. C. crescentus begins life as a motile swarmer cell with a single polar flagellum and polar pili. The swarmer cell grows and then differentiates into a stalked cell, shedding its flagellum, retracting its pili, and synthesizing a nutrient-uptaking stalk at the pole at the former site of the flagellum. The stalked cell then replicates its chromosome, becoming a predivisional cell that synthesizes a new flagellum at the opposite pole from the stalk and initiates cell constriction. Finally, cytokinesis occurs, producing one swarmer progeny and one, slightly longer, stalked cell that each enters the cell cycle at their respective stages (Fig. 1.1) [2, 3]. Stalked cells attach tightly to substrate surfaces via the holdfast, an adhesive organelle located at the end of the stalk. The C. crescentus holdfast is the strongest characterized bioadhesive to date (adhesion strength > 68 N/mm2) [4]. The ability to synchronize the cells and monitor change in the genome and proteome as cells proceed through the cell cycle has made C. crescentus a perfect model to identify gene and protein clusters on regulatory development in the cell. ‐ 2 ‐ Figure 1.1: Schematic of C. crescentus cell cycle. A C. crescentus cell begins its life as a mobile, non-reproductive swarmer cell and eventually differentiates into a stationary, fertile stalked cell. The second generation of the swarmer cell goes back to a new cell cycle after predivision cell stage, while the mother stalk cell produces another daughter swarmer cell as the black arrow indicates. Photo courtesy of Prof. Yves Brun from Indiana University [5]. Bacteria display a diversified array of cell morphologies, from spheres, rods, and helices to tapered, branched, and star shapes [6]. Both shape and size are important for cell function, particularly with respect to diffusion and nutrient uptake [7]. Bacteria can also make morphological transitions in response to changes in environmental conditions. For example: the rod-shaped plant pathogen Sinorhizobium meliloti differentiates into Yshaped nitrogen-fixing cells in plant cells [8]. The spiral-shaped pathogen Helicobacter pylori adopts a spherical shape in extended culture [9]. Uropathogenic E. coli cells lengthen into long filaments as part of an immune evasion response [10]. These morphological responses and maintenance of cell morphology indicate that sophisticated control systems must exist to regulate cell morphogenesis. The cell envelope of C. crescentus comprises the cytoplasmic membrane, a single peptidoglycan layer residing in the periplasm, the outer membrane, and the S-layer in the order from inside to outside of ‐ 3 ‐ cells. The S-layer is a hexagonal protein lattice on the cell exterior that is attached through the O-antigen portion of lipopolysaccharide [11]. The crescent-shaped morphology of C. crescentus cells can be conceived of a section of a helical cell that is shorter than one helical turn [12]. This suggestion is consensuses by the fact that elongated C. crescentus cells produced either by blocking cell division or by culturing cells for extended times (more than 10 days) display a clearly helical morphology [13, 14]. 1.2 Why Apply Proteomic Technologies for Characterizing Proteins from Complex Biological System? Current research efforts often focus on describing the detailed complexity of particular aspects of the total system in bacterial cell development and progression [1517]. It is beneficial to consider systems study, mapping multiple pathways and interactions that regulate development processes in bacteria. To capture all interactions and get a complete picture of what is really happening during pathogenesis progression and cell cycle development, conventional tools to study single protein or pathway are inadequate and large-scale, high throughput approaches are required to provide complementary information [18-20]. Advances in new high throughput technologies, such as genomics, transcriptomics and proteomics, led to the explosive growth of information and the improved molecular tools for regulatory network, subcellular component and vaccine development [15, 18, 19]. However, gene expression analysis can not accurately predict protein expression and post-translational modifications (such as phosphorylation and ‐ 4 ‐ glycosylation) [20, 21]. The transcriptome represents a set of messenger RNA (mRNA) produced by a given cell and can reflect the gene expression under specific conditions [22]. However, the transcriptome is incompetent to completely reflect the fundamental biology due to alternative splicing and post-translational modifications in response to changes of external conditions [23, 24]. As the final products for gene expression, proteins are directly responsible for the molecular functions that mediate most changes at the cellular level in biological systems. Therefore, protein-directed studies using advanced technologies are vitally important. Proteomics is the simultaneous study of numerous proteins on a large scale by high throughput analyses of cells and tissues [25]. The accelerating development of proteomics field has been enabled by multiple progresses: completion of the majority of genomes in eukaryotes and prokaryotes, advances in separation techniques on proteins/peptides, crucial improvements in mass spectrometry technologies, and progress in bioinformatics. The high frequency, powerful selectivity and extreme sensitivity of mass spectrometry (MS) makes MS-based proteomic methodologies a perfect alternative to traditional biological methods for analyses of proteins in complex biological systems [26]. The distinct advantage of proteomics and the advances in proteomic technologies have offered tremendous opportunities for protein discovery and post-translation modification analysis. Proteomics-based approaches have the potential to provide novel, systems-level insight to understand molecular mechanisms of regulatory networks, and also hold great promise for profiling subcellular proteomes in microbiology. ‐ 5 ‐ 1.3 MS-based Shotgun Proteomics The proteomics-based strategy includes the incorporation of a number of technologies in biochemistry, molecular biology, bioinformatics and bioanalytical chemistry [27]. Although varieties of options exist within each stage of the proteomic analysis, a typical approach includes the following elements: sample preparation, protein/peptide separation, protein identification (MS/MS), and data mining (Fig. 1.2). Figure 1.2: General MS-based proteomics workflow. Sample preparation ― The first critical step of a proteomics-based study is sample preparation. Proteins are isolated from biological samples and then solubilized for separation. The task of sample preparation is to maximally solubilize sample proteins under conditions which are compatible with down stream MS analysis without ‐ 6 ‐ introducing artificial modifications on proteins. There have been numerous studies that discuss various approaches to sample preparation and solubilization of proteins for MSbased shotgun proteomics [28], such as the spin-filter method [29], the gel-assisted method [30] and detergent/detergent-free methods [31]. The use of a protease and/or phosphatase inhibitor cocktail is often included to inhibit protein degradation and dephosphorylation. In addition, different combinations of reducing agents, chaotropic agents and detergents are used to disrupt intra- and inter-protein interactions and maximize protein solubilization [32]. Protein/peptide separation ― Two-dimensional polyacrylamide gel electrophoresis (2DGE) is the traditionally used technique for the separation of proteins in complex mixtures because of its high resolution [33]. The technique combines isoelectric focusing (IEF) in the first dimension and sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE) in the second dimension. The separation power of 2DGE originates from the fact that proteins are separated based on two different physicochemical properties. Proteins are separated based on their different isoelectric points in the first dimension and based on molecular weight in the second dimension. Protein profiles are then quantitatively analyzed through 2-DE gel imaging software [34]. Mass spectrometry has been applied for identification of proteins following 2DGE separation in recent decades [35, 36]. In addition to 2DGE based approaches, a number of different separation methodologies have been developed as well. In general, these methodologies fall within two categories: one dimensional (1D)-gel-based and gel-free approaches. Furthermore, various methods aimed at reducing the proteome complexity prior to analysis have been ‐ 7 ‐ introduced. One-dimensional gel-based proteomics strategies use a single dimension of electrophoretic separation at the protein level combined with advanced LC-MS/MS for protein identification. A combination of SDS-PAGE and LC-MS/MS, the so called GeLC-MS/MS approach, has been developed and used for numerous applications. For instance, GeLC has been applied to analyses of the proteome in the human pituitary [37] and pancreatic fluid in chronic pancreatitis [38]. A well-known example of a gel-free proteomics strategy is the multidimensional protein identification technology (MudPIT) [39]. MudPIT involves multidimensional chromatographic separation of complex mixtures at the peptide level. In brief, a whole protein mixture is digested directly and the resulting peptides are separated by two different types of liquid chromatography (LC), such as strong cation exchange, normal phase and reversed-phase LC, coupled to tandem mass spectrometry. Compared to 2DGE, the MudPIT method is unbiased, meaning that proteins that are normally under represented in 2D gels, including low-abundance proteins, proteins with extremes pI and MW, and membrane proteins, can be probed with MudPIT [40]. Mass spectrometry ― Mass spectrometry (MS) is a powerful technology for the identification of peptides, proteins, and their post-translation modifications. The development of advanced mass spectrometry instrumentation that allowed highsensitivity, high throughput protein identification has been the most extraordinary event in the expansion of proteomics [41]. Novel and improved instruments are being continuously developed and push the power of mass spectrometry to higher levels. ‐ 8 ‐ MS is an analytical technique that measures the molecular weight of molecules according to their mass-to-charge (m/z) ratio. A mass spectrometer is composed of three key components: ion source, mass analyzer and detector. Analytes are converted into ions in the gas phase using an ion source (Thanks to the two Mass Spectrometrists - John B. Fenn and Koichi Tanaka – who shared the Nobel Prize with an NMR Spectroscopist in 2002.). The ions are then separated according to their m/z ratio in a mass analyzer and the signal is detected by a compatible detector such as an electron multiplier. There are a number of different types of mass spectrometers that include different combination of ion sources and mass analyzers [42]. There are two broadly used ionization methods in proteomics: matrix-assisted laser desorption ionization (MALDI) and electrospray ionization (ESI). In MALDI peptides generally ionize through addition of a proton, forming a singly charged molecular ion, [M+H]+ [43]. While ESI generates multi-protonated molecular ions [M+H]n+, n ≥ 2 depending on the number of basic amino acids in the peptide sequence [44]. Additionally, ESI can directly generate gas-phase ionized molecules from a liquid solution. This feature enables the on-line coupling of ESI-based mass spectrometers with HPLC (LC-MS/MS). In my thesis work, I used a tandem quadrupole (Q) time-of-flight (TOF) mass spectrometer to generate fragment ion spectra of peptide identifications (Fig. 1.3). It is a hybrid orthogonal acceleration time-of-flight mass spectrometer which enables automated accurate mass measurement of precursor and fragment ions to yield high confidence in structural elucidation and database search results [45]. The nanospray interface allows electrospray ionization to be performed at low flow rates (5 nL/min or ‐ 9 ‐ nanoflow), which dramatically increase the sensitivity of the analysis and is compatible with on-line nanoflow LC systems. Figure 1.3: Schmetic diagram of QSTAR Elite (ESI-QTOF) mass spectrometer (ABSciex) in our lab. Mass spectrometry measurements can be carried out in two general ways: a single-stage mass spectrometry (MS) or as tandem mass spectrometry (MS/MS). An MS experiment includes ionization of the analytes and measurement of the m/z ratios for the parent ions. An MS/MS experiment is more complex than an MS experiment and includes additional steps. The steps in an MS/MS experiment are ionization of the molecules, selection of target ions (so-called precursor ions), activation of the selected precursor ion through collisions (collision-induced dissociation or CID) with a target gas, ‐ 10 ‐ and dissociation of the precursor ion into a series of product ions. Product ions are used for structure determination in the data mining process. In CID mode of MS/MS, protonated peptide ions dissociate predominantly via cleavages along the peptide backbone, theoretically generating two major ion series: the N-terminal ion series (b-ions) and the C-terminal ion series (y-ions) [46]. In theory, every peptide bond could be fragmented into b- and y- ions. Therefore the m/z patterns of the product ions can be used to derive amino acid sequence information for the peptide under analysis (Fig. 1.4). Figure 1.4: The nomenclature and structure of sequence-specific fragmentation ions in MS/MS. Database searching for protein identification ― Identification of proteins based on MS or MS/MS has been enabled by the completion of protein sequence databases that are being continuously updated and on the development of database search software programs. There are generally two ways to identify a protein based on mass spectrometric results, namely peptide mass fingerprinting (PMF) and MS/MS searching. The PMF method is designed specifically for single MS spectra, which was often obtained on MALDI-TOF MS results during the 1990s and early 2000s [47-49]. In general, the mass list of precursor ions is compared in silico to the database containing the known protein, ‐ 11 ‐ which is theoretically digested into peptides. However, one m/z value of a precursor ion probably matches to multiple peptide sequence, which will generate false positive identifications. The second approach is based on a correlation of the MS/MS product ion patterns, which correlates with the amino acid sequence of a protein in the target protein database [50, 51]. This approach has become the gold standard for protein identification in MS-based proteomics. Since a MS/MS spectrum contains the amino acid sequence information of the peptide, rather than the peptide masses alone, these searches generate more specific and discriminative results than PMF. In practice, during the database search, the search engine uses computer programs to theoretically cut the known proteins in a database (for example; the entire set of human proteins predicted from the genome) into peptides according to the protease, and each peptide is further fragmented into product ions depending on the MS/MS dissociation type. Experimental MS/MS spectra are matched against theoretical fragment ion spectra for all the peptides in the databases that have the same precursor ion mass within the experimental error. Peptides that turn out to be the first hits along with the identification scores higher than the identity threshold defined by the searching algorithm are generally considered as positive matches. The matched peptides are sequence-linked to their corresponding proteins, resulting in the identification of proteins. Many different algorithms for database searching of MS/MS spectra have been developed. They include SEQUEST (http://fields.scripps.edu/sequest/), MASCOT (http ://www. matrixscience. com/), X! Tandem (http://www.thegpm.org/TANDEM/), OMASS (http://pubchem.ncbi. nlm.nih. gov/omssa/) and ProteinPilot (http://www.absciex.com/products/software/proteinpilotsoftware). As shown in Figure 1.5, the MS/MS spectrum matched to peptide ‐ 12 ‐ GALPTELIIGTFDK using two search algorithms against the C. crescentus database. The in silico MS/MS spectrum generated from the outer membrane protein RsaF matched to the experimental MS/MS spectrum very well. Thus, we were confident that we identified this protein in our biological samples. As mentioned at the beginning, the database is the control reference for MS/MS searching algorithms. Thus, it is very important to maintain and update the protein database in order to keep accurate protein identifications. Several databases, such as Genbank [52], RefSeq [53], SWISSPROT [54] and TrEMBL [55], are supported by independent groups and can be accessed though the internet. They contain a comprehensive and updated catalog of protein sequences. ‐ 13 ‐ Figure 1.5: Identification of an outer membrane protein, RsaF (gi|16125267|type I secretion system outer membrane protein RsaF|OuterMembrane), using 2DLCMS/MS as described in chapter 2. (A) The MS/MS spectrum of peptide GALPTELIIGTFDK. This peptide was identified by a Paragon confidence value >99% (ProteinPilot) and a MASCOT score of 54. (B) Close-up view of the doubly protonated tryptic peptide GALPTELIIGTFDK at m/z 737.8804 selected for MS/MS using information dependent acquisition (IDA) mode at 59.84 min. REFERENCES [1] Poindexter, J. S., Biological properties and classification of the caulobacter group. 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[42] Patterson, S. D., Aebersold, R., Mass-spectrometric approaches for the identification of gel-separated proteins. Electrophoresis 1995, 16, 1791-1814. [43] Henzel, W. J., Billeci, T. M., Stults, J. T., Wong, S. C., et al., Identifying proteins from 2-dimensional gels by molecular mass searching of peptide-fragments in proteinsequence databases. Proc. Natl. Acad. Sci. U. S. A. 1993, 90, 5011-5015. [44] Smith, R. D., Evolution of ESI-mass spectrometry and Fourier transform ion cyclotron resonance for proteomics and other biological applications. Int. J. Mass Spectrom. 2000, 200, 509-544. [45] Bristow, T., Constantine, J., Harrison, M., Cavoit, F., Performance optimisation of a new-generation orthogonal-acceleration quadrupole-time-of-flight mass spectrometer. Rapid Commun. Mass Spectrom. 2008, 22, 1213-1222. ‐ 17 ‐ [46] Wysocki, V. H., Resing, K. A., Zhang, Q. F., Cheng, G. L., Mass spectrometry of peptides and proteins. Methods 2005, 35, 211-222. [47] Cottrell, J. S., Protein identification by peptide mass fingerprinting. Peptide Res. 1994, 7, 115-&. [48] Gevaert, K., Vandekerckhove, J., Protein identification methods in proteomics. Electrophoresis 2000, 21, 1145-1154. [49] Lahm, H. W., Langen, H., Mass spectrometry: A tool for the identification of proteins separated by gels. Electrophoresis 2000, 21, 2105-2114. [50] Eng, J. K., McCormack, A. L., Yates, J. R., An approach to correlate tandem massspectral data of peptides with amino-acid-sequences in a protein database. J. Am. Soc. Mass Spectrom. 1994, 5, 976-989. [51] Mann, M., Wilm, M., Error tolerant identification of peptides in sequence databases by peptide sequence tags. Anal. Chem. 1994, 66, 4390-4399. [52] Benson, D. A., Karsch-Mizrachi, I., Clark, K., Lipman, D. J., et al., GenBank. Nucleic Acids Res. 2012, 40, D48-D53. [53] Pruitt, K. D., Tatusova, T., Maglott, D. R., NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005, 33, D501-D504. [54] Shomer, B., Seqalert - a daily sequence alertness server for the EMBL and SWISSPROT databases. Comput. Appl. Biosci. 1997, 13, 545-547. [55] O'Donovan, C., Martin, M. J., Gattiker, A., Gasteiger, E., et al., High-quality protein knowledge resource: SWISS-PROT and TrEMBL. Briefings in bioinformatics 2002, 3, 275-284. ‐ 18 ‐ Chapter2 IMPROVED ENRICHMENT AND PROTEOMIC IDENTIFICATION OF OUTER MEMBRANE PROTEINS FROM A GRAM-NEGATIVE BACTERIUM: FOCUS ON Caulobacter crescentus ‐ 19 ‐ 2.1 INTRODUCTION The outer membrane (OM) of Gram-negative bacteria works in concert with the periplasm (PERI) and the inner membrane (IM) to preserve the cell’s structural integrity, protect the metabolic processes occurring within the cytoplasm, and anchor ‘transenvelope molecular machines’ such as flagella and protein secretion systems [1]. The biological importance of the OM is a direct result of the proteins localized to this subcellular region. Nonspecific porins, selective channels, and high affinity receptors allow the cell to sustain itself through proper nutrition and rid itself of harmful waste material or toxic substances [2]. Deletion of specific OM proteins (OMPs) or a compromise in OMP assembly negatively impacts cell growth and decreases the chemical or heat resistance of this protective barrier [3-6]. When considering pathogenic bacteria, OMPs also encompass virulence factors that are putative vaccine- or drugtargets [7, 8]. Identification and characterization of the complement of proteins found in the OM under different environmental conditions or after ablation of a key component promises to improve our understanding of fundamental bacterial processes. Proteomic characterization of OMPs initially appears to be a relatively easy endeavor. Analysis of a subset of the proteome guarantees an overall reduction in sample complexity compared to global studies. The permutations in protein products due to mRNA splicing is thought to be minimal in bacterial systems, and integral OMPs differ from other integral membrane proteins in that they do not exhibit an overall hydrophobic character [9]. Yet even with these advantages, most OMP studies fall far short of detecting the entire repertoire of predicted OM protein products in their respective systems. This could be due to a protein’s low basal level of expression or due to tight ‐ 20 ‐ control of the synthesis of a given protein until induced by external stimuli. However, failure to detect all predicted OMPs could also be due to inadequate sample handling and processing. In addition to the MS detection step, OM proteomic studies are critically dependent upon the specificity of the sample enrichment step [10-12], the efficiency of the protein dissolution step [13, 14], and the resolving capacity of the separation step [1517]. Although the first study describing an isopycnic sucrose density gradient centrifugation method to isolate Escherichia coli OMs appeared in 1968 [18], the search for a fast, selective, and general OM purification technique continues [10]. Buffers, detergents, or denaturing agents used to ensure complete solubilization of the OM fraction can interfere with the subsequent separation and identification steps, so solubilization solutions must be selected with care. Two-dimensional gel electrophoresis (2DGE) coupled with MS is by far the classic separation/analysis technique for studies of this type [11, 13, 19-27]. Alternative methods, such as one-dimensional SDS-PAGE coupled with LC-MS/MS (or GeLC-MS) [12, 16, 28-30] and gel-free shotgun proteomics [31-35] are increasingly being used. In the work presented here, we refined an OMP isolation technique using Caulobacter crescentus, a free-living gram-negative α-proteobacterium. It is an established model system for studies of cell cycle control and polar morphogenesis [36] and is expected to express a set of OMPs that differ from the more commonly studied γproteobacterium E. coli [13, 37, 38]. A previous study using C. crescentus identified 41 OMPs via 2DGE [13] and provides a good starting point for comparison. We first qualitatively evaluated two popular methods for isolation of the OM fraction. We then ‐ 21 ‐ assessed the performance of two analytical techniques that integrate protein solubilization with analysis: (i) GeLC-MS and (ii) RP/RP-MS/MS facilitated by gel-assisted digestion [39, 40]. Finally, we improved the specificity of OM isolation by lowering the target cell density prior to cell lysis and by using a mutant cell line that is devoid of a high abundance contaminate protein. 2.2 MATERIALS AND METHODS An overview of the isolation procedures described in sections 2.2.1 and 2.2.2 are shown in Figure 1. 2.2.1 Bacterial Growth and Cell Lysis Wildtype (WT) CB15 [41] or RsaA-negative (RsaA–) C. crescentus cells (kindly provided by Prof. Yves Brun at Indiana University and referred to as YB991 in [42]) were first grown to the late exponential phase in a peptone yeast extract (PYE) medium. Then, 300 mL fresh PYE medium was inoculated at a 1:30 dilution with bacterial cells. The cells were cultured at 30 ˚C with constant shaking at 100 rpm until an OD600 of either 1.0 or 0.6 was reached. Cells were harvested by centrifugation at 4000g for 10 min at 4 ˚C and washed three times with 50 mM ammonium bicarbonate (AMBIC, pH 8.0). Cells were resuspended in 2 ml of 50 mM AMBIC (pH 8.0) with protease inhibitors (Complete EDTA-free, Roche Diagnostics, Germany) before being disrupted by repeated intermittent sonic oscillation (20 × 5 s). Cellular debris was removed by centrifugation at 12,000g for 20 min. Supernatant was collected and centrifuged at 100,000g for 40 min to obtain a total membrane pellet. 2.2.2 Enrichment of C. crescentus OMPs ‐ 22 ‐ Sodium carbonate method: The OM fraction (Figure 1, P4 pellet) was obtained from the total membrane pellet essentially as described previously [13]. Briefly, total membranes were resuspended in 100 mM NaCO3, incubated on ice for 1 h, and centrifuged at 100,000g for 40 min. The recovered pellet was analyzed by SDS-PAGE and Western blotting without further processing. Protein concentrations were determined using the Pierce BCA protein assay (Thermo Fisher Scientific, Rockfold, IL). Sodium lauryl sarcosine (SLS) method: The SLS outer membrane fraction (Figure 1, P4 pellet) was obtained as described [26] with modification. Briefly, the total membrane pellet was resuspended in 20 mM AMBIC (pH 8.0) containing 1.0% (wt/vol) SLS (Affymetrix, Fremont, CA), incubated at room temperature for 30 min, and then centrifuged at 100,000g for 40 min. Next, the SLS-insoluble pellet (Figure 1, P4 pellet) was washed sequentially with 2.5 M sodium bromide and 100 mM sodium carbonate, which further removes cytoplasmic and loosely associated proteins as noted before [12, 14]. The final OM pellet was analyzed by SDS-PAGE and Western blotting or processed for gel-assisted digestion. The Pierce BCA protein assay was used to determine protein concentrations. 2.2.3 SDS-PAGE and Gel Staining OMP samples (50 μg) were mixed with lithium dodecyl sulfate (LDS) sample buffer (Invitrogen Corp., Calsbad, CA) and DTT to final concentrations of 2% (wt/vol) and 50 mM, respectively. OMPs were then denatured for 10 min at 70 °C and separated using NuPAGE Novex 10% Bis-Tris gels (Invitrogen) according to the manufacturer’s instructions. Essentially following established protocols, gels were either silver stained ‐ 23 ‐ [43] for visualization purposes only or Coomassie stained [44] to allow for downstream LC-MS/MS analysis. 2.2.4 In-gel Tryptic Digestion Coomassie blue stained gel lanes were excised into 18 bands of equal size (2 × 2 mm) and subjected to in-gel proteolysis with trypsin (Promega) and 0.1% (wt/vol) noctylglucoside (Sigma-Aldrich) according to the Katayama et al. protocol [45]. The extracted peptides were reduced to near dryness and reconstituted in 50 μL with 0.1% acetic acid for LC-MS/MS analysis. 2.2.5 Gel-assisted (Ga) Digestion The gel-assisted method described by Han [39] was applied with minor modifications. In brief, the OM pellet (50 μg) was solubilized in 50 μl of 6 M urea, 5 mM EDTA and 2% (wt/vol) SDS in 50 mM AMBIC (pH 8.0) for 30 min at 37 °C. Proteins were reduced using tris (2-carboxyethyl) phosphine (Thermo Fisher Scientific) at 5 mM final concentration for 1 h at room temperature and alkylated with fresh iodoacetamide at 10 mM final concentration in the dark for 30 min at room temperature. The protein solution was then directly incorporated into a gel by adding 20 μl of a 29:1 mixture of 40% (v/v) acrylamide solution/bis-acrylamide, 3 μl of 10% (wt/vol) ammonium persulfate and 1 μl of TEMED (Amersham Bioscience, Piscataway, NJ). The polymerized sample was then enzymatically digested with trypsin (Promega) and 0.1% (wt/vol) n-octylglucoside (Sigma-Aldrich) via the method of Katayama [45]. Extracted peptides were reduced to near dryness and reconstituted in 50 μL with 0.1% acetic acid for offline HPLC separation. ‐ 24 ‐ 2.2.6 Wheat Germ Agglutinin (WGA) Western Blotting Proteinaeous sample was electrophoretically transferred onto nitrocellulose membranes (0.45 mm, Bio-Rad, Hercules, CA) at 4 ˚C for 90 min (17 mA/30V) using the Invitrogen XCell II Blot Module. Membranes were first rinsed with wash buffer (25 mM Tris-HCl (pH 7.0), 1% Tween 20, 1 mM CaCl2, 1 mM MgCl2, 0.15 M NaCl) and subsequently blocked with the same buffer for 60 min. Incubation with biotinylatedWGA (10 μg/ml in wash buffer) was carried out overnight at 4 ˚C. Following this step, the membrane was rinsed three times with wash buffer (5 min each). HRP-conjugated streptavidin (diluted 1:1000 in wash buffer) was incubated with the membrane for 90 min. Then, the membrane was washed again (3×, 10 min each) in a buffer containing 25 mM Tris-HCl (pH 7.0), 1 mM CaCl2, 1 mM MgCl2, and 0.15 M NaCl. Finally, the blot was visualized by ECL (SuperSignal® Chemiluminescent Substrate, Thermo Fisher Scientific) 2.2.7 High-pH Reversed-phase (RP) HPLC Peptide digests were loaded onto an Agilent ZORBAX 300Extend-C18 column (150 × 2.1 mm i.d., 3.5 μm) using an Agilent 1200 binary HPLC system. Peptides were initially separated at a flow rate of 0.5 ml/min using the following gradient: 0-70% B in 4 min; 70-100% B in 8 min. A modified gradient (0-50%B in 20 min; 50-85% B in 10 min; 85% B for 5 min) was used to fractionate peptides in the final experiment. Mobile phase A consisted of 1% methanol in 20 mM ammonia (pH 10.5). Mobile phase B consisted of a 90/10 mix of acetonitrile/1% methanol in 20 mM ammonia. The column temperature was held at 30 ˚C and UV detection was performed at 214 nm. Fractions were collected ‐ 25 ‐ once per minute. Each fraction was concentrated by vacuum centrifugation and reconstituted in 0.1% acetic acid for MS analysis. 2.2.8 LC-MS/MS Analysis Peptides from the first dimension separation were analyzed by LC-MS/MS using a Tempo MDLC™ system coupled to a QSTAR Elite™ hybrid quadrupole time-of-flight mass spectrometer (ABSciex, Foster City, CA) operating in positive ionization mode. Peptides were loaded at 3 μl/min onto home-built precolumns (75 μm ID, 5 cm length of POROS 10R2; Applied Biosystems, Carlsbad, CA) and washed for 30 min with solvent A (0.1% formic acid and 2% acetonitrile in water). The peptides were then eluted at a flow rate of 100 nl/min onto analytical columns (50 μm ID, 10 cm length of Monitor 100Å-Spherical Silica C18; Column Engineering Inc., Ontario, CA) using the following gradient: 0%-30% B in 40 min; 30%-60% B in 40 min; 60%-95% B in 30 min; 95% B for 30 min. The gradient was modified slightly for the last experiment: 0%-30% B in 40 min; 30%-60% B in 40 min; 60%-75% B in 30 min; 75% B for 30 min. Solvent B consisted of 0.1% formic acid and 2% water in acetonitrile. MS data were acquired in information-dependent acquisition mode with Analyst QS 2.0 (ABSciex). MS cycles were comprised of one full scan (m/z range = 300-2000, 1 sec accumulation) followed by sequential MS/MS scans of the four most abundant ions (+2 to +4 charge state, minimum ion count = 75, collision energy = 40, exclusion time = 20 sec, maximum accumulation time = 2 sec). 2.2.9 Database Searching Tandem mass spectrometry data from .WIFF files were directly analyzed by ProteinPilot™ 2.0.1 (ABSciex) using the Paragon™ Algorithm [46] and the RefSeq C. ‐ 26 ‐ crescentus database (dated April 25, 2009) downloaded from NCBI. The search parameters permitted the identification of tryptic peptides and cysteines modified by iodoacetamide. A 95% confidence threshold for protein matches was used, which corresponded to an unused protein score ≥ 1.3. The Pro Group™ algorithm within ProteinPilot™ facilitated protein grouping and removal of redundancy. As a final filter, two unique peptides at the 95% confidence level or greater were needed to confirm the identity of each protein in each analysis. In addition, the actual false discovery rate (FDR) at the peptide level was calculated using the target-decoy database search strategy [47] and the percent FDR was < 2% for all experiments. 2.2.10 Spectral Counting and Statistical Analysis In order to evaluate protein abundance using spectral counts, we conducted a Mascot (version 2.2.0; Matrix Science, Boston, MA) search using the RefSeq C. crescentus database (dated April 25, 2009) because the output from ProteinPilot was not compatible with our spectral counting software, ProteoIQ (version 2.0.01; BioInquire, Bogart, GA). The .WIFF files were converted to .mzXML format using the TransProteomic Pipeline (version 4.4.0) [48]. The .mzXML files were then converted to .MGF files via MassMatrix MS File Conversion Tools (version 3.8; www.massmatrix.net). The .MGF files were used for the Mascot search. Mascot parameters were as follows: precursor ion mass tolerance and fragment ion tolerance were both set at 0.2 Da; carboxyamidomethylation was chosen as a fixed modification; methionine oxidation was chosen as a variable modification; trypsin was selected as the enzyme; and one missed cleavage was specified. The Mascot search results (.dat files) were filtered with ProteoIQ to generate a protein list nearly identical to the ProteinPilot results using the following ‐ 27 ‐ parameters: number of peptides: 2; Peptide Probability: 0.9; Protein Probability: 0.95; Protein Group Probability: 0.95. Finally, ProteoIQ assigned total spectral counts to each identified protein. Protein spectral count data were analyzed by the two-sided t-test for paired samples using Microsoft Excel 2002 (Redmond, WA). P values of < 0.05 were considered statistically significant. 2.2.11 Bioinformatics Two algorithms were used to predict each protein’s location in the cell: Proteome Analyst 3.0 [49] and PSORTb 2.0 [50]. The predictions made by each program had to agree in order for a given protein to be assigned to a subcellular location. Otherwise, the protein was assigned to the “unknown” category. Less than 10% of the identified proteins were categorized as such and the vast majority of these were proteins predicted by both algorithms to reside in more than one cellular compartment. 2.3. RESULTS 2.3.1 SLS Successfully Isolates OMs from C. crescentus To develop a rapid and robust OMP enrichment procedure (Fig. 2.1), we compared the two reagents most often used to isolate OMPs from whole cell lysates of gram-negative bacteria: sodium carbonate at pH 11 and sodium lauryl sarcosinate (SLS). Sodium carbonate opens and linearizes sealed membrane vesicles [51]. This allows for the removal of cytoplasmic and membrane-associated components and leaves only integral membrane proteins as pelletable material. SLS is an ionic detergent that preferentially solubilizes the inner membrane of gram-negative bacteria [52]; and after ‐ 28 ‐ differential centrifugation, the outer membrane is left as an insoluble pellet. In both cases, the protein pellet can be dissolved in the researcher’s buffer of choice. Figure 2.1. Schematic diagram of OM enrichment procedure. The OM fraction was obtained from C. crescentus whole cell lysates using differential centrifugation and SLS or Na2CO3 solubilization. First, we qualitatively assessed the effectiveness of OM enrichment using Na2CO3 or SLS via SDS-PAGE followed by silver staining. OMs were isolated at least three separate times from independently grown aliquots of cells using each method and the results were similar in each case. The isolated OMPs are strikingly different: intense ‐ 29 ‐ bands observed in the OM fraction isolated using SLS are absent in the OM fraction obtained using sodium carbonate (Fig. 2.2A). This is problematic since some of the bands contain major known or predicted OMPs (as assessed by subsequent MS analysis). For example, the 75 kDa band contains TonB-dependent receptors (CC3500, CC2194) and flagellar hook-associated protein FlaN (CC0899). The band at 55 kDa consists of OmpA (CC3494) and flageller hook protein FlgE (CC0902), and HfaB (CC 2629) and a putative outer membrane protein (CC2094) are found in the region between 35-45 kDa. Moreover, the SLS method gives a distinct profile of proteins in each subcellular fraction that is not observed when using sodium carbonate. In fact, the IM and OM fractions obtained using sodium carbonate have nearly identical banding patterns using 10% SDS-PAGE suggesting an inefficient purification of the OM from the IM. ‐ 30 ‐ Figure 2.2. The OM fraction, which contains holdfast, is most successfully isolated using SLS. (A) SDS-PAGE analysis of subcellular fractions isolated using either SLS or Na2CO3. Equal amounts of IMs or OMs (5 µg) were separated by SDS-PAGE gels and silver-stained. The experiment was performed at least 3 times with similar results. (B) Western blot analysis to determine the presence of holdfast in IM and OM fractions isolated using SLS. Equal amounts of IMs and OMs (10 µg) were separated using SDSPAGE and probed with biotinylated-WGA and HRP-strepavidin. The experiment was performed at least 2 times with similar results. Next, we assayed for the presence of an OM component in each subcellular fraction in order to further evaluate the ability of Na2CO3 and SLS to generate a purified OM pellet. The adhesin of C. crescentus (called the holdfast) is located on the tip of its stalk [41] and is partially composed of GlcNAc polymers [53, 54]. We took advantage of this distinct feature and developed an immunoblot procedure using HRP-conjugated streptavidin and biotinylated wheat germ agglutinin (WGA), a GlcNAc-specific lectin that recognizes the holdfast [53, 54]. A high molecular weight band representing the holdfast is found in the OM fraction isolated using SLS (Fig. 2.2B). As expected, this ‐ 31 ‐ band is absent from the IM fraction recovered using SLS. The band at 60 kD, found predominantly in the IM fraction, is characteristic of peptidoglycan material that can contain GlcNAc. On the other hand, both IM and OM fractions obtained using sodium carbonate contain holdfast material, demonstrating that this reagent does not effectively separate the OM from the IM (Fig. 2.3). Figure 2.3. Na2CO3 does not effectively separate the OM fraction from the IM fraction. SDS-PAGE analysis of subcellular fractions isolated using Na2CO3. Equal amounts of IMs or OMs (10 µg) were separated by SDS-PAGE. Western blot analysis was used to determine the presence of holdfast in either fraction. The holdfast was detected using biotinylated-WGA, HRP-strepavidin, and ECL. Together, the SDS-PAGE and immunoblot analyses demonstrate that our SLS method, in contrast to the traditional Na2CO3 technique, successfully isolates OMs from C. crescentus. The OMPs for all subsequent experiments described in this work were isolated using SLS. ‐ 32 ‐ 2.3.2 Gel-assisted (Ga) Digestion coupled to RP/RP-MS/MS (2DLC) Results in Increased C. crescentus OMP Identifications To determine the most effective analytical method for analysis of C. crescentus OMPs, we compared (i) SDS-PAGE combined with RP-LC-MS/MS (the “GeLC” approach; workflow shown in Fig. 2.4A) with (ii) gel-assisted digestion [39, 40] coupled with RP/RP-MS/MS (referred to as the “Ga2DLC” approach; workflow shown in Figure 2.4B). These two techniques were chosen for comparison because GeLC is increasingly employed in studies of this type [12, 16, 28-30] while 2DLC is superior in performance and fractionation efficiency [55]. Additionally, gel-assisted digestion results in enhanced digestion efficiency of membrane proteins when compared to various in-solution digestion protocols [39] and was incorporated into the Ga2DLC workflow to overcome this anticipated problem. One technical replicate was performed per analytical approach for each biological sample. ‐ 33 ‐ Figure 2.4. Overview of the GeLC and Ga2DLC methods. (A) In the GeLC method, OMP pellets are separated by SDS-PAGE after solubilization in LDS buffer. Each gel lane is cut into 18 bands, which are enzymatically digested separately and analyzed by LC-MS/MS. (B) In the Ga2DLC method, OMP pellets are co-polymerized with acrylamide solution after solubilization in a strong denaturing solution. The ‘plug’ of gel is then washed with buffer, enzymatically digested, and fractionated offline at pH 10. Each high pH fraction is analyzed by LC-MS/MS. We first assessed the solubility of the OM fraction in the sample buffer typically used for each analytical technique. We noted visually that the solubilization of the OMP pellet was only partially effective using the traditional SDS-PAGE sample reducing buffer because precipitate was still clearly observable even after heating at 70 °C for 10 min according to the standard procedure. As a consequence, this sample was centrifuged and the supernatant was taken for further processing using the GeLC approach (Fig. 2.4A). In contrast, the OMP pellet completely dissolved in the detergent mixture specified for gel-assisted digestion (see Materials and Methods) during the recommended ‐ 34 ‐ 30 min incubation at 37 °C. The solubilized protein solution was then processed according to the Ga2DLC workflow (Fig. 2.4B). We then compared the performance of GeLC to Ga2DLC by examining four parameters for each approach: (1) total number of peptides identified; (2) total number of unique proteins identified; (3) total number of OM/extracellular proteins identified; and (4) total number of spectral counts attributed to OM/extracellular proteins. The Ga2DLC approach resulted in a significant improvement in the number of total peptides identified without a corresponding enhancement in the total number of unique proteins identified (Table 2.1, column 2 versus column 1). Upon closer inspection of the data however, a noteworthy improvement in the number of identified OM/extracellular proteins using Ga2DLC (54 vs. 43) can be discerned. Furthermore, the difference in total spectral counts (Fig.2.5, C and D) assigned to the OM/extracellular protein category increased from 34% to 48% in a statistically significant manner (p = 0.0021 using the Student’s t-test). Table 2.1. Summary of proteins detected in C. crescentus OM fractions across all conditions. OD600 1.0 OD600 0.6 GeLC (n=1) Ga2DLC (n=1) WT (n=2) RsaA(n=2) Number of identified peptides 2647 3154 2518 5927 Number of unique proteins 179 182 99 234 Number of extracellular proteins 15 13 13 21 Number of OMPs 28 41 29 63 Number of PERI proteins 12 9 10 38 Number of IMPs 9 12 6 16 Number of CYT proteins 61 56 20 45 Number of proteins with unspecified location 54 51 21 51 ‐ 35 ‐ Overall, gel-assisted digestion allowed for improved solubilization of the OMP pellets through the use of very strong denaturants and facilitated the analysis of the OMP fraction by providing a procedure that effectively removed the harsh reagents prior to LC-MS/MS. The remaining studies described below were conducted using our multiplexed Ga2DLC approach in combination with SLS isolation of OMPs. Figure 2.5. Graphical representation of the predicted/annotated subcellular distribution of proteins identified in enriched OM fractions from C. crescentus using either the GeLC or Ga2DLC method. Proteins were distributed in each pie chart according to their annotated or predicted subcellular location based on the number of proteins IDs per category for either the (A) GeLC method or (B) Ga2DLC method. The total number of proteins (n) identified in each experiment is shown below each pie chart. Proteins were distributed in each pie chart according to their annotated or predicted subcellular location based on relative abundance from spectral count data for either the (C) GeLC method or (D) Ga2DLC method. The total number of spectral counts (SpC) for each experiment is shown below each pie chart. ‐ 36 ‐ 2.3.3 Low Density Culture Improves C. crescentus OMP Extraction Specificity In the work described above, the bacteria were grown in a fixed volume to an OD600 of 1.0. This is approximately 109-1010 cells/mL. Though not an experimental parameter explicitly considered in studies of this type, we postulated that this high density cell culture condition is stressful to the cells and could lead to morphological changes and/or premature cell lysis. Both scenarios could contribute to the co-purification of proteins from cellular compartments other than the OM and increase the “noise” in our proteomic experiments. Therefore, we investigated the effect that a reduction in cell density would have on the specificity of the OM enrichment step. OMPs were isolated from two independent batches of C. crescentus cells grown to an OD600 of 0.6. This optical density roughly correlates to a cell density of 108 cells/mL. The OMP fractions were then analyzed using the Ga2DLC approach. Ninetynine proteins were identified (Table 1, column 3). This low-density protocol improved the percentage of OM/extracellular protein identifications (Fig. 2.6A) with a concomitant increase in the OM/extracellular protein group abundance as accessed via spectral count data (Fig. 2.6C). The quantitative data in Figure 2.6C also indicated that this protocol resulted in a low level of contamination from other cellular locations (IM, 6%; CYT, 6%; PERI, 8%). The contribution by the unknown category (16%) to total spectral counts is considered acceptable since many of these proteins may actually reside in the OM although the predication programs are not yet able to specifically determine localization. Overall, the low-density cell culture protocol improved the specificity of OMP isolation due to a reduction in the co-purification of proteins located in other subcellular compartments. ‐ 37 ‐ Figure 2.6. Graphical representation of the predicted/annotated subcellular distribution of proteins identified in enriched OM fractions from C. crescentus (low density cell culture) using either WT or RsaA– cells. Proteins were distributed in each pie chart according to their annotated or predicted subcellular location based on the number of proteins IDs per category for either the (A) WT cells or (B) RsaA– cells. The total number of proteins (n) identified in each experiment is shown below each pie chart. Proteins were distributed in each pie chart according to their annotated or predicted subcellular location based on relative abundance from spectral count data for either the (C) WT cells or (D) RsaA– cells The total number of spectral counts (SpC) for each experiment is shown below each pie chart. 2.3.4 Deletion of the Surface-Layer Protein Improves C. crescentus OM Proteome Coverage The surface layer (S-layer) - the outermost region of a bacterium that interacts with its environment - is usually composed of one protein that can contribute up to 15% of protein mass, and serves various species-dependent functions [56, 57]. For C. crescentus, the S-layer is thought to act as a shield from invasive pathogens and the harsh environment [58] and is exclusively composed of the RsaA protein that can account for ‐ 38 ‐ an incredible 31% of the total protein mass [59]. In the previous experiments, we found that RsaA was consistently one of the top 10 most abundant proteins in each sample based on spectral counts (data not shown). Peptides derived from RsaA eluted across numerous fractions during the first dimension separation (>10 offline fractions) and, in some instances, eluted throughout the entire second dimension LC-MS/MS run as well (data not shown). Furthermore, we detected RsaA in 13 of the 18 gel bands analyzed by LC-MS/MS during the GeLC analysis. So, SDS-PAGE does not offer any special analytical advantage in this case. Unfortunately, RsaA is of concern for our proteomic study because it is possible that this highly abundant protein interfered with the detection of proteins found at lower concentrations and consequently, limited the dynamic range of our multiplex strategy. Additionally, the RsaA protein most likely compromised our OMP isolation attempts since it has been documented to interfere with the efficiency of OMP isolation [42]. To overcome these issues, a C. crescentus S-layer mutant (RsaA-) [42] that lacks the protein was used for further OMP characterization. In our hands, this mutant is phenotypically indistinguishable from the WT strain as assessed by growth (Fig. 2.7). ‐ 39 ‐ Figure 2.7. Bacterial growth profiles for WT and RsaA- C. crescentus cells. Each experiment was performed in duplicate. OMPs were isolated from two independent cultures of RsaA- grown to an OD600 of 0.6. Proteomic analyses of the OMP samples yielded 234 unique proteins (Table 2.1, column 4). The eighty-four OM/extracellular proteins identified represented only 36% of all protein identifications but accounted for 77% of the normalized spectral counts (Fig. 2.6, B and D). Individual contributions from each of the other subcellular locations were ≤ 10% based on the semi-quantitative data (Fig. 2.6D). As anticipated, RsaA was the only OM/extracellular protein identified in WT cells that was not detected in the mutant cell line. A few other non-OM proteins (23 proteins) were found in WT C. crescentus and not in RsaA- cells (data not shown). They were of low abundance and can be regarded as trivial impurities. Most importantly, the low-density cell culture procedure resulted in a two-fold increase in OM/extracellular protein identifications when RsaA- cells were analyzed instead of WT cells (Table 2.1, columns 3 and 4). ‐ 40 ‐ 2.3.5 Nearly 70% of the Expected C. crescentus OM/extracellular Proteome is Detected Using the Optimized Procedure The genome of C. crescentus contains 3,763 predicted proteins [38] and 140 of these were predicted to be OMPs based on a number of structural features [13]. In our optimization experiments described herein, we used consensus predictions from Proteome Analyst 3.0 [49] and PSORTb 2.0 [50] to categorize the location of our identified proteins. These are two of the most sensitive and most accurate algorithms currently available for global prediction of subcellular location [60]. So, we also decided to use these two programs to reevaluate the C. crescentus genome and recalculate the number of expected OM and extracellular proteins. Our analysis indicates that 99 and 105 OMPs exist in the C. crescentus proteome as predicted by Proteome Analyst and PSORTb, respectively. The list decreases to 96 after removing those hits generated by only one program. Proteome Analyst and PSORTb also indicated that 44 and 33 extracellular gene products are present within C. crescentus, respectively, with 29 predictions in common. This bioinformatic survey results in a high confidence (consensus) list of 125 predicted OM/extracellular proteins. A total of 234 proteins were identified using our optimized procedure and 84 of these proteins are predicted or known OM/extracellular proteins across four experiments (Fig. 2.8). Therefore, we have identified nearly 70% of the expected OM/extracellular proteome for C. crescentus using our optimized procedure. ‐ 41 ‐ Figure 2.8. Venn diagram illustrating the common and uniquely identified OMPs across all four datasets. Reconstruction of the extracellular, OM, and PERI regions is still difficult for prediction algorithms to do since they are trained on partial data sets [60]. As demonstrated in this work and others [10, 12], discrepancies in predictions exist between subcellular localization tools. So, inconsistencies between in silico predictions and actual locations are expected to exist as well [32]. With that in mind, if we consider the cell envelope (extracellular, OM, PERI, IM) as one unit then 59% of our protein identifications and 86% of the total spectral counts are annotated or predicted to the cell envelope region. Categorization of the 234 proteins according to their JCVI (J. Craig Venter Institute) cellular role category showed that the majority of the proteins are conserved hypothetical/hypothetical proteins (59 proteins, 25% of protein identifications) with no known function and nineteen of these are predicted OMPs. The three cellular categories ‐ 42 ‐ represented by the greatest number of proteins were: transport/binding proteins (14%); protein synthesis (9%); and cell envelope (8%). The transport/binding protein classification was largely comprised of TonB-dependent receptors (23 proteins), the second largest class of proteins encoded by the C. crescentus genome [38]. The proteins found in the protein synthesis group have unknown locations or presumably originate from the CYT and IM. The cell envelope category, which contains important structural proteins and proteins required for the biogenesis of the cell envelope, consisted of 15 OMPs, 3 PERI proteins and 1 protein of unknown subcellular localization (Fig. 2.9). Figure 2.9. The majority of the identified OMPs are conserved hypothetical/ hypothetical proteins, transport/binding proteins, or cell envelope proteins. The JCVI cellular role was used to classify the 234 proteins identified in experiment 4. Predicted subcellular locations for the proteins are indicated in the figure legend. ‐ 43 ‐ Due to the limitations of bacterial subcellular predication programs mentioned above, we subjected the 234 identified proteins to additional bioinformatic examination in order to assess their overall OMP-like character. Although integral OMPs typically adopt β-barrel conformations, recent structural analysis has revealed that they can make use of α-helices as well [61]. So, the number of α-helical or β-barrel membrane-spanning domains within each protein was predicted using TMHMM 2.0[62] and BOMP [63], respectively. In Figure 2.10, we see that 88 of the 234 identified proteins (38%) contain one or more transmembrane helical domains. Evidence for this predicted secondary structure was found in > 50% of the identified OMPs (37 of 66 proteins). The other predicted cellular locations have proteins that putatively contain α-helices as well: 21 PERI proteins; 13 “unknown” proteins; 10 IMPs; 6 extracellular proteins; and 1 CYT protein. Figure 5B shows the 28 proteins forecast to contain β-barrels. They are all predicted/known OMPs. The 16 β-barrel OMP predictions classified as highly reliable (based on BOMP categories 4 and 5) describe 14 TonB-dependent receptor family proteins, one OmpA-related protein, and one efflux system protein. All 16 of these proteins have a known role as transport and binding proteins. ‐ 44 ‐ Figure 2.10. Distribution of proteins containing (A) α-helical (88 proteins) or (B) βbarrel (28 proteins) motifs along with calculated (C) GRAVY values. This information was acquired in an effort to further assess the membrane-like qualities of the 234 identified proteins although single feature prediction programs have been shown to be less reliable than global prediction programs. In addition, a GRAVY score for each protein was determined using the ProtParam software on the ExPASY server [64]. As mentioned earlier, bacterial OMPs are distinguished by their overall hydrophilicity similar to CYT proteins [9]. As shown in Figure 2.10, the majority of the 234 identified proteins (174 proteins) have negative ‐ 45 ‐ GRAVY values indicating that these proteins are not hydrophobic. As anticipated, nearly all of the OMPs (52 out of 63 identified in this study) and CYT proteins (36 out of 45 identified in this study) are hydrophilic. Again, proteins thought to reside in other subcellular regions have negative GRAVY values as well (34 “unknown” proteins; 29 PERI proteins; 14 extracellular proteins; and 9 IMPs). Our analyses also allowed for the detection of components of three known macromolecular complexes that span the cell envelope: (1) the β-barrel assembly machine (BAM); (2) the holdfast transenvelope apparatus; and (3) the flagella. BAM is responsible for the proper folding and targeting of OMPs to the outer membrane of gramnegative bacteria [5]. In C. crescentus, the core unit is composed of four proteins (BamA, BamB, BamD, and BamE) [3, 65] and at least one additional protein (Pal) that secures the Bam complex to the peptidoglycan layer [65]. We identified BamA (CC1915), lipoprotein BamB (CC1653) (a PERI protein) and lipoprotein Pal (CC3229). The polysaccharide portion of the holdfast adhesion ‘organelle’ in C. crescentus is transported to the cell surface by a transenvelope complex composed of HfsA, HfsB, and HfsD [66]. Additionally, the holdfast is anchored to the stalk of C. crescentus by HfaB and other proteins [67]. We detected IMP HfsA (CC2431), PERI protein HfsD (CC2432), and OMP HfaB (CC2629). Interestingly, HfsD has been experimentally confirmed to localize to the OM region [68, 69] despite the contradictory PERI prediction generated by Proteome Analyst and PSORTb in our study. We also identified numerous flagellar proteins: four external flagellins (FljL (CC1460), FljM (CC0792), FljN (CC0793), FljK (CC1461)); two hook-filament junction proteins (FlgL (CC0898), FlgK (CC0899)); the flagellar hook assembly protein FlgD (CC0901); the flagellar basal body rod protein ‐ 46 ‐ FlgG (CC2064); the flagellar basal body P-ring protein FlgI (CC2582); and the flagellar hook length determination protein FlbG (CC0900). 2.4. DISCUSSION The goal of this study was to develop an optimized protocol for the improved coverage of the OM proteome of a model gram-negative bacterium, C. crescentus. We were able to do this by examining the effect of modifying several important parameters in the OMP sample isolation and processing pipeline including: (1) the protein extraction method; (2) the protein solubilization approach; (3) the mode of sample fractionation; (4) the cell growth conditions; and (4) the presence of a highly abundant protein. As we evaluated each variable, we continually improved the enrichment and detectability of the OMPs. Using the final optimized procedure, we generated a sample that overwhelmingly contained OM/extracellular proteins – 77% based on spectral counts – and less than 10% contamination from each of the other subcellular regions. Importantly, nearly 70% (84 proteins) of the expected OM/extracellular C. crescentus proteome was detected. In agreement with our expectation, this study showed that our optimized SLS method is far superior to the traditional Na2CO3 approach for the selective isolation of OMPs from C. crescentus. Numerous techniques have been used to isolate OMPs for proteomic studies of various gram-negative bacteria including sucrose density gradient centrifugation [10, 11, 18], differential detergent solubility [32, 52, 70], and washes with chaotropic agents [12, 14] and/or alkaline buffers [13, 27]. We evaluated two of the more commonly employed reagents for this type of work because they each have rapid and ‐ 47 ‐ fairly generic protocols across cell types. After obtaining SLS-insoluble OMs, we washed them with solutions containing NaBr and Na2CO3. These two reagents have been used in other membrane extraction protocols to effectively eliminate membrane-associated components [12, 14] and most likely improved the performance of our method: The OM protein profile was distinctly different from the IM protein profile. In contrast, the Na2CO3 wash of total membranes left integral membrane proteins from both the IM and the OM in the insoluble fraction (as postulated before [27]) in addition to leaving some of each membrane in the supernatant as well (Fig. 2.3). Sodium carbonate has certainly been used to generate impressive data in past OMP studies that relied on 2DGE [13, 27, 71]. However, these early OMP-focused efforts most likely benefited from the inherent bias of the 2DGE analysis technique utilized at the time. OMPs are typically hydrophilic proteins [72] and readily dissolve in IEF solution [27]; so, these proteins are not excluded by 2DGE en mass. Conversely, many inner membrane proteins are very hydrophobic and are under-represented after 2DGE separation [73]. Therefore, although adequate for a 2DGE study, an isolation step using Na2CO3 would not be the best choice for an unbiased MS-based OMP study as indicated by our results. This study also revealed that our multiplexed approach, referred to as Ga2DLC, resulted in a substantial improvement in OMP identifications in particular in addition to an increase in peptide identifications per protein on average when compared to GeLC. There are two important reasons why these results were obtained. One, we improved the efficiency of the protein dissolution step by using the gel-assisted digestion buffer. We found that OMPs dissolved completely in this buffer but did not completely solubilize in the SDS sample-reducing buffer. Although GeLC has been successfully utilized for many ‐ 48 ‐ different types of challenging samples [74-76], its general protocol has not been optimized for analysis of membrane proteins. So, its poorer performance for OMP analysis was an anticipated outcome. Second, we increased the resolving capacity of the overall analytical technique by using RP chromatography in the first dimension separation instead of SDS-PAGE. Our results concur with a previous study demonstrating that two-dimensional fractionation schemes using peptide-level high pH RP-HPLC in the first dimension have higher fractionation efficiencies than those using SDS-PAGE for protein-level separation in the first dimension [55]. Admittedly, exhaustive replicate analyses were not conducted in our proof-of-principle evaluation of GeLC versus Ga2DLC. Nevertheless, we show that the Ga2DLC approach detected more OM/extracellular proteins than the GeLC method and it nearly identified all of the OMPs – 25 out of 28 – found in the GeLC analysis (Fig. 2.11). ‐ 49 ‐ Figure 2.11. Venn diagram illustrating the overlapping and uniquely identified proteins using either the GeLC or gel-assisted methodologies. (A) Twenty-five OM proteins were observed using both techniques. (B) One-hundred twenty-four total proteins were observed in both experiments. The results from our final set of experiments proved for the first time that careful control of cell growth conditions and use of an S-layer deletion mutant [42] could further enhance OM enrichment and OMP characterization. C. crescentus morphology and physiology is certainly sensitive to its surroundings [77-80]. It is likely that overpopulation of the cell flask led to a toxic environment causing not only cell death, but also changes in protein expression and secretion due to cell stress. Indeed, high-density cell culture affects the abundance and diversity of proteins found in other bacterial internal and extracellular proteomes [81-83]. So, mitigation of this undesired situation by lowering cell density allowed normal cellular equilibrium to be established, which resulted in a decrease in co-purified non-OMPs. Ablation of RsaA even more markedly ‐ 50 ‐ facilitated OMP isolation and detection. Not only were the relative amounts of non-OMP identifications reduced but RsaA removal allowed for the more efficient capture of a greater number of OMPs and/or allowed for the detection of less abundant OMPs previously obscured by RsaA. Our strategy using the RsaA– cells is similar to that used by researchers working to discover novel biomarkers in bodily fluids. Depletion of highly abundant proteins allows access to the numerous low abundance proteins of potential biological significance [84]. This genetic approach, or alternatively low-pH extraction [85], could be used to remove the S-layer protein in other gram-negative bacteria. Overall, we suggest that careful control of cell density and the removal of the bacterial Slayer is of critical importance for “outer membrane proteomics” projects given the gain in OMP purity and identifications we achieved by doing so. The pooled results from all of the experiments described in this study allowed us to detect 98 out of the 125, or nearly 80%, of the predicted OM/extracellular proteins (Fig. 2.9). This is the largest coverage of the C. crescentus OM/extracellular proteome to date. The overlap in terms of protein identifications between each optimization was considerable (Fig. 2.9). However, the final experimental conditions clearly resulted in improved identification of OMPs. An initial analysis of the C. crescentus outer membrane proteome using 2DGE and MALDI-TOF MS was conducted by Phadke et al. in 2001 [13]. They detected 54 proteins and 41 of these were OMPs as designated by their prediction programs. As a point of comparison, 25 of the 41 “Phadke OMPs” are contained within our comprehensive list of 98 OM/extracellular C. crescentus proteins (Fig. 2.12). We should note that our protein list is the result of combining data from four separate experiments with replicate analyses. It is unclear how many experiments and/or ‐ 51 ‐ replicates were conducted by Phadke et al. We also acknowledge that our study benefits from highly sensitive, modern mass spectrometry instrumentation not available ten years ago. Figure 2.12. Venn diagrams illustrating the common and uniquely identified proteins between our studies and Dr. Phadke’s work. (A) Twenty-five OM proteins were observed in both studies. (B) Thirty-three total proteins were observed in both studies. In sum, we have demonstrated that not only is the choice of protein extraction method and analytical approach important for “outer membrane proteomics,” at least two other seemingly under-appreciated variables (i.e. cell culture conditions and presence of S-layer protein) also make a significant difference in data quality. 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Bacteriol. 1992, 174, 1783-1792. ‐ 59 ‐ Chapter3 IDENTIFICATION OF PUTATIVE SURFACE-EXPOSED PROTEINS OF C. crescentus USING A BIOTINYLATION APPROACH ‐ 60 ‐ 3.1 INTRODUCTION The surface proteins of Gram-negative bacteria are key players in the physical interactions between the cell and its environment [1]. Surface proteins recognize host molecules during pathogenic encounters, which induce a immunogenic response from the pathogen such as immune evasion [2] and adhesion [3]. Thus, these functional surface proteins provide a number of candidates for antibiotic development as well as direct vaccine production [4, 5]. At present, there are significant efforts to discover novel vaccines for protection against diseases using either genomic [6, 7] or proteomic [8, 9] strategies. In chapter 2, we developed an OM isolation and separation technique compatible with nano-flow LC-MS/MS shotgun proteomics to successfully identify 234 proteins. The results contained 36% of the predicted OM/EX proteins which covered almost 70% of the expected OM/EX products from the C. crescentus genome [10]. However, 22% of the identified proteins failed to be assigned to a specific subcellular location and several known OMPs were erroneously assigned to other regions [10]. This is most likely due to the inconsistency in performance of current subcellular localization predictors [11] and their incompetence to localize ‘anchorless’ proteins with no signal sequence residues [12]. Thus, we want to explore an alternative method to directly label surface-exposed membrane proteins in order to comprehensively map the outer membrane proteome of C. crescentus. Biotinylation has been demonstrated to be an effective technique for profiling cell surface proteomes since the low molecular weight biotin tag does not change the conformation of proteins and has high specificity for avidin purification [13-16]. The bacterial membrane is impermeable to selective labeling regents, such as Sulfo-N ‐ 61 ‐ hydrosuccinimide (NHS)-long chain (LC) biotin and Sulfo-NHS-LC-LC biotin [17]. These biotin reagents derivatize primary amines at the ε-amino group of lysine residues and N-termini of proteins which are exposed to the extracellular space. Therefore, the labeled peptides should originate from the extracellular domains of transmembrane proteins situated within the outer membrane [18]. This labeling method has been applied to both eukaryotic and prokaryotic cell systems, such as ovarian cancer [19], human mesenchymal stem cells [20] and Leptospira interrogans [21]. In those studies, the enriched labeled proteins were analyzed using MS-based shotgun proteomic analysis. This strategy generally is based on labeling cell surface proteins with biotin derivatives, capturing proteins on immobilized avidins, elution and digestion of isolated proteins, separation of resulting peptides, and identification of peptides via RPLC-MS/MS (Fig. 3.1). Here, we described a strategy for large scale and selective identification of cell surface proteins of C. crescentus using a biotinylation labeling and enrichment strategy coupled with high resolution LC-MS/MS. The approach allowed us to indentify 91 proteins, of which almost 90% had membrane characteristics. Our study used the RsaA gene deletion (RsaA-) mutant strains of C. crescentus described previously in chapter 2. To our knowledge, this study provided the first global proteomics profile of surface exposed proteins of C. crescentus. We have identified a large number of membrane proteins with different biological functions, which would provide a catalogue for potential vaccine development. Future application of this biotin-labeling approach in combination with quantitative proteomics labeling, such as stable isotope labeling would ‐ 62 ‐ assist in the determination of the expression level of cell surface proteins on C. crescentus. Figure 3.1: Schematic of biotinylation strategies for identification of surface exposed proteins using MS-based shotgun proteomics. The Sulfo-NHS-LC biotin (or other impermeable biotin reagents) is used to label the outer portion of membrane proteins. Intact cells undergo surface biotinylation and cell lysis. Labeled proteins are then captured on immobilized streptavidin substrates. Purified proteins could be either digested on bead or eluted prior to proteolytic digestion. Eluted peptides are analyzed by LC-MS/MS. Modified from [22]. ‐ 63 ‐ 3.2 MATERIALS AND METHODS An overview of the cell surface labeling and enrichment strategy described in section 3.2.1 and 3.2.2 are shown in Figure 3.2. Figure 3.2. Schematic diagram of biotinylation labeling and enrichment strategy. The avidin enrichment method was conducted on both the protein and peptide levels. 3.2.1 Bacterial Growth and In vivo Cell Surface Labeling Two biological replicates of RsaA-negative (RsaA-) C. crescentus cells were first grown to the late exponential phase in 10 mL peptone yeast extract (PYE) medium. Then, 200 mL fresh PYE medium was inoculated with 10 mL cell cultures. The cells were cultured at 30 ˚C with constant shaking at 100 rpm until an OD600 of 0.6 was reached. ‐ 64 ‐ Cells were labeled by EZ-linkTM Sulfo-NHS-LC-LC Biotin (Pierce, Rockford, IL) followed by a previously described procedure with minor modifications [23]. In general, cells were harvested by centrifugation at 4000g for 10 min at 4 ˚C and washed three times with ice-cold phosphate buffered saline (PBS, 10 mM NaH2PO4/Na2HPO4, pH 7.4, 138 mM NaCl, 2.7 mM KCl) supplemented with 1 mM CaCl2, 1 mM MgCl2. Cells were then resuspended in the same ice-cold PBS to adjust OD600 to approximately 2.0 value. After resuspension, each cell cultures were spited into two aliquots, where one was ready for in vivo biotinylation labeling, and the other half was served as negative control for further comparison. The ‘biotinylation’ cells were incubated with 0.2 mM final concentration of Sulfo-NHS-LC-LC Biotin for 1 h at room temperature with gentle rotation. The extra residue of Sulfo-NHS-LC-LC Biotin were quenched by the addition of Tris-HCl (pH 7.5) to a final concentration of 50 mM and incubated for 10min at room temperature. After three time of wash with 50 mM Tris-HCl (pH 7.5), cells were resuspended in 50 mM AMBIC (pH 7.5) with protease inhibitors (Complete EDTA-free, Roche Diagnostics). In the meantime, the ‘negative control’ cells were incubated with nano-pure water instead of the biotin reagent and underwent the same procedures. For the preparation of cell lysates, both labeled and control cells were disrupted by repeated intermittent sonic oscillation (20 × 5 s). Cellular debris was removed by centrifugation at 10,000g for 10 min. Supernatant was collected and stored for further analysis. The Pierce BCA protein assay was used to determine protein concentrations. 3.2.2 Affinity Capture of Labeled Cell Surface Proteins/Peptides The biotinylated fractions were purified through avidin cartridge column (ICATTM kit, AB SCIEX, Framingham, MA). We tested the isolation efficiency of the ‐ 65 ‐ avidin cartridge column on both biotinylate proteins and peptides (Fig. 3.2), which meant that half of the labeling fractions were isolated by avidin column first and digested by trypsin (the tryptic digestion protocol was described in 3.2.3.), the other half fractions were carried for trypsin digestion first followed by avidin purification. The purification procedure by avidin cartridge was followed as the manufacture suggested. In brief, the avidin cartridge was activated sequentially using 2 mL of Affinity Buffer-Elute (30% ACN and 0.4% TFA) and Affinity Buffer-Load (2× PBS, pH 7.5). 100 µg of labeling proteins/peptides were combined with 500 µL Affinity Buffer-Load and loaded onto the avidin column at slow injection speed (1 drop/5 seconds). After loading, the column was washed by 2 mL of Affinity Buffer-Load, Affinity Buffer-Wash (1× PBS, pH 7.5) and nano-pure water sequentially. Finally the biotinylated proteins/peptides were eluted off the column using 1 mL of Affinity Buffer-Elute and collected in Eppendof tubes. After the elutions were reduced to near dryness, the labeled peptides were reconstituted in 50 μL with 0.1% acetic acid for LC-MS/MS analysis, while the labeled proteins were first reconstituted in 50 mL AMBIC (pH 8.0) followed by tryptic digestion, nearly dried by centrivap and then reconstituted in 50 μL with 0.1% acetic acid for LC-MS/MS analysis. 3.2.3 Streptavidin Western Blotting Biotinylated proteins (50 µg) were resolved in SDS-PAGE gels as described in chapter 2. Proteinaeous samples were then electrophoretically transferred onto nitrocellulose membranes (0.45 mm, Bio-Rad, Hercules, CA) at 4 ˚C for 90 min (17 mA/30V) using the Invitrogen XCell II Blot Module. Membranes were first rinsed with wash buffer (25 mM Tris-HCl (pH 7.0), 1% Tween 20, 1 mM CaCl2, 1 mM MgCl2, 0.15 ‐ 66 ‐ M NaCl) and subsequently blocked with the same buffer for 60 min. HRP-conjugated streptavidin (diluted 1:1000 in wash buffer) was incubated with the membrane for 90 min. Then, the membrane was washed again (3×, 10 min each) in a buffer containing 25 mM Tris-HCl (pH 7.0), 1 mM CaCl2, 1 mM MgCl2, and 0.15 M NaCl. Finally, the blot was visualized by ECL (SuperSignal® Chemiluminescent Substrate, Thermo Fisher Scientific) 3.2.4 In-solution Tryptic Digestion The biotinylated proteins were reduced with 5 mM TCEP for 1 h at room temperature and alkylated with fresh IAA at 10 mM final concentration in the dark for 30 min at room temperature. The extra IAA was further quenched by 5 mM TCEP for 30 min at room temperature. Proteins were then digested with trypsin at an enzyme: substrate ratio of 1: 50 (w/w) at 37 ˚C overnight. 3.2.5 nanoLC-MS/MS Analysis LC-MS/MS analysis was performed using both QSTAR Elite QTOF mass spectrometer (AB Sciex, Foster City, CA) equipped with a Tempo nanoLC system and LTQ-Orbitrap velos (Thermo Fisher Scientific, Waltham, MA) equipped with a Agilent 1200 LC system (Agilent Technologies, Santa Clara, CA). Sample was loaded onto a precolumn (75µm × 3cm) packed with 5um Monitor C18 particles (Column Engineering. Ontario, CA) and then eluted at a flow rate of 100 nL/min (Tempo nanoLC) or 200 nL/min (Agilent 1200 LC system) onto analytical columns (75 μm ID, 10 cm length of 3 µm Monitor 100Å-Spherical Silica C18; Column Engineering Inc., Ontario, CA) using the following gradient: 0%-30% B in 60 min; 30%-60% B in 40 min; 60%-70% B in 20 ‐ 67 ‐ min; 70% B for 10 min. Solvent B consisted of 0.1% formic acid and 2% water in ACN. The ionization voltage was set at 1.8 kV. Precursor ions were scanned over the mass range from 400-2000 and MS/MS spectra were acquired for selected ions under automatic collision energy. For QSATR, MS data were acquired in informationdependent acquisition mode with Analyst QS 2.0 (ABSciex). MS cycles were comprised of one full scan (m/z range = 400-2000, 2 sec accumulation) followed by sequential MS/MS scans of the four most abundant ions (+2 to +4 charge state, minimum ion count = 100, exclusion time = 15 sec, maximum accumulation time = 2 sec). For LTQ-Orbitrap velos, the MS survey scan was performed in the FT cell recording a window between 400 and 2000 m/z. The resolution was set to 60 000, and the automatic gain control was set to 106 ions. Minimum MS signal for triggering MS/MS was set to 1000, and m/z values triggering MS/MS were put on an exclusion list for 120 s. Top 10 MS/MS spectra were acquired with a data-dependent automatic switch per survey scan (+2 to +4 charge state). 3.2.6 Protein Identification by Database Search Tandem mass spectrometry data from .WIFF and .RAW files were converted to .mzXML format using the Trans-Proteomic Pipeline (version 4.4.0). The .mzXML files were then converted to .MGF files via MassMatrix MS File Conversion Tools (version 3.8; www.massmatrix.net). The .MGF files were used for the Mascot search against the RefSeq C. crescentus database (dated April 25, 2009) downloaded from NCBI. Mascot parameters were as follows: precursor ion mass tolerance were set at 50 ppm (QSTAR) or 20 ppm (LTQ-Orbitrap) and fragment ion tolerance were set at 50 ppm (QSTAR) or 0.5 Da (LTQ-Orbitrap); carboxyamidomethylation was chosen as a fixed modification; methionine oxidation, sulfo-NHS-LC-LC biotin (K/N-term) were chosen as a variable ‐ 68 ‐ modification; trypsin was selected as the enzyme; and two missed cleavage was specified. For peptide and protein identifications in peptide-level enrichments, the search results were processed as follows. (i) The candidate peptides were screened with the Mascot score higher than 28 as well as y- or b- ions ≥ 4. (ii) The MS/MS signal corresponding to the labeled lysine and N-terminal were manually examined to ensure the presence of sulfo-NHS-LC-LC biotin modifications. (iii) Proteins identified without biotin modifications were excluded regardless of their scores (not applicable for ‘proteinlevel isolation’ and ‘negative control’ samples). For peptide and protein identifications in protein-level enrichments, only step (i) was applied and each protein was identified from two unique peptides. 3.2.7 Bioinformatics Two algorithms were used to predict each protein’s location in the cell: Proteome Analyst 3.0 and PSORTb 2.0. The predictions made by each program had to agree in order for a given protein to be assigned to a subcellular location. Otherwise, the protein was assigned to the “unknown” category. For functional analysis on each protein, TMHMM Server version 2.0 (www.cbs.dtu.dk/services/TMHMM/) was used to predict the occurrence of transmembrane helices. DAVID Bioinformatics Resources V6.7 [24] was used to extract biological functions (http://david.abcc.ncifcrf.gov/conversion.jsp). 3.3 RESULTS 3.3.1 Visualization of C. crescentus Surface Proteins ‐ 69 ‐ Most bacterial adhesins are membrane proteins containing extracellular domains that specifically interact with host cell ligands [25]. We used a membrane-impermeable biotinylation reagent (Sulfo-NHS-LC-LC Biotin) to specifically biotinylate the free amines in order to detect surface-exposed proteins. After lysis of the labeled cells, debris was removed by centrifugation. The recovered proteins were resolved on SDS-PAGE gels and visualized with Coomassie blue staining and streptavidin western blotting. Figure 3.3: Cell surface exposed proteins were successfully labeled by biotin reagents. (A) SDS-PAGE analysis of both negative control and biotinylated samples. Equal amounts of cell lysate (5 µg) were separated by SDS-PAGE gels and coomassie blue stained. (B) Western blot analysis to determine the presence of biotinylated proteins in labeled RsaA fractions. Equal amounts of cell lysate (20 µg) were separated using SDS-PAGE and probed with HRP-streptavidin. The experiment was performed 2 times with similar results. The experiment was performed 2 times with similar results. Both non-labeled negative control and the biotinylated cell lysates showed almost identical protein complexity (Fig. 3.3B). The biotinylated proteins were less complex ‐ 70 ‐ than the total whole cell lysate by comparing the protein band patterns of both results (Fig. 3.3 A and B). However, there were still around 50 bands of labeled proteins in the single gel lane from the WGA western blotting. The non-biotinylated sample only contained 4 positive protein bands (Fig. 3.3 A), which suggested that there was few nonspecific binding proteins interacting with the streptavidin. Four proteins were detected in the negative control replicates: molecular chaperone GroEL (CC_0685); alanine dehydrogenase (CC_3574); glycine dehydrogenase subunit 1 (CC_3353) and hypothetical protein (CC_1275). The theoretical molecular weights of these proteins correspond to the ~ 40 kDa and 30 kDa, which correlate with the western data. The top two gel bands may come from an oligomer of those three proteins or simply fail to be detected through LC-MS. 3.3.2 Identifications of Biotinylated Proteins by LC-MS/MS: Protein-level Analysis Most studies on bacterial surface proteins using biotinylation labeling technologies involve an avidin affinity purification step at the protein-level instead of isolating peptides in order to increase the sequence coverage of the identified proteins [15, 21, 23]. However, a batch purification process was used in those studies. Nonbiotinylated proteins were washed away using high concentrations of salts or detergents and the avidin beads were thrown away afterwards. In contrast, a reusable avidin column was used with milder wash conditions in our work. In order to evaluate the enrichment specificity of the avidin cartridge column on both labeled proteins and peptides, the ‘negative control’ fractions (protein/peptide isolations) were analyzed by LC-MS/MS at the beginning. There were four non-labeled ‐ 71 ‐ proteins identified in the peptide-level isolation of negative control fractions after combining two biological replicates, whereas the protein-level negative controls were possessed of 184 proteins (Fig. 3.4). Figure 3.4: Venn diagram representation of protein identifications in both proteinlevel and peptide-level purified fractions (negative controls). Two biological replicates were run on both samples. The protein-level analysis led to the identification of 196 and 205 proteins from each of the two biological replicates (Fig. 3.11A). In total, 233 proteins were characterized from 672 unique peptide identifications after combining the two biological replicates. Analysis of the peptide matches resulted in protein identification substantiated by the identification of non-biotinylated tryptic peptides. In our analysis, only 10 proteins (4.3 % of total proteins) were identified from MS/MS spectra of peptides possessing a biotin label. Interestingly, these 10 proteins are all predicted to reside in the cell envelope. MS analysis of the control samples yielded 184 protein identifications. After removing these 184 “background binding” proteins from the 233 protein list, 66 proteins remained and considered to be true biotinylated proteins (Fig. 3.11B). Almost 91% (167 ‐ 72 ‐ proteins) of proteins identified from the control experiments were also presente in the biotinylated samples, which indicats a high-level of non-specific binding between proteins in this sample and the avidin beads acquired from the manufacturer. Figure 3.11: Venn diagram representation of protein identifications in ‘proteinlevel’ purified biotin labeling fractions in RsaA-. Two biological replicates were performed. A) 233 total proteins were characterized from two combined biotinylated fractions. B) 66 proteins were considered as true positive biotin labeling proteins, since they were uniquely identified in the biotinylated RsaA- fractions while other 167 proteins were identified in both biotinylated fractions and negative controls. ‐ 73 ‐ Figure 3.12: Graphical representation of the predicted/annotated subcellular distribution of biotinylated proteins identified in protein-level purified fractions from C. crescentus RsaA- using Q-TOF method. Proteins were distributed in each pie chart according to their annotated or predicted subcellular location based on the number of proteins IDs per category. The percentage of biotinylated proteins identified in each experiment is shown below each pie chart. To evaluate the specificity of the biotinylation procedure for membrane proteins, we annotated the subcellular localization of each identified protein using PSORTb and Proteome Analyst as describe in chapter 2. This analysis allowed us to categorize proteins as extracellular proteins, outer membrane proteins, periplasmic proteins, inner membrane proteins, cytoplasmic proteins and proteins with unknown localization (if both predictors failed to identify cellular localizations or the prediction results from the two software were contradictory). In these protein-level avidin purifications, the 66 biotin-labeled proteins included 2 EX proteins (3%), 13 OM proteins (20%), 14 periplasmic proteins (21%), 11 IM proteins (17%), 15 cytoplasmic proteins (22%) and 11 protein with unknown subcellular localization (17%) (Fig. 3.12, Table. 3.3). Compared to the peptidelevel avidin purifications (Fig. 3.7, Table. 3.2) (to be described in the next section), the protein-level enrichments provided three-fold higher cytoplasmic protein identifications ‐ 74 ‐ (22% vs 7%) and two-fold higher inner membrane protein identifications (17% vs 8%), respectively. We believe that these were caused by the excessive non-specific interactions between the protein sample and the avidin column. In spite of having negative control samples, it should be noted that there remains uncertainty in the confidence of determining these 66 proteins as cell-surface proteins since biotinylated peptides were not detected. In the future, other types of immobilized avidin reagents followed by batch enrichment techniques [26, 27] should be used in order to specifically isolate biotinylated proteins in order to increase peptide coverage for protein identifications. Table 3.3 Summary of C. crescentus proteins identified in protein-level biotinylation experimentsa. Accession Numbers gi|16127499 gi|16127480 gi|16127277 gi|16125392 gi|16125140 gi|16125096 gi|16125017 gi|16124592 gi|16124303 gi|16124263 gi|16124419 gi|16124418 gi|16126907 gi|16124692 gi|16124519 gi|16126783 gi|16125142 gi|16127463 gi|16126575 gi|16126387 gi|16126105 gi|16125414 gi|16125261 gi|16124728 gi|16124621 gi|16124577 gi|16125024 gi|16124372 Descriptions suppressor protein SuhB aldolase, class II polymerase sigma factor RpoD reductase 4-phosphate synthase/GTP cyclohydrolase I hypothetical protein protein CheW synthetase, beta subunit pyrophosphatase protein S20 hypothetical protein hypothetical protein dipeptidase, putative protein CheYII nucleosidase aminopeptidase, putative aminopeptidase-related protein proton channel family protein proton channel family protein transporter, ATP-binding protein transporter, ATP-binding protein aminotransferase secretion system, membrane protein RsaE c reductase, cytochrome b synthase F0, B subunit transport protein ExbD esterase, putative hypothetical protein ‐ 75 ‐ Localizationa CYT CYT CYT CYT CYT CYT CYT CYT CYT CYT CYT CYT CYT CYT CYT EX EX IM IM IM IM IM IM IM IM IM IM IM Gene Locus CC_3269 CC_3250 CC_3047 CC_1140 CC_0887 CC_0843 CC_0764 CC_0337 CC_0047 CC_0007 CC_0164 CC_0163 CC_2672 CC_0437 CC_0264 CC_2544 CC_0889 CC_3233 CC_2336 CC_2148 CC_1862 CC_1162 CC_1009 CC_0473 CC_0366 CC_0322 CC_0771 CC_0117 gi|16127730 gi|16127724 gi|16127406 gi|16127376 gi|16127243 gi|16126213 gi|16125994 gi|16125716 gi|16125351 gi|16125290 gi|16125235 gi|16125177 gi|16124465 gi|16127734 gi|16127281 gi|16127056 gi|16126816 gi|16126778 gi|16126570 gi|16126393 gi|16126239 gi|16126229 gi|16126157 gi|16125396 gi|16125227 gi|16125112 gi|16124617 gi|16127587 gi|16127130 gi|16126561 gi|16126409 gi|16126164 gi|16126162 gi|16125289 gi|16125262 gi|16124953 gi|16124949 gi|16124555 TonB receptor OmpA family protein ferredoxin/flavodoxin oxidoreductase family protein TonB receptor TonB receptor TonB receptor TonB receptor single-strand binding protein TonB receptor hypothetical protein TonB receptor hypothetical protein TonB receptor M13 family protein domain family protein PmbA family protein M16 family hypothetical protein protein HU dipeptidyl peptidase IV M23/M37 family oligopeptidase family protein hypothetical protein aminopeptidase, putative hypothetical protein ABC transporter, periplasmic sugar-binding protein ABC transporter, periplasmic phosphonatesbinding protein hypothetical protein carbamoyl phosphate synthase, large subunit hypothetical protein carboxylase, beta subunit, putative uridylate kinase diphosphate synthase hypothetical protein GDP-mannose 4,6-dehydratase hypothetical protein hypothetical protein dipeptidase, putative a OM OM CC_3500 CC_3494 OM OM OM OM OM OM OM OM OM OM OM PERI PERI PERI PERI PERI PERI PERI PERI PERI PERI PERI PERI CC_3176 CC_3146 CC_3013 CC_1970 CC_1750 CC_1468 CC_1099 CC_1038 CC_0983 CC_0925 CC_0210 CC_3504 CC_3051 CC_2824 CC_2578 CC_2539 CC_2331 CC_2154 CC_1996 CC_1986 CC_1914 CC_1144 CC_0975 PERI CC_0859 PERI Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown CC_0362 CC_3357 CC_2900 CC_2322 CC_2170 CC_1921 CC_1919 CC_1037 CC_1010 CC_0700 CC_0696 CC_0300 The 66 biotinylated proteins were characterized from RsaA- biotinylation experiments filtered by negative control results. b Protein subcellular localizations were predicted by Proteome Analyst and PsortB. ‐ 76 ‐ The above results clearly show a high-level of nonspecific binding between our avidin column and our protein sample. Therefore, decided to pursue a peptide-level strategy instead. This strategy is described in the next section. 3.3.3 Identifications of Biotinylated Proteins by LC-MS/MS: Peptide-level Analysis The LC-MS/MS analysis of the avidin-purified biotinylated peptide fractions generated 1,024 (Q-TOF) and 2,133 (LTQ-Orbitrap) MS/MS spectra in a single 2.5-h analysis. These spectra were assigned to 136 (Q-TOF) and 242 (LTQ-Orbitrap) unique peptides by the Mascot search of Refseq C. crescentus CB15 database. Among those, 105 peptides (77.2%) were biotin-labeled in Q-TOF method and 196 peptides (80.9%) were biotin-labeled in LTQ-Orbitrap method (Table 3.1, Fig. 3.6). A typical MS/MS spectrum matched to a biotinylated peptide from TonB-dependent receptor is shown in Fig. 3.5. Thus, all MS/MS spectra were carefully inspected using the criteria described in the method section in order to ensure the existence of labeled biotin reagents on peptide matches. The 105 (Q-TOF) and 196 (LTQ-Orbitrap) peptides were attributed to 61 and 91 unique labeled proteins, correspondingly. The ratio of biotin-labeled peptide/protein was 1.7 in Q-TOF and 2.1 in LTQ-Orbitrap result. Although, LTQ-Orbitrap analysis increased the MS spectra by 1.1 fold, number of unique peptides by 0.9 fold and number of proteins by 0.5 fold, respectively, the biotinylated peptide coverage of each protein only increased by 0.2 fold. These results clearly show that the low abundant proteins were able to be identified by LTQ-Orbitrap duo to its higher frequent MS duty cycles and higher number of parent ion selections than those in Q-TOF, but the ratio of biotinylated peptide per protein remained at similar level in both methods. Further investigation ‐ 77 ‐ showed significant overlap between both MS methods, in which all 61 labeled proteins from Q-TOF were also identified in 91 proteins from LTQ-Orbitrap (data were not shown.) Table 3.1 Summary of detected biotinylated proteins in C. crescentus RsaAfractions using Q-TOF and LTQ-Orbitrapa. Q-TOF LTQ-Orbitrap BioRep1 & (2) Total c BioRep1 & (2) Total Number of identified biotinylated peptides 81 (91) 105 152 (160) 196 Number of proteins 50 (56) 61 69 (73) 91 Number of extracellular proteins 1 (1) 1 3 (3) 4 Number of OMPs 8 (9) 10 15 (18) 19 22 (27) 30 33 (35) 41 Number of IMPs 3 (3) 4 2 (4) 6 Number of CYT proteins 2 (5) 5 4 (4) 5 Number of proteins with unspecified location 8 (8) 11 11 (14) 16 b Number of PERI proteins a The protein identifications in the table were from the (peptide level) enrichment fractions by avidin columns. Since the (protein level) enrichment fractions had significant non-specific binding contaminants from non-labeled proteins, all MS/MS data were processed using the peptide-level fractions. b The biotinylated peptides were unique peptides. c The total number of proteins/peptides are the combined results from two biological replicates. ‐ 78 ‐ Figure 3.5: A MS/MS spectrum of a biotinylated peptides assigning to the TonBdependent receptor, a typical OMP in C. crescentus. The arrow annotations indicate how the Sulfo-NHS-LC-LC biotin was interpreted by mass shift of 452.2 on the lysine residue (labeled with the star) in both series of b and y ion fragments. Among the total of 61 biotin-labeled protein identified from Q-TOF, (Table 3.1, Fig. 3.7), 11 (18%) were predicted as OM/EX proteins. 30 (49%) are predicted as PERI proteins which was almost half of the total identified proteins. 4 (7%) were predicted as IM proteins. Finally, 5 (8%) and 11 (18%) were predicted as CYT proteins and proteins with unknown localizations, respectively. Thus, at least 76% of the identified proteins was predicted located in the cell envelop. Correspondingly, the LTQ-Orbitrap results (they could also be considered as total combined results from both methods, since the LTQ-Orbitrap contained all protein identifications from Q-TOF.) represented 23 (25%) OM/EX proteins, 41 (45%) PERI proteins, 6 (7%) IM proteins, 5 (5%) CYT proteins and ‐ 79 ‐ 16 (18%) proteins with unknown localizations. In total, At least 77% of cell envelop proteins were identified using both MS analysis. The under-representation of highly abundant cytoplasmic proteins (8%) including 2 elongation factor proteins, 1 translation factor protein, 1 ribosomal protein and 1 RNA synthesize protein indicated that the biotinylation procedure did not cause cell lysis. A possible explanation is that these cytoplasmic proteins were secreted out into the cell membrane during the biotin-labeling process [28]. Figure 3.6: Venn diagrams illustrating the common and uniquely identified biontinylated proteins between two biological replicates using both Q-TOF and LTQ-Orbitrap LC-MS/MS. ‐ 80 ‐ Figure 3.7: Graphical representation of the predicted/annotated subcellular distribution of biotinylated proteins identified in peptide-level purified fractions from C. crescentus RsaA- using Q-TOF or LTQ-Orbitrap method. Proteins were distributed in each pie chart according to their annotated or predicted subcellular location based on the number of proteins IDs per category for either the (A) Q-TOF or (B) LTQOrbitrap method. The percentage of biotinylated proteins identified in each experiment is shown below each pie chart. 3.3.4 Functional Characterization of Biotinylated Proteins from RsaA-. The molecular functions of the total 91 potential surface-exposed proteins in this study were classified according to the DAVID functional annotation clustering tool. Functional annotation clustering [29] in DAVID generated the functional classification by measuring the relationships among all Gene Ontology (GO) annotation terms on the basis of the degree of their correlations with our protein lists to cluster into similar annotation groups. This reduced the redundancy of different GO terms associating with similar biological functions. In general, each functional cluster had one Enrichment Score representing the geometric mean value of all p-values of each annotation term in this group, which the p-value of the functional annotation was calculated by a Fisher’s exact test to compare the degree of significance on each functional annotation in our results to those in the total bacteria genome. The classification stringency was set at ‘highest’ level ‐ 81 ‐ and a higher Enrichment Score of the functional cluster indicated the annotation members in this group played more important roles. Enrichment Score 1.3 correlated to non-log number of 0.05. Thus, the annotation clusters with score ≥ 1.3 were considered as significant in the study. Figure 3.8: Functional classification of total 91 putative surface-exposed proteins identified using both Q-TOF and LTQ-Orbitrap method. The Enrichment Score of each cluster was labeled on the corresponding pie charts. The annotation terms were according to the GO databases. Among the total six functional groups, four groups were significantly enriched (≥ 1.3) in the results including ion transporter, peptidase activity, cell membrane and protein binding. Metal ion transporters was the top 1 score functional cluster consisting of 27 proteins (30.0% of total identified proteins). This group included various types of zinc transporters, iron transporters, transition metal ion transporters and metallopeptidase. The second ranking cluster was peptidase group, which consisted of 17 proteins (18.7%), including serine-type peptidase and PDZ proteins. A subgroup of serine peptidase [30] and PDZ proteins [31] were often associating with membrane domain to regulate ‐ 82 ‐ membrane transporters. Another two major categories were cell membrane proteins and protein binding partners which included 17 (18.7%) and 12 (13.1%) proteins, respectively. 11 conserved hypothetical proteins were not assigned into the functional clusters which accounts for approximately 10% of total identified proteins. Figure 3.9: Distribution of biotinylated proteins with multiple tranmembrane αhelices. To further interpret the membrane features of our surface-labeled proteins, transmembrane α-helices, a distinguishing characteristic of integral membrane proteins, were inferred from primary structure of identified proteins (Fig. 3.9). Among 70 predicted cell envelope proteins, 67 proteins (95.7) had at least 1 transmembrane helice. Since almost 50% of the identified proteins were predicted as periplasmic proteins, it suggested that those periplasmic proteins could also have surface-exposed protein segments reacting to the biotin reagent as well as OM/EX proteins. Interestingly, all 16 ‐ 83 ‐ proteins with unknown localizations were in possession of at least 1 transmembrane domain which indicated that they would be associated or localized on the bacterial membranes. The proteins with TM domains ≥ 5 were from IMPs and OMPs. Only 8 proteins had no membrane domains containing 5 predicted cytoplasmic proteins, 1 IM protein and 2 predicted periplasmic proteins. 3.4 DISCUSSION The strategy described above demonstrated that the combination of cell surface labeling with Sulfo-NHS-LC-LC biotin, purification of labeled peptides with avidin affinity chromatography and high throughout LC-MS/MS analysis was a powerful method to characterize cell surface subproteome of Gram-negative bacteria. We were able to identify 83 (total 91) putative or potential cell surface proteins using this method. In order to further validate the isolation specificity of membrane proteins using the biotinylation strategy, we compared these results with the combined four OM isolation results described in chapter 2 (Table 3.2, Fig. 3.9). There were a number of membrane proteins identified in both two methods. Out of the 91 potential surfaceexposed proteins, 63 proteins were also identified in the OM isolations, which accounts for 69.2% of the total identifications. After closely investigating the distribution of subcellular localizations between the commonly identified proteins and uniquely identified proteins only in biotinylation fractions, it indicated that the overlap between proteins identified in both experiments depended on their predicted subcellular localizations. Thus, out of the 63 common proteins, 23.8% were predicted OMPs, ‐ 84 ‐ whereas only 14.3% of unique biotin-labeled proteins were OMPs (Fig. 3.10). The difference of distributions laid out in other subcellular localizations as well, which EX proteins were 4.8% over 7.1%, periplasmic proteins were 43.2% over 50.0%, IMPs were 4.8% over 10.7% and cytoplasmic proteins were 8.0% over 0% out of the commonly identified proteins over uniquely identified proteins in biotinylation fractions. In addition, the OM isolation strategy obviously provided 2.2 fold more protein identifications (201 extra proteins) than the biotinylation method. However, the biotin-labeling approach labels the lysine residues in the protein regions exposed to the cell surface, the bias for enrichment was inherent. The 2DLC separation method would potentially be applied prior to MS/MS analysis for the biotinylated samples as well in order to boost up the number of protein identifications. Table 3.2 Summary of C. crescentus proteins identified in both biotinylation experiments and in OM isolation experiments. Accession Numbers gi|16124614 Gene Locus Localization b Biotinylation OM fraction CC_0359 CYT × × gi|16124757 synthase, putative RNA polymerase, beta subunit CC_0502 CYT × × gi|16124838 transaminase, putative CC_0584 CYT × × gi|16124994 factor Tu family protein CC_0741 CYT × × gi|16127430 elongation factor G CC_3200 CYT × × gi|16124543 CC_0288 EX × × gi|16125142 hypothetical protein aminopeptidase-related protein CC_0889 EX × gi|16125963 hypothetical protein CC_1719 EX × gi|16126783 aminopeptidase, putative CC_2544 EX × gi|16124298 CC_0042 IM × gi|16125261 initiation factor IF-2 secretion system, membrane protein RsaE CC_1009 IM × gi|16125433 histidine kinase CC_1181 IM × gi|16126400 carboxypeptidase CC_2161 IM × gi|16126858 malic enzyme cytochrome C, membrane-bound CC_2622 IM × CC_2935 IM × gi|16127165 Descriptions ‐ 85 ‐ × × × × gi|16124343 conserved hypothetical protein CC_0088 OM × × gi|16124419 hypothetical protein CC_0164 OM × × gi|16124465 TonB-dependent receptor CC_0210 OM × × gi|16124833 TonB-dependent receptor alkaline metalloproteinase, putative CC_0579 OM × × CC_0746 OM × × CC_0806 OM × × gi|16125096 efflux system protein conserved hypothetical protein CC_0843 OM × gi|16125177 hypothetical protein CC_0925 OM × × gi|16125235 TonB-dependent receptor CC_0983 OM × × gi|16125290 hypothetical protein CC_1038 OM × gi|16124999 gi|16125059 gi|16125584 TPR domain protein CC_1335 OM × gi|16125994 TonB-dependent receptor CC_1750 OM × × gi|16126213 TonB-dependent receptor CC_1970 OM × × gi|16126433 TonB-dependent receptor CC_2194 OM × × gi|16127243 TonB-dependent receptor CC_3013 OM × × gi|16127376 TonB-dependent receptor CC_3146 OM × × gi|16127724 OmpA family protein CC_3494 OM × × gi|16127730 TonB-dependent receptor CC_3500 OM × × gi|16127731 hypothetical protein CC_3501 OM × gi|16124371 CC_0116 PERI × × gi|16124617 hypothetical protein ABC transporter, periplasmic binding protein CC_0362 PERI × × gi|16124649 hypothetical protein CC_0394 PERI × × gi|16124950 hypothetical protein CC_0697 PERI × × gi|16124952 hypothetical protein CC_0699 PERI × gi|16125073 Cgr1 family protein CC_0820 PERI × gi|16125236 M16 family CC_0984 PERI × gi|16125396 aminopeptidase, putative CC_1144 PERI × × gi|16125531 CC_1282 PERI × × gi|16125754 serine protease conserved hypothetical protein CC_1507 PERI × × gi|16126023 Thij family protein CC_1779 PERI × gi|16126137 rotamase family protein CC_1894 PERI × gi|16126157 CC_1914 PERI × gi|16126229 hypothetical protein oligopeptidase family protein CC_1986 PERI × gi|16126239 M23/M37 family CC_1996 PERI × gi|16126393 peptidase IV CC_2154 PERI × gi|16126437 M1 family protein CC_2198 PERI × ‐ 86 ‐ × × gi|16126468 hypothetical protein CC_2229 PERI × × gi|16126496 hypothetical protein CC_2257 PERI × × gi|16126700 phosphatase, putative CC_2461 PERI × × gi|16126778 hypothetical protein CC_2539 PERI × × gi|16126816 M16 family CC_2578 PERI × × gi|16126820 flagellar P-ring protein CC_2582 PERI × × gi|16126873 M16 family CC_2638 PERI × × gi|16126990 protease HtrA CC_2758 PERI × × gi|16127041 M20/M25/M40 family CC_2809 PERI × gi|16127072 hypothetical protein pilus assembly protein CpaD CC_2840 PERI × × CC_2944 PERI × × CC_2987 PERI × × CC_3051 PERI × × gi|16127174 gi|16127217 gi|16127281 gi|16127622 CC_3392 PERI × gi|16127665 hypothetical protein carboxyl-terminal protease CC_3435 PERI × gi|16127671 hypothetical protein CC_3441 PERI × gi|16127674 CC_3444 PERI × gi|16127719 hypothetical protein penicillin-binding protein AmpH, putative CC_3489 PERI × × gi|16127734 M13 family protein CC_3504 PERI × × gi|16127782 hypothetical protein CC_3552 PERI × gi|16127814 M16 family CC_3584 PERI × × gi|16127896 hypothetical protein CC_3666 PERI × × gi|16127978 hypothetical protein CC_3748 PERI × gi|16124752 protein L7/L12 CC_0497 Unknown × gi|16124949 hypothetical protein CC_0696 Unknown × gi|16125357 beta-glucosidase CC_1105 Unknown × gi|16125497 hypothetical protein CC_1245 Unknown × × gi|16125516 protein L15 CC_1267 Unknown × × gi|16125631 aminotransferase class I CC_1382 Unknown × gi|16125812 CC_1565 Unknown × × CC_1972 Unknown × × gi|16126217 phosphatase D conserved hypothetical protein DNA topoisomerase IV, subunit B CC_1974 Unknown × × gi|16126250 hypothetical protein CC_2007 Unknown × gi|16126500 phosphoglycerate mutase CC_2261 Unknown × × gi|16126750 protein S4 CC_2511 Unknown × × gi|16126770 hydrolase family protein conserved hypothetical protein CC_2531 Unknown × × CC_2536 Unknown × × gi|16126215 gi|16126775 hypothetical protein PDZ domain family protein ‐ 87 ‐ × × gi|16127055 CC_2823 Unknown × gi|16127130 PmbA family protein carbamoyl phosphate synthase, large subunit CC_2900 Unknown × × gi|16127941 hypothetical protein CC_3711 Unknown × × a The 91 biotinylated protein identifications were correlated against the OM isolation results which contains 292 proteins from all four experiments (chapter 2). b Protein subcellular localizations were predicted by Proteome Analyst and PsortB. Figure 3.10: Venn diagrams illustrating the common and uniquely identified proteins between biotinylation experiments and OM isolation experiments. Pie charts represented the subcellular locations of the uniquely indentified 28 proteins or the commonly identified 63 proteins from the biotinylation experiments. The 292 proteins were identified from OM isolations in the four experiments described in Chapter 2. Among the total 28 uniquely identified proteins in biotin-labeled fractions, putative aminopeptidase (CC_2544), TPR domain protein (CC_1335), rotamase family protein (CC_1894) and hypothetical proteins (CC_3392, CC_3444, CC_3552) had been ‐ 88 ‐ previously characterized in a stalk isolated fractions [32, 33]. The hypothetical protein (CC_1038) was confirmed as an outer microtubule [34]. M23/M37 family protein (CC_1996) was localized at the base of the stalk at specific stages associating to the peptidoglycan during the cell cycle of C. crescentus [35]. In addition, in a genomic localization analysis using N-/C-terminal fluorescent protein fusions, five predicted periplasmic proteins (CC_1996, CC_2944, CC_3051, CC_3435 and CC_3441) from our biotin-labeled experiments were detected to either localized at ‘pole’ position of cells or appeared ‘patchy/spotty’ patterns as cytoskeletal proteins involving in uncharacterized filament systems [36]. Even though, our biotin-labeling results showed considerable specificity on cell envelope proteins, we thought the potential permeation of the labeling reagent though outer membrane of C. crescentus may still exist. In the total 91 putative surface-exposed proteins, there were low amount of cytoplasmic proteins and IMPs accounting for 12% of total protein identifications. The periplasmic proteins were the majority portion (45%) in the sample pool, of which about 95% had TM domains. It was still not clear that all periplasmic proteins had surface-exposed segments through outer membranes. As a membrane-impermeable chemical, Sulfo-NHS-LC biotin had been suggested that it could enter though the periplasm through OM porins [23], because the molecular weight of this reagent is 557 Da, which is below the exclusion limit of 600-800 Da of outer membrane porin channels in E. coli [37]. In this study, we used Sulfo-NHS-LC-LC biotin with higher molecular weight of 670 Da and longer space arm (30.5 Å) to reduce the permeation of membranes of C. crescentus. However, our labeling and isolation strategy did not yield membrane proteins completely free of contamination. To fully overcome ‐ 89 ‐ this issue, polyethylene glycol (PEG) polymers would be substituted for LC arms on the biotin labeling reagents to prevent permeation [38, 39]. In recent decade, it has been clearly demonstrated that the spatial positioning of proteins play a key role in controlling the cell cycle of both prokaryotic and eukaryotic systems [40, 41] thanks to the development of powerful imagining technologies and genome engineering. Numerous proteins localize to specific subcellular locations in bacteria, and this localization is essential for many important physiological and behavioral processes. Next to temporal changes, spatial control of gene expressions is utilized to coordinate chromosome replication [42] and segregation though cell division [43, 44] and polar organelle development [45]. As examples in C. crescentus, the localization of the tubulin-like FtsZ determines where cytokinesis will take place in bacteria [46]. The localization of FtsZ changes from being dispersed from specific polar to being positioned near midcell where cell division occurs [47] (Fig. 3.13), which simultaneously initiate the peptidoglycan synthesis at predivision cell stage [48]. Additionally, FtsZ ring presents a high mobility until it is stabilized at the future polar site of the daughter swarmer cell [49]. Moreover, many essential proteins that regulate the progression of the cell cycle and cell differentiation in C. crescentus such as DivK, PleC, and DivJ temporally localize to specific subcellular positions throughout the cell cycle, and this localization is important for their functions as well [50, 51]. ‐ 90 ‐ Figure 3.13: FtsZ forms a ring-structure to mediate the peptidoglycan (PG) synthesis near midcell compartment during C. crescentus cell cycle [48]. FtsZ assembles into a ring structure and immigrates from one side of pole to the midcell position, where it mediates the PG synthesis by coordinating with MurG and MreB and thereby redirecting the localization of the PG precursor synthesis near midcell. At the end of cell cycle, MurG and MreB dissociates from the constriction site of FtsZ ring to disperse the PG precursor along the side wall of the daughter cells. Despite the impressive fluorescent analytical tools, analyses of the spatial distribution of proteins in bacteria remain difficult and often suffer from great uncertainty. One recent study of spatial process on the entire genome of C. crescentus inserted fluorescent mCherry fusions into each gene sequence in order to globally image the spatial distribution of proteins [36]. However, only 3,184 entry vectors (85%) were successfully isolated from the total 3,763 predicted genes, and more regrettably only 289 (8% of whole genome) proteins were detectable using fluorescent microscopy. Besides the low recovery of mCherry fusions, the extreme labor involvement is a primary limitation for this technology as well. Therefore, our SLS containing differential ultracentrifugation approach (chapter 2) and biotinylation of cell surface proteome approach (chapter 3) offer a novel high-throughput methodology for rapid mapping of the ‐ 91 ‐ spatial blueprint of the entire proteome in C. crescentus. The differential ultracentrifugation method not only isolates outer membranes, but also inner membranes and the cytoplasm. If we could perform the osmotic separation for periplasm in a parallel experiment [52], we could survey all possible subcellular compartments simultaneously. 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